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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "eeea7231-64f2-400a-9c4e-f648004b5a37",
+   "metadata": {},
+   "source": [
+    "# Handling model settings and observations from OGGM v1.7 onwards"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fe0400f-615f-4cd3-9cfb-953b58618fda",
+   "metadata": {},
+   "source": [
+    "This tutorial explains how model settings and observations are handled in OGGM from v1.7 onwards. It is intended for users who want to run multiple model simulations within a single glacier directory (`gdir`), for example for sensitivity studies or ensemble approaches (e.g. running the same workflow with different parameter values or perturbed observations).\n",
+    "\n",
+    "**Prerequisites:** familiarity with the basic OGGM workflow and the concept of\n",
+    "`entity_task`s (see [this introductory tutorial](../10minutes/preprocessed_directories.ipynb)).\n",
+    "\n",
+    "**What you will learn:**\n",
+    "- How `settings.yml` and `observations.yml` work and why they were introduced\n",
+    "- How to create custom settings and observations files\n",
+    "- How to run a [simple sensitivity study by perturbing mass balance parameters](#a-simple-sensitivity-study)\n",
+    "- How to [combine settings and observations perturbations in a full OGGM workflow](#combining-settings-with-observations-sensitivity)\n",
+    "- How to [perturb global parameters such as ice density](#advanced-sensitivity-study:-perturbing-global-parameters-(ice-density))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e9d84b9-548e-4822-9dda-ce4accbb2778",
+   "metadata": {},
+   "source": [
+    "## How the new system works"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39ff022d-58a1-45bc-aab2-fdfc33a00130",
+   "metadata": {},
+   "source": [
+    "Before v1.7, model parameters and settings were scattered across several locations: global settings and first-guess values were stored in `cfg.PARAMS`, mass balance parameters in `mb_calib.json`, and dynamic parameters in `gdir.get_diagnostics()`. Observations used during calibration were similarly spread across different files and had no central storage. Starting with v1.7, OGGM centralizes this through two new files per glacier directory:\n",
+    "\n",
+    "- **`settings.yml`**: stores glacier-specific parameter values. Global\n",
+    "  parameters (shared across all glaciers) remain in `cfg.PARAMS`, but you\n",
+    "  can access both through the same interface without needing to know where a\n",
+    "  parameter is actually stored.\n",
+    "- **`observations.yml`**: stores the observations used during calibration or\n",
+    "  assimilation (e.g. reference mass balance, reference volume).\n",
+    "\n",
+    "To make this easy to use in practice, all OGGM tasks accept a `settings_filesuffix` keyword argument. Tasks that use observations also accept `observations_filesuffix`. By switching these suffixes, you can run the same task with different parameter sets or different observations. This is the foundation of the sensitivity workflows shown in this tutorial.\n",
+    "\n",
+    "The new system is designed to be **backwards compatible**: if no v1.7 settings files exist, OGGM falls back to the pre-v1.7 locations automatically."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b3d4ac0-d241-4b77-9ae9-bd238412db83",
+   "metadata": {},
+   "source": [
+    "## Exploring `settings.yml`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "342b542b-e66c-4cfc-b84b-fc66be37acbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "import logging  # needed for defining custom entity tasks\n",
+    "\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from oggm import cfg, utils, workflow, DEFAULT_BASE_URL, tasks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "52c0e9b9-b3a1-4b14-b761-fbb6ae8a31c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:53:41: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.\n",
+      "2026-04-03 09:53:41: oggm.cfg: Multiprocessing switched OFF according to the parameter file.\n",
+      "2026-04-03 09:53:41: oggm.cfg: Multiprocessing: using all available processors (N=8)\n",
+      "2026-04-03 09:53:41: oggm.cfg: Multiprocessing switched ON after user settings.\n",
+      "2026-04-03 09:53:41: oggm.workflow: init_glacier_directories from prepro level 4 on 2 glaciers.\n",
+      "2026-04-03 09:53:41: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 2 glaciers\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set up oggm with two test glaciers\n",
+    "cfg.initialize()\n",
+    "\n",
+    "# use multiprocessing\n",
+    "cfg.PARAMS['use_multiprocessing'] = True\n",
+    "\n",
+    "# define a temporary working directory\n",
+    "cfg.PATHS['working_dir'] = utils.gettempdir(dirname='new_settings_handling', reset=True)\n",
+    "\n",
+    "# set up test gdirs\n",
+    "rgi_ids = ['RGI60-11.00897',  # Hintereisferner\n",
+    "           'RGI60-11.01450',  # Aletsch Glacier\n",
+    "          ]\n",
+    "\n",
+    "gdirs = workflow.init_glacier_directories(\n",
+    "    rgi_ids,  # which glaciers?\n",
+    "    prepro_base_url=DEFAULT_BASE_URL, # where to fetch the data?\n",
+    "    from_prepro_level=4,  # what kind of data?\n",
+    "    prepro_border=80  # how big of a map?\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "174ece46-b6bb-4e1a-8aa1-4295033f3dcb",
+   "metadata": {},
+   "source": [
+    "> **Note:** The default preprocessed glacier directories currently use v1.6.3. Once v1.7 preprocessed directories become available, this section will need to be updated accordingly.                                                                                                                                               \n",
+    "                                                                                                                                                                           \n",
+    "For now, let's look at what `settings.yml` contains for pre-v1.7 directories. We can open it using `gdir.read_yml`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1ab5d03a-e649-4a26-8026-edc3124ad5c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'parent_filesuffix': 'cfg.PARAMS'}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdirs[0].read_yml('settings')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9137537f-6af4-4f04-a579-c86938ba3cbd",
+   "metadata": {},
+   "source": [
+    "There is only one entry: `'parent_filesuffix'`. This tells OGGM where to look for a setting if it is not present in the current settings file. By default it points to `cfg.PARAMS`, which was the main parameter store before v1.7.\n",
+    "\n",
+    "Let's see what happens when we read a parameter using `gdir.read_settings`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f0e74194-e2f0-46aa-9400-b29194e120b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'filesuffix': '', 'ice_density': 900.0}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdirs[0].read_settings(['ice_density'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d09f363e-09f6-4838-a463-4f296e26e44d",
+   "metadata": {},
+   "source": [
+    "The result is a `dict` containing the name of the settings file (`filesuffix`) and the value of the requested parameter. Since `ice_density` is not stored in `settings.yml`, OGGM followed the `parent_filesuffix` and retrieved it from    `cfg.PARAMS`. We can verify this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "264cda0a-c35b-4728-8698-e7bcd4108627",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "900.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cfg.PARAMS['ice_density']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffe15317-568e-48f2-aac2-bd6915d301e9",
+   "metadata": {},
+   "source": [
+    "Let's try some other parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "98ff3695-1b9a-434e-93bf-a5852e80b9ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'filesuffix': '',\n",
+       " 'melt_f': 4.906856791650656,\n",
+       " 'inversion_glen_a': 5.6083977640974805e-24}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdirs[0].read_settings(['melt_f', 'inversion_glen_a'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2f89e732-1e6f-4704-a726-960238fb58b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5.0, 2.4e-24)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cfg.PARAMS['melt_f'], cfg.PARAMS['inversion_glen_a']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2f4933d-5f4d-47ab-ada1-2aa49860698c",
+   "metadata": {},
+   "source": [
+    "The values are different from `cfg.PARAMS`. But why?\n",
+    "\n",
+    "For **backwards compatibility**, OGGM checks additional locations before falling back to `cfg.PARAMS`:\n",
+    "- For mass balance parameters, it first checks `mb_calib.json`\n",
+    "- For dynamic parameters, it first checks `gdir.get_diagnostics()`\n",
+    "    \n",
+    "Only if a parameter cannot be found in any of those places does OGGM raise an error. We can confirm this fallback behaviour:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4613ce4a-d23e-412a-9810-01f00a43a45a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4.906856791650656, 5.6083977640974805e-24)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(gdirs[0].read_json('mb_calib')['melt_f'],\n",
+    " gdirs[0].get_diagnostics()['inversion_glen_a'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e200246b-5c00-4542-9198-80cc07f5fe42",
+   "metadata": {},
+   "source": [
+    "These match the values returned by `gdir.read_settings`, confirming that OGGM correctly falls back to the pre-v1.7 storage locations.\n",
+    "\n",
+    "> **Key point:** `gdir.read_settings` always tries to return the same default values as would have been used before v1.7, so existing workflows continue to work without modification (we hope).\n",
+    "\n",
+    "Now let's see how to create a custom settings file using `gdir.create_new_settings`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fbfc3d7f-6f1f-4d17-bbc9-c25a09382248",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_filesuffix = '_test'\n",
+    "gdirs[0].create_new_settings(filesuffix=test_filesuffix,\n",
+    "                             data={\n",
+    "                                 'melt_f': 5.5,\n",
+    "                             },\n",
+    "                             parent_filesuffix='',\n",
+    "                             # if the settings file already exists and this is False, you will get an error;\n",
+    "                             # if True and the file already exists, the new data is added to it\n",
+    "                             ignore_existing=True, \n",
+    "                             # if a parameter already exists, force overwrite it;\n",
+    "                             # otherwise an error is raised to prevent unnoticed parameter changes\n",
+    "                             overwrite=True,  \n",
+    "                            )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06089edf-3be5-4d09-80e7-5effde9d69b7",
+   "metadata": {},
+   "source": [
+    "You pass new parameter values as a `dict`. Setting `overwrite=True` allows overwriting parameters that are already present in the file.\n",
+    "\n",
+    "Notice that we set `parent_filesuffix` to the default `settings.yml` (no suffix). This creates a **nested lookup chain**: when reading a parameter from `settings_test.yml`, OGGM first checks `settings_test.yml`, then `settings.yml`, and finally `cfg.PARAMS`. This hierarchy means you only need to store the parameters you actually want to change."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "be58ffa3-0443-414b-a9c2-30f58574c29e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'filesuffix': '_test',\n",
+       " 'melt_f': 5.5,\n",
+       " 'inversion_glen_a': 5.6083977640974805e-24,\n",
+       " 'ice_density': 900.0}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdirs[0].read_settings(['melt_f', 'inversion_glen_a', 'ice_density'],\n",
+    "                       filesuffix=test_filesuffix\n",
+    "                      )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "342291e1-6c80-4dbd-964c-9807d797c227",
+   "metadata": {},
+   "source": [
+    "You can also call `read_settings` without a list of keys to inspect all currently available parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "eeb9c2cc-fae5-4e33-b25f-3cbe68ed12ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'filesuffix': '_test', 'melt_f': 5.5, 'parent_filesuffix': ''}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdirs[0].read_settings(filesuffix=test_filesuffix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7ce087c-a2e1-46e0-8cd0-a05ba0dfd776",
+   "metadata": {},
+   "source": [
+    "Now that we understand how `settings.yml` works, let's use it to run a simple sensitivity study. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b993de5-8836-4ca5-b9d9-83aff1ab66b0",
+   "metadata": {},
+   "source": [
+    "## A simple sensitivity study"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3403abcc-a308-4539-8dd2-b1e1b8b002c1",
+   "metadata": {},
+   "source": [
+    "In this example we perturb the default calibrated values of the mass balance parameters `melt_f` and `temp_bias`. We first define an `entity_task` that creates new settings files with the perturbed values. Using the `entity_task` framework makes it straightforward to apply the same perturbations to multiple glaciers, and allows multiprocessing via `workflow.execute_entity_task` (with `cfg.PARAMS['use_multiprocessing'] = True`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "106b3d3f-b48f-4256-a905-7749177d0cdd",
+   "metadata": {},
+   "source": [
+    "### Define your own `entity_task`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "717c2d04-73b2-4ae3-bad6-1fdfccee6ba8",
+   "metadata": {},
+   "source": [
+    "A function becomes an `entity_task` by applying the `@utils.entity_task` decorator and passing it the module logger via `logging.getLogger(__name__)`. By convention, every `entity_task` must have a docstring and `gdir` as its first argument. If you plan to use your own `entity_task` together with multiprocessing (`cfg.PARAMS['use_multiprocessing'] = True`), you also need to call `workflow.reset_multiprocessing()`. It is therefore good practice to define all your custom entity tasks at the beginning of your workflow and call `workflow.reset_multiprocessing()` once after all definitions.\n",
+    "\n",
