# Tutorial 07: IOManager

Posted on Tue 16 January 2018 in misc by Kazuhiro Terao

In this notebook we go over how to use larcv's dedicated IO interface, larcv::IOManager C++ class to browse the file contents. If you are looking for a tutorial to use bare ROOT APIs, checkout this tutorial. That said, here's an outline of what's covered in this notebook.

We assume you already set up larcv and cloned larcv-tutorial repository. If not, checkout our installation tutorial. Let's start with the basic imports.

In [2]:
from __future__ import print_function
import ROOT
from larcv import larcv
import numpy
import matplotlib.pyplot as plt
%matplotlib inline


We will use a ROOT file that comes with the tutorial repository.

In [3]:
%%bash
ls ../*.root

../electron.root
../out_v2.root
../proton.root
../sample.root


## Instantiation of IOManager (by hand)¶

larcv::IOManager is writen in C++ but is accessible from Python. The default constructor creates an instance with two arguments: IOMode_t and std::string indicating the unique name of an instance. The IOMode_t is defined in IOManager scope as enum IOMode_t {kREAD, kWRITE, kBOTH}. You can give either enum expression or value.

In [4]:
io = larcv.IOManager(0,"IOManager") # identical to IOManager(larcv.IOManager.kREAD,"IOManager")
# Calling initialize() will open up all files and prepare IO.
# You cannot add more files after initialize() is called.
print('initialize() return:',io.initialize())

initialize() return: True
[NORMAL]  <IOManager::prepare_input> Opening a file in READ mode: ../proton.root
[NORMAL]  <IOManager::prepare_input> Opening a file in READ mode: ../electron.root
[NORMAL]  <IOManager::initialize> Prepared input with 20 entries...


The colored messages with [NORMAL] prefix are sent from the IOManager instance for the set verbosity level (by default, NORMAL, WARNING, ERROR and CRITICAL are shown). You can see that 2 files requested to open are in fact recognized. The total of 20 entries is a stacked sum of events stored in both files. If you are wondering **what does it mean by entries??**, we will cover that in later section :).

## Instantiation of IOManager (by configuration file)¶

You can instantiate IOManager using a configuration text (ASCII) file, too. This may become handy if you prefer a formatted list of many configuration options (which we have not explored yet). Here's an example.

In [5]:
io_config = \
"""
IOManager: {
IOMode:    0
Verbosity: 2
InputFiles:   ["../proton.root","../electron.root"]
}
"""

import tempfile
test_io_config = tempfile.NamedTemporaryFile('w')
test_io_config.write(io_config)
test_io_config.flush()

io=larcv.IOManager(test_io_config.name)
io.initialize()

Out[5]:
True
    [NORMAL]  <IOManager::prepare_input> Opening a file in READ mode: ../proton.root
[NORMAL]  <IOManager::prepare_input> Opening a file in READ mode: ../electron.root
[NORMAL]  <IOManager::initialize> Prepared input with 20 entries...


The verbsoity levels are also set by an enum defined here. NORMAL corresponds to the enum value 2. If you would like to see more detailed output, you can set it to a lower value. But we won't try this in this jupyter notebook since the text output can overwhelm the kernel. If you would like to, try it in a plain python script by changing the Verbosity parameter value in the above configuration example to 1.

## Browsing the contents¶

Once we opened a file, next we want to browse the contents. There are few useful functions to get the file meta (i.e. summary) data.

In [6]:
# Get a list of opened files
print('Listing files opened...')
for count, name in enumerate(io.file_list()):
print('  file {:d}: {:s}'.format(count,name))
# Get number of entries in the file
print('\nNumber of entries:', io.get_n_entries())
# Get a list of data products stored
print('\nListing data products stored...')
for name in io.product_list():
print('  product type:\033[91m',name,'\033[00m')

Listing files opened...
file 0: ../proton.root
file 1: ../electron.root

Number of entries: 20

Listing data products stored...
product type: image2d
product type: particle


The last function, IOManager::product_list(), is useful to know the list of data product types found (i.e. available) in the files (don't be distracted by \033[91m and such: that's just me preferring to color-highlighting the printed message). The function returns a list of strings where each string is a unique identifier for a certain data product type. For example, image2d refers to larcv::EventImage2D data product. To learn about the list of data products, you can read here.

