repository_name stringlengths 5 67 | func_path_in_repository stringlengths 4 234 | func_name stringlengths 0 314 | whole_func_string stringlengths 52 3.87M | language stringclasses 6
values | func_code_string stringlengths 52 3.87M | func_documentation_string stringlengths 1 47.2k | func_code_url stringlengths 85 339 |
|---|---|---|---|---|---|---|---|
ml4ai/delphi | delphi/utils/fp.py | foldl | def foldl(f: Callable[[T, U], T], x: T, xs: Iterable[U]) -> T:
""" Returns the accumulated result of a binary function applied to elements
of an iterable.
.. math::
foldl(f, x_0, [x_1, x_2, x_3]) = f(f(f(f(x_0, x_1), x_2), x_3)
Examples
--------
>>> from delphi.utils.fp import foldl
... | python | def foldl(f: Callable[[T, U], T], x: T, xs: Iterable[U]) -> T:
""" Returns the accumulated result of a binary function applied to elements
of an iterable.
.. math::
foldl(f, x_0, [x_1, x_2, x_3]) = f(f(f(f(x_0, x_1), x_2), x_3)
Examples
--------
>>> from delphi.utils.fp import foldl
... | Returns the accumulated result of a binary function applied to elements
of an iterable.
.. math::
foldl(f, x_0, [x_1, x_2, x_3]) = f(f(f(f(x_0, x_1), x_2), x_3)
Examples
--------
>>> from delphi.utils.fp import foldl
>>> foldl(lambda x, y: x + y, 10, range(5))
20 | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/utils/fp.py#L146-L161 |
ml4ai/delphi | delphi/utils/fp.py | foldl1 | def foldl1(f: Callable[[T, T], T], xs: Iterable[T]) -> T:
""" Returns the accumulated result of a binary function applied to elements
of an iterable.
.. math::
foldl1(f, [x_0, x_1, x_2, x_3]) = f(f(f(f(x_0, x_1), x_2), x_3)
Examples
--------
>>> from delphi.utils.fp import foldl1
... | python | def foldl1(f: Callable[[T, T], T], xs: Iterable[T]) -> T:
""" Returns the accumulated result of a binary function applied to elements
of an iterable.
.. math::
foldl1(f, [x_0, x_1, x_2, x_3]) = f(f(f(f(x_0, x_1), x_2), x_3)
Examples
--------
>>> from delphi.utils.fp import foldl1
... | Returns the accumulated result of a binary function applied to elements
of an iterable.
.. math::
foldl1(f, [x_0, x_1, x_2, x_3]) = f(f(f(f(x_0, x_1), x_2), x_3)
Examples
--------
>>> from delphi.utils.fp import foldl1
>>> foldl1(lambda x, y: x + y, range(5))
10 | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/utils/fp.py#L164-L179 |
ml4ai/delphi | delphi/utils/fp.py | flatten | def flatten(xs: Union[List, Tuple]) -> List:
""" Flatten a nested list or tuple. """
return (
sum(map(flatten, xs), [])
if (isinstance(xs, list) or isinstance(xs, tuple))
else [xs]
) | python | def flatten(xs: Union[List, Tuple]) -> List:
""" Flatten a nested list or tuple. """
return (
sum(map(flatten, xs), [])
if (isinstance(xs, list) or isinstance(xs, tuple))
else [xs]
) | Flatten a nested list or tuple. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/utils/fp.py#L182-L188 |
ml4ai/delphi | delphi/utils/fp.py | iterate | def iterate(f: Callable[[T], T], x: T) -> Iterator[T]:
""" Makes infinite iterator that returns the result of successive
applications of a function to an element
.. math::
iterate(f, x) = [x, f(x), f(f(x)), f(f(f(x))), ...]
Examples
--------
>>> from delphi.utils.fp import iterate, tak... | python | def iterate(f: Callable[[T], T], x: T) -> Iterator[T]:
""" Makes infinite iterator that returns the result of successive
applications of a function to an element
.. math::
iterate(f, x) = [x, f(x), f(f(x)), f(f(f(x))), ...]
Examples
--------
>>> from delphi.utils.fp import iterate, tak... | Makes infinite iterator that returns the result of successive
applications of a function to an element
.. math::
iterate(f, x) = [x, f(x), f(f(x)), f(f(f(x))), ...]
Examples
--------
>>> from delphi.utils.fp import iterate, take
>>> list(take(5, iterate(lambda x: x*2, 1)))
[1, 2, 4... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/utils/fp.py#L191-L204 |
ml4ai/delphi | delphi/utils/fp.py | ptake | def ptake(n: int, xs: Iterable[T]) -> Iterable[T]:
""" take with a tqdm progress bar. """
return tqdm(take(n, xs), total=n) | python | def ptake(n: int, xs: Iterable[T]) -> Iterable[T]:
""" take with a tqdm progress bar. """
return tqdm(take(n, xs), total=n) | take with a tqdm progress bar. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/utils/fp.py#L211-L213 |
ml4ai/delphi | delphi/utils/fp.py | ltake | def ltake(n: int, xs: Iterable[T]) -> List[T]:
""" A non-lazy version of take. """
return list(take(n, xs)) | python | def ltake(n: int, xs: Iterable[T]) -> List[T]:
""" A non-lazy version of take. """
return list(take(n, xs)) | A non-lazy version of take. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/utils/fp.py#L216-L218 |
ml4ai/delphi | delphi/utils/fp.py | compose | def compose(*fs: Any) -> Callable:
""" Compose functions from left to right.
e.g. compose(f, g)(x) = f(g(x))
"""
return foldl1(lambda f, g: lambda *x: f(g(*x)), fs) | python | def compose(*fs: Any) -> Callable:
""" Compose functions from left to right.
e.g. compose(f, g)(x) = f(g(x))
"""
return foldl1(lambda f, g: lambda *x: f(g(*x)), fs) | Compose functions from left to right.
e.g. compose(f, g)(x) = f(g(x)) | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/utils/fp.py#L221-L226 |
ml4ai/delphi | delphi/utils/fp.py | rcompose | def rcompose(*fs: Any) -> Callable:
""" Compose functions from right to left.
e.g. rcompose(f, g)(x) = g(f(x))
"""
return foldl1(lambda f, g: lambda *x: g(f(*x)), fs) | python | def rcompose(*fs: Any) -> Callable:
""" Compose functions from right to left.
e.g. rcompose(f, g)(x) = g(f(x))
"""
return foldl1(lambda f, g: lambda *x: g(f(*x)), fs) | Compose functions from right to left.
e.g. rcompose(f, g)(x) = g(f(x)) | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/utils/fp.py#L229-L234 |
ml4ai/delphi | delphi/utils/fp.py | flatMap | def flatMap(f: Callable, xs: Iterable) -> List:
""" Map a function onto an iterable and flatten the result. """
return flatten(lmap(f, xs)) | python | def flatMap(f: Callable, xs: Iterable) -> List:
""" Map a function onto an iterable and flatten the result. """
return flatten(lmap(f, xs)) | Map a function onto an iterable and flatten the result. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/utils/fp.py#L237-L239 |
ml4ai/delphi | delphi/utils/fp.py | grouper | def grouper(xs: Iterable, n: int, fillvalue=None):
"""Collect data into fixed-length chunks or blocks.
>>> from delphi.utils.fp import grouper
>>> list(grouper('ABCDEFG', 3, 'x'))
[('A', 'B', 'C'), ('D', 'E', 'F'), ('G', 'x', 'x')]
"""
args = [iter(xs)] * n
return zip_longest(*args, fillvalu... | python | def grouper(xs: Iterable, n: int, fillvalue=None):
"""Collect data into fixed-length chunks or blocks.
>>> from delphi.utils.fp import grouper
>>> list(grouper('ABCDEFG', 3, 'x'))
[('A', 'B', 'C'), ('D', 'E', 'F'), ('G', 'x', 'x')]
"""
args = [iter(xs)] * n
return zip_longest(*args, fillvalu... | Collect data into fixed-length chunks or blocks.
>>> from delphi.utils.fp import grouper
>>> list(grouper('ABCDEFG', 3, 'x'))
[('A', 'B', 'C'), ('D', 'E', 'F'), ('G', 'x', 'x')] | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/utils/fp.py#L254-L261 |
ml4ai/delphi | scripts/process_climis_unicef_ieconomics_data.py | process_climis_crop_production_data | def process_climis_crop_production_data(data_dir: str):
""" Process CliMIS crop production data """
climis_crop_production_csvs = glob(
"{data_dir}/Climis South Sudan Crop Production Data/"
"Crops_EstimatedProductionConsumptionBalance*.csv"
)
state_county_df = pd.read_csv(
f"{da... | python | def process_climis_crop_production_data(data_dir: str):
""" Process CliMIS crop production data """
climis_crop_production_csvs = glob(
"{data_dir}/Climis South Sudan Crop Production Data/"
"Crops_EstimatedProductionConsumptionBalance*.csv"
)
state_county_df = pd.read_csv(
f"{da... | Process CliMIS crop production data | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/scripts/process_climis_unicef_ieconomics_data.py#L154-L205 |
ml4ai/delphi | scripts/process_climis_unicef_ieconomics_data.py | process_climis_livestock_data | def process_climis_livestock_data(data_dir: str):
""" Process CliMIS livestock data. """
records = []
livestock_data_dir = f"{data_dir}/Climis South Sudan Livestock Data"
for filename in glob(
f"{livestock_data_dir}/Livestock Body Condition/*2017.csv"
):
records += process_file_wi... | python | def process_climis_livestock_data(data_dir: str):
""" Process CliMIS livestock data. """
records = []
livestock_data_dir = f"{data_dir}/Climis South Sudan Livestock Data"
for filename in glob(
f"{livestock_data_dir}/Livestock Body Condition/*2017.csv"
):
records += process_file_wi... | Process CliMIS livestock data. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/scripts/process_climis_unicef_ieconomics_data.py#L208-L353 |
ml4ai/delphi | delphi/translators/for2py/preprocessor.py | separate_trailing_comments | def separate_trailing_comments(lines: List[str]) -> List[Tuple[int, str]]:
"""Given a list of numbered Fortran source code lines, i.e., pairs of the
form (n, code_line) where n is a line number and code_line is a line
of code, separate_trailing_comments() behaves as follows: for each
pair (n, c... | python | def separate_trailing_comments(lines: List[str]) -> List[Tuple[int, str]]:
"""Given a list of numbered Fortran source code lines, i.e., pairs of the
form (n, code_line) where n is a line number and code_line is a line
of code, separate_trailing_comments() behaves as follows: for each
pair (n, c... | Given a list of numbered Fortran source code lines, i.e., pairs of the
form (n, code_line) where n is a line number and code_line is a line
of code, separate_trailing_comments() behaves as follows: for each
pair (n, code_line) where code_line can be broken into two parts -- a
code portion co... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/preprocessor.py#L39-L61 |
ml4ai/delphi | delphi/translators/for2py/preprocessor.py | merge_continued_lines | def merge_continued_lines(lines):
"""Given a list of numered Fortran source code lines, i.e., pairs of the
form (n, code_line) where n is a line number and code_line is a line
of code, merge_continued_lines() merges sequences of lines that are
indicated to be continuation lines.
