The toyzero package provides first steps toward LS4GAN. See also next-steps.org
After some pre-requirements are met, this package automates the preparation for and running of various Wire-Cell Toolkit jobs in order to produce some initial datasets for initial exploration of the LS4GAN concept.
The final results are in the form raw ADC (Z-axis) waveform arrays in per-channel rows (Y-axis) of time-sampled columns (X-axis). Each array spans one of three wire planes from one of six anode plane assemblies (APA) of the ProtoDUNE-SP detector.
There are two domains named real and fake (both simulated in toyzero). The real domain uses the full, best known 2D response model. The fake domain uses an intentionally “more wrong” quasi-1D model which is derived from the 2D model by zeroing all responses except those covering the central wire of interest. (Note that this quasi-1D model is not the same of the “yet more wronger” 1D model as q1D includes the variation within the central wire region.)
For this initial development we accept a number of simplifying “cheats” which we will rectify as we gain experience and develop the next phase. They are at least the following:
- Use of simulation for both “real” and “fake”. Eventually, “real” will be detector data and “fake” will from our best available simulation (currently 2D Wire Cell). We may use paired data for evaluating the technique in this stage but can not rely on it going forward.
- Our initial data tier is that of ADC waveforms and those from only ionization electrons from ideal tracks and in particular excluding noise effects. We eventually will use realistic track simulation (Geant4) and will include noise. When adding noise we will switch to a data tier of reconstructed signal waveforms. To produce them we will include noise models in the simulation and apply noise filtering to both the fake simulation and the real detector data just prior to applying signal processing. We may stop there or go one step further and apply Wire-Cell 3D tomographic imaging to form our data tier. Each step brings “real” and “fake” closer and thus allows LS4GAN to expend its deep neural nodes to find ever more subtle differences.
This package requires a few user environment settings to support use of Wire-Cell Toolkit and various Python packages. We will assume direnv is used to manage the environment. To use another environment management system, translate the contents of the example dot.envrc.
A one time setup of direnv:
$ cd toyzero $ cp dot.envrc .envrc $ emacs .envrc # edit to taste $ direnv allow
The Wire-Cell Toolkit program wire-cell and its libraries are
required. See https://wirecell.bnl.gov/ for installation information.
Check if WCT is available with these commands:
$ wire-cell --help $ wcsonnet wirecell.jsonnet $ echo $WIRECELL_PATH
The WIRECELL_PATH should include at least the cfg/ directory provided
by WCT. This toyzero package provides additional configuration in
./cfg which will found automatically.
The stand-alone wire-cell-python package, the snakemake program and
various other Python 3 packages are required.
$ cd toyzero $ pip install -r requirements.txt
Some checks:
$ wirecell-<TAB> $ wirecell-util --help $ snakemake --help
See also section Hacking for info on providing a developer version of
wire-cell-python code.
Exercising toyzero will automatically download various files from Wire-Cell GitHub and run various programs. In principle, the user need run only:
$ snakemake -jall all
This can take ten minutes or so depending on how fast your CPU is. To see what is produced:
$ tree data plots
To generate the minimum needed for the “images” data tier (see below) you can do:
$ snakemake -jall just_images
By default, the above does not retain intermediate data files from the “depos”, “wires”, “resp” and “frames” data tier files (see below for descriptions), only keeping “images”. To keep the intermediates you may run as:
$ snakemake -jall just_images --notemp
A one “event” run produce about 2MB of “images” and 360 MB if intermediates are kept.
More information on the use of snakemake in toyzero is below and in
sections Configuration and Snakemake.
Looking into the Snakefile one will see the all target aggregates a
number of all_* sub-targets. You may run each ordered, piece-wise
manner:
$ snakemake -jall all_resp $ snakemake -jall all_wires $ snakemake -jall all_depos $ snakemake -jall all_frames $ snakemake -jall all_images
Each of these steps are described more in the next subsections.
The all_resp target downloads 2D WCT “response file” and from it
derives the quasi-1D response file. then various diagnostic plots are
made for each.
Visualization of q1D and 2D responses are generated and shown below. First “fake” q1D and “real” 2D. These show the instantaneous induced current (Z-axis) on a “wire of interest” over the drift path of an electron. One row gives the response for an electron path that begins at the transverse (aka “pitch”) location relative to the wire center and as given on the Y-axis. Along that path/row, the Z/color value of each pixel gives the instantaneous current at a moment of time (X-axis).
Note, the actual paths follow some trajectory in space but this information is not important for applying the response (though is very important for calculating the responses). Also note, the current is in a “signed log10” unit with an arbitrary scaling in order to see both fine and gross features.
- Fake/q1D field response for ProtoDUNE-SP
- Real/2D field response for ProtoDUNE-SP
The all_wires target downloads WCT “wires file” and makes a multipage
PDF file with diagnostic plots at plots/wires-diagnostic.pdf.
