Problem with _inference with constant weight

Problem with _inference?.ipynb

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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "from selectinf.learning.core import _inference\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_fn = lambda z: np.ones_like(z)\n",
    "pivots = []\n",
    "intervals = []\n",
    "delta = 1\n",
    "for _ in range(2000):\n",
    "    Z = np.random.standard_normal() + delta\n",
    "    result = _inference(Z, np.array([[1.]]), weight_fn, hypothesis=delta, alpha=0.05)\n",
    "    pivots.append(result[0])\n",
    "    intervals.append(result[1])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=((8,8)))\n",
    "U = np.linspace(0, 1, 101)\n",
    "plt.plot(U, sm.distributions.ECDF(pivots)(U), linewidth=3)\n",
    "plt.plot([0, 1], [0, 1], 'k--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "intervals = np.array(intervals)\n",
    "np.mean((intervals[:,0] < delta) * (intervals[:,1] > delta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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