repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 | partition stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|
aaugustin/django-sesame | sesame/backends.py | UrlAuthBackendMixin.parse_token | def parse_token(self, token):
"""
Obtain a user from a signed token.
"""
try:
data = self.unsign(token)
except signing.SignatureExpired:
logger.debug("Expired token: %s", token)
return
except signing.BadSignature:
logger.de... | python | def parse_token(self, token):
"""
Obtain a user from a signed token.
"""
try:
data = self.unsign(token)
except signing.SignatureExpired:
logger.debug("Expired token: %s", token)
return
except signing.BadSignature:
logger.de... | [
"def",
"parse_token",
"(",
"self",
",",
"token",
")",
":",
"try",
":",
"data",
"=",
"self",
".",
"unsign",
"(",
"token",
")",
"except",
"signing",
".",
"SignatureExpired",
":",
"logger",
".",
"debug",
"(",
"\"Expired token: %s\"",
",",
"token",
")",
"ret... | Obtain a user from a signed token. | [
"Obtain",
"a",
"user",
"from",
"a",
"signed",
"token",
"."
] | 4bf902c637b0674b8fc78bc95937271948c99282 | https://github.com/aaugustin/django-sesame/blob/4bf902c637b0674b8fc78bc95937271948c99282/sesame/backends.py#L114-L147 | train |
aaugustin/django-sesame | sesame/backends.py | ModelBackend.authenticate | def authenticate(self, request, url_auth_token=None):
"""
Check the token and return the corresponding user.
"""
try:
return self.parse_token(url_auth_token)
except TypeError:
backend = "%s.%s" % (self.__module__, self.__class__.__name__)
logg... | python | def authenticate(self, request, url_auth_token=None):
"""
Check the token and return the corresponding user.
"""
try:
return self.parse_token(url_auth_token)
except TypeError:
backend = "%s.%s" % (self.__module__, self.__class__.__name__)
logg... | [
"def",
"authenticate",
"(",
"self",
",",
"request",
",",
"url_auth_token",
"=",
"None",
")",
":",
"try",
":",
"return",
"self",
".",
"parse_token",
"(",
"url_auth_token",
")",
"except",
"TypeError",
":",
"backend",
"=",
"\"%s.%s\"",
"%",
"(",
"self",
".",
... | Check the token and return the corresponding user. | [
"Check",
"the",
"token",
"and",
"return",
"the",
"corresponding",
"user",
"."
] | 4bf902c637b0674b8fc78bc95937271948c99282 | https://github.com/aaugustin/django-sesame/blob/4bf902c637b0674b8fc78bc95937271948c99282/sesame/backends.py#L155-L166 | train |
nlppln/nlppln | nlppln/commands/frog_to_saf.py | _add_pos1 | def _add_pos1(token):
"""
Adds a 'pos1' element to a frog token.
"""
result = token.copy()
result['pos1'] = _POSMAP[token['pos'].split("(")[0]]
return result | python | def _add_pos1(token):
"""
Adds a 'pos1' element to a frog token.
"""
result = token.copy()
result['pos1'] = _POSMAP[token['pos'].split("(")[0]]
return result | [
"def",
"_add_pos1",
"(",
"token",
")",
":",
"result",
"=",
"token",
".",
"copy",
"(",
")",
"result",
"[",
"'pos1'",
"]",
"=",
"_POSMAP",
"[",
"token",
"[",
"'pos'",
"]",
".",
"split",
"(",
"\"(\"",
")",
"[",
"0",
"]",
"]",
"return",
"result"
] | Adds a 'pos1' element to a frog token. | [
"Adds",
"a",
"pos1",
"element",
"to",
"a",
"frog",
"token",
"."
] | 1155191921289a65ba2becd2bf8dfabb48eaf1f1 | https://github.com/nlppln/nlppln/blob/1155191921289a65ba2becd2bf8dfabb48eaf1f1/nlppln/commands/frog_to_saf.py#L53-L59 | train |
nlppln/nlppln | nlppln/commands/frog_to_saf.py | frog_to_saf | def frog_to_saf(tokens):
"""
Convert frog tokens into a new SAF document
"""
tokens = [_add_pos1(token) for token in tokens]
module = {'module': "frog",
"started": datetime.datetime.now().isoformat()}
return {"header": {'format': "SAF",
'format-version': "0.0... | python | def frog_to_saf(tokens):
"""
Convert frog tokens into a new SAF document
"""
tokens = [_add_pos1(token) for token in tokens]
module = {'module': "frog",
"started": datetime.datetime.now().isoformat()}
return {"header": {'format': "SAF",
'format-version': "0.0... | [
"def",
"frog_to_saf",
"(",
"tokens",
")",
":",
"tokens",
"=",
"[",
"_add_pos1",
"(",
"token",
")",
"for",
"token",
"in",
"tokens",
"]",
"module",
"=",
"{",
"'module'",
":",
"\"frog\"",
",",
"\"started\"",
":",
"datetime",
".",
"datetime",
".",
"now",
"... | Convert frog tokens into a new SAF document | [
"Convert",
"frog",
"tokens",
"into",
"a",
"new",
"SAF",
"document"
] | 1155191921289a65ba2becd2bf8dfabb48eaf1f1 | https://github.com/nlppln/nlppln/blob/1155191921289a65ba2becd2bf8dfabb48eaf1f1/nlppln/commands/frog_to_saf.py#L62-L72 | train |
nlppln/nlppln | nlppln/copycwl.py | copy_cwl_files | def copy_cwl_files(from_dir=CWL_PATH, to_dir=None):
"""Copy cwl files to a directory where the cwl-runner can find them.
Args:
from_dir (str): Path to directory where to copy files from (default:
the cwl directory of nlppln).
to_dir (str): Path to directory where the files should be... | python | def copy_cwl_files(from_dir=CWL_PATH, to_dir=None):
"""Copy cwl files to a directory where the cwl-runner can find them.
Args:
from_dir (str): Path to directory where to copy files from (default:
the cwl directory of nlppln).
to_dir (str): Path to directory where the files should be... | [
"def",
"copy_cwl_files",
"(",
"from_dir",
"=",
"CWL_PATH",
",",
"to_dir",
"=",
"None",
")",
":",
"cwl_files",
"=",
"glob",
".",
"glob",
"(",
"'{}{}*.cwl'",
".",
"format",
"(",
"from_dir",
",",
"os",
".",
"sep",
")",
")",
"if",
"len",
"(",
"cwl_files",
... | Copy cwl files to a directory where the cwl-runner can find them.
Args:
from_dir (str): Path to directory where to copy files from (default:
the cwl directory of nlppln).
to_dir (str): Path to directory where the files should be copied to
(e.g., the CWL working directory). | [
"Copy",
"cwl",
"files",
"to",
"a",
"directory",
"where",
"the",
"cwl",
"-",
"runner",
"can",
"find",
"them",
"."
] | 1155191921289a65ba2becd2bf8dfabb48eaf1f1 | https://github.com/nlppln/nlppln/blob/1155191921289a65ba2becd2bf8dfabb48eaf1f1/nlppln/copycwl.py#L10-L27 | train |
nlppln/nlppln | nlppln/copycwl.py | main | def main(to_dir, from_dir):
"""Copy CWL files."""
num = copy_cwl_files(from_dir=from_dir, to_dir=to_dir)
if num > 0:
click.echo('Copied {} CWL files to "{}".'.format(num, to_dir))
else:
msg = 'No CWL files found in "{}". Copied 0 files'.format(from_dir)
click.echo(msg) | python | def main(to_dir, from_dir):
"""Copy CWL files."""
num = copy_cwl_files(from_dir=from_dir, to_dir=to_dir)
if num > 0:
click.echo('Copied {} CWL files to "{}".'.format(num, to_dir))
else:
msg = 'No CWL files found in "{}". Copied 0 files'.format(from_dir)
click.echo(msg) | [
"def",
"main",
"(",
"to_dir",
",",
"from_dir",
")",
":",
"num",
"=",
"copy_cwl_files",
"(",
"from_dir",
"=",
"from_dir",
",",
"to_dir",
"=",
"to_dir",
")",
"if",
"num",
">",
"0",
":",
"click",
".",
"echo",
"(",
"'Copied {} CWL files to \"{}\".'",
".",
"f... | Copy CWL files. | [
"Copy",
"CWL",
"files",
"."
] | 1155191921289a65ba2becd2bf8dfabb48eaf1f1 | https://github.com/nlppln/nlppln/blob/1155191921289a65ba2becd2bf8dfabb48eaf1f1/nlppln/copycwl.py#L35-L42 | train |
sileht/python-jsonpath-rw-ext | jsonpath_rw_ext/_iterable.py | SortedThis.find | def find(self, datum):
"""Return sorted value of This if list or dict."""
if isinstance(datum.value, dict) and self.expressions:
return datum
if isinstance(datum.value, dict) or isinstance(datum.value, list):
key = (functools.cmp_to_key(self._compare)
... | python | def find(self, datum):
"""Return sorted value of This if list or dict."""
if isinstance(datum.value, dict) and self.expressions:
return datum
if isinstance(datum.value, dict) or isinstance(datum.value, list):
key = (functools.cmp_to_key(self._compare)
... | [
"def",
"find",
"(",
"self",
",",
"datum",
")",
":",
"if",
"isinstance",
"(",
"datum",
".",
"value",
",",
"dict",
")",
"and",
"self",
".",
"expressions",
":",
"return",
"datum",
"if",
"isinstance",
"(",
"datum",
".",
"value",
",",
"dict",
")",
"or",
... | Return sorted value of This if list or dict. | [
"Return",
"sorted",
"value",
"of",
"This",
"if",
"list",
"or",
"dict",
"."
] | 30dd6f209ae16d448e1e071aac90cc9d3f10cff5 | https://github.com/sileht/python-jsonpath-rw-ext/blob/30dd6f209ae16d448e1e071aac90cc9d3f10cff5/jsonpath_rw_ext/_iterable.py#L46-L56 | train |
nlppln/nlppln | nlppln/wfgenerator.py | WorkflowGenerator.save | def save(self, fname, mode=None, validate=True, wd=False, inline=False,
relative=True, pack=False, encoding='utf-8'):
"""Save workflow to file
For nlppln, the default is to save workflows with relative paths.
"""
super(WorkflowGenerator, self).save(fname,
... | python | def save(self, fname, mode=None, validate=True, wd=False, inline=False,
relative=True, pack=False, encoding='utf-8'):
"""Save workflow to file
For nlppln, the default is to save workflows with relative paths.
"""
super(WorkflowGenerator, self).save(fname,
... | [
"def",
"save",
"(",
"self",
",",
"fname",
",",
"mode",
"=",
"None",
",",
"validate",
"=",
"True",
",",
"wd",
"=",
"False",
",",
"inline",
"=",
"False",
",",
"relative",
"=",
"True",
",",
"pack",
"=",
"False",
",",
"encoding",
"=",
"'utf-8'",
")",
... | Save workflow to file
For nlppln, the default is to save workflows with relative paths. | [
"Save",
"workflow",
"to",
"file"
] | 1155191921289a65ba2becd2bf8dfabb48eaf1f1 | https://github.com/nlppln/nlppln/blob/1155191921289a65ba2becd2bf8dfabb48eaf1f1/nlppln/wfgenerator.py#L15-L28 | train |
sileht/python-jsonpath-rw-ext | jsonpath_rw_ext/parser.py | match | def match(pattern, data, **parse_kwargs):
"""Returns all matched values of pattern in data"""
return [m.value for m in parse(pattern, **parse_kwargs).find(data)] | python | def match(pattern, data, **parse_kwargs):
"""Returns all matched values of pattern in data"""
return [m.value for m in parse(pattern, **parse_kwargs).find(data)] | [
"def",
"match",
"(",
"pattern",
",",
"data",
",",
"**",
"parse_kwargs",
")",
":",
"return",
"[",
"m",
".",
"value",
"for",
"m",
"in",
"parse",
"(",
"pattern",
",",
"**",
"parse_kwargs",
")",
".",
"find",
"(",
"data",
")",
"]"
] | Returns all matched values of pattern in data | [
"Returns",
"all",
"matched",
"values",
"of",
"pattern",
"in",
"data"
] | 30dd6f209ae16d448e1e071aac90cc9d3f10cff5 | https://github.com/sileht/python-jsonpath-rw-ext/blob/30dd6f209ae16d448e1e071aac90cc9d3f10cff5/jsonpath_rw_ext/parser.py#L182-L184 | train |
sileht/python-jsonpath-rw-ext | jsonpath_rw_ext/parser.py | match1 | def match1(pattern, data, **parse_kwargs):
"""Returns first matched value of pattern in data or None if no matches"""
matches = match(pattern, data, **parse_kwargs)
return matches[0] if matches else None | python | def match1(pattern, data, **parse_kwargs):
"""Returns first matched value of pattern in data or None if no matches"""
matches = match(pattern, data, **parse_kwargs)
return matches[0] if matches else None | [
"def",
"match1",
"(",
"pattern",
",",
"data",
",",
"**",
"parse_kwargs",
")",
":",
"matches",
"=",
"match",
"(",
"pattern",
",",
"data",
",",
"**",
"parse_kwargs",
")",
"return",
"matches",
"[",
"0",
"]",
"if",
"matches",
"else",
"None"
] | Returns first matched value of pattern in data or None if no matches | [
"Returns",
"first",
"matched",
"value",
"of",
"pattern",
"in",
"data",
"or",
"None",
"if",
"no",
"matches"
] | 30dd6f209ae16d448e1e071aac90cc9d3f10cff5 | https://github.com/sileht/python-jsonpath-rw-ext/blob/30dd6f209ae16d448e1e071aac90cc9d3f10cff5/jsonpath_rw_ext/parser.py#L187-L190 | train |
nlppln/nlppln | nlppln/commands/create_chunked_list.py | create_chunked_list | def create_chunked_list(in_dir, size, out_dir, out_name):
"""Create a division of the input files in chunks.
The result is stored to a JSON file.
"""
create_dirs(out_dir)
in_files = get_files(in_dir)
chunks = chunk(in_files, size)
division = {}
for i, files in enumerate(chunks):
... | python | def create_chunked_list(in_dir, size, out_dir, out_name):
"""Create a division of the input files in chunks.
The result is stored to a JSON file.
"""
create_dirs(out_dir)
in_files = get_files(in_dir)
chunks = chunk(in_files, size)
division = {}
for i, files in enumerate(chunks):
... | [
"def",
"create_chunked_list",
"(",
"in_dir",
",",
"size",
",",
"out_dir",
",",
"out_name",
")",
":",
"create_dirs",
"(",
"out_dir",
")",
"in_files",
"=",
"get_files",
"(",
"in_dir",
")",
"chunks",
"=",
"chunk",
"(",
"in_files",
",",
"size",
")",
"division"... | Create a division of the input files in chunks.
The result is stored to a JSON file. | [
"Create",
"a",
"division",
"of",
"the",
"input",
"files",
"in",
"chunks",
"."
] | 1155191921289a65ba2becd2bf8dfabb48eaf1f1 | https://github.com/nlppln/nlppln/blob/1155191921289a65ba2becd2bf8dfabb48eaf1f1/nlppln/commands/create_chunked_list.py#L22-L39 | train |
nlppln/nlppln | nlppln/utils.py | remove_ext | def remove_ext(fname):
"""Removes the extension from a filename
"""
bn = os.path.basename(fname)
return os.path.splitext(bn)[0] | python | def remove_ext(fname):
"""Removes the extension from a filename
"""
bn = os.path.basename(fname)
return os.path.splitext(bn)[0] | [
"def",
"remove_ext",
"(",
"fname",
")",
":",
"bn",
"=",
"os",
".",
"path",
".",
"basename",
"(",
"fname",
")",
"return",
"os",
".",
"path",
".",
"splitext",
"(",
"bn",
")",
"[",
"0",
"]"
] | Removes the extension from a filename | [
"Removes",
"the",
"extension",
"from",
"a",
"filename"
] | 1155191921289a65ba2becd2bf8dfabb48eaf1f1 | https://github.com/nlppln/nlppln/blob/1155191921289a65ba2becd2bf8dfabb48eaf1f1/nlppln/utils.py#L14-L18 | train |
nlppln/nlppln | nlppln/utils.py | out_file_name | def out_file_name(out_dir, fname, ext=None):
"""Return path of output file, given a directory, file name and extension.
If fname is a path, it is converted to its basename.
Args:
out_dir (str): path to the directory where output should be written.
fname (str): path to the input file.
... | python | def out_file_name(out_dir, fname, ext=None):
"""Return path of output file, given a directory, file name and extension.
If fname is a path, it is converted to its basename.
Args:
out_dir (str): path to the directory where output should be written.
fname (str): path to the input file.
... | [
"def",
"out_file_name",
"(",
"out_dir",
",",
"fname",
",",
"ext",
"=",
"None",
")",
":",
"if",
"ext",
"is",
"None",
":",
"return",
"os",
".",
"path",
".",
"join",
"(",
"out_dir",
",",
"os",
".",
"path",
".",
"basename",
"(",
"fname",
")",
")",
"f... | Return path of output file, given a directory, file name and extension.