Now, in the file, you may have more than one instance of a certain product type. For example, maybe Kazu decided to store EventImage2D (= just 2D image) of his daughter while Corey insists to store his daughter's image. LArCV supports both fathers' request to co-exist in the file by assigning a unique instance label which is also a string. In other words, in larcv file, a unique data product instance is identified by a combination of two strings: product type and instance label where the latter is also called producer label sometimes. Here is how to list a list of available instance labels in the file.

In [7]:
msg = 'product found... type: "\033[91m{:s}\033[00m" ... label "\033[94m{:s}\033[00m"'
for product_type in io.product_list():
for instance_label in io.producer_list(product_type):
print(msg.format(product_type,instance_label))

product found... type: "image2d" ... label "data"
product found... type: "particle" ... label "mctruth"


Just in case you did not checkout a more basic tutorial on using bare ROOT APIs, this information can also be retrieved simply by:

In [8]:
ROOT.TFile.Open('../electron.root','READ').ls()

TFile**		../electron.root
TFile*		../electron.root
KEY: TTree	particle_mctruth_tree;1	mctruth tree
KEY: TTree	image2d_data_tree;1	data tree


... which lists bare TTree names that encodes two strings of our interest: data product type and instance label.

## Entries: structure of data in file¶

In the first example of opening a file in this notebook, we postponeded to discuss what "entries" mean. If you are from high energy physics community and familiar with ROOT, you can certainly skip this section.

• Each data product instance (identified by a type and label) is stored in a dedicated ROOT TTree
• Each TTree entry stores attribute values' snapshot at the moment when data is recorded.

For instance, say we create an instance of EventImage2D to store 2D image in file. Let's say we filled this instance with an image of Kazu's daughter, and stored this snapshot. Then we re-filled the data product instance with an image of Corey's daughter, and again called the relevant function to store this snapshot. In this sequence of actions, TTree is filled with _two entries_ while there is only one data product instance _uniquely identified by the type and label strings_. This is how data is stored in larcv file.

Finally one more important point to be noted:

• Entries across TTrees are aligned. This means the same entry of different TTrees (i.e. data product instances) correspond to the same event.

Individual data product instance stores an aspect of an event, a word that refers to single instance of physical phenomenon happening. You may have as many data products as you need to record the full details of physics phenomenon. To make this possible, we need all data product's snapshot to be aligned so that we know which snapshot (i.e. data values) correspond to each other. This is achieved by using TTree's entry index number as a unique identifier of an event, and therefore the statement, "entries across TTrees are aligned."

## Accessing TTree entry (i.e. snapshot of data)¶

In order to access a certain entry, you can use IOManager::read_entry function.

In [9]:
# Access entry 0
# Access a product instance (type,label) = (image2d,data)
image2d_data = io.get_data("image2d","data")
print("Retrieved data snapshot",image2d_data)

Retrieved data snapshot <ROOT.larcv::EventImage2D object at 0x7fcd797719e0>


Once entry is specified, IOManager::get_data function can be used to retrieve the data product instance filled with the snapshot value (i.e. entry=0). In this case the resulting object is larcv::EventImage2D C++ object exposed to python. You can call any attribute function defined in C++ to interact with the instance.

In [10]:
numpy_image = larcv.as_ndarray(image2d_data.as_vector().front())
fig = plt.figure(figsize=(8,8))
plt.imshow(numpy_image, interpolation='none',cmap='jet')
plt.show()


What is this image?? Remember we have another data product type particle, and that records a type of particle stored in the same event.

In [11]:
particle_data = io.get_data("particle","mctruth")
print("PDG Code:",particle_data.as_vector().front().pdg_code())

PDG Code: 2212


where PDG code 2212 corresponds to a proton. So this is a 2D projection of a proton's trajectory!

## Creating a new larcv file¶

When you don't need to read an existing file but want to create a new file to store brandnew events, all you need to change is the IOMode_t to construct an IOManager instance.

