Develop

.github/workflows/pull_request.yml

@@ -29,26 +29,6 @@ jobs:
       run: |
         pre-commit run -a
 
-  test_code_python3p8:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ["3.8"]
-    steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install .[dev]
-    - name: Test code
-      run: |
-        mkdir test_report
-        tox
-
 
   test_code_and_coverage_report_python3p11:
     runs-on: ubuntu-latest

.gitignore

@@ -57,6 +57,7 @@ coverage.xml
 *.cover
 .hypothesis/
 .pytest_cache/
+test_report/
 
 # Translations
 *.mo

.gitlab-ci.yml

@@ -1,5 +1,5 @@
 # Default image, if not specified
-image: "python:3.8"
+image: "python:3.11"
 
 stages:
   - Static Analysis
@@ -96,34 +96,34 @@ mypy:
       - test_report/cov_report
     expire_in: 1 hour
 
-tox-3.6:
-  extends: ".tox"
-  stage: "Test"
-  image: "python:3.6"
-  rules:
-    - if: '$CI_COMMIT_TAG'
-      when: always
-
-tox-3.7:
-  extends: ".tox"
-  stage: "Test"
-  image: "python:3.7"
+#tox-3.7:
+#  extends: ".tox"
+#  stage: "Test"
+#  image: "python:3.7"
 #  rules:
 #    - if: '$CI_COMMIT_TAG'
 #      when: always
 
-tox-3.8:
+tox-3.9:
   extends: ".tox"
   stage: "Test"
-  image: "python:3.8"
+  image: "python:3.9"
   rules:
     - if: '$CI_COMMIT_TAG'
       when: always
 
-tox-3.9:
+tox-3.11:
   extends: ".tox"
   stage: "Test"
-  image: "python:3.9"
+  image: "python:3.11"
+
+#tox-3.13:
+#  extends: ".tox"
+#  stage: "Test"
+#  image: "python:3.13"
+#  rules:
+#    - if: '$CI_COMMIT_TAG'
+#      when: always
 
 
 # tox_slowtests:
@@ -153,7 +153,7 @@ tox-3.9:
 #     needs: []
 
 test_report:
-  needs: ["tox-3.9"]
+  needs: ["tox-3.11"]
   stage: Publish test report
   image: 
      name: oberonamsterdam/wkhtmltopdf

docs/content/api.rst

@@ -39,7 +39,7 @@ LPUPropagation
    lpu.lpu_propagation.LPUPropagation.propagate_random
    lpu.lpu_propagation.LPUPropagation.propagate_systematic
    lpu.lpu_propagation.LPUPropagation.propagate_cov
-   lpu.lpu_propagation.LPUPropagation.propagate_flattened_cov
+   lpu.lpu_propagation.LPUPropagation.propagate_cov_flattened
    lpu.lpu_propagation.LPUPropagation.process_jacobian
 
 Digital Effects Tables

punpy/_version.py

@@ -1 +1 @@
-__version__ = "1.0.1"
+__version__ = "1.0.7"

punpy/digital_effects_table/measurement_function.py

@@ -337,18 +337,24 @@ def propagate_ds(
                 repeat_dim_err_corrs=repeat_dim_err_corrs,
             )
 
+            if not include_corr:
+                ds_vars = copy.deepcopy(list(template.keys()))
+                for key in ds_vars:
+                    if key[:8] == "err_corr":
+                        template.pop(key)
+
             # create dataset template
             ds_out = obsarray.create_ds(template, self.sizes_dict)
 
         # add trivial first dimension to so we can loop over output_vars later
         if self.output_vars == 1:
-            if u_rand_y is not None:
+            if isinstance(u_rand_y, np.ndarray):
                 u_rand_y = u_rand_y[None, ...]
-            if u_syst_y is not None:
+            if isinstance(u_syst_y, np.ndarray):
                 u_syst_y = u_syst_y[None, ...]
-            if u_stru_y is not None:
+            if isinstance(u_stru_y, np.ndarray):
                 u_stru_y = u_stru_y[None, ...]
-            if corr_stru_y is not None:
+            if isinstance(corr_stru_y, np.ndarray):
                 corr_stru_y = corr_stru_y[None, ...]
 
