Repository: sblunt/orbitize (language: python)

Issue:
OFTI time warning 
If OFTI doesn't accept its first orbit in 60 seconds, it will now print a warning statement following issue #303.
OFTI should have a time warning
If OFTI goes more than x mins without accepting any orbits, new users often think the software isn't working. We should return some sort of warning, like "OFTI is taking a while, you may want to consider MCMC, check out the MCMC vs OFTI tutorial"


Discussion / hints:

Fix patch:
diff --git a/docs/tutorials/MCMC_vs_OFTI.ipynb b/docs/tutorials/MCMC_vs_OFTI.ipynb
index 114c384d..433d67f4 100644
--- a/docs/tutorials/MCMC_vs_OFTI.ipynb
+++ b/docs/tutorials/MCMC_vs_OFTI.ipynb
@@ -43,6 +43,8 @@
     "import astropy.table\n",
     "import time\n",
     "\n",
+    "np.random.seed(5)\n",
+    "\n",
     "from orbitize.kepler import calc_orbit\n",
     "from orbitize import system, sampler\n",
     "from orbitize.read_input import read_file"
@@ -63,17 +65,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mtot = 1.2 # total system mass [M_sol]\n",
-    "plx = 60.0 # parallax [mas]\n",
+    "mtot = 1.2  # total system mass [M_sol]\n",
+    "plx = 60.0  # parallax [mas]\n",
+    "\n",
+    "\n",
+    "def generate_synthetic_data(sma=30.0, num_obs=4, unc=10.0):\n",
+    "    \"\"\"Generate an orbitize-table of synethic data\n",
     "\n",
-    "def generate_synthetic_data(sma=30., num_obs=4, unc=10.0):\n",
-    "    \"\"\" Generate an orbitize-table of synethic data\n",
-    "    \n",
     "    Args:\n",
     "        sma (float): semimajor axis (au)\n",
     "        num_obs (int): number of observations to generate\n",
     "        unc (float): uncertainty on all simulated RA & Dec measurements (mas)\n",
-    "        \n",
+    "\n",
     "    Returns:\n",
     "        2-tuple:\n",
     "            - `astropy.table.Table`: data table of generated synthetic data\n",
@@ -81,41 +84,48 @@
     "    \"\"\"\n",
     "\n",
     "    # assumed ground truth for non-input orbital parameters\n",
-    "    ecc = 0.5 # eccentricity\n",
-    "    inc = np.pi/4 # inclination [rad]\n",
-    "    argp = 0.\n",
-    "    lan = 0. \n",
-    "    tau = 0.8 \n",
+    "    ecc = 0.5  # eccentricity\n",
+    "    inc = np.pi / 4  # inclination [rad]\n",
+    "    argp = 0.0\n",
+    "    lan = 0.0\n",
+    "    tau = 0.8\n",
     "\n",
     "    # calculate RA/Dec at three observation epochs\n",
-    "    observation_epochs = np.linspace(51550., 52650., num_obs) # `num_obs` epochs between ~2000 and ~2003 [MJD]\n",
+    "    observation_epochs = np.linspace(\n",
+    "        51550.0, 52650.0, num_obs\n",
+    "    )  # `num_obs` epochs between ~2000 and ~2003 [MJD]\n",
     "    num_obs = len(observation_epochs)\n",
-    "    ra, dec, _ = calc_orbit(observation_epochs, sma, ecc, inc, argp, lan, tau, plx, mtot)\n",
+    "    ra, dec, _ = calc_orbit(\n",
+    "        observation_epochs, sma, ecc, inc, argp, lan, tau, plx, mtot\n",
+    "    )\n",
     "\n",
     "    # add Gaussian noise to simulate measurement\n",
     "    ra += np.random.normal(scale=unc, size=num_obs)\n",
     "    dec += np.random.normal(scale=unc, size=num_obs)\n",
-    "    \n",
+    "\n",
     "    # define observational uncertainties\n",
-    "    ra_err = dec_err = np.ones(num_obs)*unc\n",
-    "    \n",
+    "    ra_err = dec_err = np.ones(num_obs) * unc\n",
+    "\n",
     "    # make a plot of the data\n",
     "    plt.figure()\n",
-    "    plt.errorbar(ra, dec, xerr=ra_err, yerr=dec_err, linestyle='')\n",
-    "    plt.xlabel('$\\\\Delta$ RA'); plt.ylabel('$\\\\Delta$ Dec')\n",
-    "    \n",
+    "    plt.errorbar(ra, dec, xerr=ra_err, yerr=dec_err, linestyle=\"\")\n",
+    "    plt.xlabel(\"$\\\\Delta$ RA\")\n",
+    "    plt.ylabel(\"$\\\\Delta$ Dec\")\n",
+    "\n",
     "    # calculate the orbital fraction\n",
-    "    period = np.sqrt((sma**3)/mtot)\n",
-    "    orbit_coverage = (max(observation_epochs) - min(observation_epochs))/365.25 # [yr]\n",
-    "    orbit_fraction = 100*orbit_coverage/period\n",
+    "    period = np.sqrt((sma**3) / mtot)\n",
+    "    orbit_coverage = (\n",
+    "        max(observation_epochs) - min(observation_epochs)\n",
+    "    ) / 365.25  # [yr]\n",
+    "    orbit_fraction = 100 * orbit_coverage / period\n",
     "\n",
     "    data_table = astropy.table.Table(\n",
-    "        [observation_epochs, [1]*num_obs, ra, ra_err, dec, dec_err], \n",
-    "        names=('epoch', 'object', 'raoff', 'raoff_err', 'decoff', 'decoff_err')\n",
+    "        [observation_epochs, [1] * num_obs, ra, ra_err, dec, dec_err],\n",
+    "        names=(\"epoch\", \"object\", \"raoff\", \"raoff_err\", \"decoff\", \"decoff_err\"),\n",
     "    )\n",
     "    # read into orbitize format\n",
     "    data_table = read_file(data_table)\n",
-    "    \n",
+    "\n",
     "    return data_table, orbit_fraction"
    ]
   },
@@ -147,8 +157,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -162,12 +171,12 @@
    "source": [
     "# generate data with default kwargs\n",
     "short_data_table, short_orbit_fraction = generate_synthetic_data()\n",
-    "print('The orbit fraction is {}%'.format(np.round(short_orbit_fraction),1))\n",
+    "print(\"The orbit fraction is {}%\".format(np.round(short_orbit_fraction), 1))\n",
     "\n",
     "# initialize orbitize `System` object\n",
     "short_system = system.System(1, short_data_table, mtot, plx)\n",
     "\n",
-    "num2accept = 500 # run sampler until this many orbits are accepted"
+    "num2accept = 500  # run sampler until this many orbits are accepted"
    ]
   },
   {
@@ -180,7 +189,19 @@
      "output_type": "stream",
      "text": [
       "Converting ra/dec data points in data_table to sep/pa. Original data are stored in input_table.\n",
-      "OFTI took 4.926873683929443 seconds to accept 500 orbits.\n"
+      "497/500 orbits found\r"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m short_OFTI_sampler \u001b[38;5;241m=\u001b[39m sampler\u001b[38;5;241m.\u001b[39mOFTI(short_system)\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# perform OFTI fit\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m short_OFTI_orbits \u001b[38;5;241m=\u001b[39m \u001b[43mshort_OFTI_sampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_sampler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum2accept\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOFTI took \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m seconds to accept \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m orbits.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m     11\u001b[0m         time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m start_time, num2accept\n\u001b[1;32m     12\u001b[0m     )\n\u001b[1;32m     13\u001b[0m )\n",
+      "File \u001b[0;32m~/Documents/GitHub/orbitize/orbitize/sampler.py:593\u001b[0m, in \u001b[0;36mOFTI.run_sampler\u001b[0;34m(self, total_orbits, num_samples, num_cores, OFTI_warning)\u001b[0m\n\u001b[1;32m    588\u001b[0m             OFTI_warning \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    589\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m    590\u001b[0m         \u001b[38;5;28mstr\u001b[39m(orbits_saved\u001b[38;5;241m.\u001b[39mvalue) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(total_orbits) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m orbits found\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    591\u001b[0m         end\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    592\u001b[0m     )\n\u001b[0;32m--> 593\u001b[0m     \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m    596\u001b[0m     \u001b[38;5;28mstr\u001b[39m(total_orbits) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(total_orbits) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m orbits found\u001b[39m\u001b[38;5;124m\"\u001b[39m, end\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    597\u001b[0m )\n\u001b[1;32m    599\u001b[0m \u001b[38;5;66;03m# join the processes\u001b[39;00m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -193,43 +214,18 @@
     "# perform OFTI fit\n",
     "short_OFTI_orbits = short_OFTI_sampler.run_sampler(num2accept)\n",
     "\n",
-    "print(\"OFTI took {} seconds to accept {} orbits.\".format(time.time()-start_time, num2accept))"
+    "print(\n",
+    "    \"OFTI took {} seconds to accept {} orbits.\".format(\n",
+    "        time.time() - start_time, num2accept\n",
+    "    )\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting Burn in\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/sblunt/Projects/orbitize/orbitize/priors.py:354: RuntimeWarning: invalid value encountered in log\n",
-      "  lnprob = -np.log((element_array*normalizer))\n",
-      "/home/sblunt/Projects/orbitize/orbitize/priors.py:463: RuntimeWarning: invalid value encountered in log\n",
-      "  lnprob = np.log(np.sin(element_array)/normalization)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "100/100 steps of burn-in complete\n",
-      "Burn in complete. Sampling posterior now.\n",
-      "250/250 steps completed\n",
-      "Run complete\n",
-      "MCMC took 5000 steps in 47.53257179260254 seconds.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "start_time = time.time()\n",
     "\n",
@@ -238,47 +234,40 @@
     "short_MCMC_sampler = sampler.MCMC(short_system, num_temps=5, num_walkers=num_walkers)\n",
     "\n",
     "# perform MCMC fit\n",
-    "num2accept_mcmc = 10*num2accept\n",
+    "num2accept_mcmc = 10 * num2accept\n",
     "_ = short_MCMC_sampler.run_sampler(num2accept_mcmc, burn_steps=100)\n",
     "short_MCMC_orbits = short_MCMC_sampler.results.post\n",
     "\n",
-    "print(\"MCMC took {} steps in {} seconds.\".format(num2accept_mcmc, time.time()-start_time))"
+    "print(\n",
+    "    \"MCMC took {} steps in {} seconds.\".format(\n",
+    "        num2accept_mcmc, time.time() - start_time\n",
+    "    )\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f1506e50d90>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plt.hist(short_OFTI_orbits[:, short_system.param_idx['ecc1']], bins=40, density=True, alpha=.5, label='OFTI')\n",
-    "plt.hist(short_MCMC_orbits[:, short_system.param_idx['ecc1']], bins=40, density=True, alpha=.5, label='MCMC')\n",
-    "\n",
-    "plt.xlabel('Eccentricity'); plt.ylabel('Prob.')\n",
+    "plt.hist(\n",
+    "    short_OFTI_orbits[:, short_system.param_idx[\"ecc1\"]],\n",
+    "    bins=40,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    label=\"OFTI\",\n",
+    ")\n",
+    "plt.hist(\n",
+    "    short_MCMC_orbits[:, short_system.param_idx[\"ecc1\"]],\n",
+    "    bins=40,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    label=\"MCMC\",\n",
+    ")\n",
+    "\n",
+    "plt.xlabel(\"Eccentricity\")\n",
+    "plt.ylabel(\"Prob.\")\n",
     "plt.legend()"
    ]
   },
@@ -302,54 +291,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The orbit fraction is 10.0%\n"
-     ]
-    },
-    {
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-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# generate data\n",
     "long_data_table, long_orbit_fraction = generate_synthetic_data(sma=10, num_obs=5)\n",
-    "print('The orbit fraction is {}%'.format(np.round(long_orbit_fraction),1))\n",
+    "print(\"The orbit fraction is {}%\".format(np.round(long_orbit_fraction), 1))\n",
     "\n",
     "# initialize orbitize `System` object\n",
     "long_system = system.System(1, long_data_table, mtot, plx)\n",
-    "num2accept = 500 # run sampler until this many orbits are accepted"
+    "num2accept = 500  # run sampler until this many orbits are accepted"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converting ra/dec data points in data_table to sep/pa. Original data are stored in input_table.\n",
-      "OFTI took 3.6707496643066406 seconds to accept 1 orbit.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "start_time = time.time()\n",
     "\n",
@@ -359,27 +318,14 @@
     "# perform OFTI fit\n",
     "long_OFTI_orbits = long_OFTI_sampler.run_sampler(1)\n",
     "\n",
-    "print(\"OFTI took {} seconds to accept 1 orbit.\".format(time.time()-start_time))"
+    "print(\"OFTI took {} seconds to accept 1 orbit.\".format(time.time() - start_time))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting Burn in\n",
-      "100/100 steps of burn-in complete\n",
-      "Burn in complete. Sampling posterior now.\n",
-      "25/25 steps completed\n",
-      "Run complete\n",
-      "MCMC took 500 steps in 35.78284978866577 seconds.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "start_time = time.time()\n",
     "\n",
@@ -391,41 +337,18 @@
     "_ = long_MCMC_sampler.run_sampler(num2accept, burn_steps=100)\n",
     "long_MCMC_orbits = long_MCMC_sampler.results.post\n",
     "\n",
-    "print(\"MCMC took {} steps in {} seconds.\".format(num2accept, time.time()-start_time))"
+    "print(\"MCMC took {} steps in {} seconds.\".format(num2accept, time.time() - start_time))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Prob.')"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plt.hist(long_MCMC_orbits[:, short_system.param_idx['ecc1']], bins=15, density=True)\n",
-    "plt.xlabel('Eccentricity'); plt.ylabel('Prob.')"
+    "plt.hist(long_MCMC_orbits[:, short_system.param_idx[\"ecc1\"]], bins=15, density=True)\n",
+    "plt.xlabel(\"Eccentricity\")\n",
+    "plt.ylabel(\"Prob.\")"
    ]
   },
   {
@@ -469,7 +392,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/orbitize/sampler.py b/orbitize/sampler.py
index ca7fe9af..5251cb09 100644
--- a/orbitize/sampler.py
+++ b/orbitize/sampler.py
@@ -18,6 +18,7 @@
 import orbitize.results
 import matplotlib.pyplot as plt
 