+    "A further feature of entity tasks is that if the function includes a `settings_filesuffix` argument, the decorator automatically sets `gdir.settings` to point to the corresponding settings file, so you can access the correct parameters inside the task via `gdir.settings`. The same logic applies to `observations_filesuffix` and `gdir.observations`. Here is a small example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "54139ebc-64fb-4c8a-ae53-a38a87e48322",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# module-level logger\n",
+    "log = logging.getLogger(__name__)\n",
+    "\n",
+    "# apply the decorator before the function definition\n",
+    "@utils.entity_task(log)\n",
+    "def print_melt_f_from_settings(gdir, settings_filesuffix=''):\n",
+    "    \"\"\" Print the melt_f value from the current settings.\n",
+    "    \"\"\"\n",
+    "    # note: there is no code here using settings_filesuffix directly —\n",
+    "    # the decorator handles setting gdir.settings automatically\n",
+    "    print(gdir.settings['melt_f'])\n",
+    "\n",
+    "# reset the multiprocessing pool so that forked workers are aware of the\n",
+    "# new function definition (only needed when cfg.PARAMS['use_multiprocessing']=True)\n",
+    "workflow.reset_multiprocessing()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "eff965ef-19b5-4f7a-919a-443e38bfb31a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:53:45: __main__: (RGI60-11.00897) print_melt_f_from_settings\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.906856791650656\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_melt_f_from_settings(gdirs[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0c6680c6-0854-4ebe-a895-9e211ef01825",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:53:45: __main__: (RGI60-11.00897) print_melt_f_from_settings_test\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_melt_f_from_settings(gdirs[0], settings_filesuffix=test_filesuffix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53acf6cf-bfae-4a16-9119-f76729157449",
+   "metadata": {},
+   "source": [
+    "In the second call we pass the `settings_filesuffix` from our earlier example, and the decorator ensures that `gdir.settings` points to the correct settings file inside the task. Outside of an `entity_task`, you can access different settings directly using `gdir.read_settings`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ac84e0b-4c41-4daf-89ea-1a7bf45fd575",
+   "metadata": {},
+   "source": [
+    "### Define an entity task for perturbing mass balance parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d69e71fc-2ad5-447d-92a1-1a327d45de6b",
+   "metadata": {},
+   "source": [
+    "We now define an `entity_task` that reads the default values of `melt_f` and `temp_bias` from `settings.yml`, adds a given perturbation to each, and saves the result into a new settings file identified by `new_filesuffix`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ca635fb1-05f4-4ff3-8132-00fd6d60320f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log = logging.getLogger(__name__)\n",
+    "\n",
+    "@utils.entity_task(log)\n",
+    "def create_perturbed_mb_settings(gdir, new_filesuffix, melt_f_perturbation, temp_bias_perturbation):\n",
+    "    \"\"\" Reads the default values of melt_f and temp_bias from settings.yml, adds a perturbation,\n",
+    "    and saves the result in a new settings file identified by new_filesuffix.\n",
+    "    \"\"\"\n",
+    "    # read the default values from the default settings file\n",
+    "    default_settings = gdir.read_settings(keys=['melt_f', 'temp_bias'], filesuffix='')\n",
+    "\n",
+    "    # create a new settings file with the perturbed parameters\n",
+    "    gdir.create_new_settings(filesuffix=new_filesuffix,\n",
+    "                             # provide the perturbed values as a dict\n",
+    "                             data={\n",
+    "                                 'melt_f': default_settings['melt_f'] + melt_f_perturbation,\n",
+    "                                 'temp_bias': default_settings['temp_bias'] + temp_bias_perturbation,\n",
+    "                             },\n",
+    "                             # use the default settings.yml as the parent for all other parameters\n",
+    "                             parent_filesuffix='',\n",
+    "                             # in this example we ignore existing files and allow overwriting;\n",
+    "                             # in a full workflow it may be safer to set both to False\n",
+    "                             # to avoid unintentionally overwriting existing parameters\n",
+    "                             ignore_existing=True,\n",
+    "                             overwrite=True,\n",
+    "                            )\n",
+    "\n",
+    "workflow.reset_multiprocessing()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97feb17e-aef0-4fb9-beda-2f4d4614ea6d",
+   "metadata": {},
+   "source": [
+    "We use `itertools.product` to generate all combinations of predefined perturbation values and call our new entity task for each combination. This is a simple way to sample a parameter space. For more advanced sampling strategies, see for example the [SAFE Toolbox](https://safetoolbox.github.io/) or [SALib](https://salib.readthedocs.io/en/latest/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "174c1ef6-fe1c-4637-954c-545c3d6c4d5c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:53:45: oggm.workflow: Execute entity tasks [create_perturbed_mb_settings] on 2 glaciers\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.00897) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.01450) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: oggm.workflow: Execute entity tasks [create_perturbed_mb_settings] on 2 glaciers\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.00897) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.01450) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: oggm.workflow: Execute entity tasks [create_perturbed_mb_settings] on 2 glaciers\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.00897) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.01450) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: oggm.workflow: Execute entity tasks [create_perturbed_mb_settings] on 2 glaciers\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.01450) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.00897) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: oggm.workflow: Execute entity tasks [create_perturbed_mb_settings] on 2 glaciers\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.01450) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.00897) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: oggm.workflow: Execute entity tasks [create_perturbed_mb_settings] on 2 glaciers\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.00897) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.01450) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: oggm.workflow: Execute entity tasks [create_perturbed_mb_settings] on 2 glaciers\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.00897) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.01450) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: oggm.workflow: Execute entity tasks [create_perturbed_mb_settings] on 2 glaciers\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.01450) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.00897) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: oggm.workflow: Execute entity tasks [create_perturbed_mb_settings] on 2 glaciers\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.00897) create_perturbed_mb_settings\n",
+      "2026-04-03 09:53:45: __main__: (RGI60-11.01450) create_perturbed_mb_settings\n"
+     ]
+    }
+   ],
+   "source": [
+    "from itertools import product\n",
+    "\n",
+    "# define the perturbations for each parameter\n",
+    "melt_f_perturbations = [-0.5, 0, 0.5]\n",
+    "temp_bias_perturbations = [-0.2, 0, 0.2]\n",
+    "\n",
+    "# create all possible combinations of perturbations\n",
+    "all_combinations = list(product(melt_f_perturbations, temp_bias_perturbations))\n",
+    "\n",
+    "# common name for this set of experiments; each settings file gets a unique integer suffix\n",
+    "mb_experiment_name = '_mb_sensitivity'\n",
+    "\n",
+    "# container for all created settings filesuffixes\n",
+    "all_created_settings = []\n",
+    "\n",
+    "# loop through all combinations\n",
+    "for i, params in enumerate(all_combinations):\n",
+    "    # extract the individual perturbations\n",
+    "    melt_f_perturbation, temp_bias_perturbation = params\n",
+    "\n",
+    "    # define a unique filesuffix using a running index\n",
+    "    new_filesuffix = f\"{mb_experiment_name}_{i}\"\n",
+    "\n",
+    "    # apply the same perturbation to all glaciers at once\n",
+    "    workflow.execute_entity_task(create_perturbed_mb_settings, gdirs,\n",
+    "                                 new_filesuffix=new_filesuffix,\n",
+    "                                 melt_f_perturbation=melt_f_perturbation,\n",
+    "                                 temp_bias_perturbation=temp_bias_perturbation)\n",
+    "\n",
+    "    # store the filesuffix for later reference\n",
+    "    all_created_settings.append(new_filesuffix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41dde701-f0e7-48da-9acb-e850899a0445",
+   "metadata": {},
+   "source": [
+    "This should have created a new settings file for each combination. To inspect parameter values across multiple settings files, use `utils.compile_settings`. It works on a single `gdir` or a list of `gdir`s. Pass the parameters of interest via `keys` and a list of file suffixes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "49c0edb2-22df-4f61-a264-28a0d402fda9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\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>rgi_id</th>\n",
+       "      <th>filesuffix</th>\n",
+       "      <th>melt_f</th>\n",
+       "      <th>temp_bias</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RGI60-11.00897</td>\n",
+       "      <td>_mb_sensitivity_0</td>\n",
+       "      <td>4.406857</td>\n",
+       "      <td>1.505036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>RGI60-11.00897</td>\n",
+       "      <td>_mb_sensitivity_1</td>\n",
+       "      <td>4.406857</td>\n",
+       "      <td>1.705036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>RGI60-11.00897</td>\n",
+       "      <td>_mb_sensitivity_2</td>\n",
+       "      <td>4.406857</td>\n",
+       "      <td>1.905036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>RGI60-11.00897</td>\n",
+       "      <td>_mb_sensitivity_3</td>\n",
+       "      <td>4.906857</td>\n",
+       "      <td>1.505036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>RGI60-11.00897</td>\n",
+       "      <td>_mb_sensitivity_4</td>\n",
+       "      <td>4.906857</td>\n",
+       "      <td>1.705036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>RGI60-11.00897</td>\n",
+       "      <td>_mb_sensitivity_5</td>\n",
+       "      <td>4.906857</td>\n",
+       "      <td>1.905036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>RGI60-11.00897</td>\n",
+       "      <td>_mb_sensitivity_6</td>\n",
+       "      <td>5.406857</td>\n",
+       "      <td>1.505036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>RGI60-11.00897</td>\n",
+       "      <td>_mb_sensitivity_7</td>\n",
+       "      <td>5.406857</td>\n",
+       "      <td>1.705036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>RGI60-11.00897</td>\n",
+       "      <td>_mb_sensitivity_8</td>\n",
+       "      <td>5.406857</td>\n",
+       "      <td>1.905036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>RGI60-11.01450</td>\n",
+       "      <td>_mb_sensitivity_0</td>\n",
+       "      <td>5.831368</td>\n",
+       "      <td>0.608339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>RGI60-11.01450</td>\n",
+       "      <td>_mb_sensitivity_1</td>\n",
+       "      <td>5.831368</td>\n",
+       "      <td>0.808339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>RGI60-11.01450</td>\n",
+       "      <td>_mb_sensitivity_2</td>\n",
+       "      <td>5.831368</td>\n",
+       "      <td>1.008339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>RGI60-11.01450</td>\n",
+       "      <td>_mb_sensitivity_3</td>\n",
+       "      <td>6.331368</td>\n",
+       "      <td>0.608339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>RGI60-11.01450</td>\n",
+       "      <td>_mb_sensitivity_4</td>\n",
+       "      <td>6.331368</td>\n",
+       "      <td>0.808339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>RGI60-11.01450</td>\n",
+       "      <td>_mb_sensitivity_5</td>\n",
+       "      <td>6.331368</td>\n",
+       "      <td>1.008339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>RGI60-11.01450</td>\n",
+       "      <td>_mb_sensitivity_6</td>\n",
+       "      <td>6.831368</td>\n",
+       "      <td>0.608339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>RGI60-11.01450</td>\n",
+       "      <td>_mb_sensitivity_7</td>\n",
+       "      <td>6.831368</td>\n",
+       "      <td>0.808339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>RGI60-11.01450</td>\n",
+       "      <td>_mb_sensitivity_8</td>\n",
+       "      <td>6.831368</td>\n",
+       "      <td>1.008339</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            rgi_id         filesuffix    melt_f  temp_bias\n",
+       "0   RGI60-11.00897  _mb_sensitivity_0  4.406857   1.505036\n",
+       "1   RGI60-11.00897  _mb_sensitivity_1  4.406857   1.705036\n",
+       "2   RGI60-11.00897  _mb_sensitivity_2  4.406857   1.905036\n",
+       "3   RGI60-11.00897  _mb_sensitivity_3  4.906857   1.505036\n",
+       "4   RGI60-11.00897  _mb_sensitivity_4  4.906857   1.705036\n",
+       "5   RGI60-11.00897  _mb_sensitivity_5  4.906857   1.905036\n",
+       "6   RGI60-11.00897  _mb_sensitivity_6  5.406857   1.505036\n",
+       "7   RGI60-11.00897  _mb_sensitivity_7  5.406857   1.705036\n",
+       "8   RGI60-11.00897  _mb_sensitivity_8  5.406857   1.905036\n",
+       "9   RGI60-11.01450  _mb_sensitivity_0  5.831368   0.608339\n",
+       "10  RGI60-11.01450  _mb_sensitivity_1  5.831368   0.808339\n",
+       "11  RGI60-11.01450  _mb_sensitivity_2  5.831368   1.008339\n",
+       "12  RGI60-11.01450  _mb_sensitivity_3  6.331368   0.608339\n",
+       "13  RGI60-11.01450  _mb_sensitivity_4  6.331368   0.808339\n",
+       "14  RGI60-11.01450  _mb_sensitivity_5  6.331368   1.008339\n",