"""
# ... | python | def merge_continued_lines(lines):
"""Given a list of numered Fortran source code lines, i.e., pairs of the
form (n, code_line) where n is a line number and code_line is a line
of code, merge_continued_lines() merges sequences of lines that are
indicated to be continuation lines.
"""
# ... | Given a list of numered Fortran source code lines, i.e., pairs of the
form (n, code_line) where n is a line number and code_line is a line
of code, merge_continued_lines() merges sequences of lines that are
indicated to be continuation lines. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/preprocessor.py#L64-L106 |
ml4ai/delphi | delphi/translators/for2py/preprocessor.py | type_of_line | def type_of_line(line):
"""Given a line of code, type_of_line() returns a string indicating
what kind of code it is."""
if line_is_comment(line):
return "comment"
elif line_is_executable(line):
return "exec_stmt"
elif line_is_pgm_unit_end(line):
return "pgm_unit_end"
... | python | def type_of_line(line):
"""Given a line of code, type_of_line() returns a string indicating
what kind of code it is."""
if line_is_comment(line):
return "comment"
elif line_is_executable(line):
return "exec_stmt"
elif line_is_pgm_unit_end(line):
return "pgm_unit_end"
... | Given a line of code, type_of_line() returns a string indicating
what kind of code it is. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/preprocessor.py#L140-L154 |
ml4ai/delphi | delphi/translators/for2py/preprocessor.py | extract_comments | def extract_comments(
lines: List[Tuple[int, str]]
) -> Tuple[List[Tuple[int, str]], Dict[str, List[str]]]:
"""Given a list of numbered lines from a Fortran file where comments
internal to subprogram bodies have been moved out into their own lines,
extract_comments() extracts comments into a dicti... | python | def extract_comments(
lines: List[Tuple[int, str]]
) -> Tuple[List[Tuple[int, str]], Dict[str, List[str]]]:
"""Given a list of numbered lines from a Fortran file where comments
internal to subprogram bodies have been moved out into their own lines,
extract_comments() extracts comments into a dicti... | Given a list of numbered lines from a Fortran file where comments
internal to subprogram bodies have been moved out into their own lines,
extract_comments() extracts comments into a dictionary and replaces
each comment internal to subprogram bodies with a marker statement.
It returns a pair ... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/preprocessor.py#L157-L250 |
ml4ai/delphi | delphi/translators/for2py/preprocessor.py | split_trailing_comment | def split_trailing_comment(line: str) -> str:
"""Takes a line and splits it into two parts (code_part, comment_part)
where code_part is the line up to but not including any trailing
comment (the '!' comment character and subsequent characters
to the end of the line), while comment_part is the trailing c... | python | def split_trailing_comment(line: str) -> str:
"""Takes a line and splits it into two parts (code_part, comment_part)
where code_part is the line up to but not including any trailing
comment (the '!' comment character and subsequent characters
to the end of the line), while comment_part is the trailing c... | Takes a line and splits it into two parts (code_part, comment_part)
where code_part is the line up to but not including any trailing
comment (the '!' comment character and subsequent characters
to the end of the line), while comment_part is the trailing comment.
Args:
line: A line of Fortran so... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/preprocessor.py#L262-L303 |
ml4ai/delphi | delphi/translators/for2py/preprocessor.py | process | def process(inputLines: List[str]) -> str:
"""process() provides the interface used by an earlier version of this
preprocessor."""
lines = separate_trailing_comments(inputLines)
merge_continued_lines(lines)
(lines, comments) = extract_comments(lines)
actual_lines = [
line[1]
f... | python | def process(inputLines: List[str]) -> str:
"""process() provides the interface used by an earlier version of this
preprocessor."""
lines = separate_trailing_comments(inputLines)
merge_continued_lines(lines)
(lines, comments) = extract_comments(lines)
actual_lines = [
line[1]
f... | process() provides the interface used by an earlier version of this
preprocessor. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/preprocessor.py#L306-L317 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.assign_uuids_to_nodes_and_edges | def assign_uuids_to_nodes_and_edges(self):
""" Assign uuids to nodes and edges. """
for node in self.nodes(data=True):
node[1]["id"] = str(uuid4())
for edge in self.edges(data=True):
edge[2]["id"] = str(uuid4()) | python | def assign_uuids_to_nodes_and_edges(self):
""" Assign uuids to nodes and edges. """
for node in self.nodes(data=True):
node[1]["id"] = str(uuid4())
for edge in self.edges(data=True):
edge[2]["id"] = str(uuid4()) | Assign uuids to nodes and edges. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L67-L73 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.from_statements_file | def from_statements_file(cls, file: str):
""" Construct an AnalysisGraph object from a pickle file containing a
list of INDRA statements. """
with open(file, "rb") as f:
sts = pickle.load(f)
return cls.from_statements(sts) | python | def from_statements_file(cls, file: str):
""" Construct an AnalysisGraph object from a pickle file containing a
list of INDRA statements. """
with open(file, "rb") as f:
sts = pickle.load(f)
return cls.from_statements(sts) | Construct an AnalysisGraph object from a pickle file containing a
list of INDRA statements. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L80-L87 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.from_statements | def from_statements(
cls, sts: List[Influence], assign_default_polarities: bool = True
):
""" Construct an AnalysisGraph object from a list of INDRA statements.
Unknown polarities are set to positive by default.
Args:
sts: A list of INDRA Statements
Returns:
... | python | def from_statements(
cls, sts: List[Influence], assign_default_polarities: bool = True
):
""" Construct an AnalysisGraph object from a list of INDRA statements.
Unknown polarities are set to positive by default.
Args:
sts: A list of INDRA Statements
Returns:
... | Construct an AnalysisGraph object from a list of INDRA statements.
Unknown polarities are set to positive by default.
Args:
sts: A list of INDRA Statements
Returns:
An AnalysisGraph instance constructed from a list of INDRA
statements. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L90-L126 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.from_text | def from_text(cls, text: str):
""" Construct an AnalysisGraph object from text, using Eidos to perform
machine reading. """
eidosProcessor = process_text(text)
return cls.from_statements(eidosProcessor.statements) | python | def from_text(cls, text: str):
""" Construct an AnalysisGraph object from text, using Eidos to perform
machine reading. """
eidosProcessor = process_text(text)
return cls.from_statements(eidosProcessor.statements) | Construct an AnalysisGraph object from text, using Eidos to perform
machine reading. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L129-L134 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.from_uncharted_json_file | def from_uncharted_json_file(cls, file):
""" Construct an AnalysisGraph object from a file containing INDRA
statements serialized exported by Uncharted's CauseMos webapp.
"""
with open(file, "r") as f:
_dict = json.load(f)
return cls.from_uncharted_json_serialized_dic... | python | def from_uncharted_json_file(cls, file):
""" Construct an AnalysisGraph object from a file containing INDRA
statements serialized exported by Uncharted's CauseMos webapp.
"""
with open(file, "r") as f:
_dict = json.load(f)
return cls.from_uncharted_json_serialized_dic... | Construct an AnalysisGraph object from a file containing INDRA
statements serialized exported by Uncharted's CauseMos webapp. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L151-L157 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.from_uncharted_json_serialized_dict | def from_uncharted_json_serialized_dict(
cls, _dict, minimum_evidence_pieces_required: int = 1
):
""" Construct an AnalysisGraph object from a dict of INDRA statements
exported by Uncharted's CauseMos webapp. """
sts = _dict["statements"]
G = nx.DiGraph()
for s in sts... | python | def from_uncharted_json_serialized_dict(
cls, _dict, minimum_evidence_pieces_required: int = 1
):
""" Construct an AnalysisGraph object from a dict of INDRA statements
exported by Uncharted's CauseMos webapp. """
sts = _dict["statements"]
G = nx.DiGraph()
for s in sts... | Construct an AnalysisGraph object from a dict of INDRA statements
exported by Uncharted's CauseMos webapp. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L160-L228 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.assemble_transition_model_from_gradable_adjectives | def assemble_transition_model_from_gradable_adjectives(self):
""" Add probability distribution functions constructed from gradable
adjective data to the edges of the analysis graph data structure.
