fix: 🐛 Correct path to logo (#947)

* fix: 🐛 Correct path to logo

* build: 🔖 v0.22.4

Add poetry.lock and pyproject.toml to git

* refactor: ♻️ Using reference class

.pre-commit-config.yaml

@@ -48,6 +48,7 @@ repos:
             types-PyYAML==6.0.12.10,
             types-tabulate==0.9.0.3,
             types-mock==5.1.0.1,
+            types-setuptools==68.0.0.3,
             numpy==1.23.4,
             pydantic==2.1,
             pytest==7.1.3,

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "SpectraFit"
-version = "0.22.3"
+version = "0.22.4"
 description = "Fast fitting of 2D- and 3D-Spectra with established routines"
 readme = "README.md"
 authors = ["Anselm Hahn <[email protected]>"]
@@ -100,6 +100,7 @@ prospector = { extras = [
   "pylint",
 ], version = "^1.10.2" }
 bump-pydantic = "^0.6.0"
+types-setuptools = "^68.0.0.3"
 
 [tool.poetry.group.docs.dependencies]
 mkdocstrings = { extras = ["python"], version = ">=0.19,<0.23" }

spectrafit/__init__.py

@@ -1,2 +1,2 @@
 """SpectraFit, fast command line tool for fitting data."""
-__version__ = "0.22.3"
+__version__ = "0.22.4"

spectrafit/api/pptx_model.py

@@ -10,6 +10,7 @@
 from typing import Union
 
 import pandas as pd
+import pkg_resources
 
 from matplotlib import pyplot as plt
 from pptx.util import Pt
@@ -336,7 +337,10 @@ class PPTXBasicTitleAPI(BaseModel):
     table_2_description: str = "Table 2: Regression Metrics"
     table_3_description: str = "Table 3: Variables"
 
-    credit_logo: Path = Path("spectrafit/plugins/img/SpectraFit.png")
+    credit_logo: Path = (
+        Path(pkg_resources.get_distribution("spectrafit").location)
+        / "spectrafit/plugins/img/SpectraFit.png"
+    )
     credit_description: str = f"SpectraFit: v{__version__}"
 
 

spectrafit/api/test/test_cmd_model.py

@@ -15,7 +15,7 @@
 
 def test_default_cmd() -> None:
     """Test for default settings of CMD Model."""
-    result = CMDModelAPI(infile="").dict()
+    result = CMDModelAPI(infile="").model_dump()
     assert result["infile"] == ""
     assert result["outfile"] == "spectrafit_results"
     assert result["input"] == "fitting_input.toml"
@@ -51,7 +51,7 @@ def test_overwrite_cmd() -> None:
             "width": [1],
             "wlen": 1,
         },
-    ).dict()
+    ).model_dump()
     assert result["infile"] == ""
     assert result["outfile"] == "test"
     assert result["input"] == "fitting_input.toml"
@@ -89,7 +89,7 @@ def test_sha256() -> None:
 
 def test_default_description() -> None:
     """Test for default settings of Description Model."""
-    result = DescriptionAPI().dict()
+    result = DescriptionAPI().model_dump()
     assert result["project_name"] == "FittingProject"
     assert result["project_details"] == f"Fitting Project via SpectraFit v{__version__}"
     assert result["keywords"] == ["spectra"]
@@ -103,7 +103,7 @@ def test_default_description() -> None:
 @pytest.mark.parametrize("metadata", [{"test": "test"}, ["test"]])
 def test_overwrite_description(metadata: Any) -> None:
     """Test for overwriting settings of Description Model."""
-    result = DescriptionAPI(metadata=metadata).dict()
+    result = DescriptionAPI(metadata=metadata).model_dump()
     assert result["metadata"] == metadata
 
 
@@ -117,7 +117,7 @@ def test_overwrite_description(metadata: Any) -> None:
 )
 def test_overwrite_references(refs: Any) -> None:
     """Test for overwriting settings of Description Model."""
-    result = DescriptionAPI(refs=refs).dict()
+    result = DescriptionAPI(refs=refs).model_dump()
     assert result["references"] == refs
 
 

spectrafit/models.py

@@ -737,7 +737,7 @@ def pearson4(
 class ReferenceKeys:
     """Reference keys for model fitting and peak detection."""
 
