Add write function to FitResult

gammapy/modeling/fit.py

@@ -4,7 +4,11 @@
 import logging
 import numpy as np
 from astropy.table import Table
+import yaml
+from gammapy.utils.check import add_checksum
+from gammapy.utils.metadata import CreatorMetaData
 from gammapy.utils.pbar import progress_bar
+from gammapy.utils.scripts import make_path
 from .covariance import Covariance
 from .iminuit import (
     confidence_iminuit,
@@ -721,6 +725,75 @@ def covariance_result(self):
         """Optimize result."""
         return self._covariance_result
 
+    def to_dict(self, full_output=False, overwrite_templates=False):
+        """Convert to dictionary."""
+
+        models_dict = self.models.to_dict(
+            full_output=full_output, overwrite_templates=overwrite_templates
+        )
+
+        return {
+            "models": models_dict,
+            "covariance_result": {
+                "backend": self.covariance_result.backend,
+                "method": self.covariance_result.method,
+                "success": self.covariance_result.success,
+                "message": self.covariance_result.message,
+            },
+            "optimize_result": {
+                "backend": self.optimize_result.backend,
+                "method": self.optimize_result.method,
+                "success": self.optimize_result.success,
+                "message": self.optimize_result.message,
+                "nfev": self.optimize_result.nfev,
+                "total_stat": self.optimize_result._total_stat,
+            },
+        }
+
+    def to_yaml(self, full_output=False, overwrite_templates=False):
+        """Convert to YAML string."""
+        data = self.to_dict(
+            full_output=full_output, overwrite_templates=overwrite_templates
+        )
+        text = yaml.dump(
+            data, sort_keys=False, indent=4, width=80, default_flow_style=False
+        )
+        creation = CreatorMetaData()
+        return text + creation.to_yaml()
+
+    def write(
+        self,
+        path,
+        overwrite=False,
+        full_output=False,
+        overwrite_templates=False,
+        checksum=False,
+    ):
+        """Write to file.
+
+        Parameters
+        ----------
+        path : `pathlib.Path` or str
+            Path to write files.
+        overwrite : bool, optional
+            Overwrite existing file. Default is False.
+        full_output : bool, optional
+            Store full parameter output. Default is False.
+        overwrite_templates : bool, optional
+            Overwrite templates FITS files. Default is False.
+        checksum : bool, optional
+            When True adds a CHECKSUM entry to the file.
+            Default is False.
+        """
+        path = make_path(path)
+        if path.exists() and not overwrite:
+            raise IOError(f"File exists already: {path}")
+
+        yaml_str = self.to_yaml(full_output, overwrite_templates)
+        if checksum:
+            yaml_str = add_checksum(yaml_str)
+        path.write_text(yaml_str)
+
     def __str__(self):
         string = ""
         if self.optimize_result:

gammapy/modeling/tests/test_fit.py

@@ -3,10 +3,15 @@
 import pytest
 from numpy.testing import assert_allclose
 from astropy.table import Table
-from gammapy.datasets import Dataset
+from gammapy.datasets import Dataset, Datasets, SpectrumDatasetOnOff
 from gammapy.modeling import Fit, Parameter
-from gammapy.modeling.models import ModelBase, Models
-from gammapy.utils.testing import requires_dependency
+from gammapy.modeling.models import (
+    LogParabolaSpectralModel,
+    ModelBase,
+    Models,
+    SkyModel,
+)
+from gammapy.utils.testing import requires_data, requires_dependency
 
 
 class MyModel(ModelBase):
@@ -315,3 +320,31 @@ def test_stat_contour():
 
     # Check that original value state wasn't changed
     assert_allclose(dataset.models.parameters["y"].value, 300)
+
+
+@requires_data()
+def test_write(tmpdir):
+    datasets = Datasets()
+    for obs_id in [23523, 23526]:
+        dataset = SpectrumDatasetOnOff.read(
+            f"$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs{obs_id}.fits"
+        )
+        datasets.append(dataset)
+
+    datasets = datasets.stack_reduce(name="HESS")
+    model = SkyModel(spectral_model=LogParabolaSpectralModel(), name="crab")
+    datasets.models = model
+    fit = Fit()
+    result = fit.run(datasets)
+
+    result_dict = result.to_dict()
+    assert (
+        result_dict["covariance_result"]["backend"] == result.covariance_result.backend
+    )
+    assert result_dict["optimize_result"]["nfev"] == result.optimize_result.nfev
+    assert (
+        result_dict["optimize_result"]["total_stat"]
+        == result.optimize_result.total_stat
+    )
+
+    result.write(path=tmpdir / "test-fit-result.yaml")