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reader.py
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reader.py
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#!/usr/bin/env python
#-*- coding: utf-8 -*-
#pylint: disable=E1004,R0912,R0913,R0914,R0915,R0903,R0902,C0302
"""
File : reader.py
Author : Valentin Kuznetsov <vkuznet AT gmail dot com>
Reader module provides various readers for different data-formats:
JSON, CSV, Parquet, ROOT. It also allows access files either on local file
system, HDFS or viw xrootd (for ROOT data format). The support for HDFS
is provided by pyarrow library while for xrootd via uproot one.
There are 3 parameters each reader uses: nevts, chunk_size, nrows
nevts represents total number of events to read (for non-ROOT files it
is assigned to chunk_size). chunk_size is total buffer of events to read
from a file, nrows is total number events represented in a file.
RootDataReader reads data in chunks while others read entire file.
"""
from __future__ import print_function, division, absolute_import
# system modules
import os
import sys
import time
import json
import random
import argparse
import traceback
import itertools
import gzip
# numpy modules
import numpy as np
# pandas modules
# pd = None
# try: # https://github.com/modin-project/modin
# import modin.pandas as pd
# except ImportError:
# try:
# import pandas as pd
# except ImportError:
# pass
# uproot
try:
import uproot
try:
# uproot verion 3.X
from awkward import JaggedArray
except ImportError:
# uproot verion 2.X
from uproot.interp.jagged import JaggedArray
except ImportError:
pass
# numba
# try:
# from numba import jit
# except ImportError:
# def jit(f):
# "Simple decorator which calls underlying function"
# def new_f():
# "Action function"
# f()
# return new_f
# psutil
try:
import psutil
except ImportError:
psutil = None
# histogrammar
try:
import histogrammar as hg
# import matplotlib
# matplotlib.use('Agg')
# from matplotlib.backends.backend_pdf import PdfPages
# import matplotlib.pyplot as plt
except ImportError:
hg = None
# pyarrow module for accessing HDFS
# https://wesmckinney.com/blog/python-hdfs-interfaces/
# https://arrow.apache.org
try:
import pyarrow
import pyarrow.parquet as pq
except ImportError:
pyarrow = None
# MLaaS4HEP modules
from MLaaS4HEP.utils import nrows, dump_histograms, mem_usage, performance
from MLaaS4HEP.utils import steps, fopen, file_type, load_code
class OptionParser(object):
"Option parser class for reader arguments"
def __init__(self):
"User based option parser"
self.parser = argparse.ArgumentParser(prog='PROG')
self.parser.add_argument("--fin", action="store", \
dest="fin", default="", help="Input ROOT file")
self.parser.add_argument("--fout", action="store", \
dest="fout", default="", help="Output file name for ROOT specs")
self.parser.add_argument("--preproc", action="store", \
dest="preproc", default=None, \
help="File name containing pre-processing function")
self.parser.add_argument("--nan", action="store", \
dest="nan", default=np.nan, \
help="NaN value for padding, default np.nan [for ROOT file]")
self.parser.add_argument("--branch", action="store", \
dest="branch", default="Events", \
help="Input ROOT file branch, default Events [for ROOT file]")
self.parser.add_argument("--identifier", action="store", \
dest="identifier", default="run,event,luminosityBlock", \
help="Event identifier, default run,event,luminosityBlock [for ROOT file]")
self.parser.add_argument("--branches", action="store", \
dest="branches", default="", \
help="Comma separated list of branches to read, default all [for ROOT file]")
self.parser.add_argument("--exclude-branches", action="store", \
dest="exclude_branches", default="", \
help="Comma separated list of branches to exclude, default None [for ROOT file]")
self.parser.add_argument("--nevts", action="store", \
dest="nevts", default=5, \
help="number of events to process, default 5, use -1 to read all events)")
self.parser.add_argument("--chunk-size", action="store", \
dest="chunk_size", default=1000, help="Chunk size (nevts) to read, default 1000")
self.parser.add_argument("--specs", action="store", \
dest="specs", default=None, \
help="Input specs file [for ROOT file]")
self.parser.add_argument("--redirector", action="store", \
dest="redirector", default='root://cms-xrd-global.cern.ch', \
help="XrootD redirector, default root://cms-xrd-global.cern.ch [for ROOT file]")
self.parser.add_argument("--info", action="store_true", \
dest="info", default=False, \
help="Provide info about ROOT tree [for ROOT file]")
self.parser.add_argument("--hists", action="store_true", \
dest="hists", default=False, help="Create historgams for ROOT tree")
self.parser.add_argument("--verbose", action="store", \
dest="verbose", default=0, help="verbosity level")
def dim_jarr(arr):
"Return dimention (max length) of jagged array"
jdim = 0
for item in arr:
if jdim < len(item):
jdim = len(item)
return jdim
def min_max_arr(arr):
"""
Helper function to find out min/max values of given array.
