##########################################################################
# basf2 (Belle II Analysis Software Framework) #
# Author: The Belle II Collaboration #
# #
# See git log for contributors and copyright holders. #
# This file is licensed under LGPL-3.0, see LICENSE.md. #
##########################################################################
from typing import List
import basf2
import variables
import tables
import numpy as np
import warnings
from pyarrow.parquet import ParquetWriter
from pyarrow.csv import CSVWriter
import pyarrow as pa
"""
Python utilities to help create or manage ntuples and work with them in pandas
"""
numpy_to_pyarrow_type_map = {
np.int32: pa.int32(),
np.int64: pa.int64(),
np.uint32: pa.uint32(),
np.uint64: pa.uint64(),
np.float32: pa.float32(),
np.float64: pa.float64(),
np.bool_: pa.bool_(),
np.object_: pa.string(),
np.str_: pa.string(),
}
class VariablesToTable(basf2.Module):
"""
Base class to dump ntuples into a non root format of your choosing
"""
def __init__(self, listname: str, variables: List[str], filename: str, format: str):
"""Constructor to initialize the internal state
Arguments:
listname(str): name of the particle list
variables(list(str)): list of variables to save for each particle
filename(str): name of the output file to be created
format(str): format of the output file, one of 'hdf5', 'parquet', 'csv'
"""
super().__init__()
#: Output filename
self._filename = filename
#: Particle list name
self._listname = listname
#: List of variables
self._variables = variables
#: Output format
self._format = format
def initialize(self):
"""Create the hdf5 file and list of variable objects to be used during
event processing."""
# Always avoid the top-level 'import ROOT'.
import ROOT # noqa
#: variable names
self._varnames = [
str(varname) for varname in variables.variables.resolveCollections(
variables.std_vector(
*self._variables))]
#: variable objects for each variable
self._var_objects = [variables.variables.getVariable(n) for n in self._varnames]
#: Event metadata
self._evtmeta = ROOT.Belle2.PyStoreObj("EventMetaData")
self._evtmeta.isRequired()
#: Pointer to the particle list
self._plist = ROOT.Belle2.PyStoreObj(self._listname)
self._plist.isRequired()
dtypes = [
("__experiment__", np.int32), ("__run__", np.int32), ("__event__", np.uint32),
("__production__", np.uint32), ("__candidate__", np.uint32), ("__ncandidates__", np.uint32)
]
for name in self._varnames:
# only float variables for now
dtypes.append((name, np.float64))
#: The data type
self._dtypes = dtypes
if self._format == "hdf5":
self.initialize_hdf5_writer()
elif self._format == "parquet":
self.initialize_parquet_writer()
elif self._format == "csv":
self.initialize_csv_writer()
else:
raise ValueError(f"Unknown format {self._format}, supported formats are 'hdf5', 'parquet', 'csv'.")
def initialize_parquet_writer(self):
"""
Initialize the parquet writer using pyarrow
"""
#: A list of tuples and py.DataTypes to define the pyarrow schema
self._schema = [(name, numpy_to_pyarrow_type_map[dt]) for name, dt in self._dtypes]
#: a writer object to write data into a parquet file
self._parquet_writer = ParquetWriter(self._filename, schema=pa.schema(self._schema))
def initialize_csv_writer(self):
"""
Initialize the csv writer using pyarrow
"""
#: A list of tuples and py.DataTypes to define the pyarrow schema
self._schema = [(name, numpy_to_pyarrow_type_map[dt]) for name, dt in self._dtypes]
#: a writer object to write data into a csv file
self._csv_writer = CSVWriter(self._filename, schema=pa.schema(self._schema))
def initialize_hdf5_writer(self):
"""
Initialize the hdf5 writer using pytables
"""
#: The pytable file
self._hdf5_writer = tables.open_file(self._filename, mode="w", title="Belle2 Variables to HDF5")
filters = tables.Filters(complevel=1, complib='blosc:lz4', fletcher32=False)
# some variable names are not just A-Za-z0-9 so pytables complains but
# seems to work. Ignore warning
with warnings.catch_warnings():
warnings.simplefilter("ignore")
#: The pytable
self._table = self._hdf5_writer.create_table("/", self._listname, obj=np.zeros(0, self._dtypes), filters=filters)
def fill_buffer(self):
"""
collect all variables for the particle in a numpy array
"""
# create a numpy array with the data
buf = np.empty(self._plist.getListSize(), dtype=self._dtypes)
# add some extra columns for bookkeeping
buf["__experiment__"] = self._evtmeta.getExperiment()
buf["__run__"] = self._evtmeta.getRun()
buf["__event__"] = self._evtmeta.getEvent()
buf["__production__"] = self._evtmeta.getProduction()
buf["__ncandidates__"] = len(buf)
buf["__candidate__"] = np.arange(len(buf))
for row, p in zip(buf, self._plist):
for name, v in zip(self._varnames, self._var_objects):
# pyroot proxy not working with callables, we should fix this.
# For now we need to go back by name and call it.
# should be `row[v.name] = v.func(p)`
row[name] = variables.variables.evaluate(v.name, p)
return buf
def event(self):
"""
Event processing function
executes the fill_buffer function and writes the data to the output file
"""
buf = self.fill_buffer()
if self._format == "hdf5":
"""Create a new row in the hdf5 file with for each particle in the list"""
self._table.append(buf)
elif self._format == "parquet":
table = {name: buf[name] for name, _ in self._dtypes}
pa_table = pa.table(table, schema=pa.schema(self._schema))
self._parquet_writer.write_table(pa_table)
elif self._format == "csv":
table = {name: buf[name] for name, _ in self._dtypes}
pa_table = pa.table(table, schema=pa.schema(self._schema))
self._csv_writer.write(pa_table)
def terminate(self):
"""save and close the output"""
import ROOT # noqa
if self._format == "hdf5":
self._table.flush()
self._hdf5_writer.close()
elif self._format == "parquet":
self._parquet_writer.close()
elif self._format == "csv":
self._csv_writer.close()
ROOT.Belle2.MetadataService.Instance().addNtuple(self._filename)
[docs]
class VariablesToHDF5(VariablesToTable):
"""
Legacy class to not break existing code
"""
def __init__(self, listname, variables, filename):
super().__init__(listname, variables, filename, "hdf5")
def make_mcerrors_readable(dataframe, column="mcErrors"):
"""
Take a dataframe containing a column with the output of the :b2:var:`mcErrors`
variable from :b2:mod:`VariablesToNTuple` and convert it to a readable set
of columns of the form ``{column}_{name}`` where column is the value of the
``column`` argument and ``name`` is one of the :ref:`mcmatching`
error flags (without the leading 'c_').
Arguments:
dataframe(pandas.DataFrame): the pandas dataframe containing an ntuple
with column containing the output of the mcErrors variable
column(str): the name containing the values from the mcErrors variable
"""
# Always avoid the top-level 'import ROOT'.
import ROOT # noqa
if column not in dataframe:
raise KeyError(f"Cannot find column '{column}'")
# convert mcErrors to int to be able to logical operate on it
mcErrors = dataframe[column].astype(int)
# and loop over all the c_ constants in the Belle2.MCMatching class
for flag in (e for e in dir(ROOT.Belle2.MCMatching) if e.startswith("c_")):
try:
value = int(getattr(ROOT.Belle2.MCMatching, flag))
except ValueError:
# probably the extraInfo column name, ignore
continue
# and set the column
name = column + flag[1:]
if value == 0:
dataframe[name] = mcErrors == 0
else:
dataframe[name] = (mcErrors & value) == value