+       "15  RGI60-11.01450  _mb_sensitivity_6  6.831368   0.608339\n",
+       "16  RGI60-11.01450  _mb_sensitivity_7  6.831368   0.808339\n",
+       "17  RGI60-11.01450  _mb_sensitivity_8  6.831368   1.008339"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "utils.compile_settings(gdirs,\n",
+    "                       keys=['melt_f', 'temp_bias'],\n",
+    "                       filesuffix=all_created_settings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d09b1a2f-a37a-4921-a5da-efd8a4be5264",
+   "metadata": {},
+   "source": [
+    "### Execute multiple experiments using multiprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cb4e199-9967-4761-a513-144b2b4d1ceb",
+   "metadata": {},
+   "source": [
+    "Running the workflow with different settings is now straightforward. To make full use of multiprocessing, we define `(gdir, filesuffix)` pairs so that each combination is treated as an independent job, and then pass them to `workflow.execute_entity_task` as usual."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b9ee2331-ad32-4787-96cb-aa5ba700e2ed",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:53:45: oggm.workflow: Execute entity tasks [run_from_climate_data] on 18 glaciers\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_sensitivity_0_mb_sensitivity_0\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_sensitivity_2_mb_sensitivity_2\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_sensitivity_1_mb_sensitivity_1\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_sensitivity_3_mb_sensitivity_3\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_sensitivity_5_mb_sensitivity_5\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_sensitivity_6_mb_sensitivity_6\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_sensitivity_4_mb_sensitivity_4\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_sensitivity_7_mb_sensitivity_7\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_sensitivity_4_mb_sensitivity_4\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_sensitivity_6_mb_sensitivity_6\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_sensitivity_7_mb_sensitivity_7\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_sensitivity_2_mb_sensitivity_2\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_sensitivity_0_mb_sensitivity_0\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_sensitivity_3_mb_sensitivity_3\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_sensitivity_5_mb_sensitivity_5\n",
+      "2026-04-03 09:53:45: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_sensitivity_1_mb_sensitivity_1\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_sensitivity_8_mb_sensitivity_8\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_sensitivity_0_mb_sensitivity_0\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_sensitivity_1_mb_sensitivity_1\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_sensitivity_2_mb_sensitivity_2\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_sensitivity_3_mb_sensitivity_3\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_sensitivity_8_mb_sensitivity_8\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_sensitivity_4_mb_sensitivity_4\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_sensitivity_0_mb_sensitivity_0\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_sensitivity_5_mb_sensitivity_5\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_sensitivity_1_mb_sensitivity_1\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_sensitivity_2_mb_sensitivity_2\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_sensitivity_6_mb_sensitivity_6\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_sensitivity_3_mb_sensitivity_3\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_sensitivity_4_mb_sensitivity_4\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_sensitivity_5_mb_sensitivity_5\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_sensitivity_6_mb_sensitivity_6\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_sensitivity_7_mb_sensitivity_7\n",
+      "2026-04-03 09:53:46: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_sensitivity_7_mb_sensitivity_7\n",
+      "2026-04-03 09:53:47: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_sensitivity_8_mb_sensitivity_8\n",
+      "2026-04-03 09:53:47: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_sensitivity_8_mb_sensitivity_8\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this is the container where we save gdir-settings pairs\n",
+    "all_experiments = []\n",
+    "# loop through all glaciers\n",
+    "for gdir in gdirs:\n",
+    "    # loop through all settings\n",
+    "    for single_settings_filesuffix in all_created_settings:\n",
+    "        # here we define the actual pairs, we use the same filesuffix also for the output\n",
+    "        all_experiments.append(\n",
+    "            (gdir,\n",
+    "             dict(settings_filesuffix=single_settings_filesuffix,\n",
+    "                  output_filesuffix=single_settings_filesuffix,\n",
+    "                 )\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "# now we can execute an entity task by providing our list of gdir-settings pairs,\n",
+    "# afterwards you can still define some kwargs which should be the same for all\n",
+    "# experiments\n",
+    "workflow.execute_entity_task(tasks.run_from_climate_data, all_experiments,\n",
+    "                             ye=2020,\n",
+    "                             fixed_geometry_spinup_yr=2000);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff29bc58-b6d2-453b-9534-b7244902930a",
+   "metadata": {},
+   "source": [
+    "### Combine the output of multiple experiments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49b9f9d2-fbea-43da-b665-d3a572472d90",
+   "metadata": {},
+   "source": [
+    "After running all experiments, we combine their outputs into a single dataset using `settings_filesuffix` as a new coordinate. We also define a small helper function to compute specific mass balance. We then calculate quantiles across all experiments:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c2de432e-9cf5-41e5-8d8a-8fdf28cae73e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_specific_mb(ds, ice_density=cfg.PARAMS['ice_density']):\n",
+    "    \"\"\" Adds the 'dynamic' specific mass balance to a dataset.\n",
+    "    \"\"\"\n",
+    "    ds['specific_mb'] = (ds.volume.diff(dim='time', label='lower').reindex(time=ds.time) /\n",
+    "                         ds.area * ice_density)\n",
+    "    ds['specific_mb'].attrs = {\n",
+    "        'description': 'Specific mass balance',\n",
+    "        'unit': 'mm w.e. yr-1',\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ebd61039-9c25-4118-9004-e10ed413b37c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:53:47: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:47: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:48: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:48: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/site-packages/numpy/lib/_nanfunctions_impl.py:1593: RuntimeWarning: All-NaN slice encountered\n",
+      "  return fnb._ureduce(a,\n"
+     ]
+    }
+   ],
+   "source": [
+    "# container for individual experiment outputs\n",
+    "ds_all = []\n",
+    "\n",
+    "# loop through the different settings files\n",
+    "for suffix in all_created_settings:\n",
+    "    # compile the run output for a single experiment\n",
+    "    ds_tmp = utils.compile_run_output(gdirs,\n",
+    "                                      input_filesuffix=suffix)\n",
+    "    # add settings_filesuffix as a new coordinate\n",
+    "    ds_tmp.coords['experiment_filesuffix'] = suffix\n",
+    "    ds_tmp = ds_tmp.expand_dims('experiment_filesuffix')\n",
+    "    # add to the container\n",
+    "    ds_all.append(ds_tmp)\n",
+    "\n",
+    "# combine all experiments using the new coordinate 'experiment_filesuffix'\n",
+    "ds_all = xr.combine_by_coords(ds_all,\n",
+    "                              fill_value=np.nan,\n",
+    "                              combine_attrs=\"override\")\n",
+    "\n",
+    "# add the specific mass balance\n",
+    "add_specific_mb(ds_all)\n",
+    "\n",
+    "# calculate quantiles across all experiments\n",
+    "ds_q = ds_all.quantile([0.05, 0.5, 0.95],\n",
+    "                       dim=['experiment_filesuffix'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1933b5f-44cf-48b3-bea1-c56e36139f81",
+   "metadata": {},
+   "source": [
+    "We also load the OGGM default run output, which is included in the preprocessed directories:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "1cb282fa-37db-4697-87fb-d7afc0c2edb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:53:48: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:48: oggm.utils: Applying compile_run_output on 2 gdirs.\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds_default = utils.compile_run_output(gdirs,\n",
+    "                                      input_filesuffix='_historical')\n",
+    "\n",
+    "add_specific_mb(ds_default)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd622bdf-5b4b-45fd-88c4-8fd36a43be61",
+   "metadata": {},
+   "source": [
+    "Let's plot the distribution of results across all experiments:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "f1422c94-5d30-4b6b-ab51-533a309d2437",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_all.sel(rgi_id=rgi_ids[0]).volume.plot(hue='experiment_filesuffix');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8754295f-4966-4f3b-96c2-80e5361cb4d1",
+   "metadata": {},
+   "source": [
+    "And now only the quantile distribution, compared to the default run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "23046953-4742-4d46-b668-e437dfc18f63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7c4255b520d0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "artists = ds_q.sel(rgi_id=rgi_ids[0]).volume.plot(hue='quantile')\n",
+    "ax = artists[0].axes; leg1 = ax.get_legend(); leg1.set_title('Quantiles sensitivity study');\n",
+    "line = ds_default.sel(rgi_id=rgi_ids[0]).volume.plot(linestyle='--')\n",
+    "ax.legend(handles=[line[0]], labels=['OGGM default static spinup'], loc='lower left')\n",
+    "ax.add_artist(leg1) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "c41ee52d-f7ee-444d-bb42-f36c8f8ea6bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7c4254a0c910>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "artists = ds_q.sel(rgi_id=rgi_ids[0]).specific_mb.plot(hue='quantile')\n",
+    "ax = artists[0].axes; leg1 = ax.get_legend(); leg1.set_title('Quantiles sensitivity study');\n",
+    "line = ds_default.sel(rgi_id=rgi_ids[0]).specific_mb.plot(linestyle='--')\n",
+    "ax.legend(handles=[line[0]], labels=['OGGM default static spinup'], loc='lower left')\n",
+    "ax.add_artist(leg1) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00c7ba14-d000-4f08-b108-e68562a1c9a3",
+   "metadata": {},
+   "source": [
+    "The median of our experiments is very close to the default run: a good sign, since we perturbed the parameters symmetrically around their default values. Comparing your ensemble distribution to a default or control run is a useful sanity check for confirming that your perturbation design is balanced."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98950e67-4e16-4cf3-9b6f-557568d271c6",
+   "metadata": {},
+   "source": [
+    "## Combining settings with observations sensitivity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2b5b6a8-05b8-4e0f-a81c-4f4a5c02130c",
+   "metadata": {},
+   "source": [
+    "Since we are using preprocessed directories from before v1.7, we first need to create a default `observations.yml` file manually. This file follows a similar logic to `settings.yml`, but is specifically intended to store the observations used during calibration and assimilation. Before v1.7, observations used during calibration were not stored in a central place. From v1.7 onwards, all tasks that use observations support an `observations_filesuffix` argument. If a task is designed for a specific dataset (e.g. `workflow.calibrate_inversion_from_consensus`, `massbalance.mb_calibration_from_hugonnet_mb`), the observations used for that glacier are automatically added to `observations.yml` when the task runs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "074f5cdf-d464-4b37-a809-1f9a4896089e",
+   "metadata": {},
+   "source": [
+    "### The principle idea of `observations.yml`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c56bf98-24b2-4255-bd23-7eb246916538",
+   "metadata": {},
+   "source": [
+    "Let's recreate `observations.yml` as it would look if the OGGM workflow had been run with v1.7. We use `gdir.create_new_observations`, which follows the same syntax as `gdir.create_new_settings` discussed above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "ea01f4aa-165d-4de5-898a-cfb4bdb7ca24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log = logging.getLogger(__name__)\n",
+    "\n",
+    "@utils.entity_task(log)\n",
+    "def create_observations_for_backward_compatibility(gdir):\n",
+    "    \"\"\" Recreates observations.yml as it would have been created automatically\n",
+    "    if the OGGM workflow had been executed with v1.7 or later.\n",
+    "    \"\"\"\n",
+    "    observation_data = {}\n",
+    "\n",
+    "    # add RGI area; since we work in full calendar years, use the original RGI year + 1 as the timestamp\n",
+    "    observation_data['ref_area_m2'] = {\n",
+    "        'value': gdir.rgi_area_m2,\n",
+    "        'year': gdir.rgi_date + 1\n",
+    "    }\n",
+    "    \n",
+    "    # add the reference volume from regional calibration to the consensus estimate of Farinotti et al. (2019);\n",
+    "    # note: this is not the glacier-specific volume from the dataset, but the value\n",
+    "    # obtained from a regional calibration\n",
+    "    observation_data['ref_volume_m3'] = {\n",
+    "        'value': tasks.get_inversion_volume.unwrapped(gdir),\n",
+    "        'year': gdir.rgi_date + 1,\n",
+    "    }\n",
+    "    \n",
+    "    # add the reference geodetic mass balance from Hugonnet et al. (2021)\n",
+    "    ref_mb_df = utils.get_geodetic_mb_dataframe().loc[gdir.rgi_id]\n",
+    "    ref_mb_df = ref_mb_df[ref_mb_df.period == cfg.PARAMS['geodetic_mb_period']]\n",
+    "    observation_data['ref_mb'] = {\n",
+    "        'period': cfg.PARAMS['geodetic_mb_period'],\n",
+    "        'err': ref_mb_df['err_dmdtda'].values[0] * 1000,\n",
+    "        'unit': 'kg m-2 yr-1',\n",