If fname is a path, it is converted to its basename.
Args:
out_dir (str): path to the directory where output should be written.
fname (str): path to the input file.
ext (str): file extension of the output file (def... | [
"Return",
"path",
"of",
"output",
"file",
"given",
"a",
"directory",
"file",
"name",
"and",
"extension",
"."
] | 1155191921289a65ba2becd2bf8dfabb48eaf1f1 | https://github.com/nlppln/nlppln/blob/1155191921289a65ba2becd2bf8dfabb48eaf1f1/nlppln/utils.py#L34-L52 | train |
nlppln/nlppln | nlppln/utils.py | get_files | def get_files(directory, recursive=False):
"""Return a list of all files in the directory."""
files_out = []
if recursive:
for root, dirs, files in os.walk(os.path.abspath(directory)):
files = [os.path.join(root, f) for f in files]
files_out.append(files)
files_out = ... | python | def get_files(directory, recursive=False):
"""Return a list of all files in the directory."""
files_out = []
if recursive:
for root, dirs, files in os.walk(os.path.abspath(directory)):
files = [os.path.join(root, f) for f in files]
files_out.append(files)
files_out = ... | [
"def",
"get_files",
"(",
"directory",
",",
"recursive",
"=",
"False",
")",
":",
"files_out",
"=",
"[",
"]",
"if",
"recursive",
":",
"for",
"root",
",",
"dirs",
",",
"files",
"in",
"os",
".",
"walk",
"(",
"os",
".",
"path",
".",
"abspath",
"(",
"dir... | Return a list of all files in the directory. | [
"Return",
"a",
"list",
"of",
"all",
"files",
"in",
"the",
"directory",
"."
] | 1155191921289a65ba2becd2bf8dfabb48eaf1f1 | https://github.com/nlppln/nlppln/blob/1155191921289a65ba2becd2bf8dfabb48eaf1f1/nlppln/utils.py#L59-L72 | train |
gplepage/lsqfit | src/lsqfit/__init__.py | _reformat | def _reformat(p, buf):
""" Apply format of ``p`` to data in 1-d array ``buf``. """
if numpy.ndim(buf) != 1:
raise ValueError("Buffer ``buf`` must be 1-d.")
if hasattr(p, 'keys'):
ans = _gvar.BufferDict(p)
if ans.size != len(buf):
raise ValueError( #
... | python | def _reformat(p, buf):
""" Apply format of ``p`` to data in 1-d array ``buf``. """
if numpy.ndim(buf) != 1:
raise ValueError("Buffer ``buf`` must be 1-d.")
if hasattr(p, 'keys'):
ans = _gvar.BufferDict(p)
if ans.size != len(buf):
raise ValueError( #
... | [
"def",
"_reformat",
"(",
"p",
",",
"buf",
")",
":",
"if",
"numpy",
".",
"ndim",
"(",
"buf",
")",
"!=",
"1",
":",
"raise",
"ValueError",
"(",
"\"Buffer ``buf`` must be 1-d.\"",
")",
"if",
"hasattr",
"(",
"p",
",",
"'keys'",
")",
":",
"ans",
"=",
"_gva... | Apply format of ``p`` to data in 1-d array ``buf``. | [
"Apply",
"format",
"of",
"p",
"to",
"data",
"in",
"1",
"-",
"d",
"array",
"buf",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/__init__.py#L1543-L1558 | train |
gplepage/lsqfit | src/lsqfit/__init__.py | _unpack_gvars | def _unpack_gvars(g):
""" Unpack collection of GVars to BufferDict or numpy array. """
if g is not None:
g = _gvar.gvar(g)
if not hasattr(g, 'flat'):
# must be a scalar (ie, not an array and not a dictionary)
g = numpy.asarray(g)
return g | python | def _unpack_gvars(g):
""" Unpack collection of GVars to BufferDict or numpy array. """
if g is not None:
g = _gvar.gvar(g)
if not hasattr(g, 'flat'):
# must be a scalar (ie, not an array and not a dictionary)
g = numpy.asarray(g)
return g | [
"def",
"_unpack_gvars",
"(",
"g",
")",
":",
"if",
"g",
"is",
"not",
"None",
":",
"g",
"=",
"_gvar",
".",
"gvar",
"(",
"g",
")",
"if",
"not",
"hasattr",
"(",
"g",
",",
"'flat'",
")",
":",
"g",
"=",
"numpy",
".",
"asarray",
"(",
"g",
")",
"retu... | Unpack collection of GVars to BufferDict or numpy array. | [
"Unpack",
"collection",
"of",
"GVars",
"to",
"BufferDict",
"or",
"numpy",
"array",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/__init__.py#L1658-L1665 | train |
gplepage/lsqfit | src/lsqfit/__init__.py | _unpack_p0 | def _unpack_p0(p0, p0file, prior):
""" Create proper p0.
Try to read from a file. If that doesn't work, try using p0,
and then, finally, the prior. If the p0 is from the file, it is
checked against the prior to make sure that all elements have the
right shape; if not the p0 elements are adjusted (u... | python | def _unpack_p0(p0, p0file, prior):
""" Create proper p0.
Try to read from a file. If that doesn't work, try using p0,
and then, finally, the prior. If the p0 is from the file, it is
checked against the prior to make sure that all elements have the
right shape; if not the p0 elements are adjusted (u... | [
"def",
"_unpack_p0",
"(",
"p0",
",",
"p0file",
",",
"prior",
")",
":",
"if",
"p0file",
"is",
"not",
"None",
":",
"try",
":",
"with",
"open",
"(",
"p0file",
",",
"\"rb\"",
")",
"as",
"f",
":",
"p0",
"=",
"pickle",
".",
"load",
"(",
"f",
")",
"ex... | Create proper p0.
Try to read from a file. If that doesn't work, try using p0,
and then, finally, the prior. If the p0 is from the file, it is
checked against the prior to make sure that all elements have the
right shape; if not the p0 elements are adjusted (using info from
the prior) to be the cor... | [
"Create",
"proper",
"p0",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/__init__.py#L1667-L1754 | train |
gplepage/lsqfit | src/lsqfit/__init__.py | _unpack_fcn | def _unpack_fcn(fcn, p0, y, x):
""" reconfigure fitting fcn so inputs, outputs = flat arrays; hide x """
if y.shape is not None:
if p0.shape is not None:
def nfcn(p, x=x, fcn=fcn, pshape=p0.shape):
po = p.reshape(pshape)
ans = fcn(po) if x is False else fcn(x,... | python | def _unpack_fcn(fcn, p0, y, x):
""" reconfigure fitting fcn so inputs, outputs = flat arrays; hide x """
if y.shape is not None:
if p0.shape is not None:
def nfcn(p, x=x, fcn=fcn, pshape=p0.shape):
po = p.reshape(pshape)
ans = fcn(po) if x is False else fcn(x,... | [
"def",
"_unpack_fcn",
"(",
"fcn",
",",
"p0",
",",
"y",
",",
"x",
")",
":",
"if",
"y",
".",
"shape",
"is",
"not",
"None",
":",
"if",
"p0",
".",
"shape",
"is",
"not",
"None",
":",
"def",
"nfcn",
"(",
"p",
",",
"x",
"=",
"x",
",",
"fcn",
"=",
... | reconfigure fitting fcn so inputs, outputs = flat arrays; hide x | [
"reconfigure",
"fitting",
"fcn",
"so",
"inputs",
"outputs",
"=",
"flat",
"arrays",
";",
"hide",
"x"
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/__init__.py#L1757-L1794 | train |
gplepage/lsqfit | src/lsqfit/__init__.py | nonlinear_fit.check_roundoff | def check_roundoff(self, rtol=0.25, atol=1e-6):
""" Check for roundoff errors in fit.p.
Compares standard deviations from fit.p and fit.palt to see if they
agree to within relative tolerance ``rtol`` and absolute tolerance
``atol``. Generates a warning if they do not (in which
c... | python | def check_roundoff(self, rtol=0.25, atol=1e-6):
""" Check for roundoff errors in fit.p.
Compares standard deviations from fit.p and fit.palt to see if they
agree to within relative tolerance ``rtol`` and absolute tolerance
``atol``. Generates a warning if they do not (in which
c... | [
"def",
"check_roundoff",
"(",
"self",
",",
"rtol",
"=",
"0.25",
",",
"atol",
"=",
"1e-6",
")",
":",
"psdev",
"=",
"_gvar",
".",
"sdev",
"(",
"self",
".",
"p",
".",
"flat",
")",
"paltsdev",
"=",
"_gvar",
".",
"sdev",
"(",
"self",
".",
"palt",
".",... | Check for roundoff errors in fit.p.
Compares standard deviations from fit.p and fit.palt to see if they
agree to within relative tolerance ``rtol`` and absolute tolerance
``atol``. Generates a warning if they do not (in which
case an SVD cut might be advisable). | [
"Check",
"for",
"roundoff",
"errors",
"in",
"fit",
".",
"p",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/__init__.py#L831-L842 | train |
gplepage/lsqfit | src/lsqfit/__init__.py | nonlinear_fit.plot_residuals | def plot_residuals(self, plot=None):
""" Plot normalized fit residuals.
The sum of the squares of the residuals equals ``self.chi2``.
Individual residuals should be distributed about one, in
a Gaussian distribution.
Args:
plot: :mod:`matplotlib` plotter. If ``None``... | python | def plot_residuals(self, plot=None):
""" Plot normalized fit residuals.
The sum of the squares of the residuals equals ``self.chi2``.
Individual residuals should be distributed about one, in
a Gaussian distribution.
Args:
plot: :mod:`matplotlib` plotter. If ``None``... | [
"def",
"plot_residuals",
"(",
"self",
",",
"plot",
"=",
"None",
")",
":",
"if",
"plot",
"is",
"None",
":",
"import",
"matplotlib",
".",
"pyplot",
"as",
"plot",
"x",
"=",
"numpy",
".",
"arange",
"(",
"1",
",",
"len",
"(",
"self",
".",
"residuals",
"... | Plot normalized fit residuals.
The sum of the squares of the residuals equals ``self.chi2``.
Individual residuals should be distributed about one, in
a Gaussian distribution.
Args:
plot: :mod:`matplotlib` plotter. If ``None``, uses
``matplotlib.pyplot`.
... | [
"Plot",
"normalized",
"fit",
"residuals",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/__init__.py#L875-L901 | train |
gplepage/lsqfit | src/lsqfit/__init__.py | nonlinear_fit.load_parameters | def load_parameters(filename):
""" Load parameters stored in file ``filename``.
``p = nonlinear_fit.load_p(filename)`` is used to recover the
values of fit parameters dumped using ``fit.dump_p(filename)`` (or
``fit.dump_pmean(filename)``) where ``fit`` is of type
:class:`lsqfit.... | python | def load_parameters(filename):
""" Load parameters stored in file ``filename``.
``p = nonlinear_fit.load_p(filename)`` is used to recover the
values of fit parameters dumped using ``fit.dump_p(filename)`` (or
``fit.dump_pmean(filename)``) where ``fit`` is of type
:class:`lsqfit.... | [
"def",
"load_parameters",
"(",
"filename",
")",
":",
"warnings",
".",
"warn",
"(",
"\"nonlinear_fit.load_parameters deprecated; use pickle.load or gvar.load instead\"",
",",
"DeprecationWarning",
",",
")",
"with",
"open",
"(",
"filename",
",",
"\"rb\"",
")",
"as",
"f",
... | Load parameters stored in file ``filename``.
``p = nonlinear_fit.load_p(filename)`` is used to recover the
values of fit parameters dumped using ``fit.dump_p(filename)`` (or
``fit.dump_pmean(filename)``) where ``fit`` is of type
:class:`lsqfit.nonlinear_fit`. The layout of the returned
... | [
"Load",
"parameters",
"stored",
"in",
"file",
"filename",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/__init__.py#L1228-L1243 | train |
gplepage/lsqfit | src/lsqfit/__init__.py | nonlinear_fit.simulated_fit_iter | def simulated_fit_iter(
self, n=None, pexact=None, add_priornoise=False, bootstrap=None, **kargs
):
""" Iterator that returns simulation copies of a fit.
Fit reliability is tested using simulated data which
replaces the mean values in ``self.y`` with random numbers
drawn... | python | def simulated_fit_iter(
self, n=None, pexact=None, add_priornoise=False, bootstrap=None, **kargs
):
""" Iterator that returns simulation copies of a fit.
Fit reliability is tested using simulated data which
replaces the mean values in ``self.y`` with random numbers
drawn... | [
"def",
"simulated_fit_iter",
"(",
"self",
",",
"n",
"=",
"None",
",",
"pexact",
"=",
"None",
",",
"add_priornoise",
"=",
"False",
",",
"bootstrap",
"=",
"None",
",",
"**",
"kargs",
")",
":",
"pexact",
"=",
"self",
".",
"pmean",
"if",
"pexact",
"is",
... | Iterator that returns simulation copies of a fit.
Fit reliability is tested using simulated data which
replaces the mean values in ``self.y`` with random numbers
drawn from a distribution whose mean equals ``self.fcn(pexact)``
and whose covariance matrix is the same as ``self.y``'s. Sim... | [
"Iterator",
"that",
"returns",
"simulation",
"copies",
"of",
"a",
"fit",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/__init__.py#L1282-L1360 | train |
gplepage/lsqfit | src/lsqfit/__init__.py | nonlinear_fit.simulated_data_iter | def simulated_data_iter(
self, n=None, pexact=None, add_priornoise=False, bootstrap=None
):
""" Iterator that returns simulated data based upon a fit's data.
Simulated data is generated from a fit's data ``fit.y`` by
replacing the mean values in that data with random numbers
... | python | def simulated_data_iter(
self, n=None, pexact=None, add_priornoise=False, bootstrap=None
):
""" Iterator that returns simulated data based upon a fit's data.
Simulated data is generated from a fit's data ``fit.y`` by
replacing the mean values in that data with random numbers
... | [
"def",
"simulated_data_iter",
"(",
"self",
",",
"n",
"=",
"None",
",",
"pexact",
"=",
"None",
",",
"add_priornoise",
"=",
"False",
",",
"bootstrap",
"=",
"None",
")",
":",
"pexact",
"=",
"self",
".",
"pmean",
"if",
"pexact",
"is",
"None",
"else",
"pexa... | Iterator that returns simulated data based upon a fit's data.
Simulated data is generated from a fit's data ``fit.y`` by
replacing the mean values in that data with random numbers
drawn from a distribution whose mean is ``self.fcn(pexact)``
and whose covariance matrix is the same as tha... | [
"Iterator",
"that",
"returns",
"simulated",
"data",
"based",
"upon",
"a",
"fit",
"s",
"data",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/__init__.py#L1362-L1434 | train |
gplepage/lsqfit | src/lsqfit/_extras.py | chained_nonlinear_fit.formatall | def formatall(self, *args, **kargs):
" Add-on method for fits returned by chained_nonlinear_fit. "
ans = ''
for x in self.chained_fits:
ans += 10 * '=' + ' ' + str(x) + '\n'
ans += self.chained_fits[x].format(*args, **kargs)
ans += '\n'
return ans[:-1] | python | def formatall(self, *args, **kargs):
" Add-on method for fits returned by chained_nonlinear_fit. "
ans = ''
for x in self.chained_fits:
ans += 10 * '=' + ' ' + str(x) + '\n'
ans += self.chained_fits[x].format(*args, **kargs)
ans += '\n'
return ans[:-1] | [
"def",
"formatall",
"(",
"self",
",",
"*",
"args",
",",
"**",
"kargs",
")",
":",
"\" Add-on method for fits returned by chained_nonlinear_fit. \"",
"ans",
"=",
"''",
"for",
"x",
"in",
"self",
".",
"chained_fits",
":",
"ans",
"+=",
"10",
"*",
"'='",
"+",
"' '... | Add-on method for fits returned by chained_nonlinear_fit. | [
"Add",
"-",
"on",
"method",
"for",
"fits",
"returned",
"by",
"chained_nonlinear_fit",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/_extras.py#L758-L765 | train |
gplepage/lsqfit | src/lsqfit/_extras.py | MultiFitter.set | def set(self, **kargs):
""" Reset default keyword parameters.
Assigns new default values from dictionary ``kargs`` to the fitter's
keyword parameters. Keywords for the underlying :mod:`lsqfit` fitters
can also be included (or grouped together in dictionary
``fitterargs``).
... | python | def set(self, **kargs):
""" Reset default keyword parameters.
Assigns new default values from dictionary ``kargs`` to the fitter's
keyword parameters. Keywords for the underlying :mod:`lsqfit` fitters
can also be included (or grouped together in dictionary
``fitterargs``).
... | [
"def",
"set",
"(",
"self",
",",
"**",
"kargs",
")",
":",
"kwords",
"=",
"set",
"(",
"[",
"'mopt'",
",",
"'fast'",
",",
"'ratio'",
",",
"'wavg_kargs'",
",",
"'wavg_all'",
",",
"'fitterargs'",
",",
"'fitname'",
",",
"]",
")",
"kargs",
"=",
"dict",
"(",... | Reset default keyword parameters.
