diff --git a/astropath/chance.py b/astropath/chance.py
index c809972..e12714b 100644
--- a/astropath/chance.py
+++ b/astropath/chance.py
@@ -3,6 +3,9 @@
 
 from scipy import interpolate
 
+from importlib.resources import files
+import os
+
 from IPython import embed
 
 
@@ -12,7 +15,8 @@
 driver_spl = interpolate.UnivariateSpline._from_tck(driver_tck)
 
 
-def driver_sigma(rmag):
+
+def driver_sigma(mag):
     """
     Estimated incidence of galaxies per sq arcsec with r > rmag
     using Driver et al. 2016 number counts.
@@ -26,7 +30,32 @@ def driver_sigma(rmag):
         float or np.ndarray:  Galaxy number density
 
     """
-    return 10**driver_spl(rmag)
+    return 10**driver_spl(mag)
+
+def windhorst_sigma(mag):
+    """
+    Estimated incidence of galaxies per sq arcsec with F200W > mag
+    using Windhorst et al. 2024 number counts.
+
+    Spline parameters (globals) are for F200W vs Num counts
+
+    Args:
+        mag (float or np.ndarray): F200W band magnitude of galaxy
+
+    Returns:
+        float or np.ndarray:  Galaxy number density
+
+    """
+    data_path = os.path.join(files('astropath'),'data','galaxy_num_counts','windhorst2023_F200W.npz')
+    data = np.load(data_path)
+    mag_f200w = data['mag']
+    Num = data['Num(N/arcsec/0.5mag))']
+
+    winhorst_spline = interpolate.interp1d(mag_f200w, Num, kind='cubic', fill_value='extrapolate')
+
+    num_counts = winhorst_spline(mag)
+
+    return num_counts
 
 
 def bloom_sigma(rmag):
diff --git a/astropath/data/galaxy_num_counts/windhorst2023_F200W.npz b/astropath/data/galaxy_num_counts/windhorst2023_F200W.npz
new file mode 100644
index 0000000..5136d33
Binary files /dev/null and b/astropath/data/galaxy_num_counts/windhorst2023_F200W.npz differ
diff --git a/astropath/path.py b/astropath/path.py
index e769b50..7bef69d 100644
--- a/astropath/path.py
+++ b/astropath/path.py
@@ -11,6 +11,8 @@
 from astropath import localization
 from astropath import priors
 
+from IPython import embed
+
 
 class PATH(object):
     """Convenience class to run PATH analysis
@@ -122,7 +124,7 @@ def init_localization(self, ltype:str, **kwargs):
         assert localization.vet_localization(self.localiz), 'Bad candidate prior input'
         logging.info("Localization is ready!")
 
-    def calc_priors(self):
+    def calc_priors(self, ifilter:str='r'):
         """Calculate and normalize the P(O) values for the candidates
 
         Raises:
@@ -138,7 +140,8 @@ def calc_priors(self):
         logging.info("Calculating priors")
         self.raw_prior_Oi = priors.raw_prior_Oi(
             self.cand_prior['P_O_method'], self.candidates['ang_size'], 
-            mag=self.candidates['mag'] if 'mag' in self.candidates.keys() else None)
+            mag=self.candidates['mag'] if 'mag' in self.candidates.keys() else None,
+            filter=ifilter)
 
         # Normalize
         logging.info("Normalizing priors")
diff --git a/astropath/priors.py b/astropath/priors.py
index 33f7691..327b75d 100644
--- a/astropath/priors.py
+++ b/astropath/priors.py
@@ -27,6 +27,10 @@
                 help='Label for this prior.'),
 }
 
+splines = {
+    'r': 'driver',
+    'F200W':'windhorst'
+}
 
 
 def raw_prior_Oi(method, ang_size, mag=None, filter='r'):
@@ -52,11 +56,18 @@ def raw_prior_Oi(method, ang_size, mag=None, filter='r'):
         float or np.ndarray:
 