+    "        'value': ref_mb_df['dmdtda'].values[0]*1000,\n",
+    "    }\n",
+    "\n",
+    "    # create the observations file using the same syntax as create_new_settings\n",
+    "    gdir.create_new_observations(filesuffix='',\n",
+    "                                 data=observation_data,\n",
+    "                                 ignore_existing=True,\n",
+    "                                 overwrite=True,\n",
+    "                                )\n",
+    "\n",
+    "workflow.reset_multiprocessing()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "39176b71-bb56-4a61-99e0-ecbc2496efc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:53:48: oggm.workflow: Execute entity tasks [create_observations_for_backward_compatibility] on 2 glaciers\n",
+      "2026-04-03 09:53:49: __main__: (RGI60-11.00897) create_observations_for_backward_compatibility\n",
+      "2026-04-03 09:53:49: __main__: (RGI60-11.01450) create_observations_for_backward_compatibility\n"
+     ]
+    }
+   ],
+   "source": [
+    "workflow.execute_entity_task(create_observations_for_backward_compatibility, gdirs);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0a3b0da-b6b7-44b0-8bee-56c2b81d3d40",
+   "metadata": {},
+   "source": [
+    "We can inspect the observations we just created using `gdir.read_observations`. It also accepts a `filesuffix` argument if you are working with multiple observation files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "694c3f46-62d3-455b-ba81-eedf4c2b1b91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'filesuffix': '',\n",
+       " 'ref_area_m2': {'value': 8036000.0, 'year': 2004},\n",
+       " 'ref_mb': {'err': 171.8,\n",
+       "  'period': '2000-01-01_2020-01-01',\n",
+       "  'unit': 'kg m-2 yr-1',\n",
+       "  'value': -1100.3},\n",
+       " 'ref_volume_m3': {'value': 593426652.7574053, 'year': 2004}}"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdirs[0].read_observations(filesuffix='')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4993cc-2948-4cbe-bd41-61286486f5c5",
+   "metadata": {},
+   "source": [
+    "As a check, we can compare the reference mass balance value to what was stored in `mb_calib.json` in v1.6:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "fcd048b1-2916-47f1-a8a1-2b93ba09cb62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1100.3"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdirs[0].read_json('mb_calib')['reference_mb']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e788c2e6-1145-419b-992d-39446ea9c2c7",
+   "metadata": {},
+   "source": [
+    "By passing `observations_filesuffix` to an entity task, you can access the correct observations inside the task via `gdir.observations`. This works through the same decorator logic described for settings above ([see here](#define-your-own-entity_task)). Each observation name is standardized across OGGM tasks and currently supports `'ref_mb'`, `'ref_area_m2'`, and `'ref_volume_m3'`. For each observation, the actual value is stored under `'value'`, and the valid time under `'year'` or `'period'`. You can add any additional metadata that is useful, such as uncertainties or units. The observation file is designed to be extended incrementally as new observations become available. For distributed or more complex data, the idea is to store the relevant filepath in `observations.yml`, so that any analysis can quickly trace which observations were used in a given run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bef336cf-be1f-4346-aca2-74015bd675d6",
+   "metadata": {},
+   "source": [
+    "### Define an entity task for perturbing settings and observations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1334d89-e7a6-40bb-8fbe-1a70519b387a",
+   "metadata": {},
+   "source": [
+    "We now define an entity task that creates matched settings and observations files for a workflow where we perturb `melt_f`, `ref_mb`, and `ref_volume_m3`. Both files use the same filesuffix so it is always clear which settings file was used together with which observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "ecd6901a-7692-4508-8950-f9a6c33a8d9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log = logging.getLogger(__name__)\n",
+    "\n",
+    "@utils.entity_task(log)\n",
+    "def create_perturbed_settings_and_observations(gdir, new_filesuffix, melt_f_perturbation,\n",
+    "                                               ref_mb_perturbation, ref_volume_perturbation):\n",
+    "    \"\"\" Reads the default values of melt_f, ref_mb, and ref_volume_m3, adds a perturbation,\n",
+    "    and saves the result in new settings and observations files identified by new_filesuffix.\n",
+    "    \"\"\"\n",
+    "    # read the default melt_f from the default settings file\n",
+    "    default_settings = gdir.read_settings(keys=['melt_f'],\n",
+    "                                          filesuffix='')\n",
+    "\n",
+    "    # save the perturbed settings under new_filesuffix\n",
+    "    gdir.create_new_settings(filesuffix=new_filesuffix,\n",
+    "                             data={\n",
+    "                                 'melt_f': default_settings['melt_f'] + melt_f_perturbation,\n",
+    "                             },\n",
+    "                             parent_filesuffix='',\n",
+    "                             ignore_existing=True,\n",
+    "                             overwrite=True,\n",
+    "                            )\n",
+    "\n",
+    "    # read the default observations from observations.yml\n",
+    "    default_observations = gdir.read_observations(keys=['ref_mb', 'ref_volume_m3'],\n",
+    "                                                  filesuffix='')\n",
+    "\n",
+    "    # copy the original structure and apply the perturbation only to the values\n",
+    "    new_observations = default_observations\n",
+    "    new_observations.pop('filesuffix', None)  # remove the old filesuffix key\n",
+    "    new_observations['ref_mb']['value'] = default_observations['ref_mb']['value'] * ref_mb_perturbation\n",
+    "    new_observations['ref_volume_m3']['value'] = default_observations['ref_volume_m3']['value'] * ref_volume_perturbation\n",
+    "\n",
+    "    # save the perturbed observations under new_filesuffix\n",
+    "    gdir.create_new_observations(filesuffix=new_filesuffix,\n",
+    "                                 data=new_observations,\n",
+    "                                 ignore_existing=True,\n",
+    "                                 overwrite=True,\n",
+    "                                )\n",
+    "\n",
+    "workflow.reset_multiprocessing()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1789e320-8bd6-4071-93bc-77f74c8d3a5c",
+   "metadata": {},
+   "source": [
+    "We again use `itertools.product` to generate all combinations of perturbations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8f2daa64-8a08-4898-b5e4-c2b0bf12685a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:53:51: oggm.workflow: Execute entity tasks [create_perturbed_settings_and_observations] on 2 glaciers\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.00897) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.01450) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: oggm.workflow: Execute entity tasks [create_perturbed_settings_and_observations] on 2 glaciers\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.01450) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.00897) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: oggm.workflow: Execute entity tasks [create_perturbed_settings_and_observations] on 2 glaciers\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.00897) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.01450) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: oggm.workflow: Execute entity tasks [create_perturbed_settings_and_observations] on 2 glaciers\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.00897) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.01450) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: oggm.workflow: Execute entity tasks [create_perturbed_settings_and_observations] on 2 glaciers\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.00897) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.01450) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: oggm.workflow: Execute entity tasks [create_perturbed_settings_and_observations] on 2 glaciers\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.00897) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.01450) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: oggm.workflow: Execute entity tasks [create_perturbed_settings_and_observations] on 2 glaciers\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.00897) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.01450) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: oggm.workflow: Execute entity tasks [create_perturbed_settings_and_observations] on 2 glaciers\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.00897) create_perturbed_settings_and_observations\n",
+      "2026-04-03 09:53:52: __main__: (RGI60-11.01450) create_perturbed_settings_and_observations\n"
+     ]
+    }
+   ],
+   "source": [
+    "from itertools import product\n",
+    "\n",
+    "# applied additively to the default melt_f\n",
+    "melt_f_perturbations = [-0.5, 0.5]\n",
+    "\n",
+    "# applied multiplicatively to the default values, i.e. +/- 10%\n",
+    "ref_mb_perturbations = [0.9, 1.1]\n",
+    "ref_volume_perturbations = [0.9, 1.1]\n",
+    "\n",
+    "# create all combinations of perturbations\n",
+    "all_combinations_advanced = list(product(melt_f_perturbations, ref_mb_perturbations, ref_volume_perturbations))\n",
+    "\n",
+    "# common name for this set of experiments\n",
+    "advanced_experiment_name = '_mb_and_obs_sensitivity'\n",
+    "\n",
+    "# container for all created filesuffixes\n",
+    "all_created_settings_advanced = []\n",
+    "\n",
+    "for i, params in enumerate(all_combinations_advanced):\n",
+    "    # extract the individual perturbations\n",
+    "    melt_f_perturbation, ref_mb_perturbation, ref_volume_perturbation = params\n",
+    "\n",
+    "    # define a unique filesuffix using a running index\n",
+    "    new_filesuffix = f\"{advanced_experiment_name}_{i}\"\n",
+    "\n",
+    "    # execute the entity task for all glaciers\n",
+    "    workflow.execute_entity_task(create_perturbed_settings_and_observations, gdirs,\n",
+    "                                 new_filesuffix=new_filesuffix,\n",
+    "                                 melt_f_perturbation=melt_f_perturbation,\n",
+    "                                 ref_mb_perturbation=ref_mb_perturbation,\n",
+    "                                 ref_volume_perturbation=ref_volume_perturbation,\n",
+    "                                )\n",
+    "\n",
+    "    # store the filesuffix for later reference\n",
+    "    all_created_settings_advanced.append(new_filesuffix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c8e326e-d51d-4aeb-bbc9-990d3ae3b1e0",
+   "metadata": {},
+   "source": [
+    "### Define your OGGM workflow combining multiple tasks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3b0b405-ad8e-474c-8e4d-a4268a45f8e2",
+   "metadata": {},
+   "source": [
+    "We now define our full OGGM workflow as an entity task. It consists of three steps: a fixed-geometry mass balance calibration matching a provided reference mass balance, a glacier bed inversion matching a provided reference volume, and a dynamic model run (without dynamic spinup)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "92fa3cea-400e-4934-8e57-578a8657b763",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log = logging.getLogger(__name__)\n",
+    "\n",
+    "@utils.entity_task(log)\n",
+    "def my_oggm_workflow(gdir, experiment_filesuffix, observations_filesuffix=None):\n",
+    "    \"\"\" Defines a full OGGM workflow as an entity task. It consists of multiple\n",
+    "    tasks executed for each gdir and experiment.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # if no observations filesuffix is provided, use the same as the experiment filesuffix\n",
+    "    if observations_filesuffix is None:\n",
+    "        observations_filesuffix = experiment_filesuffix\n",
+    "\n",
+    "    # fixed-geometry mass balance calibration: reads ref_mb from observations_filesuffix\n",
+    "    # and stores the resulting mass balance parameters in settings_filesuffix\n",
+    "    tasks.mb_calibration_from_scalar_mb(gdir,\n",
+    "                                        settings_filesuffix=experiment_filesuffix,\n",
+    "                                        observations_filesuffix=observations_filesuffix,\n",
+    "                                        calibrate_param1='prcp_fac',\n",
+    "                                        calibrate_param2='melt_f',\n",
+    "                                        calibrate_param3='temp_bias',\n",
+    "                                        overwrite_gdir=True,\n",
+    "                                       )\n",
+    "\n",
+    "    # calculate the apparent mass balance using the newly calibrated parameters;\n",
+    "    # from here on we use the same filesuffix for all outputs to keep settings,\n",
+    "    # observations, and results traceable\n",
+    "    tasks.apparent_mb_from_any_mb(gdir,\n",
+    "                                  settings_filesuffix=experiment_filesuffix,\n",
+    "                                  input_filesuffix='',  # start from the inversion flowlines derived from the DEM and outline\n",
+    "                                  output_filesuffix=experiment_filesuffix,\n",
+    "                                 )\n",
+    "\n",
+    "    # bed inversion calibrated to match ref_volume_m3 from observations_filesuffix\n",
+    "    workflow.calibrate_inversion_from_volume(\n",
+    "        [gdir],\n",
+    "        settings_filesuffix=experiment_filesuffix,\n",
+    "        observations_filesuffix=observations_filesuffix,\n",
+    "        input_filesuffix=experiment_filesuffix,  # use the inversion flowlines created by apparent_mb_from_any_mb\n",
+    "        output_filesuffix=experiment_filesuffix,\n",
+    "        apply_fs_on_mismatch=True,\n",
+    "        error_on_mismatch=True,  # set to False when running many glaciers where some may not converge\n",
+    "        filter_inversion_output=True,  # partially filters overdeepenings caused by the equilibrium\n",
+    "        # assumption for retreating glaciers (see Figure 5 of Maussion et al. 2019)\n",
+    "        add_to_log_file=False,\n",