+
 class Sampler(abc.ABC):
     """
     Abstract base class for sampler objects.
@@ -26,7 +27,9 @@ class Sampler(abc.ABC):
     Written: Sarah Blunt, 2018
     """
 
-    def __init__(self, system, like='chi2_lnlike', custom_lnlike=None, chi2_type='standard'):
+    def __init__(
+        self, system, like="chi2_lnlike", custom_lnlike=None, chi2_type="standard"
+    ):
         self.system = system
 
         # check if `like` is a string or a function
@@ -38,7 +41,7 @@ def __init__(self, system, like='chi2_lnlike', custom_lnlike=None, chi2_type='st
         self.custom_lnlike = custom_lnlike
         self.chi2_type = chi2_type
         # check if need to handle covariances
-        self.has_corr = np.any(~np.isnan(self.system.data_table['quant12_corr']))
+        self.has_corr = np.any(~np.isnan(self.system.data_table["quant12_corr"]))
 
     @abc.abstractmethod
     def run_sampler(self, total_orbits):
@@ -64,15 +67,18 @@ def _logl(self, params):
         model, jitter = self.system.compute_model(params)
 
         # fold data/errors to match model output shape. In particualr, quant1/quant2 are interleaved
-        data = np.array([self.system.data_table['quant1'], self.system.data_table['quant2']]).T
+        data = np.array(
+            [self.system.data_table["quant1"], self.system.data_table["quant2"]]
+        ).T
 
         # errors below required for lnlike function below
-        errs = np.array([self.system.data_table['quant1_err'],
-                         self.system.data_table['quant2_err']]).T
+        errs = np.array(
+            [self.system.data_table["quant1_err"], self.system.data_table["quant2_err"]]
+        ).T
         # covariances/correlations, if applicable
         # we're doing this check now because the likelihood computation is much faster if we can skip it.
         if self.has_corr:
-            corrs = self.system.data_table['quant12_corr']
+            corrs = self.system.data_table["quant12_corr"]
         else:
             corrs = None
 
@@ -80,80 +86,93 @@ def _logl(self, params):
         seppa_indices = self.system.all_seppa
 
         # compute lnlike
-        lnlikes = self.lnlike(data, errs, corrs, model, jitter, seppa_indices, chi2_type=self.chi2_type)
+        lnlikes = self.lnlike(
+            data, errs, corrs, model, jitter, seppa_indices, chi2_type=self.chi2_type
+        )
 
         # return sum of lnlikes (aka product of likeliehoods)
         lnlikes_sum = np.nansum(lnlikes, axis=(0, 1))
 
         if self.custom_lnlike is not None:
             lnlikes_sum += self.custom_lnlike(params)
-        
-        if self.system.hipparcos_IAD is not None:
 
+        if self.system.hipparcos_IAD is not None:
             # compute Ra/Dec predictions at the Hipparcos IAD epochs
             raoff_model, deoff_model, _ = self.system.compute_all_orbits(
                 params, epochs=self.system.hipparcos_IAD.epochs_mjd
-            ) 
+            )
 
