diff --git a/.github/workflows/muler-tests.yml b/.github/workflows/muler-tests.yml
index 342802f..4a830a6 100644
--- a/.github/workflows/muler-tests.yml
+++ b/.github/workflows/muler-tests.yml
@@ -11,8 +11,8 @@ jobs:
         python-version: [3.9]
         specutils-version: [1.5, 1.9.1]
         astropy-version: [5.2]
-        numpy-version: [1.18, 1.24]
-
+        #numpy-version: [1.18, 1.24]
+        
     steps:
       - uses: actions/checkout@v2
       - uses: actions/checkout@v2
diff --git a/docs/requirements_actions.txt b/docs/requirements_actions.txt
index b06dca0..88b748b 100644
--- a/docs/requirements_actions.txt
+++ b/docs/requirements_actions.txt
@@ -16,3 +16,4 @@ bokeh # needed for gollum to install on RTD
 gollum==0.2.1
 numpydoc
 sphinx-gallery
+tynt
diff --git a/docs/tutorials/IGRINS_SpecList_demo.ipynb b/docs/tutorials/IGRINS_SpecList_demo.ipynb
index 2b7d5db..b446c18 100644
--- a/docs/tutorials/IGRINS_SpecList_demo.ipynb
+++ b/docs/tutorials/IGRINS_SpecList_demo.ipynb
@@ -20,14 +20,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "from muler.igrins import IGRINSSpectrum, IGRINSSpectrumList\n",
     "from specutils import Spectrum1D, SpectrumList\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.pyplot as pltf\n",
     "import astropy.units as u\n",
     "%matplotlib inline\n",
     "%config InlineBackend.figure_format='retina'"
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,33 +53,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "Exception",
+     "evalue": "Neither .variance.fits or .sn.fits exists in the same path as the spectrum file to get the uncertainity.  Please provide one of these files in the same directory as your spectrum file.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m spec_list \u001b[38;5;241m=\u001b[39m \u001b[43mIGRINSSpectrumList\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfull_path\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mnormalize()\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/muler/igrins.py:434\u001b[0m, in \u001b[0;36mIGRINSSpectrumList.read\u001b[0;34m(file, precache_hdus, wavefile)\u001b[0m\n\u001b[1;32m    432\u001b[0m hdus \u001b[38;5;241m=\u001b[39m fits\u001b[38;5;241m.\u001b[39mopen(file, memmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m    433\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrtell\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m file: \u001b[38;5;66;03m#Default, if no rtell file is used\u001b[39;00m\n\u001b[0;32m--> 434\u001b[0m     uncertainty_filepath \u001b[38;5;241m=\u001b[39m \u001b[43mgetUncertainityFilepath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    435\u001b[0m     uncertainity_hdus \u001b[38;5;241m=\u001b[39m fits\u001b[38;5;241m.\u001b[39mopen(uncertainty_filepath, memmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)    \n\u001b[1;32m    436\u001b[0m     cached_hdus \u001b[38;5;241m=\u001b[39m [hdus, uncertainity_hdus]   \n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/muler/igrins.py:84\u001b[0m, in \u001b[0;36mgetUncertainityFilepath\u001b[0;34m(filepath)\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m path_base \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.sn.fits\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 84\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\n\u001b[1;32m     85\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNeither .variance.fits or .sn.fits exists in the same path as the spectrum file to get the uncertainity.  Please provide one of these files in the same directory as your spectrum file.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     86\u001b[0m         )\n",
+      "\u001b[0;31mException\u001b[0m: Neither .variance.fits or .sn.fits exists in the same path as the spectrum file to get the uncertainity.  Please provide one of these files in the same directory as your spectrum file."
+     ]
+    }
+   ],
    "source": [
     "spec_list = IGRINSSpectrumList.read(full_path).normalize()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1800x288 with 1 Axes>"
-      ]
-     },
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-      "image/png": {
-       "height": 264,
-       "width": 1445
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "ename": "NameError",
+     "evalue": "name 'spec_list' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mspec_list\u001b[49m\u001b[38;5;241m.\u001b[39mremove_nans()\u001b[38;5;241m.\u001b[39mtrim_edges()\u001b[38;5;241m.\u001b[39mnormalize(order_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m15\u001b[39m)\u001b[38;5;241m.\u001b[39mplot(color\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, ylo\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, yhi\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.5\u001b[39m);\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'spec_list' is not defined"
+     ]
     }
    ],
    "source": [
@@ -468,7 +477,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/flux_calibration.ipynb b/docs/tutorials/flux_calibration.ipynb
new file mode 100644
index 0000000..b91caab
--- /dev/null
+++ b/docs/tutorials/flux_calibration.ipynb
@@ -0,0 +1,246 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploratory flux calibration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from muler.hpf import HPFSpectrum, HPFSpectrumList\n",
+    "import numpy as np\n",
+    "import glob\n",
+    "\n",
+    "%config InlineBackend.figure_format='retina'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we have Goldilocks spectra:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = 'https://github.com/OttoStruve/muler_example_data/raw/main/HPF/01_A0V_standards/'\n",
+    "filename = 'Goldilocks_20210212T072837_v1.0_0037.spectra.fits'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can easily read in HPF data for a specific spectral order:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "original_spectrum = HPFSpectrum(file=path+filename, order=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spectrum = original_spectrum.sky_subtract(method='vector').trim_edges().remove_nans().deblaze().normalize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can normalize and overplot plot the observed spectrum, sky subtracted spectrum, and the sky emission itself:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 395,
+       "width": 846
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "spectrum.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gollum.phoenix import PHOENIXGrid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing Teff=10200K|log(g)=4.00|Z=+0.0: 100%|██| 2/2 [00:00<00:00, 28.38it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "grid = PHOENIXGrid(teff_range=(10_000, 10_200), logg_range=(4,4), Z_range=(0,0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_model1, raw_model2 = grid[0], grid[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Finding: we should allow the `grid` object to accept all of these arguments:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#grid.rotationally_broaden(130.0)\n",
+    "#grid.instrumental_broaden(55_000)\n",
+    "#grid.resample(spectrum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model1 = raw_model1.rotationally_broaden(130.0).instrumental_broaden(55_000).resample(spectrum)\n",
+    "model2 = raw_model2.rotationally_broaden(130.0).instrumental_broaden(55_000).resample(spectrum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "factor = 0.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mixture_model = factor * model1 + (1-factor) * model2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 395,
+       "width": 846
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = spectrum.plot()\n",
+    "mixture_model.normalize().plot(ax=ax);"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/environment.yml b/environment.yml
index ecfb4b4..15a34fc 100644
--- a/environment.yml
+++ b/environment.yml
@@ -30,8 +30,11 @@ dependencies:
   - coverage
   - coveralls
   - twine
+  - dust_extinction
   - pip
   - pip:
       - sphinx-material
       - celerite2
       - specutils==1.9.1
+      - tynt
+
diff --git a/environment_M1.yml b/environment_M1.yml
index 36df598..d15c9ec 100644
--- a/environment_M1.yml
+++ b/environment_M1.yml
@@ -30,7 +30,9 @@ dependencies:
   - coverage
   - coveralls
   - twine
+  - dust_extinction
   - pip
   - pip:
       - sphinx-material
       - celerite2
+      - tynt
diff --git a/src/muler/echelle.py b/src/muler/echelle.py
index 5a3a032..aa66259 100644
--- a/src/muler/echelle.py
+++ b/src/muler/echelle.py
@@ -26,8 +26,10 @@
 from scipy.signal import savgol_filter
 from astropy.constants import R_jup, R_sun, G, M_jup, R_earth, c
 from astropy.modeling.physical_models import BlackBody
+from scipy.ndimage import median_filter, gaussian_filter1d
 import specutils
-from muler.utilities import apply_numpy_mask, is_list
+from muler.utilities import apply_numpy_mask, is_list, resample_list
+
 
 # from barycorrpy import get_BC_vel
 from astropy.coordinates import SkyCoord, EarthLocation
@@ -39,6 +41,9 @@
 import os
 import copy
 
+from specutils.manipulation import LinearInterpolatedResampler
+
+
 from specutils.spectra.spectral_region import SpectralRegion
 from specutils.analysis import equivalent_width
 
@@ -73,6 +78,7 @@ def __init__(self, *args, **kwargs):
         # self.ancillary_spectra = None
         super().__init__(*args, **kwargs)
 
