docs/emulator_bayes_calib/latent_gp_approach.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'lily'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3hzCrDtt53qu",
    "outputId": "9a6cd7f5-a7dc-4d12-e3c5-ea598a778af9"
   },
   "outputs": [],
   "source": [
    "if name == 'lily':\n",
    "    pass\n",
    "else:\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/drive', force_remount=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_bws= np.array([4.852,4.404,4.213,3.534,3.028,3.187,3.732])\n",
    "t_ba= np.array([4.675,4.016,3.413,3.235,4.522,3.685,4.159])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.85 3.732\n"
     ]
    }
   ],
   "source": [
    "print(np.average(t_bws), np.median(t_bws))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9578571428571423 4.016\n"
     ]
    }
   ],
   "source": [
    "print(np.average(t_ba), np.median(t_ba))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fbAfZbpb6hRz"
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "C6eeIeo159F8"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "#import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import os\n",
    "import sklearn\n",
    "import sklearn.model_selection\n",
    "\n",
    "import pymc as pm\n",
    "import arviz as az\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "8Mmbgkv_icHY"
   },
   "outputs": [],
   "source": [
    "import aesara.tensor as at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ai8U3Lj5C3LJ",
    "outputId": "3372b29c-2c93-4a8f-d053-5bc93e77b687"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.2.1\n"
     ]
    }
   ],
   "source": [
    "print(pm.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "azEX4Elu6jLd"
   },
   "source": [
    "### Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "fOEYST3765f0"
   },
   "outputs": [],
   "source": [
    "if name == 'lily':\n",
    "    data_path = '/home/lilianschuster/massbalance-sandbox/docs/emulator_bayes_calib/'\n",
    "else:\n",
    "    data_path = '/content/drive/MyDrive/massbalance-sandbox/docs/emulator_bayes_calib/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "orhdImsc6ZwM"
   },
   "outputs": [],
   "source": [
    "# load specific MB mean for different MB model parameters (i.e. temp_b & melt_f, here done as a test for the Aletsch glacier): \n",
    "# choose the one with a fixed pf but variable temp. b and melt factors\n",
    "pd_spec_mb = pd.read_csv(data_path+'test_mb_RGI60-11.01450_fixed_pf_test.csv', index_col=[0])\n",
    "\n",
    "# too many values make the notebook slow ... I just removed some parameters again ... \n",
    "pd_spec_mb = pd_spec_mb.sort_values(by=['melt_f', 'temp_bias']).iloc[1::3]\n",
    "pd_spec_mb = pd_spec_mb.sort_values(by=['temp_bias','melt_f']).iloc[::2]\n",
    "\n",
    "# applied MB model parameters that were used to estimated the real OGGM MB model (partly used for training the emulator)  \n",
    "X = pd_spec_mb[['temp_bias','melt_f'] ].values \n",
    "\n",
    "# modelled specific mean MB (mean over 2000-2020) estimated from the real OGGM MB model (partly used for training the emulator) \n",
    "# we later want to match this value (i.e. y) with the geodetic observation \n",
    "y = pd_spec_mb['spec_mb_mean'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g4UsK22z7B0h"
   },
   "source": [
    "Check on the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "cSBTuuGa6-j3",
    "outputId": "47f8d943-90a4-4d54-bb24-4b657603d02f"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp_bias</th>\n",
       "      <th>melt_f</th>\n",
       "      <th>spec_mb_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>-9.95</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4447.609466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>-9.95</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4442.662098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>-9.95</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4437.714730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>-9.95</td>\n",
       "      <td>75.0</td>\n",
       "      <td>4432.767362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>-9.95</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4427.819994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19791</th>\n",
       "      <td>9.85</td>\n",
       "      <td>915.0</td>\n",
       "      <td>-74043.378533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19793</th>\n",
       "      <td>9.85</td>\n",
       "      <td>935.0</td>\n",
       "      <td>-75693.041314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19795</th>\n",
       "      <td>9.85</td>\n",
       "      <td>955.0</td>\n",
       "      <td>-77342.704096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19797</th>\n",
       "      <td>9.85</td>\n",
       "      <td>975.0</td>\n",
       "      <td>-78992.366877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19799</th>\n",
       "      <td>9.85</td>\n",
       "      <td>995.0</td>\n",
       "      <td>-80642.029659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3317 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp_bias  melt_f  spec_mb_mean\n",
       "99         -9.95    15.0   4447.609466\n",
       "101        -9.95    35.0   4442.662098\n",
       "103        -9.95    55.0   4437.714730\n",
       "105        -9.95    75.0   4432.767362\n",
       "107        -9.95    95.0   4427.819994\n",
       "...          ...     ...           ...\n",
       "19791       9.85   915.0 -74043.378533\n",
       "19793       9.85   935.0 -75693.041314\n",
       "19795       9.85   955.0 -77342.704096\n",
       "19797       9.85   975.0 -78992.366877\n",
       "19799       9.85   995.0 -80642.029659\n",
       "\n",
       "[3317 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_spec_mb[['temp_bias','melt_f', 'spec_mb_mean']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nbosPCtR7Jvs"
   },
   "source": [
    "Following Lily's normalization routine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "1ZD7F18U7JU5"
   },
   "outputs": [],
   "source": [
    "random_state = 42 \n",
    "### similar as what David Rounce does in PyGEM:\n",
    "\n",
    "# normalize parameter data used inside \"real\" MB model (partly used for training the emulator) \n",
    "X_mean = X.mean(axis=0)\n",
    "X_std = X.std(axis=0)\n",
    "X_norm = (X - X_mean) / X_std\n",
    "\n",
    "# normalize modelled MB data from \"real\" MB model (partly used for training the emulator) \n",
    "y_mean = y.mean()\n",
    "y_std = y.std()\n",
    "y_norm = (y - y_mean) / y_std\n",
    "\n",
    "# Split into training and test data (but witout having torch tensors ... )\n",
    "X_train_pm,X_test_pm,y_train_pm,y_test_pm = sklearn.model_selection.train_test_split(X_norm,y_norm, test_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qD9U25HZ9-_o",
    "outputId": "091d62e7-78a2-4933-ce6d-9568421b4764"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(663, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pm.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U1WCUpNpE6mi"
   },
   "source": [
    "### Visualize the Simulated Data in 3D to ascertain fit shape\n",
    "\n",
    "This will help us figure out priors to test for GP fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "CfFqLFtsFBvg"
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits import mplot3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "2tPyBsK1FyJe"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "L2QqurVkFPMc",
    "outputId": "d4576d6d-5032-42a5-a2da-5445afda218e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = y_norm\n",
    "x = X_norm[:,0]\n",
    "y = X_norm[:,1]\n",
    " \n",
    "fig = plt.figure(figsize = (10, 7))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    " \n",
    "\n",
    "ax.scatter3D(x, y, z)\n",
    "ax.set_xlabel('Temp Bias')\n",
    "ax.set_ylabel('Melt Factor')\n",
    "ax.set_zlabel('Mass Balance')\n",
    "\n",
    "ax.view_init(5, 85)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "likB5IddUL4h"
   },
   "source": [
    "There is some degeneracy in the y value for the melt factor and temp bias."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lXR4fiVGw-NX"
   },
   "source": [
    "### Geodetic Observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "Ke31sn_7xCnw"
   },
   "outputs": [],
   "source": [
    "sigma_geo = [171]\n",