-            raoff_model_hip_epoch, deoff_model_hip_epoch, _ = self.system.compute_all_orbits(
-                params, epochs=Time([1991.25], format='decimalyear').mjd
-            ) 
+            (
+                raoff_model_hip_epoch,
+                deoff_model_hip_epoch,
+                _,
+            ) = self.system.compute_all_orbits(
+                params, epochs=Time([1991.25], format="decimalyear").mjd
+            )
 
             # subtract off position of star at reference Hipparcos epoch
-            raoff_model[:,0,:] -= raoff_model_hip_epoch[:,0,:]
-            deoff_model[:,0,:] -= deoff_model_hip_epoch[:,0,:]
+            raoff_model[:, 0, :] -= raoff_model_hip_epoch[:, 0, :]
+            deoff_model[:, 0, :] -= deoff_model_hip_epoch[:, 0, :]
 
             # select body 0 raoff/deoff predictions & feed into Hip IAD lnlike fn
             lnlikes_sum += self.system.hipparcos_IAD.compute_lnlike(
-                raoff_model[:,0,:], deoff_model[:,0,:], params, self.system.param_idx
+                raoff_model[:, 0, :],
+                deoff_model[:, 0, :],
+                params,
+                self.system.param_idx,
             )
 
             if self.system.gaia is not None:
-
                 gaiahip_epochs = Time(
-                    [self.system.gaia.hipparcos_epoch, self.system.gaia.gaia_epoch], 
-                    format='decimalyear'
+                    [self.system.gaia.hipparcos_epoch, self.system.gaia.gaia_epoch],
+                    format="decimalyear",
                 ).mjd
 
                 # compute Ra/Dec predictions at the Gaia epoch
                 raoff_model, deoff_model, _ = self.system.compute_all_orbits(
                     params, epochs=gaiahip_epochs
-                ) 
+                )
 
                 # select body 0 raoff/deoff predictions & feed into Gaia module lnlike fn
                 lnlikes_sum += self.system.gaia.compute_lnlike(
-                    raoff_model[:,0,:], deoff_model[:,0,:], params, self.system.param_idx
+                    raoff_model[:, 0, :],
+                    deoff_model[:, 0, :],
+                    params,
+                    self.system.param_idx,
                 )
 
         return lnlikes_sum
 
 
-class OFTI(Sampler,):
+class OFTI(
+    Sampler,
+):
     """
     OFTI Sampler
 
     Args:
         system (system.System): ``system.System`` object
         like (string): name of likelihood function in ``lnlike.py``
-        custom_lnlike (func): ability to include an addition custom likelihood 
-            function in the fit. The function looks like 
+        custom_lnlike (func): ability to include an addition custom likelihood
+            function in the fit. The function looks like
             ``clnlikes = custon_lnlike(params)`` where ``params`` is a RxM array
-            of fitting parameters, where R is the number of orbital paramters 
+            of fitting parameters, where R is the number of orbital paramters
             (can be passed in system.compute_model()),
-            and M is the number of orbits we need model predictions for. 
+            and M is the number of orbits we need model predictions for.
             It returns ``clnlikes`` which is an array of
             length M, or it can be a single float if M = 1.
 
     Written: Isabel Angelo, Sarah Blunt, Logan Pearce, 2018
     """
-    def __init__(self, system, like='chi2_lnlike', custom_lnlike=None,chi2_type='standard'):
 
-        super(OFTI, self).__init__(system, like=like, chi2_type=chi2_type, custom_lnlike=custom_lnlike)
+    def __init__(
+        self, system, like="chi2_lnlike", custom_lnlike=None, chi2_type="standard"
+    ):
+        super(OFTI, self).__init__(
+            system, like=like, chi2_type=chi2_type, custom_lnlike=custom_lnlike
+        )
 
-        if (
-            (self.system.hipparcos_IAD is not None) or 
-            (len(self.system.rv[0] > 0))
-        ):
+        if (self.system.hipparcos_IAD is not None) or (len(self.system.rv[0] > 0)):
             raise NotImplementedError(
                 """
                 You can only use OFTI with relative astrometry measurements 
@@ -172,38 +191,63 @@ def __init__(self, system, like='chi2_lnlike', custom_lnlike=None,chi2_type='sta
             if len(self.system.radec[body_num]) > 0:
                 # only print the warning once.
                 if not convert_warning_print:
-                    print('Converting ra/dec data points in data_table to sep/pa. Original data are stored in input_table.')
+                    print(
+                        "Converting ra/dec data points in data_table to sep/pa. Original data are stored in input_table."
+                    )
                     convert_warning_print = True
                 self.system.convert_data_table_radec2seppa(body_num=body_num)
 
         # these are of type astropy.table.column
-        self.sep_observed = self.system.data_table[np.where(
-            self.system.data_table['quant_type'] == 'seppa')]['quant1'].copy()
-        self.pa_observed = self.system.data_table[np.where(
-            self.system.data_table['quant_type'] == 'seppa')]['quant2'].copy()
-        self.sep_err = self.system.data_table[np.where(
-            self.system.data_table['quant_type'] == 'seppa')]['quant1_err'].copy()
-        self.pa_err = self.system.data_table[np.where(
-            self.system.data_table['quant_type'] == 'seppa')]['quant2_err'].copy()
-        self.meas_object = self.system.data_table[np.where(
-            self.system.data_table['quant_type'] == 'seppa')]['object'].copy()
+        self.sep_observed = self.system.data_table[
+            np.where(self.system.data_table["quant_type"] == "seppa")
+        ]["quant1"].copy()
+        self.pa_observed = self.system.data_table[
+            np.where(self.system.data_table["quant_type"] == "seppa")
+        ]["quant2"].copy()
+        self.sep_err = self.system.data_table[
+            np.where(self.system.data_table["quant_type"] == "seppa")
+        ]["quant1_err"].copy()
+        self.pa_err = self.system.data_table[
+            np.where(self.system.data_table["quant_type"] == "seppa")
+        ]["quant2_err"].copy()
+        self.meas_object = self.system.data_table[
+            np.where(self.system.data_table["quant_type"] == "seppa")
+        ]["object"].copy()
 
         # this is OK, ONLY IF we are only using self.epochs for computing RA/Dec from Keplerian elements
-        self.epochs = np.array(self.system.data_table['epoch']) - self.system.tau_ref_epoch
+        self.epochs = (
+            np.array(self.system.data_table["epoch"]) - self.system.tau_ref_epoch
+        )
 
         # distinguishing all epochs from sep/pa epochs
-        self.epochs_seppa = np.array(self.system.data_table[np.where(
-            self.system.data_table['quant_type'] == 'seppa')]['epoch']) - self.system.tau_ref_epoch
+        self.epochs_seppa = (
+            np.array(
+                self.system.data_table[
+                    np.where(self.system.data_table["quant_type"] == "seppa")
+                ]["epoch"]
+            )
+            - self.system.tau_ref_epoch
+        )
 
-        self.epochs_rv = np.array(self.system.data_table[np.where(
-            self.system.data_table['quant_type'] == 'rv')]['epoch']) - self.system.tau_ref_epoch
+        self.epochs_rv = (
+            np.array(
+                self.system.data_table[
+                    np.where(self.system.data_table["quant_type"] == "rv")
+                ]["epoch"]
+            )
+            - self.system.tau_ref_epoch
+        )
 
         # choose scale-and-rotate epoch
         # for multiplanet support, this is now a list.
         # For each planet, we find the measurment for it that corresponds to the smallest astrometric error
         self.epoch_idx = []
-        min_sep_indices = np.argsort(self.sep_err) # indices of sep err sorted from smallest to higheset
-        min_sep_indices_body = self.meas_object[min_sep_indices] # the corresponding body_num that these sorted measurements correspond to
+        min_sep_indices = np.argsort(
+            self.sep_err
+        )  # indices of sep err sorted from smallest to higheset
+        min_sep_indices_body = self.meas_object[
+            min_sep_indices
+        ]  # the corresponding body_num that these sorted measurements correspond to
         for i in range(self.system.num_secondary_bodies):
             body_num = i + 1
             this_object_meas = np.where(min_sep_indices_body == body_num)[0]
@@ -212,17 +256,25 @@ def __init__(self, system, like='chi2_lnlike', custom_lnlike=None,chi2_type='sta
                 self.epoch_idx.append(None)
                 continue
             # get the smallest measurement belonging to this body
-            best_epoch = min_sep_indices[this_object_meas][0] # already sorted by argsort
+            best_epoch = min_sep_indices[this_object_meas][
+                0
+            ]  # already sorted by argsort
             self.epoch_idx.append(best_epoch)
 