+
     @property
     def snr(self):
         """The Signal-to-Noise Ratio :math:`\frac{S}{N}`, the flux divided by the uncertainty
@@ -699,9 +705,142 @@ def apply_boolean_mask(self, mask):
             )
 
         return spec
+
+    def get_slit_profile(self, lower=None, upper=None, slit_length=1.0):
+        """"For a 2D spectrum, returns the slit profile
+
+        Parameters
+        ----------
+        lower : AstroPy Quantity or float
+            The short wavelength limit at which to define the slit profile.
+            If the value is a float, it assume Angstrom units.
+        upper : AstroPy Quantity or float
+            The long wavelength limit at which to define the slit profiled.
+            If the value is a float, it assume Angstrom units.
+
+        Returns
+        -------
+        Array with the same height as the 2D spectrum of the median estimated slit profile
+        """
+        #Get the upper and lower wavelength limits in the correct units
+
+        assert len(np.shape(self.flux)) == 2, "Spectrum must be 2D to estimate slit profile." #Test to make sure this is a 2D spectrum
+
+        if lower is None:
+            lower = self.wavelength.min().value
+        if upper is None:
+            upper = self.wavelength.max().value
+
+        if type(lower) is not u.Quantity:
+            # Assume it's Angstroms
+            lower = lower * u.Angstrom
+        if type(upper) is not u.Quantity:
+            upper = upper * u.Angstrom
+
+        mask = (self.wavelength >= lower) & (self.wavelength <= upper)
+
+        flux = self.flux[:, mask].value
+        normalized_flux = flux / np.nansum(flux, axis=0)
+        median_slit_profile = np.nanmedian(normalized_flux, axis=1)
+
+        return median_slit_profile
+
+
+    def resample(self, target_spectrum):
+        """Resample spectrum onto a new spectral_axis.
+        Copied from gollum.
+        
+        Parameters
+        ----------
+        target_spectrum : Spectrum1D
+            Spectrum whose wavelength grid you seek to match
+
+        Returns
+        -------
+        resampled_spec : PrecomputedSpectrum
+            Resampled spectrum
+        """
+        output = LinearInterpolatedResampler()(self, target_spectrum.wavelength)
+
+        return self._copy(
+            spectral_axis=output.wavelength.value * output.wavelength.unit,
+            flux=output.flux, uncertainty=output.uncertainty, meta=self.meta,
+            wcs=None,
+        )
+
+    def instrumental_broaden(self, resolving_power=55000):
+        r"""Instrumentally broaden the spectrum for a given instrumental resolution
+        Copied verbatim from gollum.
+
+        Known limitation: If the wavelength sampling changes with wavelength,
+          the convolution becomes inaccurate.  It may be better to FFT,
+          following Starfish.
+
+        Parameters
+        ----------
+        resolving_power : int
+            Instrumental resolving power :math:`R = \frac{\lambda}{\delta \lambda}`
+
+        Returns
+        -------
+        broadened_spec : PrecomputedSpectrum
+            Instrumentally broadened spectrum
+        """
+        # In detail the spectral resolution is wavelength dependent...
+        # For now we assume a constant resolving power
+        angstroms_per_pixel = np.median(np.diff(self.wavelength.angstrom))
+        lam0 = np.median(self.wavelength.value)
+        delta_lam = lam0 / resolving_power
+
+        scale_factor = 2.355
+        sigma = delta_lam / scale_factor / angstroms_per_pixel
+
+        convolved_flux = gaussian_filter1d(self.flux.value, sigma) * self.flux.unit
+        return self._copy(flux=convolved_flux)
+    def fill_nans(self, method=median_filter, **kwargs):
+        """Fill nans with the median of surrounding pixels using 
+        scipy.ndimage.median_filter
+        
+        Parameters
+        ----------
+        method: def
+            def to apply to smooth surrounding pixels (e.g. scipy.ndimage.median_filter)
+        **kwargs:
+            Gets passed to method (e.g. size for scipy.ndimage.median_filter)
+        """
+        flux = self.flux
+        unc = self.uncertainty.array
+        filtered_flux = Quantity(method(flux.value, **kwargs), unit=self.flux.unit)
+        filtered_variance = method(unc**2, **kwargs)
+        filtered_unc = (filtered_variance**0.5)
+        found_nans = np.isnan(flux.value)
+        flux[found_nans] = filtered_flux[found_nans]
+        unc[found_nans] = filtered_unc[found_nans]
+
+        return self.__class__(
+            spectral_axis=self.spectral_axis, flux=flux, uncertainty=StdDevUncertainty(unc), meta=self.meta, wcs=None)
+    def apply(self, method=np.nansum, **kwargs):
+        """
+        Apply any method to the spectrum.  This is very general and can be used for many
+        things.  Uncertainty is propogated.
+
+        Parameters
+        ----------
+        method: def
+            def to apply to spectrum (e.g. np.nansum to collapse a multidimensional spectrum)
+        **kwargs:
+            Gets passed to method (e.g. axis for np.nansum)
+        """    
+        flux = self.flux
+        unc = self.uncertainty.array
+        flux = Quantity(method(self.flux.value, **kwargs), unit=self.flux.unit)
+        unc = method(self.uncertainty.array**2, **kwargs)**0.5
+        return self.__class__(
+            spectral_axis=self.spectral_axis, flux=flux, uncertainty=StdDevUncertainty(unc), meta=self.meta, wcs=None)
+
     def __pow__(self, power):
         """Take flux to a power while preserving the exiting flux units.
-        Uuseful for airmass correction.  Uncertainity is propogated by keeping the 
+        Uuseful for airmass correction.  Uncertainty is propogated by keeping the 
         singal-to-noise constant.
 
         Parameters
@@ -778,6 +917,34 @@ def trim_edges(self, limits=None):
 
         return spec_out
 
+    def trim_overlap(self, pivot=0.5):
+        """Trim all the edges that overlap with adjacent spectra (e.g. orders)
+        in the list.  Useful for running before stitch()."""
+        spec_out = copy.deepcopy(self)
+        n = len(spec_out)
+
+        for i in range(n): #Loop through each spectrum/order in list
+            #print('starting i ', i)
+            if i == 0: #Figure out where to trim the left side
+                left_limit = 0
+            elif self[i].spectral_axis[0] >  self[i-1].spectral_axis[-1]:
+                left_limit = 0
+            else:
+                mid_wave = self[i].spectral_axis[0]*(1-pivot) + self[i-1].spectral_axis[-1]*(pivot)
+                left_limit = np.where(self[i].spectral_axis > mid_wave)[-1][0] + 1
+            if i == n-1: #Figure out where to trim the right side
+                right_limit = len(self[i].spectral_axis)
+            elif self[i].spectral_axis[-1] <  self[i+1].spectral_axis[0]:
+                right_limit = len(self[i].spectral_axis)
+            else:
+                mid_wave = self[i].spectral_axis[-1]*(pivot) + self[i+1].spectral_axis[0]*(1-pivot)
+                right_limit = np.where(self[i].spectral_axis > mid_wave)[0][0] - 1
+
+            if left_limit > 0 or right_limit < len(self[i].spectral_axis):
+                spec_out[i] = spec_out[i].trim_edges((left_limit, right_limit))
+
+        return spec_out        
+
     def deblaze(self, method="spline"):
         """Remove blaze function from all orders by interpolating a spline function
 
@@ -949,7 +1116,7 @@ def __truediv__(self, other):
 
     def __pow__(self, power):
         """Take flux to a power while preserving the exiting flux units.
-        Uuseful for airmass correction.  Uncertainity is propagated by keeping the 
+        Uuseful for airmass correction.  Uncertainty is propagated by keeping the 
         singal-to-noise constant.
 
         Parameters
@@ -986,3 +1153,40 @@ def flatten(self, **kwargs):
             if "x_values" not in spec_out[i].meta:
                 spec_out[i].meta["x_values"] = self[i].meta["x_values"]
         return spec_out
+
+    def fill_nans(self, method=median_filter, **kwargs):
+        """Fill nans with the median of surrounding pixels using 
+        scipy.ndimage.median_filter
+        
+        Parameters
+        ----------
+        method: def
+            def to apply to smooth surrounding pixels (e.g. scipy.ndimage.median_filter)
+        **kwargs:
+            Gets passed to method (e.g. size for scipy.ndimage.median_filter)
+        """
+        spec_out = copy.deepcopy(self)
+        for i in range(len(self)):
+            spec_out[i] = self[i].fill_nans(method=method, **kwargs)
+            if "x_values" not in spec_out[i].meta:
+                spec_out[i].meta["x_values"] = self[i].meta["x_values"]
+        return spec_out
+
+    def apply(self, method=np.nansum, **kwargs):
+        """
+        Apply any method to the spectral list.  This is very general and can be used for many
+        things.  Uncertainty is propogated.
+
+        Parameters
+        ----------
+        method: def
+            def to apply to spectrum (e.g. np.nansum to collapse a multidimensional spectrum)
+        **kwargs:
+            Gets passed to method (e.g. axis for np.nansum)
+        """    
+        spec_out = copy.deepcopy(self)
+        for i in range(len(self)):
+            spec_out[i] = self[i].apply(method=method, **kwargs)
+            if "x_values" not in spec_out[i].meta:
+                spec_out[i].meta["x_values"] = self[i].meta["x_values"]
+        return spec_out
\ No newline at end of file
diff --git a/src/muler/hpf.py b/src/muler/hpf.py
index fd4160b..05cb951 100644
--- a/src/muler/hpf.py
+++ b/src/muler/hpf.py
@@ -12,12 +12,14 @@
 import warnings
 import logging
 from muler.echelle import EchelleSpectrum, EchelleSpectrumList
+from muler.utilities import resample_list
 import numpy as np
 import astropy
 from astropy.io import fits
 from astropy import units as u
 from astropy.wcs import WCS, FITSFixedWarning
 from astropy.nddata import StdDevUncertainty
+from scipy.ndimage import median_filter
 from scipy.interpolate import InterpolatedUnivariateSpline
 from astropy.constants import R_jup, R_sun, G, M_jup, R_earth, c
 from astropy.time import Time
@@ -28,6 +30,7 @@
 from . import templates
 import pandas as pd
 