Most of these pages may show content that is too esoteric for most users and they exhaustively cover all six protodune APAs. However, the pages showing wire segments may be instructive. For the most part, they can be ignored as “wires files” are typically well tested prior to making them available.
The all_depos target generates sets of ionization point depositions
for input to the WCT simulation. It produces a file:
data/depos/depos.npz
The file is in Numpy format with three types of arrays named like:
depo_data_<N> depo_info_<N> track_info_<N>
We will ignore the info arrays here.
The <N> counts the set of depos which are generated together (eg, an
“event”). The data arrays are 2D of shape (n, 7) where n is the
number of depositions over all tracks in the “event” and is
O(100k-1M). Each depo is a 7-tuple with elements:
- time
- number of electrons
- X position
- Y position
- Z position
- longitudinal extent (zero here)
- transverse extent (zero here)
A simple summary diagnostic plot for the first “event” is generated showing distribution of depo times and the depo locations.
The track_info_<N> arrays are numpy structured arrays with these
“columns”:
- pmin
- 3-vector holding track start point in Cartesian coordinates (mm)
- pmax
- 3-vector holding track end point in Cartesian coordinates (mm)
- tmin
- time at start point (ns)
- tmax
- time at end point (ns)
- step
- distance between steps along track (mm)
- eper
- number of ionization electrons at each step
Each of these columns have one row per “track” (line source) in the
“event” number <N>. By default their length is 10.
The all_frames target generates “frame” data from depos by running the
Wire-Cell simulation. The “frame” file format is described elsewhere.
For here, we treat it as a temporary.
The simulation is internally structured as a DAG of components exchanging data flow as shown:
Frames come in different flavor depending on the final output data tier. We name and describe them as:
- noiseless
- these are integer ADC waveforms reflecting existence of only ionization electrons and no electronics noise. They are bipolar on the induction planes “U” and “V and unipolar on collection plane “W”. They have a fixed “baseline” at an ADC value depending on the plane and are sparse (baseline-padded) at locations away from any ionization.
- signal
- these floating point measures of the original ionization electrons after signal processing and noise filtering is applied to signal and noise simulation. The waveforms from all planes are unipolar and give a measure of the original ionization distribution. The waveforms have a “baseline” of 0.0 and are sparse (baseline-padded) at locations away from any ionization.
The all_images target processes each “frame” data to produce one 2D
image for each of three wire planes of each of six anode plane
assemblies. The file name and name of the single array in the file
match. For example:
❯ wirecell-util npzls data/images/real/protodune-orig-0-1-W.npz protodune-orig-0-1-W (960, 6000)
These image arrays have shape (nchan, ntick). That is, each row is
the waveform from one channel and has ntick=6000 samples (3ms). When
visualized with matplotlib’s imshow() you will see channels as Y-axis,
tics as X-axis.
This “W” file holds one array of 960 channels and 6000 sample time “ticks” and is from index=0 from the “real” data, APA ID 1 and plane “W” (collection plane aka plane 2 counting from 0). “U” and “V” are induction planes and each will have 800 channels and the contemporaneous 6000 ticks.
Like frames above, images come in corresponding flavors. See section frames for description.
Diagnostic plots for each APA of the first event are made in PDF, PNG and SVG with different color maps to explore how best to display the information. These image pose problems as they are relatively high resolution and high dynamic range but are also sparse. As such we must take care that some visualization will produce very misleading artifacts.
- png
- At
matplotlibdefault DPI expect loss of visual data due to the resolution being low and potentially due to low values falling into the “center” color of the color map. Increasing DPI can help at the cost of larger files. Exploring different color maps follows. - Likely best format to use for accuracy. However beware of antialiasing that many PDF viewers apply to make images and text look “prettier”. It can obscure features and add artifacts.
- svg
- Much of the benefits of PDF but can be inlined in HTML.
As an example of DPI problems, compare the two images below. The
first is a high-DPI PNG rendering (by NETPBM) from the original PDF
made by matplotlib and the second is a PNG directly from matplotlib at
its default DPI.
to the same data as PNG at default DPI from matplotlib:
If you need more zoom from your evince PDF viewer try
❯ gsettings set org.gnome.Evince page-cache-size 2014
Selecting a good color map can have a big impact. Some guidance:
- For bipolar data of large dynamic range consider using “seismic” and
symmetric ranges (
vmin/vmax). - For bipolar data with low dynamic range, select a symmetric color map such as “coolwarm” and use a masked array to remove the 0 value.
- Avoid “rainbow” color maps with a large central region of a nearly common color.