-        if len(self.system.rv[0]) > 0 and self.system.fit_secondary_mass:  # checking for RV data
-            self.rv_observed = self.system.data_table[np.where(
-                self.system.data_table['quant_type'] == 'rv')]['quant1'].copy()
-            self.rv_err = self.system.data_table[np.where(
-                self.system.data_table['quant_type'] == 'rv')]['quant1_err'].copy()
-
-            self.epoch_rv_idx = [np.argmin(self.rv_observed),
-                                 np.argmax(self.rv_observed)]
+        if (
+            len(self.system.rv[0]) > 0 and self.system.fit_secondary_mass
+        ):  # checking for RV data
+            self.rv_observed = self.system.data_table[
+                np.where(self.system.data_table["quant_type"] == "rv")
+            ]["quant1"].copy()
+            self.rv_err = self.system.data_table[
+                np.where(self.system.data_table["quant_type"] == "rv")
+            ]["quant1_err"].copy()
+
+            self.epoch_rv_idx = [
+                np.argmin(self.rv_observed),
+                np.argmax(self.rv_observed),
+            ]
 
         # create an empty results object
         self.results = orbitize.results.Results(
@@ -230,7 +282,7 @@ def __init__(self, system, like='chi2_lnlike', custom_lnlike=None,chi2_type='sta
             sampler_name=self.__class__.__name__,
             post=None,
             lnlike=None,
-            version_number=orbitize.__version__
+            version_number=orbitize.__version__,
         )
 
     def prepare_samples(self, num_samples):
@@ -252,62 +304,80 @@ def prepare_samples(self, num_samples):
         for i in range(len(self.priors)):
             if hasattr(self.priors[i], "draw_samples"):
                 samples[i, :] = self.priors[i].draw_samples(num_samples)
-            else: # param is fixed & has no prior
+            else:  # param is fixed & has no prior
                 samples[i, :] = self.priors[i] * np.ones(num_samples)
 
         # Make Converison to Standard Basis:
         samples = self.system.basis.to_standard_basis(samples)
-        
-        for body_num in np.arange(self.system.num_secondary_bodies) + 1:
 
-            sma = samples[self.system.basis.standard_basis_idx['sma{}'.format(body_num)],:]
-            ecc = samples[self.system.basis.standard_basis_idx['ecc{}'.format(body_num)],:]
-            inc = samples[self.system.basis.standard_basis_idx['inc{}'.format(body_num)],:]
-            argp = samples[self.system.basis.standard_basis_idx['aop{}'.format(body_num)],:]
-            lan = samples[self.system.basis.standard_basis_idx['pan{}'.format(body_num)],:]
-            tau = samples[self.system.basis.standard_basis_idx['tau{}'.format(body_num)],:]
-            plx = samples[self.system.basis.standard_basis_idx['plx'],:]
+        for body_num in np.arange(self.system.num_secondary_bodies) + 1:
+            sma = samples[
+                self.system.basis.standard_basis_idx["sma{}".format(body_num)], :
+            ]
+            ecc = samples[
+                self.system.basis.standard_basis_idx["ecc{}".format(body_num)], :
+            ]
+            inc = samples[
+                self.system.basis.standard_basis_idx["inc{}".format(body_num)], :
+            ]
+            argp = samples[
+                self.system.basis.standard_basis_idx["aop{}".format(body_num)], :
+            ]
+            lan = samples[
+                self.system.basis.standard_basis_idx["pan{}".format(body_num)], :
+            ]
+            tau = samples[
+                self.system.basis.standard_basis_idx["tau{}".format(body_num)], :
+            ]
+            plx = samples[self.system.basis.standard_basis_idx["plx"], :]
             if self.system.fit_secondary_mass:
-                m0 = samples[self.system.basis.standard_basis_idx['m0'],:]
-                m1 = samples[self.system.basis.standard_basis_idx['m{}'.format(body_num)],:]
+                m0 = samples[self.system.basis.standard_basis_idx["m0"], :]
+                m1 = samples[
+                    self.system.basis.standard_basis_idx["m{}".format(body_num)], :
+                ]
                 mtot = m0 + m1
             else:
-                mtot = samples[self.system.basis.standard_basis_idx['mtot'],:]
+                mtot = samples[self.system.basis.standard_basis_idx["mtot"], :]
                 m1 = None
-            
+
             min_epoch = self.epoch_idx[body_num - 1]
             if min_epoch is None:
                 # Don't need to rotate and scale if no astrometric measurments for this body. Brute force rejection sampling
                 continue
 
             period_prescale = np.sqrt(
-                4*np.pi**2*(sma*u.AU)**3/(consts.G*(mtot*u.Msun))
+                4 * np.pi**2 * (sma * u.AU) ** 3 / (consts.G * (mtot * u.Msun))
             )
             period_prescale = period_prescale.to(u.day).value
-            meananno = self.epochs[min_epoch]/period_prescale - tau
+            meananno = self.epochs[min_epoch] / period_prescale - tau
 
             # compute sep/PA of generated orbits
             ra, dec, _ = orbitize.kepler.calc_orbit(
-                self.epochs[min_epoch], sma, ecc, inc, argp, lan, tau, plx, mtot, 
-                tau_ref_epoch=0, mass_for_Kamp=m1
+                self.epochs[min_epoch],
+                sma,
+                ecc,
+                inc,
+                argp,
+                lan,
+                tau,
+                plx,
+                mtot,
+                tau_ref_epoch=0,
+                mass_for_Kamp=m1,
             )
-            sep, pa = orbitize.system.radec2seppa(ra, dec) # sep[mas], PA[deg]
+            sep, pa = orbitize.system.radec2seppa(ra, dec)  # sep[mas], PA[deg]
 
             # generate Gaussian offsets from observational uncertainties
-            sep_offset = np.random.normal(
-                0, self.sep_err[min_epoch], size=num_samples
-            )
-            pa_offset =  np.random.normal(
-                0, self.pa_err[min_epoch], size=num_samples
-            )
+            sep_offset = np.random.normal(0, self.sep_err[min_epoch], size=num_samples)
+            pa_offset = np.random.normal(0, self.pa_err[min_epoch], size=num_samples)
 
             # calculate correction factors
-            sma_corr = (sep_offset + self.sep_observed[min_epoch])/sep
-            lan_corr = (pa_offset + self.pa_observed[min_epoch] - pa)
+            sma_corr = (sep_offset + self.sep_observed[min_epoch]) / sep
+            lan_corr = pa_offset + self.pa_observed[min_epoch] - pa
 
             # perform scale-and-rotate
-            sma *= sma_corr # [AU]
-            lan += np.radians(lan_corr) # [rad]
+            sma *= sma_corr  # [AU]
+            lan += np.radians(lan_corr)  # [rad]
             lan = (lan + 2 * np.pi) % (2 * np.pi)
 
             if self.system.restrict_angle_ranges:
@@ -316,17 +386,25 @@ def prepare_samples(self, num_samples):
                 lan[lan >= np.pi] -= np.pi
 
             period_new = np.sqrt(
-                4*np.pi**2*(sma*u.AU)**3/(consts.G*(mtot*u.Msun))
+                4 * np.pi**2 * (sma * u.AU) ** 3 / (consts.G * (mtot * u.Msun))
             )
             period_new = period_new.to(u.day).value
 
-            tau = (self.epochs[min_epoch]/period_new - meananno) % 1
+            tau = (self.epochs[min_epoch] / period_new - meananno) % 1
 
             # updates samples with new values of sma, pan, tau
-            samples[self.system.basis.standard_basis_idx['sma{}'.format(body_num)],:] = sma
-            samples[self.system.basis.standard_basis_idx['aop{}'.format(body_num)],:] = argp
-            samples[self.system.basis.standard_basis_idx['pan{}'.format(body_num)],:] = lan
-            samples[self.system.basis.standard_basis_idx['tau{}'.format(body_num)],:] = tau
+            samples[
+                self.system.basis.standard_basis_idx["sma{}".format(body_num)], :
+            ] = sma
+            samples[
+                self.system.basis.standard_basis_idx["aop{}".format(body_num)], :
+            ] = argp
+            samples[
+                self.system.basis.standard_basis_idx["pan{}".format(body_num)], :
+            ] = lan
+            samples[
+                self.system.basis.standard_basis_idx["tau{}".format(body_num)], :
+            ] = tau
 
         return samples
 
@@ -352,34 +430,32 @@ def reject(self, samples):
         lnp = self._logl(samples)
 
         # we just want the chi2 term for rejection, so compute the Gaussian normalization term and remove it
-        errs = np.array([self.system.data_table['quant1_err'],
-                         self.system.data_table['quant2_err']]).T
+        errs = np.array(
+            [self.system.data_table["quant1_err"], self.system.data_table["quant2_err"]]
+        ).T
 
         if self.has_corr:
-            corrs = self.system.data_table['quant12_corr']
+            corrs = self.system.data_table["quant12_corr"]
         else:
             corrs = None
         lnp_scaled = lnp - orbitize.lnlike.chi2_norm_term(errs, corrs)
 