The array can be either jagged one or normal numpy.ndarray
"""
try:
if isinstance(arr, JaggedArray):
arr = arr.flatten()
return float(np.min(arr)), float(np.max(arr))
except ValueError:
return 1e15, -1e15
class FileReader(object):
"""
FileReader represents generic interface to read data files
"""
def __init__(self, fin, label, chunk_size=1000, nevts=-1, preproc=None, verbose=0, reader=None):
self.fin = fin
self.label = label
self.chunk_size = chunk_size if nevts == -1 else nevts
self.nevts = nevts
self.preproc = preproc
self.verbose = verbose
self.keys = []
self.nrows = 0
self.reader = reader
self.istream = None
self.type = self.__class__.__name__
if not fin.lower().startswith('hdfs://'):
if hasattr(fin, 'readline'): # we already given a file descriptor
self.istream = fin
else:
self.istream = fopen(fin, 'r')
if self.verbose:
if self.reader:
print('init {} with {}'.format(self.type, self.reader))
else:
print('init {}'.format(self.type))
def info(self):
"Provide basic info about class attributes"
print('{} {}'.format(self.type, self))
mkey = max([len(k) for k in self.__dict__])
for key, val in self.__dict__.items():
pad = ' ' * (mkey - len(key))
print('{}{}: {}'.format(key, pad, val))
def __exit__(self, gtype, value, gtraceback):
"Exit function for our class"
if self.istream and hasattr(self.istream, 'close'):
self.istream.close()
if self.reader:
self.reader.__exit__()
@property
def columns(self):
"Return names of columns of our data"
if self.reader:
return self.reader.columns
return self.keys
def next(self):
"Read next chunk of data from out file"
return self.reader.next() if self.reader else []
#
# HDFS readers
#
def hdfs_read(fin):
"Read data from the fiven file into numpy array"
if pyarrow:
client = pyarrow.hdfs.connect()
with client.open(fin) as istream:
raw = istream.read()
if fin.endswith('gz'):
raw = getattr(gzip, "decompress")(raw)
return raw
else:
raise Exception("pyarrow is not available")
class HDFSReader(FileReader):
"""
HDFSReader represents interface to read data file from HDFS.
"""
def __init__(self, fin, label, chunk_size=1000, nevts=-1, preproc=None, verbose=0):
if sys.version.startswith('3.'):
super().__init__(fin, label, chunk_size, nevts, preproc, verbose)
else:
super(HDFSReader, self).__init__(\
fin, label, chunk_size, nevts, preproc, verbose)
self.raw = None
self.keys = None
self.pos = 0
def getdata(self):
"Read next chunk of data from out file"
if not self.raw:
if self.verbose:
print("%s reading %s" % (self.__class__.__name__, self.fin))
time0 = time.time()
self.raw = hdfs_read(self.fin)
if self.verbose:
print("read %s in %s sec" % (self.fin, time.time()-time0))
return self.raw
class HDFSJSONReader(HDFSReader):
"""
HDFSJSONReader represents interface to read JSON file from HDFS.