+
 log = logging.getLogger(__name__)
 
 for category in [
@@ -304,7 +307,21 @@ def deblaze(self, method="template"):
             log.error("This method is deprecated!  Please use the new deblaze method")
             raise NotImplementedError
 
-    def sky_subtract(self, method="scalar"):
+    def sky_resample(self):
+        """
+        Resample's sky spectrum from the sky fiber to match the spectrum from the science fiber
+
+        Returns
+        -------
+        Spectrum with sky fiber spectrum resampled to match the wavelength solution of the science fiber
+
+        """
+        spec = copy.deepcopy(self)
+        spec.meta["sky"] = spec.sky.resample(spec)
+        #spec.sky = spec.sky.resample(spec)
+        return spec
+
+    def sky_subtract(self, method="scalar", scale=0.93):
         """Subtract sky spectrum from science spectrum, with refinements for sky throughput
 
         Note: This operation does not wavelength shift or scale the sky spectrum
@@ -315,6 +332,9 @@ def sky_subtract(self, method="scalar"):
             The method for sky subtraction: "naive", "scalar", or "vector", as described in
             Gully-Santiago et al. in prep.  Default is scalar.
 
+        scale : (float)
+            When using the "scalar" method, sets the scale.  Default is 0.93.
+
         Returns
         -------
         sky_subtractedSpec : (HPFSpectrum)
@@ -327,17 +347,20 @@ def sky_subtract(self, method="scalar"):
             )
             beta = 1.0 * u.dimensionless_unscaled
         elif method == "scalar":
-            beta = 0.93 * u.dimensionless_unscaled
+            beta = scale * u.dimensionless_unscaled
         elif method == "vector":
             beta_native_spectrum = spec.get_static_sky_ratio_template()
             resampler = LinearInterpolatedResampler(extrapolation_treatment="zero_fill")
             beta = resampler(beta_native_spectrum, spec.spectral_axis)
+        # elif method == 'median': #Attempt to measure the scale of the sky lines between the fibers using a median of the pixels
+        #     resampled_sky = spec.sky.resample(spec)
+        #     good_sci_pixels = self.flux.value -
         else:
             log.error("Method must be one of 'naive', 'scalar' or 'vector'. ")
             raise NotImplementedError
 
         # These steps should propagate uncertainty?
-        sky_estimator = spec.sky.multiply(beta, handle_meta="first_found")
+        sky_estimator = spec.sky_resample().sky.multiply(beta, handle_meta="first_found")
         return spec.subtract(sky_estimator, handle_meta="first_found")
 
     def mask_tellurics(self, method="TelFit", threshold=0.999, dilation=5):
@@ -434,14 +457,48 @@ def deblaze(self):
 
         return spec_out
 
-    def sky_subtract(self, method="vector"):
+    def sky_resample(self):
+        """
+        Resample's sky spectrum from the sky fiber to match the spectrum from the science fiber
+
+        Returns
+        -------
+        Spectrum with sky fiber spectrum resampled to match the wavelength solution of the science fiber
+
+        """
+        spec_out = copy.copy(self)
+        for i in range(len(spec_out)):
+            spec_out[i] = spec_out[i].sky_resample()
+
+        return spec_out
+               
+
+    def sky_subtract(self, method="vector", scale=0.93):
         """Sky subtract the entire spectrum"""
         spec_out = copy.copy(self)
         for i in range(len(spec_out)):
-            spec_out[i] = spec_out[i].sky_subtract(method=method)
+            spec_out[i] = spec_out[i].sky_subtract(method=method, scale=scale)
 
         return spec_out
 
+    def test_print_sky_scale(self):
+        #reampled_sky = resample_list(self.sky, self) #Resample sky to match sci wavelengths
+        all_sky = np.zeros([2048, len(self)]) #Put all science and sky fiber flux into 2D arrays for easy manipulation
+        all_sci = np.zeros([2048, len(self)])
+        for i in range(len(self)):
+            all_sky[:, i] = (self[i].sky).resample(self[i]).flux.value
+            all_sci[:, i] = self[i].flux.value
+        all_sky -= median_filter(all_sky, [25, 1]) #Subtract continuum/background using a running median filter
+        all_sci -= median_filter(all_sci, [25, 1])
+        #all_sky[all_sky ]
+        max_sky_flux = np.nanmax(all_sky)
+        bad_pix = (all_sky < 0.1 * max_sky_flux) & (all_sci < 0.1 * max_sky_flux)
+        all_sky[bad_pix] = np.nan
+        all_sci[bad_pix] = np.nan
+        print(np.nanmedian(all_sci / all_sky))
+
+
+
     # def sky_subtract(self):
     #     """Sky subtract all orders
     #     """
diff --git a/src/muler/igrins.py b/src/muler/igrins.py
index 5561f28..dc418e0 100644
--- a/src/muler/igrins.py
+++ b/src/muler/igrins.py
@@ -10,7 +10,9 @@
 """
 import logging
 import warnings
+import json
 from muler.echelle import EchelleSpectrum, EchelleSpectrumList
+from muler.utilities import Slit, concatenate_orders
 from astropy.time import Time
 import numpy as np
 import astropy
@@ -18,6 +20,8 @@
 from astropy import units as u
 from astropy.wcs import WCS, FITSFixedWarning
 from astropy.nddata import StdDevUncertainty
+from specutils.manipulation import LinearInterpolatedResampler
+LinInterpResampler = LinearInterpolatedResampler()
 
 import copy
 import os
@@ -39,12 +43,58 @@
 grating_order_offsets = {"H": 98, "K": 71}
 
 
+def readPLP(plppath, date, frameno, waveframeno, dim='1D'):
+    """Convience function for easily reading in the full IGRINS Spectrum (both H and K bands)
+    from the IGRINS PLP output
+
+    Parameters
+    ----------
+    plppath: string
+        Path to the IGRINS PLP (e.g. "/Users/Username/Desktop/plp/")
+    date: int or string
+        Date for night of IGIRNS observation in format of YYYYMMDD (e.g. "201401023")
+    frameno: int or string
+        Number of frame denoting target as specified as the first frame in the
+        recipes file for the night (e.g. 54 or "0054")
+    waveframeno: int or string
+        Number of frame denoting target as specified as the first frame in the
+        recipes file for the wavelength solution (e.g. 54 or "0054") from a wvlsol_v1 file.
+        This is usually the first frame number for the sky.
+    dim: string
+        Set to "1D" to read in the 1D extracted spectrum from the .spec.fits files
+        or "2D" to read in the rectified 2D spectrum from the .spec2d.fits files
+
+    Returns
+    -------
+    IGRINSSpectrumList containing all the orders for the H and K bands for the specified target
+    """
+    if type(date) is not str: #Converhet dates and frame numbers to the proper string format
+        date = '%.8d' % int(date)
+    if type(frameno) is not str:
+        frameno = '%.4d' % int(frameno)
+    if type(waveframeno) is not str:
+        waveframeno = '%.4d' % int(waveframeno)
+    if dim.upper() == '1D': #Use proper filename for 1D or 2D extractions
+        suffix = '.spec.fits'
+    elif dim.upper() == '2D':
+        suffix = '.spec2d.fits'
+    else:
+        raise Exception(
+            "Argument 'dim' must be '1D' for .spec.fits files or '2D' for .spec2d.fits files."
+            )
+    spec_H = IGRINSSpectrumList.read(plppath+'outdata/'+date +'/'+'SDCH_'+date+'_'+frameno+suffix, #Read in H band
+                                wavefile=plppath+'calib/primary/'+date +'/SKY_SDCH_'+date+'_'+waveframeno+'.wvlsol_v1.fits')
+    spec_K = IGRINSSpectrumList.read(plppath+'outdata/'+date +'/'+'SDCK_'+date+'_'+frameno+suffix, #Read in K band
+                                wavefile=plppath+'calib/primary/'+date +'/SKY_SDCK_'+date+'_'+waveframeno+'.wvlsol_v1.fits')
+    spec_all = concatenate_orders(spec_H, spec_K) #Combine H and K bands
+    return spec_all
 
-def getUncertainityFilepath(filepath):
-    """Returns path for uncertainity file (.variance.fits or .sn.fits)
+
+def getUncertaintyFilepath(filepath):
+    """Returns path for uncertainty file (.variance.fits or .sn.fits)
 
         Will first search for a .variance.fits file but if that does not exist
-        will serach for a .sn.fits file.
+        will search for a .sn.fits file.
 