     """
+    # Setting spline_fit
     # Convenience
     if method not in ['identical']:
-        if filter != 'r':
-            raise IOError("Not ready for this.  Best to go with what you have that is closest to r-band")
-        Sigma_m = chance.driver_sigma(mag)
+        if filter not in splines.keys():
+                raise IOError("Not ready for this.  Best to go with what you have that is closest to r-band or F200W")
+        else:
+            spline_fit = splines[filter]
+            if spline_fit == 'driver':
+                Sigma_m = chance.driver_sigma(mag)
+
+            elif spline_fit == 'windhorst':
+                Sigma_m = chance.windhorst_sigma(mag)
 
     # Do it
     if method == 'inverse':
diff --git a/calculations/mag_priors/F200W_spline_fit.ipynb b/calculations/mag_priors/F200W_spline_fit.ipynb
new file mode 100644
index 0000000..020acb6
--- /dev/null
+++ b/calculations/mag_priors/F200W_spline_fit.ipynb
@@ -0,0 +1,651 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import AutoMinorLocator, LogLocator\n",
+    "import pandas as pd\n",
+    "from scipy.interpolate import UnivariateSpline\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Windhorst et al. 2023\n",
+    "windhorst_df = pd.read_csv('windhorst2023.csv') # Make sure you have this file in the same directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mag</th>\n",
+       "      <th>wave_0.153</th>\n",
+       "      <th>wave_0.225</th>\n",
+       "      <th>wave_0.356</th>\n",
+       "      <th>wave_0.474</th>\n",
+       "      <th>wave_0.631</th>\n",
+       "      <th>wave_0.758</th>\n",
+       "      <th>wave_0.883</th>\n",
+       "      <th>wave_1.02</th>\n",
+       "      <th>wave_1.25</th>\n",
+       "      <th>wave_1.65</th>\n",
+       "      <th>wave_2.15</th>\n",
+       "      <th>wave_2.77</th>\n",
+       "      <th>wave_3.54</th>\n",
+       "      <th>wave_4.49</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.50</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>4.942793e-05</td>\n",
+       "      <td>1.216234e-03</td>\n",
+       "      <td>0.000135</td>\n",
+       "      <td>0.000146</td>\n",
+       "      <td>1.862685e-04</td>\n",
+       "      <td>2.444320e-04</td>\n",
+       "      <td>2.546509e-04</td>\n",
+       "      <td>1.665367e-04</td>\n",
+       "      <td>3.456864</td>\n",
+       "      <td>4.989246e-05</td>\n",
+       "      <td>0.000021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.51</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>5.006923e-05</td>\n",
+       "      <td>1.229015e-03</td>\n",
+       "      <td>0.000136</td>\n",
+       "      <td>0.000148</td>\n",
+       "      <td>1.888277e-04</td>\n",
+       "      <td>2.477808e-04</td>\n",
+       "      <td>2.582382e-04</td>\n",
+       "      <td>1.689721e-04</td>\n",
+       "      <td>3.477715</td>\n",
+       "      <td>5.061485e-05</td>\n",
+       "      <td>0.000022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.52</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>5.071885e-05</td>\n",
+       "      <td>1.241931e-03</td>\n",
+       "      <td>0.000138</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>1.914220e-04</td>\n",
+       "      <td>2.511755e-04</td>\n",
+       "      <td>2.618760e-04</td>\n",
+       "      <td>1.714431e-04</td>\n",
+       "      <td>3.498692</td>\n",
+       "      <td>5.134770e-05</td>\n",
+       "      <td>0.000022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7.53</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>5.137690e-05</td>\n",