"""
def __init__(self, fin, label, chunk_size=1000, nevts=-1, preproc=None, verbose=0):
if sys.version.startswith('3.'):
super().__init__(fin, label, chunk_size, nevts, preproc, verbose)
else:
super(HDFSJSONReader, self).__init__(\
fin, label, chunk_size, nevts, preproc, verbose)
def next(self):
"Read next chunk of data from out file"
lines = self.getdata().splitlines()
if not self.nrows:
self.nrows = len(lines)
for idx in range(self.chunk_size):
time0 = time.time()
if len(lines) <= self.pos:
break
line = lines[self.pos]
if not line:
continue
rec = json.loads(line.decode('utf-8'))
if not rec:
continue
if self.preproc:
rec = self.preproc(rec)
if not self.keys:
self.keys = [k for k in sorted(rec.keys())]
if self.keys != sorted(rec.keys()):
rkeys = sorted(rec.keys())
msg = 'WARNING: record %s contains different set of keys from original ones\n' % idx
msg += 'original keys : %s\n' % json.dumps(self.keys)
msg += 'record keys : %s\n' % json.dumps(rkeys)
if len(self.keys) > len(rkeys):
diff = set(self.keys)-set(rkeys)
msg += 'orig-rkeys diff: %s\n' % diff
else:
diff = set(rkeys)-set(self.keys)
msg += 'rkeys-orig diff: %s\n' % diff
if self.verbose > 1:
print(msg)
if self.label in self.keys:
data = [rec.get(k, 0) for k in self.keys if k != self.label]
label = rec[self.label]
else:
data = [rec.get(k, 0) for k in self.keys]
label = self.label
self.pos += 1
data = np.array(data)
if self.verbose > 1:
print("read data chunk", self.pos, time.time()-time0, \
self.chunk_size, np.shape(data))
yield data, label
class HDFSCSVReader(HDFSReader):
"""
HDFSCSVReader represents interface to read CSV file from HDFS storage
"""
def __init__(self, fin, label, chunk_size=1000, nevts=-1, preproc=None, \
verbose=0, headers=None, separator=','):
if sys.version.startswith('3.'):
super().__init__(fin, label, chunk_size, nevts, preproc, verbose)
else:
super(HDFSCSVReader, self).__init__(\
fin, label, chunk_size, nevts, preproc, verbose)
self.headers = headers
self.sep = separator
def next(self):
"Read next chunk of data from out file"
lines = self.getdata().splitlines()
if not self.nrows:
self.nrows = len(lines)
for idx in range(self.chunk_size):
time0 = time.time()
if len(lines) <= self.pos:
break
line = lines[self.pos]
if not line:
continue
row = line.split(self.sep)
if not row:
continue
if self.preproc:
row = self.preproc(row)
if not self.keys:
self.keys = [k for k in sorted(row)]
continue
rec = dict(zip(self.keys, row))
if self.keys != sorted(rec.keys()):
msg = 'WARNING: record %s contains different set of keys from original ones' % idx
if self.verbose:
print(msg)
if self.label in self.keys:
data = [rec.get(k, 0) for k in self.keys if k != self.label]
label = rec[self.label]
else:
data = [rec.get(k, 0) for k in self.keys]
label = self.label
self.pos += 1
data = np.array(data)
if self.verbose > 1:
print("read data chunk", self.pos, time.time()-time0, \
self.chunk_size, np.shape(data))
yield data, label
class ParquetReader(HDFSReader):
"""
ParquetReader represents interface to read Parque files
"""
def __init__(self, fin, label, chunk_size=1000, nevts=-1, preproc=None, verbose=0):
if sys.version.startswith('3.'):
super().__init__(fin, label, chunk_size, nevts, preproc, verbose)
else:
super(ParquetReader, self).__init__(\
fin, label, chunk_size, nevts, preproc, verbose)
self.pos = 0
def next(self):
"Read next chunk of data from out file"
data = pq.read_table(self.fin)
xdf = data.to_pandas()
shape = np.shape(xdf)
self.nrows = shape[0]
end = self.chunk_size if self.pos + self.chunk_size < shape[0] else shape[0]
xdf = xdf[self.pos:end]
self.pos = end
self.keys = list(xdf.columns)
if self.label in self.keys:
label = xdf[self.label]
xdf.drop(self.label, axis=1)
yield xdf.values, label
else:
yield xdf.values, self.label
#
# Data reader classes
#
class JSONReader(FileReader):
"""
JSONReader represents interface to read JSON file from local file system
"""
def __init__(self, fin, label, chunk_size=1000, nevts=-1, preproc=None, verbose=0):
if sys.version.startswith('3.'):
super().__init__(fin, label, chunk_size, nevts, preproc, verbose)
else:
super(JSONReader, self).__init__(\
fin, label, chunk_size, nevts, preproc, verbose)
self.nrows = nrows(fin)
def next(self):