Assigns new default values from dictionary ``kargs`` to the fitter's
keyword parameters. Keywords for the underlying :mod:`lsqfit` fitters
can also be included (or grouped together in dictionary
``fitterargs``).
Returns tuple ``(kargs, oldkarg... | [
"Reset",
"default",
"keyword",
"parameters",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/_extras.py#L837-L878 | train |
gplepage/lsqfit | src/lsqfit/_extras.py | MultiFitter.buildfitfcn | def buildfitfcn(self):
""" Create fit function to fit models in list ``models``. """
def _fitfcn(p, flatmodels=self.flatmodels):
ans = gvar.BufferDict()
for m in flatmodels:
ans[m.datatag] = (
m.fitfcn(p) if m.ncg <= 1 else
... | python | def buildfitfcn(self):
""" Create fit function to fit models in list ``models``. """
def _fitfcn(p, flatmodels=self.flatmodels):
ans = gvar.BufferDict()
for m in flatmodels:
ans[m.datatag] = (
m.fitfcn(p) if m.ncg <= 1 else
... | [
"def",
"buildfitfcn",
"(",
"self",
")",
":",
"def",
"_fitfcn",
"(",
"p",
",",
"flatmodels",
"=",
"self",
".",
"flatmodels",
")",
":",
"ans",
"=",
"gvar",
".",
"BufferDict",
"(",
")",
"for",
"m",
"in",
"flatmodels",
":",
"ans",
"[",
"m",
".",
"datat... | Create fit function to fit models in list ``models``. | [
"Create",
"fit",
"function",
"to",
"fit",
"models",
"in",
"list",
"models",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/_extras.py#L880-L890 | train |
gplepage/lsqfit | src/lsqfit/_extras.py | MultiFitter.builddata | def builddata(self, mopt=None, data=None, pdata=None, prior=None):
""" Rebuild pdata to account for marginalization. """
if pdata is None:
if data is None:
raise ValueError('no data or pdata')
pdata = gvar.BufferDict()
for m in self.flatmodels:
... | python | def builddata(self, mopt=None, data=None, pdata=None, prior=None):
""" Rebuild pdata to account for marginalization. """
if pdata is None:
if data is None:
raise ValueError('no data or pdata')
pdata = gvar.BufferDict()
for m in self.flatmodels:
... | [
"def",
"builddata",
"(",
"self",
",",
"mopt",
"=",
"None",
",",
"data",
"=",
"None",
",",
"pdata",
"=",
"None",
",",
"prior",
"=",
"None",
")",
":",
"if",
"pdata",
"is",
"None",
":",
"if",
"data",
"is",
"None",
":",
"raise",
"ValueError",
"(",
"'... | Rebuild pdata to account for marginalization. | [
"Rebuild",
"pdata",
"to",
"account",
"for",
"marginalization",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/_extras.py#L892-L927 | train |
gplepage/lsqfit | src/lsqfit/_extras.py | MultiFitter.buildprior | def buildprior(self, prior, mopt=None):
""" Create prior to fit models in list ``models``. """
nprior = gvar.BufferDict()
for m in self.flatmodels:
nprior.update(m.buildprior(
prior, mopt=mopt,
))
if not self.fast:
for k in prior:
... | python | def buildprior(self, prior, mopt=None):
""" Create prior to fit models in list ``models``. """
nprior = gvar.BufferDict()
for m in self.flatmodels:
nprior.update(m.buildprior(
prior, mopt=mopt,
))
if not self.fast:
for k in prior:
... | [
"def",
"buildprior",
"(",
"self",
",",
"prior",
",",
"mopt",
"=",
"None",
")",
":",
"nprior",
"=",
"gvar",
".",
"BufferDict",
"(",
")",
"for",
"m",
"in",
"self",
".",
"flatmodels",
":",
"nprior",
".",
"update",
"(",
"m",
".",
"buildprior",
"(",
"pr... | Create prior to fit models in list ``models``. | [
"Create",
"prior",
"to",
"fit",
"models",
"in",
"list",
"models",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/_extras.py#L929-L940 | train |
gplepage/lsqfit | src/lsqfit/_extras.py | MultiFitter._flatten_models | def _flatten_models(tasklist):
" Create 1d-array containing all disctinct models from ``tasklist``. "
ans = gvar.BufferDict()
for task, mlist in tasklist:
if task != 'fit':
continue
for m in mlist:
id_m = id(m)
if id_m not i... | python | def _flatten_models(tasklist):
" Create 1d-array containing all disctinct models from ``tasklist``. "
ans = gvar.BufferDict()
for task, mlist in tasklist:
if task != 'fit':
continue
for m in mlist:
id_m = id(m)
if id_m not i... | [
"def",
"_flatten_models",
"(",
"tasklist",
")",
":",
"\" Create 1d-array containing all disctinct models from ``tasklist``. \"",
"ans",
"=",
"gvar",
".",
"BufferDict",
"(",
")",
"for",
"task",
",",
"mlist",
"in",
"tasklist",
":",
"if",
"task",
"!=",
"'fit'",
":",
... | Create 1d-array containing all disctinct models from ``tasklist``. | [
"Create",
"1d",
"-",
"array",
"containing",
"all",
"disctinct",
"models",
"from",
"tasklist",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/_extras.py#L943-L953 | train |
gplepage/lsqfit | src/lsqfit/_extras.py | MultiFitter.flatten_models | def flatten_models(models):
" Create 1d-array containing all disctinct models from ``models``. "
if isinstance(models, MultiFitterModel):
ans = [models]
else:
tasklist = MultiFitter._compile_models(models)
ans = MultiFitter._flatten_models(tasklist)
re... | python | def flatten_models(models):
" Create 1d-array containing all disctinct models from ``models``. "
if isinstance(models, MultiFitterModel):
ans = [models]
else:
tasklist = MultiFitter._compile_models(models)
ans = MultiFitter._flatten_models(tasklist)
re... | [
"def",
"flatten_models",
"(",
"models",
")",
":",
"\" Create 1d-array containing all disctinct models from ``models``. \"",
"if",
"isinstance",
"(",
"models",
",",
"MultiFitterModel",
")",
":",
"ans",
"=",
"[",
"models",
"]",
"else",
":",
"tasklist",
"=",
"MultiFitter... | Create 1d-array containing all disctinct models from ``models``. | [
"Create",
"1d",
"-",
"array",
"containing",
"all",
"disctinct",
"models",
"from",
"models",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/_extras.py#L956-L963 | train |
gplepage/lsqfit | src/lsqfit/_extras.py | MultiFitter.lsqfit | def lsqfit(self, data=None, pdata=None, prior=None, p0=None, **kargs):
""" Compute least-squares fit of models to data.
:meth:`MultiFitter.lsqfit` fits all of the models together, in
a single fit. It returns the |nonlinear_fit| object from the fit.
To see plots of the fit data divided ... | python | def lsqfit(self, data=None, pdata=None, prior=None, p0=None, **kargs):
""" Compute least-squares fit of models to data.
:meth:`MultiFitter.lsqfit` fits all of the models together, in
a single fit. It returns the |nonlinear_fit| object from the fit.
To see plots of the fit data divided ... | [
"def",
"lsqfit",
"(",
"self",
",",
"data",
"=",
"None",
",",
"pdata",
"=",
"None",
",",
"prior",
"=",
"None",
",",
"p0",
"=",
"None",
",",
"**",
"kargs",
")",
":",
"if",
"prior",
"is",
"None",
":",
"raise",
"ValueError",
"(",
"'no prior'",
")",
"... | Compute least-squares fit of models to data.
:meth:`MultiFitter.lsqfit` fits all of the models together, in
a single fit. It returns the |nonlinear_fit| object from the fit.
To see plots of the fit data divided by the fit function
with the best-fit parameters use
fit.show_... | [
"Compute",
"least",
"-",
"squares",
"fit",
"of",
"models",
"to",
"data",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/_extras.py#L965-L1071 | train |
gplepage/lsqfit | src/lsqfit/_extras.py | MultiFitter._compile_models | def _compile_models(models):
""" Convert ``models`` into a list of tasks.
Each task is tuple ``(name, data)`` where ``name`` indicates the task
task and ``data`` is the relevant data for that task.
Supported tasks and data:
- ``'fit'`` and list of models
- ``'... | python | def _compile_models(models):
""" Convert ``models`` into a list of tasks.
Each task is tuple ``(name, data)`` where ``name`` indicates the task
task and ``data`` is the relevant data for that task.
Supported tasks and data:
- ``'fit'`` and list of models
- ``'... | [
"def",
"_compile_models",
"(",
"models",
")",
":",
"tasklist",
"=",
"[",
"]",
"for",
"m",
"in",
"models",
":",
"if",
"isinstance",
"(",
"m",
",",
"MultiFitterModel",
")",
":",
"tasklist",
"+=",
"[",
"(",
"'fit'",
",",
"[",
"m",
"]",
")",
"]",
"task... | Convert ``models`` into a list of tasks.
Each task is tuple ``(name, data)`` where ``name`` indicates the task
task and ``data`` is the relevant data for that task.
Supported tasks and data:
- ``'fit'`` and list of models
- ``'update-kargs'`` and ``None``
... | [
"Convert",
"models",
"into",
"a",
"list",
"of",
"tasks",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/_extras.py#L1250-L1290 | train |
gplepage/lsqfit | src/lsqfit/_extras.py | MultiFitter.coarse_grain | def coarse_grain(G, ncg):
""" Coarse-grain last index of array ``G``.
Bin the last index of array ``G`` in bins of width ``ncg``, and
replace each bin by its average. Return the binned results.
Args:
G: Array to be coarse-grained.
ncg: Bin width for coarse-grain... | python | def coarse_grain(G, ncg):
""" Coarse-grain last index of array ``G``.
Bin the last index of array ``G`` in bins of width ``ncg``, and
replace each bin by its average. Return the binned results.
Args:
G: Array to be coarse-grained.
ncg: Bin width for coarse-grain... | [
"def",
"coarse_grain",
"(",
"G",
",",
"ncg",
")",
":",
"if",
"ncg",
"<=",
"1",
":",
"return",
"G",
"G",
"=",
"numpy",
".",
"asarray",
"(",
"G",
")",
"nbin",
",",
"remainder",
"=",
"divmod",
"(",
"G",
".",
"shape",
"[",
"-",
"1",
"]",
",",
"nc... | Coarse-grain last index of array ``G``.
Bin the last index of array ``G`` in bins of width ``ncg``, and
replace each bin by its average. Return the binned results.
Args:
G: Array to be coarse-grained.
ncg: Bin width for coarse-graining. | [
"Coarse",
"-",
"grain",
"last",
"index",
"of",
"array",
"G",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/_extras.py#L1355-L1374 | train |
gplepage/lsqfit | src/lsqfit/_extras.py | MultiFitter.process_data | def process_data(data, models):
""" Convert ``data`` to processed data using ``models``.
Data from dictionary ``data`` is processed by each model
in list ``models``, and the results collected into a new
dictionary ``pdata`` for use in :meth:`MultiFitter.lsqfit`
and :meth:`MultiF... | python | def process_data(data, models):
""" Convert ``data`` to processed data using ``models``.
Data from dictionary ``data`` is processed by each model
in list ``models``, and the results collected into a new
dictionary ``pdata`` for use in :meth:`MultiFitter.lsqfit`
and :meth:`MultiF... | [
"def",
"process_data",
"(",
"data",
",",
"models",
")",
":",
"pdata",
"=",
"gvar",
".",
"BufferDict",
"(",
")",
"for",
"m",
"in",
"MultiFitter",
".",
"flatten_models",
"(",
"models",
")",
":",
"pdata",
"[",
"m",
".",
"datatag",
"]",
"=",
"(",
"m",
... | Convert ``data`` to processed data using ``models``.
Data from dictionary ``data`` is processed by each model
in list ``models``, and the results collected into a new
dictionary ``pdata`` for use in :meth:`MultiFitter.lsqfit`
and :meth:`MultiFitter.chained_lsqft`. | [
"Convert",
"data",
"to",
"processed",
"data",
"using",
"models",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/_extras.py#L1377-L1391 | train |
gplepage/lsqfit | src/lsqfit/_extras.py | MultiFitter.process_dataset | def process_dataset(dataset, models, **kargs):
""" Convert ``dataset`` to processed data using ``models``.
:class:`gvar.dataset.Dataset` (or similar dictionary) object
``dataset`` is processed by each model in list ``models``,
and the results collected into a new dictionary ``pdata`` fo... | python | def process_dataset(dataset, models, **kargs):
""" Convert ``dataset`` to processed data using ``models``.
:class:`gvar.dataset.Dataset` (or similar dictionary) object
``dataset`` is processed by each model in list ``models``,
and the results collected into a new dictionary ``pdata`` fo... | [
"def",
"process_dataset",
"(",
"dataset",
",",
"models",
",",
"**",
"kargs",
")",
":",
"dset",
"=",
"collections",
".",
"OrderedDict",
"(",
")",
"for",
"m",
"in",
"MultiFitter",
".",
"flatten_models",
"(",
"models",
")",
":",
"dset",
"[",
"m",
".",
"da... | Convert ``dataset`` to processed data using ``models``.
:class:`gvar.dataset.Dataset` (or similar dictionary) object
``dataset`` is processed by each model in list ``models``,
and the results collected into a new dictionary ``pdata`` for use in
:meth:`MultiFitter.lsqfit` and :meth:`Mult... | [
"Convert",
"dataset",
"to",
"processed",
"data",
"using",
"models",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/_extras.py#L1394-L1412 | train |
gplepage/lsqfit | examples/multifitter.py | Linear.buildprior | def buildprior(self, prior, mopt=None, extend=False):
" Extract the model's parameters from prior. "
newprior = {}
# allow for log-normal, etc priors
intercept, slope = gv.get_dictkeys(
prior, [self.intercept, self.slope]
)
newprior[intercept] = prior[inte... | python | def buildprior(self, prior, mopt=None, extend=False):
" Extract the model's parameters from prior. "
newprior = {}
# allow for log-normal, etc priors
intercept, slope = gv.get_dictkeys(
prior, [self.intercept, self.slope]
)
newprior[intercept] = prior[inte... | [
"def",
"buildprior",
"(",
"self",
",",
"prior",
",",
"mopt",
"=",
"None",
",",
"extend",
"=",
"False",
")",
":",
"\" Extract the model's parameters from prior. \"",
"newprior",
"=",
"{",
"}",
"intercept",
",",
"slope",
"=",
"gv",
".",
"get_dictkeys",
"(",
"p... | Extract the model's parameters from prior. | [
"Extract",
"the",
"model",
"s",
"parameters",
"from",
"prior",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/examples/multifitter.py#L98-L109 | train |
gplepage/lsqfit | examples/pendulum.py | show_plot | def show_plot(t_array, th_array):
""" Display theta vs t plot. """
th_mean = gv.mean(th_array)
th_sdev = gv.sdev(th_array)
thp = th_mean + th_sdev
thm = th_mean - th_sdev
plt.fill_between(t_array, thp, thm, color='0.8')
plt.plot(t_array, th_mean, linewidth=0.5)
plt.xlabel('$t$')
plt.... | python | def show_plot(t_array, th_array):
""" Display theta vs t plot. """
th_mean = gv.mean(th_array)
th_sdev = gv.sdev(th_array)
thp = th_mean + th_sdev
thm = th_mean - th_sdev
plt.fill_between(t_array, thp, thm, color='0.8')
plt.plot(t_array, th_mean, linewidth=0.5)
plt.xlabel('$t$')
plt.... | [
"def",
"show_plot",
"(",
"t_array",
",",
"th_array",
")",
":",
"th_mean",
"=",
"gv",
".",
"mean",
"(",
"th_array",
")",
"th_sdev",
"=",
"gv",
".",
"sdev",
"(",
"th_array",
")",
"thp",
"=",
"th_mean",
"+",
"th_sdev",
"thm",
"=",
"th_mean",
"-",
"th_sd... | Display theta vs t plot. | [
"Display",
"theta",
"vs",
"t",
"plot",
"."
] | 6a57fd687632c175fccb47d8e8e943cda5e9ce9d | https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/examples/pendulum.py#L127-L138 | train |
obsrvbl/flowlogs-reader | flowlogs_reader/aggregation.py | aggregated_records | def aggregated_records(all_records, key_fields=KEY_FIELDS):
"""
Yield dicts that correspond to aggregates of the flow records given by
the sequence of FlowRecords in `all_records`. Skips incomplete records.
This will consume the `all_records` iterator, and requires enough memory to
be able to read i... | python | def aggregated_records(all_records, key_fields=KEY_FIELDS):
"""
Yield dicts that correspond to aggregates of the flow records given by
the sequence of FlowRecords in `all_records`. Skips incomplete records.