     Parameters
     ----------
@@ -52,29 +102,152 @@ def getUncertainityFilepath(filepath):
 
     Returns
     -------
-    uncertainityFilepath: string
+    uncertaintyFilepath: string
         Returns the file path to the uncertianity (.variance.fits or .sn.fits) file.
 
     """
-    if ".spec_a0v.fits" in filepath: #Grab base file name for the uncertainity file
+    if ".spec_a0v.fits" in filepath: #Grab base file name for the uncertainty file
+        path_base = filepath[:-14]
+    elif ".spec_flattened.fits" in filepath:
+        path_base = filepath[:-20]
+    elif ".spec.fits" in filepath:
+        path_base = filepath[:-10]
+    elif ".spec2d.fits" in filepath:
+        path_base = filepath[:-12]
+    if ".spec2d.fits" in filepath:
+        if os.path.exists(path_base + '.var2d.fits'):
+            return path_base + '.var2d.fits'
+        else:
+            raise Exception(
+                "The file .var2d.fits does not exist in the same path as the spectrum file to get the uncertainty.  Please provide one of these files in the same directory as your spectrum file."
+                )             
+    else:
+        if os.path.exists(path_base + '.variance.fits'): #Prefer .variance.fits file
+            return path_base + '.variance.fits'
+        elif os.path.exists(path_base + '.sn.fits'): #If no .variance.fits file found, try using the .sn.fits file
+            return path_base + '.sn.fits'
+        else:
+            raise Exception(
+                "Neither .variance.fits or .sn.fits exists in the same path as the spectrum file to get the uncertainty.  Please provide one of these files in the same directory as your spectrum file."
+                )             
+
+def getSlitProfile(filepath, band, slit_length):
+    """Returns the path for the slit profile.  Will first look for a 2D
+    spectrum .spec2d.fits file to calculate the profile from.  If a spec2d.fits
+    file does not exist, will look for a .slit_profile.json.
+
+    Parameters
+    ----------
+    filepath: string
+        Filepath to fits file storing the data.  Can be .spec.fits or .spec_a0v.fits.
+    band: string
+        'H' or 'K' specifying which band
+    slit_length: float
+        Length of the slit on the sky in arcsec.
+
+    Returns
+    -------
+    x: float
+        Distance in arcsec along the slit
+    y: float
+        Flux of beam profile across the slit
+    """
+    if ".spec_a0v.fits" in filepath: #Grab base file name for the uncertainty file
         path_base = filepath[:-14]
     elif ".spec_flattened.fits" in filepath:
         path_base = filepath[:-20]
     elif ".spec.fits" in filepath:
         path_base = filepath[:-10]
-    if os.path.exists(path_base + '.variance.fits'): #Prefer .variance.fits file
-        return path_base + '.variance.fits'
-    elif os.path.exists(path_base + '.sn.fits'): #If no .variance.fits file found, try using the .sn.fits file
-        return path_base + '.sn.fits'
-    elif path_base[0:4] == 'http':
-        # Try to read in the variance file...
-        return path_base + '.variance.fits'
+    elif ".spec2d.fits" in filepath:
+        path_base = filepath[:-12]
+    path_base = path_base.replace('SDCH', 'SDC'+band).replace('SDCK', 'SDC'+band)
+    spec2d_filepath = path_base + '.spec2d.fits'
+    json_filepath = path_base + '.slit_profile.json'
+    if os.path.exists(filepath): #First try to use the 2D spectrum in a .spec2d.fits file to estimate the slit proflie
+        spec2d = fits.getdata(spec2d_filepath)
+        long_spec2d = spec2d[2,:,1000:1300] #Chop off order edges at columns 800 and 1200
+        for i in range(3, len(spec2d)-2):
+            long_spec2d = np.concatenate([long_spec2d, spec2d[i,:,1000:1300]], axis=1)
+        y = np.nanmedian(long_spec2d, axis=1)
+        x = np.arange(len(y)) * (slit_length / len(y))
+    elif os.path.exists(json_filepath): #If no 2D spectrum exists, try using the PLP estimate in .slit_profile.json
+        json_file = open(filepath)
+        json_obj = json.load(json_file)
+        x = np.array(json_obj['profile_x']) * slit_length
+        y = np.array(json_obj['profile_y'])
+        json_file.close()
     else:
-        # No Uncertainty file available.  That's OK.  We will just have coarse uncertainty...
-        # TODO: support this scenario!
-        warnings.warn("Neither .variance.fits or .sn.fits exists locally in the same path as the spectrum file to get the uncertainity."
-            )         
-        raise Exception('Reading IGRINS without uncertainty files is unsupported at this time.')    
+        raise Exception(
+            "Need either .spec2d.fits or .slit_profile.json file in the same directory as "
+            + filepath
+            + " in order to get an estimate of the slit profile.  .spec2d.fits or .slit_profile.json are missing."
+        )        
+    return x, y
+
+
+
+def getIGRINSSlitThroughputABBACoefficients(file, slit_length=14.8, PA=90, guiding_error=1.5, print_info=True, plot=False):
+    """Estimate the wavelength dependent fractional slit throughput for a point source nodded ABBA on the IGRINS slit and return the 
+    coefficients of a linear fit.
+
+    Parameters
+    ----------
+    file:
+        Path to fits file (e.g. spec.fits) from which the slit_profile.json file is also in the same directory.
+        These should all be in the same IGRINS PLP output directory.
+    slit_length: float
+        Length of the slit on the sky in arcsec.
+    PA: float
+        Position angle of the slit on the sky in degrees.  Measured counterclockwise from North to East.
+    guilding_error: float
+        Estimate of the guiding error in arcsec.  This smears out the PSF fits in the East-West direction.
+        This should be used carefully and only for telescopes on equitorial mounts.
+    print_info: bool
+        Print information about the fit.
+    plot: bool
+        Visualize slit throughput calculations.
+
+    Returns
+    -------
+    m, b:
+        Coefficients for a fit of a linear trend of m*(1/wavelength)+b to the fractional slit throughput with the
+        wavelength units in microns.
+
+    """
+    igrins_slit = Slit(length=slit_length, width=slit_length*(1/14.8), PA=PA, guiding_error=guiding_error)
+    #Get throughput for H band
+    x, y = getSlitProfile(file, band='H', slit_length=slit_length) #Get slit profile
+    igrins_slit.clear()
+    igrins_slit.ABBA(y, x=x, print_info=print_info, plot=plot)
+    if plot:
+        print('2D plot of H-band')
+        igrins_slit.plot2d()
+        #breakpoint()
+    f_through_slit_H = igrins_slit.estimate_slit_throughput()
+    #Get throughput for K band
+    x, y = getSlitProfile(file, band='K', slit_length=slit_length) #Get slit profile
+    igrins_slit.clear()
+    igrins_slit.ABBA(y, x=x, print_info=print_info, plot=plot)
+    if plot:
+        print('2D plot of K-band')
+        igrins_slit.plot2d()
+        breakpoint()
+    f_through_slit_K = igrins_slit.estimate_slit_throughput()
+    #Fit linear trend through slit throughput as function of wavelength and using fitting a line through two points
+    m = (f_through_slit_K - f_through_slit_H) / ((1/2.2) - (1/1.65))
+    b = f_through_slit_H - m*(1/1.65)
+    if print_info:
+        # log.info('H-band slit throughput: ', f_through_slit_H)
+        # log.info('K-band slit throughput:', f_through_slit_K)
+        # log.info('m: ', m)
+        # log.info('b: ', b)
+        print('H-band slit throughput: ', f_through_slit_H)
+        print('K-band slit throughput:', f_through_slit_K)
+        print('m: ', m)
+        print('b: ', b)
+    return m, b
+
+
 
 class IGRINSSpectrum(EchelleSpectrum):
     r"""
@@ -100,10 +273,13 @@ def __init__(
         # self.ancillary_spectra = None
         self.noisy_edges = (450, 1950)
         self.instrumental_resolution = 45_000.0
-         #False if variance.fits file used for uncertainity, true if sn.fits file used for uncertainity
+        self.file = file
+
+         #False if variance.fits file used for uncertainty, true if sn.fits file used for uncertainty
 
         if file is not None:
-            assert (".spec_a0v.fits" in file) or (".spec.fits" in file) or (".spec_flattened.fits")
+            
+            assert (".spec_a0v.fits" in file) or (".spec.fits" in file) or (".spec_flattened.fits" in file) or ('.spec2d.fits' in file)
             # Determine the band
             if "SDCH" in file:
                 band = "H"
@@ -113,13 +289,14 @@ def __init__(
                 raise NameError("Cannot identify file as an IGRINS spectrum")
             grating_order = grating_order_offsets[band] + order
 