"Read next chunk of data from out file"
for idx in range(self.chunk_size):
line = self.istream.readline()
if not line:
continue
rec = json.loads(line)
if not rec:
continue
if self.preproc:
rec = self.preproc(rec)
if not self.keys:
self.keys = [k for k in sorted(rec.keys())]
if self.keys != sorted(rec.keys()):
rkeys = sorted(rec.keys())
msg = 'WARNING: record %s contains different set of keys from original ones\n' % idx
msg += 'original keys : %s\n' % json.dumps(self.keys)
msg += 'record keys : %s\n' % json.dumps(rkeys)
if len(self.keys) > len(rkeys):
diff = set(self.keys)-set(rkeys)
msg += 'orig-rkeys diff: %s\n' % diff
else:
diff = set(rkeys)-set(self.keys)
msg += 'rkeys-orig diff: %s\n' % diff
if self.verbose:
print(msg)
if self.label in self.keys:
data = [rec.get(k, 0) for k in self.keys if k != self.label]
label = rec[self.label]
else:
data = [rec.get(k, 0) for k in self.keys]
label = self.label
yield np.array(data), label
class CSVReader(FileReader):
"""
CSVReader represents interface to read CSV file from local file system
"""
def __init__(self, fin, label, chunk_size=1000, nevts=-1, preproc=None, \
verbose=0, headers=None, separator=','):
if sys.version.startswith('3.'):
super().__init__(fin, label, chunk_size, nevts, preproc, verbose)
else:
super(CSVReader, self).__init__(\
fin, label, chunk_size, nevts, preproc, verbose)
self.headers = headers
self.keys = headers if headers else None
self.sep = separator
self.nrows = nrows(fin)
def next(self):
"Read next chunk of data from out file"
for idx in range(self.chunk_size):
line = self.istream.readline()
if not line:
continue
row = line.split(self.sep)
if not row:
continue
if self.preproc:
row = self.preproc(row)
if not self.keys:
self.keys = [k for k in sorted(row)]
continue
rec = dict(zip(self.keys, row))
if self.keys != sorted(rec.keys()):
msg = 'WARNING: record %s contains different set of keys from original ones' % idx
if self.verbose:
print(msg)
if self.label in self.keys:
data = [rec.get(k, 0) for k in self.keys if k != self.label]
label = rec[self.label]
else:
data = [rec.get(k, 0) for k in self.keys]
label = self.label
self.nrows += 1
yield np.array(data), label
class AvroReader(FileReader):
"""
AvroReader represents interface to read Avro file.
Depends on: https://issues.apache.org/jira/browse/ARROW-1209
"""
def __init__(self, fin, label, chunk_size=1000, nevts=-1, preproc=None, verbose=0):
if sys.version.startswith('3.'):
super().__init__(fin, label, chunk_size, nevts, preproc, verbose)
else:
super(AvroReader, self).__init__(\
fin, label, chunk_size, nevts, preproc, verbose)
def next(self):
"Read next chunk of data from out file"
raise NotImplementedError
#
# User based classes
#
class JsonReader(FileReader):
"""
JsonReader represents interface to read jSON file either from local file system or HDFS
"""
def __init__(self, fin, label, chunk_size=1000, nevts=-1, preproc=None, verbose=0):
if fin.lower().startswith('hdfs://'):
reader = HDFSJSONReader(fin, label, chunk_size, nevts, preproc)
else:
reader = JSONReader(fin, label, chunk_size, nevts, preproc)
if sys.version.startswith('3.'):
super().__init__(fin, label, chunk_size, nevts, preproc, verbose, reader)
else:
super(JsonReader, self).__init__(\
fin, label, chunk_size, nevts, preproc, verbose, reader)
self.nrows = reader.nrows
class CsvReader(FileReader):
"""
CsvReader represents interface to read CSV file either from local file system or HDFS
"""
def __init__(self, fin, label, chunk_size=1000, nevts=-1, preproc=None, verbose=0):
if fin.lower().startswith('hdfs://'):
reader = HDFSCSVReader(fin, label, chunk_size, nevts, preproc)
else:
reader = CSVReader(fin, label, chunk_size, nevts, preproc)
if sys.version.startswith('3.'):
super().__init__(fin, label, chunk_size, nevts, preproc, verbose, reader)
else:
super(CsvReader, self).__init__(\
fin, label, chunk_size, nevts, preproc, verbose, reader)
self.nrows = reader.nrows
class RootDataReader(object):
"""
RootDataReader class provide interface to read ROOT files
and APIs to access its data. It uses two-pass procedure
unless specs file is provided. The first pass parse entire
file and identifies flat/jagged keys, their dimensionality
and min/max values. All of them are stored in a file specs.