This will consume the `all_records` iterator, and requires enough memory to
be able to read i... | [
"def",
"aggregated_records",
"(",
"all_records",
",",
"key_fields",
"=",
"KEY_FIELDS",
")",
":",
"flow_table",
"=",
"defaultdict",
"(",
"_FlowStats",
")",
"for",
"flow_record",
"in",
"all_records",
":",
"key",
"=",
"tuple",
"(",
"getattr",
"(",
"flow_record",
... | Yield dicts that correspond to aggregates of the flow records given by
the sequence of FlowRecords in `all_records`. Skips incomplete records.
This will consume the `all_records` iterator, and requires enough memory to
be able to read it entirely.
`key_fields` optionally contains the fields over which t... | [
"Yield",
"dicts",
"that",
"correspond",
"to",
"aggregates",
"of",
"the",
"flow",
"records",
"given",
"by",
"the",
"sequence",
"of",
"FlowRecords",
"in",
"all_records",
".",
"Skips",
"incomplete",
"records",
".",
"This",
"will",
"consume",
"the",
"all_records",
... | 248d8cb3cc586859b6744d30cebce0f359d9900c | https://github.com/obsrvbl/flowlogs-reader/blob/248d8cb3cc586859b6744d30cebce0f359d9900c/flowlogs_reader/aggregation.py#L48-L67 | train |
obsrvbl/flowlogs-reader | flowlogs_reader/__main__.py | action_print | def action_print(reader, *args):
"""Simply print the Flow Log records to output."""
arg_count = len(args)
if arg_count == 0:
stop_after = 0
elif arg_count == 1:
stop_after = int(args[0])
else:
raise RuntimeError("0 or 1 arguments expected for action 'print'")
for i, reco... | python | def action_print(reader, *args):
"""Simply print the Flow Log records to output."""
arg_count = len(args)
if arg_count == 0:
stop_after = 0
elif arg_count == 1:
stop_after = int(args[0])
else:
raise RuntimeError("0 or 1 arguments expected for action 'print'")
for i, reco... | [
"def",
"action_print",
"(",
"reader",
",",
"*",
"args",
")",
":",
"arg_count",
"=",
"len",
"(",
"args",
")",
"if",
"arg_count",
"==",
"0",
":",
"stop_after",
"=",
"0",
"elif",
"arg_count",
"==",
"1",
":",
"stop_after",
"=",
"int",
"(",
"args",
"[",
... | Simply print the Flow Log records to output. | [
"Simply",
"print",
"the",
"Flow",
"Log",
"records",
"to",
"output",
"."
] | 248d8cb3cc586859b6744d30cebce0f359d9900c | https://github.com/obsrvbl/flowlogs-reader/blob/248d8cb3cc586859b6744d30cebce0f359d9900c/flowlogs_reader/__main__.py#L31-L44 | train |
obsrvbl/flowlogs-reader | flowlogs_reader/__main__.py | action_ipset | def action_ipset(reader, *args):
"""Show the set of IPs seen in Flow Log records."""
ip_set = set()
for record in reader:
if record.log_status in (SKIPDATA, NODATA):
continue
ip_set.add(record.srcaddr)
ip_set.add(record.dstaddr)
for ip in ip_set:
print(ip) | python | def action_ipset(reader, *args):
"""Show the set of IPs seen in Flow Log records."""
ip_set = set()
for record in reader:
if record.log_status in (SKIPDATA, NODATA):
continue
ip_set.add(record.srcaddr)
ip_set.add(record.dstaddr)
for ip in ip_set:
print(ip) | [
"def",
"action_ipset",
"(",
"reader",
",",
"*",
"args",
")",
":",
"ip_set",
"=",
"set",
"(",
")",
"for",
"record",
"in",
"reader",
":",
"if",
"record",
".",
"log_status",
"in",
"(",
"SKIPDATA",
",",
"NODATA",
")",
":",
"continue",
"ip_set",
".",
"add... | Show the set of IPs seen in Flow Log records. | [
"Show",
"the",
"set",
"of",
"IPs",
"seen",
"in",
"Flow",
"Log",
"records",
"."
] | 248d8cb3cc586859b6744d30cebce0f359d9900c | https://github.com/obsrvbl/flowlogs-reader/blob/248d8cb3cc586859b6744d30cebce0f359d9900c/flowlogs_reader/__main__.py#L50-L60 | train |
obsrvbl/flowlogs-reader | flowlogs_reader/__main__.py | action_findip | def action_findip(reader, *args):
"""Find Flow Log records involving a specific IP or IPs."""
target_ips = set(args)
for record in reader:
if (record.srcaddr in target_ips) or (record.dstaddr in target_ips):
print(record.to_message()) | python | def action_findip(reader, *args):
"""Find Flow Log records involving a specific IP or IPs."""
target_ips = set(args)
for record in reader:
if (record.srcaddr in target_ips) or (record.dstaddr in target_ips):
print(record.to_message()) | [
"def",
"action_findip",
"(",
"reader",
",",
"*",
"args",
")",
":",
"target_ips",
"=",
"set",
"(",
"args",
")",
"for",
"record",
"in",
"reader",
":",
"if",
"(",
"record",
".",
"srcaddr",
"in",
"target_ips",
")",
"or",
"(",
"record",
".",
"dstaddr",
"i... | Find Flow Log records involving a specific IP or IPs. | [
"Find",
"Flow",
"Log",
"records",
"involving",
"a",
"specific",
"IP",
"or",
"IPs",
"."
] | 248d8cb3cc586859b6744d30cebce0f359d9900c | https://github.com/obsrvbl/flowlogs-reader/blob/248d8cb3cc586859b6744d30cebce0f359d9900c/flowlogs_reader/__main__.py#L66-L71 | train |
obsrvbl/flowlogs-reader | flowlogs_reader/__main__.py | action_aggregate | def action_aggregate(reader, *args):
"""Aggregate flow records by 5-tuple and print a tab-separated stream"""
all_aggregated = aggregated_records(reader)
first_row = next(all_aggregated)
keys = sorted(first_row.keys())
print(*keys, sep='\t')
# Join the first row with the rest of the rows and pr... | python | def action_aggregate(reader, *args):
"""Aggregate flow records by 5-tuple and print a tab-separated stream"""
all_aggregated = aggregated_records(reader)
first_row = next(all_aggregated)
keys = sorted(first_row.keys())
print(*keys, sep='\t')
# Join the first row with the rest of the rows and pr... | [
"def",
"action_aggregate",
"(",
"reader",
",",
"*",
"args",
")",
":",
"all_aggregated",
"=",
"aggregated_records",
"(",
"reader",
")",
"first_row",
"=",
"next",
"(",
"all_aggregated",
")",
"keys",
"=",
"sorted",
"(",
"first_row",
".",
"keys",
"(",
")",
")"... | Aggregate flow records by 5-tuple and print a tab-separated stream | [
"Aggregate",
"flow",
"records",
"by",
"5",
"-",
"tuple",
"and",
"print",
"a",
"tab",
"-",
"separated",
"stream"
] | 248d8cb3cc586859b6744d30cebce0f359d9900c | https://github.com/obsrvbl/flowlogs-reader/blob/248d8cb3cc586859b6744d30cebce0f359d9900c/flowlogs_reader/__main__.py#L77-L87 | train |
erdc/RAPIDpy | RAPIDpy/gis/weight.py | get_poly_area_geo | def get_poly_area_geo(poly):
"""
Calculates the area in meters squared of the individual polygon
"""
minx, miny, maxx, maxy = poly.bounds
# reproject polygon to get area
reprojected_for_area = Proj("+proj=aea +lat_1={0} +lat_1={1} "
"+lat_0={2} +lon_0={3}"
... | python | def get_poly_area_geo(poly):
"""
Calculates the area in meters squared of the individual polygon
"""
minx, miny, maxx, maxy = poly.bounds
# reproject polygon to get area
reprojected_for_area = Proj("+proj=aea +lat_1={0} +lat_1={1} "
"+lat_0={2} +lon_0={3}"
... | [
"def",
"get_poly_area_geo",
"(",
"poly",
")",
":",
"minx",
",",
"miny",
",",
"maxx",
",",
"maxy",
"=",
"poly",
".",
"bounds",
"reprojected_for_area",
"=",
"Proj",
"(",
"\"+proj=aea +lat_1={0} +lat_1={1} \"",
"\"+lat_0={2} +lon_0={3}\"",
".",
"format",
"(",
"miny",... | Calculates the area in meters squared of the individual polygon | [
"Calculates",
"the",
"area",
"in",
"meters",
"squared",
"of",
"the",
"individual",
"polygon"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/weight.py#L30-L47 | train |
erdc/RAPIDpy | RAPIDpy/gis/weight.py | CreateWeightTableECMWF | def CreateWeightTableECMWF(in_ecmwf_nc,
in_catchment_shapefile,
river_id,
in_connectivity_file,
out_weight_table,
area_id=None,
file_geodatabase=None):
""... | python | def CreateWeightTableECMWF(in_ecmwf_nc,
in_catchment_shapefile,
river_id,
in_connectivity_file,
out_weight_table,
area_id=None,
file_geodatabase=None):
""... | [
"def",
"CreateWeightTableECMWF",
"(",
"in_ecmwf_nc",
",",
"in_catchment_shapefile",
",",
"river_id",
",",
"in_connectivity_file",
",",
"out_weight_table",
",",
"area_id",
"=",
"None",
",",
"file_geodatabase",
"=",
"None",
")",
":",
"data_ecmwf_nc",
"=",
"Dataset",
"... | Create Weight Table for ECMWF Grids
.. note:: The grids are in the RAPIDpy package under
the gis/lsm_grids folder.
Parameters
----------
in_ecmwf_nc: str
Path to the ECMWF NetCDF grid.
in_catchment_shapefile: str
Path to the Catchment shapefile.
river_id: str
... | [
"Create",
"Weight",
"Table",
"for",
"ECMWF",
"Grids"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/weight.py#L277-L346 | train |
erdc/RAPIDpy | RAPIDpy/gis/weight.py | CreateWeightTableLDAS | def CreateWeightTableLDAS(in_ldas_nc,
in_nc_lon_var,
in_nc_lat_var,
in_catchment_shapefile,
river_id,
in_connectivity_file,
out_weight_table,
... | python | def CreateWeightTableLDAS(in_ldas_nc,
in_nc_lon_var,
in_nc_lat_var,
in_catchment_shapefile,
river_id,
in_connectivity_file,
out_weight_table,
... | [
"def",
"CreateWeightTableLDAS",
"(",
"in_ldas_nc",
",",
"in_nc_lon_var",
",",
"in_nc_lat_var",
",",
"in_catchment_shapefile",
",",
"river_id",
",",
"in_connectivity_file",
",",
"out_weight_table",
",",
"area_id",
"=",
"None",
",",
"file_geodatabase",
"=",
"None",
")",... | Create Weight Table for NLDAS, GLDAS grids as well as for 2D Joules,
or LIS Grids
Parameters
----------
in_ldas_nc: str
Path to the land surface model NetCDF grid.
in_nc_lon_var: str
The variable name in the NetCDF file for the longitude.
in_nc_lat_var: str
The variable ... | [
"Create",
"Weight",
"Table",
"for",
"NLDAS",
"GLDAS",
"grids",
"as",
"well",
"as",
"for",
"2D",
"Joules",
"or",
"LIS",
"Grids"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/weight.py#L349-L419 | train |
erdc/RAPIDpy | RAPIDpy/postprocess/generate_seasonal_averages.py | generate_single_seasonal_average | def generate_single_seasonal_average(args):
"""
This function calculates the seasonal average for a single day of the year
for all river segments
"""
qout_file = args[0]
seasonal_average_file = args[1]
day_of_year = args[2]
mp_lock = args[3]
min_day = day_of_year - 3
max_day = d... | python | def generate_single_seasonal_average(args):
"""
This function calculates the seasonal average for a single day of the year
for all river segments
"""
qout_file = args[0]
seasonal_average_file = args[1]
day_of_year = args[2]
mp_lock = args[3]
min_day = day_of_year - 3
max_day = d... | [
"def",
"generate_single_seasonal_average",
"(",
"args",
")",
":",
"qout_file",
"=",
"args",
"[",
"0",
"]",
"seasonal_average_file",
"=",
"args",
"[",
"1",
"]",
"day_of_year",
"=",
"args",
"[",
"2",
"]",
"mp_lock",
"=",
"args",
"[",
"3",
"]",
"min_day",
"... | This function calculates the seasonal average for a single day of the year
for all river segments | [
"This",
"function",
"calculates",
"the",
"seasonal",
"average",
"for",
"a",
"single",
"day",
"of",
"the",
"year",
"for",
"all",
"river",
"segments"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/postprocess/generate_seasonal_averages.py#L20-L67 | train |
erdc/RAPIDpy | RAPIDpy/postprocess/generate_seasonal_averages.py | generate_seasonal_averages | def generate_seasonal_averages(qout_file, seasonal_average_file,
num_cpus=multiprocessing.cpu_count()):
"""
This function loops through a CF compliant rapid streamflow
file to produce a netCDF file with a seasonal average for
365 days a year
"""
with RAPIDDataset(q... | python | def generate_seasonal_averages(qout_file, seasonal_average_file,
num_cpus=multiprocessing.cpu_count()):
"""
This function loops through a CF compliant rapid streamflow
file to produce a netCDF file with a seasonal average for
365 days a year
"""
with RAPIDDataset(q... | [
"def",
"generate_seasonal_averages",
"(",
"qout_file",
",",
"seasonal_average_file",
",",
"num_cpus",
"=",
"multiprocessing",
".",
"cpu_count",
"(",
")",
")",
":",
"with",
"RAPIDDataset",
"(",
"qout_file",
")",
"as",
"qout_nc_file",
":",
"print",
"(",
"\"Generatin... | This function loops through a CF compliant rapid streamflow
file to produce a netCDF file with a seasonal average for
365 days a year | [
"This",
"function",
"loops",
"through",
"a",
"CF",
"compliant",
"rapid",
"streamflow",
"file",
"to",
"produce",
"a",
"netCDF",
"file",
"with",
"a",
"seasonal",
"average",
"for",
"365",
"days",
"a",
"year"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/postprocess/generate_seasonal_averages.py#L70-L143 | train |
erdc/RAPIDpy | RAPIDpy/gis/__init__.py | open_shapefile | def open_shapefile(shapefile_path, file_geodatabase=None):
"""Opens a shapefile using either a shapefile path
or a file geodatabase
"""
if file_geodatabase:
gdb_driver = ogr.GetDriverByName("OpenFileGDB")
ogr_shapefile = gdb_driver.Open(file_geodatabase)
ogr_shapefile_lyr = o... | python | def open_shapefile(shapefile_path, file_geodatabase=None):
"""Opens a shapefile using either a shapefile path
or a file geodatabase
"""
if file_geodatabase:
gdb_driver = ogr.GetDriverByName("OpenFileGDB")
ogr_shapefile = gdb_driver.Open(file_geodatabase)
ogr_shapefile_lyr = o... | [
"def",
"open_shapefile",
"(",
"shapefile_path",
",",
"file_geodatabase",
"=",
"None",
")",
":",
"if",
"file_geodatabase",
":",
"gdb_driver",
"=",
"ogr",
".",
"GetDriverByName",
"(",
"\"OpenFileGDB\"",
")",
"ogr_shapefile",
"=",
"gdb_driver",
".",
"Open",
"(",
"f... | Opens a shapefile using either a shapefile path
or a file geodatabase | [
"Opens",
"a",
"shapefile",
"using",
"either",
"a",
"shapefile",
"path",
"or",
"a",
"file",
"geodatabase"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/__init__.py#L12-L23 | train |
erdc/RAPIDpy | RAPIDpy/rapid.py | RAPID._get_cygwin_path | def _get_cygwin_path(self, windows_path):
"""
Convert windows path to cygpath
"""
conv_cmd = [os.path.join(self._cygwin_bin_location, "cygpath.exe"),
"-u", windows_path]
process = Popen(conv_cmd,
stdout=PIPE, stderr=PIPE, shell=False)
... | python | def _get_cygwin_path(self, windows_path):
"""
Convert windows path to cygpath
"""
conv_cmd = [os.path.join(self._cygwin_bin_location, "cygpath.exe"),
"-u", windows_path]
process = Popen(conv_cmd,
stdout=PIPE, stderr=PIPE, shell=False)
... | [
"def",
"_get_cygwin_path",
"(",
"self",
",",
"windows_path",
")",
":",
"conv_cmd",
"=",
"[",
"os",
".",
"path",
".",
"join",
"(",
"self",
".",
"_cygwin_bin_location",
",",
"\"cygpath.exe\"",
")",
",",
"\"-u\"",
",",
"windows_path",
"]",
"process",
"=",
"Po... | Convert windows path to cygpath | [
"Convert",
"windows",
"path",
"to",
"cygpath"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/rapid.py#L255-L268 | train |
erdc/RAPIDpy | RAPIDpy/rapid.py | RAPID._create_symlink_cygwin | def _create_symlink_cygwin(self, initial_path, final_path):
"""
Use cygqin to generate symbolic link
"""
symlink_cmd = [os.path.join(self._cygwin_bin_location, "ln.exe"),
"-s", self._get_cygwin_path(initial_path),
self._get_cygwin_path(final_... | python | def _create_symlink_cygwin(self, initial_path, final_path):
"""
Use cygqin to generate symbolic link
"""
symlink_cmd = [os.path.join(self._cygwin_bin_location, "ln.exe"),
"-s", self._get_cygwin_path(initial_path),
self._get_cygwin_path(final_... | [
"def",
"_create_symlink_cygwin",
"(",
"self",
",",
"initial_path",
",",
"final_path",
")",
":",
"symlink_cmd",
"=",
"[",
"os",
".",
"path",
".",
"join",
"(",
"self",
".",
"_cygwin_bin_location",
",",
"\"ln.exe\"",
")",
",",
"\"-s\"",
",",
"self",
".",
"_ge... | Use cygqin to generate symbolic link | [
"Use",
"cygqin",
"to",
"generate",
"symbolic",
"link"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/rapid.py#L270-L284 | train |
erdc/RAPIDpy | RAPIDpy/rapid.py | RAPID._dos2unix_cygwin | def _dos2unix_cygwin(self, file_path):
"""
Use cygwin to convert file to unix format
"""
dos2unix_cmd = \
[os.path.join(self._cygwin_bin_location, "dos2unix.exe"),
self._get_cygwin_path(file_path)]
process = Popen(dos2unix_cmd,
std... | python | def _dos2unix_cygwin(self, file_path):
"""
Use cygwin to convert file to unix format
"""
dos2unix_cmd = \
[os.path.join(self._cygwin_bin_location, "dos2unix.exe"),
self._get_cygwin_path(file_path)]
process = Popen(dos2unix_cmd,
std... | [
"def",
"_dos2unix_cygwin",
"(",
"self",
",",
"file_path",
")",
":",
"dos2unix_cmd",
"=",
"[",
"os",
".",
"path",
".",
"join",
"(",
"self",
".",
"_cygwin_bin_location",
",",
"\"dos2unix.exe\"",
")",
",",
"self",
".",
"_get_cygwin_path",
"(",
"file_path",
")",... | Use cygwin to convert file to unix format | [
"Use",
"cygwin",
"to",
"convert",
"file",
"to",
"unix",
"format"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/rapid.py#L286-L295 | train |
erdc/RAPIDpy | RAPIDpy/rapid.py | RAPID.update_reach_number_data | def update_reach_number_data(self):
"""
Update the reach number data for the namelist based on input files.
