Adding Wall loss fitting prelim

docs/_toc.yml

@@ -25,6 +25,7 @@ parts:
     sections:
     - file: examples/wall_loss/chamber_smps_data
     - file: examples/wall_loss/wall_loss_forward_simulation
+    - file: examples/wall_loss/forward_fit_simple
   - file: examples/lagrangian/lagrangian_intro
     sections:
     - file: examples/lagrangian/basic_lagrangian_box

docs/examples/wall_loss/chamber_smps_data.ipynb

@@ -152,7 +152,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# 1 convert to dn/dlogDp\n",
+    "# 1 convert to dn/dDp\n",
     "stream_smps_2d.data = convert.convert_sizer_dn(\n",
     "    diameter=np.array(stream_smps_2d.header, dtype=float),\n",
     "    dn_dlogdp=stream_smps_2d.data,\n",

docs/examples/wall_loss/ktp_fits.csv

@@ -0,0 +1,29 @@
+ktp_fits
+9.114052578201235377e+00
+1.056410118397569153e+00
+8.603020104153245029e-01
+7.286903885282755011e-01
+6.143790155101150896e-01
+4.683403011824088802e-01
+2.261290201180816417e-01
+7.938569470737081302e-01
+1.236364213294667697e-01
+2.037340385477289517e-01
+4.034193294551781328e+00
+9.682521668505555956e-02
+3.843420712626977043e-01
+3.079963755986986906e-01
+2.090019848554948989e-01
+1.864405270290740019e-02
+3.500866924013069648e-01
+1.996353182015291050e-01
+3.041341740204304145e-01
+1.749759101214403978e-01
+1.688639371712058745e-01
+5.139506188290942079e-01
+3.359766187966383710e-01
+3.015341445928835795e-01
+2.662682003934179753e-01
+2.973109238698287693e-01
+4.444325626562330434e-01
+3.505578700593284247e-01

particula/util/convert.py

@@ -547,3 +547,42 @@ def data_shape_check(
         raise ValueError("Header list must be a single entry if data_new \
                               is 1D.")
     return data
+
+
+def distribution_convert_pdf_pms(
+        x_array: np.ndarray,
+        distribution: np.ndarray,
+        to_pdf: bool = True
+) -> np.ndarray:
+    """
+    Convert between a probability density function (PDF) and a probability
+    mass spectrum (PMS) based on the specified direction.
+
+    Args:
+    x_array
+        An array of radii corresponding to the bins of the distribution,
+        shape (m).
+    distribution
+        The concentration values of the distribution (either PDF or PMS)
+        at the given radii. Supports broadcasting across x_array (n,m).
+    to_PDF
+        Direction of conversion. If True, converts PMS to PDF. If False,
+        converts PDF to PMS.
+
+    Returns:
+    converted_distribution
+        The converted distribution array (either PDF or PMS).
+    """
+
+    # Calculate the differences between consecutive x_array values for bin
+    # widths.
+    delta_x_array = np.empty_like(x_array)
+    delta_x_array[:-1] = np.diff(x_array)
+    # For the last bin, extrapolate the width assuming constant growth
+    # rate from the last two bins.
+    delta_x_array[-1] = delta_x_array[-2]**2 / delta_x_array[-3]
+
+    # Converting PMS to PDF by dividing the PMS values by the bin widths. or
+    # Converting PDF to PMS by multiplying the PDF values by the bin widths.
+    return distribution / delta_x_array if to_pdf \
+        else distribution * delta_x_array

particula/util/stats.py

@@ -286,3 +286,39 @@ def mask_outliers(
     # invert the mask to get true= non-outliers and false=outliers
     mask = np.logical_not(mask)
     return mask
+
+
+def distribution_integration(
+        distribution: np.ndarray,
+        x_array: Optional[np.ndarray] = None,
+        axis: int = 0
+) -> np.ndarray:
+    """
+    Performs either PDF integration or PMS integration based on the input.
+    This function supports broadcasting where x_array has shape (m,) and
+    distribution has shape (n, m).
+
+    Args:
+    -----
+        distribution: The distribution array to integrate.
+            It should have a shape of (n, m).
+        x_array: The x-values array for PDF
+            integration. It should have a shape of (m,).
+            If None, PMS integration is performed. Defaults to None.
+        axis: The axis along which to perform the integration
+            for PDF or the sum for PMS.
+            Defaults to 0.
+
+    Return:
+    -------
+        np.ndarray: The result of the integration. If PDF integration is
+        performed, the result will have a shape of (n,) if axis=0 or (m,)
+        if axis=1. If PMS integration is performed, the result will be a
+        single value if axis=None, or an array with reduced dimensionality
+        otherwise.
+    """
+    if x_array is not None:
+        # Perform PDF integration along the specified axis
+        return np.trapz(y=distribution, x=x_array, axis=axis)
+    # Perform PMS integration
+    return np.sum(distribution, axis=axis)