+            uncertainty_hdus = None #Default values
+            uncertainty = None
             if cached_hdus is not None:
                 hdus = cached_hdus[0]
                 if "rtell" in file:
                     sn = hdus["SNR"].data[order]
-                    uncertainity_hdus = None
                 else:
-                    uncertainity_hdus = cached_hdus[1]
+                    uncertainty_hdus = cached_hdus[1]
                 if wavefile is not None:
                     wave_hdus = cached_hdus[2]
             else: #Read in files if cached_hdus are not provided
@@ -131,15 +308,14 @@ def __init__(
                         base_path = os.path.dirname(file)
                         full_path = base_path + '/' + os.path.basename(wavefile)
                         wave_hdus = fits.open(full_path)
-                if "rtell" not in file:  
-                    uncertainty_filepath = getUncertainityFilepath(file)
-                    uncertainity_hdus = fits.open(uncertainty_filepath, memmap=False)   
+                if "rtell" not in file and "spec_a0v" not in file:  
+                    uncertainty_filepath = getUncertaintyFilepath(file)
+                    uncertainty_hdus = fits.open(uncertainty_filepath, memmap=False)   
                     if '.sn.fits' in uncertainty_filepath:
                         sn_used = True
-                else: #If rtell file is used, grab SNR stored in extension
+                elif "rtell" in file: #If rtell file is used, grab SNR stored in extension
                     sn = hdus["SNR"].data[order]
                     sn_used = True
-                    uncertainity_hdus = None
             hdr = hdus[0].header
             if ("spec_a0v.fits" in file) and (wavefile is not None):
                 log.warn(
@@ -147,12 +323,22 @@ def __init__(
                 )
                 lamb = hdus["WAVELENGTH"].data[order].astype(np.float64) * u.micron
                 flux = hdus["SPEC_DIVIDE_A0V"].data[order].astype(np.float64) * u.ct
+                try:
+                    uncertainty_hdus = [hdus["SPEC_DIVIDE_A0V_VARIANCE"]]
+                    sn_used = False
+                except:
+                    print("Warning: Using older PLP versions of .spec_a0v.fits files which have no variance saved.  Will grab .variance.fits file.")
             elif ".spec_a0v.fits" in file:
                 lamb = hdus["WAVELENGTH"].data[order].astype(float) * u.micron
                 flux = hdus["SPEC_DIVIDE_A0V"].data[order].astype(float) * u.ct
-            elif (("spec.fits" in file) or ("spec_flattened.fits" in file)) and (wavefile is not None):
+                try:
+                    uncertainty_hdus = [hdus["SPEC_DIVIDE_A0V_VARIANCE"]]
+                    sn_used = False
+                except:
+                    print("Warning: Using older PLP versions of .spec_a0v.fits files which have no variance saved.  Will grab .variance.fits file.")
+            elif (("spec.fits" in file) or ("spec_flattened.fits" in file) or ('.spec2d.fits' in file)) and (wavefile is not None):
                 lamb = (
-                    wave_hdus[0].data[order].astype(float) * 1e-3 * u.micron
+                    wave_hdus[0].data[order].astype(float) * u.micron
                 )  # Note .wave.fits and .wavesol_v1.fts files store their wavelenghts in nm so they need to be converted to microns
                 flux = hdus[0].data[order].astype(float) * u.ct
             elif (("spec.fits" in file) or ("spec_flattened.fits" in file)) and (wavefile is None):
@@ -170,24 +356,23 @@ def __init__(
                 "m": grating_order,
                 "header": hdr,
             }
-            if uncertainity_hdus is not None or ("rtell" in file):
+            if uncertainty_hdus is not None or ("rtell" in file):
                 if not sn_used: #If .variance.fits used
-                    variance = uncertainity_hdus[0].data[order].astype(np.float64)
+                    variance = uncertainty_hdus[0].data[order].astype(np.float64)
                     stddev = np.sqrt(variance)
-                    if ("rtell" in file) or ("spec_a0v" in file): #If using a rtell or spec_a0v file with a variance file, scale the stddev to preserve signal-to-noise
+                    if ("rtell" in file): #If using a rtell or spec_a0v file with a variance file, scale the stddev to preserve signal-to-noise
                         unprocessed_flux = hdus["TGT_SPEC"].data[order].astype(np.float64)
                         stddev *= (flux.value / unprocessed_flux)
                 else: #Else if .sn.fits (or SNR HDU in rtell file) used
                     if not "rtell" in file:
-                        sn = uncertainity_hdus[0].data[order].astype(np.float64)
-                    dw = np.gradient(lamb) #Divide out stuff the IGRINS PLP did to calculate the uncertainity per resolution element to get the uncertainity per pixel
+                        sn = uncertainty_hdus[0].data[order].astype(np.float64)
+                    dw = np.gradient(lamb) #Divide out stuff the IGRINS PLP did to calculate the uncertainty per resolution element to get the uncertainty per pixel
                     pixel_per_res_element = (lamb/40000.)/dw
                     sn_per_pixel =  sn / np.sqrt(pixel_per_res_element)
                     stddev = flux.value / sn_per_pixel.value
                 uncertainty = StdDevUncertainty(np.abs(stddev))
                 mask = np.isnan(flux) | np.isnan(uncertainty.array)
             else:
-                uncertainity = None
                 mask = np.isnan(flux)
 
             super().__init__(
@@ -236,6 +421,39 @@ def astropy_time(self):
         mjd = self.meta["header"]["MJD-OBS"]
         return Time(mjd, format="mjd", scale="utc")
 
+    def getSlitThroughput(self, slit_length=14.8, PA=90, guiding_error=1.5, print_info=True, plot=False):
+        """Estimate the wavelength dependent fractional slit throughput for a point source nodded ABBA on the IGRINS slit.
+
+        Parameters
+        ----------
+        h_band_slitprofile_filepath:
+            Filepath to *.slit_profile.json file outputted by the IGRINS PLP storing the spatial
+            profile of the target along the slit for the H band.
+        k_band_slitprofile_filepath:
+            Filepath to *.slit_profile.json file outputted by the IGRINS PLP storing the spatial
+            profile of the target along the slit for the K band.
+        slit_length: float
+            Length of the slit on the sky in arcsec.
+        PA: float
+            Position angle of the slit on the sky in degrees.  Measured counterclockwise from North to East.
+        guilding_error: float
+            Estimate of the guiding error in arcsec.  This smears out the PSF fits in the East-West direction.
+            This should be used carefully and only for telescopes on equitorial mounts.
+        print_info: bool
+            Print information about the fit.
+
+        Returns
+        -------
+        Returns array of fractional slit throughput as a function of wavelength
+        """
+
+        m, b = getIGRINSSlitThroughputABBACoefficients(self.file, slit_length=slit_length, PA=PA, guiding_error=guiding_error, print_info=print_info, plot=plot)
+        return m*(1/self.wavelength.um) + b
+
+
+
+
+
 
 class IGRINSSpectrumList(EchelleSpectrumList):
     r"""
@@ -244,6 +462,7 @@ class IGRINSSpectrumList(EchelleSpectrumList):
     """
 
     def __init__(self, *args, **kwargs):
+        self.file = None
         self.normalization_order_index = 14
         super().__init__(*args, **kwargs)
 
@@ -254,18 +473,26 @@ def read(file, precache_hdus=True, wavefile=None):
         Parameters
         ----------
         file : (str)
-            A path to a reduced IGRINS spectrum from plp
+            A path to a reduced IGRINS spectrum from plp.
         wavefile : (str)
+            Path to a file storing a wavelength soultion for a night from the plp.
+            Wave files are found in the IGRINS PLP callib/primary/DATE/ directory with
+            the extension wvlsol_v1.fits.
 