In [12]:
io = larcv.IOManager(1,"IOManager") # identical to IOManager(larcv.IOManager.kWRITE,"IOManager")
# Set the output file name
io.set_out_file('remove_me.root')
# Calling initialize() will prepare IO.
# You cannot change output file name after initialize() is called.
print('initialize() return:',io.initialize())

initialize() return: True


Or, equivalently using a configuration file:

In [13]:
io_config = \
"""
IOManager: {
IOMode:    1
Verbosity: 2
OutFileName: "remove_me.root"
}
"""

import tempfile
test_io_config = tempfile.NamedTemporaryFile('w')
test_io_config.write(io_config)
test_io_config.flush()

io=larcv.IOManager(test_io_config.name)
io.initialize()

Out[13]:
True

Let's create something, and close (i.e. store) the output file. In order to create a new data product, you can use the exact same function IOManager::get_data we used earlier. This function, when you instantiate IOManager with kWRITE mode, creates a new data product instance if you have not yet created. Here is an example of creating 3 entries and closing the file.

In [14]:
for entry in xrange(3):
print('Recording entry',entry)
my_particle = io.get_data('particle','toy')
io.set_id(0,0,entry)
io.save_entry()
io.finalize()

Recording entry 0
Recording entry 1
Recording entry 2
[NORMAL]  <IOManager::get_data> Created TTree particle_toy_tree (id=0) w/ 0 entries...
[NORMAL]  <IOManager::finalize> Writing particle_toy_tree with 3 entries
[NORMAL]  <IOManager::finalize> Closing output file


As you can tell, for this minimal example, I did not fill any data attributes. However I note two important function calls: IOManager::set_id and IOManager::save_entry(). The latter is very self-descriptive: it saves a snapshot of all created data product instances. The former is often forgotten! so it is important to note. IOManager::set_id function is used to set a unique identifier for TTree entry.

You might argue "Why not using an integer entry index number?" and that's a good point! That's because sometimes (actually often) we want to uniquely identify an entry, or physics event, across different files. TTree entry indexes, which is 0 or positive integers, are only useful to identify a unique entry within a file. For this purpose, larcv uses a combination of 3 integer attributes called run, subrun, and event. These are forced, common attributes to all larcv data product types since they are used to align data products uniquely. As long as they are unique, they can be of any value combinations. You may define the meaning of these attributes for your own convenience. For instance, you may vary run and event and keep subrun always the same value like 0.

Lastly, let's make sure we can read-in this file!

In [15]:
io = larcv.IOManager(0,"IOManager")
io.initialize()

# Get a list of opened files
print('Listing files opened...')
for count, name in enumerate(io.file_list()):
print('  file {:d}: {:s}'.format(count,name))
# Get number of entries in the file
print('\nNumber of entries:', io.get_n_entries())
# Get a list of data products stored
print('\nListing data products stored...')
for name in io.product_list():
print('  product type:\033[91m',name,'\033[00m')

Listing files opened...
file 0: remove_me.root

Number of entries: 3

Listing data products stored...
product type: particle
[NORMAL]  <IOManager::prepare_input> Opening a file in READ mode: remove_me.root
[NORMAL]  <IOManager::initialize> Prepared input with 3 entries...


## Making modification/addition to existing file¶

The last example case of IOManager use is read-and-write which uses IOMode_t enum value 2, or kBOTH symbol. Again you can do either interactively:

In [16]:
io = larcv.IOManager(2,"IOManager") # identical to IOManager(larcv.IOManager.kWRITE,"IOManager")
# Add a list of input files
# Set the output file name
io.set_out_file('remove_me.root')
# Initialize
print('initialize() return:',io.initialize())

initialize() return: True
[NORMAL]  <IOManager::prepare_input> Opening a file in READ mode: ../proton.root
[NORMAL]  <IOManager::prepare_input> Opening a file in READ mode: ../electron.root
[NORMAL]  <IOManager::initialize> Prepared input with 20 entries...


... or using a configuration file:

In [17]:
io_config = \
"""
IOManager: {
IOMode:    2
Verbosity: 2
InputFiles:  ["../proton.root","../electron.root"]
OutFileName: "remove_me.root"
}
"""

import tempfile
test_io_config = tempfile.NamedTemporaryFile('w')
test_io_config.write(io_config)
test_io_config.flush()

io=larcv.IOManager(test_io_config.name)
io.initialize()

Out[17]:
True
    [NORMAL]  <IOManager::prepare_input> Opening a file in READ mode: ../proton.root
[NORMAL]  <IOManager::prepare_input> Opening a file in READ mode: ../electron.root
[NORMAL]  <IOManager::initialize> Prepared input with 20 entries...


