Merge pull request #5 from jeriek/dev

Minor updates for v1.0.2

README.md

@@ -20,11 +20,12 @@ see [https://arxiv.org/abs/2006.16273](https://arxiv.org/abs/2006.16273).
 `xsec` is compatible with both Python 2.7 and 3.
 The following external Python libraries are required to run `xsec`:
 
-- Setuptools 20.7.0 or later
-- NumPy 1.14 or later
-- SciPy 1.0.0 or later
-- Joblib 0.12.2 or later
-- PySLHA 3.2.3 or later
+- setuptools 20.7.0 or later
+- numpy 1.14 or later
+- scipy 1.0.0 or later
+- joblib 0.12.2 or later
+- dill 0.3.2 or later
+- pyslha 3.2.3 or later
 
 ### `pip` installation
 `xsec` can be installed from [PyPI](https://pypi.org/project/xsec/) using
@@ -67,9 +68,10 @@ The default option is `all`.
 ### Testing the installation
 To check whether the `xsec` installation works properly, download the gluino pair production data (`gg`) and try a test cross-section evaluation:
 ```
-xsec-test [GP_DIR]
+xsec-download-gprocs -t gg [-g GP_DIR]
+xsec-test [-g GP_DIR]
 ```
-If no argument is given, the data is assumed to be in a directory called `gprocs` in the current working directory, otherwise the user-specified path `GP_DIR` is used.
+If no argument is given to `xsec-test`, the data files are assumed to be in a subdirectory called `gprocs` in the current working directory, otherwise the user-specified path `GP_DIR` is used.
 