.. warning:: You need to make sure you set *rapid_connect_file*
and *riv_bas_id_file* before running this function.
Example:
.. code:: python
... | python | def update_reach_number_data(self):
"""
Update the reach number data for the namelist based on input files.
.. warning:: You need to make sure you set *rapid_connect_file*
and *riv_bas_id_file* before running this function.
Example:
.. code:: python
... | [
"def",
"update_reach_number_data",
"(",
"self",
")",
":",
"if",
"not",
"self",
".",
"rapid_connect_file",
":",
"log",
"(",
"\"Missing rapid_connect_file. \"",
"\"Please set before running this function ...\"",
",",
"\"ERROR\"",
")",
"if",
"not",
"self",
".",
"riv_bas_id... | Update the reach number data for the namelist based on input files.
.. warning:: You need to make sure you set *rapid_connect_file*
and *riv_bas_id_file* before running this function.
Example:
.. code:: python
from RAPIDpy import RAPID
rapid_man... | [
"Update",
"the",
"reach",
"number",
"data",
"for",
"the",
"namelist",
"based",
"on",
"input",
"files",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/rapid.py#L342-L427 | train |
erdc/RAPIDpy | RAPIDpy/rapid.py | RAPID.generate_namelist_file | def generate_namelist_file(self, rapid_namelist_file):
"""
Generate rapid_namelist file.
Parameters
----------
rapid_namelist_file: str
Path of namelist file to generate from
parameters added to the RAPID manager.
"""
log("Generating RAPID... | python | def generate_namelist_file(self, rapid_namelist_file):
"""
Generate rapid_namelist file.
Parameters
----------
rapid_namelist_file: str
Path of namelist file to generate from
parameters added to the RAPID manager.
"""
log("Generating RAPID... | [
"def",
"generate_namelist_file",
"(",
"self",
",",
"rapid_namelist_file",
")",
":",
"log",
"(",
"\"Generating RAPID namelist file ...\"",
",",
"\"INFO\"",
")",
"try",
":",
"os",
".",
"remove",
"(",
"rapid_namelist_file",
")",
"except",
"OSError",
":",
"pass",
"wit... | Generate rapid_namelist file.
Parameters
----------
rapid_namelist_file: str
Path of namelist file to generate from
parameters added to the RAPID manager. | [
"Generate",
"rapid_namelist",
"file",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/rapid.py#L469-L501 | train |
erdc/RAPIDpy | RAPIDpy/rapid.py | RAPID.update_namelist_file | def update_namelist_file(self, rapid_namelist_file,
new_namelist_file=None):
"""
Update existing namelist file with new parameters
Parameters
----------
rapid_namelist_file: str
Path of namelist file to use in the simulation. It will be
... | python | def update_namelist_file(self, rapid_namelist_file,
new_namelist_file=None):
"""
Update existing namelist file with new parameters
Parameters
----------
rapid_namelist_file: str
Path of namelist file to use in the simulation. It will be
... | [
"def",
"update_namelist_file",
"(",
"self",
",",
"rapid_namelist_file",
",",
"new_namelist_file",
"=",
"None",
")",
":",
"if",
"os",
".",
"path",
".",
"exists",
"(",
"rapid_namelist_file",
")",
"and",
"rapid_namelist_file",
":",
"log",
"(",
"\"Adding missing input... | Update existing namelist file with new parameters
Parameters
----------
rapid_namelist_file: str
Path of namelist file to use in the simulation. It will be
updated with any parameters added to the RAPID manager.
new_namelist_file: str, optional
Path t... | [
"Update",
"existing",
"namelist",
"file",
"with",
"new",
"parameters"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/rapid.py#L503-L553 | train |
erdc/RAPIDpy | RAPIDpy/rapid.py | RAPID.generate_qinit_from_past_qout | def generate_qinit_from_past_qout(self, qinit_file, time_index=-1,
out_datetime=None):
"""
Generate qinit from a RAPID qout file
Parameters
----------
qinit_file: str
Path to output qinit_file.
time_index: int, optional
... | python | def generate_qinit_from_past_qout(self, qinit_file, time_index=-1,
out_datetime=None):
"""
Generate qinit from a RAPID qout file
Parameters
----------
qinit_file: str
Path to output qinit_file.
time_index: int, optional
... | [
"def",
"generate_qinit_from_past_qout",
"(",
"self",
",",
"qinit_file",
",",
"time_index",
"=",
"-",
"1",
",",
"out_datetime",
"=",
"None",
")",
":",
"if",
"not",
"self",
".",
"Qout_file",
"or",
"not",
"os",
".",
"path",
".",
"exists",
"(",
"self",
".",
... | Generate qinit from a RAPID qout file
Parameters
----------
qinit_file: str
Path to output qinit_file.
time_index: int, optional
Index of simulation to generate initial flow file.
Default is the last index.
out_datetime: :obj:`datetime.datetim... | [
"Generate",
"qinit",
"from",
"a",
"RAPID",
"qout",
"file"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/rapid.py#L802-L880 | train |
erdc/RAPIDpy | RAPIDpy/rapid.py | RAPID.generate_seasonal_intitialization | def generate_seasonal_intitialization(
self,
qinit_file,
datetime_start_initialization=datetime.datetime.utcnow()
):
"""This creates a seasonal qinit file from a RAPID qout file. This
requires a simulation Qout file with a longer time period of record and
to be CF com... | python | def generate_seasonal_intitialization(
self,
qinit_file,
datetime_start_initialization=datetime.datetime.utcnow()
):
"""This creates a seasonal qinit file from a RAPID qout file. This
requires a simulation Qout file with a longer time period of record and
to be CF com... | [
"def",
"generate_seasonal_intitialization",
"(",
"self",
",",
"qinit_file",
",",
"datetime_start_initialization",
"=",
"datetime",
".",
"datetime",
".",
"utcnow",
"(",
")",
")",
":",
"if",
"not",
"self",
".",
"Qout_file",
"or",
"not",
"os",
".",
"path",
".",
... | This creates a seasonal qinit file from a RAPID qout file. This
requires a simulation Qout file with a longer time period of record and
to be CF compliant. It takes the average of the current date +- 3 days
and goes back as far as possible.
Parameters
----------
qinit_fi... | [
"This",
"creates",
"a",
"seasonal",
"qinit",
"file",
"from",
"a",
"RAPID",
"qout",
"file",
".",
"This",
"requires",
"a",
"simulation",
"Qout",
"file",
"with",
"a",
"longer",
"time",
"period",
"of",
"record",
"and",
"to",
"be",
"CF",
"compliant",
".",
"It... | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/rapid.py#L882-L989 | train |
erdc/RAPIDpy | RAPIDpy/inflow/CreateInflowFileFromGriddedRunoff.py | CreateInflowFileFromGriddedRunoff.read_in_weight_table | def read_in_weight_table(self, in_weight_table):
"""
Read in weight table
"""
print("Reading the weight table...")
with open_csv(in_weight_table, "r") as csvfile:
reader = csv.reader(csvfile)
header_row = next(reader)
# check number of ... | python | def read_in_weight_table(self, in_weight_table):
"""
Read in weight table
"""
print("Reading the weight table...")
with open_csv(in_weight_table, "r") as csvfile:
reader = csv.reader(csvfile)
header_row = next(reader)
# check number of ... | [
"def",
"read_in_weight_table",
"(",
"self",
",",
"in_weight_table",
")",
":",
"print",
"(",
"\"Reading the weight table...\"",
")",
"with",
"open_csv",
"(",
"in_weight_table",
",",
"\"r\"",
")",
"as",
"csvfile",
":",
"reader",
"=",
"csv",
".",
"reader",
"(",
"... | Read in weight table | [
"Read",
"in",
"weight",
"table"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/inflow/CreateInflowFileFromGriddedRunoff.py#L52-L86 | train |
erdc/RAPIDpy | RAPIDpy/inflow/CreateInflowFileFromGriddedRunoff.py | CreateInflowFileFromGriddedRunoff._write_lat_lon | def _write_lat_lon(data_out_nc, rivid_lat_lon_z_file):
"""Add latitude and longitude each netCDF feature
Lookup table is a CSV file with rivid, Lat, Lon, columns.
Columns must be in that order and these must be the first
three columns.
"""
# only add if user adds
... | python | def _write_lat_lon(data_out_nc, rivid_lat_lon_z_file):
"""Add latitude and longitude each netCDF feature
Lookup table is a CSV file with rivid, Lat, Lon, columns.
Columns must be in that order and these must be the first
three columns.
"""
# only add if user adds
... | [
"def",
"_write_lat_lon",
"(",
"data_out_nc",
",",
"rivid_lat_lon_z_file",
")",
":",
"if",
"rivid_lat_lon_z_file",
"and",
"os",
".",
"path",
".",
"exists",
"(",
"rivid_lat_lon_z_file",
")",
":",
"lookup_table",
"=",
"np",
".",
"loadtxt",
"(",
"rivid_lat_lon_z_file"... | Add latitude and longitude each netCDF feature
Lookup table is a CSV file with rivid, Lat, Lon, columns.
Columns must be in that order and these must be the first
three columns. | [
"Add",
"latitude",
"and",
"longitude",
"each",
"netCDF",
"feature",
"Lookup",
"table",
"is",
"a",
"CSV",
"file",
"with",
"rivid",
"Lat",
"Lon",
"columns",
".",
"Columns",
"must",
"be",
"in",
"that",
"order",
"and",
"these",
"must",
"be",
"the",
"first",
... | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/inflow/CreateInflowFileFromGriddedRunoff.py#L89-L156 | train |
erdc/RAPIDpy | RAPIDpy/postprocess/goodness_of_fit.py | filter_nan | def filter_nan(s, o):
"""
this functions removed the data from simulated and observed data
whereever the observed data contains nan
this is used by all other functions, otherwise they will produce nan as
output
"""
data = np.array([s.flatten(), o.flatten()])
data = ... | python | def filter_nan(s, o):
"""
this functions removed the data from simulated and observed data
whereever the observed data contains nan
this is used by all other functions, otherwise they will produce nan as
output
"""
data = np.array([s.flatten(), o.flatten()])
data = ... | [
"def",
"filter_nan",
"(",
"s",
",",
"o",
")",
":",
"data",
"=",
"np",
".",
"array",
"(",
"[",
"s",
".",
"flatten",
"(",
")",
",",
"o",
".",
"flatten",
"(",
")",
"]",
")",
"data",
"=",
"np",
".",
"transpose",
"(",
"data",
")",
"data",
"=",
"... | this functions removed the data from simulated and observed data
whereever the observed data contains nan
this is used by all other functions, otherwise they will produce nan as
output | [
"this",
"functions",
"removed",
"the",
"data",
"from",
"simulated",
"and",
"observed",
"data",
"whereever",
"the",
"observed",
"data",
"contains",
"nan"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/postprocess/goodness_of_fit.py#L21-L32 | train |
erdc/RAPIDpy | RAPIDpy/postprocess/goodness_of_fit.py | find_goodness_of_fit | def find_goodness_of_fit(rapid_qout_file, reach_id_file, observed_file,
out_analysis_file, daily=False):
"""
Finds the goodness of fit comparing observed streamflow in a rapid Qout
file with simulated flows in a csv file.
Parameters
----------
rapid_qout_file: str
... | python | def find_goodness_of_fit(rapid_qout_file, reach_id_file, observed_file,
out_analysis_file, daily=False):
"""
Finds the goodness of fit comparing observed streamflow in a rapid Qout
file with simulated flows in a csv file.
Parameters
----------
rapid_qout_file: str
... | [
"def",
"find_goodness_of_fit",
"(",
"rapid_qout_file",
",",
"reach_id_file",
",",
"observed_file",
",",
"out_analysis_file",
",",
"daily",
"=",
"False",
")",
":",
"reach_id_list",
"=",
"np",
".",
"loadtxt",
"(",
"reach_id_file",
",",
"delimiter",
"=",
"\",\"",
"... | Finds the goodness of fit comparing observed streamflow in a rapid Qout
file with simulated flows in a csv file.
Parameters
----------
rapid_qout_file: str
Path to the RAPID Qout file.
reach_id_file: str
ath to file with river reach ID's associate with the RAPID Qout file.
I... | [
"Finds",
"the",
"goodness",
"of",
"fit",
"comparing",
"observed",
"streamflow",
"in",
"a",
"rapid",
"Qout",
"file",
"with",
"simulated",
"flows",
"in",
"a",
"csv",
"file",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/postprocess/goodness_of_fit.py#L183-L271 | train |
erdc/RAPIDpy | RAPIDpy/postprocess/goodness_of_fit.py | find_goodness_of_fit_csv | def find_goodness_of_fit_csv(observed_simulated_file, out_file=None):
"""
Finds the goodness of fit comparing observed and simulated flows
In the file, the first column is the observed flows and the
second column is the simulated flows.
Example::
33.5, 77.2
34.7, 73.0
Paramete... | python | def find_goodness_of_fit_csv(observed_simulated_file, out_file=None):
"""
Finds the goodness of fit comparing observed and simulated flows
In the file, the first column is the observed flows and the
second column is the simulated flows.
Example::
33.5, 77.2
34.7, 73.0
Paramete... | [
"def",
"find_goodness_of_fit_csv",
"(",
"observed_simulated_file",
",",
"out_file",
"=",
"None",
")",
":",
"observed_simulated_table",
"=",
"np",
".",
"loadtxt",
"(",
"observed_simulated_file",
",",
"ndmin",
"=",
"2",
",",
"delimiter",
"=",
"\",\"",
",",
"usecols"... | Finds the goodness of fit comparing observed and simulated flows
In the file, the first column is the observed flows and the
second column is the simulated flows.
Example::
33.5, 77.2
34.7, 73.0
Parameters
----------
observed_simulated_file: str
Path to the csv file wi... | [
"Finds",
"the",
"goodness",
"of",
"fit",
"comparing",
"observed",
"and",
"simulated",
"flows",
"In",
"the",
"file",
"the",
"first",
"column",
"is",
"the",
"observed",
"flows",
"and",
"the",
"second",
"column",
"is",
"the",
"simulated",
"flows",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/postprocess/goodness_of_fit.py#L274-L341 | train |
erdc/RAPIDpy | RAPIDpy/helper_functions.py | log | def log(message, severity="INFO", print_debug=True):
"""Logs, prints, or raises a message.
Arguments:
message -- message to report
severity -- string of one of these values:
CRITICAL|ERROR|WARNING|INFO|DEBUG
"""
print_me = ['WARNING', 'INFO', 'DEBUG']
if severity in pri... | python | def log(message, severity="INFO", print_debug=True):
"""Logs, prints, or raises a message.