    "y_geo = [-1210]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vuqa6P1H7YjM"
   },
   "source": [
    "### Model Observations on Latent GP trained on Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 94
    },
    "id": "tjQwsgdO7EUU",
    "outputId": "fbd8ece8-fb46-4a49-d977-74b844c651cb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Sequential sampling (2 chains in 1 job)\n",
      "NUTS: [latent_gp_rotated_]\n"
     ]
    },
    {
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       "      100.00% [1500/1500 00:08&lt;00:00 Sampling chain 0, 0 divergences]\n",
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       "      <progress value='1500' class='' max='1500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [1500/1500 00:08&lt;00:00 Sampling chain 1, 0 divergences]\n",
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     "output_type": "stream",
     "text": [
      "Sampling 2 chains for 1_000 tune and 500 draw iterations (2_000 + 1_000 draws total) took 17 seconds.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as model:\n",
    "    \n",
    "    #define the covariance parameters and function\n",
    "    input_dim = 2 #number of fit variables\n",
    "    cov = pm.gp.cov.ExpQuad(input_dim, ls=0.1)\n",
    "    \n",
    "    #instantiate latent GP\n",
    "    gp = pm.gp.Latent(cov_func=cov)\n",
    "    f = gp.prior('latent_gp', X=X_train_pm, \n",
    "                 jitter=1e-7)\n",
    "    \n",
    "    #assuming marginal GP likelihood is normally distributed\n",
    "    gp_likelihood = pm.Normal('gp_likelihood', mu=f, observed=y_train_pm)\n",
    "\n",
    "    ###### For future, but not necessary now\n",
    "    #assuming marginal GP likelihood is student-t distributed\n",
    "    # gp_sigma = pm.HalfNormal('gp_sigma', sigma=2.0)\n",
    "    # nu = 1 + pm.Gamma('nu', alpha=2, beta=0.1)\n",
    "    # y_ = pm.StudentT('y', mu=f, lam=1.0/gp_sigma, nu=nu, \n",
    "    #                  observed=y_train_pm)\n",
    "\n",
    "    # #assuming GP has some scatter\n",
    "    # gp_sigma = pm.HalfCauchy('gp_sigma', beta=3)\n",
    "    # latent_likelihood = gp.marginal_likelihood('gp_likelihood', \n",
    "    #                                            X=X_train_pm, y=y_train_pm, \n",
    "    #                                            sigma=gp_sigma)\n",
    "    ######\n",
    "\n",
    "    latent_trace = pm.sample(500, chains=2, target_accept=0.9, cores=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "opxYDAC283lU",
    "outputId": "36814e9c-e007-42d8-f479-ca1e3a4c9304"
   },
   "outputs": [],
   "source": [
    "with model:\n",
    "\n",
    "    #define the \"observational\" variables for the geodetic best fit\n",
    "    sigma = pm.ConstantData('sigma', sigma_geo)      # Lily: changed because of warning\n",
    "    t_b = pm.TruncatedNormal('t_b', mu=0, sigma=2,\n",
    "                            lower=-8, upper=8)\n",
    "    melt_f = pm.TruncatedNormal('melt_f',\n",
    "                                mu=250,\n",
    "                                sigma=200,\n",
    "                                lower=1, upper=1000)\n",
    "    \n",
    "    Xnew = at.stack(([t_b], [melt_f])).T\n",
    "        \n",
    "    #instantiate the conditional fit of GP to new \"data\"\n",
    "    gp_mu = gp.conditional('conditional_fit', Xnew=Xnew)\n",
    "    \n",
    "    #specify the manifest model\n",
    "    observed = pm.ConstantData('observed', y_geo)  # Lily: changed because of warning\n",
    "    log_l = pm.Normal('log_mb', mu=gp_mu, sigma=sigma,\n",
    "                      observed=observed) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Sequential sampling (2 chains in 1 job)\n",
      "NUTS: [latent_gp_rotated_, t_b, melt_f, conditional_fit]\n"
     ]
    },
    {
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       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
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       "\n",
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       "      <progress value='0' class='progress-bar-interrupted' max='1500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      Interrupted\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Intel MKL ERROR: Parameter 8 was incorrect on entry to DGEMV .\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "array must not contain infs or NaNs\nApply node that caused the error: SolveTriangular{lower=False, trans=0, unit_diagonal=False, check_finite=True}(TensorConstant{[[ 1.00000..671e-001]]}, CGemv{inplace}.0)\nToposort index: 89\nInputs types: [TensorType(float64, (663, 663)), TensorType(float64, (None,))]\nInputs shapes: [(663, 663), (663,)]\nInputs strides: [(5304, 8), (8,)]\nInputs values: ['not shown', 'not shown']\nOutputs clients: [[CGemv{no_inplace}(latent_gp_rotated_, (d__logp/dmelt_f_interval___logprob){1.0}, TensorConstant{[[ 1.00000..042e-001]]}, SolveTriangular{lower=False, trans=0, unit_diagonal=False, check_finite=True}.0, TensorConstant{-1.0})]]\n\nBacktrace when the node is created (use Aesara flag traceback__limit=N to make it longer):\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 623, in grad\n    _rval: Sequence[Variable] = _populate_grad_dict(\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1434, in _populate_grad_dict\n    rval = [access_grad_cache(elem) for elem in wrt]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1434, in <listcomp>\n    rval = [access_grad_cache(elem) for elem in wrt]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1387, in access_grad_cache\n    term = access_term_cache(node)[idx]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1058, in access_term_cache\n    output_grads = [access_grad_cache(var) for var in node.outputs]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1058, in <listcomp>\n    output_grads = [access_grad_cache(var) for var in node.outputs]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1387, in access_grad_cache\n    term = access_term_cache(node)[idx]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1213, in access_term_cache\n    input_grads = node.op.L_op(inputs, node.outputs, new_output_grads)\n\nHINT: Use the Aesara flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/compile/function/types.py:971\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    969\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    970\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 971\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    972\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m output_subset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    973\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm(output_subset\u001b[38;5;241m=\u001b[39moutput_subset)\n\u001b[1;32m    974\u001b[0m     )\n\u001b[1;32m    975\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/graph/op.py:543\u001b[0m, in \u001b[0;36mOp.make_py_thunk.<locals>.rval\u001b[0;34m(p, i, o, n, params)\u001b[0m\n\u001b[1;32m    539\u001b[0m \u001b[38;5;129m@is_thunk_type\u001b[39m\n\u001b[1;32m    540\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrval\u001b[39m(\n\u001b[1;32m    541\u001b[0m     p\u001b[38;5;241m=\u001b[39mp, i\u001b[38;5;241m=\u001b[39mnode_input_storage, o\u001b[38;5;241m=\u001b[39mnode_output_storage, n\u001b[38;5;241m=\u001b[39mnode, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    542\u001b[0m ):\n\u001b[0;32m--> 543\u001b[0m     r \u001b[38;5;241m=\u001b[39m \u001b[43mp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    544\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m node\u001b[38;5;241m.