def get_statistics(filter_expression, include_attrs, include_runattrs,
include_itervars, include_param_assignments,
include_config_entries):
df = _read_result_files(filter_expression, 't')
df = _pivot_results(df, include_attrs, include_runattrs, include_itervars,
include_param_assignments, include_config_entries)
_ensure_columns_exist(df, _STATISTIC_COLUMN_NAMES)
return df
def get_parameters(filter_expression, include_attrs, include_runattrs,
include_itervars, include_param_assignments,
include_config_entries):
df = _read_result_files(filter_expression, 'p')
df = _pivot_results(df, include_attrs, include_runattrs, include_itervars,
include_param_assignments, include_config_entries)
_ensure_columns_exist(df, _PARAMETER_COLUMN_NAMES)
return df
def get_scalars(filter_expression, include_attrs, include_fields, include_runattrs, include_itervars, include_param_assignments, include_config_entries):
args = []
if include_fields:
args.append("--add-fields-as-scalars")
# TODO filter row types based on include_ args, as optimization
df = _read_result_files(filter_expression, 's', *args)
df = _pivot_results(df, include_attrs, include_runattrs, include_itervars, include_param_assignments, include_config_entries)
df["value"] = pd.to_numeric(df["value"], errors="raise")
return df
def get_vectors(filter_expression, include_attrs, include_runattrs,
include_itervars, include_param_assignments,
include_config_entries, start_time, end_time):
df = _read_result_files(filter_expression, 'v', '--start-time',
str(start_time), '--end-time', str(end_time))
df = _pivot_results(df, include_attrs, include_runattrs, include_itervars,
include_param_assignments, include_config_entries)
_ensure_columns_exist(df, _VECTOR_COLUMN_NAMES)
return df
def get_scalars(filter_or_dataframe="",
include_attrs=False,
include_fields=False,
include_runattrs=False,
include_itervars=False,
include_param_assignments=False,
include_config_entries=False):
"""
Returns a filtered list of scalar results.
Parameters:
- `filter_or_dataframe` (string): The filter expression to select the
desired scalars, or a dataframe in the "raw" format. Example: `name =~
"channelUtilization*" AND runattr:replication =~ "#0"`
- `include_attrs` (bool): Optional. When set to `True`, result attributes
(like `unit` or `source` for example) are appended to the DataFrame,
pivoted into columns.
- `include_fields` (bool): Optional. If `True`, the fields of statistics and
histograms (`:min`, `:mean`, etc.) are also returned as synthetic scalars.
- `include_runattrs`, `include_itervars`, `include_param_assignments`,
`include_config_entries` (bool): Optional. When set to `True`, additional
pieces of metadata about the run is appended to the DataFrame, pivoted
into columns. See the "Metadata columns" section of the module
documentation for details.
Columns of the returned DataFrame:
- `runID` (string): Identifies the simulation run
- `module` (string): Hierarchical name (a.k.a. full path) of the module that
recorded the result item
- `name` (string): The name of the scalar
- `value` (double): The value of the scalar
- Additional metadata items (result attributes, run attributes, iteration
variables, etc.), as requested
"""
if type(filter_or_dataframe) is str:
filter_expression = filter_or_dataframe
del filter_or_dataframe
return impl.get_scalars(**locals())
else:
if include_fields:
raise ValueError(
"include_fields is not supported when filter_or_dataframe is a dataframe"
)
df = filter_or_dataframe
row_types = ["scalar", "itervar", "runattr", "config", "attr"]
df = df[df["type"].isin(row_types)]
df = _pivot_results(df, include_attrs, include_runattrs,
include_itervars, include_param_assignments,
include_config_entries)
df.dropna(axis='columns', how='all', inplace=True)
if "value" in df: # it might be empty
df["value"] = pd.to_numeric(df["value"], errors="raise")
return df
def get_parameters(filter_or_dataframe="",
include_attrs=False,
include_runattrs=False,
include_itervars=False,
include_param_assignments=False,
include_config_entries=False):
"""
Returns a filtered list of parameters - actually computed values of
individual `cPar` instances in the fully built network.
Parameters are considered "pseudo-results", similar to scalars - except
their values are strings. Even though they act mostly as input to the actual
simulation run, the actually assigned value of individual `cPar` instances
is valuable information, as it is the result of the network setup process.
For example, even if a parameter is set up as an expression like `normal(3,
0.4)` from `omnetpp.ini`, the returned DataFrame will contain the single
concrete value picked for every instance of the parameter.
Parameters:
- `filter_or_dataframe` (string): The filter expression to select the
desired parameters, or a dataframe in the "raw" format. Example: `name =~
"x" AND module =~ Aloha.server`
- `include_attrs` (bool): Optional. When set to `True`, result attributes
(like `unit` for example) are appended to the DataFrame, pivoted into
columns.
- `include_runattrs`, `include_itervars`, `include_param_assignments`,
`include_config_entries` (bool): Optional. When set to `True`, additional
pieces of metadata about the run is appended to the DataFrame, pivoted
into columns. See the "Metadata columns" section of the module
documentation for details.
Columns of the returned DataFrame:
- `runID` (string): Identifies the simulation run
- `module` (string): Hierarchical name (a.k.a. full path) of the module that
recorded the result item
- `name` (string): The name of the parameter
- `value` (string): The value of the parameter.
