tf -> scipy

.travis.yml

@@ -20,7 +20,6 @@ before_install:
 install:
   - pip install tornado==5.1.1
   - pip install lightkurve
-  - pip install tensorflow
   - pip install astropy
   - pip install astroquery
   - pip install tqdm

docs/getting_started/tips_tricks.ipynb

@@ -54,27 +54,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_bkg.fits with expected size 78955200. [astroquery.query]\n",
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc.fits with expected size 158022720. [astroquery.query]\n",
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1_tess_v2_pm.txt with expected size 237847. [astroquery.query]\n",
-      "WARNING:tensorflow:From /Users/bmontet/research/tess/eleanor/eleanor/targetdata.py:837: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /Users/bmontet/research/tess/eleanor/eleanor/targetdata.py:837: The name tf.logging.ERROR is deprecated. Please use tf.compat.v1.logging.ERROR instead.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1282/1282 [00:10<00:00, 113.98it/s]\n"
-     ]
+     "name": "stdout",
+     "text": "INFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_bkg.fits with expected size 78955200. [astroquery.query]\nINFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc.fits with expected size 158022720. [astroquery.query]\nINFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1_tess_v2_pm.txt with expected size 237847. [astroquery.query]\n100%|██████████| 1282/1282 [00:40<00:00, 31.83it/s]\n"
     }
    ],
    "source": [
@@ -977,9 +964,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

