bug fix: hard-coded the attributes of scatterer in experiment module …

…as the previous method based on dataclass attribute order was messing the medium_properties of scatterers

PyMieSim/_version.py

@@ -12,5 +12,5 @@
 __version_tuple__: VERSION_TUPLE
 version_tuple: VERSION_TUPLE
 
-__version__ = version = '3.1.0.1'
-__version_tuple__ = version_tuple = (3, 1, 0, 1)
+__version__ = version = '3.1.0.6'
+__version_tuple__ = version_tuple = (3, 1, 0, 6)

PyMieSim/cpp/experiment/includes/experiment.cpp

@@ -12,6 +12,7 @@
 
 typedef std::complex<double> complex128;
 
+
 #define DEFINE_SCATTERER_FUNCTION(scatterer, SCATTERER, dtype, name) \
     pybind11::array_t<dtype> get_##scatterer##_##name() const { return get_scatterer_data<double, SCATTERER::Set>(scatterer##Set, &SCATTERER::Scatterer::get_##name); } \
     pybind11::array_t<dtype> get_##scatterer##_##name##_sequential() const { return get_scatterer_data_sequential<double, SCATTERER::Set>(scatterer##Set, &SCATTERER::Scatterer::get_##name); }
@@ -24,9 +25,6 @@ typedef std::complex<double> complex128;
     pybind11::array_t<double> get_##scatterer##_coupling() const { return get_scatterer_coupling<SCATTERER::Set>(scatterer##Set); } \
     pybind11::array_t<double> get_##scatterer##_coupling_sequential() const { return get_scatterer_coupling_sequential<SCATTERER::Set>(scatterer##Set); }
 
-
-
-#include <iostream>
 class Experiment
 {
     public:
@@ -118,6 +116,7 @@ class Experiment
             return _vector_to_numpy(output_array, {full_size});
         }
 
+
         template<typename ScattererSet>
         pybind11::array_t<double> get_scatterer_coupling(const ScattererSet& scattererSet) const {
             std::vector<size_t> array_shape = concatenate_vector(sourceSet.shape, scattererSet.shape, detectorSet.shape);

PyMieSim/experiment/scatterer/base.py

@@ -5,7 +5,6 @@
 from pydantic import field_validator
 from typing import List
 from pydantic.dataclasses import dataclass
-from dataclasses import fields
 from pydantic import ConfigDict
 from PyMieSim.units import Quantity, meter, RIU
 from pint_pandas import PintArray
@@ -98,8 +97,7 @@ def _generate_mapping(self) -> None:
         list
             A list of visual representations for each property in the `mapping` dictionary that has been populated.
         """
-
-        for attr in [f.name for f in fields(self) if f.name != 'source']:
+        for attr in self.attributes:
             values = getattr(self, attr)
             if values is None:
                 continue
@@ -108,3 +106,4 @@ def _generate_mapping(self) -> None:
                 self.mapping["scatterer:" + attr] = PintArray(values.magnitude, dtype=values.units)
             else:
                 self.mapping["scatterer:" + attr] = [repr(m) for m in values]
+

PyMieSim/experiment/scatterer/core_shell.py

@@ -46,6 +46,8 @@ class CoreShell(BaseScatterer):
         'a2', 'a3', 'b1', 'b2', 'b3', 'g', 'coupling',
     ]
 
+    attributes = ['core_diameter', 'shell_thickness', 'core_property', 'shell_property', 'medium_property']
+
     def __post_init__(self) -> None:
         """
         Assembles the keyword arguments necessary for C++ binding, tailored for core-shell scatterers.

PyMieSim/experiment/scatterer/cylinder.py

@@ -34,6 +34,8 @@ class Cylinder(BaseScatterer):
         'b22', 'b13', 'b23', 'coupling',
     ]
 
+    attributes = ['diameter', 'property', 'medium_property']
+
     def __post_init__(self) -> None:
         """
         Constructs the keyword arguments necessary for the C++ binding interface, specifically tailored for spherical scatterers.

PyMieSim/experiment/scatterer/sphere.py

@@ -38,6 +38,8 @@ class Sphere(BaseScatterer):
         'a2', 'a3', 'b1', 'b2', 'b3', 'g', 'coupling',
     ]
 
+    attributes = ['diameter', 'property', 'medium_property']
+
     def __post_init__(self) -> None:
         """
         Constructs the keyword arguments necessary for the C++ binding interface, specifically tailored for spherical scatterers.