Args:
adjective_data
res
"""
df = pd.read_sql_table("grad... | python | def assemble_transition_model_from_gradable_adjectives(self):
""" Add probability distribution functions constructed from gradable
adjective data to the edges of the analysis graph data structure.
Args:
adjective_data
res
"""
df = pd.read_sql_table("grad... | Add probability distribution functions constructed from gradable
adjective data to the edges of the analysis graph data structure.
Args:
adjective_data
res | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L276-L303 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.sample_from_prior | def sample_from_prior(self):
""" Sample elements of the stochastic transition matrix from the prior
distribution, based on gradable adjectives. """
# simple_path_dict caches the results of the graph traversal that finds
# simple paths between pairs of nodes, so that it doesn't have to b... | python | def sample_from_prior(self):
""" Sample elements of the stochastic transition matrix from the prior
distribution, based on gradable adjectives. """
# simple_path_dict caches the results of the graph traversal that finds
# simple paths between pairs of nodes, so that it doesn't have to b... | Sample elements of the stochastic transition matrix from the prior
distribution, based on gradable adjectives. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L314-L354 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.sample_observed_state | def sample_observed_state(self, s: pd.Series) -> Dict:
""" Sample observed state vector. This is the implementation of the
emission function.
Args:
s: Latent state vector.
Returns:
Observed state vector.
"""
return {
n[0]: {
... | python | def sample_observed_state(self, s: pd.Series) -> Dict:
""" Sample observed state vector. This is the implementation of the
emission function.
Args:
s: Latent state vector.
Returns:
Observed state vector.
"""
return {
n[0]: {
... | Sample observed state vector. This is the implementation of the
emission function.
Args:
s: Latent state vector.
Returns:
Observed state vector. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L356-L373 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.sample_from_likelihood | def sample_from_likelihood(self, n_timesteps=10):
""" Sample a collection of observed state sequences from the likelihood
model given a collection of transition matrices.
Args:
n_timesteps: The number of timesteps for the sequences.
"""
self.latent_state_sequences =... | python | def sample_from_likelihood(self, n_timesteps=10):
""" Sample a collection of observed state sequences from the likelihood
model given a collection of transition matrices.
Args:
n_timesteps: The number of timesteps for the sequences.
"""
self.latent_state_sequences =... | Sample a collection of observed state sequences from the likelihood
model given a collection of transition matrices.
Args:
n_timesteps: The number of timesteps for the sequences. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L375-L396 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.sample_from_proposal | def sample_from_proposal(self, A: pd.DataFrame) -> None:
""" Sample a new transition matrix from the proposal distribution,
given a current candidate transition matrix. In practice, this amounts
to the in-place perturbation of an element of the transition matrix
currently being used by t... | python | def sample_from_proposal(self, A: pd.DataFrame) -> None:
""" Sample a new transition matrix from the proposal distribution,
given a current candidate transition matrix. In practice, this amounts
to the in-place perturbation of an element of the transition matrix
currently being used by t... | Sample a new transition matrix from the proposal distribution,
given a current candidate transition matrix. In practice, this amounts
to the in-place perturbation of an element of the transition matrix
currently being used by the sampler.
Args | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L398-L412 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.get_timeseries_values_for_indicators | def get_timeseries_values_for_indicators(
self, resolution: str = "month", months: Iterable[int] = range(6, 9)
):
""" Attach timeseries to indicators, for performing Bayesian inference.
"""
if resolution == "month":
funcs = [
partial(get_indicator_value, ... | python | def get_timeseries_values_for_indicators(
self, resolution: str = "month", months: Iterable[int] = range(6, 9)
):
""" Attach timeseries to indicators, for performing Bayesian inference.
"""
if resolution == "month":
funcs = [
partial(get_indicator_value, ... | Attach timeseries to indicators, for performing Bayesian inference. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L414-L434 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.sample_from_posterior | def sample_from_posterior(self, A: pd.DataFrame) -> None:
""" Run Bayesian inference - sample from the posterior distribution."""
self.sample_from_proposal(A)
self.set_latent_state_sequence(A)
self.update_log_prior(A)
self.update_log_likelihood()
candidate_log_joint_prob... | python | def sample_from_posterior(self, A: pd.DataFrame) -> None:
""" Run Bayesian inference - sample from the posterior distribution."""
self.sample_from_proposal(A)
self.set_latent_state_sequence(A)
self.update_log_prior(A)
self.update_log_likelihood()
candidate_log_joint_prob... | Run Bayesian inference - sample from the posterior distribution. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L436-L457 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.infer_transition_matrix_coefficient_from_data | def infer_transition_matrix_coefficient_from_data(
self,
source: str,
target: str,
state: Optional[str] = None,
crop: Optional[str] = None,
):
""" Infer the distribution of a particular transition matrix
coefficient from data.
Args:
source... | python | def infer_transition_matrix_coefficient_from_data(
self,
source: str,
target: str,
state: Optional[str] = None,
crop: Optional[str] = None,
):
""" Infer the distribution of a particular transition matrix
coefficient from data.
Args:
source... | Infer the distribution of a particular transition matrix
coefficient from data.
Args:
source: The source of the edge corresponding to the matrix element
to infer.
target: The target of the edge corresponding to the matrix element
to infer.
... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L459-L491 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.create_bmi_config_file | def create_bmi_config_file(self, filename: str = "bmi_config.txt") -> None:
""" Create a BMI config file to initialize the model.
Args:
filename: The filename with which the config file should be saved.
"""
s0 = self.construct_default_initial_state()
s0.to_csv(filena... | python | def create_bmi_config_file(self, filename: str = "bmi_config.txt") -> None:
""" Create a BMI config file to initialize the model.
Args:
filename: The filename with which the config file should be saved.
"""
s0 = self.construct_default_initial_state()
s0.to_csv(filena... | Create a BMI config file to initialize the model.
Args:
filename: The filename with which the config file should be saved. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L497-L504 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.default_update_function | def default_update_function(self, n: Tuple[str, dict]) -> List[float]:
""" The default update function for a CAG node.
n: A 2-tuple containing the node name and node data.
Returns:
A list of values corresponding to the distribution of the value of
the real-valued var... | python | def default_update_function(self, n: Tuple[str, dict]) -> List[float]:
""" The default update function for a CAG node.
n: A 2-tuple containing the node name and node data.
Returns:
A list of values corresponding to the distribution of the value of
the real-valued var... | The default update function for a CAG node.
n: A 2-tuple containing the node name and node data.
Returns:
A list of values corresponding to the distribution of the value of
the real-valued variable representing the node. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L506-L518 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.initialize | def initialize(
self, config_file: str = "bmi_config.txt", initialize_indicators=True
):
""" Initialize the executable AnalysisGraph with a config file.
Args:
config_file
Returns:
AnalysisGraph
"""
self.t = 0.0
if not os.path.isfile(c... | python | def initialize(
self, config_file: str = "bmi_config.txt", initialize_indicators=True
):
""" Initialize the executable AnalysisGraph with a config file.
Args:
config_file
Returns:
AnalysisGraph
"""
self.t = 0.0
if not os.path.isfile(c... | Initialize the executable AnalysisGraph with a config file.
Args:
config_file
Returns:
AnalysisGraph | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L520-L556 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.update | def update(self, τ: float = 1.0, update_indicators=True, dampen=False):
""" Advance the model by one time step. """
for n in self.nodes(data=True):
n[1]["next_state"] = n[1]["update_function"](n)
for n in self.nodes(data=True):
n[1]["rv"].dataset = n[1]["next_state"]
... | python | def update(self, τ: float = 1.0, update_indicators=True, dampen=False):
""" Advance the model by one time step. """
for n in self.nodes(data=True):
n[1]["next_state"] = n[1]["update_function"](n)
for n in self.nodes(data=True):
n[1]["rv"].dataset = n[1]["next_state"]
... | Advance the model by one time step. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L558-L581 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.export_node | def export_node(self, n) -> Dict[str, Union[str, List[str]]]:
""" Return dict suitable for exporting to JSON.
Args:
n: A dict representing the data in a networkx AnalysisGraph node.
Returns:
The node dict with additional fields for name, units, dtype, and
ar... | python | def export_node(self, n) -> Dict[str, Union[str, List[str]]]:
""" Return dict suitable for exporting to JSON.
Args:
n: A dict representing the data in a networkx AnalysisGraph node.
Returns:
The node dict with additional fields for name, units, dtype, and
ar... | Return dict suitable for exporting to JSON.
Args:
n: A dict representing the data in a networkx AnalysisGraph node.
Returns:
The node dict with additional fields for name, units, dtype, and
arguments. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L623-L653 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.to_dict | def to_dict(self) -> Dict:
""" Export the CAG to a dict that can be serialized to JSON. """
return {
"name": self.name,
"dateCreated": str(self.dateCreated),
"variables": lmap(
lambda n: self.export_node(n), self.nodes(data=True)
),
... | python | def to_dict(self) -> Dict:
""" Export the CAG to a dict that can be serialized to JSON. """
return {
"name": self.name,
"dateCreated": str(self.dateCreated),
"variables": lmap(
lambda n: self.export_node(n), self.nodes(data=True)
),
... | Export the CAG to a dict that can be serialized to JSON. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L655-L665 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.map_concepts_to_indicators | def map_concepts_to_indicators(
self, n: int = 1, min_temporal_res: Optional[str] = None
):
""" Map each concept node in the AnalysisGraph instance to one or more
tangible quantities, known as 'indicators'.
Args:
n: Number of matches to keep
min_temporal_res:... | python | def map_concepts_to_indicators(
self, n: int = 1, min_temporal_res: Optional[str] = None
):
""" Map each concept node in the AnalysisGraph instance to one or more
tangible quantities, known as 'indicators'.
Args:
n: Number of matches to keep
min_temporal_res:... | Map each concept node in the AnalysisGraph instance to one or more
tangible quantities, known as 'indicators'.