 ### Removing the package
 Uninstalling `xsec` is as simple as running
@@ -78,7 +80,7 @@ pip uninstall xsec
 ```
 
 ## Code examples
-An example main program showing how to use `xsec` can be found in the [`example_xsec_eval.py`](examples/example_xsec_eval.py) file. This shows evaluation both by specifying the model parameters by hand and by importing an SLHA file. An example showing a simple loop over parameters is available in [`example_xsec_loop.py`](examples/example_xsec_loop.py)
+An example main program showing how to use `xsec` can be found in the [`example_xsec_eval.py`](examples/example_xsec_eval.py) file. This shows evaluation both by specifying the model parameters by hand and by importing an SLHA file. An example showing a simple loop over parameters is available in [`example_xsec_loop.py`](examples/example_xsec_loop.py).
 
 ## Licence
 

VERSION

@@ -1 +1 @@
-1.0.1
+1.0.2

requirements.txt

@@ -2,4 +2,5 @@ setuptools>=20.7.0
 numpy>=1.14
 scipy>=1.0.0
 joblib>=0.13.2
+dill>=0.3.2
 pyslha>=3.2.3

scripts/xsec-test

@@ -6,67 +6,97 @@ Run a simple test instance of the evaluation module.
 This script can be used as a basis for a more complete routine to
 evaluate cross sections.
 
-Use: xsec-test <directory-for-xsec-data>
-    If no argument <directory-for-xsec-data> is given, the code looks
-    for a folder called 'gprocs' in the current working directory.
+Use: xsec-test [-g GP_DIR]
+    If no argument is given, the code looks for a folder called 'gprocs'
+    in the current working directory.
 """
 
 import os
 import sys
+import argparse
 
-# Import xsec, first assume we have pip installed it
-try:
-    import xsec
-# Someone is using our fine programme without pip installing
-except ImportError:
-    # Our current absolute directory
-    abs_dir = os.path.dirname(os.path.abspath(__file__))
-    # Parent directory containing xsec
-    parent_dir = os.path.dirname(abs_dir)
-    sys.path.append(parent_dir)
-    import xsec
-
-try:
-    input_dir = sys.argv[1]
-except IndexError:
-    # By default, look for './gprocs'
-    cwd = os.getcwd()
-    input_dir = os.path.join(cwd, "gprocs")
-
-# Set directory and cache choices
-xsec.init(data_dir=input_dir)  # run with default settings (no caching)
-
-# Set center-of-mass energy (in GeV)
-xsec.set_energy(13000)
-
-# Load GP models for the specified process(es)
-processes = [(1000021, 1000021)]
-xsec.load_processes(processes)
-
-# Enter parameter values
-xsec.set_parameters(
-    {
-        "m1000021": 1000,
-        "m1000001": 500,
-        "m1000002": 500,
-        "m1000003": 500,
-        "m1000004": 500,
-        "m1000005": 500,
-        "m1000006": 500,
-        "m2000001": 500,
-        "m2000002": 500,
-        "m2000003": 500,
-        "m2000004": 500,
-        "m2000005": 500,
-        "m2000006": 500,
-        "sbotmix11": 0,
-        "stopmix11": 0,
-        "mean": 500,
-    }
-)
-
-# Evaluate the cross section with the given input parameters
-xsec.eval_xsection()
-
-# Finalise the evaluation procedure
-xsec.finalise()
+
+def main():
+    """
+    Run a test cross-section evaluation for gluino pair production.
+    """
+
+    # Get the script name
+    prog_name = "xsec-test"
+
+    # Set up the argument parser
+    parser = argparse.ArgumentParser(
+        prog=prog_name,
+        description="Tool for checking the xsec setup with a test evaluation.",
+    )
+
+    # Take the download directory as an optional argument
+    parser.add_argument(
+        "-g",
+        "--gp_dir",
+        nargs="?",
+        metavar="PATH",
+        type=str,
+        action="store",
+        default=os.path.join(os.getcwd(), "gprocs"),
+        help="set the path where the downloaded files are stored. "
+        "The default path is %(default)s",
+    )
+
+    # Parse the arguments
+    args = parser.parse_args()
+
+    # Import xsec, first assume we have pip installed it
+    try:
+        import xsec
+    # Someone is using our fine programme without pip installing
+    except ImportError:
+        # Our current absolute directory
+        abs_dir = os.path.dirname(os.path.abspath(__file__))
+        # Parent directory containing xsec
+        parent_dir = os.path.dirname(abs_dir)
+        sys.path.append(parent_dir)
+        import xsec
+
+    # Set directory and cache choices
+    xsec.init(data_dir=args.gp_dir)  # run with default settings (no caching)
+
+    # Set center-of-mass energy (in GeV)
+    xsec.set_energy(13000)
+
+    # Load GP models for the specified process(es)
+    processes = [(1000021, 1000021)]
+    xsec.load_processes(processes)
+
+    # Enter parameter values
+    xsec.set_parameters(
+        {
+            "m1000021": 1000,
+            "m1000001": 500,
+            "m1000002": 500,
+            "m1000003": 500,
+            "m1000004": 500,
+            "m1000005": 500,
+            "m1000006": 500,
+            "m2000001": 500,
+            "m2000002": 500,
+            "m2000003": 500,
+            "m2000004": 500,
+            "m2000005": 500,
+            "m2000006": 500,
+            "sbotmix11": 0,
+            "stopmix11": 0,
+            "mean": 500,
+        }
+    )
+
+    # Evaluate the cross section with the given input parameters
+    xsec.eval_xsection()
+
+    # Finalise the evaluation procedure
+    xsec.finalise()
+
+
+# When the code is executed as a script, run the following.
+if __name__ == "__main__":
+    main()

xsec/evaluation.py

@@ -142,25 +142,31 @@ def eval_xsection(verbose=2, check_consistency=True):
     #    the xsection value at the lower scale, but at the higher one,
     #    and vice versa for scaleup_rel.
     # --- Get the DGP medians, discard regression errors on the variations
-    mu_dgp_scldn_rel, _ = np.array(list(zip(*dgp_results["scldn"])))
-    mu_dgp_sclup_rel, _ = np.array(list(zip(*dgp_results["sclup"])))
+    mu_dgp_scldn_rel, _ = np.array(
+        list(zip(*dgp_results["scldn"])), dtype=object
+    )
+    mu_dgp_sclup_rel, _ = np.array(
+        list(zip(*dgp_results["sclup"])), dtype=object
+    )
 
     scaledown_rel = (
         np.array(
-            list(map(np.min, list(zip(mu_dgp_scldn_rel, mu_dgp_sclup_rel))))
+            list(map(np.min, list(zip(mu_dgp_scldn_rel, mu_dgp_sclup_rel)))),
+            dtype=object,
         )
         - 1.0
     )
     scaleup_rel = (
         np.array(
-            list(map(np.max, list(zip(mu_dgp_scldn_rel, mu_dgp_sclup_rel))))
+            list(map(np.max, list(zip(mu_dgp_scldn_rel, mu_dgp_sclup_rel)))),
+            dtype=object,
         )
         - 1.0
     )
 
     # -- Signed PDF errors divided by xsection_central
     # --- Get the DGP medians, discard regression errors on the variations
-    delta_pdf_rel, _ = np.array(list(zip(*dgp_results["pdf"])))
+    delta_pdf_rel, _ = np.array(list(zip(*dgp_results["pdf"])), dtype=object)
 
     pdfdown_rel = (-1.0) * np.abs(delta_pdf_rel)
     pdfup_rel = np.abs(delta_pdf_rel)
@@ -172,8 +178,8 @@ def eval_xsection(verbose=2, check_consistency=True):
     #    calculated with upper and lower (1 sigma) alpha_s values.
     #    (Therefore this cross-section uncertainty is symmetric.)
     # --- Get the DGP medians, discard regression errors on the variations
-    mu_dgp_adn_rel, _ = np.array(list(zip(*dgp_results["adn"])))
-    mu_dgp_aup_rel, _ = np.array(list(zip(*dgp_results["aup"])))
+    mu_dgp_adn_rel, _ = np.array(list(zip(*dgp_results["adn"])), dtype=object)
+    mu_dgp_aup_rel, _ = np.array(list(zip(*dgp_results["aup"])), dtype=object)
 
     delta_alphas_rel = np.array(
         [