\u001b[39moutputs:\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/tensor/slinalg.py:366\u001b[0m, in \u001b[0;36mSolveTriangular.perform\u001b[0;34m(self, node, inputs, outputs)\u001b[0m\n\u001b[1;32m    365\u001b[0m A, b \u001b[38;5;241m=\u001b[39m inputs\n\u001b[0;32m--> 366\u001b[0m outputs[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mscipy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve_triangular\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    367\u001b[0m \u001b[43m    \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    368\u001b[0m \u001b[43m    \u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlower\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrans\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrans\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m    \u001b[49m\u001b[43munit_diagonal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munit_diagonal\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcheck_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/scipy/linalg/_basic.py:334\u001b[0m, in \u001b[0;36msolve_triangular\u001b[0;34m(a, b, trans, lower, unit_diagonal, overwrite_b, check_finite)\u001b[0m\n\u001b[1;32m    333\u001b[0m a1 \u001b[38;5;241m=\u001b[39m _asarray_validated(a, check_finite\u001b[38;5;241m=\u001b[39mcheck_finite)\n\u001b[0;32m--> 334\u001b[0m b1 \u001b[38;5;241m=\u001b[39m \u001b[43m_asarray_validated\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_finite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    335\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(a1\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m a1\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m a1\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]:\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/scipy/_lib/_util.py:287\u001b[0m, in \u001b[0;36m_asarray_validated\u001b[0;34m(a, check_finite, sparse_ok, objects_ok, mask_ok, as_inexact)\u001b[0m\n\u001b[1;32m    286\u001b[0m toarray \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray_chkfinite \u001b[38;5;28;01mif\u001b[39;00m check_finite \u001b[38;5;28;01melse\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray\n\u001b[0;32m--> 287\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[43mtoarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m objects_ok:\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/numpy/lib/function_base.py:627\u001b[0m, in \u001b[0;36masarray_chkfinite\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    626\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m a\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mchar \u001b[38;5;129;01min\u001b[39;00m typecodes[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAllFloat\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(a)\u001b[38;5;241m.\u001b[39mall():\n\u001b[0;32m--> 627\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    628\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray must not contain infs or NaNs\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    629\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n",
      "\u001b[0;31mValueError\u001b[0m: array must not contain infs or NaNs",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [40], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# lily: this gives a ValueError: array must not contain infs or NaNs\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m model:\n\u001b[0;32m----> 3\u001b[0m     trace \u001b[38;5;241m=\u001b[39m \u001b[43mpm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m500\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mchains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcores\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_inferencedata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/sampling.py:655\u001b[0m, in \u001b[0;36msample\u001b[0;34m(draws, step, init, n_init, initvals, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, **kwargs)\u001b[0m\n\u001b[1;32m    653\u001b[0m         _log\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSequential sampling (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mchains\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m chains in 1 job)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    654\u001b[0m         _print_step_hierarchy(step)\n\u001b[0;32m--> 655\u001b[0m         mtrace \u001b[38;5;241m=\u001b[39m \u001b[43m_sample_many\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msample_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    657\u001b[0m t_sampling \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m t_start\n\u001b[1;32m    658\u001b[0m \u001b[38;5;66;03m# count the number of tune/draw iterations that happened\u001b[39;00m\n\u001b[1;32m    659\u001b[0m \u001b[38;5;66;03m# ideally via the \"tune\" statistic, but not all samplers record it!\u001b[39;00m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/sampling.py:769\u001b[0m, in \u001b[0;36m_sample_many\u001b[0;34m(draws, chain, chains, start, random_seed, step, callback, **kwargs)\u001b[0m\n\u001b[1;32m    767\u001b[0m traces: List[BaseTrace] \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    768\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(chains):\n\u001b[0;32m--> 769\u001b[0m     trace \u001b[38;5;241m=\u001b[39m \u001b[43m_sample\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    770\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdraws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdraws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    771\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchain\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    772\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstart\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    773\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    774\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    775\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    776\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    777\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    778\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m trace \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    779\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(traces) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/sampling.py:915\u001b[0m, in \u001b[0;36m_sample\u001b[0;34m(chain, progressbar, random_seed, start, draws, step, trace, tune, model, callback, **kwargs)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    914\u001b[0m     strace \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 915\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m it, (strace, diverging) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(sampling):\n\u001b[1;32m    916\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m it \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m skip_first \u001b[38;5;129;01mand\u001b[39;00m diverging:\n\u001b[1;32m    917\u001b[0m             _pbar_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdivergences\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/fastprogress/fastprogress.py:50\u001b[0m, in \u001b[0;36mProgressBar.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_interrupt()\n\u001b[0;32m---> 50\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/fastprogress/fastprogress.py:41\u001b[0m, in \u001b[0;36mProgressBar.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtotal \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 41\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i,o \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgen):\n\u001b[1;32m     42\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtotal \u001b[38;5;129;01mand\u001b[39;00m i \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtotal: \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m     43\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m o\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/sampling.py:1062\u001b[0m, in \u001b[0;36m_iter_sample\u001b[0;34m(draws, step, start, trace, chain, tune, model, random_seed, callback)\u001b[0m\n\u001b[1;32m   1060\u001b[0m     step \u001b[38;5;241m=\u001b[39m stop_tuning(step)\n\u001b[1;32m   1061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step\u001b[38;5;241m.\u001b[39mgenerates_stats:\n\u001b[0;32m-> 1062\u001b[0m     point, stats \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoint\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1063\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m strace\u001b[38;5;241m.\u001b[39msupports_sampler_stats:\n\u001b[1;32m   1064\u001b[0m         strace\u001b[38;5;241m.\u001b[39mrecord(point, stats)\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/step_methods/arraystep.py:286\u001b[0m, in \u001b[0;36mGradientSharedStep.step\u001b[0;34m(self, point)\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, point):\n\u001b[1;32m    285\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_logp_dlogp_func\u001b[38;5;241m.\u001b[39m_extra_are_set \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 286\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoint\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/step_methods/arraystep.py:208\u001b[0m, in \u001b[0;36mArrayStepShared.step\u001b[0;34m(self, point)\u001b[0m\n\u001b[1;32m    204\u001b[0m     shared_var\u001b[38;5;241m.