Args:
n: Number of matches to keep
min_temporal_res: Minimum temporal resolution that the indicators
must have data for. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L675-L719 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.parameterize | def parameterize(
self,
country: Optional[str] = "South Sudan",
state: Optional[str] = None,
year: Optional[int] = None,
month: Optional[int] = None,
unit: Optional[str] = None,
fallback_aggaxes: List[str] = ["year", "month"],
aggfunc: Callable = np.mean,
... | python | def parameterize(
self,
country: Optional[str] = "South Sudan",
state: Optional[str] = None,
year: Optional[int] = None,
month: Optional[int] = None,
unit: Optional[str] = None,
fallback_aggaxes: List[str] = ["year", "month"],
aggfunc: Callable = np.mean,
... | Parameterize the analysis graph.
Args:
country
year
month
fallback_aggaxes:
An iterable of strings denoting the axes upon which to perform
fallback aggregation if the desired constraints cannot be met.
aggfunc: The func... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L721-L764 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.delete_nodes | def delete_nodes(self, nodes: Iterable[str]):
""" Iterate over a set of nodes and remove the ones that are present in
the graph. """
for n in nodes:
if self.has_node(n):
self.remove_node(n) | python | def delete_nodes(self, nodes: Iterable[str]):
""" Iterate over a set of nodes and remove the ones that are present in
the graph. """
for n in nodes:
if self.has_node(n):
self.remove_node(n) | Iterate over a set of nodes and remove the ones that are present in
the graph. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L775-L780 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.delete_node | def delete_node(self, node: str):
""" Removes a node if it is in the graph. """
if self.has_node(node):
self.remove_node(node) | python | def delete_node(self, node: str):
""" Removes a node if it is in the graph. """
if self.has_node(node):
self.remove_node(node) | Removes a node if it is in the graph. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L782-L785 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.delete_edge | def delete_edge(self, source: str, target: str):
""" Removes an edge if it is in the graph. """
if self.has_edge(source, target):
self.remove_edge(source, target) | python | def delete_edge(self, source: str, target: str):
""" Removes an edge if it is in the graph. """
if self.has_edge(source, target):
self.remove_edge(source, target) | Removes an edge if it is in the graph. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L787-L790 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.delete_edges | def delete_edges(self, edges: Iterable[Tuple[str, str]]):
""" Iterate over a set of edges and remove the ones that are present in
the graph. """
for edge in edges:
if self.has_edge(*edge):
self.remove_edge(*edge) | python | def delete_edges(self, edges: Iterable[Tuple[str, str]]):
""" Iterate over a set of edges and remove the ones that are present in
the graph. """
for edge in edges:
if self.has_edge(*edge):
self.remove_edge(*edge) | Iterate over a set of edges and remove the ones that are present in
the graph. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L792-L797 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.prune | def prune(self, cutoff: int = 2):
""" Prunes the CAG by removing redundant paths. If there are multiple
(directed) paths between two nodes, this function removes all but the
longest paths. Subsequently, it restricts the graph to the largest
connected component.
Args:
... | python | def prune(self, cutoff: int = 2):
""" Prunes the CAG by removing redundant paths. If there are multiple
(directed) paths between two nodes, this function removes all but the
longest paths. Subsequently, it restricts the graph to the largest
connected component.
Args:
... | Prunes the CAG by removing redundant paths. If there are multiple
(directed) paths between two nodes, this function removes all but the
longest paths. Subsequently, it restricts the graph to the largest
connected component.
Args:
cutoff: The maximum path length to consider f... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L799-L823 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.merge_nodes | def merge_nodes(self, n1: str, n2: str, same_polarity: bool = True):
""" Merge node n1 into node n2, with the option to specify relative
polarity.
Args:
n1
n2
same_polarity
"""
for p in self.predecessors(n1):
for st in self[p][n1]... | python | def merge_nodes(self, n1: str, n2: str, same_polarity: bool = True):
""" Merge node n1 into node n2, with the option to specify relative
polarity.
Args:
n1
n2
same_polarity
"""
for p in self.predecessors(n1):
for st in self[p][n1]... | Merge node n1 into node n2, with the option to specify relative
polarity.
Args:
n1
n2
same_polarity | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L825-L868 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.get_subgraph_for_concept | def get_subgraph_for_concept(
self, concept: str, depth: int = 1, reverse: bool = False
):
""" Returns a new subgraph of the analysis graph for a single concept.
Args:
concept: The concept that the subgraph will be centered around.
depth: The depth to which the depth... | python | def get_subgraph_for_concept(
self, concept: str, depth: int = 1, reverse: bool = False
):
""" Returns a new subgraph of the analysis graph for a single concept.
Args:
concept: The concept that the subgraph will be centered around.
depth: The depth to which the depth... | Returns a new subgraph of the analysis graph for a single concept.
Args:
concept: The concept that the subgraph will be centered around.
depth: The depth to which the depth-first search must be performed.
reverse: Sets the direction of causal influence flow to examine.
... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L874-L903 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.get_subgraph_for_concept_pair | def get_subgraph_for_concept_pair(
self, source: str, target: str, cutoff: Optional[int] = None
):
""" Get subgraph comprised of simple paths between the source and the
target.
Args:
source
target
cutoff
"""
paths = nx.all_simple_p... | python | def get_subgraph_for_concept_pair(
self, source: str, target: str, cutoff: Optional[int] = None
):
""" Get subgraph comprised of simple paths between the source and the
target.
Args:
source
target
cutoff
"""
paths = nx.all_simple_p... | Get subgraph comprised of simple paths between the source and the
target.
Args:
source
target
cutoff | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L905-L917 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.get_subgraph_for_concept_pairs | def get_subgraph_for_concept_pairs(
self, concepts: List[str], cutoff: Optional[int] = None
):
""" Get subgraph comprised of simple paths between the source and the
target.
Args:
concepts
cutoff
"""
path_generator = (
nx.all_simple... | python | def get_subgraph_for_concept_pairs(
self, concepts: List[str], cutoff: Optional[int] = None
):
""" Get subgraph comprised of simple paths between the source and the
target.
Args:
concepts
cutoff
"""
path_generator = (
nx.all_simple... | Get subgraph comprised of simple paths between the source and the
target.
Args:
concepts
cutoff | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L919-L934 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.to_sql | def to_sql(self, app=None, last_known_value_date: Optional[date] = None):
""" Inserts the model into the SQLite3 database associated with Delphi,
for use with the ICM REST API. """
from delphi.apps.rest_api import create_app, db
self.assign_uuids_to_nodes_and_edges()
icm_metada... | python | def to_sql(self, app=None, last_known_value_date: Optional[date] = None):
""" Inserts the model into the SQLite3 database associated with Delphi,
for use with the ICM REST API. """
from delphi.apps.rest_api import create_app, db
self.assign_uuids_to_nodes_and_edges()
icm_metada... | Inserts the model into the SQLite3 database associated with Delphi,
for use with the ICM REST API. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L940-L1060 |
ml4ai/delphi | delphi/AnalysisGraph.py | AnalysisGraph.to_agraph | def to_agraph(
self,
indicators: bool = False,
indicator_values: bool = False,
nodes_to_highlight=None,
*args,
**kwargs,
):
""" Exports the CAG as a pygraphviz AGraph for visualization.
Args:
indicators: Whether to display indicators in th... | python | def to_agraph(
self,
indicators: bool = False,
indicator_values: bool = False,
nodes_to_highlight=None,
*args,
**kwargs,
):
""" Exports the CAG as a pygraphviz AGraph for visualization.
Args:
indicators: Whether to display indicators in th... | Exports the CAG as a pygraphviz AGraph for visualization.
Args:
indicators: Whether to display indicators in the AGraph
indicator_values: Whether to display indicator values in the AGraph
nodes_to_highlight: Nodes to highlight in the AGraph.