         # account for user-set priors on PAN that were destroyed by scale-and-rotate
         for body_num in np.arange(self.system.num_secondary_bodies) + 1:
-
-            pan_idx = self.system.basis.standard_basis_idx['pan{}'.format(body_num)]
+            pan_idx = self.system.basis.standard_basis_idx["pan{}".format(body_num)]
 
             pan_prior = self.system.sys_priors[pan_idx]
             if pan_prior is not orbitize.priors.UniformPrior:
-
                 # apply PAN prior
-                lnp_scaled += pan_prior.compute_lnprob(samples[pan_idx,:])
+                lnp_scaled += pan_prior.compute_lnprob(samples[pan_idx, :])
 
             # prior is uniform but with different bounds that OFTI expects
             elif (pan_prior.minval != 0) or (
-                (pan_prior.maxval != np.pi) or (pan_prior.maxval != 2*np.pi)
+                (pan_prior.maxval != np.pi) or (pan_prior.maxval != 2 * np.pi)
             ):
-                
                 samples_outside_pan_prior = np.where(
-                    (samples[pan_idx,:] < pan_prior.minval) or 
-                    (samples[pan_idx,:] > pan_prior.maxval)
+                    (samples[pan_idx, :] < pan_prior.minval)
+                    or (samples[pan_idx, :] > pan_prior.maxval)
                 )[0]
 
                 lnp_scaled[samples_outside_pan_prior] = -np.inf
@@ -392,7 +468,9 @@ def reject(self, samples):
 
         return saved_orbits, lnlikes
 
-    def _sampler_process(self, output, total_orbits, num_samples=10000, Value=0, lock=None):
+    def _sampler_process(
+        self, output, total_orbits, num_samples=10000, Value=0, lock=None
+    ):
         """
         Runs OFTI until it finds the number of total accepted orbits desired.
         Meant to be called by run_sampler.
@@ -424,7 +502,6 @@ def _sampler_process(self, output, total_orbits, num_samples=10000, Value=0, loc
 
         # add orbits to `output_orbits` until `total_orbits` are saved
         while n_orbits_saved < total_orbits:
-
             samples = self.prepare_samples(num_samples)
             accepted_orbits, lnlikes = self.reject(samples)
 
@@ -433,9 +510,12 @@ def _sampler_process(self, output, total_orbits, num_samples=10000, Value=0, loc
             else:
                 n_accepted = len(accepted_orbits)
                 maxindex2save = np.min([n_accepted, total_orbits - n_orbits_saved])
-                output_orbits[n_orbits_saved: n_orbits_saved +
-                              n_accepted] = accepted_orbits[0:maxindex2save]
-                output_lnlikes[n_orbits_saved: n_orbits_saved+n_accepted] = lnlikes[0:maxindex2save]
+                output_orbits[
+                    n_orbits_saved : n_orbits_saved + n_accepted
+                ] = accepted_orbits[0:maxindex2save]
+                output_lnlikes[n_orbits_saved : n_orbits_saved + n_accepted] = lnlikes[
+                    0:maxindex2save
+                ]
                 n_orbits_saved += maxindex2save
 
                 # add to the value of the global variable
@@ -445,7 +525,9 @@ def _sampler_process(self, output, total_orbits, num_samples=10000, Value=0, loc
         output.put((np.array(output_orbits), output_lnlikes))
         return (np.array(output_orbits), output_lnlikes)
 
-    def run_sampler(self, total_orbits, num_samples=10000, num_cores=None):
+    def run_sampler(
+        self, total_orbits, num_samples=10000, num_cores=None, OFTI_warning=60.0
+    ):
         """
         Runs OFTI in parallel on multiple cores until we get the number of total accepted orbits we want.
 
@@ -455,6 +537,9 @@ def run_sampler(self, total_orbits, num_samples=10000, num_cores=None):
                 rejection sampling on. Defaults to 10000.
             num_cores (int): the number of cores to run OFTI on. Defaults to
                 number of cores availabe.
+            OFTI_warning (float): if OFTI doesn't accept a single orbit before
+                this amount of time (in seconds), print a warning suggesting to
+                try MCMC. If None, don't print a warning.
         Return:
             np.array: array of accepted orbits. Size: total_orbits.
 
@@ -462,27 +547,29 @@ def run_sampler(self, total_orbits, num_samples=10000, num_cores=None):
 
         """
 
-        if num_cores!=1:
-            if num_cores==None:
-                num_cores=mp.cpu_count()
-            
-            results=[]
-            # orbits_saved is a global counter for the number of orbits generated 
-            orbits_saved=mp.Value('i',0)
-            
-            manager = mp.Manager()            
+        start_time = time.time()
+
+        if num_cores != 1:
+            if num_cores is None:
+                num_cores = mp.cpu_count()
+
+            results = []
+            # orbits_saved is a global counter for the number of orbits generated
+            orbits_saved = mp.Value("i", 0)
+
+            manager = mp.Manager()
             output = manager.Queue()
 
             # setup the processes
             lock = mp.Lock()
-            nrun_per_core = int(np.ceil(float(total_orbits)/float(num_cores)))
+            nrun_per_core = int(np.ceil(float(total_orbits) / float(num_cores)))
 
             processes = [
                 mp.Process(
                     target=self._sampler_process,
-                    args=(output, nrun_per_core, num_samples,
-                          orbits_saved, lock)
-                ) for x in range(num_cores)
+                    args=(output, nrun_per_core, num_samples, orbits_saved, lock),
+                )
+                for x in range(num_cores)
             ]
 
             # start the processes
@@ -491,10 +578,23 @@ def run_sampler(self, total_orbits, num_samples=10000, num_cores=None):
 
             # print out the number of orbits generated every second
             while orbits_saved.value < total_orbits:
-                print(str(orbits_saved.value)+'/'+str(total_orbits)+' orbits found', end='\r')
+                if OFTI_warning is not None and orbits_saved.value == 0:
+                    check_time = time.time() - start_time
+                    if check_time > OFTI_warning:
+                        print(
+                            "Warning! OFTI is taking a while, and you may want to consider MCMC, check out the MCMC vs OFTI tutorial.",
+                            end="\n",
+                        )
+                        OFTI_warning = None
+                print(
+                    str(orbits_saved.value) + "/" + str(total_orbits) + " orbits found",
+                    end="\r",
+                )
                 time.sleep(0.1)
 
-            print(str(total_orbits)+'/'+str(total_orbits)+' orbits found', end='\r')
+            print(
+                str(total_orbits) + "/" + str(total_orbits) + " orbits found", end="\r"
+            )
 
             # join the processes
             for p in processes:
@@ -510,14 +610,11 @@ def run_sampler(self, total_orbits, num_samples=10000, num_cores=None):
 
             for p in results:
                 num_to_fill = np.min([len(p[0]), total_orbits - pos])
-                output_orbits[pos:pos+num_to_fill] = p[0][0:num_to_fill]
-                output_lnlikes[pos:pos+num_to_fill] = p[1][0:num_to_fill]
+                output_orbits[pos : pos + num_to_fill] = p[0][0:num_to_fill]
+                output_lnlikes[pos : pos + num_to_fill] = p[1][0:num_to_fill]
                 pos += num_to_fill
 
-            self.results.add_samples(
-                np.array(output_orbits),
-                output_lnlikes
-            )
+            self.results.add_samples(np.array(output_orbits), output_lnlikes)
             return output_orbits
 
         else:
@@ -532,24 +629,34 @@ def run_sampler(self, total_orbits, num_samples=10000, num_cores=None):
                 accepted_orbits, lnlikes = self.reject(samples)
 
                 if len(accepted_orbits) == 0:
+                    check_time = time.time() - start_time
+                    if OFTI_warning is not None:
+                        if check_time > OFTI_warning:
+                            print(
+                                "Warning! OFTI is taking a while, and you may want to consider MCMC, check out the MCMC vs OFTI tutorial.",
+                                end="\n",
+                            )
+                            OFTI_warning = None
                     pass
                 else:
                     n_accepted = len(accepted_orbits)
                     maxindex2save = np.min([n_accepted, total_orbits - n_orbits_saved])
 
-                    output_orbits[n_orbits_saved: n_orbits_saved +
-                                  n_accepted] = accepted_orbits[0:maxindex2save]
-                    output_lnlikes[n_orbits_saved: n_orbits_saved +
-                                   n_accepted] = lnlikes[0:maxindex2save]
+                    output_orbits[
+                        n_orbits_saved : n_orbits_saved + n_accepted
+                    ] = accepted_orbits[0:maxindex2save]
+                    output_lnlikes[
+                        n_orbits_saved : n_orbits_saved + n_accepted
+                    ] = lnlikes[0:maxindex2save]
                     n_orbits_saved += maxindex2save
 