Arguments:
message -- message to report
severity -- string of one of these values:
CRITICAL|ERROR|WARNING|INFO|DEBUG
"""
print_me = ['WARNING', 'INFO', 'DEBUG']
if severity in pri... | [
"def",
"log",
"(",
"message",
",",
"severity",
"=",
"\"INFO\"",
",",
"print_debug",
"=",
"True",
")",
":",
"print_me",
"=",
"[",
"'WARNING'",
",",
"'INFO'",
",",
"'DEBUG'",
"]",
"if",
"severity",
"in",
"print_me",
":",
"if",
"severity",
"==",
"'DEBUG'",
... | Logs, prints, or raises a message.
Arguments:
message -- message to report
severity -- string of one of these values:
CRITICAL|ERROR|WARNING|INFO|DEBUG | [
"Logs",
"prints",
"or",
"raises",
"a",
"message",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/helper_functions.py#L36-L53 | train |
erdc/RAPIDpy | RAPIDpy/helper_functions.py | csv_to_list | def csv_to_list(csv_file, delimiter=','):
"""
Reads in a CSV file and returns the contents as list,
where every row is stored as a sublist, and each element
in the sublist represents 1 cell in the table.
"""
with open_csv(csv_file) as csv_con:
if len(delimiter) > 1:
dialect =... | python | def csv_to_list(csv_file, delimiter=','):
"""
Reads in a CSV file and returns the contents as list,
where every row is stored as a sublist, and each element
in the sublist represents 1 cell in the table.
"""
with open_csv(csv_file) as csv_con:
if len(delimiter) > 1:
dialect =... | [
"def",
"csv_to_list",
"(",
"csv_file",
",",
"delimiter",
"=",
"','",
")",
":",
"with",
"open_csv",
"(",
"csv_file",
")",
"as",
"csv_con",
":",
"if",
"len",
"(",
"delimiter",
")",
">",
"1",
":",
"dialect",
"=",
"csv",
".",
"Sniffer",
"(",
")",
".",
... | Reads in a CSV file and returns the contents as list,
where every row is stored as a sublist, and each element
in the sublist represents 1 cell in the table. | [
"Reads",
"in",
"a",
"CSV",
"file",
"and",
"returns",
"the",
"contents",
"as",
"list",
"where",
"every",
"row",
"is",
"stored",
"as",
"a",
"sublist",
"and",
"each",
"element",
"in",
"the",
"sublist",
"represents",
"1",
"cell",
"in",
"the",
"table",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/helper_functions.py#L56-L70 | train |
erdc/RAPIDpy | RAPIDpy/helper_functions.py | add_latlon_metadata | def add_latlon_metadata(lat_var, lon_var):
"""Adds latitude and longitude metadata"""
lat_var.long_name = 'latitude'
lat_var.standard_name = 'latitude'
lat_var.units = 'degrees_north'
lat_var.axis = 'Y'
lon_var.long_name = 'longitude'
lon_var.standard_name = 'longitude'
lon_var.units = ... | python | def add_latlon_metadata(lat_var, lon_var):
"""Adds latitude and longitude metadata"""
lat_var.long_name = 'latitude'
lat_var.standard_name = 'latitude'
lat_var.units = 'degrees_north'
lat_var.axis = 'Y'
lon_var.long_name = 'longitude'
lon_var.standard_name = 'longitude'
lon_var.units = ... | [
"def",
"add_latlon_metadata",
"(",
"lat_var",
",",
"lon_var",
")",
":",
"lat_var",
".",
"long_name",
"=",
"'latitude'",
"lat_var",
".",
"standard_name",
"=",
"'latitude'",
"lat_var",
".",
"units",
"=",
"'degrees_north'",
"lat_var",
".",
"axis",
"=",
"'Y'",
"lo... | Adds latitude and longitude metadata | [
"Adds",
"latitude",
"and",
"longitude",
"metadata"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/helper_functions.py#L126-L136 | train |
erdc/RAPIDpy | RAPIDpy/inflow/lsm_rapid_process.py | generate_inflows_from_runoff | def generate_inflows_from_runoff(args):
"""
prepare runoff inflow file for rapid
"""
runoff_file_list = args[0]
file_index_list = args[1]
weight_table_file = args[2]
grid_type = args[3]
rapid_inflow_file = args[4]
rapid_inflow_tool = args[5]
mp_lock = args[6]
time_start_all ... | python | def generate_inflows_from_runoff(args):
"""
prepare runoff inflow file for rapid
"""
runoff_file_list = args[0]
file_index_list = args[1]
weight_table_file = args[2]
grid_type = args[3]
rapid_inflow_file = args[4]
rapid_inflow_tool = args[5]
mp_lock = args[6]
time_start_all ... | [
"def",
"generate_inflows_from_runoff",
"(",
"args",
")",
":",
"runoff_file_list",
"=",
"args",
"[",
"0",
"]",
"file_index_list",
"=",
"args",
"[",
"1",
"]",
"weight_table_file",
"=",
"args",
"[",
"2",
"]",
"grid_type",
"=",
"args",
"[",
"3",
"]",
"rapid_in... | prepare runoff inflow file for rapid | [
"prepare",
"runoff",
"inflow",
"file",
"for",
"rapid"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/inflow/lsm_rapid_process.py#L38-L87 | train |
erdc/RAPIDpy | RAPIDpy/inflow/lsm_rapid_process.py | determine_start_end_timestep | def determine_start_end_timestep(lsm_file_list,
file_re_match=None,
file_datetime_pattern=None,
expected_time_step=None,
lsm_grid_info=None):
"""
Determine the start and end date f... | python | def determine_start_end_timestep(lsm_file_list,
file_re_match=None,
file_datetime_pattern=None,
expected_time_step=None,
lsm_grid_info=None):
"""
Determine the start and end date f... | [
"def",
"determine_start_end_timestep",
"(",
"lsm_file_list",
",",
"file_re_match",
"=",
"None",
",",
"file_datetime_pattern",
"=",
"None",
",",
"expected_time_step",
"=",
"None",
",",
"lsm_grid_info",
"=",
"None",
")",
":",
"if",
"lsm_grid_info",
"is",
"None",
":"... | Determine the start and end date from LSM input files | [
"Determine",
"the",
"start",
"and",
"end",
"date",
"from",
"LSM",
"input",
"files"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/inflow/lsm_rapid_process.py#L471-L561 | train |
erdc/RAPIDpy | RAPIDpy/gis/voronoi.py | _get_voronoi_centroid_array | def _get_voronoi_centroid_array(lsm_lat_array, lsm_lon_array, extent):
"""
This function generates a voronoi centroid point
list from arrays of latitude and longitude
"""
YMin = extent[2]
YMax = extent[3]
XMin = extent[0]
XMax = extent[1]
ptList = []
if (lsm_lat_array.ndim == 2)... | python | def _get_voronoi_centroid_array(lsm_lat_array, lsm_lon_array, extent):
"""
This function generates a voronoi centroid point
list from arrays of latitude and longitude
"""
YMin = extent[2]
YMax = extent[3]
XMin = extent[0]
XMax = extent[1]
ptList = []
if (lsm_lat_array.ndim == 2)... | [
"def",
"_get_voronoi_centroid_array",
"(",
"lsm_lat_array",
",",
"lsm_lon_array",
",",
"extent",
")",
":",
"YMin",
"=",
"extent",
"[",
"2",
"]",
"YMax",
"=",
"extent",
"[",
"3",
"]",
"XMin",
"=",
"extent",
"[",
"0",
"]",
"XMax",
"=",
"extent",
"[",
"1"... | This function generates a voronoi centroid point
list from arrays of latitude and longitude | [
"This",
"function",
"generates",
"a",
"voronoi",
"centroid",
"point",
"list",
"from",
"arrays",
"of",
"latitude",
"and",
"longitude"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/voronoi.py#L19-L86 | train |
erdc/RAPIDpy | RAPIDpy/gis/voronoi.py | _get_voronoi_poly_points | def _get_voronoi_poly_points(vert_index_list, voronoi_vertices,
voronoi_centroid):
"""
This function returns the corner points for a
polygon from scipy voronoi information
"""
voronoi_poly_points = []
if -1 not in vert_index_list and len(vert_index_list) > 3:
... | python | def _get_voronoi_poly_points(vert_index_list, voronoi_vertices,
voronoi_centroid):
"""
This function returns the corner points for a
polygon from scipy voronoi information
"""
voronoi_poly_points = []
if -1 not in vert_index_list and len(vert_index_list) > 3:
... | [
"def",
"_get_voronoi_poly_points",
"(",
"vert_index_list",
",",
"voronoi_vertices",
",",
"voronoi_centroid",
")",
":",
"voronoi_poly_points",
"=",
"[",
"]",
"if",
"-",
"1",
"not",
"in",
"vert_index_list",
"and",
"len",
"(",
"vert_index_list",
")",
">",
"3",
":",... | This function returns the corner points for a
polygon from scipy voronoi information | [
"This",
"function",
"returns",
"the",
"corner",
"points",
"for",
"a",
"polygon",
"from",
"scipy",
"voronoi",
"information"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/voronoi.py#L89-L146 | train |
erdc/RAPIDpy | RAPIDpy/gis/voronoi.py | pointsToVoronoiGridShapefile | def pointsToVoronoiGridShapefile(lat, lon, vor_shp_path, extent=None):
"""
Converts points to shapefile grid via voronoi
"""
voronoi_centroids = _get_voronoi_centroid_array(lat, lon, extent)
# set-up output polygon shp
log("Creating output polygon shp {0}"
.format(os.path.basename(vor_s... | python | def pointsToVoronoiGridShapefile(lat, lon, vor_shp_path, extent=None):
"""
Converts points to shapefile grid via voronoi
"""
voronoi_centroids = _get_voronoi_centroid_array(lat, lon, extent)
# set-up output polygon shp
log("Creating output polygon shp {0}"
.format(os.path.basename(vor_s... | [
"def",
"pointsToVoronoiGridShapefile",
"(",
"lat",
",",
"lon",
",",
"vor_shp_path",
",",
"extent",
"=",
"None",
")",
":",
"voronoi_centroids",
"=",
"_get_voronoi_centroid_array",
"(",
"lat",
",",
"lon",
",",
"extent",
")",
"log",
"(",
"\"Creating output polygon sh... | Converts points to shapefile grid via voronoi | [
"Converts",
"points",
"to",
"shapefile",
"grid",
"via",
"voronoi"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/voronoi.py#L149-L197 | train |
erdc/RAPIDpy | RAPIDpy/gis/voronoi.py | pointsToVoronoiGridArray | def pointsToVoronoiGridArray(lat, lon, extent=None):
"""
Converts points to grid array via voronoi
"""
voronoi_centroids = _get_voronoi_centroid_array(lat, lon, extent)
# find nodes surrounding polygon centroid
# sort nodes in counterclockwise order
# create polygon perimeter through nodes
... | python | def pointsToVoronoiGridArray(lat, lon, extent=None):
"""
Converts points to grid array via voronoi
"""
voronoi_centroids = _get_voronoi_centroid_array(lat, lon, extent)
# find nodes surrounding polygon centroid
# sort nodes in counterclockwise order
# create polygon perimeter through nodes
... | [
"def",
"pointsToVoronoiGridArray",
"(",
"lat",
",",
"lon",
",",
"extent",
"=",
"None",
")",
":",
"voronoi_centroids",
"=",
"_get_voronoi_centroid_array",
"(",
"lat",
",",
"lon",
",",
"extent",
")",
"log",
"(",
"\"Building Voronoi polygons...\"",
")",
"voronoi_mana... | Converts points to grid array via voronoi | [
"Converts",
"points",
"to",
"grid",
"array",
"via",
"voronoi"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/voronoi.py#L200-L226 | train |
erdc/RAPIDpy | RAPIDpy/postprocess/merge.py | ConvertRAPIDOutputToCF._generate_time_values | def _generate_time_values(self):
"""
Generates time values for out nc file
"""
# Populate time values
log('writing times', 'INFO')
d1970 = datetime(1970, 1, 1, tzinfo=utc)
time_array = [[int((self.start_datetime - d1970).total_seconds())]]
dateti... | python | def _generate_time_values(self):
"""
Generates time values for out nc file
"""
# Populate time values
log('writing times', 'INFO')
d1970 = datetime(1970, 1, 1, tzinfo=utc)
time_array = [[int((self.start_datetime - d1970).total_seconds())]]
dateti... | [
"def",
"_generate_time_values",
"(",
"self",
")",
":",
"log",
"(",
"'writing times'",
",",
"'INFO'",
")",
"d1970",
"=",
"datetime",
"(",
"1970",
",",
"1",
",",
"1",
",",
"tzinfo",
"=",
"utc",
")",
"time_array",
"=",
"[",
"[",
"int",
"(",
"(",
"self",... | Generates time values for out nc file | [
"Generates",
"time",
"values",
"for",
"out",
"nc",
"file"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/postprocess/merge.py#L401-L424 | train |
erdc/RAPIDpy | RAPIDpy/postprocess/merge.py | ConvertRAPIDOutputToCF.convert | def convert(self):
"""
Copies data from RAPID netCDF output to a CF-compliant netCDF file.
"""
try:
log('Processing %s ...' % self.rapid_output_file_list[0])
time_start_conversion = datetime.utcnow()
# Validate the raw netCDF file
... | python | def convert(self):
"""
Copies data from RAPID netCDF output to a CF-compliant netCDF file.
"""
try:
log('Processing %s ...' % self.rapid_output_file_list[0])
time_start_conversion = datetime.utcnow()
# Validate the raw netCDF file
... | [
"def",
"convert",
"(",
"self",
")",
":",
"try",
":",
"log",
"(",
"'Processing %s ...'",
"%",
"self",
".",
"rapid_output_file_list",
"[",
"0",
"]",
")",
"time_start_conversion",
"=",
"datetime",
".",
"utcnow",
"(",
")",
"log",
"(",
"'validating input netCDF fil... | Copies data from RAPID netCDF output to a CF-compliant netCDF file. | [
"Copies",
"data",
"from",
"RAPID",
"netCDF",
"output",
"to",
"a",
"CF",
"-",
"compliant",
"netCDF",
"file",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/postprocess/merge.py#L495-L543 | train |
erdc/RAPIDpy | RAPIDpy/gis/muskingum.py | CreateMuskingumKFile | def CreateMuskingumKFile(lambda_k,
in_kfac_file,
out_k_file):
"""
Creates muskingum k file from kfac file.
Parameters
----------
lambda_k: float
The value for lambda given from RAPID after the calibration process.
If no calibration h... | python | def CreateMuskingumKFile(lambda_k,
in_kfac_file,
out_k_file):
"""
Creates muskingum k file from kfac file.
Parameters
----------
lambda_k: float
The value for lambda given from RAPID after the calibration process.
If no calibration h... | [
"def",
"CreateMuskingumKFile",
"(",
"lambda_k",
",",
"in_kfac_file",
",",
"out_k_file",
")",
":",
"kfac_table",
"=",
"csv_to_list",
"(",
"in_kfac_file",
")",
"with",
"open_csv",
"(",
"out_k_file",
",",
"'w'",
")",
"as",
"kfile",
":",
"k_writer",
"=",
"csv_writ... | Creates muskingum k file from kfac file.
Parameters
----------
lambda_k: float
The value for lambda given from RAPID after the calibration process.
If no calibration has been performed, 0.35 is reasonable.
in_kfac_file: str
The path to the input kfac file.
out_k_file: str
... | [
"Creates",
"muskingum",
"k",
"file",
"from",
"kfac",
"file",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/muskingum.py#L227-L259 | train |
erdc/RAPIDpy | RAPIDpy/gis/muskingum.py | CreateMuskingumXFileFromDranageLine | def CreateMuskingumXFileFromDranageLine(in_drainage_line,
x_id,
out_x_file,
file_geodatabase=None):
"""
Create muskingum X file from drainage line.
Parameters
----------
in_draina... | python | def CreateMuskingumXFileFromDranageLine(in_drainage_line,
x_id,
out_x_file,
file_geodatabase=None):
"""
Create muskingum X file from drainage line.
Parameters
----------
in_draina... | [
"def",
"CreateMuskingumXFileFromDranageLine",
"(",
"in_drainage_line",
",",
"x_id",
",",
"out_x_file",
",",
"file_geodatabase",
"=",
"None",
")",
":",
"ogr_drainage_line_shapefile_lyr",
",",
"ogr_drainage_line_shapefile",
"=",
"open_shapefile",
"(",
"in_drainage_line",
",",... | Create muskingum X file from drainage line.
Parameters
----------
in_drainage_line: str
Path to the stream network (i.e. Drainage Line) shapefile.
x_id: str
The name of the muksingum X field (i.e. 'Musk_x').
out_x_file: str
The path to the output x file.
file_geodatabase... | [
"Create",
"muskingum",
"X",
"file",
"from",
"drainage",
"line",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/muskingum.py#L262-L301 | train |
erdc/RAPIDpy | RAPIDpy/gis/muskingum.py | CreateConstMuskingumXFile | def CreateConstMuskingumXFile(x_value,
in_connectivity_file,
out_x_file):
"""
Create muskingum X file from value that is constant all the way through
for each river segment.
Parameters
----------
x_value: float
Value for the mu... | python | def CreateConstMuskingumXFile(x_value,
in_connectivity_file,
out_x_file):
"""
Create muskingum X file from value that is constant all the way through
for each river segment.