         """
         # still works
-        assert (".spec_a0v.fits" in file) or (".spec.fits" in file) or (".spec_flattened.fits" in file)
+        assert (".spec_a0v.fits" in file) or (".spec.fits" in file) or (".spec_flattened.fits" in file) or (".spec2d.fits" in file)
+        
         sn_used = False #Default
         hdus = fits.open(file, memmap=False)
-        if "rtell" not in file: #Default, if no rtell file is used
-            uncertainty_filepath = getUncertainityFilepath(file)
-            uncertainity_hdus = fits.open(uncertainty_filepath, memmap=False)    
-            cached_hdus = [hdus, uncertainity_hdus]   
+
+        #hdus["SPEC_DIVIDE_A0V_VARIANCE"] 
+        if "SPEC_DIVIDE_A0V_VARIANCE" in hdus:
+            cached_hdus = [hdus, [hdus["SPEC_DIVIDE_A0V_VARIANCE"]]] 
+        elif "rtell" not in file: #Default, if no rtell file is used
+            uncertainty_filepath = getUncertaintyFilepath(file)
+            uncertainty_hdus = fits.open(uncertainty_filepath, memmap=False)    
+            cached_hdus = [hdus, uncertainty_hdus]   
             if '.sn.fits' in uncertainty_filepath:
                 sn_used = True     
         else: #If rtell file is used
@@ -280,7 +507,14 @@ def read(file, precache_hdus=True, wavefile=None):
                 wave_hdus = fits.open(full_path, memmap=False)
             cached_hdus.append(wave_hdus)
 
-        n_orders, n_pix = hdus[0].data.shape
+        if hdus[0].data is not None:
+            hdus0_shape = hdus[0].data.shape #Normally we read from the 0th extension
+        else:
+            hdus0_shape = hdus[1].data.shape #To insure compatibility with new version of the PLP for spec_a0v.fits files
+        if len(hdus0_shape) == 2: #1D spectrum
+            n_orders, n_pix = hdus0_shape
+        elif len(hdus0_shape) == 3: #2D spectrum
+            n_orders, n_height, n_pix = hdus0_shape
 
         list_out = []
         for i in range(n_orders - 1, -1, -1):
@@ -288,5 +522,37 @@ def read(file, precache_hdus=True, wavefile=None):
                 file=file, wavefile=wavefile, order=i, sn_used=sn_used, cached_hdus=cached_hdus
             )
             list_out.append(spec)
-        return IGRINSSpectrumList(list_out)
+        specList = IGRINSSpectrumList(list_out)
+        specList.file = file
+        return specList
+    def getSlitThroughput(self, slit_length=14.8, PA=90, guiding_error=1.5,  print_info=True, plot=False):
+        """Estimate the wavelength dependent fractional slit throughput for a point source nodded ABBA on the IGRINS slit.
+
+        Parameters
+        ----------
+        h_band_slitprofile_filepath:
+            Filepath to *.slit_profile.json file outputted by the IGRINS PLP storing the spatial
+            profile of the target along the slit for the H band.
+        k_band_slitprofile_filepath:
+            Filepath to *.slit_profile.json file outputted by the IGRINS PLP storing the spatial
+            profile of the target along the slit for the K band.
+        slit_length: float
+            Length of the slit on the sky in arcsec.
+        PA: float
+            Position angle of the slit on the sky in degrees.  Measured counterclockwise from North to East.
+        guilding_error: float
+            Estimate of the guiding error in arcsec.  This smears out the PSF fits in the East-West direction.
+            This should be used carefully and only for telescopes on equitorial mounts.
+        print_info: bool
+            Print information about the fit.
+
+        Returns
+        -------
+        Returns list of arrays of fractional slit throughput as a function of wavelength
+        """
 
+        m, b = getIGRINSSlitThroughputABBACoefficients(self.file, slit_length=slit_length, PA=PA, guiding_error=guiding_error, print_info=print_info, plot=plot)
+        f_throughput = []
+        for i in range(len(self)):
+            f_throughput.append(m*(1/self[i].wavelength.um) + b)
+        return f_throughput
diff --git a/src/muler/utilities.py b/src/muler/utilities.py
index 9587082..c548038 100644
--- a/src/muler/utilities.py
+++ b/src/muler/utilities.py
@@ -1,8 +1,78 @@
+import logging
+
 import numpy as np
 import copy
 from specutils.spectra import Spectrum1D
 from astropy.nddata.nduncertainty import StdDevUncertainty
+from astropy.modeling import models, fitting #import the astropy model fitting package
+from astropy import units as u
 from scipy.stats import binned_statistic
+from scipy.interpolate import interp1d
+from specutils.manipulation import LinearInterpolatedResampler
+from matplotlib import pyplot as plt
+LinInterpResampler = LinearInterpolatedResampler()
+from tynt import FilterGenerator
+
+log = logging.getLogger(__name__)
+
+
+def resample_combine_spectra(input_spec, spec_to_match, weights=1.0):
+        """Linearly resample input_spectra, which can be a list of spectra, to match specrum_to_match and return an EchelleSpectrum
+        or EchelleSpectrumList object with the same spectral axis and naned pixels as specrum_to_match.  One main applications
+        for this is to match multiple synthetic spectra generated from stellar atmosphere models to a real spectrum.
+
+        Parameters
+        -------
+        input_spec :
+            A EchelleSpectrumm EchelleSpectrumList, or similar specutils object (or list of objects) to be resampled to match spec_to_match.
+        specrum_to_match :
+            A EchelleSpectrum or EchelleSpectrumLis spectrum which the input_spec will be resampled to match in both wavelength and naned pixels
+        weights :
+            A list or array giving the fraction of each spectrum in input_spec that makes up the final resampled spectrum.
+            Useful for grid interpolation for stellar atmosphere models or just stacking spectra from multiple objects
+            into one spectrum.
+    
+        Returns
+        -------
+        An EchelleSpectrum or EchelleSpectrumList object with the same wavelength arrays and naned pixels as spec_to_match.
+        """
+
+        if is_list(input_spec): #
+            weights = np.array(weights) #Check that weights are a list and their sum equals 1
+            sum_weights = np.sum(weights)
+            assert (len(weights)==1 and weights[0] == 1) or (len(weights) > 1), "If providing weights, You need to provide a weight for each input spectrum.."
+            assert sum_weights == 1, "Total weights in weights list is "+str(sum_weights)+" but total must equal to 1."
+           
+            if is_list(spec_to_match):
+                resampled_spec = resample_list(input_spec[0], spec_to_match)*(weights[0]) #Resample spectra
+                for i in range(1, len(input_spec)):
+                    if len(weights)==1 and weights[0] == 1:
+                        resampled_spec = resampled_spec + resample_list(input_spec[i], spec_to_match)*(weights[i])
+                    else:
+                        resampled_spec = resampled_spec + resample_list(input_spec[i], spec_to_match)
+            else:
+                resampled_spec = LinInterpResampler(input_spec[0], spec_to_match.spectral_axis)*(weights[0]) #Resample spectra
+                for i in range(1, len(input_spec)):
+                    if len(weights)==1 and weights[0] == 1:
+                        resampled_spec = resampled_spec + LinInterpResampler(input_spec[i], spec_to_match.spectral_axis)*(weights[i])
+                    else:
+                        resampled_spec = resampled_spec + LinInterpResampler(input_spec[i], spec_to_match.spectral_axis)
+        else:
+            if is_list(spec_to_match):
+                resampled_spec = resample_list(input_spec, spec_to_match) #Resample spectrum
+            else:
+                resampled_spec = LinInterpResampler(input_spec, spec_to_match.spectral_axis)
+                resampled_spec = spec_to_match.__class__( #Ensure resampled_spec is the same object as spec_to_match
+                    spectral_axis=resampled_spec.spectral_axis, flux=resampled_spec.flux, meta=resampled_spec.meta, wcs=None)
+
+        if is_list(spec_to_match): #Propogate nans from spec_to_match to avoid wierd errors
+            for i in range(len(spec_to_match)):
+                resampled_spec[i].flux[np.isnan(spec_to_match[i].flux.value)] = np.nan
+        else:
+            resampled_spec.flux[np.isnan(spec_to_match.flux.value)] = np.nan
+
+        return resampled_spec
+
 
 
 def combine_spectra(spec_list):
@@ -150,16 +220,6 @@ def apply_numpy_mask(spec, mask):
             " The boolean mask should have the same shape as the spectrum."
         )
 
-    if spec.uncertainty is not None:
-        masked_unc = spec.uncertainty[mask]
-    else:
-        masked_unc = None
-
-    if spec.mask is not None:
-        mask_out = spec.mask[mask]
-    else:
-        mask_out = None
-
     if spec.meta is not None:
         meta_out = copy.deepcopy(spec.meta)
         if "x_values" in spec.meta.keys():
@@ -167,14 +227,45 @@ def apply_numpy_mask(spec, mask):
     else:
         meta_out = None
 