The second pass uses specs to convert jagged structure of
ROOT file into flat DataFrame format.
"""
def __init__(self, fin, branch='Events', selected_branches=None, \
exclude_branches=None, identifier=None, label=None, \
chunk_size=1000, nevts=-1, specs=None, nan=np.nan, histograms=False, \
redirector='root://cms-xrd-global.cern.ch', verbose=0):
self.type = self.__class__.__name__
self.fin = xfile(fin, redirector)
self.verbose = verbose
if self.verbose:
print("Reading {}".format(self.fin))
self.istream = uproot.open(self.fin)
self.branches = {}
self.gen = None
self.out_branches = []
self.identifier = identifier if identifier else ['run', 'event', 'luminosityBlock']
self.tree = self.istream[branch]
self.nrows = self.tree.numentries
self.nevts = nevts if nevts != -1 else self.nrows
self.label = label
self.idx = -1
self.chunk_idx = 0
self.chunk_size = chunk_size if chunk_size < self.nrows else self.nrows
self.nan = float(nan)
self.attrs = []
self.shape = None
self.cache = {}
self.hdict = {}
self.hists = histograms
self.idx_label = 0
self.flat_keys_encoded = []
self.jagged_keys_encoded = []
self.keys = []
self.min_list = []
self.max_list = []
self.jdimension = []
self.dimension_list = []
if specs:
self.load_specs(specs)
else:
self.jdim = {}
self.minv = {}
self.maxv = {}
self.jkeys = []
self.fkeys = []
self.nans = {}
# perform initialization
time0 = time.time()
self.init()
if self.verbose:
print("{} init is complete in {} sec".format(self, time.time()-time0))
if selected_branches:
self.out_branches = []
for attr in self.attrs:
for name in selected_branches:
if name.find('*') != -1:
if attr.startswith(name):
self.out_branches.append(attr)
else:
if attr == name:
self.out_branches.append(attr)
if self.out_branches:
if self.verbose:
print("Select branches ...")
for name in sorted(self.out_branches):
print(name)
if exclude_branches:
out_branches = set()
for attr in self.attrs:
count = 0
for name in exclude_branches:
if name.find('*') != -1:
if attr.startswith(name):
count += 1
else:
if attr == name:
count += 1
if not count:
out_branches.add(attr)
self.out_branches = list(out_branches)
if self.out_branches:
if self.verbose:
print("Select branches ...")