-    __models__ = list(DistributionModelAPI.schema()["properties"].keys())
+    __models__ = list(DistributionModelAPI.model_json_schema()["properties"].keys())
 
     __automodels__ = [
         "gaussian",
@@ -1507,8 +1507,8 @@ def __init__(self, df: pd.DataFrame, args: Dict[str, Any]) -> None:
                  additional information beyond the command line arguments.
         """
         super().__init__(df=df, args=args)
-        self.args_solver = SolverModelsAPI(**args).dict()
-        self.args_global = GlobalFittingAPI(**args).dict()
+        self.args_solver = SolverModelsAPI(**args).model_dump()
+        self.args_global = GlobalFittingAPI(**args).model_dump()
         self.params = self.return_params
 
     def __call__(self) -> Tuple[Minimizer, Any]:

spectrafit/plotting.py

@@ -88,7 +88,7 @@ def plot_global_spectra(self) -> None:
             peaks = [
                 peak
                 for peak in self.df.columns
-                if not peak.startswith(tuple(ColumnNamesAPI().dict().values()))
+                if not peak.startswith(tuple(ColumnNamesAPI().model_dump().values()))
                 and peak.endswith(f"_{i+1}")
             ]
             color_peaks = sns.color_palette("rocket", len(peaks))
@@ -154,7 +154,7 @@ def plot_local_spectra(self) -> None:
         peaks = [
             peak
             for peak in self.df.columns
-            if peak not in list(ColumnNamesAPI().dict().values())
+            if peak not in list(ColumnNamesAPI().model_dump().values())
         ]
         color_peaks = sns.color_palette("rocket", len(peaks))
         for i, peak in enumerate(peaks):

spectrafit/plugins/notebook.py

@@ -1107,7 +1107,9 @@ def solver_model(
         self.initial_model = initial_model
 
         if isinstance(conf_interval, bool):
-            conf_interval = ConfIntervalAPI().dict() if conf_interval is True else False
+            conf_interval = (
+                ConfIntervalAPI().model_dump() if conf_interval is True else False
+            )
         elif isinstance(conf_interval, dict):
             conf_interval = ConfIntervalAPI(**conf_interval).dict(exclude_none=True)
 

spectrafit/plugins/rixs_converter.py

@@ -220,7 +220,7 @@ def __call__(self) -> None:
                 emission_energy=args["emission_energy"],
                 rixs_map=args["rixs_map"],
                 mode=args["mode"],
-            ).dict(),
+            ).model_dump(),
             fname=args["infile"],
             export_format=args["export_format"],
         )

spectrafit/plugins/rixs_visualizer.py

@@ -652,7 +652,7 @@ def load_data(infile: Path) -> RIXSModelAPI:
 
     def __call__(self) -> None:  # pragma: no cover
         """Run the RIXS Visualizer."""
-        app = RIXSApp(**self.load_data(self.get_args()["infile"]).dict())
+        app = RIXSApp(**self.load_data(self.get_args()["infile"]).model_dump())
         app.app_run()
 
 

spectrafit/plugins/test/test_converter.py

@@ -3,6 +3,7 @@
 import gzip
 import json
 import pickle
+import shutil
 
 from pathlib import Path
 from typing import Any
@@ -21,6 +22,7 @@
 from nptyping import Float
 from nptyping import NDArray
 from nptyping import Shape
+from spectrafit.api.pptx_model import PPTXBasicTitleAPI
 from spectrafit.plugins.data_converter import DataConverter
 from spectrafit.plugins.file_converter import FileConverter
 from spectrafit.plugins.pkl_converter import ExportData
@@ -680,7 +682,6 @@ def test_cmd_pkl_converter(
             "--export-format",
             export_format,
         )
-
         assert ret.success
         assert ret.stdout == ""
         assert ret.stderr == ""
@@ -899,7 +900,7 @@ def test_create_rixs(
         fname, keys = tmp_list_dict_rixs
         converter = RIXSConverter()
         data = converter.convert(fname, file_format="latin1")
-        rixs_map = converter.create_rixs(data, *keys, mode=mode).dict()
+        rixs_map = converter.create_rixs(data, *keys, mode=mode).model_dump()
 
         assert isinstance(rixs_map["rixs_map"], np.ndarray)
         if mode == "mean":
@@ -1070,13 +1071,22 @@ def test_cmd_pkl_converter(
             script_runner (Any): Script runner.
             tmp_toml_data (Path): Path to temporary file.
             file_format (str): File format.
+
         """
+        source_path = Path("spectrafit/plugins/img/SpectraFit.png")
+        destination_path = PPTXBasicTitleAPI().credit_logo
+        destination_path.parent.mkdir(parents=True, exist_ok=True)
+        shutil.copy(source_path, destination_path)
+
         ret = script_runner.run(
-            "spectrafit-pptx-converter",
-            str(tmp_toml_data),
-            "--file-format",
-            file_format,
+            [
+                "spectrafit-pptx-converter",
+                str(tmp_toml_data),
+                "--file-format",
+                file_format,
+            ]
         )
+        destination_path.unlink()
         assert ret.success
         assert ret.stderr == ""
         assert ret.stdout == ""

spectrafit/spectrafit.py

@@ -235,7 +235,7 @@ def extracted_from_command_line_runner() -> Dict[str, Any]:
     if "settings" in _args.keys():
         for key in _args["settings"].keys():
             result[key] = _args["settings"][key]
-    result = CMDModelAPI(**result).dict()
+    result = CMDModelAPI(**result).model_dump()
     if "description" in _args["fitting"].keys():
         result["description"] = _args["fitting"]["description"]
     if "parameters" in _args["fitting"].keys():