\u001b[39mset_value(point[name])\n\u001b[1;32m    206\u001b[0m q \u001b[38;5;241m=\u001b[39m DictToArrayBijection\u001b[38;5;241m.\u001b[39mmap({v\u001b[38;5;241m.\u001b[39mname: point[v\u001b[38;5;241m.\u001b[39mname] \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvars})\n\u001b[0;32m--> 208\u001b[0m step_res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerates_stats:\n\u001b[1;32m    211\u001b[0m     apoint, stats \u001b[38;5;241m=\u001b[39m step_res\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/step_methods/hmc/base_hmc.py:156\u001b[0m, in \u001b[0;36mBaseHMC.astep\u001b[0;34m(self, q0)\u001b[0m\n\u001b[1;32m    153\u001b[0m p0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpotential\u001b[38;5;241m.\u001b[39mrandom()\n\u001b[1;32m    154\u001b[0m p0 \u001b[38;5;241m=\u001b[39m RaveledVars(p0, q0\u001b[38;5;241m.\u001b[39mpoint_map_info)\n\u001b[0;32m--> 156\u001b[0m start \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(start\u001b[38;5;241m.\u001b[39menergy):\n\u001b[1;32m    159\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/step_methods/hmc/integration.py:47\u001b[0m, in \u001b[0;36mCpuLeapfrogIntegrator.compute_state\u001b[0;34m(self, q, p)\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m q\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dtype \u001b[38;5;129;01mor\u001b[39;00m p\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dtype:\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid dtype. Must be \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dtype)\n\u001b[0;32m---> 47\u001b[0m logp, dlogp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_logp_dlogp_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     49\u001b[0m v \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_potential\u001b[38;5;241m.\u001b[39mvelocity(p\u001b[38;5;241m.\u001b[39mdata)\n\u001b[1;32m     50\u001b[0m kinetic \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_potential\u001b[38;5;241m.\u001b[39menergy(p\u001b[38;5;241m.\u001b[39mdata, velocity\u001b[38;5;241m=\u001b[39mv)\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/model.py:410\u001b[0m, in \u001b[0;36mValueGradFunction.__call__\u001b[0;34m(self, grad_vars, grad_out, extra_vars)\u001b[0m\n\u001b[1;32m    407\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(grad_vars, RaveledVars):\n\u001b[1;32m    408\u001b[0m     grad_vars \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(DictToArrayBijection\u001b[38;5;241m.\u001b[39mrmap(grad_vars)\u001b[38;5;241m.\u001b[39mvalues())\n\u001b[0;32m--> 410\u001b[0m cost, \u001b[38;5;241m*\u001b[39mgrads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_aesara_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mgrad_vars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    412\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grads:\n\u001b[1;32m    413\u001b[0m     grads_raveled \u001b[38;5;241m=\u001b[39m DictToArrayBijection\u001b[38;5;241m.\u001b[39mmap(\n\u001b[1;32m    414\u001b[0m         {v\u001b[38;5;241m.\u001b[39mname: gv \u001b[38;5;28;01mfor\u001b[39;00m v, gv \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_grad_vars, grads)}\n\u001b[1;32m    415\u001b[0m     )\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/compile/function/types.py:984\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    982\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthunks\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    983\u001b[0m         thunk \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm\u001b[38;5;241m.\u001b[39mthunks[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm\u001b[38;5;241m.\u001b[39mposition_of_error]\n\u001b[0;32m--> 984\u001b[0m     \u001b[43mraise_with_op\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    985\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaker\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfgraph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    986\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnodes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mposition_of_error\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    987\u001b[0m \u001b[43m        \u001b[49m\u001b[43mthunk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthunk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    988\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    989\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    990\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    991\u001b[0m     \u001b[38;5;66;03m# old-style linkers raise their own exceptions\u001b[39;00m\n\u001b[1;32m    992\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/link/utils.py:534\u001b[0m, in \u001b[0;36mraise_with_op\u001b[0;34m(fgraph, node, thunk, exc_info, storage_map)\u001b[0m\n\u001b[1;32m    529\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    530\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexc_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m error does not allow us to add an extra error message\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    531\u001b[0m     )\n\u001b[1;32m    532\u001b[0m     \u001b[38;5;66;03m# Some exception need extra parameter in inputs. So forget the\u001b[39;00m\n\u001b[1;32m    533\u001b[0m     \u001b[38;5;66;03m# extra long error message in that case.\u001b[39;00m\n\u001b[0;32m--> 534\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc_value\u001b[38;5;241m.\u001b[39mwith_traceback(exc_trace)\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/compile/function/types.py:971\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    968\u001b[0m t0_fn \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m    969\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    970\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 971\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    972\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m output_subset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    973\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm(output_subset\u001b[38;5;241m=\u001b[39moutput_subset)\n\u001b[1;32m    974\u001b[0m     )\n\u001b[1;32m    975\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m    976\u001b[0m     restore_defaults()\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/graph/op.py:543\u001b[0m, in \u001b[0;36mOp.make_py_thunk.<locals>.rval\u001b[0;34m(p, i, o, n, params)\u001b[0m\n\u001b[1;32m    539\u001b[0m \u001b[38;5;129m@is_thunk_type\u001b[39m\n\u001b[1;32m    540\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrval\u001b[39m(\n\u001b[1;32m    541\u001b[0m     p\u001b[38;5;241m=\u001b[39mp, i\u001b[38;5;241m=\u001b[39mnode_input_storage, o\u001b[38;5;241m=\u001b[39mnode_output_storage, n\u001b[38;5;241m=\u001b[39mnode, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    542\u001b[0m ):\n\u001b[0;32m--> 543\u001b[0m     r \u001b[38;5;241m=\u001b[39m \u001b[43mp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    544\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m node\u001b[38;5;241m.\u001b[39moutputs:\n\u001b[1;32m    545\u001b[0m         compute_map[o][\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/tensor/slinalg.py:366\u001b[0m, in \u001b[0;36mSolveTriangular.perform\u001b[0;34m(self, node, inputs, outputs)\u001b[0m\n\u001b[1;32m    364\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mperform\u001b[39m(\u001b[38;5;28mself\u001b[39m, node, inputs, outputs):\n\u001b[1;32m    365\u001b[0m     A, b \u001b[38;5;241m=\u001b[39m inputs\n\u001b[0;32m--> 366\u001b[0m     outputs[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mscipy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve_triangular\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    367\u001b[0m \u001b[43m        \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    368\u001b[0m \u001b[43m        \u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlower\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrans\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrans\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m        \u001b[49m\u001b[43munit_diagonal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munit_diagonal\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/scipy/linalg/_basic.py:334\u001b[0m, in \u001b[0;36msolve_triangular\u001b[0;34m(a, b, trans, lower, unit_diagonal, overwrite_b, check_finite)\u001b[0m\n\u001b[1;32m    267\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    268\u001b[0m \u001b[38;5;124;03mSolve the equation `a x = b` for `x`, assuming a is a triangular matrix.