for name in sorted(self.out_branches):
print(name)
# declare histograms for original and normilized values
if hg and self.hists:
for key in self.attrs:
low = self.minv[key]
high = self.maxv[key]
self.hdict['%s_orig' % key] = \
hg.Bin(num=100, low=low, high=high, quantity=lambda x: x, value=hg.Count())
self.hdict['%s_norm' % key] = \
hg.Bin(num=100, low=0, high=1, quantity=lambda x: x, value=hg.Count())
def load_specs(self, specs):
"load given specs"
if not isinstance(specs, dict):
if self.verbose:
print(f"load specs from {specs} for {self.fin}")
specs = json.load(open(specs))
if self.verbose > 1:
print("ROOT specs: {}".format(json.dumps(specs)))
self.jdim = specs['jdim']
self.minv = specs['minv']
self.maxv = specs['maxv']
self.jkeys = specs['jkeys']
self.fkeys = specs['fkeys']
self.nans = specs['nans']
self.flat_keys_encoded = sorted([key.encode('ascii') for key in self.flat_keys()])
self.jagged_keys_encoded = sorted([key.encode('ascii') for key in self.jagged_keys()])
self.keys = self.flat_keys_encoded + self.jagged_keys_encoded
self.min_list = [self.minv[key.decode('ascii')] for key in self.keys]
self.max_list = [self.maxv[key.decode('ascii')] for key in self.keys]
self.jdimension = [self.jdim[key.decode('ascii')] for key in self.jagged_keys_encoded]
self.dimension_list = [1] * len(self.flat_keys_encoded)
self.dimension_list = self.dimension_list + self.jdimension
def fetch_data(self, key):
"fetch data for given key from underlying ROOT tree"
if sys.version.startswith('3.') and isinstance(key, str):
key = key.encode('ascii') # convert string to binary
if key in self.branches:
return self.branches[key]
raise Exception('Unable to find "%s" key in ROOT branches' % key)
def read_chunk(self, nevts, set_branches=False, set_min_max=False):
"Reach chunk of events and determine min/max values as well as load branch values"
# read some portion of the data to determine branches
start_time = time.time()
if not self.gen:
if self.out_branches:
self.gen = self.tree.iterate(\
branches=self.out_branches+self.identifier, \
entrysteps=nevts, keycache=self.cache)
else:
self.gen = self.tree.iterate(\
entrysteps=nevts, keycache=self.cache)
self.branches = {} # start with fresh dict
try:
self.branches = next(self.gen) # python 3.X and 2.X
except StopIteration:
if self.out_branches:
self.gen = self.tree.iterate(\
branches=self.out_branches+self.identifier, \
entrysteps=nevts, keycache=self.cache)
else:
self.gen = self.tree.iterate(entrysteps=nevts, keycache=self.cache)
self.branches = next(self.gen) # python 3.X and 2.X
end_time = time.time()
self.idx += nevts
if self.verbose:
performance(nevts, self.tree, self.branches, start_time, end_time)
if set_branches:
for key, val in self.branches.items():
if isinstance(key, bytes):
key = key.decode()
self.minv[key], self.maxv[key] = min_max_arr(val)
if isinstance(val, JaggedArray):
self.jkeys.append(key)
else:
self.fkeys.append(key)
if set_min_max:
for key, val in self.branches.items():
if isinstance(key, bytes):
key = key.decode()
minv, maxv = min_max_arr(val)
if minv < self.minv[key]:
self.minv[key] = minv
if maxv > self.maxv[key]:
self.maxv[key] = maxv
def columns(self):
"Return columns of produced output vector"
cols = self.flat_keys()
for key in self.jagged_keys():
for idx in range(self.jdim[key]):
cols.append('%s_%s' % (key, idx))
return cols
def init(self):
"Initialize class data members by scaning ROOT tree"
if self.minv and self.maxv:
self.attrs = sorted(self.flat_keys()) + sorted(self.jagged_keys())
self.shape = len(self.flat_keys()) + sum(self.jdim.values())
msg = "+++ first pass: %s events, (%s-flat, %s-jagged) branches, %s attrs" \
% (self.nrows, len(self.flat_keys()), len(self.jagged_keys()), self.shape)
if self.verbose:
print(msg)
if self.verbose > 1:
print("\n### Flat attributes:")
for key in self.flat_keys():
print(key)
print("\n### Jagged array attributes:")
for key in self.jagged_keys():
print(key)
self.idx = -1
return
if psutil and self.verbose:
vmem0 = psutil.virtual_memory()
swap0 = psutil.swap_memory()
msg = ''