The wire-cell-python package provides a command to easily visualize a
2D Numpy image array in a file with many plotting parameters exposed
as command line options:
❯ wirecell-util npz-to-img --help
Usage: wirecell-util npz-to-img [OPTIONS] NPZFILE
Make an image from an array in an numpy file.
Options:
-o, --output TEXT Output image file
-a, --array TEXT Array to plot
-c, --cmap TEXT Color map
-b, --baseline TEXT Apply median baseline subtraction or if number,
subtract directly
-m, --mask TEXT Value or range to mask, range is lower-edge inclusive
--vmin TEXT Minimum value (z/color axis)
--vmax TEXT Maximum value (z/color axis)
--dpi TEXT The dots-per-inch resolution
-z, --zoom TEXT A zoom range as 'rmin:rmax,cmin:cmax'
--help Show this message and exit.
❯ wirecell-util npz-to-img --cmap Spectral \
--zoom 300:500,0:1000 --mask 0 --vmin -50 --vmax 50 \
--dpi 600 --baseline=median \
-o junk.png data/images/real/protodune-orig-0-2-U.npz
A few color map examples with command like tha bove. Note, 0 value is masked to white regardless of color map.
- “coolwarm” (real)
- “seismic” (real)
- “Spectral” (real)
- “viridis” (real)
We select one event region (U plane) to show “real” 2D vs “fake” q1D, both as SVG. In particular, not how there is “long range induction” effects in 2D which are lacking in q1D.
- Real:
- Fake:
By default, toyzero operates as a generator. That is, every time a
particular version is run it should always produce essentially an
identical result.
Some high-level parameters may be changed to produce different results. These can be controlled by a configuration file. For example the default configuration is held in:
To customize,
$ cp toyzero.yaml mycfg.yaml $ emacs mycfg.yaml $ snakemake -jall --configfile mycfg.yaml all
Files carry identical names between the different variant runs. To
avoid collision be sure to set datadir and plotdir to locations not
yet used.
You may also customize specific parameters from the command line:
$ snakemake -jall --config outdir=junk ntracks=10 all_depos $ eom junk/plots/depos-diagnostic.png
A --config wins over an entry in a --configfile. Note, a single
--config flag should be used to indicate all parameter overrides.
Adding another --config will negate all prior.
As already introduced, this “toyzero” package is organized around a
snakemake Snakefile which provides automated running of a
directed, acyclic graph (DAG). A node of the DAG is a process and
directional edges are formed through input or output files.
It is instructive to visualize the overall DAG and snakemake provides
this by running:
snakemake --dag all just_images > toyzero-all-dag.dot
dot -Tsvg -o toyzero-all-dag.svg toyzero-all-dag.dot
dot -Tpdf -o toyzero-all-dag.pdf toyzero-all-dag.dotBelow is the result as SVG or download the toyzero-all-dag.pdf to zoom around.
The “real” documentation on toyzero is the Snakefile as it describes
exactly what is run. In principle, with the Snakefile one can always
reproduce its results. In practice changes in the wire-cell and
wirecell-* programs, or external data can lead to unexpected changes.
Much of the toyzero package relies on the wire-cell-python package.
You can develop that code locally (instead of push to GitHub +
reinstall). We can leverage the direnv environment for toyzero to do
so like:
$ git clone [email protected]:wirecell/wire-cell-python.git $ cd wire-cell-python/ $ pip install -U -e .
You can now hack on wire-cell-python and your changes are immediately made available. If the changes are generally useful, please consider making a PR!
We run from containers. Eg, as a mongo docker command that runs
snakemake in the toyzero container.
$ docker run \ --user user \ --volume (pwd):/data \ -ti ls4gan/toyzero:0.3.0 \ "cd toyzero && \ snakemake just_tar --notemp -j1 -p \ --config seed=1234 outdir=/data ntracks=100 nevents=10 wcloglvl=debug threads=8"
Or, one layer down in the wirecell container, assuming you have
toyzero checked out locally:
$ cd .. # parent holding local toyzero/
$ mkdir run
$ cd run/
$ cp -a ../toyzero/{Snakefile,cfg,toyzero.yaml} .
$ docker run \
--user user \
--volume (pwd):/data \
-ti ls4gan/wirecell:0.16.0 \
"cd /data && \
snakemake just_tar -j1 -p \
--config seed=1234 ntracks=100 nevents=10 wcloglvl=debug threads=8"
$ ls -l toyzero-100-10-1234.tar
Note: will likely want to set threads=1 for batch and MUST set
seed=XYZ uniquely for each submission. The outdir setting may be used
if output should not go to the CWD. The tar file base name can be
controlled with outname. Intermediate files that eventually go in to
the tar file land in {outdir}/seed-{seed}/. The ./.snakemake/
directory receives snakemake control and log files. If run with
--notemp then (ironically, should be named --yestemp) temporary files
marked with temp() in the Snakefile are kept.