- Additional metadata items (result attributes, run attributes, iteration
variables, etc.), as requested
"""
if type(filter_or_dataframe) is str:
filter_expression = filter_or_dataframe
del filter_or_dataframe
return impl.get_parameters(**locals())
else:
df = filter_or_dataframe
row_types = ["param", "itervar", "runattr", "config", "attr"]
df = df[df["type"].isin(row_types)]
df = _pivot_results(df, include_attrs, include_runattrs,
include_itervars, include_param_assignments,
include_config_entries)
df.dropna(axis='columns', how='all', inplace=True)
return df
def get_histograms(filter_or_dataframe="",
include_attrs=False,
include_runattrs=False,
include_itervars=False,
include_param_assignments=False,
include_config_entries=False):
"""
Returns a filtered list of histogram results.
Parameters:
- `filter_or_dataframe` (string): The filter expression to select the
desired histograms, or a dataframe in the "raw" format. Example: `name =~
"collisionMultiplicity:histogram" AND itervar:iaMean =~ "2"`
- `include_attrs` (bool): Optional. When set to `True`, result attributes
(like `unit` or `source` for example) are appended to the DataFrame,
pivoted into columns.
- `include_runattrs`, `include_itervars`, `include_param_assignments`,
`include_config_entries` (bool): Optional. When set to `True`, additional
pieces of metadata about the run is appended to the DataFrame, pivoted
into columns. See the "Metadata columns" section of the module
documentation for details.
Columns of the returned DataFrame:
- `runID` (string): Identifies the simulation run
- `module` (string): Hierarchical name (a.k.a. full path) of the module that
recorded the result item
- `name` (string): The name of the vector
- `count`, `sumweights`, `mean`, `stddev`, `min`, `max` (double): The
characteristic mathematical properties of the histogram
- `binedges`, `binvalues` (np.array): The histogram edge locations and the
weighted sum of the collected samples in each bin. `len(binedges) ==
len(binvalues) + 1`
- `underflows`, `overflows` (double): The weighted sum of the samples that
fell outside of the histogram bin range in the two directions
- Additional metadata items (result attributes, run attributes, iteration
variables, etc.), as requested
"""
if type(filter_or_dataframe) is str:
filter_expression = filter_or_dataframe
del filter_or_dataframe
return impl.get_histograms(**locals())
else:
df = filter_or_dataframe
row_types = ["histogram", "itervar", "runattr", "config", "attr"]
df = df[df["type"].isin(row_types)]
df = _pivot_results(df, include_attrs, include_runattrs,
include_itervars, include_param_assignments,
include_config_entries)
df.dropna(axis='columns', how='all', inplace=True)
return df
def get_vectors(filter_or_dataframe="",
include_attrs=False,
include_runattrs=False,
include_itervars=False,
include_param_assignments=False,
include_config_entries=False,
start_time=-inf,
end_time=inf):
"""
Returns a filtered list of vector results.
Parameters:
- `filter_or_dataframe` (string): The filter expression to select the
desired vectors, or a dataframe in the "raw" format. Example: `name =~
"radioState*" AND runattr:replication =~ "#0"`
- `include_attrs` (bool): Optional. When set to `True`, result attributes
(like `unit` or `source` for example) are appended to the DataFrame,
pivoted into columns.
- `include_runattrs`, `include_itervars`, `include_param_assignments`,
`include_config_entries` (bool): Optional. When set to `True`, additional
pieces of metadata about the run is appended to the DataFrame, pivoted
into columns. See the "Metadata columns" section of the module
documentation for details.
- `start_time`, `end_time` (double): Optional time limits to trim the data
of vector type results. The unit is seconds, both the `vectime` and
`vecvalue` arrays will be affected, the interval is left-closed,
right-open.
Columns of the returned DataFrame:
- `runID` (string): Identifies the simulation run
- `module` (string): Hierarchical name (a.k.a. full path) of the module that
recorded the result item
- `name` (string): The name of the vector
- `vectime`, `vecvalue` (np.array): The simulation times and the
corresponding values in the vector
- Additional metadata items (result attributes, run attributes, iteration
variables, etc.), as requested
"""
if type(filter_or_dataframe) is str:
filter_expression = filter_or_dataframe
del filter_or_dataframe
return impl.get_vectors(**locals())
else:
df = filter_or_dataframe
row_types = ["vector", "itervar", "runattr", "config", "attr"]
df = df[df["type"].isin(row_types)]
df = _pivot_results(df, include_attrs, include_runattrs,
include_itervars, include_param_assignments,
include_config_entries)
df.dropna(axis='columns', how='all', inplace=True)
if start_time != -inf or end_time != inf:
def crop(row):
t = row['vectime']
v = row['vecvalue']
from_index = np.searchsorted(t, start_time, 'left')
to_index = np.searchsorted(t, end_time, 'left')
row['vectime'] = t[from_index:to_index]
row['vecvalue'] = v[from_index:to_index]
return row
df = df.transform(crop, axis='columns')
return df
def get_parameters(filter_expression, include_attrs, include_runattrs, include_itervars, include_param_assignments, include_config_entries):
df = _read_result_files(filter_expression, 'p')
df = _pivot_results(df, include_attrs, include_runattrs, include_itervars, include_param_assignments, include_config_entries)
return df