\u001b[39;00m\n\u001b[1;32m    269\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    330\u001b[0m \n\u001b[1;32m    331\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    333\u001b[0m a1 \u001b[38;5;241m=\u001b[39m _asarray_validated(a, check_finite\u001b[38;5;241m=\u001b[39mcheck_finite)\n\u001b[0;32m--> 334\u001b[0m b1 \u001b[38;5;241m=\u001b[39m \u001b[43m_asarray_validated\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_finite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    335\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(a1\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m a1\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m a1\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]:\n\u001b[1;32m    336\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexpected square matrix\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/scipy/_lib/_util.py:287\u001b[0m, in \u001b[0;36m_asarray_validated\u001b[0;34m(a, check_finite, sparse_ok, objects_ok, mask_ok, as_inexact)\u001b[0m\n\u001b[1;32m    285\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmasked arrays are not supported\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    286\u001b[0m toarray \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray_chkfinite \u001b[38;5;28;01mif\u001b[39;00m check_finite \u001b[38;5;28;01melse\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray\n\u001b[0;32m--> 287\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[43mtoarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m objects_ok:\n\u001b[1;32m    289\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m a\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01mis\u001b[39;00m np\u001b[38;5;241m.\u001b[39mdtype(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mO\u001b[39m\u001b[38;5;124m'\u001b[39m):\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/numpy/lib/function_base.py:627\u001b[0m, in \u001b[0;36masarray_chkfinite\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    625\u001b[0m a \u001b[38;5;241m=\u001b[39m asarray(a, dtype\u001b[38;5;241m=\u001b[39mdtype, order\u001b[38;5;241m=\u001b[39morder)\n\u001b[1;32m    626\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m a\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mchar \u001b[38;5;129;01min\u001b[39;00m typecodes[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAllFloat\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(a)\u001b[38;5;241m.\u001b[39mall():\n\u001b[0;32m--> 627\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    628\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray must not contain infs or NaNs\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    629\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n",
      "\u001b[0;31mValueError\u001b[0m: array must not contain infs or NaNs\nApply node that caused the error: SolveTriangular{lower=False, trans=0, unit_diagonal=False, check_finite=True}(TensorConstant{[[ 1.00000..671e-001]]}, CGemv{inplace}.0)\nToposort index: 89\nInputs types: [TensorType(float64, (663, 663)), TensorType(float64, (None,))]\nInputs shapes: [(663, 663), (663,)]\nInputs strides: [(5304, 8), (8,)]\nInputs values: ['not shown', 'not shown']\nOutputs clients: [[CGemv{no_inplace}(latent_gp_rotated_, (d__logp/dmelt_f_interval___logprob){1.0}, TensorConstant{[[ 1.00000..042e-001]]}, SolveTriangular{lower=False, trans=0, unit_diagonal=False, check_finite=True}.0, TensorConstant{-1.0})]]\n\nBacktrace when the node is created (use Aesara flag traceback__limit=N to make it longer):\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 623, in grad\n    _rval: Sequence[Variable] = _populate_grad_dict(\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1434, in _populate_grad_dict\n    rval = [access_grad_cache(elem) for elem in wrt]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1434, in <listcomp>\n    rval = [access_grad_cache(elem) for elem in wrt]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1387, in access_grad_cache\n    term = access_term_cache(node)[idx]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1058, in access_term_cache\n    output_grads = [access_grad_cache(var) for var in node.outputs]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1058, in <listcomp>\n    output_grads = [access_grad_cache(var) for var in node.outputs]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1387, in access_grad_cache\n    term = access_term_cache(node)[idx]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1213, in access_term_cache\n    input_grads = node.op.L_op(inputs, node.outputs, new_output_grads)\n\nHINT: Use the Aesara flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node."
     ]
    }
   ],
   "source": [
    "# lily: this gives a ValueError: array must not contain infs or NaNs\n",
    "with model:\n",
    "    trace = pm.sample(500,chains=2, cores=1, return_inferencedata=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "id": "VeUlIFvB-mrP",
    "outputId": "60b09773-6e0e-4256-e112-5fed999f1af7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag_grad...\n",
      "Sequential sampling (4 chains in 1 job)\n",
      "NUTS: [latent_gp_rotated_, t_b, melt_f, conditional_fit]\n"
     ]
    },
    {
     "data": {
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       "\n",
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     "text": [
      "\n",
      "Intel MKL ERROR: Parameter 8 was incorrect on entry to DGEMV .\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "array must not contain infs or NaNs\nApply node that caused the error: SolveTriangular{lower=False, trans=0, unit_diagonal=False, check_finite=True}(TensorConstant{[[ 1.00000..671e-001]]}, CGemv{inplace}.0)\nToposort index: 89\nInputs types: [TensorType(float64, (663, 663)), TensorType(float64, (None,))]\nInputs shapes: [(663, 663), (663,)]\nInputs strides: [(5304, 8), (8,)]\nInputs values: ['not shown', 'not shown']\nOutputs clients: [[CGemv{no_inplace}(latent_gp_rotated_, (d__logp/dmelt_f_interval___logprob){1.0}, TensorConstant{[[ 1.00000..042e-001]]}, SolveTriangular{lower=False, trans=0, unit_diagonal=False, check_finite=True}.0, TensorConstant{-1.0})]]\n\nBacktrace when the node is created (use Aesara flag traceback__limit=N to make it longer):\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 623, in grad\n    _rval: Sequence[Variable] = _populate_grad_dict(\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1434, in _populate_grad_dict\n    rval = [access_grad_cache(elem) for elem in wrt]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1434, in <listcomp>\n    rval = [access_grad_cache(elem) for elem in wrt]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1387, in access_grad_cache\n    term = access_term_cache(node)[idx]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1058, in access_term_cache\n    output_grads = [access_grad_cache(var) for var in node.outputs]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1058, in <listcomp>\n    output_grads = [access_grad_cache(var) for var in node.outputs]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1387, in access_grad_cache\n    term = access_term_cache(node)[idx]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1213, in access_term_cache\n    input_grads = node.op.L_op(inputs, node.outputs, new_output_grads)\n\nHINT: Use the Aesara flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/compile/function/types.py:971\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    969\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    970\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 971\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    972\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m output_subset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    973\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm(output_subset\u001b[38;5;241m=\u001b[39moutput_subset)\n\u001b[1;32m    974\u001b[0m     )\n\u001b[1;32m    975\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/graph/op.py:543\u001b[0m, in \u001b[0;36mOp.make_py_thunk.