# if self.nevnts=0 we'll use 2x self.chunk_size to determine
# the dimensions otherwise job waits too long to possibly scan
# all events in a file which can be too large.
tot_rows = self.nrows
if not self.nevts:
tot_rows = 2*self.chunk_size
if self.verbose:
print("# will use {} events to obtain dimensionality".format(tot_rows))
# scan all rows to find out largest jagged array dimension
tot = 0
set_branches = True
set_min_max = True
for chunk in steps(tot_rows, self.chunk_size):
if tot + self.chunk_size > self.nevts:
nevts = self.nevts - tot
tot = self.nevts
else:
nevts = len(chunk) # chunk here contains event indexes
tot += nevts
self.read_chunk(nevts, set_branches=set_branches, set_min_max=set_min_max)
set_branches = False # we do it once
for key in self.jkeys:
if key not in self.jdim:
if isinstance(key, bytes):
key = key.decode()
self.jdim[key] = 0
dim = dim_jarr(self.fetch_data(key))
if dim > self.jdim.get(key, 0):
self.jdim[key] = dim
if self.nevts > 0 and tot >= self.nevts:
break
# if we've been asked to read all or zero events we determine
# number of events as all available rows in TTree which is set as
# self.tree.numentries in __init__
if self.nevts < 1:
self.nevts = self.nrows
# initialize all nan values (zeros) in normalize phase-space
# this should be done after we get all min/max values
for key in self.branches.keys():
if isinstance(key, bytes):
key = key.decode()
self.nans[key] = self.normalize(key, 0)
# reset internal indexes since we done with first pass reading
self.idx = -1
self.gen = None
# define final list of attributes
self.attrs = sorted(self.flat_keys()) + sorted(self.jagged_keys())
if self.verbose > 1:
print("\n### Dimensionality")
for key, val in self.jdim.items():
print(key, val)
print("\n### min/max values")
for key, val in self.minv.items():
print(key, val, self.maxv[key])
self.shape = len(self.flat_keys()) + sum(self.jdim.values())
msg = "--- first pass: %s events, (%s-flat, %s-jagged) branches, %s attrs" \
% (self.nrows, len(self.flat_keys()), len(self.jagged_keys()), self.shape)
if self.verbose:
print(msg)
if self.verbose > 1:
print("\n### Flat attributes:")
for key in self.flat_keys():
print(key)
print("\n### Jagged array attributes:")
for key in self.jagged_keys():
print(key)
if psutil and self.verbose:
vmem1 = psutil.virtual_memory()
swap1 = psutil.swap_memory()
mem_usage(vmem0, swap0, vmem1, swap1)
def write_specs(self, fout):
"Write specs about underlying file"
out = {'jdim': self.jdim, 'minv': self.minv, 'maxv': self.maxv}
out['fkeys'] = self.flat_keys()
out['jkeys'] = self.jagged_keys()
out['nans'] = self.nans
if self.verbose:
print("write {}".format(fout))
with open(fout, 'w') as ostream:
ostream.write(json.dumps(out))
def next(self):
"Provides read interface for next event using vectorize approach"
self.idx = self.idx + 1
# read new chunk of records if necessary
if not self.idx % self.chunk_size:
if self.idx + self.chunk_size > self.nrows:
nevts = self.nrows - self.idx
else:
nevts = self.chunk_size
self.read_chunk(nevts)
self.chunk_idx = 0 # reset chunk index after we read the chunk of data
self.idx = self.idx - nevts # reset index after chunk read by nevents offset
if self.verbose > 1:
print("idx", self.idx, "read", nevts, "events")
# form DataFrame record
try:
rec = [self.branches[key][self.chunk_idx] for key in self.keys]
except:
if len(rec) <= self.chunk_idx:
raise Exception("For key='%s' unable to find data at pos=%s while got %s" \
% (key, self.chunk_idx, len(self.branches[key])))
print("failed key", key)
print("failed idx", self.chunk_idx)
print("len(fdata)", len(self.branches[key]))
raise
# normalise and adjust dimension of the events
result = [x1 if x3 == x2 else (x1 - x2) / (x3 - x2) for (x1, x2, x3) in \
zip(rec, self.min_list, self.max_list) ]
result = [[result[i]] if i < len(self.flat_keys_encoded) else result[i].tolist() \
