Merge pull request #26 from comet-toolkit/v1.0.2

V1.0.2

Author: pdevis

.github/workflows/pull_request.yml

@@ -29,27 +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
     strategy:

.gitignore

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

.gitlab-ci.yml

@@ -1,5 +1,5 @@
 # Default image, if not specified
-image: "python:3.8"
+image: "python:3.11"
 
 stages:
   - Static Analysis
@@ -98,23 +98,15 @@ mypy:
       junit: cov_report.xml
     expire_in: 1 hour
 
-tox-3.10:
-  extends: ".tox"
-  stage: "Test"
-  image: "python:3.6"
-  rules:
-    - if: '$CI_COMMIT_TAG'
-      when: always
-
 tox-3.11:
   extends: ".tox"
   stage: "Test"
   image: "python:3.11"
 
-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

comet_maths/_version.py

@@ -1 +1 @@
-__version__ = "1.0.0"
+__version__ = "1.0.6"

comet_maths/generate_sample/generate_sample.py

@@ -212,8 +212,10 @@ def generate_sample_same(MCsteps, param, dtype=None):
     :return: generated sample
     :rtype: array
     """
-    tileshape = generate_sample_shape(MCsteps, param)
-    MC_sample = np.tile(param, tileshape).astype(dtype)
+    # tileshape = generate_sample_shape(MCsteps, param)
+    MC_sample = np.repeat(np.array(param)[np.newaxis], MCsteps, axis=0)
+    if dtype is not None:
+        MC_sample = MC_sample.astype(dtype)
     return MC_sample
 
 
@@ -696,33 +698,39 @@ def correlate_sample_corr(
             "comet_math.correlate_sample_corr: it is not yet possible to provide a sample with inhomogeneous dimensions."
         )
 
-    try:
-        L = np.array(np.linalg.cholesky(corr))
-    except:
-        L = cm.nearestPD_cholesky(corr, corr=True)
-
-    if maintain_sample_unmodified:
-        sample_out = copy.deepcopy(sample)
-    else:
-        sample_out = sample
-
     if iterate_sample:
         sample_out_iter = np.empty(len(sample), dtype=object)
         for i in range(len(sample)):
-            sample_i = np.roll(sample_out, i, axis=0)
+            sample_i = np.roll(sample, i, axis=0)
             if mean is not None:
                 mean_i = np.roll(mean, i, axis=0)
             if std is not None:
                 std_i = np.roll(std, i, axis=0)
             sample_out_iter[i] = np.roll(
                 correlate_sample_corr(
-                    sample_i, corr, mean_i, std_i, dtype=dtype, iterate_sample=False
+                    sample_i,
+                    corr,
+                    mean_i,
+                    std_i,
+                    dtype=dtype,
+                    iterate_sample=False,
+                    maintain_sample_unmodified=True,
                 ),
                 -i,
                 axis=0,
             )
         return np.mean(sample_out_iter, axis=0)
 
+    try:
+        L = np.array(np.linalg.cholesky(corr))
+    except:
+        L = cm.nearestPD_cholesky(corr, corr=True)
+
+    if maintain_sample_unmodified:
+        sample_out = copy.deepcopy(sample)
+    else:
+        sample_out = sample
+
     if mean is None:
         mean = np.array(
             [np.mean(sample[i], axis=0) for i in range(len(sample))],

comet_maths/generate_sample/tests/test_generate_sample.py

@@ -112,6 +112,7 @@ def test_generate_sample_same(self):
         sample = generate_sample_same(100, x0)
         npt.assert_equal(sample.shape, (100,))
         npt.assert_equal(sample[:-1], sample[1:])
+        assert sample.dtype == int
 
         sample = generate_sample_same(100, x1)
         npt.assert_equal(sample.shape, (100, 20))
@@ -121,7 +122,13 @@ def test_generate_sample_same(self):
         npt.assert_equal(sample.shape, (100, 5, 6))
         npt.assert_equal(sample[:-1], sample[1:])
 
+        sample = generate_sample_same(100, "test")
+        npt.assert_equal(sample.shape, (100,))
+        npt.assert_equal(sample[:-1], sample[1:])
+        assert sample.dtype.type is np.str_
+
     def test_generate_sample_systematic(self):
+        np.random.seed(123456)
         sample = generate_sample_systematic(1000, x0, u_x0)
         npt.assert_equal(sample.shape, (1000,))
         npt.assert_allclose(u_x0, np.std(sample, axis=0), rtol=0.1)