+       "      <td>1.254982e-03</td>\n",
+       "      <td>0.000140</td>\n",
+       "      <td>0.000152</td>\n",
+       "      <td>1.940520e-04</td>\n",
+       "      <td>2.546167e-04</td>\n",
+       "      <td>2.655651e-04</td>\n",
+       "      <td>1.739503e-04</td>\n",
+       "      <td>3.519796</td>\n",
+       "      <td>5.209117e-05</td>\n",
+       "      <td>0.000022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.54</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>5.204349e-05</td>\n",
+       "      <td>1.268170e-03</td>\n",
+       "      <td>0.000142</td>\n",
+       "      <td>0.000154</td>\n",
+       "      <td>1.967182e-04</td>\n",
+       "      <td>2.581050e-04</td>\n",
+       "      <td>2.693062e-04</td>\n",
+       "      <td>1.764941e-04</td>\n",
+       "      <td>3.541027</td>\n",
+       "      <td>5.284539e-05</td>\n",
+       "      <td>0.000023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2346</th>\n",
+       "      <td>30.96</td>\n",
+       "      <td>90033.516323</td>\n",
+       "      <td>456470.331499</td>\n",
+       "      <td>834433.635681</td>\n",
+       "      <td>1.122963e+06</td>\n",
+       "      <td>1.023564e+06</td>\n",
+       "      <td>915924.484447</td>\n",
+       "      <td>689278.586090</td>\n",
+       "      <td>1.197895e+06</td>\n",
+       "      <td>1.225102e+06</td>\n",
+       "      <td>1.648079e+06</td>\n",
+       "      <td>1.488937e+06</td>\n",
+       "      <td>2808.040405</td>\n",
+       "      <td>1.129466e+06</td>\n",
+       "      <td>879554.187073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2347</th>\n",
+       "      <td>30.97</td>\n",
+       "      <td>90481.565264</td>\n",
+       "      <td>459134.836892</td>\n",
+       "      <td>838970.792439</td>\n",
+       "      <td>1.128896e+06</td>\n",
+       "      <td>1.027828e+06</td>\n",
+       "      <td>919985.388994</td>\n",
+       "      <td>691838.709115</td>\n",
+       "      <td>1.203583e+06</td>\n",
+       "      <td>1.230742e+06</td>\n",
+       "      <td>1.656139e+06</td>\n",
+       "      <td>1.496393e+06</td>\n",
+       "      <td>2741.623910</td>\n",
+       "      <td>1.134617e+06</td>\n",
+       "      <td>883204.432965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2348</th>\n",
+       "      <td>30.98</td>\n",
+       "      <td>90931.843906</td>\n",
+       "      <td>461814.895517</td>\n",
+       "      <td>843532.619572</td>\n",
+       "      <td>1.134861e+06</td>\n",
+       "      <td>1.032110e+06</td>\n",
+       "      <td>924064.298240</td>\n",
+       "      <td>694408.340967</td>\n",
+       "      <td>1.209298e+06</td>\n",
+       "      <td>1.236409e+06</td>\n",
+       "      <td>1.664238e+06</td>\n",
+       "      <td>1.503886e+06</td>\n",
+       "      <td>2676.778316</td>\n",
+       "      <td>1.139792e+06</td>\n",
+       "      <td>886869.827776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2349</th>\n",
+       "      <td>30.99</td>\n",
+       "      <td>91384.363346</td>\n",
+       "      <td>464510.598161</td>\n",
+       "      <td>848119.251224</td>\n",
+       "      <td>1.140857e+06</td>\n",
+       "      <td>1.036410e+06</td>\n",
+       "      <td>928161.292014</td>\n",
+       "      <td>696987.516961</td>\n",
+       "      <td>1.215041e+06</td>\n",
+       "      <td>1.242102e+06</td>\n",
+       "      <td>1.672378e+06</td>\n",
+       "      <td>1.511417e+06</td>\n",
+       "      <td>2613.466465</td>\n",
+       "      <td>1.144990e+06</td>\n",
+       "      <td>890550.434376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2350</th>\n",