+    "    )\n",
+    "    \n",
+    "    # initialise the dynamic flowlines for this experiment\n",
+    "    tasks.init_present_time_glacier(gdir,\n",
+    "                                    settings_filesuffix=experiment_filesuffix,\n",
+    "                                    input_filesuffix=experiment_filesuffix,\n",
+    "                                    output_filesuffix=experiment_filesuffix,\n",
+    "                                   )\n",
+    "\n",
+    "    # run the dynamic model, saving all outputs under the same filesuffix\n",
+    "    tasks.run_from_climate_data(gdir,\n",
+    "                                settings_filesuffix=experiment_filesuffix,\n",
+    "                                model_flowlines_filesuffix=experiment_filesuffix,\n",
+    "                                output_filesuffix=experiment_filesuffix,\n",
+    "                                ye=2020,\n",
+    "                                fixed_geometry_spinup_yr=2000)\n",
+    "\n",
+    "workflow.reset_multiprocessing()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da9c55d2-b223-4e65-9a7f-dcd06bc9bd65",
+   "metadata": {},
+   "source": [
+    "### Run the custom OGGM workflow and combine outputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfce79f-a466-4108-81e1-6ec3482d00db",
+   "metadata": {},
+   "source": [
+    "As before, we define `(gdir, filesuffix)` pairs to make full use of multiprocessing ([see here](#execute-multiple-experiments-using-multiprocessing))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "e5f8e895-5645-433f-9e56-1199c64dfb1e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:53:52: oggm.workflow: Execute entity tasks [my_oggm_workflow] on 16 glaciers\n",
+      "2026-04-03 09:53:54: __main__: (RGI60-11.00897) my_oggm_workflow\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) apparent_mb_from_any_mb_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 6.930182382573753 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 3.5150911912868765 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: __main__: (RGI60-11.00897) my_oggm_workflow\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 1.8075455956434383 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 1.5036278259099738 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) apparent_mb_from_any_mb_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: __main__: (RGI60-11.00897) my_oggm_workflow\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) apparent_mb_from_any_mb_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 4.5974516185606635 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: __main__: (RGI60-11.00897) my_oggm_workflow\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: __main__: (RGI60-11.00897) my_oggm_workflow\n",
+      "2026-04-03 09:53:54: __main__: (RGI60-11.00897) my_oggm_workflow\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 2.4183169242385434 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: __main__: (RGI60-11.00897) my_oggm_workflow\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 1.59506824882265 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 1.2591584621192717 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) apparent_mb_from_any_mb_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) apparent_mb_from_any_mb_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: __main__: (RGI60-11.00897) my_oggm_workflow\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) apparent_mb_from_any_mb_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 6.875444876925506 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 0.6795792310596359 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 1.5870929075775366 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) apparent_mb_from_any_mb_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 3.487722438462753 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: calibrate_inversion_from_volume converged after 7 iterations and fs=0. The resulting Glen A factor is 1.5870929075775366.\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.massbalance: (RGI60-11.00897) apparent_mb_from_any_mb_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:54: oggm.workflow: Volume estimate optimisation with A factor: 5.109168604938157 and fs: 0\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:54: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:54: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.7938612192313765 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) init_present_time_glacier_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 4.531627247767725 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 7.35709915094174 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 2.6045843024690782 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.444650541605288 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 7.315258558487386 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 3.7285495754708697 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 2.3568711747269204 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.3522921512345392 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.49103151435109094 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 3.707629279243693 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.914274787735435 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 5.058964709873942 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.7261460756172696 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.2284355873634603 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: __main__: (RGI60-11.01450) my_oggm_workflow\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.massbalance: (RGI60-11.01450) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.9038146396218465 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.5420967606064622 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 2.0318620565409984 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.6642177936817302 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.5356305962628083 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.9718456753278368 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 2.579482354936971 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.core.massbalance: (RGI60-11.01450) apparent_mb_from_any_mb_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.5329524432804943 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.3397411774684855 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.5529334394516074 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: calibrate_inversion_from_volume converged after 9 iterations and fs=0. The resulting Glen A factor is 0.5356305962628083.\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.7198705887342428 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.543986645981422 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.6578576616636538 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 6.9588511374353565 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) init_present_time_glacier_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.4763187897343454 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.9951711354243573 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.9499490573384308 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.536266712750515 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.6728290868912127 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 3.529425568717678 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.5271380330935669 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: calibrate_inversion_from_volume converged after 8 iterations and fs=0. The resulting Glen A factor is 1.543986645981422.\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.9851952797462356 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.9401993120507386 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.6694649414557566 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.814712784358839 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: __main__: (RGI60-11.01450) my_oggm_workflow\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.massbalance: (RGI60-11.01450) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: calibrate_inversion_from_volume converged after 7 iterations and fs=0. The resulting Glen A factor is 1.9951711354243573.\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.5205631015760241 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) init_present_time_glacier_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.workflow: calibrate_inversion_from_volume converged after 8 iterations and fs=0. The resulting Glen A factor is 0.6728290868912127.\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: calibrate_inversion_from_volume converged after 7 iterations and fs=0. The resulting Glen A factor is 1.9499490573384308.\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.6402185113848609 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.5750542378692842 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.5179602860671441 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) init_present_time_glacier_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) init_present_time_glacier_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.6580181098738542 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.massbalance: (RGI60-11.01450) apparent_mb_from_any_mb_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) init_present_time_glacier_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.6435683692804575 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: calibrate_inversion_from_volume converged after 9 iterations and fs=0. The resulting Glen A factor is 0.5205631015760241.\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: __main__: (RGI60-11.01450) my_oggm_workflow\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 0.654728019323485 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.massbalance: (RGI60-11.01450) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Volume estimate optimisation with A factor: 1.635350527433055 and fs: 0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) init_present_time_glacier_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:55: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: calibrate_inversion_from_volume converged after 7 iterations and fs=0. The resulting Glen A factor is 1.6435683692804575.\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: calibrate_inversion_from_volume converged after 8 iterations and fs=0. The resulting Glen A factor is 0.6580181098738542.\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: __main__: (RGI60-11.01450) my_oggm_workflow\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.massbalance: (RGI60-11.01450) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 4.608055673387995 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: __main__: (RGI60-11.01450) my_oggm_workflow\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.massbalance: (RGI60-11.01450) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: __main__: (RGI60-11.01450) my_oggm_workflow\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.massbalance: (RGI60-11.01450) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:56: oggm.core.massbalance: (RGI60-11.01450) apparent_mb_from_any_mb_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) init_present_time_glacier_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.00897) init_present_time_glacier_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.massbalance: (RGI60-11.01450) apparent_mb_from_any_mb_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 2.432062077425435 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: __main__: (RGI60-11.01450) my_oggm_workflow\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.massbalance: (RGI60-11.01450) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.massbalance: (RGI60-11.01450) apparent_mb_from_any_mb_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.massbalance: (RGI60-11.01450) apparent_mb_from_any_mb_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_and_obs_sensitivity_0_mb_and_obs_sensitivity_0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.2660310387127176 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.6830155193563588 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 6.931569642257738 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.massbalance: (RGI60-11.01450) apparent_mb_from_any_mb_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 4.5754280686957065 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.5075974343045274 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: __main__: (RGI60-11.01450) my_oggm_workflow\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 3.515784821128869 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.massbalance: (RGI60-11.01450) mb_calibration_from_scalar_mb_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 7.273883373628777 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 4.982711247701716 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 2.4014367226627056 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.8078924105644345 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 3.6869416868143885 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.55337351794182 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 2.797416279942582 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.massbalance: (RGI60-11.01450) apparent_mb_from_any_mb_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 7.251437501924944 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.5449290190197944 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.8934708434071943 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.5479989078343085 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.2507183613313528 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.448708139971291 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 3.675718750962472 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.6212859898345786 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.5452589132941369 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.9604197199768099 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.6753591806656765 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.7743540699856456 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.887859375481236 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 4.955923657917181 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.6131795598844056 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.4998935558504479 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.9389349296481972 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: calibrate_inversion_from_volume converged after 9 iterations and fs=0. The resulting Glen A factor is 0.5479989078343085.