Returns:
A Py... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/AnalysisGraph.py#L1062-L1217 |
ml4ai/delphi | delphi/translators/for2py/genPGM.py | dump | def dump(node, annotate_fields=True, include_attributes=False, indent=" "):
"""
Return a formatted dump of the tree in *node*. This is mainly useful for
debugging purposes. The returned string will show the names and the values
for fields. This makes the code impossible to evaluate, so if evaluation... | python | def dump(node, annotate_fields=True, include_attributes=False, indent=" "):
"""
Return a formatted dump of the tree in *node*. This is mainly useful for
debugging purposes. The returned string will show the names and the values
for fields. This makes the code impossible to evaluate, so if evaluation... | Return a formatted dump of the tree in *node*. This is mainly useful for
debugging purposes. The returned string will show the names and the values
for fields. This makes the code impossible to evaluate, so if evaluation
is wanted *annotate_fields* must be set to False. Attributes such as line
numbe... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/genPGM.py#L1375-L1424 |
ml4ai/delphi | delphi/translators/for2py/genPGM.py | create_pgm_dict | def create_pgm_dict(
lambdaFile: str,
asts: List,
file_name: str,
mode_mapper_dict: dict,
save_file=False,
) -> Dict:
""" Create a Python dict representing the PGM, with additional metadata for
JSON output. """
lambdaStrings = ["import math\n\n"]
state = PGMState(lambdaStrings)
... | python | def create_pgm_dict(
lambdaFile: str,
asts: List,
file_name: str,
mode_mapper_dict: dict,
save_file=False,
) -> Dict:
""" Create a Python dict representing the PGM, with additional metadata for
JSON output. """
lambdaStrings = ["import math\n\n"]
state = PGMState(lambdaStrings)
... | Create a Python dict representing the PGM, with additional metadata for
JSON output. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/genPGM.py#L1622-L1655 |
ml4ai/delphi | scripts/evaluations/create_reference_CAG.py | filter_and_process_statements | def filter_and_process_statements(
sts,
grounding_score_cutoff: float = 0.8,
belief_score_cutoff: float = 0.85,
concepts_of_interest: List[str] = [],
):
""" Filter preassembled statements according to certain rules. """
filtered_sts = []
counters = {}
def update_counter(counter_name):
... | python | def filter_and_process_statements(
sts,
grounding_score_cutoff: float = 0.8,
belief_score_cutoff: float = 0.85,
concepts_of_interest: List[str] = [],
):
""" Filter preassembled statements according to certain rules. """
filtered_sts = []
counters = {}
def update_counter(counter_name):
... | Filter preassembled statements according to certain rules. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/scripts/evaluations/create_reference_CAG.py#L7-L63 |
ml4ai/delphi | scripts/evaluations/create_CAG_with_indicators.py | create_CAG_with_indicators | def create_CAG_with_indicators(input, output, filename="CAG_with_indicators.pdf"):
""" Create a CAG with mapped indicators """
with open(input, "rb") as f:
G = pickle.load(f)
G.map_concepts_to_indicators(min_temporal_res="month")
G.set_indicator("UN/events/weather/precipitation", "Historical Ave... | python | def create_CAG_with_indicators(input, output, filename="CAG_with_indicators.pdf"):
""" Create a CAG with mapped indicators """
with open(input, "rb") as f:
G = pickle.load(f)
G.map_concepts_to_indicators(min_temporal_res="month")
G.set_indicator("UN/events/weather/precipitation", "Historical Ave... | Create a CAG with mapped indicators | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/scripts/evaluations/create_CAG_with_indicators.py#L5-L18 |
ml4ai/delphi | delphi/GrFN/networks.py | ComputationalGraph.run | def run(
self,
inputs: Dict[str, Union[float, Iterable]],
torch_size: Optional[int] = None,
) -> Union[float, Iterable]:
"""Executes the GrFN over a particular set of inputs and returns the
result.
Args:
inputs: Input set where keys are the names of input... | python | def run(
self,
inputs: Dict[str, Union[float, Iterable]],
torch_size: Optional[int] = None,
) -> Union[float, Iterable]:
"""Executes the GrFN over a particular set of inputs and returns the
result.
Args:
inputs: Input set where keys are the names of input... | Executes the GrFN over a particular set of inputs and returns the
result.
Args:
inputs: Input set where keys are the names of input nodes in the
GrFN and each key points to a set of input values (or just one).
Returns:
A set of outputs from executing the G... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L87-L124 |
ml4ai/delphi | delphi/GrFN/networks.py | ComputationalGraph.to_CAG | def to_CAG(self):
""" Export to a Causal Analysis Graph (CAG) PyGraphviz AGraph object.
The CAG shows the influence relationships between the variables and
elides the function nodes."""
G = nx.DiGraph()
for (name, attrs) in self.nodes(data=True):
if attrs["type"] == ... | python | def to_CAG(self):
""" Export to a Causal Analysis Graph (CAG) PyGraphviz AGraph object.
The CAG shows the influence relationships between the variables and
elides the function nodes."""
G = nx.DiGraph()
for (name, attrs) in self.nodes(data=True):
if attrs["type"] == ... | Export to a Causal Analysis Graph (CAG) PyGraphviz AGraph object.
The CAG shows the influence relationships between the variables and
elides the function nodes. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L126-L152 |
ml4ai/delphi | delphi/GrFN/networks.py | GroundedFunctionNetwork.traverse_nodes | def traverse_nodes(self, node_set, depth=0):
"""BFS traversal of nodes that returns name traversal as large string.
Args:
node_set: Set of input nodes to begin traversal.
depth: Current traversal depth for child node viewing.
Returns:
type: String containing... | python | def traverse_nodes(self, node_set, depth=0):
"""BFS traversal of nodes that returns name traversal as large string.
Args:
node_set: Set of input nodes to begin traversal.
depth: Current traversal depth for child node viewing.
Returns:
type: String containing... | BFS traversal of nodes that returns name traversal as large string.
Args:
node_set: Set of input nodes to begin traversal.
depth: Current traversal depth for child node viewing.
Returns:
type: String containing tabbed traversal view. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L186-L210 |
ml4ai/delphi | delphi/GrFN/networks.py | GroundedFunctionNetwork.from_json_and_lambdas | def from_json_and_lambdas(cls, file: str, lambdas):
"""Builds a GrFN from a JSON object.
Args:
cls: The class variable for object creation.
file: Filename of a GrFN JSON file.
Returns:
type: A GroundedFunctionNetwork object.
"""
with open(fi... | python | def from_json_and_lambdas(cls, file: str, lambdas):
"""Builds a GrFN from a JSON object.
Args:
cls: The class variable for object creation.
file: Filename of a GrFN JSON file.
Returns:
type: A GroundedFunctionNetwork object.
"""
with open(fi... | Builds a GrFN from a JSON object.
Args:
cls: The class variable for object creation.
file: Filename of a GrFN JSON file.
Returns:
type: A GroundedFunctionNetwork object. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L213-L227 |
ml4ai/delphi | delphi/GrFN/networks.py | GroundedFunctionNetwork.from_dict | def from_dict(cls, data: Dict, lambdas):
"""Builds a GrFN object from a set of extracted function data objects
and an associated file of lambda functions.
Args:
cls: The class variable for object creation.
data: A set of function data object that specify the wiring of a
... | python | def from_dict(cls, data: Dict, lambdas):
"""Builds a GrFN object from a set of extracted function data objects
and an associated file of lambda functions.
Args:
cls: The class variable for object creation.
data: A set of function data object that specify the wiring of a
... | Builds a GrFN object from a set of extracted function data objects
and an associated file of lambda functions.
Args:
cls: The class variable for object creation.
data: A set of function data object that specify the wiring of a
GrFN object.
lambdas: ... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L230-L356 |
ml4ai/delphi | delphi/GrFN/networks.py | GroundedFunctionNetwork.from_python_file | def from_python_file(
cls, python_file, lambdas_path, json_filename: str, stem: str
):
"""Builds GrFN object from Python file."""
with open(python_file, "r") as f:
pySrc = f.read()
return cls.from_python_src(pySrc, lambdas_path, json_filename, stem) | python | def from_python_file(
cls, python_file, lambdas_path, json_filename: str, stem: str
):
"""Builds GrFN object from Python file."""
with open(python_file, "r") as f:
pySrc = f.read()
return cls.from_python_src(pySrc, lambdas_path, json_filename, stem) | Builds GrFN object from Python file. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L359-L365 |
ml4ai/delphi | delphi/GrFN/networks.py | GroundedFunctionNetwork.from_python_src | def from_python_src(
cls,
pySrc,
lambdas_path,
json_filename: str,
stem: str,
save_file: bool = False,
):
"""Builds GrFN object from Python source code."""
asts = [ast.parse(pySrc)]
pgm_dict = genPGM.create_pgm_dict(
lambdas_path,
... | python | def from_python_src(
cls,
pySrc,
lambdas_path,
json_filename: str,
stem: str,
save_file: bool = False,
):
"""Builds GrFN object from Python source code."""
asts = [ast.parse(pySrc)]
pgm_dict = genPGM.create_pgm_dict(
lambdas_path,
... | Builds GrFN object from Python source code. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L368-L385 |
ml4ai/delphi | delphi/GrFN/networks.py | GroundedFunctionNetwork.from_fortran_file | def from_fortran_file(cls, fortran_file: str, tmpdir: str = "."):
"""Builds GrFN object from a Fortran program."""
stem = Path(fortran_file).stem
if tmpdir == "." and "/" in fortran_file:
tmpdir = Path(fortran_file).parent
preprocessed_fortran_file = f"{tmpdir}/{stem}_preproc... | python | def from_fortran_file(cls, fortran_file: str, tmpdir: str = "."):
"""Builds GrFN object from a Fortran program."""
stem = Path(fortran_file).stem
if tmpdir == "." and "/" in fortran_file:
tmpdir = Path(fortran_file).parent
preprocessed_fortran_file = f"{tmpdir}/{stem}_preproc... | Builds GrFN object from a Fortran program. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L390-L425 |
ml4ai/delphi | delphi/GrFN/networks.py | GroundedFunctionNetwork.from_fortran_src | def from_fortran_src(cls, fortran_src: str, dir: str = "."):
""" Create a GroundedFunctionNetwork instance from a string with raw
Fortran code.
Args:
fortran_src: A string with Fortran source code.
dir: (Optional) - the directory in which the temporary Fortran file
... | python | def from_fortran_src(cls, fortran_src: str, dir: str = "."):
""" Create a GroundedFunctionNetwork instance from a string with raw
Fortran code.
Args:
fortran_src: A string with Fortran source code.
dir: (Optional) - the directory in which the temporary Fortran file
... | Create a GroundedFunctionNetwork instance from a string with raw
Fortran code.
Args:
fortran_src: A string with Fortran source code.
dir: (Optional) - the directory in which the temporary Fortran file
will be created (make sure you have write permission!) Default... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L428-L446 |
ml4ai/delphi | delphi/GrFN/networks.py | GroundedFunctionNetwork.clear | def clear(self):
"""Clear variable nodes for next computation."""
for n in self.nodes():
if self.nodes[n]["type"] == "variable":
self.nodes[n]["value"] = None
elif self.nodes[n]["type"] == "function":
self.nodes[n]["func_visited"] = False | python | def clear(self):
"""Clear variable nodes for next computation."""
for n in self.nodes():
if self.nodes[n]["type"] == "variable":
self.nodes[n]["value"] = None
elif self.nodes[n]["type"] == "function":
self.nodes[n]["func_visited"] = False | Clear variable nodes for next computation. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L448-L454 |
ml4ai/delphi | delphi/GrFN/networks.py | GroundedFunctionNetwork.to_FIB | def to_FIB(self, other):
""" Creates a ForwardInfluenceBlanket object representing the
intersection of this model with the other input model.