-    return spec.__class__(
-        spectral_axis=spec.wavelength.value[mask] * spec.wavelength.unit,
-        flux=spec.flux[mask],
-        mask=mask_out,
-        uncertainty=masked_unc,
-        wcs=None,
-        meta=meta_out,
-    )
+    ndim = spec.flux.ndim #Grab dimensionality of spec, can be 1D or 2D
+    if ndim == 1: #For 1D spectra
+        if spec.uncertainty is not None:
+            masked_unc = spec.uncertainty[mask]
+        else:
+            masked_unc = None
+
+        if spec.mask is not None:
+            mask_out = spec.mask[mask]
+        else:
+            mask_out = None
+
+        return spec.__class__(
+            spectral_axis=spec.wavelength.value[mask] * spec.wavelength.unit,
+            flux=spec.flux[mask],
+            mask=mask_out,
+            uncertainty=masked_unc,
+            wcs=None,
+            meta=meta_out,
+        )
+    elif ndim == 2: #For 2D (e.g. slit) spectra
+        if spec.uncertainty is not None:
+            masked_unc = spec.uncertainty[:, mask]
+        else:
+            masked_unc = None
+
+        if spec.mask is not None:
+            mask_out = spec.mask[:, mask]
+        else:
+            mask_out = None
+
+        return spec.__class__(
+            spectral_axis=spec.wavelength.value[mask] * spec.wavelength.unit,
+            flux=spec.flux[:, mask],
+            mask=mask_out,
+            uncertainty=masked_unc,
+            wcs=None,
+            meta=meta_out,
+        )
 
 
 def resample_list(spec_to_resample, specList, **kwargs):
@@ -195,7 +286,14 @@ def resample_list(spec_to_resample, specList, **kwargs):
     """
     spec_out = copy.deepcopy(specList)
     for i in range(len(specList)):
-        spec_out[i] = spec_to_resample.resample(specList[i], **kwargs)
+        meta_out = specList[i].meta
+        resampled_spec = spec_to_resample.resample(specList[i], **kwargs)
+        if hasattr(resampled_spec, "unc"):
+            spec_out[i] = specList[i].__class__(
+                spectral_axis=resampled_spec.spectral_axis, flux=resampled_spec.flux, uncertainty=resampled_spec.unc, meta=meta_out, wcs=None)
+        else:
+            spec_out[i] = specList[i].__class__(
+                spectral_axis=resampled_spec.spectral_axis, flux=resampled_spec.flux, meta=meta_out, wcs=None)            
     return spec_out
 
