Setup
Clone the repository:
git clone ssh://git@gitlab.cern.ch:7999/clcheng/quickstats.git
1. CERN User
To set up from lxplus, just do
source setup.sh
2. Genearl User
To set up locally, make sure you have pyROOT 6.24+ installed (using conda is recommended), and do
pip install quickstats
Installing pyROOT
Simplest way to install pyROOT is via conda
conda install -c conda-forge ROOT
Important: First-time compilation
To compile c++ dependencies, do this for first time use
quickstats compile
Command Line Tools
Run Nuisance Parameter Pulls and Ranking
quickstats run_pulls -i <input_ws_path> -d <dataset_name> -p <np_name/pattern> --poi <poi_name> --parallel -1 -o <output_dir>
The following options are available
| Option | Description | Default |
|---|---|---|
-i/--input_file |
Path to the input workspace file | - |
-w/--workspace |
Name of workspace. Auto-detect by default. | None |
-m/--model_config |
Name of model config. Auto-detect by default. | None |
-d/--data |
Name of dataset | "combData" |
-p/--parameter |
Nuisance parameter(s) to run pulls on. Multiple parameters are separated by commas. Wildcards are accepted. All NPs will be run over by default | "" |
-x/--poi |
POIs to measure. If empty, impact on POI will not be calculated. | "" |
-r/--profile |
Parameters to profile | "" |
-f/--fix |
Parameters to fix | "" |
-s/--snapshot |
Name of initial snapshot | "nominalNuis" |
-o/--outdir |
Output directory | "pulls" |
-t/--minimizer_type |
Minimizer type | "Minuit2" |
-a/--minimizer_algo |
Minimizer algorithm | "Migrad" |
-c/--num_cpu |
Number of CPUs to use per parameter | 1 |
--binned/--unbinned |
Whether to use binned likelihood | True |
-q/--precision |
Precision for scan | 0.001 |
-e/--eps |
Tolerance | 1.0 |
-l/--log_level |
Log level | "INFO" |
--eigen/--no-eigen |
Compute eigenvalues and vectors | False |
--strategy |
Default fit strategy | 0 |
--fix-cache/--no-fix-cache |
Fix StarMomentMorph cache | True |
--fix-multi/--no-fix-multi |
Fix MultiPdf level 2 | True |
--offset/--no-offset |
Offset likelihood | True |
--optimize/--no-optimize |
Optimize constant terms | True |
--max_calls |
Maximum number of function calls | -1 |
--max_iters |
Maximum number of Minuit iterations | -1 |
--parallel |
Parallelize job across different nuisanceparameters using N workers. Use -1 for N_CPU workers. | 0 |
--cache/--no-cache |
Cache existing result | True |
--exclude |
Exclude NPs (wildcard is accepted) | "" |
Plot Nuisance Parameter Pulls and Ranking
quickstats plot_pulls --help
Likelihood Fit (Best-fit)
quickstats likelihood_fit --help
Run Likelihood Scan
quickstats likelihood_scan --help
Asymptotic CLs Limit
quickstats cls_limit --help
CLs Limit Scan
quickstats limit_scan --help
Generate Asimov dataset
quickstats generate_standard_asimov --help
Inspect Workspace
quickstats inspect_workspace --help
Create Workspace from XML Cards
quickstats build_xml_ws --help
Modify Workspace from XML Cards or Json Config
quickstats modify_ws --help
Combine Workspace from XML Cards or Json Config
quickstats combine_ws --help
Compare Workspaces
quickstats compare_ws --help
Run Event Loop from Custom Config File
quickstats process_rfile --help
Syntax for setting parameter values
One may modify the values and/or range of variables in a ROOT workspace via a so-called parameter expression. This is used in the class AnalysisBase (or ExtendedModel) via the method set_parameters, or through the CLIs likelihood_fit, likelihood_scan, significance_scan, cls_limit, limit_scan via the arguments --fix and --profile.
