Minor fixes in fake measurement, full response and corrections (#404)

* Added path conversions to fake measurement + some debug messages
* Skip modifiers if none (in lhc.py)
* Value error for all-zero weights in correction.
* Bumpversion

CHANGELOG.md

@@ -1,5 +1,10 @@
 # OMC3 Changelog
 
+#### 2022-11-01 - v0.6.6
+
+- Bugfixes 
+  - correction: fullresponse is converted to Path.
+  - fake measurement from model: dont randomize errors and values by default. 
 
 #### 2022-10-15 - v0.6.5
 
@@ -11,7 +16,6 @@
 
 - Fixed the phase filtering for coupling calculation to not forget columns.
 
-
 #### 2022-09-27 - v0.6.3
 
 - Pandafied `knob_extractor` internally and python output.

omc3/__init__.py

@@ -11,7 +11,7 @@
 __title__ = "omc3"
 __description__ = "An accelerator physics tools package for the OMC team at CERN."
 __url__ = "https://github.com/pylhc/omc3"
-__version__ = "0.6.5"
+__version__ = "0.6.6"
 __author__ = "pylhc"
 __author_email__ = "[email protected]"
 __license__ = "MIT"

omc3/correction/handler.py

@@ -122,6 +122,11 @@ def get_measurement_data(
     filtered_keys = keys
     if w_dict is not None:
         filtered_keys = [key for key in keys if w_dict[key] != 0]
+        if not len(filtered_keys):
+            raise ValueError(
+                "All given Parameters have been discarded due to all-zero weights. "
+                "Check given weights and weight default values."
+            )
 
     for key in filtered_keys:
         if key.startswith(f"{PHASE}"):

omc3/global_correction.py

@@ -175,6 +175,7 @@
                                        NORM_DISP, PHASE, TUNE)
 from omc3.model import manager
 from omc3.utils import logging_tools
+from omc3.utils.iotools import PathOrStr
 
 LOG = logging_tools.get_logger(__name__)
 
@@ -201,11 +202,14 @@ def correction_params():
     params = EntryPointParameters()
     params.add_parameter(name="meas_dir",
                          required=True,
+                         type=PathOrStr,
                          help="Path to the directory containing the measurement files.",)
     params.add_parameter(name="output_dir",
                          required=True,
+                         type=PathOrStr,
                          help="Path to the directory where to write the output files.", )
     params.add_parameter(name="fullresponse_path",
+                         type=PathOrStr,
                          help="Path to the fullresponse binary file.If not given, "
                               "calculates the response analytically.",)
     params.add_parameter(name="optics_params",
@@ -293,6 +297,8 @@ def _check_opt_add_dicts(opt: dict) -> dict:  # acts inplace...
         opt[key] = dict(zip(opt.optics_params, opt[key]))
     opt.meas_dir = Path(opt.meas_dir)
     opt.output_dir = Path(opt.output_dir)
+    if opt.fullresponse_path:
+        opt.fullresponse_path = Path(opt.fullresponse_path)
     return opt
 
 
@@ -352,5 +358,4 @@ def _get_default_values() -> Dict[str, Dict[str, float]]:
 
 
 if __name__ == "__main__":
-    with logging_tools.DebugMode(active=False, log_file="iterative_correction.log"):
-        global_correction_entrypoint()
+    global_correction_entrypoint()

omc3/model/accelerators/lhc.py

@@ -363,10 +363,11 @@ def get_base_madx_script(self, best_knowledge: bool = False) -> str:
             f"{self.load_main_seq_madx()}\n"
             f"exec, define_nominal_beams();\n"
         )
-        madx_script += "".join(
-            f"call, file = '{self.model_dir / modifier}'; {MODIFIER_TAG}\n"
-            for modifier in self.modifiers
-        )
+        if self.modifiers is not None:
+            madx_script += "".join(
+                f"call, file = '{self.model_dir / modifier}'; {MODIFIER_TAG}\n"
+                for modifier in self.modifiers
+            )
         madx_script += (
             f"\n! ----- Defining Configuration Specifics -----\n"
             f"exec, cycle_sequences();\n"

omc3/scripts/fake_measurement_from_model.py

@@ -47,7 +47,7 @@
 
     choices: ``['values', 'errors']``
 
-    default: ``['values', 'errors']``
+    default: ``[]``
 
 
 - **relative_errors** *(float)*:
@@ -143,7 +143,7 @@ def get_params():
               "values and the errors will be equal to the relative error * measurement."
               ),
         choices=[VALUES, ERRORS],
-        default=[VALUES, ERRORS],
+        default=[],
         type=str,
         nargs="*",
     )
@@ -202,6 +202,7 @@ def generate(opt) -> Dict[str, tfs.TfsDataFrame]:
     if opt.outputdir is not None:
         LOG.info("Writing fake measurements to files.")
         output_path = Path(opt.outputdir)
+        output_path.mkdir(parents=True, exist_ok=True)
         for filename, df in results.items():
             full_path = output_path / f"{filename}{EXT}"
             tfs.write(full_path, df, save_index=NAME)
@@ -472,11 +473,16 @@ def _get_loop_parameters(parameters: Sequence[str], errors: Sequence[float]) ->
 
 def _get_random_errors(errors: np.array, values: np.array) -> np.array:
     """ Creates normal distributed error-values that will not be lower than EPSILON. """
+    LOG.debug("Calculating normal distributed random errors.")
     random_errors = np.zeros_like(errors)
     too_small = np.ones_like(errors, dtype=bool)
-    while sum(too_small):
+    n_too_small = 1
+    while n_too_small:
         random_errors[too_small] = np.random.normal(errors[too_small], errors[too_small])
         too_small = random_errors < EPSILON * np.abs(values)
+        n_too_small = sum(too_small)
+        LOG.debug(f"{n_too_small} error values are smaller than given eps.")
+    LOG.debug("Random errors created.")
     return random_errors