Args:
other: The GroundedFunctionNetwork object to compare this model to.
Returns:
A ForwardInfluenceBlanket object to u... | python | def to_FIB(self, other):
""" Creates a ForwardInfluenceBlanket object representing the
intersection of this model with the other input model.
Args:
other: The GroundedFunctionNetwork object to compare this model to.
Returns:
A ForwardInfluenceBlanket object to u... | Creates a ForwardInfluenceBlanket object representing the
intersection of this model with the other input model.
Args:
other: The GroundedFunctionNetwork object to compare this model to.
Returns:
A ForwardInfluenceBlanket object to use for model comparison. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L550-L590 |
ml4ai/delphi | delphi/GrFN/networks.py | GroundedFunctionNetwork.to_agraph | def to_agraph(self):
""" Export to a PyGraphviz AGraph object. """
A = nx.nx_agraph.to_agraph(self)
A.graph_attr.update(
{"dpi": 227, "fontsize": 20, "fontname": "Menlo", "rankdir": "TB"}
)
A.node_attr.update({"fontname": "Menlo"})
def build_tree(cluster_name... | python | def to_agraph(self):
""" Export to a PyGraphviz AGraph object. """
A = nx.nx_agraph.to_agraph(self)
A.graph_attr.update(
{"dpi": 227, "fontsize": 20, "fontname": "Menlo", "rankdir": "TB"}
)
A.node_attr.update({"fontname": "Menlo"})
def build_tree(cluster_name... | Export to a PyGraphviz AGraph object. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L592-L618 |
ml4ai/delphi | delphi/GrFN/networks.py | GroundedFunctionNetwork.to_CAG_agraph | def to_CAG_agraph(self):
"""Returns a variable-only view of the GrFN in the form of an AGraph.
Returns:
type: A CAG constructed via variable influence in the GrFN object.
"""
CAG = self.to_CAG()
A = nx.nx_agraph.to_agraph(CAG)
A.graph_attr.update({"dpi": 227... | python | def to_CAG_agraph(self):
"""Returns a variable-only view of the GrFN in the form of an AGraph.
Returns:
type: A CAG constructed via variable influence in the GrFN object.
"""
CAG = self.to_CAG()
A = nx.nx_agraph.to_agraph(CAG)
A.graph_attr.update({"dpi": 227... | Returns a variable-only view of the GrFN in the form of an AGraph.
Returns:
type: A CAG constructed via variable influence in the GrFN object. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L620-L639 |
ml4ai/delphi | delphi/GrFN/networks.py | GroundedFunctionNetwork.to_call_agraph | def to_call_agraph(self):
""" Build a PyGraphviz AGraph object corresponding to a call graph of
functions. """
A = nx.nx_agraph.to_agraph(self.call_graph)
A.graph_attr.update({"dpi": 227, "fontsize": 20, "fontname": "Menlo"})
A.node_attr.update(
{"shape": "rectangle"... | python | def to_call_agraph(self):
""" Build a PyGraphviz AGraph object corresponding to a call graph of
functions. """
A = nx.nx_agraph.to_agraph(self.call_graph)
A.graph_attr.update({"dpi": 227, "fontsize": 20, "fontname": "Menlo"})
A.node_attr.update(
{"shape": "rectangle"... | Build a PyGraphviz AGraph object corresponding to a call graph of
functions. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L641-L651 |
ml4ai/delphi | delphi/GrFN/networks.py | ForwardInfluenceBlanket.run | def run(
self,
inputs: Dict[str, Union[float, Iterable]],
covers: Dict[str, Union[float, Iterable]],
torch_size: Optional[int] = None,
) -> Union[float, Iterable]:
"""Executes the FIB over a particular set of inputs and returns the
result.
Args:
in... | python | def run(
self,
inputs: Dict[str, Union[float, Iterable]],
covers: Dict[str, Union[float, Iterable]],
torch_size: Optional[int] = None,
) -> Union[float, Iterable]:
"""Executes the FIB over a particular set of inputs and returns the
result.
Args:
in... | Executes the FIB over a particular set of inputs and returns the
result.
Args:
inputs: Input set where keys are the names of input nodes in the
GrFN and each key points to a set of input values (or just one).
Returns:
A set of outputs from executing the GrFN... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L745-L768 |
ml4ai/delphi | delphi/GrFN/networks.py | ForwardInfluenceBlanket.S2_surface | def S2_surface(self, sizes, bounds, presets, covers, use_torch=False,
num_samples = 10):
"""Calculates the sensitivity surface of a GrFN for the two variables with
the highest S2 index.
Args:
num_samples: Number of samples for sensitivity analysis.
sizes: Tup... | python | def S2_surface(self, sizes, bounds, presets, covers, use_torch=False,
num_samples = 10):
"""Calculates the sensitivity surface of a GrFN for the two variables with
the highest S2 index.
Args:
num_samples: Number of samples for sensitivity analysis.
sizes: Tup... | Calculates the sensitivity surface of a GrFN for the two variables with
the highest S2 index.
Args:
num_samples: Number of samples for sensitivity analysis.
sizes: Tuple of (number of x inputs, number of y inputs).
bounds: Set of bounds for GrFN inputs.
p... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/networks.py#L806-L862 |
ml4ai/delphi | delphi/translators/for2py/translate.py | XMLToJSONTranslator.process_direct_map | def process_direct_map(self, root, state) -> List[Dict]:
"""Handles tags that are mapped directly from xml to IR with no
additional processing other than recursive translation of any child
nodes."""
val = {"tag": root.tag, "args": []}
for node in root:
val["args"] +=... | python | def process_direct_map(self, root, state) -> List[Dict]:
"""Handles tags that are mapped directly from xml to IR with no
additional processing other than recursive translation of any child
nodes."""
val = {"tag": root.tag, "args": []}
for node in root:
val["args"] +=... | Handles tags that are mapped directly from xml to IR with no
additional processing other than recursive translation of any child
nodes. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/translate.py#L629-L637 |
ml4ai/delphi | delphi/translators/for2py/translate.py | XMLToJSONTranslator.parseTree | def parseTree(self, root, state: ParseState) -> List[Dict]:
"""
Parses the XML ast tree recursively to generate a JSON AST
which can be ingested by other scripts to generate Python
scripts.
Args:
root: The current root of the tree.
state: The current stat... | python | def parseTree(self, root, state: ParseState) -> List[Dict]:
"""
Parses the XML ast tree recursively to generate a JSON AST
which can be ingested by other scripts to generate Python
scripts.
Args:
root: The current root of the tree.
state: The current stat... | Parses the XML ast tree recursively to generate a JSON AST
which can be ingested by other scripts to generate Python
scripts.
Args:
root: The current root of the tree.
state: The current state of the tree defined by an object of the
ParseState class.
... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/translate.py#L738-L763 |
ml4ai/delphi | delphi/translators/for2py/translate.py | XMLToJSONTranslator.loadFunction | def loadFunction(self, root):
"""
Loads a list with all the functions in the Fortran File
Args:
root: The root of the XML ast tree.
Returns:
None
Does not return anything but populates a list (self.functionList) that
contains all the functions i... | python | def loadFunction(self, root):
"""
Loads a list with all the functions in the Fortran File
Args:
root: The root of the XML ast tree.
Returns:
None
Does not return anything but populates a list (self.functionList) that
contains all the functions i... | Loads a list with all the functions in the Fortran File
Args:
root: The root of the XML ast tree.
Returns:
None
Does not return anything but populates a list (self.functionList) that
contains all the functions in the Fortran File. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/translate.py#L765-L780 |
ml4ai/delphi | delphi/translators/for2py/translate.py | XMLToJSONTranslator.analyze | def analyze(
self, trees: List[ET.ElementTree], comments: OrderedDict
) -> Dict:
outputDict = {}
ast = []
# Parse through the ast once to identify and grab all the functions
# present in the Fortran file.
for tree in trees:
self.loadFunction(tree)
... | python | def analyze(
self, trees: List[ET.ElementTree], comments: OrderedDict
) -> Dict:
outputDict = {}
ast = []
# Parse through the ast once to identify and grab all the functions
# present in the Fortran file.
for tree in trees:
self.loadFunction(tree)
... | Find the entry point for the Fortran file.
The entry point for a conventional Fortran file is always the PROGRAM
section. This 'if' statement checks for the presence of a PROGRAM
segment.
If not found, the entry point can be any of the functions or
subroutines in the file. So, a... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/translate.py#L782-L823 |
ml4ai/delphi | delphi/apps/cli.py | main | def main():
"""Run the CLI."""
parser = ArgumentParser(
description="Dynamic Bayes Net Executable Model",
formatter_class=ArgumentDefaultsHelpFormatter,
)
def add_flag(short_arg: str, long_arg: str, help: str):
parser.add_argument(
"-" + short_arg, "--" + long_arg, h... | python | def main():
"""Run the CLI."""
parser = ArgumentParser(
description="Dynamic Bayes Net Executable Model",
formatter_class=ArgumentDefaultsHelpFormatter,
)
def add_flag(short_arg: str, long_arg: str, help: str):
parser.add_argument(
"-" + short_arg, "--" + long_arg, h... | Run the CLI. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/apps/cli.py#L93-L155 |
ml4ai/delphi | scripts/data_processing/process_FAO_and_WDI_data.py | construct_FAO_ontology | def construct_FAO_ontology():
""" Construct FAO variable ontology for use with Eidos. """
df = pd.read_csv("south_sudan_data_fao.csv")
gb = df.groupby("Element")
d = [
{
"events": [
{
k: [
{e: [process_variable_name(k, e)]}... | python | def construct_FAO_ontology():
""" Construct FAO variable ontology for use with Eidos. """
df = pd.read_csv("south_sudan_data_fao.csv")
gb = df.groupby("Element")
d = [
{
"events": [
{
k: [
{e: [process_variable_name(k, e)]}... | Construct FAO variable ontology for use with Eidos. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/scripts/data_processing/process_FAO_and_WDI_data.py#L96-L119 |
ml4ai/delphi | delphi/inspection.py | inspect_edge | def inspect_edge(G: AnalysisGraph, source: str, target: str):
""" 'Drill down' into an edge in the analysis graph and inspect its
provenance. This function prints the provenance.