A parameter expression is a comma-separated string with the general syntax "<parameter_name>=<value_expression>,...". Here, <parameter_name> can be a string that matches the name of a given variable in the workspace, or a wildcard pattern that matches a collection of variables. There are also special magic keywords that allows matching a specific type of variables via the use of angular brackets. Currently, the following special keywords are supported:
<poi>: matches all Parameters of Interests (POIs)<global_observable>: matches all Global Observables (GOs)<nuisance_parameter>: matches all Nuisance Parameters (NPs)<constrained_nuisance_parameter>: matches all NPs with associated constraint pdfs<unconstrained_nuisance_parameter>: matches all NPs without associated constraint pdfs<gaussian_constraint_np>: matches all NPs with Gaussian constraint pdfs<poisson_constraint_np>: matches all NPs with Poisson constraint pdfs
The <value_expression> supports the following syntax:
- Set only the nominal value:
"<parameter_name>=<nominal_value>" - Set only the range:
"<parameter_name>=<min_value>_<max_value>" - Set both nominal value and range:
"<parameter_name>=<nominal_value>_<min_value>_<max_value>" - Set nominal value, range, and error:
"<parameter_name>=<nominal_value>_<min_value>_<max_value>_<error_value>"
Note that one can ommit values if you do not want to modify a particular data, i.e.:
- Set only the nominal value and the minimal range:
"<parameter_name>=<nominal_value>_<min_value>_" - Set only the error value:
"<parameter_name>=___<error_value>"
Additional, if you want to change only the state of the variable(s), you may completely ommit the value expression. The state can be controlled via the mode argument in set_parameters if you are using the API. Or through the use of --fix (variables marked as constant) or --profile (variables marked as floating) if you are using the CLI.
Some CLI examples:
- Fixing the values of all NPs starting with the substring "ATLAS_" to 0 and those with substring "CMS_" to 1:
--fix "ATLAS_*=0",CMS_*=1" - Fixing (Floating) the values of all constrained (unconstrained) NPs:
--fix "<constrained_nuisance_parameter>" --profile "<unconstrained_nuisance_parameter>" - Float all POIs and setting their range to be [-10, 10] with value 1:
--profile "<poi>=1_-10_10"
Some API examples:
- Fixing the values of all NPs starting with the substring "ATLAS_" to 0 and those with substring "CMS_" to 1:
analysis.set_parameters("ATLAS_*=0,CMS_*=1", mode="fix") - Floating all constrained NPs:
analysis.set_parameters("<constrained_nuisance_parameters>", mode="float") - Set all POIs' range to be [-10, 10] with value 1:
analysis.set_parameters("<poi>=1_-10_10", mode="unchanged")
1 maintainer
Metadata
1 maintainer
Release files for quickstats 0.8.5.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| quickstats-0.8.5.6.tar.gz | 9.2 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| quickstats-0.8.5.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 10.2 MB
Release files / quickstats-0.8.5.6.tar.gz
| Download URL | quickstats-0.8.5.6.tar.gz |
|---|---|
| Size | 9.2 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
76cc62fc5d502bd2cf88fbccb2821e6ff9f7bf7b851bf050d0c6a58740bf7f87
|
|
BLAKE2b-256 checksum How to use checksums |
afb7d33c3968a194f01eacb2af2d919c720a28929ea9844d6189ab52f5c9697c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.13
|
Release files / quickstats-0.8.5.6-py3-none-any.whl
| Download URL | quickstats-0.8.5.6-py3-none-any.whl |
|---|---|
| Size | 995.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
446ee363882c28db0a302e8da978d67590eaa4385a4e31928f07b25ba8f8a989
|
|
BLAKE2b-256 checksum How to use checksums |
d8c30ee10535f76f3cdc8a7571af5281066f7c62649239b6bad8fc8a6efa7de9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.13
|
Security for quickstats 0.8.5.6
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
