🔥 fix wrong assert

arpes/fits/fit_models/x_model_mixin.py

@@ -3,8 +3,8 @@
 
 import operator
 import warnings
-from logging import DEBUG, Formatter, StreamHandler, getLogger
-from typing import TYPE_CHECKING
+from logging import DEBUG, INFO, Formatter, StreamHandler, getLogger
+from typing import TYPE_CHECKING, reveal_type
 
 import lmfit as lf
 import numpy as np
@@ -21,7 +21,7 @@
 __all__ = ["XModelMixin", "gaussian_convolve"]
 
 
-LOGLEVEL = DEBUG
+LOGLEVEL = (DEBUG, INFO)[0]
 logger = getLogger(__name__)
 fmt = "%(asctime)s %(levelname)s %(name)s :%(message)s"
 formatter = Formatter(fmt)
@@ -112,7 +112,8 @@ def guess_fit(
         """
         if params is not None and not isinstance(params, lf.Parameters):
             params = dict_to_parameters(params)
-        assert isinstance(params, lf.Parameters)
+        param_type_ = f"params type : {reveal_type(params)}"
+        logger.debug(param_type_)
         coord_values = {}
         if "x" in kwargs:
             coord_values["x"] = kwargs.pop("x")

arpes/utilities/collections.py

@@ -19,10 +19,9 @@ def __add__(self, other: MappableDict) -> MappableDict:
         if set(self.keys()) != set(other.keys()):
             msg = "You can only add two MappableDicts with the same keys."
             raise ValueError(msg)
-
         return MappableDict({k: self.get(k) + other.get(k) for k in self})
 
-    def __sub__(self, other: MappableDict):
+    def __sub__(self, other: MappableDict) -> MappableDict:
         """Applies `-` onto values."""
         if set(self.keys()) != set(other.keys()):
             msg = "You can only subtract two MappableDicts with the same keys."
@@ -80,12 +79,27 @@ def deep_update(destination: dict[str, Any], source: dict[str, Any]) -> dict[str
     return destination
 
 
-def deep_equals(a: Any, b: Any) -> bool | None:
+def deep_equals(
+    a: float
+    | str
+    | list[float | str]
+    | tuple[str, ...]
+    | tuple[float, ...]
+    | set[str | float]
+    | dict[str, float | str],
+    b: float
+    | str
+    | list[float | str]
+    | tuple[str, ...]
+    | tuple[float, ...]
+    | set[str | float]
+    | dict[str, float | str],
+) -> bool | None:
     """An equality check that looks into common collection types."""
     if not isinstance(b, type(a)):
         return False
 
-    if isinstance(a, int | str | float):
+    if isinstance(a, str | float):
         return a == b
 
     if a is None:
@@ -100,14 +114,14 @@ def deep_equals(a: Any, b: Any) -> bool | None:
             msg,
         )
 
-    if isinstance(a, set):
+    if isinstance(a, set) and isinstance(b, set):
         for item in a:
             if item not in b:
                 return False
 
         return all(item in a for item in b)
 
-    if isinstance(a, list | tuple):
+    if isinstance(a, list | tuple) and isinstance(b, list | tuple):
         if len(a) != len(b):
             return False
 
@@ -119,7 +133,7 @@ def deep_equals(a: Any, b: Any) -> bool | None:
 
         return True
 
-    if isinstance(a, dict):
+    if isinstance(a, dict) and isinstance(b, dict):
         if set(a.keys()) != set(b.keys()):
             return False
 

arpes/xarray_extensions.py

@@ -45,8 +45,8 @@
 import warnings
 from collections import OrderedDict
 from collections.abc import Sequence
-from logging import INFO, Formatter, StreamHandler, getLogger
-from typing import TYPE_CHECKING, Any, Literal, Self, TypeAlias, Unpack
+from logging import DEBUG, INFO, Formatter, StreamHandler, getLogger
+from typing import TYPE_CHECKING, Any, Literal, Self, TypeAlias, Unpack, reveal_type
 
 import lmfit
 import matplotlib.pyplot as plt
@@ -102,7 +102,7 @@
 
 ANGLE_VARS = ("alpha", "beta", "chi", "psi", "phi", "theta")
 
-LOGLEVEL = INFO
+LOGLEVEL = (DEBUG, INFO)[0]
 logger = getLogger(__name__)
 fmt = "%(asctime)s %(levelname)s %(name)s :%(message)s"
 formatter = Formatter(fmt)
@@ -3189,7 +3189,9 @@ def mean_square_error(self) -> xr.DataArray:
         def safe_error(model_result_instance: lmfit.model.ModelResult | None) -> float:
             if model_result_instance is None:
                 return np.nan
-            assert isinstance(model_result_instance.residual, lmfit.model.ModelResult)
+            model_result_instance_residual_type_ = f"model_result_instance_residual_type: {reveal_type(model_result_instance.residual)}"
+            logger.debug(model_result_instance_residual_type_)
+            assert isinstance(model_result_instance.residual, np.ndarray)
             return (model_result_instance.residual**2).mean()
 
         return self._obj.G.map(safe_error)

docs/source/notebooks/converting-to-kspace.ipynb

@@ -44,11 +44,11 @@
     "\n",
     "**Note**: We will use Fermi edge corrected data for conversion. The function `load_energy_corrected` below handles this. You can see the document on Fermi edge corrections for more details.\n",
     "\n",
-    "**Imports note**: This example uses a lot of imports, we have been explicit about them here so you know where to look in the code if you want to learn more, but you can also import more or less everything you need for day-to-day analysis with\n",
+    "~~**Imports note**: This example uses a lot of imports, we have been explicit about them here so you know where to look in the code if you want to learn more, but you can also import more or less everything you need for day-to-day analysis with~~\n",
     "\n",
-    "```python\n",
-    "from arpes.all import *\n",
-    "```\n",
+    "~~```python~~\n",
+    "~~from arpes.all import *~~\n",
+    "~~```~~\n",
     "\n",
     "## Converting Volumetric Data\n",
     "\n",

docs/source/notebooks/fermi-edge-correction.ipynb

@@ -57,7 +57,7 @@
    "outputs": [],
    "source": [
     "from arpes.io import example_data\n",
-    "from arpes.all import broadcast_model, AffineBroadenedFD\n",
+    "from arpes.fits import broadcast_model, AffineBroadenedFD\n",
     "\n",
     "photon_energy = example_data.photon_energy"
    ]