\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 2.770480689737655 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.928973200961334 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.6095050524148413 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.9292402549989562 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.5456687917648256 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: calibrate_inversion_from_volume converged after 7 iterations and fs=0. The resulting Glen A factor is 1.6131795598844056.\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) init_present_time_glacier_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.9155650248129295 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.6519375522324462 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.5402267005638456 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.4352403448688276 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) init_present_time_glacier_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: calibrate_inversion_from_volume converged after 7 iterations and fs=0. The resulting Glen A factor is 1.9389349296481972.\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_and_obs_sensitivity_1_mb_and_obs_sensitivity_1\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.5375255670600264 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 1.9059871996878648 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_and_obs_sensitivity_2_mb_and_obs_sensitivity_2\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.6478395565727534 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.7676201724344138 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) init_present_time_glacier_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: calibrate_inversion_from_volume converged after 7 iterations and fs=0. The resulting Glen A factor is 1.9155650248129295.\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: calibrate_inversion_from_volume converged after 9 iterations and fs=0. The resulting Glen A factor is 0.5402267005638456.\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_and_obs_sensitivity_4_mb_and_obs_sensitivity_4\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.6446003587888897 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.6014243331388687 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) init_present_time_glacier_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) init_present_time_glacier_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: calibrate_inversion_from_volume converged after 9 iterations and fs=0. The resulting Glen A factor is 0.6478395565727534.\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.6442800022049535 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_and_obs_sensitivity_6_mb_and_obs_sensitivity_6\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_and_obs_sensitivity_3_mb_and_obs_sensitivity_3\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) init_present_time_glacier_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.6400691600168531 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_and_obs_sensitivity_5_mb_and_obs_sensitivity_5\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:56: oggm.workflow: Volume estimate optimisation with A factor: 0.6368688142157688 and fs: 0\n",
+      "2026-04-03 09:53:56: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:56: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:56: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:57: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:57: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:57: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:57: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:57: oggm.workflow: calibrate_inversion_from_volume converged after 9 iterations and fs=0. The resulting Glen A factor is 0.6400691600168531.\n",
+      "2026-04-03 09:53:57: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:53:57: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:57: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:57: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:53:57: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:57: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:53:57: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:57: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:53:57: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:53:57: oggm.core.flowline: (RGI60-11.01450) init_present_time_glacier_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:57: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n",
+      "2026-04-03 09:53:57: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_mb_and_obs_sensitivity_7_mb_and_obs_sensitivity_7\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_experiments_advanced = []\n",
+    "for gdir in gdirs:\n",
+    "    for single_experiments_filesuffix in all_created_settings_advanced:\n",
+    "        all_experiments_advanced.append((gdir,\n",
+    "                                dict(experiment_filesuffix=single_experiments_filesuffix)\n",
+    "                               )\n",
+    "                              )\n",
+    "\n",
+    "workflow.execute_entity_task(my_oggm_workflow, all_experiments_advanced);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "374722b8-4dcb-4271-934f-b3409c36afae",
+   "metadata": {},
+   "source": [
+    "We combine the outputs the same way as in the simple sensitivity study above ([see here](#combine-the-output-of-multiple-experiments))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "b0a6ecf2-d8bc-4781-9a1e-87a05bae70d0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:53:57: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:53:57: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/site-packages/numpy/lib/_nanfunctions_impl.py:1593: RuntimeWarning: All-NaN slice encountered\n",
+      "  return fnb._ureduce(a,\n"
+     ]
+    }
+   ],
+   "source": [
+    "# container for individual experiment outputs\n",
+    "ds_all_advanced = []\n",
+    "for suffix in all_created_settings_advanced:\n",
+    "    ds_tmp = utils.compile_run_output(gdirs,\n",
+    "                                      input_filesuffix=suffix)\n",
+    "    ds_tmp.coords['experiment_filesuffix'] = suffix\n",
+    "    ds_tmp = ds_tmp.expand_dims('experiment_filesuffix')\n",
+    "    ds_all_advanced.append(ds_tmp)\n",
+    "\n",
+    "ds_all_advanced = xr.combine_by_coords(ds_all_advanced, fill_value=np.nan, combine_attrs=\"override\")\n",
+    "\n",
+    "# add specific mass balance\n",
+    "add_specific_mb(ds_all_advanced)\n",
+    "\n",
+    "# calculate quantiles across all experiments\n",
+    "ds_q_advanced = ds_all_advanced.quantile([0.05, 0.5, 0.95], dim=['experiment_filesuffix'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87822bc0-5f95-475e-858b-293a1373f372",
+   "metadata": {},
+   "source": [
+    "### Look at some results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1aec803-ba63-4c73-b5cf-64eeb342a26a",
+   "metadata": {},
+   "source": [
+    "Let's start by looking at the glacier volume across all experiments:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "fb2d867e-d1af-4270-b644-f2d1a76d1547",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_all_advanced.sel(rgi_id=rgi_ids[0]).volume.plot(hue='experiment_filesuffix');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf442417-e14c-44c9-9bac-19f5012e757c",
+   "metadata": {},
+   "source": [
+    "Runs sharing the same `ref_volume_m3` intersect at the RGI reference year, reflecting the two different reference volumes we used. Within each pair, the two distinct slopes correspond to the two different `ref_mb` values. The small remaining differences within each slope correspond to the two different perturbations of `melt_f`.\n",
+    "\n",
+    "**Important:** when combining settings and observations perturbations, calibration steps may overwrite some settings values. It is therefore good practice to check the distribution of final settings after running your experiments. You can do this with `utils.compile_settings`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6a2b2e50-0300-45f8-93f7-a924fd350436",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_settings_advanced = utils.compile_settings(gdirs[0],\n",
+    "                                              keys=['melt_f', 'prcp_fac', 'inversion_glen_a'],\n",
+    "                                              filesuffix=all_created_settings_advanced)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "77a991f2-3f54-4738-9eaa-675aae1dd2ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_settings_advanced['melt_f'].hist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82465102-bb2b-40bf-9e92-7696c79ca8ae",
+   "metadata": {},
+   "source": [
+    "In the `melt_f` distribution, the two prescribed values were never changed by a calibration step. However, this could happen depending on how the mass balance calibration is configured, in our workflow we set `calibrate_param2='melt_f'` (for more details on mass balance calibration see [this tutorial](massbalance_calibration.ipynb))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "1b6c81ad-1ae2-4e26-8dcc-8e17c8a26399",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+YerUqXpqqQ0bNvjdf/PNN6WwsFA+//xzGTZsmHe+GqlJSEiw+3IAAABaF25aq76+Xm7cuCE9evTwm3/z5k3p16+f3Lt3T4YOHSq5ubl+4aeh2tpaPXlUV1fr27q6Oj05KSbKcnR5zf69SMvvNhRO94FTPK8r0Otr7352im8twepzO5NrU6jPve9DN647O/3sxGeCE9qif9tqnUVYlhVyb6nRloKCApkxY0aLn/POO+/I22+/Ld9884306tVLzzt58qScP39ennjiCR1S3n//fX0Yq6ysTAYOHBhwOdnZ2ZKTk9Nofn5+vsTFxYVaEgAAaEc1NTWSnp4uVVVV+hQV14WbnTt3ysKFC/VhqcmTJwcd3Xnqqadk/PjxsnHjxhaP3CQlJcnVq1cd6xyP5Gz/E6DbmkrnuSn1sqYkUmrrI0JaxtfZaRKOVEovKirS52FFR0d3aD87xbevg9XndibXplCfe9+Hpaufdd22aaefnfhMcEJbfK6o953KBE6Hm3Y7LLV7925ZsGCBfPbZZ0GDjRIZGSkjRoyQc+fONdkmJiZGTw2pDdvpjbv2XsdsTGojDvVvh/sbPNB66qh+bq1Afd0W22G4MLk2pbPX58b3oaceN627UPq5NZ8JTnBL37bb99yoEZt58+bpQ0bPPfdcs+3VYNKZM2ckMTGxPV4eAAAwiO2RG3Xirzo/xuPChQs6iKgThPv27StZWVly6dIl2bFjhzfYzJkzR59HM3r0aKmsrNTzu3XrJt27d9f/r86dUY+p82vU4SV1KEotc/Pmzc5VCgAAOgXbIzclJSX6KibPlUyZmZn6/19//XV9X30/TXl5ubf9z3/+c7l7964sWbJEj8R4pmXLlnnbXL9+XRYtWiSDBg2SKVOm6HCkvh9n5MiRzlQJAAA6DdsjNxMmTNCHjZqivm3Y1+HDh5td5nvvvacnAACA1uK3pQAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAABA5w43xcXFMn36dOnTp49ERETIvn37mn3OkSNHZPjw4RIbGysDBgyQbdu2NWqzZ88eGTx4sMTExOjbgoICuy8NAADAfri5deuWDBkyRDZt2tSi9hcuXJBp06bJuHHjpLS0VFatWiVLly7VYcbjxIkTMnv2bMnIyJCysjJ9O2vWLDl16hSrCAAA2NLFXnORqVOn6qml1ChN3759ZcOGDfr+oEGDpKSkRNatWycvvPCCnqceS01NlaysLH1f3arRHjV/586ddl8iAADoxGyHG7vUqMyUKVP85qWlpcn27dulrq5OoqOjdZsVK1Y0auMJRIHU1tbqyaO6ulrfqmWqyUkxUZajy2v270VafrehcLoPnOJ5XYFeX3v3s1N8awlWn9uZXJtCfe59H7px3dnpZyc+E5zQFv3bVusswrKskHtLnXOjzo2ZMWNGk22+//3vy7x58/ThKI/jx4/L008/LZcvX5bExETp2rWr5OXlSXp6urdNfn6+zJ8/3y/A+MrOzpacnJxG89Xz4uLiQi0JAAC0o5qaGv35X1VVJfHx8e4YufGEIF+ePOU7P1CbhvN8qUNXmZmZfiM3SUlJepTIqc7xSM4+IO1JpfPclHpZUxIptfVN90EwX2enSThSKb2oqEgfhlSjdh3Zz07x7etg9bmdybUp1Ofe92Hp6mddt23a6WcnPhOc0BafK+p9V1hY6Phy2zzcJCQkSGVlpd+8K1euSJcuXeT+++8P2qZ3795NLlddVaWmhtSG7fTGXXuvYzYmtRGH+rfD/Q0eaD11VD+3VqC+bovtMFyYXJvS2etz4/vQU4+b1l0o/dyazwQnuKVv2+V7bsaMGaMTta+DBw9KSkqKt6OaajN27Ni2fnkAAMAwtkdubt68KefPn/e71PvMmTPSo0cPfVWUOlx06dIl2bFjh3588eLF+rJxdQjpxz/+sT55WJ1M7HsV1LJly2T8+PGydu1aef755/UQ1aFDh+TYsWNO1QkAADoJ2yM36jLuYcOG6UlRoUX9/+uvv67vV1RUSHl5ubd9//79Zf/+/XL48GEZOnSo5ObmysaNG72XgStqhGbXrl3y8ccfy5NPPqlPLt69e7eMGjXKmSoBAECnYXvkZsKECd4TggNRwaShZ555Rr788sugy505c6aeAAAAWoPflgIAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARgkp3GzZskX69+8vsbGxMnz4cDl69GiTbefNmycRERGNpscff9zbJi8vL2Cb27dvh1YVAADotGyHm927d8vy5ctl9erVUlpaKuPGjZOpU6dKeXl5wPbvv/++VFRUeKeLFy9Kjx495Ec/+pFfu/j4eL92alLhCQAAoE3Dzfr162XBggWycOFCGTRokGzYsEGSkpJk69atAdt3795dEhISvFNJSYl89913Mn/+fL92aqTGt52aAAAA7Opip/GdO3fk9OnTsnLlSr/5U6ZMkePHj7doGdu3b5fJkydLv379/ObfvHlTz7t3754MHTpUcnNzZdiwYU0up7a2Vk8e1dXV+raurk5PToqJshxdXrN/L9Lyuw2F033gFM/rCvT62rufneJbS7D63M7k2hTqc+/70I3rzk4/O/GZ4IS26N+2WmcRlmW1uLcuX74sDz74oHzxxRcyduxY7/w333xTPvnkEzl79mzQ56tDTWqUJz8/X2bNmuWdf/LkSTl//rw88cQTOqSoQ1n79++XsrIyGThwYMBlZWdnS05OTqP5atlxcXEtLQkAAHSgmpoaSU9Pl6qqKn2KSruP3PgeQvKl8lHDeYGoE4fvu+8+mTFjht/80aNH68nj6aeflqeeeko++OAD2bhxY8BlZWVlSWZmpve+CkUqOKlRJKc6xyM5+4C0J5XOc1PqZU1JpNTWN9+vgXydnSbhSKX0oqIiSU1Nlejo6A7tZ6f49nWw+tzO5NoU6nPv+7B09bOu2zbt9LMTnwlOaIvPFfW+KywsdHy5tsJNz549JSoqSiorK/3mX7lyRXr37h30uSoAffTRR5KRkSFdu3YN2jYyMlJGjBgh586da7JNTEyMnhpSG7bTG3ftvY7ZmNRGHOrfDvc3eKD11FH93FqB+rottsNwYXJtSmevz43vQ089blp3ofRzaz4TnOCWvrV9QrEKJerSb5WQfan7voepAjly5Ig+9KRORm6OCkJnzpyRxMREOy8PAADA/mEpdShIjb6kpKTImDFj5MMPP9SXgS9evNh7uOjSpUuyY8eORicSjxo1SpKTkxstU507ow5LqfNr1OEldShKhZvNmzezigAAQNuGm9mzZ8u1a9fkjTfe0CcIq7CiTv71XP2k5jX8zht1ktCePXv0icKBXL9+XRYtWqQPd6lLx9VVUsXFxTJy5Ei7Lw8AAHRyIZ1Q/NJLL+mpqZOGG1KBRZ0N3ZT33ntPTwAAAK3Fb0sBAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKOEFG62bNki/fv3l9jYWBk+fLgcPXq0ybaHDx+WiIiIRtO3337r127Pnj0yePBgiYmJ0bcFBQWhvDQAANDJ2Q43u3fvluXLl8vq1aultLRUxo0bJ1OnTpXy8vKgzzt79qxUVFR4p4EDB3ofO3HihMyePVsyMjKkrKxM386aNUtOnToVWlUAAKDTsh1u1q9fLwsWLJCFCxfKoEGDZMOGDZKUlCRbt24N+rxevXpJQkKCd4qKivI+ppaRmpoqWVlZ8thjj+nbSZMm6fkAAAB2dLHT+M6dO3L69GlZuXKl3/wpU6bI8ePHgz532LBhcvv2bX3I6bXXXpOJEyf6jdysWLHCr31aWlrQcFNbW6snj+rqan1bV1enJyfFRFmOLq/Zvxdp+d2Gwuk+cIrndQV6fe3dz07xrSVYfW5ncm0K9bn3fejGdWenn534THBCW/RvW62zCMuyWtxbly9flgcffFC++OILGTt2rHf+m2++KZ988ok+9NSQmldcXKzPzVFh5Be/+IVs27ZNn4szfvx43aZr166Sl5cn6enp3ufl5+fL/Pnz/QKMr+zsbMnJyWk0Xz0vLi6upSUBAIAOVFNToz//q6qqJD4+vv1HbjzUCcG+VD5qOM/j0Ucf1ZPHmDFj5OLFi7Ju3TpvuLG7TEUdusrMzPQbuVGHx9QoklOd45GcfUDak0rnuSn1sqYkUmrrm+6DYL7OTpNwpFJ6UVGRPgwZHR3dof3sFN++Dlaf25lcm0J97n0flq5+1nXbpp1+duIzwQlt8bmi3neFhYWOL9dWuOnZs6c+V6aystJv/pUrV6R3794tXs7o0aPl008/9d5X5+DYXaa6qkpNDakN2+mNu/Zex2xMaiMO9W+H+xs80HrqqH5urUB93RbbYbgwuTals9fnxvehpx43rbtQ+rk1nwlOcEvf2j6hWB0+UoeXVEL2pe77HqZqjrrKKjEx0W80p+EyDx48aGuZAAAAIR2WUoeC1KXaKSkpOpR8+OGH+jLwxYsXew8XXbp0SXbs2KHvq5OCH374YXn88cf1CclqxEZ9p42aPJYtW6YPUa1du1aef/55PUR16NAhOXbsGGsJAAC0bbhR30dz7do1eeONN/T31SQnJ8v+/fulX79++nE1z/c7b1SgeeWVV3Tg6datmw45v/nNb2TatGneNmqEZteuXfoqqjVr1sgjjzyiv09n1KhRdl8eAADo5EI6ofill17SUyDqqidfr776qp6aM3PmTD0BAAC0Br8tBQAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMElK42bJli/Tv319iY2Nl+PDhcvTo0Sbb7t27V1JTU