<locals>.rval\u001b[0;34m(p, i, o, n, params)\u001b[0m\n\u001b[1;32m    539\u001b[0m \u001b[38;5;129m@is_thunk_type\u001b[39m\n\u001b[1;32m    540\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrval\u001b[39m(\n\u001b[1;32m    541\u001b[0m     p\u001b[38;5;241m=\u001b[39mp, i\u001b[38;5;241m=\u001b[39mnode_input_storage, o\u001b[38;5;241m=\u001b[39mnode_output_storage, n\u001b[38;5;241m=\u001b[39mnode, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    542\u001b[0m ):\n\u001b[0;32m--> 543\u001b[0m     r \u001b[38;5;241m=\u001b[39m \u001b[43mp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    544\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m node\u001b[38;5;241m.\u001b[39moutputs:\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/tensor/slinalg.py:366\u001b[0m, in \u001b[0;36mSolveTriangular.perform\u001b[0;34m(self, node, inputs, outputs)\u001b[0m\n\u001b[1;32m    365\u001b[0m A, b \u001b[38;5;241m=\u001b[39m inputs\n\u001b[0;32m--> 366\u001b[0m outputs[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mscipy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve_triangular\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    367\u001b[0m \u001b[43m    \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    368\u001b[0m \u001b[43m    \u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlower\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrans\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrans\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m    \u001b[49m\u001b[43munit_diagonal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munit_diagonal\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcheck_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/scipy/linalg/_basic.py:334\u001b[0m, in \u001b[0;36msolve_triangular\u001b[0;34m(a, b, trans, lower, unit_diagonal, overwrite_b, check_finite)\u001b[0m\n\u001b[1;32m    333\u001b[0m a1 \u001b[38;5;241m=\u001b[39m _asarray_validated(a, check_finite\u001b[38;5;241m=\u001b[39mcheck_finite)\n\u001b[0;32m--> 334\u001b[0m b1 \u001b[38;5;241m=\u001b[39m \u001b[43m_asarray_validated\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_finite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    335\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(a1\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m a1\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m a1\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]:\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/scipy/_lib/_util.py:287\u001b[0m, in \u001b[0;36m_asarray_validated\u001b[0;34m(a, check_finite, sparse_ok, objects_ok, mask_ok, as_inexact)\u001b[0m\n\u001b[1;32m    286\u001b[0m toarray \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray_chkfinite \u001b[38;5;28;01mif\u001b[39;00m check_finite \u001b[38;5;28;01melse\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray\n\u001b[0;32m--> 287\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[43mtoarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m objects_ok:\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/numpy/lib/function_base.py:627\u001b[0m, in \u001b[0;36masarray_chkfinite\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    626\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m a\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mchar \u001b[38;5;129;01min\u001b[39;00m typecodes[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAllFloat\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(a)\u001b[38;5;241m.\u001b[39mall():\n\u001b[0;32m--> 627\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    628\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray must not contain infs or NaNs\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    629\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n",
      "\u001b[0;31mValueError\u001b[0m: array must not contain infs or NaNs",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [38], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# lily: this gives a ValueError: array must not contain infs or NaNs\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m model:\n\u001b[0;32m----> 3\u001b[0m     trace \u001b[38;5;241m=\u001b[39m \u001b[43mpm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m        \u001b[49m\u001b[43minit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mjitter+adapt_diag_grad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m42\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_accept\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.8\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcores\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/sampling.py:655\u001b[0m, in \u001b[0;36msample\u001b[0;34m(draws, step, init, n_init, initvals, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, **kwargs)\u001b[0m\n\u001b[1;32m    653\u001b[0m         _log\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSequential sampling (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mchains\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m chains in 1 job)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    654\u001b[0m         _print_step_hierarchy(step)\n\u001b[0;32m--> 655\u001b[0m         mtrace \u001b[38;5;241m=\u001b[39m \u001b[43m_sample_many\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msample_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    657\u001b[0m t_sampling \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m t_start\n\u001b[1;32m    658\u001b[0m \u001b[38;5;66;03m# count the number of tune/draw iterations that happened\u001b[39;00m\n\u001b[1;32m    659\u001b[0m \u001b[38;5;66;03m# ideally via the \"tune\" statistic, but not all samplers record it!\u001b[39;00m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/sampling.py:769\u001b[0m, in \u001b[0;36m_sample_many\u001b[0;34m(draws, chain, chains, start, random_seed, step, callback, **kwargs)\u001b[0m\n\u001b[1;32m    767\u001b[0m traces: List[BaseTrace] \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    768\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(chains):\n\u001b[0;32m--> 769\u001b[0m     trace \u001b[38;5;241m=\u001b[39m \u001b[43m_sample\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    770\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdraws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdraws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    771\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchain\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    772\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstart\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    773\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    774\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    775\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    776\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    777\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    778\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m trace \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    779\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(traces) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/sampling.py:915\u001b[0m, in \u001b[0;36m_sample\u001b[0;34m(chain, progressbar, random_seed, start, draws, step, trace, tune, model, callback, **kwargs)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    914\u001b[0m     strace \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 915\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m it, (strace, diverging) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(sampling):\n\u001b[1;32m    916\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m it \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m skip_first \u001b[38;5;129;01mand\u001b[39;00m diverging:\n\u001b[1;32m    917\u001b[0m             _pbar_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdivergences\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/fastprogress/fastprogress.py:50\u001b[0m, in \u001b[0;36mProgressBar.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_interrupt()\n\u001b[0;32m---> 50\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/fastprogress/fastprogress.py:41\u001b[0m, in \u001b[0;36mProgressBar.