Parameters
----------
x_value: float
Value for the mu... | [
"def",
"CreateConstMuskingumXFile",
"(",
"x_value",
",",
"in_connectivity_file",
",",
"out_x_file",
")",
":",
"num_rivers",
"=",
"0",
"with",
"open_csv",
"(",
"in_connectivity_file",
",",
"\"r\"",
")",
"as",
"csvfile",
":",
"reader",
"=",
"csv_reader",
"(",
"csv... | Create muskingum X file from value that is constant all the way through
for each river segment.
Parameters
----------
x_value: float
Value for the muskingum X parameter [0-0.5].
in_connectivity_file: str
The path to the RAPID connectivity file.
out_x_file: str
The path t... | [
"Create",
"muskingum",
"X",
"file",
"from",
"value",
"that",
"is",
"constant",
"all",
"the",
"way",
"through",
"for",
"each",
"river",
"segment",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/muskingum.py#L304-L340 | train |
erdc/RAPIDpy | RAPIDpy/gis/network.py | StreamIDNextDownIDToConnectivity | def StreamIDNextDownIDToConnectivity(stream_id_array,
next_down_id_array,
out_csv_file):
"""
Creates RAPID connect file from stream_id array and next down id array
"""
list_all = []
max_count_upstream = 0
for hydroid in n... | python | def StreamIDNextDownIDToConnectivity(stream_id_array,
next_down_id_array,
out_csv_file):
"""
Creates RAPID connect file from stream_id array and next down id array
"""
list_all = []
max_count_upstream = 0
for hydroid in n... | [
"def",
"StreamIDNextDownIDToConnectivity",
"(",
"stream_id_array",
",",
"next_down_id_array",
",",
"out_csv_file",
")",
":",
"list_all",
"=",
"[",
"]",
"max_count_upstream",
"=",
"0",
"for",
"hydroid",
"in",
"np",
".",
"sort",
"(",
"stream_id_array",
")",
":",
"... | Creates RAPID connect file from stream_id array and next down id array | [
"Creates",
"RAPID",
"connect",
"file",
"from",
"stream_id",
"array",
"and",
"next",
"down",
"id",
"array"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/network.py#L25-L57 | train |
erdc/RAPIDpy | RAPIDpy/gis/network.py | CreateNetworkConnectivity | def CreateNetworkConnectivity(in_drainage_line,
river_id,
next_down_id,
out_connectivity_file,
file_geodatabase=None):
"""
Creates Network Connectivity input CSV file for RAPID
based on th... | python | def CreateNetworkConnectivity(in_drainage_line,
river_id,
next_down_id,
out_connectivity_file,
file_geodatabase=None):
"""
Creates Network Connectivity input CSV file for RAPID
based on th... | [
"def",
"CreateNetworkConnectivity",
"(",
"in_drainage_line",
",",
"river_id",
",",
"next_down_id",
",",
"out_connectivity_file",
",",
"file_geodatabase",
"=",
"None",
")",
":",
"ogr_drainage_line_shapefile_lyr",
",",
"ogr_drainage_line_shapefile",
"=",
"open_shapefile",
"("... | Creates Network Connectivity input CSV file for RAPID
based on the Drainage Line shapefile with river ID and
next downstream ID fields.
Parameters
----------
in_drainage_line: str
Path to the stream network (i.e. Drainage Line) shapefile.
river_id: str
The name of the field with... | [
"Creates",
"Network",
"Connectivity",
"input",
"CSV",
"file",
"for",
"RAPID",
"based",
"on",
"the",
"Drainage",
"Line",
"shapefile",
"with",
"river",
"ID",
"and",
"next",
"downstream",
"ID",
"fields",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/network.py#L60-L115 | train |
erdc/RAPIDpy | RAPIDpy/gis/network.py | CreateNetworkConnectivityTauDEMTree | def CreateNetworkConnectivityTauDEMTree(network_connectivity_tree_file,
out_csv_file):
"""
Creates Network Connectivity input CSV file for RAPID
based on the TauDEM network connectivity tree file
"""
stream_id_array = []
next_down_id_array = []
with op... | python | def CreateNetworkConnectivityTauDEMTree(network_connectivity_tree_file,
out_csv_file):
"""
Creates Network Connectivity input CSV file for RAPID
based on the TauDEM network connectivity tree file
"""
stream_id_array = []
next_down_id_array = []
with op... | [
"def",
"CreateNetworkConnectivityTauDEMTree",
"(",
"network_connectivity_tree_file",
",",
"out_csv_file",
")",
":",
"stream_id_array",
"=",
"[",
"]",
"next_down_id_array",
"=",
"[",
"]",
"with",
"open_csv",
"(",
"network_connectivity_tree_file",
",",
"\"r\"",
")",
"as",... | Creates Network Connectivity input CSV file for RAPID
based on the TauDEM network connectivity tree file | [
"Creates",
"Network",
"Connectivity",
"input",
"CSV",
"file",
"for",
"RAPID",
"based",
"on",
"the",
"TauDEM",
"network",
"connectivity",
"tree",
"file"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/network.py#L118-L139 | train |
erdc/RAPIDpy | RAPIDpy/gis/network.py | CreateNetworkConnectivityNHDPlus | def CreateNetworkConnectivityNHDPlus(in_drainage_line,
out_connectivity_file,
file_geodatabase=None):
"""
Creates Network Connectivity input CSV file for RAPID
based on the NHDPlus drainage lines with
COMID, FROMNODE, TONODE, and ... | python | def CreateNetworkConnectivityNHDPlus(in_drainage_line,
out_connectivity_file,
file_geodatabase=None):
"""
Creates Network Connectivity input CSV file for RAPID
based on the NHDPlus drainage lines with
COMID, FROMNODE, TONODE, and ... | [
"def",
"CreateNetworkConnectivityNHDPlus",
"(",
"in_drainage_line",
",",
"out_connectivity_file",
",",
"file_geodatabase",
"=",
"None",
")",
":",
"ogr_drainage_line_shapefile_lyr",
",",
"ogr_drainage_line_shapefile",
"=",
"open_shapefile",
"(",
"in_drainage_line",
",",
"file_... | Creates Network Connectivity input CSV file for RAPID
based on the NHDPlus drainage lines with
COMID, FROMNODE, TONODE, and DIVERGENCE fields.
Parameters
----------
in_drainage_line: str
Path to the stream network (i.e. Drainage Line) shapefile.
out_connectivity_file: str
The pa... | [
"Creates",
"Network",
"Connectivity",
"input",
"CSV",
"file",
"for",
"RAPID",
"based",
"on",
"the",
"NHDPlus",
"drainage",
"lines",
"with",
"COMID",
"FROMNODE",
"TONODE",
"and",
"DIVERGENCE",
"fields",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/network.py#L142-L247 | train |
erdc/RAPIDpy | RAPIDpy/gis/network.py | CreateSubsetFile | def CreateSubsetFile(in_drainage_line,
river_id,
out_riv_bas_id_file,
file_geodatabase=None):
"""
Creates River Basin ID subset input CSV file for RAPID
based on the Drainage Line shapefile with river ID and
next downstream ID fields
Pa... | python | def CreateSubsetFile(in_drainage_line,
river_id,
out_riv_bas_id_file,
file_geodatabase=None):
"""
Creates River Basin ID subset input CSV file for RAPID
based on the Drainage Line shapefile with river ID and
next downstream ID fields
Pa... | [
"def",
"CreateSubsetFile",
"(",
"in_drainage_line",
",",
"river_id",
",",
"out_riv_bas_id_file",
",",
"file_geodatabase",
"=",
"None",
")",
":",
"ogr_drainage_line_shapefile_lyr",
",",
"ogr_drainage_line_shapefile",
"=",
"open_shapefile",
"(",
"in_drainage_line",
",",
"fi... | Creates River Basin ID subset input CSV file for RAPID
based on the Drainage Line shapefile with river ID and
next downstream ID fields
Parameters
----------
in_drainage_line: str
Path to the stream network (i.e. Drainage Line) shapefile.
river_id: str
The name of the field with... | [
"Creates",
"River",
"Basin",
"ID",
"subset",
"input",
"CSV",
"file",
"for",
"RAPID",
"based",
"on",
"the",
"Drainage",
"Line",
"shapefile",
"with",
"river",
"ID",
"and",
"next",
"downstream",
"ID",
"fields"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/network.py#L250-L326 | train |
erdc/RAPIDpy | RAPIDpy/gis/workflow.py | CreateAllStaticECMWFFiles | def CreateAllStaticECMWFFiles(in_catchment,
catchment_river_id,
rapid_output_folder,
rapid_connect_file,
file_geodatabase=None
):
"""
This creates all of the ECMW... | python | def CreateAllStaticECMWFFiles(in_catchment,
catchment_river_id,
rapid_output_folder,
rapid_connect_file,
file_geodatabase=None
):
"""
This creates all of the ECMW... | [
"def",
"CreateAllStaticECMWFFiles",
"(",
"in_catchment",
",",
"catchment_river_id",
",",
"rapid_output_folder",
",",
"rapid_connect_file",
",",
"file_geodatabase",
"=",
"None",
")",
":",
"lsm_grid_folder",
"=",
"os",
".",
"path",
".",
"join",
"(",
"os",
".",
"path... | This creates all of the ECMWF grid weight tables using an area
weighted method based on Esri's RAPID_Toolbox.
Parameters
----------
in_catchment: str
Path to the Catchment shapefile.
catchment_river_id: str
The name of the field with the river ID (Ex. 'DrainLnID' or 'LINKNO').
r... | [
"This",
"creates",
"all",
"of",
"the",
"ECMWF",
"grid",
"weight",
"tables",
"using",
"an",
"area",
"weighted",
"method",
"based",
"on",
"Esri",
"s",
"RAPID_Toolbox",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/workflow.py#L151-L226 | train |
erdc/RAPIDpy | RAPIDpy/gis/workflow.py | CreateAllStaticECMWFRAPIDFiles | def CreateAllStaticECMWFRAPIDFiles(in_drainage_line,
river_id,
length_id,
slope_id,
next_down_id,
in_catchment,
... | python | def CreateAllStaticECMWFRAPIDFiles(in_drainage_line,
river_id,
length_id,
slope_id,
next_down_id,
in_catchment,
... | [
"def",
"CreateAllStaticECMWFRAPIDFiles",
"(",
"in_drainage_line",
",",
"river_id",
",",
"length_id",
",",
"slope_id",
",",
"next_down_id",
",",
"in_catchment",
",",
"catchment_river_id",
",",
"rapid_output_folder",
",",
"kfac_celerity",
"=",
"1000.0",
"/",
"3600.0",
"... | This creates all of the static RAPID files and ECMWF grid weight tables.
Parameters
----------
in_drainage_line: str
Path to the stream network (i.e. Drainage Line) shapefile.
river_id: str
The name of the field with the river ID
(Ex. 'HydroID', 'COMID', or 'LINKNO').
length... | [
"This",
"creates",
"all",
"of",
"the",
"static",
"RAPID",
"files",
"and",
"ECMWF",
"grid",
"weight",
"tables",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/workflow.py#L229-L332 | train |
erdc/RAPIDpy | RAPIDpy/dataset.py | compare_qout_files | def compare_qout_files(dataset1_path, dataset2_path):
"""
This function compares the output of RAPID Qout and tells you where
they are different.
"""
qout_same = False
d1 = RAPIDDataset(dataset1_path)
d2 = RAPIDDataset(dataset2_path)
if len(d1.get_river_id_array()) != len(d2.get_river_... | python | def compare_qout_files(dataset1_path, dataset2_path):
"""
This function compares the output of RAPID Qout and tells you where
they are different.
"""
qout_same = False
d1 = RAPIDDataset(dataset1_path)
d2 = RAPIDDataset(dataset2_path)
if len(d1.get_river_id_array()) != len(d2.get_river_... | [
"def",
"compare_qout_files",
"(",
"dataset1_path",
",",
"dataset2_path",
")",
":",
"qout_same",
"=",
"False",
"d1",
"=",
"RAPIDDataset",
"(",
"dataset1_path",
")",
"d2",
"=",
"RAPIDDataset",
"(",
"dataset2_path",
")",
"if",
"len",
"(",
"d1",
".",
"get_river_id... | This function compares the output of RAPID Qout and tells you where
they are different. | [
"This",
"function",
"compares",
"the",
"output",
"of",
"RAPID",
"Qout",
"and",
"tells",
"you",
"where",
"they",
"are",
"different",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/dataset.py#L25-L95 | train |
erdc/RAPIDpy | RAPIDpy/dataset.py | RAPIDDataset.is_time_variable_valid | def is_time_variable_valid(self):
"""
This function returns whether or not the time variable
is valid.
Returns
-------
boolean
True if the time variable is valid, otherwise false.
Example::
from RAPIDpy import RAPIDDataset
... | python | def is_time_variable_valid(self):
"""
This function returns whether or not the time variable
is valid.
Returns
-------
boolean
True if the time variable is valid, otherwise false.
Example::
from RAPIDpy import RAPIDDataset
... | [
"def",
"is_time_variable_valid",
"(",
"self",
")",
":",
"time_var_valid",
"=",
"False",
"if",
"'time'",
"in",
"self",
".",
"qout_nc",
".",
"variables",
".",
"keys",
"(",
")",
":",
"if",
"len",
"(",
"self",
".",
"qout_nc",
".",
"dimensions",
"[",
"'time'"... | This function returns whether or not the time variable
is valid.
Returns
-------
boolean
True if the time variable is valid, otherwise false.
Example::
from RAPIDpy import RAPIDDataset
path_to_rapid_qout = '/path/to/Qout.nc'
wi... | [
"This",
"function",
"returns",
"whether",
"or",
"not",
"the",
"time",
"variable",
"is",
"valid",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/dataset.py#L248-L288 | train |
erdc/RAPIDpy | RAPIDpy/dataset.py | RAPIDDataset.get_time_array | def get_time_array(self,
datetime_simulation_start=None,
simulation_time_step_seconds=None,
return_datetime=False,
time_index_array=None):
"""
This method extracts or generates an array of time.
The new v... | python | def get_time_array(self,
datetime_simulation_start=None,
simulation_time_step_seconds=None,
return_datetime=False,
time_index_array=None):
"""
This method extracts or generates an array of time.
The new v... | [
"def",
"get_time_array",
"(",
"self",
",",
"datetime_simulation_start",
"=",
"None",
",",
"simulation_time_step_seconds",
"=",
"None",
",",
"return_datetime",
"=",
"False",
",",
"time_index_array",
"=",
"None",
")",
":",
"if",
"datetime_simulation_start",
"is",
"not... | This method extracts or generates an array of time.
The new version of RAPID output has the time array stored.
However, the old version requires the user to know when the
simulation began and the time step of the output.
Parameters
----------
datetime_simulation_start: :... | [
"This",
"method",
"extracts",
"or",
"generates",
"an",
"array",
"of",
"time",
".",
"The",
"new",
"version",
"of",
"RAPID",
"output",
"has",
"the",
"time",
"array",
"stored",
".",
"However",
"the",
"old",
"version",
"requires",
"the",
"user",
"to",
"know",
... | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/dataset.py#L296-L412 | train |
erdc/RAPIDpy | RAPIDpy/dataset.py | RAPIDDataset.get_time_index_range | def get_time_index_range(self,
date_search_start=None,
date_search_end=None,
time_index_start=None,
time_index_end=None,
time_index=None):
"""
Generates a time... | python | def get_time_index_range(self,
date_search_start=None,
date_search_end=None,
time_index_start=None,
time_index_end=None,
time_index=None):
"""
Generates a time... | [
"def",
"get_time_index_range",
"(",
"self",
",",
"date_search_start",
"=",
"None",
",",
"date_search_end",
"=",
"None",
",",
"time_index_start",
"=",
"None",
",",
"time_index_end",
"=",
"None",
",",
"time_index",
"=",
"None",
")",
":",
"time_range",
"=",
"None... | Generates a time index range based on time bounds given.
This is useful for subset data extraction.
Parameters
----------
date_search_start: :obj:`datetime.datetime`, optional
This is a datetime object with the date of the minimum date for
starting.
date_... | [
"Generates",
"a",
"time",
"index",
"range",
"based",
"on",
"time",
"bounds",
"given",
".",
"This",
"is",
"useful",
"for",
"subset",
"data",
"extraction",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/dataset.py#L414-L532 | train |
erdc/RAPIDpy | RAPIDpy/dataset.py | RAPIDDataset.get_river_index | def get_river_index(self, river_id):
"""
This method retrieves the river index in the netCDF
dataset corresponding to the river ID.
Parameters
----------
river_id: int
The ID of the river segment.
Returns
-------
int:
The ... | python | def get_river_index(self, river_id):
"""
This method retrieves the river index in the netCDF
dataset corresponding to the river ID.
Parameters
----------
river_id: int
The ID of the river segment.