+WBBx6Q+Ph4GTNmjBw4cMCvTV5enkRERDSabt++HcrLAwAAnZjtcLN7925Zvny5rF69WkpLS2XcuHEydepUKS8vD9i+uLhYh5v9+/fL6dOnZeLEiTJ9+nT9XF8q+FRUVPhNKjwBAADY0cVWaxFZv369LFiwQBYuXKjvb9iwQY/EbN26Vd56661G7dXjvt58800pLCyUzz//XIYNG+adr0ZqEhIS7L4cAACA0MPNnTt39OjLypUr/eZPmTJFjh8/3qJl1NfXy40bN6RHjx5+82/evCn9+vWTe/fuydChQyU3N9cv/DRUW1urJ4/q6mp9W1dXpycnxURZji6v2b8XafndhsLpPnCK53UFen3t3c9O8a0lWH1uZ3JtCvW5933oxnVnp5+d+ExwQlv0b1utswjLslrcW5cvX5YHH3xQvvjiCxk7dqzfaMwnn3wiZ8+ebXYZ77zzjrz99tvyzTffSK9evfS8kydPyvnz5+WJJ57QIeX999/Xh7HKyspk4MCBAZeTnZ0tOTk5jebn5+dLXFxcS0sCAAAdqKamRtLT06WqqkqfotIhh6U8h5B8qXzUcF4gO3fu1KFEHZbyBBtl9OjRevJ4+umn5amnnpIPPvhANm7cGHBZWVlZkpmZ6b2vQlFSUpIeRXKqczySs/1PgG5rKp3nptTLmpJIqa1vvl8D+To7TcKRSulFRUX6PKzo6OgO7Wen+PZ1sPrczuTaFOpz7/uwdPWzrts27fSzE58JTmiLzxX1vlOZwGm2wk3Pnj0lKipKKisr/eZfuXJFevfu3eyJyOpcnc8++0wmT54ctG1kZKSMGDFCzp0712SbmJgYPTWkNmynN+7aex2zMamNONS/He5v8EDrqaP6ubUC9XVbbIfhwuTalM5enxvfh5563LTuQunn1nwmOMEtfWv7aqmuXbvqS79VQval7vsepgo0YjNv3jx9yOi5555r9u+okaAzZ85IYmKinZcHAABg/7CUOhSUkZEhKSkp+jtrPvzwQ30Z+OLFi72Hiy5duiQ7duzwBps5c+bo82jUoSfPqE+3bt2ke/fu+v/VuTPqMXV+jTq8pA5FqXCzefNmVhEAAGjbcDN79my5du2avPHGG/q7aJKTk/XJv+pKJ0XN8/3Om5///Ody9+5dWbJkiZ485s6dq7+8T7l+/bosWrRIBx8VeNRVUur7cUaOHGn35QEAgE4upBOKX3rpJT0F4gksHocPH252ee+9956eAAAAWovflgIAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARgkp3GzZskX69+8vsbGxMnz4cDl69GjQ9keOHNHtVPsBAwbItm3bGrXZs2ePDB48WGJiYvRtQUFBKC8NAAB0crbDze7du2X58uWyevVqKS0tlXHjxsnUqVOlvLw8YPsLFy7ItGnTdDvVftWqVbJ06VIdZjxOnDghs2fPloyMDCkrK9O3s2bNklOnTrWuOgAA0OnYDjfr16+XBQsWyMKFC2XQoEGyYcMGSUpKkq1btwZsr0Zp+vbtq9up9up5L774oqxbt87bRj2WmpoqWVlZ8thjj+nbSZMm6fkAAAB2dLHT+M6dO3L69GlZuXKl3/wpU6bI8ePHAz5Hjcqox32lpaXJ9u3bpa6uTqKjo3WbFStWNGoTLNzU1tbqyaOqqkrf/v3vf9fLdVKXu7ccXV6zf6/ekpqaeulSFyn36iNCWsa1a9ckHKl1U1NTo1+fWvcd2c9O8e3rYPW5ncm1KdTn3veh2ibdtm3a6WcnPhOc0BafK573nWJZVseEm6tXr8q9e/ekd+/efvPV/crKyoDPUfMDtb97965eXmJiYpNtmlqm8tZbb0lOTk6j+epcIBOkt/L5Pd916IWAvgbCXGIn2N+19jPBCW39uXLjxg3p3r17+4cbj4gI/+So0lbDec21bzjf7jLVoavMzEzv/fr6ej1qc//99wd9nhtUV1frQ30XL16U+Ph4MYnJtZlen8m1KdTnXqw796+7P/3pT9KnTx/Hlmsr3PTs2VOioqIajahcuXKl0ciLR0JCQsD2Xbp00UEkWJumlqmoq6rU5Ou+++4Tk6gPEBM/REyvzfT6TK5NoT73Yt2514MPPiiRkc59O42tJXXt2lVf0l1UVOQ3X90fO3ZswOeMGTOmUfuDBw9KSkqK99hoU22aWiYAAIBjh6XUoSB1qbYKJyqUfPjhh/oy8MWLF3sPF126dEl27Nih76v5mzZt0s/78Y9/rE8eVicT79y507vMZcuWyfjx42Xt2rXy/PPPS2FhoRw6dEiOHTtm9+UBAIBOzna4Ud9Ho86YfuONN6SiokKSk5Nl//790q9fP/24muf7nTfqBF/1uLoaavPmzfqY2saNG+WFF17wtlEjNLt27ZLXXntN1qxZI4888oj+Pp1Ro0ZJZ6QOt/3sZz9rdNjNBCbXZnp9JtemUJ97se7cK6aN9isRlpPXXgEAAHQwflsKAAAYhXADAACMQrgBAABGIdwAAACjEG7akfrJiBEjRsj3vvc96dWrl8yYMUPOnj3b7PN++ctfypAhQyQuLk7/XMX8+fPD8rej1I+nPvnkk94v0lJfFfDb3/426HOOHDmivzspNjZWBgwYoH9oNVzZrW/v3r36B2EfeOABb/sDBw5IOApl3Xl88cUX+ks5hw4dKuEqlPrUb9etXr1aXwmqruRQV3F+9NFHYkp9btmvBNqPqm+hX758uTH7Fju1uWm/Euq6c2LfQrhpR+rNtmTJEjl58qT+0kL1+1rqR0Vv3Wr6B9TUd/3MmTNH/xL7H//4R/nss8/kv//7v/Wvq4ebhx56SN5++20pKSnR07PPPqu/t0i97kAuXLgg06ZNk3HjxklpaamsWrVKli5dKnv27JFwZLe+4uJivRNSX4WgfnB24sSJMn36dF2r22vz/cFatX1OmjRJwlko9c2aNUt+//vf6+/lUv8IUd/N9dhjj4kJ9blpv+JLvUb13WoqyAXjtn2LndrctF8JpT7H9i3qUnB0jCtXrqjL8K0jR4402eadd96xBgwY4Ddv48aN1kMPPWS5wT//8z9b//mf/xnwsVdffdV67LHH/Ob927/9mzV69GjLLYLVF8jgwYOtnJwcy5TaZs+ebb322mvWz372M2vIkCGWmwSr77e//a3VvXt369q1a5ZbBavPjfuVGzduWAMHDrSKioqsZ555xlq2bFmTbd22b7FTmxv3KzdCqK+1+xZGbjqQSqZKjx49mmyjvuDwb3/7m07p6iuJ/u///k9+9atfyXPPPSfhTP16vPpiRjUqpYZNA1HfVq1GrnylpaXpf3nW1dWJ2+trSP24q/rV22Dr2021ffzxx/KXv/xFfwGXm7Skvl//+tf6W9j/4z/+Q//mzfe//3155ZVX5B//+IeYUJ8b9ytq1Fu9vsmTJzfb1m37Fju1uXG/ssRmfU7sW0L6VXC0ntqhqJ+k+MEPfqC/5bkpaiekjo2rb4a+ffu2PpT1wx/+UD744IOwXA3/8z//o3eo6rX+0z/9kxQUFMjgwYMDtlU/ltrwx1HVfVXj1atX9XkAbq6voXfffVd/4KjDHeHITm3nzp2TlStXytGjR/UxcTewU99f//pXfehGna+h2qnt8aWXXpK///3vYXvejZ363LZfUWHtyy+/1Ic2WsJN+xa7tbltv7LLZn1O7VsYuekgL7/8snz11Vd+v7EViPoZeHWs+PXXX9fHV3/3u9/p48me3/IKN48++qicOXNGn1f0k5/8RObOnatraIo6ucyX5wuzG853a30eaj1nZ2frnxVRJ5O7uTY1MpCeni45OTl6RMMt7Kw79a9htQ2qADBy5Eh9/sb69eslLy8vbEdv7NTnpv3KxYsX9e8PfvrppzpstpQb9i2h1uaW/cpFm/U5um8J8RAaWuHll1/Wx7b/+te/Ntv2X//1X62ZM2f6zTt69Kg+V+fy5cthvx4mTZpkLVq0KOBj48aNs5YuXeo3b+/evVaXLl2sO3fuWG4QrD6PXbt2Wd26dbP+67/+y3KTpmr77rvv9PYXFRXlnSIiIrzzfv/731tuX3dz5syxHnnkEb95f/rTn3SNf/7zny231+em/UpBQUGj7U3dV9uc+v+7d++6dt8SSm1u2q8U2KzPyX2LO8aTDaH+5fDTn/5UDxcfPnxY/6hoc2pqahoNzUVFRXmXF+7Ua1SX1AaihtA///xzv3kHDx7U5zpER0eLGwSrz/MvqxdffFHfhvP5DHZqU5efqkMgvrZs2SJ/+MMf9HkbLdmuw33dPf300/oKops3b+pDPMqf//xniYyM1Fcmub0+N+1X1NUyDbc3ddm6unLt3//9372v2437llBqc9N+ZZLN+hzdt7RBWEMTfvKTn+grMA4fPmxVVFR4p5qaGm+blStXWhkZGd77H3/8sf7XxpYtW6y//OUv1rFjx6yUlBRr5MiRYdfPWVlZVnFxsXXhwgXrq6++slatWmVFRkZaBw8eDFibGrmKi4uzVqxYof9VvH37dis6Otr61a9+ZYUju/Xl5+frdbd582a/9X39+nXL7bU1FO5XS9mtT13doUZX1ejGH//4R31Fo7raY+HChZYJ9blpvxJIwytu3L5vsVObm/YrodTn1L6FcNOOVJYMNKkdjcfcuXP1ym94iaa61E8NQSYmJlr/8i//Yv3tb3+zws2LL75o9evXz+ratav1wAMP6GFxz861qdpU0Bs2bJh+zsMPP2xt3brVCld261P/H2h9q3YmrDs3hZtQ6vvmm2+syZMn6/edCjqZmZl+/xBxe31u2a+05APS7fsWO7W5ab8S6rpzYt8Sof7TuoEnAACA8MHVUgAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAACISf4ftXOSfyg+0hYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_settings_advanced['prcp_fac'].hist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2215875-2198-480e-84d1-992fae66110d",
+   "metadata": {},
+   "source": [
+    "The `prcp_fac` distribution shows four distinct values. This is because `prcp_fac` is determined by matching the mass balance to the provided `ref_mb` (two values) using the provided `melt_f` (also two values), resulting in four unique combinations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "67804c1e-989a-4f3a-827b-6496fda8468f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_settings_advanced['inversion_glen_a'].hist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a16c3783-5895-4a08-bd2d-2379ecee962d",
+   "metadata": {},
+   "source": [
+    "The spread in `inversion_glen_a` reflects the interconnected nature of the OGGM workflow. This parameter is calibrated during inversion depending on both the target volume and the calibrated apparent mass balance, which is itself a combination of `ref_mb` and `melt_f`.\n",
+    "\n",
+    "Let's also look at some plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "bead1301-6858-4660-b8c2-d122da92bc70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7c41a96d4e10>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "artists = ds_q_advanced.sel(rgi_id=rgi_ids[0]).volume.plot(hue='quantile')\n",
+    "ax = artists[0].axes; leg1 = ax.get_legend(); leg1.set_title('Quantiles sensitivity study');\n",
+    "line = ds_default.sel(rgi_id=rgi_ids[0]).volume.plot(linestyle='--')\n",
+    "ax.legend(handles=[line[0]], labels=['OGGM default static spinup'], loc='lower left')\n",
+    "ax.add_artist(leg1) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "4cb7127f-4530-4cc8-97df-74dde25b703b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7c41a953fd90>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "artists = ds_q_advanced.sel(rgi_id=rgi_ids[0]).specific_mb.plot(hue='quantile')\n",
+    "ax = artists[0].axes; leg1 = ax.get_legend(); leg1.set_title('Quantiles sensitivity study');\n",
+    "line = ds_default.sel(rgi_id=rgi_ids[0]).specific_mb.plot(linestyle='--')\n",
+    "ax.legend(handles=[line[0]], labels=['OGGM default static spinup'], loc='best')\n",
+    "ax.add_artist(leg1) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a731405a-59cd-4c92-8f4d-f112c89d480a",
+   "metadata": {},
+   "source": [
+    "Again, we see our median is very close to the OGGM default, which is an indication that our perturbation design is balanced."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1885963c-6432-4af5-96e1-5e05d0251f73",
+   "metadata": {},
+   "source": [
+    "## Advanced sensitivity study: perturbing global parameters (ice density)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "m5gopf2bcp",
+   "metadata": {},
+   "source": [
+    "So far we have perturbed glacier-specific parameters such as `melt_f` or `ref_mb`. Some parameters, however, are global. They apply to all glaciers equally and are stored in `cfg.PARAMS` rather than in `settings.yml`. A typical example is `ice_density`. The new settings system also supports perturbing global parameters: you can store an override value in a custom `settings.yml` file, and any task that reads the parameter through `gdir.settings` will use the overridden value for that experiment. This makes it possible to include global parameters in sensitivity studies using exactly the same workflow as for glacier-specific parameters. Here we demonstrate this for `ice_density`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "d07ef90e-1574-4817-a991-52ae01f238e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from itertools import product\n",
+    "\n",
+    "ice_density_variations = [850, 900, 950]\n",
+    "\n",
+    "density_experiment_name = '_ice_density_sensitivity'\n",
+    "\n",
+    "all_created_settings_density = []\n",
+    "\n",
+    "for i, new_ice_density in enumerate(ice_density_variations):\n",
+    "    new_filesuffix = f\"{density_experiment_name}_{i}\"\n",
+    "\n",
+    "    for gdir in gdirs:\n",
+    "        gdir.create_new_settings(filesuffix=new_filesuffix,\n",
+    "                                 data={'ice_density': new_ice_density,},\n",
+    "                                 parent_filesuffix='',\n",
+    "                                 ignore_existing=True,\n",
+    "                                 overwrite=True,\n",
+    "                                )\n",
+    "\n",
+    "    all_created_settings_density.append(new_filesuffix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "npz394yywip",
+   "metadata": {},
+   "source": [
+    "We now run the same OGGM workflow as before, using the default observations and the new settings files with different `ice_density` values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "656dc154-2767-4305-a3c8-879446734fdf",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:53:58: oggm.workflow: Execute entity tasks [my_oggm_workflow] on 6 glaciers\n",
+      "2026-04-03 09:54:00: __main__: (RGI60-11.00897) my_oggm_workflow\n",
+      "2026-04-03 09:54:00: oggm.core.massbalance: (RGI60-11.00897) mb_calibration_from_scalar_mb_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.massbalance: (RGI60-11.00897) apparent_mb_from_any_mb_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: __main__: (RGI60-11.00897) my_oggm_workflow\n",
+      "2026-04-03 09:54:00: __main__: (RGI60-11.00897) my_oggm_workflow\n",
+      "2026-04-03 09:54:00: oggm.core.massbalance: (RGI60-11.00897) mb_calibration_from_scalar_mb_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.core.massbalance: (RGI60-11.00897) mb_calibration_from_scalar_mb_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.massbalance: (RGI60-11.00897) apparent_mb_from_any_mb_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.massbalance: (RGI60-11.00897) apparent_mb_from_any_mb_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 6.450810327634147 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 3.2754051638170734 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: __main__: (RGI60-11.01450) my_oggm_workflow\n",
+      "2026-04-03 09:54:00: __main__: (RGI60-11.01450) my_oggm_workflow\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.massbalance: (RGI60-11.01450) mb_calibration_from_scalar_mb_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: __main__: (RGI60-11.01450) my_oggm_workflow\n",
+      "2026-04-03 09:54:00: oggm.core.massbalance: (RGI60-11.01450) mb_calibration_from_scalar_mb_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.massbalance: (RGI60-11.01450) mb_calibration_from_scalar_mb_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 1.6877025819085367 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 5.988129921382223 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 5.533626481389863 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 1.0652480206517403 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.massbalance: (RGI60-11.01450) apparent_mb_from_any_mb_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 3.044064960691111 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.core.massbalance: (RGI60-11.01450) apparent_mb_from_any_mb_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.massbalance: (RGI60-11.01450) apparent_mb_from_any_mb_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 2.8168132406949313 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 1.2970489471809503 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task calibrate_inversion_from_volume on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 0.1 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 1.5720324803455556 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 1.4584066203474657 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 1.2626198187502378 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 10.0 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 0.8360162401727779 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 1.2563067196554867 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 0.7792033101737329 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 5.9508061171059055 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 6.412762782542346 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 5.497587680126281 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 1.0549447566204668 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: calibrate_inversion_from_volume converged after 8 iterations and fs=0. The resulting Glen A factor is 1.2563067196554867.\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 0.8190767310635729 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 3.0254030585529526 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 2.7987938400631402 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 1.0070293495479614 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Volume estimate optimisation with A factor: 3.256381391271173 and fs: 0\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:00: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:00: oggm.core.flowline: (RGI60-11.00897) init_present_time_glacier_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:00: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 0.8072147878457062 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 1.5627015292764763 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 1.001790491708867 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 1.4493969200315702 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 1.6781906956355865 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 0.8031787139054777 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: calibrate_inversion_from_volume converged after 8 iterations and fs=0. The resulting Glen A factor is 1.001790491708867.