         # loop through measurands
@@ -364,7 +370,7 @@ def propagate_ds(
                 ucomp_sys = "u_sys_" + self.yvariable[i]
                 ucomp_str = "u_str_" + self.yvariable[i]
 
-            if u_rand_y is None:
+            if not isinstance(u_rand_y, np.ndarray):
                 ds_out = self.templ.remove_unc_component(
                     ds_out, self.yvariable[i], ucomp_ran
                 )
@@ -374,7 +380,7 @@ def propagate_ds(
                 else:
                     ds_out[ucomp_ran].values = u_rand_y[i]
 
-            if u_syst_y is None:
+            if not isinstance(u_syst_y, np.ndarray):
                 ds_out = self.templ.remove_unc_component(
                     ds_out, self.yvariable[i], ucomp_sys
                 )
@@ -384,7 +390,7 @@ def propagate_ds(
                 else:
                     ds_out[ucomp_sys].values = u_syst_y[i]
 
-            if u_stru_y is None:
+            if not isinstance(u_stru_y, np.ndarray):
                 ds_out = self.templ.remove_unc_component(
                     ds_out,
                     self.yvariable[i],
@@ -405,10 +411,6 @@ def propagate_ds(
                         i,
                         use_ds_out_pre_unmodified,
                     )
-                else:
-                    ds_out.drop("err_corr_str_" + self.yvariable[i])
-
-                    # ds_out.drop("err_corr_str_between")
 
             if (ds_out_pre is not None) and not use_ds_out_pre_unmodified:
                 self.templ.join_with_preexisting_ds(
@@ -500,6 +502,12 @@ def propagate_ds_total(
                 repeat_dim_err_corrs=repeat_dim_err_corrs,
             )
 
+            if not include_corr:
+                ds_vars = copy.deepcopy(list(template.keys()))
+                for key in ds_vars:
+                    if key[:8] == "err_corr":
+                        template.pop(key)
+
             # create dataset template
             ds_out = obsarray.create_ds(template, self.sizes_dict)
 
@@ -537,18 +545,6 @@ def propagate_ds_total(
                         use_ds_out_pre_unmodified,
                     )
 
-                else:
-                    if len(self.str_corr_dims) == 1:
-                        ds_out.drop("err_corr_tot_" + self.yvariable[i])
-                    else:
-                        for ii in range(len(self.str_corr_dims)):
-                            ds_out.drop(
-                                "err_corr_tot_"
-                                + self.yvariable[i]
-                                + "_"
-                                + self.str_corr_dims[ii]
-                            )
-
             if (ds_out_pre is not None) and not use_ds_out_pre_unmodified:
                 self.templ.join_with_preexisting_ds(
                     ds_out, ds_out_pre, drop=self.yvariable[i]
@@ -645,6 +641,12 @@ def propagate_ds_specific(
                 simple_systematic=simple_systematic,
             )
 
+            if not include_corr:
+                ds_vars = copy.deepcopy(list(template.keys()))
+                for key in ds_vars:
+                    if key[:8] == "err_corr":
+                        template.pop(key)
+
             # create dataset template
             ds_out = obsarray.create_ds(template, self.sizes_dict)
 
@@ -805,7 +807,7 @@ def propagate_ds_all(
             include_corr=include_corr,
         )
 
-    def run(self, *args, expand=False):
+    def run_meas_function(self, *args, expand=False):
         """
         Function to calculate the measurand by running input quantities through measurement function.
 
@@ -1219,7 +1221,7 @@ def _check_sizes_and_run(self, *args, expand=False, ds_out_pre=None):
                     "punpy.MeasurementFunction: When using a measurement function with multiple measurands with different shapes, you cannot set parallel_cores to 0 (the default) when creating the prop object."
                 )
 
-        # define dictionary with dimension sizes (needs to be done before self.run() when expand==True)
+        # define dictionary with dimension sizes (needs to be done before self.run_meas_function() when expand==True)
         if self.sizes_dict is None and expand:
             self.sizes_dict = {}
             for i in range(self.output_vars):
@@ -1239,7 +1241,7 @@ def _check_sizes_and_run(self, *args, expand=False, ds_out_pre=None):
                             continue
 
         # run the measurement function
-        y = self.run(*args, expand=expand)
+        y = self.run_meas_function(*args, expand=expand)
 
         if self.output_vars == 1:
             y = y[None, ...]

punpy/digital_effects_table/tests/test_measurement_function.py

@@ -141,7 +141,7 @@ def test_gaslaw(self):
         )
         ds_y_tot = gl.propagate_ds_total(ds)
 
-        prop = MCPropagation(3000, dtype="float32", verbose=False)
+        prop = MCPropagation(1000, dtype="float32", verbose=False)
         gl = IdealGasLaw(
             prop,
             ["pressure", "temperature", "n_moles"],
@@ -156,10 +156,10 @@ def test_gaslaw(self):
         )
 
         npt.assert_allclose(ds_y["volume"].values, volume, rtol=0.002)
-        npt.assert_allclose(ds_y["u_ran_volume"].values, u_ran_volume, rtol=0.08)
-        npt.assert_allclose(ds_y["u_sys_volume"].values, u_sys_volume, rtol=0.08)
-        npt.assert_allclose(ds_y["u_str_volume"].values, u_str_volume, rtol=0.08)
-        npt.assert_allclose(ds_y["u_str_volume"].values, u_str_volume, rtol=0.08)
+        npt.assert_allclose(ds_y["u_ran_volume"].values, u_ran_volume, rtol=0.1)
+        npt.assert_allclose(ds_y["u_sys_volume"].values, u_sys_volume, rtol=0.1)
+        npt.assert_allclose(ds_y["u_str_volume"].values, u_str_volume, rtol=0.1)
+        npt.assert_allclose(ds_y["u_str_volume"].values, u_str_volume, rtol=0.1)
         npt.assert_allclose(
             ds_y.unc["volume"].total_unc().values, u_tot_volume, rtol=0.12
         )
@@ -349,7 +349,7 @@ def test_hypernets(self):
         )
 