-                    # print progress statement
-                    print(str(n_orbits_saved)+'/'+str(total_orbits)+' orbits found', end='\r')
+                # print progress statement
+                print(
+                    str(n_orbits_saved) + "/" + str(total_orbits) + " orbits found",
+                    end="\r",
+                )
 
-            self.results.add_samples(
-                np.array(output_orbits),
-                output_lnlikes
-            )
+            self.results.add_samples(np.array(output_orbits), output_lnlikes)
 
             return output_orbits
 
@@ -564,31 +671,40 @@ class MCMC(Sampler):
 
     Args:
         system (system.System): system.System object
-        num_temps (int): number of temperatures to run the sampler at. 
+        num_temps (int): number of temperatures to run the sampler at.
             Parallel tempering will be used if num_temps > 1 (default=20)
         num_walkers (int): number of walkers at each temperature (default=1000)
         num_threads (int): number of threads to use for parallelization (default=1)
         chi2_type (str, optional): either  "standard", or "log"
         like (str): name of likelihood function in ``lnlike.py``
-        custom_lnlike (func): ability to include an addition custom likelihood 
-            function in the fit. The function looks like 
-            ``clnlikes = custon_lnlike(params)`` where ``params`` is a RxM array 
-            of fitting parameters, where R is the number of orbital paramters 
-            (can be passed in system.compute_model()), and M is the number of 
-            orbits we need model predictions for. It returns ``clnlikes`` 
+        custom_lnlike (func): ability to include an addition custom likelihood
+            function in the fit. The function looks like
+            ``clnlikes = custon_lnlike(params)`` where ``params`` is a RxM array
+            of fitting parameters, where R is the number of orbital paramters
+            (can be passed in system.compute_model()), and M is the number of
+            orbits we need model predictions for. It returns ``clnlikes``
             which is an array of length M, or it can be a single float if M = 1.
-        prev_result_filename (str): if passed a filename to an HDF5 file 
-            containing a orbitize.Result data, MCMC will restart from where it 
-            left off. 
+        prev_result_filename (str): if passed a filename to an HDF5 file
+            containing a orbitize.Result data, MCMC will restart from where it
+            left off.
 
     Written: Jason Wang, Henry Ngo, 2018
     """
+
     def __init__(
-        self, system, num_temps=20, num_walkers=1000, num_threads=1, chi2_type='standard', 
-        like='chi2_lnlike', custom_lnlike=None, prev_result_filename=None
+        self,
+        system,
+        num_temps=20,
+        num_walkers=1000,
+        num_threads=1,
+        chi2_type="standard",
+        like="chi2_lnlike",
+        custom_lnlike=None,
+        prev_result_filename=None,
     ):
-
-        super(MCMC, self).__init__(system, like=like, chi2_type=chi2_type, custom_lnlike=custom_lnlike)
+        super(MCMC, self).__init__(
+            system, like=like, chi2_type=chi2_type, custom_lnlike=custom_lnlike
+        )
 
         self.num_temps = num_temps
         self.num_walkers = num_walkers
@@ -600,7 +716,7 @@ def __init__(
             sampler_name=self.__class__.__name__,
             post=None,
             lnlike=None,
-            version_number=orbitize.__version__
+            version_number=orbitize.__version__,
         )
 
         if self.num_temps > 1:
@@ -616,7 +732,6 @@ def __init__(
         self.sampled_param_idx = {}
         sampled_param_counter = 0
         for i, prior in enumerate(system.sys_priors):
-
             # check for fixed parameters
             if not hasattr(prior, "draw_samples"):
                 self.fixed_params.append((i, prior))
@@ -635,7 +750,7 @@ def __init__(
             for prior in self.priors:
                 # draw them uniformly becase we don't know any better right now
                 # TODO: be smarter in the future
-                random_init = prior.draw_samples(num_walkers*self.num_temps)
+                random_init = prior.draw_samples(num_walkers * self.num_temps)
                 if self.num_temps > 1:
                     random_init = random_init.reshape([self.num_temps, num_walkers])
 
@@ -662,7 +777,11 @@ def __init__(
             if self.use_pt:
                 expected_shape = (self.num_temps,) + expected_shape
             if prev_pos.shape != expected_shape:
-                raise ValueError("Unable to restart chain. Saved walker positions has shape {0}, while current sampler needs {1}".format(prev_pos.shape, expected_shape))
+                raise ValueError(
+                    "Unable to restart chain. Saved walker positions has shape {0}, while current sampler needs {1}".format(
+                        prev_pos.shape, expected_shape
+                    )
+                )
 
             self.curr_pos = prev_pos
 
@@ -671,11 +790,11 @@ def _fill_in_fixed_params(self, sampled_params):
         Fills in the missing parameters from the chain that aren't being sampled
 
         Args:
-            sampled_params (np.array): either 1-D array of size = number of 
+            sampled_params (np.array): either 1-D array of size = number of
                 sampled params, or 2-D array of shape (num_models, num_params)
 
         Returns:
-            np.array: same number of dimensions as sampled_params, 
+            np.array: same number of dimensions as sampled_params,
                 but with num_params including the fixed parameters
         """
         if len(self.fixed_params) == 0:
@@ -718,8 +837,9 @@ def _logl(self, params, include_logp=False):
                 if np.isinf(logp):
                     return -np.inf
             else:
-                logp = np.array([orbitize.priors.all_lnpriors(pset, self.priors)
-                                 for pset in params])
+                logp = np.array(
+                    [orbitize.priors.all_lnpriors(pset, self.priors) for pset in params]
+                )
         else:
             logp = 0  # don't include prior
 
@@ -746,7 +866,9 @@ def _update_chains_from_sampler(self, sampler, num_steps=None):
 
         if self.use_pt:
             # chain is shape: Ntemp x Nwalkers x Nsteps x Nparams
-            self.post = sampler.chain[0, :, :num_steps].reshape(-1, num_params) # the reshaping flattens the chain
+            self.post = sampler.chain[0, :, :num_steps].reshape(
+                -1, num_params
+            )  # the reshaping flattens the chain
             # should also be picking out the lowest temperature logps
             self.lnlikes = sampler.loglikelihood[0, :, :num_steps].flatten()
             self.lnlikes_alltemps = sampler.loglikelihood[:, :, :num_steps]
@@ -764,21 +886,28 @@ def _update_chains_from_sampler(self, sampler, num_steps=None):
 
     def validate_xyz_positions(self):
         """
-        If using the XYZ basis, walkers might be initialized in an invalid 
-        region of parameter space. This function fixes that by replacing invalid 
+        If using the XYZ basis, walkers might be initialized in an invalid
+        region of parameter space. This function fixes that by replacing invalid
         positions by new randomly generated positions until all are valid.
         """
-        if self.system.fitting_basis == 'XYZ':
+        if self.system.fitting_basis == "XYZ":
             if self.use_pt:
                 all_valid = False
                 while not all_valid:
                     total_invalids = 0
                     for temp in range(self.num_temps):
-                        to_stand = self.system.basis.to_standard_basis(self.curr_pos[temp,:,:].T.copy()).T
+                        to_stand = self.system.basis.to_standard_basis(
+                            self.curr_pos[temp, :, :].T.copy()
+                        ).T
 
                         # Get invalids by checking ecc values for each companion
-                        indices = [((i * 6) + 1) for i in range(self.system.num_secondary_bodies)]
-                        invalids = np.where((to_stand[:, indices] < 0.) | (to_stand[:, indices] >= 1.))[0]
+                        indices = [
+                            ((i * 6) + 1)
+                            for i in range(self.system.num_secondary_bodies)
+                        ]
+                        invalids = np.where(
+                            (to_stand[:, indices] < 0.0) | (to_stand[:, indices] >= 1.0)
+                        )[0]
 
                         # Redraw samples for the invalid ones
                         if len(invalids) > 0:
@@ -786,20 +915,26 @@ def validate_xyz_positions(self):
                             for prior in self.priors:
                                 randompos = prior.draw_samples(len(invalids))
                                 newpos.append(randompos)
-                            self.curr_pos[temp, invalids, :] = np.stack(newpos).T 
+                            self.curr_pos[temp, invalids, :] = np.stack(newpos).T
                             total_invalids += len(invalids)
                     if total_invalids == 0:
                         all_valid = True
-                        print('All walker positions validated.')
+                        print("All walker positions validated.")
             else:
                 all_valid = False
                 while not all_valid:
                     total_invalids = 0
-                    to_stand = self.system.basis.to_standard_basis(self.curr_pos[:,:].T.copy()).T
+                    to_stand = self.system.basis.to_standard_basis(
+                        self.curr_pos[:, :].T.copy()
+                    ).T
 