comet_maths/interpolation/interpolation.py

@@ -190,7 +190,7 @@ def interpolate_1d(
     return_corr: Optional[bool] = False,
     include_model_uncertainties: Optional[bool] = True,
     add_model_error: Optional[bool] = False,
-    MCsteps: Optional[int] = 100,
+    MCsteps: Optional[int] = 50,
     parallel_cores: Optional[int] = 4,
     interpolate_axis: Optional[int] = 0,
 ) -> Union[
@@ -402,14 +402,26 @@ def _redo_extrapolation(
     :param extrapolate: extrapolation method, which can be set to "extrapolate" (in which case extrapolation is used using interpolation method defined in "method"), "nearest" (in which case nearest values are used for extrapolation), or "linear" (in which case linear extrapolation is used). Defaults to "extrapolate".
     :return: interpolated values with correct extrapolation
     """
-    if extrapolate == "nearest":
-        y[x < x_i[0]] = y_i[0]
-        y[x > x_i[-1]] = y_i[-1]
+    if hasattr(x, "__len__") and len(x) > 1:
+        if extrapolate == "nearest":
+            y[x < x_i[0]] = y_i[0]
+            y[x > x_i[-1]] = y_i[-1]
+
+        elif extrapolate == "linear":
+            f_lin = interp1d(x_i, y_i, kind="linear", fill_value="extrapolate")
+            y[x < x_i[0]] = f_lin(x[x < x_i[0]])
+            y[x > x_i[-1]] = f_lin(x[x > x_i[-1]])
+    else:
+        if extrapolate == "nearest":
+            if x < x_i[0]:
+                y = y_i[0]
+            elif x > x_i[-1]:
+                y = y_i[-1]
 
-    elif extrapolate == "linear":
-        f_lin = interp1d(x_i, y_i, kind="linear", fill_value="extrapolate")
-        y[x < x_i[0]] = f_lin(x[x < x_i[0]])
-        y[x > x_i[-1]] = f_lin(x[x > x_i[-1]])
+        elif extrapolate == "linear":
+            if x < x_i[0] or x > x_i[-1]:
+                f_lin = interp1d(x_i, y_i, kind="linear", fill_value="extrapolate")
+                y = f_lin(x)
 
     return y
 
@@ -628,7 +640,7 @@ def interpolate_1d_along_example(
     include_model_uncertainties: Optional[bool] = True,
     add_model_error: Optional[bool] = False,
     plot_residuals: Optional[bool] = False,
-    MCsteps: Optional[int] = 100,
+    MCsteps: Optional[int] = 50,
     parallel_cores: Optional[int] = 4,
 ) -> Union[
     np.ndarray, Tuple[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray, np.ndarray]

comet_maths/interpolation/tests/test_interpolation.py

@@ -145,6 +145,33 @@ def test_interpolation_1d(self):
         fig2.colorbar(p2)
         fig2.savefig("interpolation_test_1d_corrs.png", bbox_inches="tight")
 
+    def test_interpolation_1d(self):
+        xi = np.arange(0, 3.0, 0.2)
+        yi = function1(xi)
+        u_yi = 0.05 * np.abs(yi)
+        # yi = cm.generate_sample(1, yi, u_yi, corr_x="rand")
+
+        x = np.array([0.33333, 0.666666, 1, 1.33333, 1.66666, 2.00, 2.3333])
+        # t1=time.time()
+        y = cm.interpolate_1d(xi, yi, x, method="cubic")
+        y2 = cm.interpolate_1d(xi, yi, x, method="quadratic")
+
+        y, u_y, corr_y = cm.interpolate_1d(
+            xi,
+            yi,
+            x,
+            u_y_i=u_yi,
+            corr_y_i="syst",
+            method="linear",
+            return_uncertainties=True,
+            return_corr=True,
+            MCsteps=20000,
+            include_model_uncertainties=False,
+        )
+
+        print(u_y / y)
+        print(u_y, u_yi)
+
     def test_interpolation_1d_along_example(self):
         np.random.seed(1234567)
         xi = np.arange(0, 3.0, 0.2)
@@ -296,7 +323,7 @@ def test_interpolation_1d_along_example_unc(self):
 
         # npt.assert_allclose(y_hr_gpr,y_hr_gpr2,atol=0.01)
 
-        mcprop = punpy.MCPropagation(100, parallel_cores=4)
+        mcprop = punpy.MCPropagation(50, parallel_cores=1)
 
         inp2 = cm.Interpolator(
             relative=False,

quality_documentation/QualityDocumentation.tex

@@ -110,13 +110,14 @@ \section{Test report}\label{testreport}
     \clearpage
 }
 
-\part*{User Manual}
-\phantomsection
-\addcontentsline{toc}{part}{User Manual}
-\appendix
-\label{UserManual}
-\def\maketitle{}
-\def\tableofcontents{}
-\input{./latex/user_manual.tex}
+%\part*{User Manual}
+%\usepackage{./latex/sphinxmanual}
+%\phantomsection
+%\addcontentsline{toc}{part}{User Manual}
+%\appendix
+%\label{UserManual}
+%\def\maketitle{}
+%\def\tableofcontents{}
+%\input{./latex/user_manual.tex}
 
 \end{document}

quality_documentation/base/CookiecutterMacros.tex

@@ -4,4 +4,5 @@
 \verbdef\authorname{Pieter De Vis}
 \verbdef\authoremail{[email protected]}
 \newcommand{\packagedescription}{Mathematical algorithms and tools to use within CoMet toolkit.}
-\newcommand{\sil}{sil3}
+\newcommand{\sil}{sil3}
+\newcommand{\sphinxAtStartPar}{\hspace{0}}