         hmf.setup(0.1)
-        y = hmf.run(calib_data, L0data.variables, L0data)
+        y = hmf.run_meas_function(calib_data, L0data.variables, L0data)
         u_y_rand = hmf.propagate_random(L0data, calib_data)
         print(u_y_rand)
         # print(u_y_rand,L0data)
@@ -427,7 +427,7 @@ def test_hypernets_repeat_dim(self):
             corr_dims=-99,
         )
         hmf.setup(0.1)
-        y = hmf.run(calib_data, L0data)
+        y = hmf.run_meas_function(calib_data, L0data)
         u_y_rand = hmf.propagate_random(L0data, calib_data)
         # print(list(L1data.variables))
         mask = np.where(

punpy/lpu/lpu_propagation.py

@@ -424,7 +424,7 @@ def propagate_standard(
                 return_Jacobian,
             )
 
-    def propagate_flattened_cov(
+    def propagate_cov_flattened(
         self,
         func,
         x,

punpy/mc/mc_propagation.py

@@ -999,7 +999,7 @@ def _perform_checks(
         count = 0
         for i in range(len(x)):
             if u_x[i] is None:
-                if hasattr(x[i], "__len__"):
+                if hasattr(x[i], "shape"):
                     u_x[i] = np.zeros(x[i].shape)
                 else:
                     u_x[i] = 0.0
@@ -1519,6 +1519,10 @@ def run_samples(
                                 [
                                     np.any(np.isfinite(MC_y[i][ivar]))
                                     for ivar in range(output_vars)
+                                    if (
+                                        hasattr(MC_y[i][ivar], "__len__")
+                                        and (len(MC_y[i][ivar]) > 0)
+                                    )
                                 ]
                             )
                         ],
@@ -1533,6 +1537,10 @@ def run_samples(
                                 [
                                     np.any(np.isfinite(MC_y[i][ivar]))
                                     for ivar in range(output_vars)
+                                    if (
+                                        hasattr(MC_y[i][ivar], "__len__")
+                                        and (len(MC_y[i][ivar]) > 0)
+                                    )
                                 ]
                             )
                         ],
@@ -1549,6 +1557,10 @@ def run_samples(
                                 [
                                     np.all(np.isfinite(MC_y[i][ivar]))
                                     for ivar in range(output_vars)
+                                    if (
+                                        hasattr(MC_y[i][ivar], "__len__")
+                                        and (len(MC_y[i][ivar]) > 0)
+                                    )
                                 ]
                             )
                         ],
@@ -1563,6 +1575,10 @@ def run_samples(
                                 [
                                     np.all(np.isfinite(MC_y[i][ivar]))
                                     for ivar in range(output_vars)
+                                    if (
+                                        hasattr(MC_y[i][ivar], "__len__")
+                                        and (len(MC_y[i][ivar]) > 0)
+                                    )
                                 ]
                             )
                         ],
@@ -1667,14 +1683,18 @@ def process_samples(
                 complex_shapes = False
 
             if complex_shapes:
-                MC_y2 = np.empty(output_vars, dtype=object)
                 u_func = np.empty(output_vars, dtype=object)
 
                 for i in range(output_vars):
-                    MC_y2[i] = np.empty((self.MCsteps,) + yshapes[i])
-                    for j in range(self.MCsteps):
-                        MC_y2[i][j] = MC_y[j, i]
-                    u_func[i] = np.std(np.array(MC_y2[i]), axis=0, dtype=self.dtype)
+                    u_func[i] = np.std(
+                        np.array(list(MC_y[:, i])), axis=0, dtype=self.dtype
+                    )
+
+                # for i in range(output_vars):
+                #     MC_y2[i] = np.empty((self.MCsteps,) + yshapes[i])
+                #     for j in range(self.MCsteps):
+                #         MC_y2[i][j] = MC_y[j, i]
+                #     u_func[i] = np.std(np.array(MC_y2[i]), axis=0, dtype=self.dtype)
 
             else:
                 u_func = np.std(MC_y, axis=0, dtype=self.dtype)
@@ -1728,7 +1748,10 @@ def process_samples(
                     if fixed_corr is None:
                         if complex_shapes:
                             corr_ys[i] = cm.calculate_corr(
-                                MC_y2[i], corrdims, PD_corr, self.dtype
+                                np.array(list(MC_y[:, i])),
+                                corrdims,
+                                PD_corr,
+                                self.dtype,
                             )
                         else:
                             corr_ys[i] = cm.calculate_corr(