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/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..67f5f5a 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
@@ -699,6 +705,139 @@ 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.  Uncertainity 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 
@@ -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
 
@@ -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.  Uncertainity 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..3bd741e 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)
 
         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
     ----------
@@ -62,19 +112,142 @@ def getUncertainityFilepath(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]
+    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 uncertainity.  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 uncertainity.  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 uncertainity 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]
+    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
+        self.file = file
+
          #False if variance.fits file used for uncertainity, true if sn.fits file used for uncertainity
 
         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"
@@ -150,9 +326,9 @@ def __init__(
             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):
+            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):
@@ -236,6 +412,39 @@ 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 +453,7 @@ class IGRINSSpectrumList(EchelleSpectrumList):
     """
 
     def __init__(self, *args, **kwargs):
+        self.file = None
         self.normalization_order_index = 14
         super().__init__(*args, **kwargs)
 
@@ -254,12 +464,16 @@ 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
@@ -280,7 +494,14 @@ 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 +509,37 @@ 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