+       "      <td>31.00</td>\n",
+       "      <td>91839.134734</td>\n",
+       "      <td>467222.036142</td>\n",
+       "      <td>852730.822268</td>\n",
+       "      <td>1.146885e+06</td>\n",
+       "      <td>1.040727e+06</td>\n",
+       "      <td>932276.450495</td>\n",
+       "      <td>699576.272548</td>\n",
+       "      <td>1.220810e+06</td>\n",
+       "      <td>1.247821e+06</td>\n",
+       "      <td>1.680557e+06</td>\n",
+       "      <td>1.518986e+06</td>\n",
+       "      <td>2551.652083</td>\n",
+       "      <td>1.150213e+06</td>\n",
+       "      <td>894246.315895</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2351 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        mag    wave_0.153     wave_0.225     wave_0.356    wave_0.474  \\\n",
+       "0      7.50      0.000002       0.000001       0.000007  4.942793e-05   \n",
+       "1      7.51      0.000002       0.000001       0.000007  5.006923e-05   \n",
+       "2      7.52      0.000002       0.000001       0.000007  5.071885e-05   \n",
+       "3      7.53      0.000002       0.000001       0.000007  5.137690e-05   \n",
+       "4      7.54      0.000002       0.000001       0.000007  5.204349e-05   \n",
+       "...     ...           ...            ...            ...           ...   \n",
+       "2346  30.96  90033.516323  456470.331499  834433.635681  1.122963e+06   \n",
+       "2347  30.97  90481.565264  459134.836892  838970.792439  1.128896e+06   \n",
+       "2348  30.98  90931.843906  461814.895517  843532.619572  1.134861e+06   \n",
+       "2349  30.99  91384.363346  464510.598161  848119.251224  1.140857e+06   \n",
+       "2350  31.00  91839.134734  467222.036142  852730.822268  1.146885e+06   \n",
+       "\n",
+       "        wave_0.631     wave_0.758     wave_0.883     wave_1.02     wave_1.25  \\\n",
+       "0     1.216234e-03       0.000135       0.000146  1.862685e-04  2.444320e-04   \n",
+       "1     1.229015e-03       0.000136       0.000148  1.888277e-04  2.477808e-04   \n",
+       "2     1.241931e-03       0.000138       0.000150  1.914220e-04  2.511755e-04   \n",
+       "3     1.254982e-03       0.000140       0.000152  1.940520e-04  2.546167e-04   \n",
+       "4     1.268170e-03       0.000142       0.000154  1.967182e-04  2.581050e-04   \n",
+       "...            ...            ...            ...           ...           ...   \n",
+       "2346  1.023564e+06  915924.484447  689278.586090  1.197895e+06  1.225102e+06   \n",
+       "2347  1.027828e+06  919985.388994  691838.709115  1.203583e+06  1.230742e+06   \n",
+       "2348  1.032110e+06  924064.298240  694408.340967  1.209298e+06  1.236409e+06   \n",
+       "2349  1.036410e+06  928161.292014  696987.516961  1.215041e+06  1.242102e+06   \n",
+       "2350  1.040727e+06  932276.450495  699576.272548  1.220810e+06  1.247821e+06   \n",
+       "\n",
+       "         wave_1.65     wave_2.15    wave_2.77     wave_3.54      wave_4.49  \n",
+       "0     2.546509e-04  1.665367e-04     3.456864  4.989246e-05       0.000021  \n",
+       "1     2.582382e-04  1.689721e-04     3.477715  5.061485e-05       0.000022  \n",
+       "2     2.618760e-04  1.714431e-04     3.498692  5.134770e-05       0.000022  \n",
+       "3     2.655651e-04  1.739503e-04     3.519796  5.209117e-05       0.000022  \n",
+       "4     2.693062e-04  1.764941e-04     3.541027  5.284539e-05       0.000023  \n",
+       "...            ...           ...          ...           ...            ...  \n",
+       "2346  1.648079e+06  1.488937e+06  2808.040405  1.129466e+06  879554.187073  \n",