                     # Get invalids by checking ecc values for each companion
-                    indices = [((i * 6) + 1) for i in range(self.system.num_secondary_bodies)]
-                    invalids = np.where((to_stand[:, indices] < 0.) | (to_stand[:, indices] >= 1.))[0]                    
+                    indices = [
+                        ((i * 6) + 1) for i in range(self.system.num_secondary_bodies)
+                    ]
+                    invalids = np.where(
+                        (to_stand[:, indices] < 0.0) | (to_stand[:, indices] >= 1.0)
+                    )[0]
 
                     # Redraw saples for the invalid ones
                     if len(invalids) > 0:
@@ -807,17 +942,20 @@ def validate_xyz_positions(self):
                         for prior in self.priors:
                             randompos = prior.draw_samples(len(invalids))
                             newpos.append(randompos)
-                        self.curr_pos[invalids, :] = np.stack(newpos).T 
+                        self.curr_pos[invalids, :] = np.stack(newpos).T
                         total_invalids += len(invalids)
                     if total_invalids == 0:
                         all_valid = True
-                        print('All walker positions validated.')
-
-
+                        print("All walker positions validated.")
 
     def run_sampler(
-        self, total_orbits, burn_steps=0, thin=1, examine_chains=False, 
-        output_filename=None, periodic_save_freq=None
+        self,
+        total_orbits,
+        burn_steps=0,
+        thin=1,
+        examine_chains=False,
+        output_filename=None,
+        periodic_save_freq=None,
     ):
         """
         Runs PT MCMC sampler. Results are stored in ``self.chain`` and ``self.lnlikes``.
@@ -845,7 +983,9 @@ def run_sampler(
         """
 
         if periodic_save_freq is not None and output_filename is None:
-            raise ValueError("output_filename must be defined for periodic saving of the chains")
+            raise ValueError(
+                "output_filename must be defined for periodic saving of the chains"
+            )
         if periodic_save_freq is not None and not isinstance(periodic_save_freq, int):
             raise TypeError("periodic_save_freq must be an integer")
 
@@ -853,100 +993,145 @@ def run_sampler(
         if nsteps <= 0:
             raise ValueError("Total_orbits must be greater than num_walkers.")
 
-        with Pool(processes=self.num_threads) as pool: 
+        with Pool(processes=self.num_threads) as pool:
             if self.use_pt:
                 sampler = ptemcee.Sampler(
-                    self.num_walkers, self.num_params, self._logl, orbitize.priors.all_lnpriors,
-                    ntemps=self.num_temps, threads=self.num_threads, logpargs=[self.priors, ]
+                    self.num_walkers,
+                    self.num_params,
+                    self._logl,
+                    orbitize.priors.all_lnpriors,
+                    ntemps=self.num_temps,
+                    threads=self.num_threads,
+                    logpargs=[
+                        self.priors,
+                    ],
                 )
             else:
                 sampler = emcee.EnsembleSampler(
-                    self.num_walkers, self.num_params, self._logl, pool=pool,
-                    kwargs={'include_logp': True}
+                    self.num_walkers,
+                    self.num_params,
+                    self._logl,
+                    pool=pool,
+                    kwargs={"include_logp": True},
                 )
 
             print("Starting Burn in")
-            for i, state in enumerate(sampler.sample(self.curr_pos, iterations=burn_steps, thin=thin)):
+            for i, state in enumerate(
+                sampler.sample(self.curr_pos, iterations=burn_steps, thin=thin)
+            ):
                 if self.use_pt:
                     self.curr_pos = state[0]
                 else:
                     self.curr_pos = state.coords
 
-                if (i+1) % 5 == 0:
-                    print(str(i+1)+'/'+str(burn_steps)+' steps of burn-in complete', end='\r')
+                if (i + 1) % 5 == 0:
+                    print(
+                        str(i + 1)
+                        + "/"
+                        + str(burn_steps)
+                        + " steps of burn-in complete",
+                        end="\r",
+                    )
 
                 if periodic_save_freq is not None:
-                    if (i+1) % periodic_save_freq == 0: # we've completed i+1 steps
+                    if (i + 1) % periodic_save_freq == 0:  # we've completed i+1 steps
                         self.results.curr_pos = self.curr_pos
                         self.results.save_results(output_filename)
 
             sampler.reset()
-            print('')
-            print('Burn in complete. Sampling posterior now.')
+            print("")
+            print("Burn in complete. Sampling posterior now.")
 
-            saved_upto = 0 # keep track of how many steps of this chain we've saved. this is the next index that needs to be saved 
-            for i, state in enumerate(sampler.sample(self.curr_pos, iterations=nsteps, thin=thin)):
+            saved_upto = 0  # keep track of how many steps of this chain we've saved. this is the next index that needs to be saved
+            for i, state in enumerate(
+                sampler.sample(self.curr_pos, iterations=nsteps, thin=thin)
+            ):
                 if self.use_pt:
                     self.curr_pos = state[0]
                 else:
                     self.curr_pos = state.coords
-                    
+
                 # print progress statement
-                if (i+1) % 5 == 0:
-                    print(str(i+1)+'/'+str(nsteps)+' steps completed', end='\r')
+                if (i + 1) % 5 == 0:
+                    print(str(i + 1) + "/" + str(nsteps) + " steps completed", end="\r")
 
                 if periodic_save_freq is not None:
-                    if (i+1) % periodic_save_freq == 0: # we've completed i+1 steps
-                        self._update_chains_from_sampler(sampler, num_steps=i+1)
+                    if (i + 1) % periodic_save_freq == 0:  # we've completed i+1 steps
+                        self._update_chains_from_sampler(sampler, num_steps=i + 1)
 
                         # figure out what is the new chunk of the chain and corresponding lnlikes that have been computed before last save
                         # grab the current posterior and lnlikes and reshape them to have the Nwalkers x Nsteps dimension again
                         post_shape = self.post.shape
-                        curr_chain_shape = (self.num_walkers, post_shape[0]//self.num_walkers, post_shape[-1])
+                        curr_chain_shape = (
+                            self.num_walkers,
+                            post_shape[0] // self.num_walkers,
+                            post_shape[-1],
+                        )
                         curr_chain = self.post.reshape(curr_chain_shape)
                         curr_lnlike_chain = self.lnlikes.reshape(curr_chain_shape[:2])
                         # use the reshaped arrays and find the new steps we computed
-                        curr_chunk = curr_chain[:, saved_upto:i+1]
-                        curr_chunk = curr_chunk.reshape(-1, curr_chunk.shape[-1]) # flatten nwalkers x nsteps dim
-                        curr_lnlike_chunk = curr_lnlike_chain[:, saved_upto:i+1].flatten()
+                        curr_chunk = curr_chain[:, saved_upto : i + 1]
+                        curr_chunk = curr_chunk.reshape(
+                            -1, curr_chunk.shape[-1]
+                        )  # flatten nwalkers x nsteps dim
+                        curr_lnlike_chunk = curr_lnlike_chain[
+                            :, saved_upto : i + 1
+                        ].flatten()
 
                         # add this current chunk to the results object (which already has all the previous chunks saved)
-                        self.results.add_samples(curr_chunk, curr_lnlike_chunk, 
-                                                    curr_pos=self.curr_pos)
+                        self.results.add_samples(
+                            curr_chunk, curr_lnlike_chunk, curr_pos=self.curr_pos
+                        )
                         self.results.save_results(output_filename)
-                        saved_upto = i+1
+                        saved_upto = i + 1
 
-            print('')
+            print("")
             self._update_chains_from_sampler(sampler)
 
             if periodic_save_freq is None:
                 # need to save everything
-                self.results.add_samples(self.post, self.lnlikes, curr_pos=self.curr_pos)
+                self.results.add_samples(
+                    self.post, self.lnlikes, curr_pos=self.curr_pos
+                )
             elif saved_upto < nsteps:
                 # just need to save the last few
                 # same code as above except we just need to grab the last few
                 post_shape = self.post.shape
-                curr_chain_shape = (self.num_walkers, post_shape[0]//self.num_walkers, post_shape[-1])
+                curr_chain_shape = (
+                    self.num_walkers,
+                    post_shape[0] // self.num_walkers,
+                    post_shape[-1],
+                )
                 curr_chain = self.post.reshape(curr_chain_shape)
                 curr_lnlike_chain = self.lnlikes.reshape(curr_chain_shape[:2])
                 curr_chunk = curr_chain[:, saved_upto:]
-                curr_chunk = curr_chunk.reshape(-1, curr_chunk.shape[-1]) # flatten nwalkers x nsteps dim
+                curr_chunk = curr_chunk.reshape(
+                    -1, curr_chunk.shape[-1]
+                )  # flatten nwalkers x nsteps dim
                 curr_lnlike_chunk = curr_lnlike_chain[:, saved_upto:].flatten()
 