Returns
-------
int:
The ... | [
"def",
"get_river_index",
"(",
"self",
",",
"river_id",
")",
":",
"try",
":",
"return",
"np",
".",
"where",
"(",
"self",
".",
"get_river_id_array",
"(",
")",
"==",
"river_id",
")",
"[",
"0",
"]",
"[",
"0",
"]",
"except",
"IndexError",
":",
"raise",
"... | This method retrieves the river index in the netCDF
dataset corresponding to the river ID.
Parameters
----------
river_id: int
The ID of the river segment.
Returns
-------
int:
The index of the river ID's in the file.
Example::
... | [
"This",
"method",
"retrieves",
"the",
"river",
"index",
"in",
"the",
"netCDF",
"dataset",
"corresponding",
"to",
"the",
"river",
"ID",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/dataset.py#L555-L586 | train |
erdc/RAPIDpy | RAPIDpy/dataset.py | RAPIDDataset.get_subset_riverid_index_list | def get_subset_riverid_index_list(self, river_id_list):
"""
Gets the subset riverid_list from the netcdf file
Optional returns include the list of valid river ids in the dataset
as well as a list of missing rive rids
Parameters
----------
river_id_list: list or :... | python | def get_subset_riverid_index_list(self, river_id_list):
"""
Gets the subset riverid_list from the netcdf file
Optional returns include the list of valid river ids in the dataset
as well as a list of missing rive rids
Parameters
----------
river_id_list: list or :... | [
"def",
"get_subset_riverid_index_list",
"(",
"self",
",",
"river_id_list",
")",
":",
"netcdf_river_indices_list",
"=",
"[",
"]",
"valid_river_ids",
"=",
"[",
"]",
"missing_river_ids",
"=",
"[",
"]",
"for",
"river_id",
"in",
"river_id_list",
":",
"try",
":",
"net... | Gets the subset riverid_list from the netcdf file
Optional returns include the list of valid river ids in the dataset
as well as a list of missing rive rids
Parameters
----------
river_id_list: list or :obj:`numpy.array`
Array of river ID's for the river segments you... | [
"Gets",
"the",
"subset",
"riverid_list",
"from",
"the",
"netcdf",
"file",
"Optional",
"returns",
"include",
"the",
"list",
"of",
"valid",
"river",
"ids",
"in",
"the",
"dataset",
"as",
"well",
"as",
"a",
"list",
"of",
"missing",
"rive",
"rids"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/dataset.py#L588-L631 | train |
erdc/RAPIDpy | RAPIDpy/dataset.py | RAPIDDataset.get_qout | def get_qout(self,
river_id_array=None,
date_search_start=None,
date_search_end=None,
time_index_start=None,
time_index_end=None,
time_index=None,
time_index_array=None,
daily=False,
... | python | def get_qout(self,
river_id_array=None,
date_search_start=None,
date_search_end=None,
time_index_start=None,
time_index_end=None,
time_index=None,
time_index_array=None,
daily=False,
... | [
"def",
"get_qout",
"(",
"self",
",",
"river_id_array",
"=",
"None",
",",
"date_search_start",
"=",
"None",
",",
"date_search_end",
"=",
"None",
",",
"time_index_start",
"=",
"None",
",",
"time_index_end",
"=",
"None",
",",
"time_index",
"=",
"None",
",",
"ti... | This method extracts streamflow data by a single river ID
or by a river ID array. It has options to extract by date
or by date index.
Parameters
----------
river_id_array: :obj:`numpy.array` or list or int, optional
A single river ID or an array of river IDs.
... | [
"This",
"method",
"extracts",
"streamflow",
"data",
"by",
"a",
"single",
"river",
"ID",
"or",
"by",
"a",
"river",
"ID",
"array",
".",
"It",
"has",
"options",
"to",
"extract",
"by",
"date",
"or",
"by",
"date",
"index",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/dataset.py#L633-L732 | train |
erdc/RAPIDpy | RAPIDpy/dataset.py | RAPIDDataset.get_qout_index | def get_qout_index(self,
river_index_array=None,
date_search_start=None,
date_search_end=None,
time_index_start=None,
time_index_end=None,
time_index=None,
tim... | python | def get_qout_index(self,
river_index_array=None,
date_search_start=None,
date_search_end=None,
time_index_start=None,
time_index_end=None,
time_index=None,
tim... | [
"def",
"get_qout_index",
"(",
"self",
",",
"river_index_array",
"=",
"None",
",",
"date_search_start",
"=",
"None",
",",
"date_search_end",
"=",
"None",
",",
"time_index_start",
"=",
"None",
",",
"time_index_end",
"=",
"None",
",",
"time_index",
"=",
"None",
"... | This method extracts streamflow data by river index.
It allows for extracting single or multiple river streamflow arrays
It has options to extract by date or by date index.
See: :meth:`RAPIDpy.RAPIDDataset.get_qout` | [
"This",
"method",
"extracts",
"streamflow",
"data",
"by",
"river",
"index",
".",
"It",
"allows",
"for",
"extracting",
"single",
"or",
"multiple",
"river",
"streamflow",
"arrays",
"It",
"has",
"options",
"to",
"extract",
"by",
"date",
"or",
"by",
"date",
"ind... | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/dataset.py#L734-L819 | train |
erdc/RAPIDpy | RAPIDpy/dataset.py | RAPIDDataset.write_flows_to_csv | def write_flows_to_csv(self, path_to_output_file,
river_index=None,
river_id=None,
date_search_start=None,
date_search_end=None,
daily=False,
filter_mode="mea... | python | def write_flows_to_csv(self, path_to_output_file,
river_index=None,
river_id=None,
date_search_start=None,
date_search_end=None,
daily=False,
filter_mode="mea... | [
"def",
"write_flows_to_csv",
"(",
"self",
",",
"path_to_output_file",
",",
"river_index",
"=",
"None",
",",
"river_id",
"=",
"None",
",",
"date_search_start",
"=",
"None",
",",
"date_search_end",
"=",
"None",
",",
"daily",
"=",
"False",
",",
"filter_mode",
"="... | Write out RAPID output to CSV file.
.. note:: Need either *reach_id* or *reach_index* parameter,
but either can be used.
Parameters
----------
path_to_output_file: str
Path to the output csv file.
river_index: :obj:`datetime.datetime`, optional
... | [
"Write",
"out",
"RAPID",
"output",
"to",
"CSV",
"file",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/dataset.py#L821-L946 | train |
erdc/RAPIDpy | RAPIDpy/dataset.py | RAPIDDataset.write_flows_to_gssha_time_series_xys | def write_flows_to_gssha_time_series_xys(self,
path_to_output_file,
series_name,
series_id,
river_index=None,
... | python | def write_flows_to_gssha_time_series_xys(self,
path_to_output_file,
series_name,
series_id,
river_index=None,
... | [
"def",
"write_flows_to_gssha_time_series_xys",
"(",
"self",
",",
"path_to_output_file",
",",
"series_name",
",",
"series_id",
",",
"river_index",
"=",
"None",
",",
"river_id",
"=",
"None",
",",
"date_search_start",
"=",
"None",
",",
"date_search_end",
"=",
"None",
... | Write out RAPID output to GSSHA WMS time series xys file.
Parameters
----------
path_to_output_file: str
Path to the output xys file.
series_name: str
The name for the series.
series_id: int
The ID to give the series.
river_index: :obj... | [
"Write",
"out",
"RAPID",
"output",
"to",
"GSSHA",
"WMS",
"time",
"series",
"xys",
"file",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/dataset.py#L948-L1077 | train |
erdc/RAPIDpy | RAPIDpy/dataset.py | RAPIDDataset.write_flows_to_gssha_time_series_ihg | def write_flows_to_gssha_time_series_ihg(self,
path_to_output_file,
connection_list_file,
date_search_start=None,
date_search_end=None,
... | python | def write_flows_to_gssha_time_series_ihg(self,
path_to_output_file,
connection_list_file,
date_search_start=None,
date_search_end=None,
... | [
"def",
"write_flows_to_gssha_time_series_ihg",
"(",
"self",
",",
"path_to_output_file",
",",
"connection_list_file",
",",
"date_search_start",
"=",
"None",
",",
"date_search_end",
"=",
"None",
",",
"daily",
"=",
"False",
",",
"filter_mode",
"=",
"\"mean\"",
")",
":"... | Write out RAPID output to GSSHA time series ihg file
.. note:: See: http://www.gsshawiki.com/Surface_Water_Routing:Introducing_Dischage/Constituent_Hydrographs
.. note:: GSSHA project card is CHAN_POINT_INPUT
Parameters
----------
path_to_output_file: str
Path to t... | [
"Write",
"out",
"RAPID",
"output",
"to",
"GSSHA",
"time",
"series",
"ihg",
"file"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/dataset.py#L1079-L1232 | train |
erdc/RAPIDpy | RAPIDpy/utilities.py | case_insensitive_file_search | def case_insensitive_file_search(directory, pattern):
"""
Looks for file with pattern with case insensitive search
"""
try:
return os.path.join(
directory,
[filename for filename in os.listdir(directory)
if re.search(pattern, filename, re.IGNORECASE)][0])
... | python | def case_insensitive_file_search(directory, pattern):
"""
Looks for file with pattern with case insensitive search
"""
try:
return os.path.join(
directory,
[filename for filename in os.listdir(directory)
if re.search(pattern, filename, re.IGNORECASE)][0])
... | [
"def",
"case_insensitive_file_search",
"(",
"directory",
",",
"pattern",
")",
":",
"try",
":",
"return",
"os",
".",
"path",
".",
"join",
"(",
"directory",
",",
"[",
"filename",
"for",
"filename",
"in",
"os",
".",
"listdir",
"(",
"directory",
")",
"if",
"... | Looks for file with pattern with case insensitive search | [
"Looks",
"for",
"file",
"with",
"pattern",
"with",
"case",
"insensitive",
"search"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/utilities.py#L18-L29 | train |
erdc/RAPIDpy | RAPIDpy/utilities.py | partition | def partition(lst, n):
"""
Divide list into n equal parts
"""
q, r = divmod(len(lst), n)
indices = [q*i + min(i, r) for i in xrange(n+1)]
return [lst[indices[i]:indices[i+1]] for i in xrange(n)], \
[list(xrange(indices[i], indices[i+1])) for i in xrange(n)] | python | def partition(lst, n):
"""
Divide list into n equal parts
"""
q, r = divmod(len(lst), n)
indices = [q*i + min(i, r) for i in xrange(n+1)]
return [lst[indices[i]:indices[i+1]] for i in xrange(n)], \
[list(xrange(indices[i], indices[i+1])) for i in xrange(n)] | [
"def",
"partition",
"(",
"lst",
",",
"n",
")",
":",
"q",
",",
"r",
"=",
"divmod",
"(",
"len",
"(",
"lst",
")",
",",
"n",
")",
"indices",
"=",
"[",
"q",
"*",
"i",
"+",
"min",
"(",
"i",
",",
"r",
")",
"for",
"i",
"in",
"xrange",
"(",
"n",
... | Divide list into n equal parts | [
"Divide",
"list",
"into",
"n",
"equal",
"parts"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/utilities.py#L32-L39 | train |
erdc/RAPIDpy | RAPIDpy/utilities.py | get_valid_directory_list | def get_valid_directory_list(input_directory):
"""
Get a list of folders
"""
valid_input_directories = []
for directory in os.listdir(input_directory):
if os.path.isdir(os.path.join(input_directory, directory)):
valid_input_directories.append(directory)
else:
... | python | def get_valid_directory_list(input_directory):
"""
Get a list of folders
"""
valid_input_directories = []
for directory in os.listdir(input_directory):
if os.path.isdir(os.path.join(input_directory, directory)):
valid_input_directories.append(directory)
else:
... | [
"def",
"get_valid_directory_list",
"(",
"input_directory",
")",
":",
"valid_input_directories",
"=",
"[",
"]",
"for",
"directory",
"in",
"os",
".",
"listdir",
"(",
"input_directory",
")",
":",
"if",
"os",
".",
"path",
".",
"isdir",
"(",
"os",
".",
"path",
... | Get a list of folders | [
"Get",
"a",
"list",
"of",
"folders"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/utilities.py#L42-L52 | train |
erdc/RAPIDpy | RAPIDpy/gis/taudem.py | TauDEM._run_mpi_cmd | def _run_mpi_cmd(self, cmd):
"""
This runs the command you send in
"""
log("Number of Processes: {0}".format(self.num_processors))
time_start = datetime.utcnow()
# Construct the taudem command line.
cmd = [self.mpiexec_path, '-n', str(self.num_processors)] + cmd
... | python | def _run_mpi_cmd(self, cmd):
"""
This runs the command you send in
"""
log("Number of Processes: {0}".format(self.num_processors))
time_start = datetime.utcnow()
# Construct the taudem command line.
cmd = [self.mpiexec_path, '-n', str(self.num_processors)] + cmd
... | [
"def",
"_run_mpi_cmd",
"(",
"self",
",",
"cmd",
")",
":",
"log",
"(",
"\"Number of Processes: {0}\"",
".",
"format",
"(",
"self",
".",
"num_processors",
")",
")",
"time_start",
"=",
"datetime",
".",
"utcnow",
"(",
")",
"cmd",
"=",
"[",
"self",
".",
"mpie... | This runs the command you send in | [
"This",
"runs",
"the",
"command",
"you",
"send",
"in"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/taudem.py#L79-L97 | train |
erdc/RAPIDpy | RAPIDpy/gis/taudem.py | TauDEM._add_prj_file | def _add_prj_file(original_gis_file, new_gis_file):
"""
Adds projection file
"""
out_prj_file = "{0}.prj".format(os.path.splitext(new_gis_file)[0])
if original_gis_file.endswith(".shp"):
dataset = ogr.Open(original_gis_file)
layer = dataset.GetLayer()
... | python | def _add_prj_file(original_gis_file, new_gis_file):
"""
Adds projection file
"""
out_prj_file = "{0}.prj".format(os.path.splitext(new_gis_file)[0])
if original_gis_file.endswith(".shp"):
dataset = ogr.Open(original_gis_file)
layer = dataset.GetLayer()
... | [
"def",
"_add_prj_file",
"(",
"original_gis_file",
",",
"new_gis_file",
")",
":",
"out_prj_file",
"=",
"\"{0}.prj\"",
".",
"format",
"(",
"os",
".",
"path",
".",
"splitext",
"(",
"new_gis_file",
")",
"[",
"0",
"]",
")",
"if",
"original_gis_file",
".",
"endswi... | Adds projection file | [
"Adds",
"projection",
"file"
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/taudem.py#L100-L116 | train |
erdc/RAPIDpy | RAPIDpy/gis/taudem.py | TauDEM.extractSubNetwork | def extractSubNetwork(network_file,
out_subset_network_file,
outlet_ids,
river_id_field,
next_down_id_field,
river_magnitude_field,
safe_mode=True):
"""
... | python | def extractSubNetwork(network_file,
out_subset_network_file,
outlet_ids,
river_id_field,
next_down_id_field,
river_magnitude_field,
safe_mode=True):
"""
... | [
"def",
"extractSubNetwork",
"(",
"network_file",
",",
"out_subset_network_file",
",",
"outlet_ids",
",",
"river_id_field",
",",
"next_down_id_field",
",",
"river_magnitude_field",
",",
"safe_mode",
"=",
"True",
")",
":",
"network_shapefile",
"=",
"ogr",
".",
"Open",
... | Extracts a subset river network from the main river network based on
the outlet IDs.
Parameters
----------
network_file: str
Path to the stream network shapefile.
out_subset_network_file: str
Path to the output subset stream network shapefile.
out... | [
"Extracts",
"a",
"subset",
"river",
"network",
"from",
"the",
"main",
"river",
"network",
"based",
"on",
"the",
"outlet",
"IDs",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/taudem.py#L119-L268 | train |
erdc/RAPIDpy | RAPIDpy/gis/taudem.py | TauDEM.extractLargestSubNetwork | def extractLargestSubNetwork(cls,
network_file,
out_subset_network_file,
river_id_field,
next_down_id_field,
river_magnitude_field,
... | python | def extractLargestSubNetwork(cls,
network_file,
out_subset_network_file,
river_id_field,
next_down_id_field,
river_magnitude_field,
... | [
"def",
"extractLargestSubNetwork",
"(",
"cls",
",",
"network_file",
",",
"out_subset_network_file",
",",
"river_id_field",
",",
"next_down_id_field",
",",
"river_magnitude_field",
",",
"safe_mode",
"=",
"True",
")",
":",
"network_shapefile",
"=",
"ogr",
".",
"Open",
... | Extracts the larges sub network from the watershed based on the
magnitude parameter.
Parameters
----------
network_file: str
Path to the stream network shapefile.
out_subset_network_file: str
Path to the output subset stream network shapefile.
riv... | [
"Extracts",
"the",
"larges",
"sub",
"network",
"from",
"the",
"watershed",
"based",
"on",
"the",
"magnitude",
"parameter",
"."
] | 50e14e130554b254a00ff23b226cd7e4c6cfe91a | https://github.com/erdc/RAPIDpy/blob/50e14e130554b254a00ff23b226cd7e4c6cfe91a/RAPIDpy/gis/taudem.py#L271-L338 | train |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.