+       "2347  1.656139e+06  1.496393e+06  2741.623910  1.134617e+06  883204.432965  \n",
+       "2348  1.664238e+06  1.503886e+06  2676.778316  1.139792e+06  886869.827776  \n",
+       "2349  1.672378e+06  1.511417e+06  2613.466465  1.144990e+06  890550.434376  \n",
+       "2350  1.680557e+06  1.518986e+06  2551.652083  1.150213e+06  894246.315895  \n",
+       "\n",
+       "[2351 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "windhorst_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mag = windhorst_df['mag']\n",
+    "N = windhorst_df['wave_2.15']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax1 = plt.subplots()\n",
+    "ax1.scatter(mag, np.log10(N), color='k', label='Spline Points',s=1)\n",
+    "plt.xlabel('Magnitude (mag)')\n",
+    "plt.ylabel(r'$log_{10}N \\delta_m$ [$deg^{-2}$ (0.5 $mag^{-1}$)]')\n",
+    "plt.title(r'Number Counts $\\lambda=2.15\\mu m$')\n",
+    "plt.grid(linestyle='--',alpha=0.5)\n",
+    "ax1.tick_params(axis='both', right=True, top=True, width=2, length=8, direction='in', which='both', labelsize=14) #Inward pointing ticks are so much easier to see where the *data* are.  Also, let's have ticks on all sides.\n",
+    "ax1.set_xlim(7., 31.)\n",
+    "ax1.set_ylim(-4, 6.5)\n",
+    "ax1.legend(loc='upper right', fontsize=14)\n",
+    "ax1.xaxis.set_minor_locator(AutoMinorLocator())\n",
+    "ax1.yaxis.set_minor_locator(AutoMinorLocator())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert spline counts from per deg^2 to per arcsec^2\n",
+    "from astropy import units as u\n",
+    "\n",
+    "Num = np.asarray(N)*u.deg**-2\n",
+    "Num = (Num.to(u.arcsec**-2).value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from importlib.resources import files\n",
+    "import os\n",
+    "\n",
+    "# Make an an npz file with the spline\n",
+    "data = {'mag': mag, 'Num(N/arcsec/0.5mag))': Num}\n",
+    "data_path = os.path.join(files('astropath'),'data','galaxy_num_counts','windhorst2023_F200W.npz')\n",
+    "np.savez(data_path, **data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0006276780144064129\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Testing chance.py code\n",
+    "data = np.load(data_path)\n",
+    "mag = data['mag']\n",
+    "Num = data['Num(N/arcsec/0.5mag))']\n",
+    "\n",
+    "# Make a spline\n",
+    "from scipy.interpolate import interp1d\n",
+    "\n",
+    "def windhorst_sigma(mag):\n",
+    "    \"\"\"\n",
+    "    Estimated incidence of galaxies per sq arcsec with F200W > mag\n",
+    "    using Windhorst et al. 2024 number counts.\n",
+    "\n",
+    "    Spline parameters (globals) are for F200W vs sigma\n",
+    "\n",
+    "    Args:\n",
+    "        mag (float or np.ndarray): F200 band magnitude of galaxy\n",
+    "\n",
+    "    Returns:\n",
+    "        float or np.ndarray:  Galaxy number density\n",
+    "\n",
+    "    \"\"\"\n",
+    "    data = np.load(data_path)\n",
+    "    mag_f200w = data['mag']\n",
+    "    Num = data['Num(N/arcsec/0.5mag))']\n",
+    "\n",
+    "    winhorst_spline = interp1d(mag_f200w, Num, kind='cubic')\n",
+    "\n",
+    "    num_counts = winhorst_spline(mag)\n",
+    "\n",
+    "    return num_counts\n",
+    "\n",
+    "# Test the function\n",
+    "print(windhorst_sigma(21.))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7291a7d8f9b0>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy import interpolate\n",
+    "# Spline parameters(globals) are for rmag vs sigma\n",
+    "driver_tck = (np.array([15., 15., 15., 15., 30., 30., 30., 30.]),\n",