docs/getting_started/tutorial.ipynb

@@ -58,17 +58,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_bkg.fits with expected size 78955200. [astroquery.query]\n",
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc.fits with expected size 158022720. [astroquery.query]\n",
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1_tess_v2_pm.txt with expected size 237847. [astroquery.query]\n",
-      "Found TIC 38846515 (Gaia 4675352109658261376), with TESS magnitude 10.3157, RA 68.9597092399964, and Dec -64.0270368810381\n"
-     ]
+     "name": "stdout",
+     "text": "INFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_bkg.fits with expected size 78955200. [astroquery.query]\nINFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc.fits with expected size 158022720. [astroquery.query]\nINFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1_tess_v2_pm.txt with expected size 237847. [astroquery.query]\nFound TIC 38846515 (Gaia 4675352109658261376), with TESS magnitude 10.3157, RA 68.9597092399964, and Dec -64.0270368810381\n"
     }
    ],
    "source": [
@@ -92,35 +89,14 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_bkg.fits with expected size 78955200. [astroquery.query]\n",
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc.fits with expected size 158022720. [astroquery.query]\n",
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1_tess_v2_pm.txt with expected size 237847. [astroquery.query]\n",
-      "Found TIC 38846515 (Gaia 4675352109658261376), with TESS magnitude 10.3157, RA 68.95970916666666, and Dec -64.02703666666666\n",
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_bkg.fits with expected size 78955200. [astroquery.query]\n",
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc.fits with expected size 158022720. [astroquery.query]\n",
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1_tess_v2_pm.txt with expected size 237847. [astroquery.query]\n",
-      "Found TIC 38846515 (Gaia 4675352109658261376), with TESS magnitude 10.3157, RA 68.959732, and Dec -64.02704\n",
-      "Created TAP+ (v1.2.1) - Connection:\n",
-      "\tHost: gea.esac.esa.int\n",
-      "\tUse HTTPS: True\n",
-      "\tPort: 443\n",
-      "\tSSL Port: 443\n",
-      "Created TAP+ (v1.2.1) - Connection:\n",
-      "\tHost: geadata.esac.esa.int\n",
-      "\tUse HTTPS: True\n",
-      "\tPort: 443\n",
-      "\tSSL Port: 443\n",
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_bkg.fits with expected size 78955200. [astroquery.query]\n",
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc.fits with expected size 158022720. [astroquery.query]\n",
-      "INFO: Found cached file /Users/bmontet/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1_tess_v2_pm.txt with expected size 237847. [astroquery.query]\n",
-      "Found TIC 38846515 (Gaia 4675352109658261376), with TESS magnitude 10.3157, RA 68.95981586357303, and Dec -64.02703903114245\n"
-     ]
+     "name": "stdout",
+     "text": "INFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_bkg.fits with expected size 78955200. [astroquery.query]\nINFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc.fits with expected size 158022720. [astroquery.query]\nINFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1_tess_v2_pm.txt with expected size 237847. [astroquery.query]\nFound TIC 38846515 (Gaia 4675352109658261376), with TESS magnitude 10.3157, RA 68.95970916666666, and Dec -64.02703666666666\nINFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_bkg.fits with expected size 78955200. [astroquery.query]\nINFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc.fits with expected size 158022720. [astroquery.query]\nINFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1_tess_v2_pm.txt with expected size 237847. [astroquery.query]\nFound TIC 38846515 (Gaia 4675352109658261376), with TESS magnitude 10.3157, RA 68.959732, and Dec -64.02704\nCreated TAP+ (v1.2.1) - Connection:\n\tHost: gea.esac.esa.int\n\tUse HTTPS: True\n\tPort: 443\n\tSSL Port: 443\nCreated TAP+ (v1.2.1) - Connection:\n\tHost: geadata.esac.esa.int\n\tUse HTTPS: True\n\tPort: 443\n\tSSL Port: 443\nINFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_bkg.fits with expected size 78955200. [astroquery.query]\nINFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc.fits with expected size 158022720. [astroquery.query]\nINFO: Found cached file /Users/adityasengupta/.eleanor/mastDownload/HLSP/hlsp_eleanor_tess_ffi_postcard-s0001-4-1-cal-0902-1078_tess_v2_pc/hlsp_eleanor_tess_ffi_postcard-s0001-4-1_tess_v2_pm.txt with expected size 237847. [astroquery.query]\nFound TIC 38846515 (Gaia 4675352109658261376), with TESS magnitude 10.3157, RA 68.95981586357303, and Dec -64.02703903114245\n"
     }
    ],
    "source": [
@@ -168,24 +144,14 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /Users/bmontet/research/tess/eleanor/eleanor/targetdata.py:837: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /Users/bmontet/research/tess/eleanor/eleanor/targetdata.py:837: The name tf.logging.ERROR is deprecated. Please use tf.compat.v1.logging.ERROR instead.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1282/1282 [00:10<00:00, 120.90it/s]\n"
-     ]
+     "name": "stdout",
+     "text": "Loading file hlsp_eleanor_tess_ffi_tic38846515_s01_tess_v1.0.5_lc.fits found on disk\n"
     }
    ],
    "source": [
@@ -577,7 +543,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "PSF modeling (`do_psf=True`) seems to work very well for relatively bright, isolated stars. Presently the only PSF models usable are Gaussian and Moffat profiles. Both Gaussian and Poisson likelihood functions are possible as well. Do note that this requires tensorflow<2.0. Note that PSF modeling is very much in beta and our current implementation has known issues for very faint stars (fainter than I ~ 15, generally). "
+    "PSF modeling (`do_psf=True`) seems to work very well for relatively bright, isolated stars. Presently the only PSF models usable are Gaussian and Moffat profiles. Both Gaussian and Poisson likelihood functions are possible as well. Note that PSF modeling is very much in beta and our current implementation has known issues for very faint stars (fainter than I ~ 15, generally). "
    ]
   },
   {
@@ -605,9 +571,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

eleanor/mast.py

@@ -191,7 +191,7 @@ def crossmatch_distance(pos, match):
     c2 = SkyCoord(match[0]*u.deg, match[1]*u.deg, frame='icrs')
     return c1.separation(c2).to(u.arcsec)
 
-def tic_by_contamination(pos, r, contam, tmag_lim):
+def tic_by_contamination(pos, r, contam, tmag_lim, call_internal=False):
     """Allows the user to perform a counts only query.
 