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtotal \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 41\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i,o \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgen):\n\u001b[1;32m     42\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtotal \u001b[38;5;129;01mand\u001b[39;00m i \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtotal: \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m     43\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m o\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/sampling.py:1062\u001b[0m, in \u001b[0;36m_iter_sample\u001b[0;34m(draws, step, start, trace, chain, tune, model, random_seed, callback)\u001b[0m\n\u001b[1;32m   1060\u001b[0m     step \u001b[38;5;241m=\u001b[39m stop_tuning(step)\n\u001b[1;32m   1061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step\u001b[38;5;241m.\u001b[39mgenerates_stats:\n\u001b[0;32m-> 1062\u001b[0m     point, stats \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoint\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1063\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m strace\u001b[38;5;241m.\u001b[39msupports_sampler_stats:\n\u001b[1;32m   1064\u001b[0m         strace\u001b[38;5;241m.\u001b[39mrecord(point, stats)\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/step_methods/arraystep.py:286\u001b[0m, in \u001b[0;36mGradientSharedStep.step\u001b[0;34m(self, point)\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, point):\n\u001b[1;32m    285\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_logp_dlogp_func\u001b[38;5;241m.\u001b[39m_extra_are_set \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 286\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoint\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/step_methods/arraystep.py:208\u001b[0m, in \u001b[0;36mArrayStepShared.step\u001b[0;34m(self, point)\u001b[0m\n\u001b[1;32m    204\u001b[0m     shared_var\u001b[38;5;241m.\u001b[39mset_value(point[name])\n\u001b[1;32m    206\u001b[0m q \u001b[38;5;241m=\u001b[39m DictToArrayBijection\u001b[38;5;241m.\u001b[39mmap({v\u001b[38;5;241m.\u001b[39mname: point[v\u001b[38;5;241m.\u001b[39mname] \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvars})\n\u001b[0;32m--> 208\u001b[0m step_res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerates_stats:\n\u001b[1;32m    211\u001b[0m     apoint, stats \u001b[38;5;241m=\u001b[39m step_res\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/step_methods/hmc/base_hmc.py:156\u001b[0m, in \u001b[0;36mBaseHMC.astep\u001b[0;34m(self, q0)\u001b[0m\n\u001b[1;32m    153\u001b[0m p0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpotential\u001b[38;5;241m.\u001b[39mrandom()\n\u001b[1;32m    154\u001b[0m p0 \u001b[38;5;241m=\u001b[39m RaveledVars(p0, q0\u001b[38;5;241m.\u001b[39mpoint_map_info)\n\u001b[0;32m--> 156\u001b[0m start \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(start\u001b[38;5;241m.\u001b[39menergy):\n\u001b[1;32m    159\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/step_methods/hmc/integration.py:47\u001b[0m, in \u001b[0;36mCpuLeapfrogIntegrator.compute_state\u001b[0;34m(self, q, p)\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m q\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dtype \u001b[38;5;129;01mor\u001b[39;00m p\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dtype:\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid dtype. Must be \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dtype)\n\u001b[0;32m---> 47\u001b[0m logp, dlogp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_logp_dlogp_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     49\u001b[0m v \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_potential\u001b[38;5;241m.\u001b[39mvelocity(p\u001b[38;5;241m.\u001b[39mdata)\n\u001b[1;32m     50\u001b[0m kinetic \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_potential\u001b[38;5;241m.\u001b[39menergy(p\u001b[38;5;241m.\u001b[39mdata, velocity\u001b[38;5;241m=\u001b[39mv)\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/pymc/model.py:410\u001b[0m, in \u001b[0;36mValueGradFunction.__call__\u001b[0;34m(self, grad_vars, grad_out, extra_vars)\u001b[0m\n\u001b[1;32m    407\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(grad_vars, RaveledVars):\n\u001b[1;32m    408\u001b[0m     grad_vars \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(DictToArrayBijection\u001b[38;5;241m.\u001b[39mrmap(grad_vars)\u001b[38;5;241m.\u001b[39mvalues())\n\u001b[0;32m--> 410\u001b[0m cost, \u001b[38;5;241m*\u001b[39mgrads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_aesara_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mgrad_vars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    412\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grads:\n\u001b[1;32m    413\u001b[0m     grads_raveled \u001b[38;5;241m=\u001b[39m DictToArrayBijection\u001b[38;5;241m.\u001b[39mmap(\n\u001b[1;32m    414\u001b[0m         {v\u001b[38;5;241m.\u001b[39mname: gv \u001b[38;5;28;01mfor\u001b[39;00m v, gv \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_grad_vars, grads)}\n\u001b[1;32m    415\u001b[0m     )\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/compile/function/types.py:984\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    982\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthunks\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    983\u001b[0m         thunk \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm\u001b[38;5;241m.\u001b[39mthunks[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm\u001b[38;5;241m.\u001b[39mposition_of_error]\n\u001b[0;32m--> 984\u001b[0m     \u001b[43mraise_with_op\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    985\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaker\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfgraph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    986\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnodes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mposition_of_error\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    987\u001b[0m \u001b[43m        \u001b[49m\u001b[43mthunk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthunk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    988\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    989\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    990\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    991\u001b[0m     \u001b[38;5;66;03m# old-style linkers raise their own exceptions\u001b[39;00m\n\u001b[1;32m    992\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/link/utils.py:534\u001b[0m, in \u001b[0;36mraise_with_op\u001b[0;34m(fgraph, node, thunk, exc_info, storage_map)\u001b[0m\n\u001b[1;32m    529\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    530\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexc_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m error does not allow us to add an extra error message\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    531\u001b[0m     )\n\u001b[1;32m    532\u001b[0m     \u001b[38;5;66;03m# Some exception need extra parameter in inputs. So forget the\u001b[39;00m\n\u001b[1;32m    533\u001b[0m     \u001b[38;5;66;03m# extra long error message in that case.\u001b[39;00m\n\u001b[0;32m--> 534\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc_value\u001b[38;5;241m.\u001b[39mwith_traceback(exc_trace)\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/compile/function/types.py:971\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    968\u001b[0m t0_fn \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m    969\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    970\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 971\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    972\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m output_subset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    973\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm(output_subset\u001b[38;5;241m=\u001b[39moutput_subset)\n\u001b[1;32m    974\u001b[0m     )\n\u001b[1;32m    975\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m    976\u001b[0m     restore_defaults()\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/graph/op.py:543\u001b[0m, in \u001b[0;36mOp.make_py_thunk.