if len(result[i]) == self.dimension_list[i] else \
self.add_dim(result[i], i) for i in range(0, len(result))]
xdf = list(itertools.chain.from_iterable(result))
mask = list(np.isnan(xdf) * 1)
self.chunk_idx = self.chunk_idx + 1
return np.array(xdf), np.array(mask), self.idx_label
def add_dim(self, elem, index):
"Allows to extend dimension of an array after reading the max dimension from the specs file"
a = np.empty(self.dimension_list[index]) * np.nan
a[:elem.shape[0]] = elem
return a.tolist()
def next_old(self):
'''Provides read interface for next event using vectorize approach
This is the old function, slower than the new one. It is kept for completeness'''
self.idx = self.idx + 1
# build output matrix
time0 = time.time()
shape = len(self.flat_keys())
for key in sorted(self.jagged_keys()):
shape += self.jdim[key]
xdf = np.ndarray(shape=(shape,))
mask = np.ndarray(shape=(shape,), dtype=np.int)
idx_label = 0
# read new chunk of records if necessary
if not self.idx % self.chunk_size:
if self.idx + self.chunk_size > self.nrows:
nevts = self.nrows - self.idx
else:
nevts = self.chunk_size
self.read_chunk(nevts)
self.chunk_idx = 0 # reset chunk index after we read the chunk of data
self.idx = self.idx - nevts # reset index after chunk read by nevents offset
if self.verbose > 1:
print("idx", self.idx, "read", nevts, "events")
# read event info
event = []
for key in self.identifier:
fdata = self.fetch_data(key)
if len(fdata) <= self.chunk_idx:
raise Exception("For key='%s' unable to find data at pos=%s while got %s" \
% (key, self.chunk_idx, len(fdata)))
event.append(fdata[self.chunk_idx])
# form DataFrame record
rec = {}
for key in self.branches.keys():
try:
fdata = self.fetch_data(key)
if len(fdata) <= self.chunk_idx:
raise Exception("For key='%s' unable to find data at pos=%s while got %s" \
% (key, self.chunk_idx, len(fdata)))
rec[key] = fdata[self.chunk_idx]
except:
print("failed key", key)
print("failed idx", self.chunk_idx)
print("len(fdata)", len(fdata))
raise
# advance chunk index since we read the record
self.chunk_idx = self.chunk_idx + 1
idx = 0
for idx, key in enumerate(sorted(self.flat_keys())):
if sys.version.startswith('3.') and isinstance(key, str):
key = key.encode('ascii') # convert string to binary
if key.decode() != self.label:
xdf[idx] = self.normalize(key, rec[key])
else:
idx_label = idx
xdf[idx] = rec[key]
if hg and self.hists:
self.hdict['%s_orig' % key].fill(rec[key])
if xdf[idx] != self.nan:
self.hdict['%s_norm' % key].fill(xdf[idx])
mask[idx] = 1
if idx: # only advance position if we read something from flat_keys
pos = idx + 1 # position in xdf for jagged branches
else:
pos = 0
for key in sorted(self.jagged_keys()):
# check if key in our record
if key in rec.keys():
vals = rec.get(key, [])
else: # if not convert key to bytes key and use it to look-up a value
vals = rec.get(key.encode('utf-8'), [])
for jdx in range(self.jdim[key]):
# assign np.nan in case if we get empty array
val = vals[jdx] if len(vals) > jdx else np.nan
idx = pos+jdx
xdf[idx] = self.normalize(key, val)
if hg and self.hists:
self.hdict['%s_orig' % key].fill(val)
if xdf[idx] != self.nan:
self.hdict['%s_norm' % key].fill(xdf[idx])
if np.isnan(val):
mask[idx] = 0
else:
mask[idx] = 1
pos = idx + 1
if self.verbose > 1:
print("# idx=%s event=%s shape=%s proc.time=%s" % (
self.idx, event, np.shape(xdf), (time.time()-time0)))
if self.idx < 3:
# pick-up 3 branches for cross checking
if self.jagged_keys():
aidx = [random.randint(0, len(self.jagged_keys())-1) for _ in range(3)]
try:
keys = [self.jagged_keys()[i] for i in aidx]
for key in keys:
data = self.tree[key].array()
idx = self.attrs.index(key)
start_idx, end_idx = self.find_branch_idx(key)
print("+ branch=%s, row %s, position %s:%s, min=%s max=%s" \
% (key, self.idx, start_idx, end_idx, \
self.minv[key], self.maxv[key]))