+    "       np.array([-6.41580144, -3.53188049, -1.68500105, -0.63090954, 0., 0., 0., 0.]), 3)\n",
+    "driver_spl = interpolate.UnivariateSpline._from_tck(driver_tck)\n",
+    "\n",
+    "\n",
+    "\n",
+    "def driver_sigma(mag):\n",
+    "    \"\"\"\n",
+    "    Estimated incidence of galaxies per sq arcsec with r > rmag\n",
+    "    using Driver et al. 2016 number counts.\n",
+    "\n",
+    "    Spline parameters (globals) are for rmag vs sigma\n",
+    "\n",
+    "    Args:\n",
+    "        rmag (float or np.ndarray): r band magnitude of galaxy\n",
+    "\n",
+    "    Returns:\n",
+    "        float or np.ndarray:  Galaxy number density\n",
+    "\n",
+    "    \"\"\"\n",
+    "    return 10**driver_spl(mag)\n",
+    "\n",
+    "# Test the function\n",
+    "mags = np.linspace(15., 30., 100)\n",
+    "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax1.plot(mags, np.log10(driver_sigma(mags)), label='Driver et al. 2016 - rmag', color='r')\n",
+    "ax1.plot(mags, np.log10(windhorst_sigma(mags)), label='Windhorst et al. 2023 - F200W', color='b') \n",
+    "plt.xlabel('Magnitude (mag)')\n",
+    "plt.ylabel(r'$log_{10}N \\delta_m$ [$deg^{-2}$ (0.5 $mag^{-1}$)]')\n",
+    "plt.title(r'Number Counts')\n",
+    "plt.grid(linestyle='--',alpha=0.5)\n",
+    "ax1.tick_params(axis='both', right=True, top=True, width=2, length=8, direction='in', which='both', labelsize=14)\n",
+    "ax1.legend(loc='lower right', fontsize=14)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from scipy.interpolate import splrep, splev\n",
+    "\n",
+    "# # Generate smooth points for plotting\n",
+    "# mag_fine = np.linspace(min(mag), max(mag), 200)\n",
+    "# N_spline = spline(mag_fine)\n",
+    "\n",
+    "# # Save the spline coefficients (t, c, k) representation\n",
+    "# tck = splrep(mag, N, k=3, s=0)  # k=3 for cubic spline\n",
+    "\n",
+    "# # Function to recreate the spline from saved coefficients\n",
+    "# def create_spline_from_tck(tck):\n",
+    "#     return UnivariateSpline._from_tck(tck)\n",
+    "\n",
+    "# # Save coefficients for future use\n",
+    "# spline_tck = (tck[0], tck[1], tck[2])  # (knots, coefficients, degree)\n",
+    "\n",
+    "# # Example of how to recreate the spline\n",
+    "# recreated_spline = create_spline_from_tck(spline_tck)\n",
+    "\n",
+    "# # Testing the recreated spline with new data points\n",
+    "# N_recreated = recreated_spline(mag_fine)\n",
+    "\n",
+    "# # Plot the recreated spline\n",
+    "# plt.figure(figsize=(8, 5))\n",
+    "# plt.scatter(mag, np.log10(N), color='red', label='Data Points')\n",
+    "# plt.plot(mag_fine, np.log10(N_recreated), label='Recreated Spline Fit', linewidth=2, linestyle='dashed')\n",
+    "# plt.xlabel('Magnitude (mag)')\n",
+    "# plt.ylabel('N')\n",
+    "# plt.title('Recreated Spline Fit from Saved Coefficients')\n",
+    "# plt.legend()\n",
+    "# plt.grid()\n",
+    "# plt.show()\n",
+    "\n",
+    "# # Output the saved coefficients\n",
+    "# spline_tck\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
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+   "execution_count": null,
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+   "source": []
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+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
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+   "metadata": {},
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+   "execution_count": null,
+   "metadata": {},
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+   "source": []
+  }
+ ],
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