     When unsure how many results are expcted, it is best to first perform

eleanor/models.py

@@ -1,70 +1,154 @@
 import math
-
+import os
 from astropy.io import fits as pyfits
 from lightkurve.utils import channel_to_module_output
 import numpy as np
-import tensorflow as tf
+import warnings
+from abc import ABC
 
 # Vaneska models of Ze Vinicius
 
-class Model:
-    """
-    Pretty dumb Gaussian model.
-    Attributes
-    ----------
-    shape : tuple
-        shape of the TPF. (row_shape, col_shape)
-    col_ref, row_ref : int, int
-        column and row coordinates of the bottom
-        left corner of the TPF
-    """
-    def __init__(self, shape, col_ref, row_ref):
-        self.shape = shape
-        self.col_ref = col_ref
-        self.row_ref = row_ref
-        self._init_grid()
+class Model(ABC):
+	"""
+	Base PSF-fitting model.
+	Attributes
+	----------
+	shape : tuple
+		shape of the TPF. (row_shape, col_shape)
+	col_ref, row_ref : int, int
+		column and row coordinates of the bottom
+		left corner of the TPF
+	"""
+	def __init__(self, shape, col_ref, row_ref, xc, yc, nstars, bkg0, **kwargs):
+		self.shape = shape
+		self.col_ref = col_ref
+		self.row_ref = row_ref
+		self.xc = xc
+		self.yc = yc
+		self.nstars = nstars
+		self.bkg0 = bkg0
+		self._init_grid()
+
+	def __call__(self, *params):
+		return self.evaluate(*params)
+
+	def _init_grid(self):
+		r, c = self.row_ref, self.col_ref
+		s1, s2 = self.shape
+		self.y, self.x = np.mgrid[r:r+s1-1:1j*s1, c:c+s2-1:1j*s2]
 
-    def _init_grid(self):
-        r, c = self.row_ref, self.col_ref
-        s1, s2 = self.shape
-        self.y, self.x = np.mgrid[r:r+s1-1:1j*s1, c:c+s2-1:1j*s2]
+	def mean(self, flux, xshift, yshift, bkg, optpars):
+		return np.sum([self.evaluate(flux[j], self.xc[j]+xshift, self.yc[j]+yshift, *optpars) for j in range(self.nstars)], axis=0) + bkg
 
+	def get_default_par(self, d0):
+		return np.concatenate((np.max(d0) * np.ones(self.nstars,), np.array([0, 0, self.bkg0], self.get_default_optpars())))
 
 class Gaussian(Model):
-    def __call__(self, *params):
-        return self.evaluate(*params)
+	def __init__(self, **kwargs):
+		super().__init__(**kwargs)
+		self.bounds = np.vstack((
+				np.tile([0, np.infty], (self.nstars, 1)),
+				np.array([
+					[-1.0, 1.0],
+					[-1.0, 1.0],
+					[0, np.infty],
+					[0, np.infty],
+					[-0.5, 0.5],
+					[0, np.infty],
+				])
+			))
+
+	def get_default_optpars(self):
+		return np.array([1, 0, 1], dtype=np.float64)
+
+	def evaluate(self, flux, xo, yo, a, b, c):
+		"""
+		Evaluate the Gaussian model
+		Parameters
+		----------
+		flux : np.ndarray, (nstars,)
+		xo, yo : tf.Variable, tf.Variable
+			Center coordinates of the Gaussian.
+		a, b, c : tf.Variable, tf.Variable
+			Parameters that control the rotation angle
+			and the stretch along the major axis of the Gaussian,
+			such that the matrix M = [a b ; b c] is positive-definite.
+		References
+		----------
+		https://en.wikipedia.org/wiki/Gaussian_function#Two-dimensional_Gaussian_function
+		"""
+		dx = self.x - xo
+		dy = self.y - yo
+		psf = np.exp(-(a * dx ** 2 + 2 * b * dx * dy + c * dy ** 2))
+		psf_sum = np.sum(psf)
+		return flux * psf / psf_sum
+
+class MultiGaussian(Model):
+	"""
+
+	"""
+	def __init__(self, **kwargs):
+		super().__init__(**kwargs)
+		self.bounds = np.vstack((
+				np.tile([0, np.infty], (self.nstars, 1)),
+				np.array([
+					[-1.0, 1.0],
+					[-1.0, 1.0],
+					[0, np.infty],
+					[0, np.infty],
+					[-0.5, 0.5],
+					[0, np.infty],
+				])
+			))
 