Args:
G
source
target
"""
return create_statement_inspection_table(
G[source][target]["... | python | def inspect_edge(G: AnalysisGraph, source: str, target: str):
""" 'Drill down' into an edge in the analysis graph and inspect its
provenance. This function prints the provenance.
Args:
G
source
target
"""
return create_statement_inspection_table(
G[source][target]["... | 'Drill down' into an edge in the analysis graph and inspect its
provenance. This function prints the provenance.
Args:
G
source
target | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/inspection.py#L13-L25 |
ml4ai/delphi | delphi/inspection.py | _get_edge_sentences | def _get_edge_sentences(
G: AnalysisGraph, source: str, target: str
) -> List[str]:
""" Return the sentences that led to the construction of a specified edge.
Args:
G
source: The source of the edge.
target: The target of the edge.
"""
return chain.from_iterable(
[
... | python | def _get_edge_sentences(
G: AnalysisGraph, source: str, target: str
) -> List[str]:
""" Return the sentences that led to the construction of a specified edge.
Args:
G
source: The source of the edge.
target: The target of the edge.
"""
return chain.from_iterable(
[
... | Return the sentences that led to the construction of a specified edge.
Args:
G
source: The source of the edge.
target: The target of the edge. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/inspection.py#L28-L44 |
ml4ai/delphi | delphi/GrFN/utils.py | get_node_type | def get_node_type(type_str):
"""Returns the NodeType given a name of a JSON function object."""
if type_str == "container":
return NodeType.CONTAINER
elif type_str == "loop_plate":
return NodeType.LOOP
elif type_str == "assign":
return NodeType.ASSIGN
elif type_str == "condit... | python | def get_node_type(type_str):
"""Returns the NodeType given a name of a JSON function object."""
if type_str == "container":
return NodeType.CONTAINER
elif type_str == "loop_plate":
return NodeType.LOOP
elif type_str == "assign":
return NodeType.ASSIGN
elif type_str == "condit... | Returns the NodeType given a name of a JSON function object. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/GrFN/utils.py#L51-L64 |
ml4ai/delphi | delphi/translators/for2py/format.py | list_output_formats | def list_output_formats(type_list):
"""This function takes a list of type names and returns a list of
format specifiers for list-directed output of values of those types."""
out_format_list = []
for type_item in type_list:
item_format = default_output_format(type_item)
out_format_list.ap... | python | def list_output_formats(type_list):
"""This function takes a list of type names and returns a list of
format specifiers for list-directed output of values of those types."""
out_format_list = []
for type_item in type_list:
item_format = default_output_format(type_item)
out_format_list.ap... | This function takes a list of type names and returns a list of
format specifiers for list-directed output of values of those types. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/format.py#L405-L413 |
ml4ai/delphi | delphi/translators/for2py/format.py | list_data_type | def list_data_type(type_list):
"""This function takes a list of format specifiers and returns a list of data
types represented by the format specifiers."""
data_type = []
for item in type_list:
match = re.match(r"(\d+)(.+)", item)
if not match:
reps = 1
if item[0]... | python | def list_data_type(type_list):
"""This function takes a list of format specifiers and returns a list of data
types represented by the format specifiers."""
data_type = []
for item in type_list:
match = re.match(r"(\d+)(.+)", item)
if not match:
reps = 1
if item[0]... | This function takes a list of format specifiers and returns a list of data
types represented by the format specifiers. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/format.py#L421-L448 |
ml4ai/delphi | delphi/translators/for2py/format.py | Format.init_read_line | def init_read_line(self):
"""init_read_line() initializes fields relevant to input matching"""
format_list = self._format_list
self._re_cvt = self.match_input_fmt(format_list)
regexp0_str = "".join([subs[0] for subs in self._re_cvt])
self._regexp_str = regexp0_str
self._r... | python | def init_read_line(self):
"""init_read_line() initializes fields relevant to input matching"""
format_list = self._format_list
self._re_cvt = self.match_input_fmt(format_list)
regexp0_str = "".join([subs[0] for subs in self._re_cvt])
self._regexp_str = regexp0_str
self._r... | init_read_line() initializes fields relevant to input matching | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/format.py#L73-L87 |
ml4ai/delphi | delphi/translators/for2py/format.py | Format.init_write_line | def init_write_line(self):
"""init_write_line() initializes fields relevant to output generation"""
format_list = self._format_list
output_info = self.gen_output_fmt(format_list)
self._output_fmt = "".join([sub[0] for sub in output_info])
self._out_gen_fmt = [sub[1] for sub in ou... | python | def init_write_line(self):
"""init_write_line() initializes fields relevant to output generation"""
format_list = self._format_list
output_info = self.gen_output_fmt(format_list)
self._output_fmt = "".join([sub[0] for sub in output_info])
self._out_gen_fmt = [sub[1] for sub in ou... | init_write_line() initializes fields relevant to output generation | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/format.py#L89-L96 |
ml4ai/delphi | delphi/translators/for2py/format.py | Format.read_line | def read_line(self, line):
"""
Match a line of input according to the format specified and return a
tuple of the resulting values
"""
if not self._read_line_init:
self.init_read_line()
match = self._re.match(line)
assert match is not None, f"Format m... | python | def read_line(self, line):
"""
Match a line of input according to the format specified and return a
tuple of the resulting values
"""
if not self._read_line_init:
self.init_read_line()
match = self._re.match(line)
assert match is not None, f"Format m... | Match a line of input according to the format specified and return a
tuple of the resulting values | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/format.py#L99-L138 |
ml4ai/delphi | delphi/translators/for2py/format.py | Format.write_line | def write_line(self, values):
"""
Process a list of values according to the format specified to generate
a line of output.
"""
if not self._write_line_init:
self.init_write_line()
if len(self._out_widths) > len(values):
raise For2PyError(f"ERROR:... | python | def write_line(self, values):
"""
Process a list of values according to the format specified to generate
a line of output.
"""
if not self._write_line_init:
self.init_write_line()
if len(self._out_widths) > len(values):
raise For2PyError(f"ERROR:... | Process a list of values according to the format specified to generate
a line of output. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/format.py#L140-L167 |
ml4ai/delphi | delphi/translators/for2py/format.py | Format.match_input_fmt | def match_input_fmt(self, fmt_list):
"""Given a list of Fortran format specifiers, e.g., ['I5', '2X', 'F4.1'],
this function constructs a list of tuples for matching an input
string against those format specifiers."""
rexp_list = []
for fmt in fmt_list:
rexp_list.ext... | python | def match_input_fmt(self, fmt_list):
"""Given a list of Fortran format specifiers, e.g., ['I5', '2X', 'F4.1'],
this function constructs a list of tuples for matching an input
string against those format specifiers."""
rexp_list = []
for fmt in fmt_list:
rexp_list.ext... | Given a list of Fortran format specifiers, e.g., ['I5', '2X', 'F4.1'],
this function constructs a list of tuples for matching an input
string against those format specifiers. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/format.py#L178-L187 |
ml4ai/delphi | delphi/translators/for2py/format.py | Format.match_input_fmt_1 | def match_input_fmt_1(self, fmt):
"""
Given a single format specifier, e.g., '2X', 'I5', etc., this function
constructs a list of tuples for matching against that specifier. Each
element of this list is a tuple
(xtract_re, cvt_re, divisor, cvt_fn)
where:
... | python | def match_input_fmt_1(self, fmt):
"""
Given a single format specifier, e.g., '2X', 'I5', etc., this function
constructs a list of tuples for matching against that specifier. Each
element of this list is a tuple
(xtract_re, cvt_re, divisor, cvt_fn)
where:
... | Given a single format specifier, e.g., '2X', 'I5', etc., this function
constructs a list of tuples for matching against that specifier. Each
element of this list is a tuple
(xtract_re, cvt_re, divisor, cvt_fn)
where:
xtract_re is a regular expression that extracts an... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/format.py#L189-L258 |
ml4ai/delphi | delphi/translators/for2py/format.py | Format.gen_output_fmt | def gen_output_fmt(self, fmt_list):
"""given a list of Fortran format specifiers, e.g., ['I5', '2X', 'F4.1'],
this function constructs a list of tuples for constructing an output
string based on those format specifiers."""
rexp_list = []
for fmt in fmt_list:
rexp_lis... | python | def gen_output_fmt(self, fmt_list):
"""given a list of Fortran format specifiers, e.g., ['I5', '2X', 'F4.1'],
this function constructs a list of tuples for constructing an output
string based on those format specifiers."""
rexp_list = []
for fmt in fmt_list:
rexp_lis... | given a list of Fortran format specifiers, e.g., ['I5', '2X', 'F4.1'],
this function constructs a list of tuples for constructing an output
string based on those format specifiers. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/format.py#L266-L275 |
ml4ai/delphi | delphi/translators/for2py/format.py | Format.gen_output_fmt_1 | def gen_output_fmt_1(self, fmt):
"""given a single format specifier, get_output_fmt_1() constructs and returns
a list of tuples for matching against that specifier.