-                self.results.add_samples(curr_chunk, curr_lnlike_chunk, 
-                                                     curr_pos=self.curr_pos)
+                self.results.add_samples(
+                    curr_chunk, curr_lnlike_chunk, curr_pos=self.curr_pos
+                )
 
             if output_filename is not None:
                 self.results.save_results(output_filename)
 
-            print('Run complete')
+            print("Run complete")
         # Close pool
         if examine_chains:
             self.examine_chains()
 
         return sampler
 
-    def examine_chains(self, param_list=None, walker_list=None, n_walkers=None, step_range=None, transparency = 1):
+    def examine_chains(
+        self,
+        param_list=None,
+        walker_list=None,
+        n_walkers=None,
+        step_range=None,
+        transparency=1,
+    ):
         """
         Plots position of walkers at each step from Results object. Returns list of figures, one per parameter
         Args:
@@ -972,13 +1157,17 @@ def examine_chains(self, param_list=None, walker_list=None, n_walkers=None, step
         # Get the flattened chain from Results object (nwalkers*nsteps, nparams)
         flatchain = np.copy(self.results.post)
         total_samples, n_params = flatchain.shape
-        n_steps = int(total_samples/self.num_walkers)
+        n_steps = int(total_samples / self.num_walkers)
         # Reshape it to (nwalkers, nsteps, nparams)
         chn = flatchain.reshape((self.num_walkers, n_steps, n_params))
 
         # Get list of walkers to use
-        if n_walkers is not None:  # If n_walkers defined, randomly choose that many walkers
-            walkers_to_plot = np.random.choice(self.num_walkers, size=n_walkers, replace=False)
+        if (
+            n_walkers is not None
+        ):  # If n_walkers defined, randomly choose that many walkers
+            walkers_to_plot = np.random.choice(
+                self.num_walkers, size=n_walkers, replace=False
+            )
         elif walker_list is not None:  # if walker_list is given, use that list
             walkers_to_plot = np.array(walker_list)
         else:  # both n_walkers and walker_list are none, so use all walkers
@@ -993,7 +1182,9 @@ def examine_chains(self, param_list=None, walker_list=None, n_walkers=None, step
                 if i in self.system.basis.param_idx:
                     params_plot_list.append(self.system.basis.param_idx[i])
                 else:
-                    raise Exception('Invalid param name: {}. See system.basis.param_idx.'.format(i))
+                    raise Exception(
+                        "Invalid param name: {}. See system.basis.param_idx.".format(i)
+                    )
             params_to_plot = np.array(params_plot_list)
 
         # Loop through each parameter and make plot
@@ -1001,8 +1192,8 @@ def examine_chains(self, param_list=None, walker_list=None, n_walkers=None, step
         for pp in params_to_plot:
             fig, ax = plt.subplots()
             for ww in walkers_to_plot:
-                ax.plot(chn[ww, :, pp], 'k-', alpha = transparency)
-            ax.set_xlabel('Step')
+                ax.plot(chn[ww, :, pp], "k-", alpha=transparency)
+            ax.set_xlabel("Step")
             if step_range is not None:  # Limit range shown if step_range is set
                 ax.set_xlim(step_range)
             output_figs.append(fig)
@@ -1012,8 +1203,8 @@ def examine_chains(self, param_list=None, walker_list=None, n_walkers=None, step
 
     def chop_chains(self, burn, trim=0):
         """
-        Permanently removes steps from beginning (and/or end) of chains from the 
-        Results object. Also updates `curr_pos` if steps are removed from the 
+        Permanently removes steps from beginning (and/or end) of chains from the
+        Results object. Also updates `curr_pos` if steps are removed from the
         end of the chain.
 
         Args:
@@ -1028,7 +1219,7 @@ def chop_chains(self, burn, trim=0):
         # Retrieve information from results object
         flatchain = np.copy(self.results.post)
         total_samples, n_params = flatchain.shape
-        n_steps = int(total_samples/self.num_walkers)
+        n_steps = int(total_samples / self.num_walkers)
         flatlnlikes = np.copy(self.results.lnlike)
 
         # Reshape chain to (nwalkers, nsteps, nparams)
@@ -1050,21 +1241,25 @@ def chop_chains(self, burn, trim=0):
             self.curr_pos = chopped_chain[:, -1, :]
 
         # Flatten likelihoods and samples
-        flat_chopped_chain = chopped_chain.reshape(self.num_walkers*n_chopped_steps, n_params)
-        flat_chopped_lnlikes = chopped_lnlikes.reshape(self.num_walkers*n_chopped_steps)
+        flat_chopped_chain = chopped_chain.reshape(
+            self.num_walkers * n_chopped_steps, n_params
+        )
+        flat_chopped_lnlikes = chopped_lnlikes.reshape(
+            self.num_walkers * n_chopped_steps
+        )
 
         # Update results object associated with this sampler
         self.results = orbitize.results.Results(
-            self.system, 
+            self.system,
             sampler_name=self.__class__.__name__,
             post=flat_chopped_chain,
             lnlike=flat_chopped_lnlikes,
-            version_number = orbitize.__version__,
-            curr_pos = self.curr_pos
+            version_number=orbitize.__version__,
+            curr_pos=self.curr_pos,
         )
 
         # Print a confirmation
-        print('Chains successfully chopped. Results object updated.')
+        print("Chains successfully chopped. Results object updated.")
 
     def check_prior_support(self):
         """
@@ -1076,8 +1271,10 @@ def check_prior_support(self):
         """
 
         # Flatten the walker/temperature positions for ease of manipulation.
-        all_positions = self.curr_pos.reshape(self.num_walkers*self.num_temps,self.num_params)
-        
+        all_positions = self.curr_pos.reshape(
+            self.num_walkers * self.num_temps, self.num_params
+        )
+
         # Placeholder list to track any bad parameters that come up.
         bad_parameters = []
 
@@ -1094,14 +1291,19 @@ def check_prior_support(self):
 
             # Throw our ValueError if necessary,
             if len(bad_parameters) > 0:
-                raise ValueError("Attempting to start with walkers outside of prior support: check parameter(s) "+', '.join(bad_parameters))
+                raise ValueError(
+                    "Attempting to start with walkers outside of prior support: check parameter(s) "
+                    + ", ".join(bad_parameters)
+                )
 
         # We're not done yet, however. There may be errors in covariant priors; run a check for that.
         else:
             for y in all_positions:
-                lnprob = orbitize.priors.all_lnpriors(y,self.priors)
+                lnprob = orbitize.priors.all_lnpriors(y, self.priors)
                 if not np.isfinite(lnprob).all():
-                    raise ValueError("Attempting to start with walkers outside of prior support: covariant prior failure.")
-        
+                    raise ValueError(
+                        "Attempting to start with walkers outside of prior support: covariant prior failure."
+                    )
+
         # otherwise exit the function and continue.
         return


Test patch:
diff --git a/tests/test_chi2_lnlike.py b/tests/test_chi2_lnlike.py
index 331ca8dd..5aee6716 100644
--- a/tests/test_chi2_lnlike.py
+++ b/tests/test_chi2_lnlike.py
@@ -156,7 +156,7 @@ def test_log_vs_standard():
         56.95,
     )
     s_standard = myDriver_standard.sampler
-    _ = s_standard.run_sampler(500)
+    _ = s_standard.run_sampler(750)
 
     # initiate driver with log chi2
     myDriver_log = orbitize.driver.Driver(
@@ -168,7 +168,7 @@ def test_log_vs_standard():
         chi2_type="log",
     )
     s_log = myDriver_log.sampler
-    _ = s_log.run_sampler(500)
+    _ = s_log.run_sampler(750)
 
     # take mean of result objects
     myResults_standard = np.mean(s_standard.results.post, axis=0)
@@ -177,7 +177,7 @@ def test_log_vs_standard():
     print(myResults_log[1], myResults_standard[1])
 
     # check that the eccentricity means are about the same
-    assert myResults_log[1] == pytest.approx(myResults_standard[1], rel=0.1)
+    assert myResults_log[1] == pytest.approx(myResults_standard[1], rel=0.15)
 
 
 if __name__ == "__main__":
diff --git a/tests/test_hipparcos.py b/tests/test_hipparcos.py
index 091ce5fd..3a3d64d6 100644
--- a/tests/test_hipparcos.py
+++ b/tests/test_hipparcos.py
@@ -156,7 +156,7 @@ def test_iad_refitting():
 
     # check that we get reasonable values for the posteriors of the refit IAD
     # (we're only running the MCMC for a few steps, so these are not strict)
-    assert np.isclose(0, np.median(post[:, -1]), atol=0.5)
+    assert np.isclose(0, np.median(post[:, -1]), atol=0.7)
     assert np.isclose(myHipLogProb.plx0, np.median(post[:, 0]), atol=0.5)