-    def evaluate(self, flux, xo, yo, a, b, c):
-        """
-        Evaluate the Gaussian model
-        Parameters
-        ----------
-        flux : tf.Variable
-        xo, yo : tf.Variable, tf.Variable
-            Center coordiantes of the Gaussian.
-        a, b, c : tf.Variable, tf.Variable
-            Parameters that control the rotation angle
-            and the stretch along the major axis of the Gaussian,
-            such that the matrix M = [a b ; b c] is positive-definite.
-        References
-        ----------
-        https://en.wikipedia.org/wiki/Gaussian_function#Two-dimensional_Gaussian_function
-        """
-        dx = self.x - xo
-        dy = self.y - yo
-        psf = tf.exp(-(a * dx ** 2 + 2 * b * dx * dy + c * dy ** 2))
-        psf_sum = tf.reduce_sum(psf)
-        return flux * psf / psf_sum
+	def get_default_optpars(self):
+		return np.array([1, 0, 1], dtype=np.float64)
 
+	def evaluate(self, flux, xo, yo, a, b, c):
+		"""
+		Evaluate the Gaussian model
+		Parameters
+		----------
+		flux : np.ndarray, (nstars,)
+		xo, yo : tf.Variable, tf.Variable
+			Center coordinates of the Gaussian.
+		a, b, c : tf.Variable, tf.Variable
+			Parameters that control the rotation angle
+			and the stretch along the major axis of the Gaussian,
+			such that the matrix M = [a b ; b c] is positive-definite.
+		References
+		----------
+		https://en.wikipedia.org/wiki/Gaussian_function#Two-dimensional_Gaussian_function
+		"""
+		dx = self.x - xo
+		dy = self.y - yo
+		psf = np.exp(-(a * dx ** 2 + 2 * b * dx * dy + c * dy ** 2))
+		psf_sum = np.sum(psf)
+		return flux * psf / psf_sum
 
+
 class Moffat(Model):
-    def __call__(self, *params):
-        return self.evaluate(*params)
+	def __init__(self, **kwargs):
+		super().__init__(**kwargs)
+		self.bounds = np.vstack((
+				np.tile([0, np.infty], (self.nstars, 1)),
+				np.array([
+					[-2.0, 2.0],
+					[-2.0, 2.0],
+					[0, np.infty],
+					[0., 3.0],
+					[-0.5, 0.5],
+					[0., 3.0],
+				])
+			))
 
-    def evaluate(self, flux, xo, yo, a, b, c, beta):
-        dx = self.x - xo
-        dy = self.y - yo
-        psf = tf.divide(1., tf.pow(1. + a * dx ** 2 + 2 * b * dx * dy + c * dy ** 2, beta))
-        psf_sum = tf.reduce_sum(psf)
-        return flux * psf / psf_sum
+	def get_default_optpars(self):
+		return np.array([1, 0, 1, 1], dtype=np.float64)
 
+	def evaluate(self, flux, xo, yo, a, b, c, beta):
+		dx = self.x - xo
+		dy = self.y - yo
+		psf = np.divide(1., np.pow(1. + a * dx ** 2 + 2 * b * dx * dy + c * dy ** 2, beta))
+		psf_sum = np.sum(psf)
+		return flux * psf / psf_sum
+