 
@@ -228,8 +326,239 @@ def is_list(check_this):
     True: Object has more than one element (e.g. is a list or array)
     False: Object has a single element (e.g. a single variable like 10.0)
     """
-    if np.size(check_this) > 1:
-        return True
-    else:
-        return False
-
+    return isinstance(check_this, list)
+
+class Slit:
+    def __init__(self, length=14.8, width=1.0, PA=90.0, guiding_error=1.5, n_axis=5000):
+        """
+        A  class to handle information about a spectrometer's slit, used for calculating things like slit losses
+
+        Parameters 
+        ----------
+        length: float
+            Length of the slit on the sky in arcsec.
+        width: float
+            Width of the slit on the sky in arcsec.
+        PA: float
+            Position angle of the slit on the sky in degrees.  Measured counterclockwise from North to East.
+        guilding_error: float
+            Estimate of the guiding error in arcsec.  This smears out the PSF fits in the East-West direction.
+            This should be used carefully and only for telescopes on equitorial mounts.
+        n_axis: float
+            Size of axis for a 2D square array storing estimated profiles along the slit in 2D for later masking
+
+        """
+        self.length = length
+        self.width = width
+        self.PA = PA
+        self.guiding_error = guiding_error
+
+        half_n_axis = n_axis / 2
+        dx = 1.2 * (length / n_axis)
+        dy = 1.2 * (length / n_axis)
+        x2d, y2d = np.meshgrid(np.arange(n_axis), np.arange(n_axis))
+        x2d = (x2d - half_n_axis) * dx
+        y2d = (y2d - half_n_axis) * dy
+        self.x2d = x2d #Store x coordinates of 2D grid
+        self.y2d = y2d #Store y coordinates on 2D grid
+        self.f2d = np.zeros(np.shape(y2d)) #Store 2D grid of estimated fluxes'
+        half_length = 0.5 * self.length
+        half_width = 0.5 * self.width        
+        self.mask = (x2d <= -half_width) | (x2d >= half_width) | (y2d <= -half_length) | (y2d >= half_length) #Create mask where every pixel inside slit is True and outside is False
+    def ABBA(self, y, x=None, print_info=True, plot=False):
+        """
+        Given a collapsed spatial profile long slit for a point (stellar) source nodded
+        ABBA along the slit, generate an estimate of A and B nods' 2D PSFs.
+        The A and B nods are fit with Moffat functions which are then projected from 1D to 2D and then
+        a mask is applied representing the slit and the the fraction of light in the PSFs inside the mask
+        are integrated to estimate the fraction of light that passes through the slit.
+
+        Parameters 
+        ----------
+        y: numpy array of floats
+            Array representing the spatial profile of the source on the slit.  It should be the PSF for
+            a point source nodded ABBA on the slit.
+        x: numpy array of floats (optional)
+            Array representing the spatial position along the slit in pixel space corrisponding to y.
+        print_info: bool
+            Print information about the fit.
+        plot: bool
+            Set to True to plot the 1D profile along the slit, Moffat fits, and residuals
+        """
+        slit_width_to_length_ratio = self.width / self.length
+        if x is None: #Generate equally spaced x array if it is not provided
+            ny = len(y)
+            x = (np.arange(ny) / ny) * self.length
+        #Find maximum and minimum
+        i_max = np.where(y == np.nanmax(y))[0][0]
+        i_min = np.where(y == np.nanmin(y))[0][0]
+        if np.size(i_max) > 1: #Error catch for the rare event when two or more pixels match the max or min y values
+            i_max = i_max[0]
+        if np.size(i_min) > 1:
+            i_min = i_min[0]
+        #Fit 2 Moffat distributions to the psfs from A and B positions (see https://docs.astropy.org/en/stable/modeling/compound-models.html)
+        g1 = models.Moffat1D(amplitude=y[i_max], x_0=x[i_max], alpha=1.0, gamma=1.0)
+        g2 = models.Moffat1D(amplitude=y[i_min], x_0=x[i_min], alpha=1.0, gamma=1.0)
+        gg_init = g1 + g2
+        fitter = fitting.TRFLSQFitter()
+        gg_fit = fitter(gg_init, x, y)
+        if plot:
+            plt.figure()
+            plt.plot(x, y, '.', label='Std Star Data')
+            plt.plot(x, gg_fit(x), label='Moffat Distribution Fit')
+            plt.plot(x, y-gg_fit(x), label='Residuals')
+            plt.xlabel('Distance along slit (arcsec)')
+            plt.ylabel('Flux')
+            plt.legend()
+            plt.show()
+        if print_info:
+            #log.info('FWHM A beam:', gg_fit[0].fwhm)
+            #log.info('FWHM B beam:', gg_fit[1].fwhm)
+            print('FWHM A beam:', gg_fit[0].fwhm)
+            print('FWHM B beam:', gg_fit[1].fwhm)
+        #Numerically estimate light through slit
+        g1_fit = models.Moffat2D(amplitude=np.abs(gg_fit[0].amplitude), x_0=gg_fit[0].x_0 - 0.5*self.length, alpha=gg_fit[0].alpha, gamma=gg_fit[0].gamma)
+        g2_fit = models.Moffat2D(amplitude=np.abs(gg_fit[1].amplitude), x_0=gg_fit[1].x_0 - 0.5*self.length, alpha=gg_fit[1].alpha, gamma=gg_fit[1].gamma)
+        #simulate  guiding error by "smearing out" PSF
+        position_angle_in_radians = self.PA * (np.pi)/180.0 #PA in radians
+        fraction_guiding_error = np.cos(position_angle_in_radians)*self.guiding_error #arcsec, estimated by doubling average fwhm of moffet functions
+        diff_x0 = fraction_guiding_error * np.cos(position_angle_in_radians)
+        diff_y0 = fraction_guiding_error * np.sin(position_angle_in_radians)
+        g1_fit.x_0 += 0.5*diff_x0
+        g2_fit.x_0 += 0.5*diff_x0
+        g1_fit.y_0 += 0.5*diff_y0
+        g2_fit.y_0 += 0.5*diff_y0
+        n = 5
+        for i in range(n):
+            self.f2d += (1/n)*(g1_fit(self.y2d, self.x2d) + g2_fit(self.y2d, self.x2d))
+            g1_fit.x_0 -= (1/(n-1))*diff_x0
+            g2_fit.x_0 -= (1/(n-1))*diff_x0
+            g1_fit.y_0 -= (1/(n-1))*diff_y0
+            g2_fit.y_0 -= (1/(n-1))*diff_y0
+    def estimate_slit_throughput(self, normalize=True):
+        """
+        a mask is applied representing the slit and the the fraction of light in the PSFs inside the mask
+        are integrated to estimate the fraction of light that passes through the slit.
+        """
+        if normalize: #You almost always want to normalize
+            self.normalize()
+        fraction_through_slit = np.nansum(self.f2d[~self.mask]) #Get fraction of light inside the slit mask
+        return fraction_through_slit
+    def clear(self):
+        """
+        Clear 2D flux array
+        """
+        self.f2d[:] = 0.0
+    def normalize(self):
+        """
+        #Normalize each pixel by fraction of starlight
+        """
+        self.f2d = self.f2d / np.nansum(self.f2d)
+    def plot2d(self, **kwarg):
+        """
+        Visualize the 2D distribution with slit overplotted
+        """
+        plt.figure()
+        plt.imshow(self.f2d, origin='lower', aspect='auto', **kwarg)
+        plt.colorbar()
+        half_width = 0.5*self.width #Pkit slit outline
+        half_length = 0.5*self.length
+        # slit_ouline_x = np.array([-half_width, half_width, half_width, -half_width, -half_width])
+        # slit_ouline_y = np.array([-half_length, -half_length, half_length, half_length, -half_length])
+        # plt.plot(slit_ouline_x, slit_ouline_y, color='White', linewidth=3.0)
+        numerical_mask = np.ones(np.shape(self.mask))
+        plt.contour(self.mask, levels=[0.0,0.5, 1.0], colors='white', linewidths=2)
+        plt.show()
+
+
+class absoluteFluxCalibration:
+    def __init__(self, std_spec, synth_spec):
+        """
+        A  class to handle absolute flux calibration using a standard star spectrum and synthetic spectrum of the
+        standard star.
+
+        Parameters 
+        ----------
+        std_spec: EchelleSpectrum, EchelleSpectrumList, Spectrum1D, or SpectrumList like object 
+            Actual spectrum of the standard star
+        synth_spec: Spectrum1D, or SpectrumList like object from gollum
+            Synethic spectrum of the standard star from a stellar atmosphere model read in with gollum, or something similar
+        """
+        self.std_spec = std_spec
+        self.synth_spec = synth_spec
+
+
+class photometry:
+    def __init__(self):
+        f = FilterGenerator()
+        johnson_bands = np.array(['U', 'B','V','R','I']) #2MASS
+        twoMass_bands = np.array(['J', 'H', 'Ks']) #Johnson filters
+        self.bands =  np.concatenate((johnson_bands, twoMass_bands))
+        self.f0_lambda = np.array([3.96526e-9*1e4, 6.13268e-9*1e4, 3.62708e-9*1e4, 2.17037e-9*1e4, 1.12588e-9*1e4, #Source: http://svo2.cab.inta-csic.es/theory/fps3/index.php?mode=browse&gname=Generic&gname2=Bessell&asttype=, with units converted from erg cm^-2 s^-1 ang^-1 to erg cm^-2 s^-1 um^-1 by multiplying by 1e-4
+                3.129e-13*1e7, 1.133e-13*1e7, 4.283e-14*1e7]) #2MASS: Convert units to from W cm^-2 um^-1 to erg s^-1 cm^-2 um^-1
+        self.x = np.arange(0.0, 10.0, 1e-6)
+        self.delta_lambda = np.abs(self.x[1]-self.x[0])
+        n = len(self.bands)
+        tcurve_interp = []
+        tcurve_resampled = []
+        for i in range(n):
+            if self.bands[i] in twoMass_bands:
+                filt = f.reconstruct('2MASS/2MASS.'+self.bands[i])
+            elif self.bands[i] in johnson_bands:
+                filt = f.reconstruct('Generic/Johnson.'+self.bands[i])
+            interp_obj = interp1d(filt.wavelength.to('um'), filt.transmittance, kind='cubic', fill_value=0.0, bounds_error=False)
+            tcurve_interp.append(interp_obj)
+            tcurve_resampled.append(interp_obj(self.x))
+        self.tcurve_interp = tcurve_interp
+        self.tcurve_resampled = tcurve_resampled
+
+        # if band == 'K':
+        #     band = 'Ks' #Catch to set K band band name to 'Ks'
+        # twoMass_bands = np.array(['J', 'H', 'Ks'])
+        # johnson_bands = np.array(['U', 'B','V','R','I'])
+        # if band in twoMass_bands: #2MASS NIR filters
+        #     f0_lambda = (np.array([3.129e-13, 1.133e-13, 4.283e-14]) * 1e7) [band == twoMass_bands][0] #Convert units to from W cm^-2 um^-1 to erg s^-1 cm^-2 um^-1
+        #     filt = f.reconstruct('2MASS/2MASS.'+band)
+        # elif band in johnson_bands: #Johnson filters
+        #     f0_lambda = (np.array([417.5e-11, 632e-11, 363.1e-11, 217.7e-11, 112.6e-11]) * 1e4 )[band == johnson_bands][0] #Source: Table A2 from Bessel (1998), with units converted from erg cm^-2 s^-1 ang^-1 to erg cm^-2 s^-1 um^-1 by multiplying by 1e-4
+        #     filt = f.reconstruct('Generic/Johnson.'+band)
+        # else:
+        #     raise Exception(
+        #         "Band"+band+" not recognized. Must be U, B, V, R, I, J, H, or Ks."
+        #     )        
+        #self.f0_lambda = f0_lambda
+        
+        # self.tcurve_interp = interp1d(filt.wavelength.to('um'), filt.transmittance, kind='cubic', fill_value=0.0, bounds_error=False) #Create interp obj for the transmission curve
+        # self.tcurve_resampled = self.tcurve_interp(self.x)
+        #self.vega_V_flambdla_zero_point = 363.1e-7 #Vega flux zero point for V band from Bessell et al. (1998) in erg cm^2 s^-1 um^-1
+    def scale(self, synth_spec, band='V', mag=0.0):
+        i = self.grab_band_index(band)
+        resampled_synthetic_spectrum =  LinInterpResampler(synth_spec , self.x*u.um).flux.value
+        f_lambda = np.nansum(resampled_synthetic_spectrum * self.tcurve_resampled[i] * self.x * self.delta_lambda) / np.nansum(self.tcurve_resampled[i] * self.x * self.delta_lambda)
+        magnitude_scale = 10**(0.4*(-mag))
+        # print('self.f0_lambda', self.f0_lambda[i])
+        # print('f_lambda', f_lambda)
+        # print('magnitude_scale', magnitude_scale)
+        return synth_spec * (self.f0_lambda[i] / f_lambda) * magnitude_scale
+    def get(self, synth_spec, band='V', resample=True):
+        i = self.grab_band_index(band)
+        if resample:
+            resampled_synthetic_spectrum =  LinInterpResampler(synth_spec , self.x*u.um).flux.value
+            f_lambda = np.nansum(resampled_synthetic_spectrum * self.tcurve_resampled[i] * self.x * self.delta_lambda) / np.nansum(self.tcurve_resampled[i] * self.x * self.delta_lambda)
+        else:
+            x = synth_spec.wavelength.to('um').value
+            delta_lambda = np.concatenate([[x[1]-x[0]], x[1:] - x[:-1]])
+            interp_obj = interp1d(self.x, self.tcurve_resampled[i], kind='linear', fill_value=0.0, bounds_error=False)
+            resampled_tcurve = interp_obj(x)
+            goodpix = (synth_spec.flux.value > 1e-20) & (synth_spec.flux.value < 1e10)
+            f_lambda = np.nansum(synth_spec.flux.value[goodpix] * resampled_tcurve[goodpix] * x[goodpix] * delta_lambda[goodpix]) / np.nansum(resampled_tcurve[goodpix] * x[goodpix] * delta_lambda[goodpix])
+            print(np.sum(np.isfinite(synth_spec.flux.value)))
+            #print(np.nansum(synth_spec.flux.value * resampled_tcurve * x * delta_lambda))
+            print(np.nansum(resampled_tcurve * x * delta_lambda))
+        magnitude = -2.5 * np.log10(f_lambda / self.f0_lambda[i])
+        return magnitude
+    def grab_band_index(self, band):
+        if band == 'K':
+            band = 'Ks' #Catch to set K band band name to 'Ks' 
+        i = np.where(band == self.bands)[0][0]   
+        return i    
diff --git a/tests/test_igrins.py b/tests/test_igrins.py
index 278e6ea..de5fd94 100644
--- a/tests/test_igrins.py
+++ b/tests/test_igrins.py
@@ -231,7 +231,7 @@ def test_deblaze():
 def test_bandmath():
     """Does band math work?"""
     spec1 = IGRINSSpectrumList.read(file=file)
-    spec2 = IGRINSSpectrumList.read(file=file_2, wavefile="SDCH_20201202_0063.wave.fits")
+    spec2 = IGRINSSpectrumList.read(file=file_2, wavefile="SKY_SDCH_20201202_0033.wvlsol_v1.fits")
 
     #Test band math for orders
     new_order = spec1[10] + spec2[10]