Create 3 toy data product instances per event and store.

In [18]:
%%time

for entry in xrange(3):
oiriginal = io.get_data('particle','mctruth')
mine      = io.get_data('particle','toy')
io.save_entry()
io.finalize()

CPU times: user 13.8 ms, sys: 3.22 ms, total: 17 ms
Wall time: 17.3 ms
[NORMAL]  <IOManager::get_data> Created TTree particle_toy_tree (id=2) w/ 0 entries...
[NORMAL]  <IOManager::finalize> Writing particle_mctruth_tree with 3 entries
[NORMAL]  <IOManager::finalize> Writing image2d_data_tree with 3 entries
[NORMAL]  <IOManager::finalize> Writing particle_toy_tree with 3 entries
[NORMAL]  <IOManager::finalize> Closing output file


You may have noticed, in this example, we did not call IOManager::set_id() function. This is because it was not necessary. You see we first called oiriginal = io.get_data('particle','mctruth'). When this is executed, IOManager reads-in this data product from the disk, and fetch (run,subrun,event) data from this data product. Then this combination of integers are used as the default event identifier key for newly created data product (the next line). Of course, if you would like to, you can still over-ride this combination by calling IOManager::set_id explicitly.

You may have noticed, in the last code block we executed, IOManager::finalize reported that 3 TTrees are written: particle_mctruth_tree, image2d_data_tree, and particle_toy_tree where the last one is what we added. This shows you that the kBOTH mode by default reads and writes all data products. But this is not always what you want to do, and it may slow down your IO by doing extra read/write. Sometimes you want to read a subset of input, and write out a (possibly different) subset to the output. You can do this by specifying a list of data products to be read-in and write-out separately.

In [19]:
io_config = \
"""
IOManager: {
IOMode:    2
Verbosity: 2
InputFiles:    ["../proton.root","../electron.root"]
OutFileName:   "remove_me.root"
WriteOnlyType: ["particle","particle"]
WriteOnlyName: ["toy","mctruth"]
}
"""

import tempfile
test_io_config = tempfile.NamedTemporaryFile('w')
test_io_config.write(io_config)
test_io_config.flush()

io=larcv.IOManager(test_io_config.name)
io.initialize()

Out[19]:
True
    [NORMAL]  <IOManager::prepare_input> Opening a file in READ mode: ../proton.root
[NORMAL]  <IOManager::prepare_input> Skipping: producer=data type= image2d
[NORMAL]  <IOManager::prepare_input> Opening a file in READ mode: ../electron.root
[NORMAL]  <IOManager::prepare_input> Skipping: producer=data type= image2d
[NORMAL]  <IOManager::initialize> Prepared input with 20 entries...


Note the additional configuration parameters, ReadOnlyName, ReadOnlyType, WriteOnlyName, and WriteOnlyType. These should be the array of strings specifying a list of data product types and labels to read/write. The order of strings should be matching for ReadOnly and WriteOnly parameters.

In the resulting output, first, you notice IOManager reports it skipped reading data product type image2d with label data as we configured!

In [20]:
%%time
for entry in xrange(3):
oiriginal = io.get_data('particle','mctruth')
mine      = io.get_data('particle','toy')
io.save_entry()
io.finalize()

CPU times: user 4.46 ms, sys: 1.57 ms, total: 6.04 ms
Wall time: 6.32 ms
[NORMAL]  <IOManager::get_data> Created TTree particle_toy_tree (id=1) w/ 0 entries...
[NORMAL]  <IOManager::finalize> Writing particle_mctruth_tree with 3 entries
[NORMAL]  <IOManager::finalize> Writing particle_toy_tree with 3 entries
[NORMAL]  <IOManager::finalize> Closing output file


Comparing the measured CPU and wall time in the cell just above this to the one we previously executed without avoiding to read/write image2d data product, you can see we gained a significant speed up. Use a combination of ReadOnlyX and WriteOnlyX whenever appropriate for your use of IOManager!

## Closing remark¶

In this notebook we covered how to use larcv's C++ file handler called IOManager. Please note, for browsing files interactively, this is a complementary approach to using bare ROOT APIs. Both methods are useful. However you IOManager is the IO interface used for programming larcv modules, which you will eventually see if you are following larcv wiki/manual!