<locals>.rval\u001b[0;34m(p, i, o, n, params)\u001b[0m\n\u001b[1;32m    539\u001b[0m \u001b[38;5;129m@is_thunk_type\u001b[39m\n\u001b[1;32m    540\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrval\u001b[39m(\n\u001b[1;32m    541\u001b[0m     p\u001b[38;5;241m=\u001b[39mp, i\u001b[38;5;241m=\u001b[39mnode_input_storage, o\u001b[38;5;241m=\u001b[39mnode_output_storage, n\u001b[38;5;241m=\u001b[39mnode, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    542\u001b[0m ):\n\u001b[0;32m--> 543\u001b[0m     r \u001b[38;5;241m=\u001b[39m \u001b[43mp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    544\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m node\u001b[38;5;241m.\u001b[39moutputs:\n\u001b[1;32m    545\u001b[0m         compute_map[o][\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/tensor/slinalg.py:366\u001b[0m, in \u001b[0;36mSolveTriangular.perform\u001b[0;34m(self, node, inputs, outputs)\u001b[0m\n\u001b[1;32m    364\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mperform\u001b[39m(\u001b[38;5;28mself\u001b[39m, node, inputs, outputs):\n\u001b[1;32m    365\u001b[0m     A, b \u001b[38;5;241m=\u001b[39m inputs\n\u001b[0;32m--> 366\u001b[0m     outputs[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mscipy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve_triangular\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    367\u001b[0m \u001b[43m        \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    368\u001b[0m \u001b[43m        \u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlower\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrans\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrans\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m        \u001b[49m\u001b[43munit_diagonal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munit_diagonal\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/scipy/linalg/_basic.py:334\u001b[0m, in \u001b[0;36msolve_triangular\u001b[0;34m(a, b, trans, lower, unit_diagonal, overwrite_b, check_finite)\u001b[0m\n\u001b[1;32m    267\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    268\u001b[0m \u001b[38;5;124;03mSolve the equation `a x = b` for `x`, assuming a is a triangular matrix.\u001b[39;00m\n\u001b[1;32m    269\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    330\u001b[0m \n\u001b[1;32m    331\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    333\u001b[0m a1 \u001b[38;5;241m=\u001b[39m _asarray_validated(a, check_finite\u001b[38;5;241m=\u001b[39mcheck_finite)\n\u001b[0;32m--> 334\u001b[0m b1 \u001b[38;5;241m=\u001b[39m \u001b[43m_asarray_validated\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_finite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    335\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(a1\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m a1\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m a1\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]:\n\u001b[1;32m    336\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexpected square matrix\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/scipy/_lib/_util.py:287\u001b[0m, in \u001b[0;36m_asarray_validated\u001b[0;34m(a, check_finite, sparse_ok, objects_ok, mask_ok, as_inexact)\u001b[0m\n\u001b[1;32m    285\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmasked arrays are not supported\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    286\u001b[0m toarray \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray_chkfinite \u001b[38;5;28;01mif\u001b[39;00m check_finite \u001b[38;5;28;01melse\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray\n\u001b[0;32m--> 287\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[43mtoarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m objects_ok:\n\u001b[1;32m    289\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m a\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01mis\u001b[39;00m np\u001b[38;5;241m.\u001b[39mdtype(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mO\u001b[39m\u001b[38;5;124m'\u001b[39m):\n",
      "File \u001b[0;32m~/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/numpy/lib/function_base.py:627\u001b[0m, in \u001b[0;36masarray_chkfinite\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    625\u001b[0m a \u001b[38;5;241m=\u001b[39m asarray(a, dtype\u001b[38;5;241m=\u001b[39mdtype, order\u001b[38;5;241m=\u001b[39morder)\n\u001b[1;32m    626\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m a\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mchar \u001b[38;5;129;01min\u001b[39;00m typecodes[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAllFloat\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(a)\u001b[38;5;241m.\u001b[39mall():\n\u001b[0;32m--> 627\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    628\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray must not contain infs or NaNs\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    629\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n",
      "\u001b[0;31mValueError\u001b[0m: array must not contain infs or NaNs\nApply node that caused the error: SolveTriangular{lower=False, trans=0, unit_diagonal=False, check_finite=True}(TensorConstant{[[ 1.00000..671e-001]]}, CGemv{inplace}.0)\nToposort index: 89\nInputs types: [TensorType(float64, (663, 663)), TensorType(float64, (None,))]\nInputs shapes: [(663, 663), (663,)]\nInputs strides: [(5304, 8), (8,)]\nInputs values: ['not shown', 'not shown']\nOutputs clients: [[CGemv{no_inplace}(latent_gp_rotated_, (d__logp/dmelt_f_interval___logprob){1.0}, TensorConstant{[[ 1.00000..042e-001]]}, SolveTriangular{lower=False, trans=0, unit_diagonal=False, check_finite=True}.0, TensorConstant{-1.0})]]\n\nBacktrace when the node is created (use Aesara flag traceback__limit=N to make it longer):\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 623, in grad\n    _rval: Sequence[Variable] = _populate_grad_dict(\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1434, in _populate_grad_dict\n    rval = [access_grad_cache(elem) for elem in wrt]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1434, in <listcomp>\n    rval = [access_grad_cache(elem) for elem in wrt]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1387, in access_grad_cache\n    term = access_term_cache(node)[idx]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1058, in access_term_cache\n    output_grads = [access_grad_cache(var) for var in node.outputs]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1058, in <listcomp>\n    output_grads = [access_grad_cache(var) for var in node.outputs]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1387, in access_grad_cache\n    term = access_term_cache(node)[idx]\n  File \"/home/lilianschuster/micromamba/envs/oggm_pymc/lib/python3.9/site-packages/aesara/gradient.py\", line 1213, in access_term_cache\n    input_grads = node.op.L_op(inputs, node.outputs, new_output_grads)\n\nHINT: Use the Aesara flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node."
     ]
    }
   ],
   "source": [
    "# lily: this gives a ValueError: array must not contain infs or NaNs\n",
    "with model:\n",
    "    trace = pm.sample(\n",
    "        init=\"jitter+adapt_diag_grad\",\n",
    "        random_seed=42, chains=4, target_accept=0.8, cores=1\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "id": "LwsddgTKj2Hd",
    "outputId": "6c5fdaf3-47ec-47bd-d94f-06790b7abfed"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = az.plot_trace(trace, var_names=['t_b', 'melt_f', \n",
    "                                      'conditional_fit'], filter_vars=\"like\")\n",
    "axs[0,0].figure.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 37
    },
    "id": "quAwSYGmpB3A",
    "outputId": "f5debad7-3ffa-4c04-d812-396714ae1ff8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='4000' class='' max='4000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [4000/4000 00:00&lt;00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with model:\n",
    "  predicted_samples = pm.sample_posterior_predictive(trace, var_names=['log_mb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "id": "CeJNhUPRtvjd",
    "outputId": "034bca5b-3374-431c-91ab-5e83bdadc6dd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = az.plot_trace(predicted_samples.posterior_predictive, filter_vars=\"like\")\n",
    "axs[0,0].figure.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WkmIomRw0ObD"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "oggm_pymc",
   "language": "python",
   "name": "oggm_pymc"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}