Each element of this list is a tuple
(gen_fmt, cvt_fmt, sz)
where:
gen_fmt is the Python forma... | python | def gen_output_fmt_1(self, fmt):
"""given a single format specifier, get_output_fmt_1() constructs and returns
a list of tuples for matching against that specifier.
Each element of this list is a tuple
(gen_fmt, cvt_fmt, sz)
where:
gen_fmt is the Python forma... | given a single format specifier, get_output_fmt_1() constructs and returns
a list of tuples for matching against that specifier.
Each element of this list is a tuple
(gen_fmt, cvt_fmt, sz)
where:
gen_fmt is the Python format specifier for assembling this value into
... | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/translators/for2py/format.py#L277-L358 |
ml4ai/delphi | delphi/assembly.py | constructConditionalPDF | def constructConditionalPDF(
gb, rs: np.ndarray, e: Tuple[str, str, Dict]
) -> gaussian_kde:
""" Construct a conditional probability density function for a particular
AnalysisGraph edge. """
adjective_response_dict = {}
all_θs = []
# Setting σ_X and σ_Y that are in Eq. 1.21 of the model docume... | python | def constructConditionalPDF(
gb, rs: np.ndarray, e: Tuple[str, str, Dict]
) -> gaussian_kde:
""" Construct a conditional probability density function for a particular
AnalysisGraph edge. """
adjective_response_dict = {}
all_θs = []
# Setting σ_X and σ_Y that are in Eq. 1.21 of the model docume... | Construct a conditional probability density function for a particular
AnalysisGraph edge. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/assembly.py#L24-L89 |
ml4ai/delphi | delphi/assembly.py | get_variable_and_source | def get_variable_and_source(x: str):
""" Process the variable name to make it more human-readable. """
xs = x.replace("\/", "|").split("/")
xs = [x.replace("|", "/") for x in xs]
if xs[0] == "FAO":
return " ".join(xs[2:]), xs[0]
else:
return xs[-1], xs[0] | python | def get_variable_and_source(x: str):
""" Process the variable name to make it more human-readable. """
xs = x.replace("\/", "|").split("/")
xs = [x.replace("|", "/") for x in xs]
if xs[0] == "FAO":
return " ".join(xs[2:]), xs[0]
else:
return xs[-1], xs[0] | Process the variable name to make it more human-readable. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/assembly.py#L96-L103 |
ml4ai/delphi | delphi/assembly.py | construct_concept_to_indicator_mapping | def construct_concept_to_indicator_mapping(n: int = 1) -> Dict[str, List[str]]:
""" Create a dictionary mapping high-level concepts to low-level indicators
Args:
n: Number of indicators to return
Returns:
Dictionary that maps concept names to lists of indicator names.
"""
df = pd.... | python | def construct_concept_to_indicator_mapping(n: int = 1) -> Dict[str, List[str]]:
""" Create a dictionary mapping high-level concepts to low-level indicators
Args:
n: Number of indicators to return
Returns:
Dictionary that maps concept names to lists of indicator names.
"""
df = pd.... | Create a dictionary mapping high-level concepts to low-level indicators
Args:
n: Number of indicators to return
Returns:
Dictionary that maps concept names to lists of indicator names. | https://github.com/ml4ai/delphi/blob/6d03d8aafeab99610387c51b89c99738ff2abbe3/delphi/assembly.py#L106-L123 |
ihmeuw/vivarium | src/vivarium/examples/disease_model/mortality.py | Mortality.setup | def setup(self, builder: Builder):
"""Performs this component's simulation setup.
The ``setup`` method is automatically called by the simulation
framework. The framework passes in a ``builder`` object which
provides access to a variety of framework subsystems and metadata.
Para... | python | def setup(self, builder: Builder):
"""Performs this component's simulation setup.
The ``setup`` method is automatically called by the simulation
framework. The framework passes in a ``builder`` object which
provides access to a variety of framework subsystems and metadata.
Para... | Performs this component's simulation setup.
The ``setup`` method is automatically called by the simulation
framework. The framework passes in a ``builder`` object which
provides access to a variety of framework subsystems and metadata.
Parameters
----------
builder :
... | https://github.com/ihmeuw/vivarium/blob/c5f5d50f775c8bf337d3aae1ff7c57c025a8e258/src/vivarium/examples/disease_model/mortality.py#L25-L43 |
ihmeuw/vivarium | src/vivarium/examples/disease_model/mortality.py | Mortality.base_mortality_rate | def base_mortality_rate(self, index: pd.Index) -> pd.Series:
"""Computes the base mortality rate for every individual.
Parameters
----------
index :
A representation of the simulants to compute the base mortality
rate for.
Returns
-------
... | python | def base_mortality_rate(self, index: pd.Index) -> pd.Series:
"""Computes the base mortality rate for every individual.
Parameters
----------
index :
A representation of the simulants to compute the base mortality
rate for.
Returns
-------
... | Computes the base mortality rate for every individual.
Parameters
----------
index :
A representation of the simulants to compute the base mortality
rate for.
Returns
-------
The base mortality rate for all simulants in the index. | https://github.com/ihmeuw/vivarium/blob/c5f5d50f775c8bf337d3aae1ff7c57c025a8e258/src/vivarium/examples/disease_model/mortality.py#L45-L58 |
ihmeuw/vivarium | src/vivarium/examples/disease_model/mortality.py | Mortality.determine_deaths | def determine_deaths(self, event: Event):
"""Determines who dies each time step.
Parameters
----------
event :
An event object emitted by the simulation containing an index
representing the simulants affected by the event and timing
information.
... | python | def determine_deaths(self, event: Event):
"""Determines who dies each time step.
Parameters
----------
event :
An event object emitted by the simulation containing an index
representing the simulants affected by the event and timing
information.
... | Determines who dies each time step.
Parameters
----------
event :
An event object emitted by the simulation containing an index
representing the simulants affected by the event and timing
information. | https://github.com/ihmeuw/vivarium/blob/c5f5d50f775c8bf337d3aae1ff7c57c025a8e258/src/vivarium/examples/disease_model/mortality.py#L60-L74 |
ihmeuw/vivarium | src/vivarium/framework/components/parser.py | _prep_components | def _prep_components(component_list: Sequence[str]) -> List[Tuple[str, Tuple[str]]]:
"""Transform component description strings into tuples of component paths and required arguments.
Parameters
----------
component_list :
The component descriptions to transform.
Returns
-------
Lis... | python | def _prep_components(component_list: Sequence[str]) -> List[Tuple[str, Tuple[str]]]:
"""Transform component description strings into tuples of component paths and required arguments.
Parameters
----------
component_list :
The component descriptions to transform.
Returns
-------
Lis... | Transform component description strings into tuples of component paths and required arguments.
Parameters
----------
component_list :
The component descriptions to transform.
Returns
-------
List of component/argument tuples. | https://github.com/ihmeuw/vivarium/blob/c5f5d50f775c8bf337d3aae1ff7c57c025a8e258/src/vivarium/framework/components/parser.py#L100-L117 |
ihmeuw/vivarium | src/vivarium/framework/components/parser.py | ComponentConfigurationParser.get_components | def get_components(self, component_config: Union[ConfigTree, List]) -> List:
"""Extracts component specifications from configuration information and returns initialized components.
Parameters
----------
component_config :
A hierarchical component specification blob. This con... | python | def get_components(self, component_config: Union[ConfigTree, List]) -> List:
"""Extracts component specifications from configuration information and returns initialized components.
Parameters
----------
component_config :
A hierarchical component specification blob. This con... | Extracts component specifications from configuration information and returns initialized components.
Parameters
----------
component_config :
A hierarchical component specification blob. This configuration information needs to be parsable
into a full import path and a se... | https://github.com/ihmeuw/vivarium/blob/c5f5d50f775c8bf337d3aae1ff7c57c025a8e258/src/vivarium/framework/components/parser.py#L35-L55 |
ihmeuw/vivarium | src/vivarium/framework/components/parser.py | ComponentConfigurationParser.parse_component_config | def parse_component_config(self, component_config: Dict[str, Union[Dict, List]]) -> List[str]:
"""Parses a hierarchical component specification into a list of standardized component definitions.
This default parser expects component configurations as a list of dicts. Each dict at the top level
... | python | def parse_component_config(self, component_config: Dict[str, Union[Dict, List]]) -> List[str]:
"""Parses a hierarchical component specification into a list of standardized component definitions.
This default parser expects component configurations as a list of dicts. Each dict at the top level
... | Parses a hierarchical component specification into a list of standardized component definitions.
This default parser expects component configurations as a list of dicts. Each dict at the top level
corresponds to a different package and has a single key. This key may be just the name of the package
... | https://github.com/ihmeuw/vivarium/blob/c5f5d50f775c8bf337d3aae1ff7c57c025a8e258/src/vivarium/framework/components/parser.py#L57-L79 |
casastorta/python-sar | sar/parser.py | Parser.load_file | def load_file(self):
"""
Loads SAR format logfile in ASCII format (sarXX).
:return: ``True`` if loading and parsing of file went fine, \
``False`` if it failed (at any point)
"""
# We first split file into pieces
searchunks = self._split_file()
i... | python | def load_file(self):
"""
Loads SAR format logfile in ASCII format (sarXX).
:return: ``True`` if loading and parsing of file went fine, \
``False`` if it failed (at any point)
"""
# We first split file into pieces
searchunks = self._split_file()
i... | Loads SAR format logfile in ASCII format (sarXX).
:return: ``True`` if loading and parsing of file went fine, \
``False`` if it failed (at any point) | https://github.com/casastorta/python-sar/blob/e6d8bb86524102d677f37e985302fad34e3297c1/sar/parser.py#L39-L62 |
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