PyMieSim/plotting.py

@@ -109,18 +109,49 @@ def plot_with_std(dataframe: pd.DataFrame, ax, x: str, std: str, alpha: float =
         plt.show()
 
 
-def plot_without_std(dataframe: pd.DataFrame, ax: plt.Axes, x: str, show: bool = True, **kwargs) -> None:
+def plot_without_std(
+    dataframe: pd.DataFrame,
+    ax: plt.Axes,
+    x: str,
+    y: str = None,
+    show: bool = True,
+    log_scale_x: bool = False,
+    log_scale_y: bool = False,
+    **kwargs) -> plt.Axes:
     """
-    Plots the data without standard deviation shading. Handles real and imaginary parts if the data is complex.
+    Plots data without standard deviation shading, handling both real and imaginary parts for complex data.
 
     Parameters
     ----------
-    df_unstacked : pd.DataFrame
-        The DataFrame after unstacking.
+    dataframe : pd.DataFrame
+        The DataFrame containing the data to plot. If the DataFrame includes
+        multi-index columns, it will handle stacked levels for plotting.
     ax : plt.Axes
-        The matplotlib axis on which to plot.
+        The matplotlib axis on which to plot the data.
     x : str
-        The index level for the x-axis.
+        The column or index level to use as the x-axis.
+    y : str, optional
+        The column to use as the y-axis. If not specified, the first column is used.
+    show : bool, default=True
+        Whether to display the plot immediately.
+    log_scale_x : bool, default=False
+        Whether to apply logarithmic scaling to the x-axis.
+    log_scale_y : bool, default=False
+        Whether to apply logarithmic scaling to the y-axis.
+    **kwargs : dict, optional
+        Additional keyword arguments passed to `matplotlib.pyplot.plot`,
+        such as line styles, colors, markers, etc.
+
+    Returns
+    -------
+    plt.Axes
+        The matplotlib axis with the plotted data.
+
+    Notes
+    -----
+    - If the `dataframe` contains complex data, the function plots the real part by default.
+    - The `kwargs` can be used to customize the appearance of the plot.
+    - For multi-indexed DataFrames, groups are automatically handled and labeled.
     """
     if 'type' in dataframe.columns.names:
         dataframe = dataframe.stack('type', future_stack=True)
@@ -150,6 +181,12 @@ def plot_without_std(dataframe: pd.DataFrame, ax: plt.Axes, x: str, show: bool =
             **kwargs
         )
 
+    # Apply log scaling if specified
+    if log_scale_x:
+        ax.set_xscale('log')
+    if log_scale_y:
+        ax.set_yscale('log')
+
     ax.legend(title=" : ".join(groupby_levels))
 
     if show:

development/dev1.py

@@ -1,11 +1,4 @@
-"""
-Sphere: Qsca vs diameter
-========================
 
-"""
-
-# %%
-# Importing the package dependencies: numpy, PyMieSim
 import numpy as np
 
 from PyMieSim.experiment.scatterer import Sphere
@@ -14,45 +7,33 @@
 from PyMieSim.experiment import Setup
 from PyMieSim.units import nanometer, degree, watt, AU, RIU
 
-# %%
-# Defining the source to be employed.
 source = Gaussian(
-    wavelength=[500] * nanometer,
-    polarization=30 * degree,
+    wavelength=488 * nanometer,
+    polarization=0 * degree,
     optical_power=1e-3 * watt,
     NA=0.2 * AU
 )
-# %%
-# Defining the ranging parameters for the scatterer distribution
+
 scatterer = Sphere(
-    diameter=np.linspace(10, 1000, 1000) * nanometer,
-    medium_property=1.33 * RIU,
-    property=[1.40, 1.45, 1.50] * RIU,
+    diameter=np.linspace(30, 300, 200) * nanometer,
+    medium_property=[1.33, 1.3299] * RIU,
+    property=[1.42] * RIU,
     source=source
 )
 
-# %%
-# Defining the detector to be employed.
 detector = Photodiode(
-    NA=1.2 * AU,
+    NA=0.2 * AU,
     phi_offset=[0.0] * degree,
     gamma_offset=0.0 * degree,
     sampling=600 * AU,
     polarization_filter=None
 )
 
 
-
-# %%
-# Defining the experiment setup
 experiment = Setup(scatterer=scatterer, source=source, detector=detector)
 
-# %%
-# Measuring the properties
-dataframe = experiment.get('coupling', scale_unit=True, drop_unique_level=True)
+
+dataframe = experiment.get('coupling', scale_unit=True)
 
 
-print(dataframe[280:])
-# %%
-# Plotting the results
-# dataframe.plot_data(x='scatterer:diameter')
+dataframe.plot_data(x='scatterer:diameter', log_scale_y=False)