\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 0.8313507646382382 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 0.7746984600157851 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 1.0757882552755587 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: calibrate_inversion_from_volume converged after 8 iterations and fs=0. The resulting Glen A factor is 0.8072147878457062.\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 1.05652255490783 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 0.8219302839704133 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.00897) init_present_time_glacier_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 1.2953589197332143 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.00897) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 0.8081268737231314 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.00897) init_present_time_glacier_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 1.0082501590648167 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.00897) run_from_climate_data_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 1.263449467793793 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 0.8040862393535158 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.00897) flowline_model_run_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 1.0028057503182393 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Volume estimate optimisation with A factor: 1.2571322204538242 and fs: 0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: calibrate_inversion_from_volume converged after 8 iterations and fs=0. The resulting Glen A factor is 0.8081268737231314.\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: calibrate_inversion_from_volume converged after 8 iterations and fs=0. The resulting Glen A factor is 1.0028057503182393.\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: calibrate_inversion_from_volume converged after 8 iterations and fs=0. The resulting Glen A factor is 1.263449467793793.\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Applying global task inversion_tasks on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [prepare_for_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) prepare_for_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [mass_conservation_inversion] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) mass_conservation_inversion_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.01450) init_present_time_glacier_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [filter_inversion_output] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.01450) init_present_time_glacier_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) filter_inversion_output_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.workflow: Execute entity tasks [get_inversion_volume] on 1 glaciers\n",
+      "2026-04-03 09:54:01: oggm.core.inversion: (RGI60-11.01450) get_inversion_volume\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.01450) init_present_time_glacier_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.01450) run_from_climate_data_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_ice_density_sensitivity_2_ice_density_sensitivity_2\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_ice_density_sensitivity_1_ice_density_sensitivity_1\n",
+      "2026-04-03 09:54:01: oggm.core.flowline: (RGI60-11.01450) flowline_model_run_ice_density_sensitivity_0_ice_density_sensitivity_0\n",
+      "Process ForkPoolWorker-62:\n",
+      "Process ForkPoolWorker-63:\n",
+      "Process ForkPoolWorker-60:\n",
+      "Process ForkPoolWorker-59:\n",
+      "Process ForkPoolWorker-56:\n",
+      "Process ForkPoolWorker-61:\n",
+      "Process ForkPoolWorker-57:\n",
+      "Process ForkPoolWorker-58:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/multiprocessing/process.py\", line 313, in _bootstrap\n",
+      "    self.run()\n",
+      "    ~~~~~~~~^^\n",
+      "  File \"/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "    ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/multiprocessing/pool.py\", line 114, in worker\n",
+      "    task = get()\n",
+      "  File \"/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/multiprocessing/queues.py\", line 385, in get\n",
+      "    res = self._reader.recv_bytes()\n",
+      "  File \"/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/multiprocessing/connection.py\", line 216, in recv_bytes\n",
+      "    buf = self._recv_bytes(maxlength)\n",
+      "  File \"/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/multiprocessing/connection.py\", line 430, in _recv_bytes\n",
+      "    buf = self._recv(4)\n",
+      "  File \"/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/multiprocessing/connection.py\", line 395, in _recv\n",
+      "    chunk = read(handle, remaining)\n",
+      "KeyboardInterrupt\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/multiprocessing/process.py\", line 313, in _bootstrap\n",
+      "    self.run()\n",
+      "    ~~~~~~~~^^\n",
+      "  File \"/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/multiprocessing/process.py\", line 313, in _bootstrap\n",
+      "    self.run()\n",
+      "    ~~~~~~~~^^\n",
+      "  File \"/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/multiprocessing/process.py\", line 313, in _bootstrap\n",
+      "    self.run()\n",
+      "    ~~~~~~~~^^\n",
+      "  File \"/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/multiprocessing/process.py\", line 313, in _bootstrap\n",
+      "    self.run()\n",
+      "    ~~~~~~~~^^\n",
+      "  File \"/media/patrick/SD_CARD/mambaforge/envs/oggm_env_26/lib/python3.13/multiprocessing/process.py\", line 313, in _bootstrap\n",
+      "    self.run()\n",
+      "    ~~~~~~~~^^\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_experiments_density = []\n",
+    "for gdir in gdirs:\n",
+    "    for single_experiments_filesuffix in all_created_settings_density:\n",
+    "        all_experiments_density.append(\n",
+    "            (gdir, dict(\n",
+    "                experiment_filesuffix=single_experiments_filesuffix,\n",
+    "                observations_filesuffix='',  # use the default observations\n",
+    "            ))\n",
+    "        )\n",
+    "\n",
+    "workflow.execute_entity_task(my_oggm_workflow, all_experiments_density);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9wj3a6yvx2",
+   "metadata": {},
+   "source": [
+    "Let's combine the outputs and look at the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "b6981b3a-3a0a-4b2b-a4eb-8d930fd6e5c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-03 09:54:01: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:54:01: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:54:01: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:54:01: oggm.utils: Applying compile_run_output on 2 gdirs.\n",
+      "2026-04-03 09:54:01: oggm.utils: Applying global task compile_run_output on 2 glaciers\n",
+      "2026-04-03 09:54:01: oggm.utils: Applying compile_run_output on 2 gdirs.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# container for individual experiment outputs\n",
+    "ds_all_density = []\n",
+    "for suffix in all_created_settings_density:\n",
+    "    ds_tmp = utils.compile_run_output(gdirs,\n",
+    "                                      input_filesuffix=suffix)\n",
+    "    add_specific_mb(ds_tmp,\n",
+    "                    # use the correct ice density for each experiment\n",
+    "                    ice_density=gdirs[0].read_settings(['ice_density'], filesuffix=suffix)['ice_density'])\n",
+    "    ds_tmp.coords['experiment_filesuffix'] = suffix\n",
+    "    ds_tmp = ds_tmp.expand_dims('experiment_filesuffix')\n",
+    "    ds_all_density.append(ds_tmp)\n",
+    "\n",
+    "ds_all_density = xr.combine_by_coords(ds_all_density, fill_value=np.nan, combine_attrs=\"override\")\n",
+    "\n",
+    "#add_specific_mb(ds_all_density)\n",
+    "\n",
+    "#ds_q_density = ds_all_density.quantile([0.05, 0.5, 0.95], dim=['experiment_filesuffix'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "cfcbcafb-afe6-4d7f-b2d5-5ec660af04ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7c41a9421950>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jz4kcsuCoO4mm+TIMwzAMk/0JDg4WRgr1OJ4W8pzIUbuoSOCwyGEYhmGYnMXXhJpw4DHDMAzDMLkSFjkMwzAMw+RKWOQwDMMwDJMryXMxOQzDMClBucMonQvDMFmDlpZWmqeHpwUWOQzD5Hlo/Q1PT08EBgbm+b5gmKyEBA6lryGxkx6wyGEYJs+jFjhWVlYi0S4vFMowWbdY76dPn5A/f/50+X/IIodhGOR1F5Va4HDyU4bJWiwtLYXQiY2Nhaam5nfXx4HHDMPkadQxOGTBYRgma1G7qejHR3rAIodhGOYrFxhjGCZn/D9kkcMwDMMwTK6ERQ7DMAzz3bi7u4tf4ffv38+xweeNGjWCvr4+TExMxDFqz/79+7OsfeHh4WjXrp1IQUT3ptix5I45OTnhr7/+yrTnyklw4DHDMAzz3VDiRJoVY2FhkeW9OX36dCFOvkaQLFq0SDw/XWNsbCyO0b6pqSmyio0bN+LSpUu4evWq6Fd6rpUrV3527NatW0KcMZ/DIicdeewRBCcLfRhoc7cyDJN3iI6OFgGjNjY2yKm8evUKFSpUQOHCheOPZXV76JmKFSuGkiVLpnqMZiQxycPuqnQiLCoWLZZeRslpJ1Bjzln0XHcTfxx2xa5b73H3XQCCI3kVVYZh0mfhwrlz58LZ2Rm6urooU6YMdu/eLY43bNgQTZs2Fa8JcmXQeiOTJk0S++fPnxcujiNHjojrdHR0UKVKFTx69CjRPchKULt2bVE/WWhGjBiBsLCw+PPkHvnjjz/Qq1cvYUno37//Z+4c9b1OnDiBcuXKibrq168Pb29vHDt2TAzU5HL56aefhAvmS+1To673zJkzqFixopgVV716dTx//lyc37BhA2bMmIEHDx6IcrTRsdSg9uzZswebNm0S5aldSd1VyeHq6opmzZrBwMAA1tbW6N69O3x9fePP03OXKlVKtIOWJ6D3R92PdevWxahRoxLV9+OPP8bfm84vWLAAFy9eFM9B+8kdUz+/2l1F/UOCk6w9augasvqQZSqvwSaHdMIzOBIWBtrwDY2CR2CE2C688ElUxtZYB4WsDOBibYjCVgYobG2AQlaGMNb9/rUAGIbJG0yePBl79+7FihUrhNWBBrxu3bqJX/Pk3qBBdcmSJRg5ciQGDRokBl9y3yRk3LhxWLx4sbBUTJw4Ea1atcKLFy/EuiQkeJo0aYLff/8da9euhY+PD4YNGya29evXx9cxb948TJkyRTxPatC9//77byFGOnbsKDZtbW1s27YNoaGhaNOmDZYuXYrx48d/sX116tSJr5eEGw3edJza2adPH1y5cgWdOnXC48ePcfz4cZw+fVqUVbufUoLcPT169BCii/qFRMmXIMFAz0MCb+HChYiIiBBtoPadPXtWnCcBR4KN2hgSEiKEh1qAfgnqg19//VW0hV6rp1YndywhavFEguvBgwdCfFJfbd++Hba2tshrsMhJJwpaGuD25IYICIuGm08oXniF4KVXKNy84157h0ThU1Ck2C69/E/pE9ZG2ihsZShED/11+f9fYz0WPwzD/AdZAWhApUG0WrVq4hhZPC5fvox//vlHCAf6SwOcl5cXDh06hHv37n22qNq0adNEkC1BwihfvnzYt2+fGKBJvHTp0iXeykBCg0QTDegkPMj6Q5BVZuzYsfF10mCaHGTxqVGjhnjdt29fTJgwQbhc6LmJ9u3b49y5c0IgfKl9CUXOzJkz4/dp4G/evDkiIyOFQCHLioaGRprdTSSUSHjRtWm9hvqifPnymDVrVvyxdevWCcsXCUYScLSgXdu2beHo6CjOkwBNK2ZmZkIYJnUDJncsuT4ngTdgwAA8efJEfB5IaOVFWOSkF8oYYHFZmJoVQCWLwqhk4QIULwzUdgGMKiEoUgk3nxC88AoV4ueld5wIIguQV3CU2C67JRY/loba8YKniI0hfihpAxO99MnnwTBMzoPcIzSQqwVKwpgYcgkRHTp0EIJl9uzZYiB2cXH5rB61gFAPpkWKFMHTp0/F/p07d+Dm5oatW7fGlyHrAy25/+bNG+FmIshVlBZKly4d/5qsSjRIqwWO+tjNmzfT3L7k6lVbKMgVRu65zID6icQZCaqkkIhr3LgxGjRoIIQNWcZonwRdZgQykwjasmWL6CMSWHl55hWLnPQiwB0I/hC3uV9K0su6MLYohAoWLmJDgcJAJRfAvDSClRrC2vPy/5afF96hcPMKwcegSPiERIntipufqGbmkafoUc0R/Wo5w0yfxQ7D5DVIaBAUU2Nvb5/oHFkiCIpvoQFYoVDg5cuXX70IG91j4MCBIg4nKQkFRFpn8yS0ItE9klqV6Ji6XWlpX0r1Jrw+M6B7tWzZEn/++edn50h0Uf+fOnVKxDedPHlSuOTIbXTjxg2RgJISUSZ1XalX304P6L6Ev7+/2PLq7CsWOemFSX7c6/APHCJCYR7oAZnfS8D3JeD3CoiNADwfxW2JkMHIxAHlLVzEBpvCQEkXwKIUQhQmcPMJw0sSPd6huPjCB888Q7D8/CtsuOqOblUd0b+Ws7D2MAyTNyhevLgY7N+9e5fIdZOQn3/+WQygFNxLQbHkxiHXUkKuX78eL1gCAgKEe6Vo0aJin1ww5OIoVKgQsmP70mrJSK+0AClB/UTByhT0S66x5CDxRa462qZOnSqsKmRlGzNmjHCRJQwEpuelWJt69ep997ORJWn06NFYvXo1du3aJeKNKFCbPhd5DRY56USoKgY9bs8Ur/U19eFo5AhH2zpwNOgGR4UenGJi4RgWBMOAt4DvC8D3ORAZBAS+i9vc4gLk1BjqGKOchYvYYFEYvzYpgrMxJfHXhXd47BGMVRdfY9M1d3Sp7IiBdZxhbRTnJ2cYJvdiaGgo4mBoACNLQs2aNREcHCx+tZPbhGbQUFzItWvXxCBMsSo9e/bEw4cPE7lJfvvtNzHbh1xFZF2g62hmD0GxMVWrVsXQoUNFUC1ZAMiVRVYJskZkZfuoLWmBhAe51mimF8UbUb1JLUHfC/UPiQgKLqZAbupDcvPt2LFDHL99+7YQFuSmouSvZMGhIG61u4+EJ4kdsloVLFhQrNNDs+G+FxJLFIND9+3duzd++OEH4TKjIG16zrwGi5x0wj/SH/kM8uFj2EeExYTB1c9VbEkx0zGDk70THItVg6OOBZwkORwjI+AQ4gttsvqQACLRQwLow6247f9z/RvqW6FBlUG4XKc1Flzywv33gVh35Q223HiLzpUcMKhOQdiZfHlWAMMwORea9USDJsXcvH79WqzOS4KGAnppZhHNZqJ9dYAxuUpo9tHOnTvj65gzZ46YfUXuLJqiffDgwfiZOhTHceHCBSF+atWqJVwqNAhT3VnZPpoFllZoRWCafURWERIONCtMPTU7vbCzsxOzuUgUUsxNVFSUsNTQFH6ymNBMLZoZRvEwJNToHAkNEh0EzQaj2U9kZSFLEAm79LDiUEA2BYFT0DlBAcpr1qwRQeUU61S2bFnkJWRSWuez5RLow0bTCYOCgsSHML2JVkbjQ8gHuAe7423wW7GpX/tGJA4sTogMMtgZ2MVZgAzs4SjXg2OsEo7hIbAL/AjFu2tAyMe4wlqGkCr0wg3rTph/LQS33waIw5oKGTpUdMDgOgXhYMYZlRkmLVCgK/3qpzgJ9cyh3AqtoUIDKbmo1KkLGCan/H/8lvGbLTnpjJZCC84mzmJLSmh0KN6FvEskfN4Gxb0OjQmFR6iH2OLCxf5DU64Jx4JF0FS3Pjq63YapzzPIri1FVflK/Fu6I+5X6YE/b0u4/tof2268EwsQti1vj6H1CsHRPG8GmzEMwzAMW3KyAWRMI3eX2vKT0AL0LvgdolXR8WW1FdpoYV4W3T65o9C7OFeWoEhzuDr3wezHhvHr8CjkMrQuY4eh9QuJdXwYhvkctuTkfmg6PM0YSw5yI1GgNZM7LTkscrI5KkkFzzBP3PG6gy1PtySK86lmWhzdQ8JR4+XF//Jz5K8GN5d+mPkiH869iJt6TrMrW5a2w7D6hcRqywzD5E2Rk1eh1YZpccTkoKno6sX6mKyHRU42j8nJaIvPPe972Oy6GWffnxUCiCigb49uKl20eH4Zesr/W30si+FdsX6Y+a4ETjzzjxc7tKDgsHqFUdwuZ7WdYTIKFjkMk31gkZOHRU5CKLh527Nt2Ptyr5jNRRhpGqCDli06u92CTWRwXEEje3wq3hdzvKrgwNOg+OsbFbfGiPqFUSpf6jldGCa3wyKHYbIPLHK+k9wichIGMx94dQBbXLfgQ+gHcUxDpkAjPUd0f/8UpYL+b6LVMYFv8e6YH1gPO59GQj2nrn5RK3Sq5CCSi1KiUBM9TfFXU5H3Fo1i8iYschgm+8Ai5zvJbSJHjVKlxIUPF4Qr67bX7fjjZfXs0M3HCw2838RNpdPQQVCRjlgS0RTrn1LMT/L16WspRJ4sIxI+upqJBBAlDhX7ulqfHTfQ0oBcHrfEOsPkBFjkMEz2gUXOd5JbRU5Cnvo9FUHKR98cRawqVhyz1TJGl/AYtPV4DiNSNjI5Qgu1wHqpFU4G2CIwIhpB4TEIjowr/62QvhGCR1dT5Nca06gIaha2SKeWMUz6wyKHYbIPLHK+k7wgctTQ4oM7nu3Arue7EBAVt2CgrlwbP0q66PbOFflj/y9oLIsB+haAjjFUOsaI1jBChMIAYXIDhEAfQdBHoFIXfko9+MTowCtGF76RMgRFxCIoIiZOIEXEIDLm8+R4lFvr9Jg6QvQwTHaERQ7DZB9Y5HwneUnkqIlSRuHI6yPCleUW6Ba/wnIdhTG6e7xEpYgIfLWDSaEl4nxIGEE37q9S2xhRGoaIUBgiTKaPLY+jsDGoDNpXKYSZbUplRNMY5rvJjSKnbt26Yvl+SimQF+5PqSz2798vclXlRjZs2IBRo0Z9MbcVJQSlBKDqPGRpyfFF9dKWXWCR853kRZGTcAr69U/XhSvr4oeL8cfNNA1gotCBoUwThpDDUAIMVRIMVUoYxMbAKCYShtERMIgKg2FUKAyVsf8/r4KOJKUqkC4qS6FHzK/YM7g6KjiaZUo7GSavixx/f3+x/gslpswLIic0NFTkjqKkowTlqSJBQMInNxARESHW+qGcXqmJOk9PT5GINa3JSH18fEQCVj09vW8SSWmBMspTMtOzZ89CV1cXXbp0wfz58+NzpSWF0zow3wx9gKvZVRPbm6A32Pp0Kw6+Ogj/mFCxpYjm/zd9+o+QOCeWhkwOQ4UuDOVaMJBpCJFkJAEGkgQLvzfoHOiKzqpzmLjXCIdH1ORZWwyTCZiZ5a0fFJShnLbcCokD2r4EJeP8GiwtLZGRUEb05s2bi/tcvnwZfn5+IpM8/eDO6Iz2aniecB6lgHEBTK46GWc7nMXOFjuxtvFa/FX3L/xW/Tf8UukXDC4zGN2KdUPrgq1R36E+KttURjGzYrA3sIextjHksriPTqykQkBsGN5FB8A1ygc3orxwKtoL+2K8sdpIHwNtLDFOaytCvN5g1cXXWd1shskTkCUloQuCrBy//PILHBwcxK/8woULY+3atfHnXV1d0axZMyEUrK2t0b17d/j6ppxQOCFhYWEikzZda2trKzJtJyU6Olrc397eXlgOqlSpIpKFJnTHUMLQEydOoFixYqIuyub96dOn+DJUvnLlyuJ6KlujRg28ffs23rKhzq5Nrzdu3IgDBw6IH3a00bX169fHsGHDEj0XDbrUH2Rl+BLLly8X/UbWPuqj9u3bx5+jQXvu3LlwdnYWYoQyu+/evTvRs9NznDlzBhUrVhSWk+rVq+P58+fxZSgjOSVPJesbeRkqVKiA27dvJ+of9esZM2aI8ur20TGCXqutV9WqVcOvv/76meWGLHznzp2Ld1eprW30mmjTpo2oh/YpmzllVFc/hxoSKLRK9Jfye588eVJ8trZs2YJy5cqhYcOG4vOxevVq4VXJDDhBZx7HQMsAxc2Lf/V19OGOiI1AcHQwQqJDRIJR+ptwI0uRG/ywxlQbc2JXo/8ZS7QobctJQ5lsj/h8xygz/b66mgoxwKQ3JEKuXbuGJUuWiAGY3HNqEUNCok6dOujfvz8WLlwoXCPjx49Hx44d0zT4jxs3Tgya5OYgS8LEiRNx586deNFB9O7dWwyYO3bsgJ2dnShLIubRo0dCOBDh4eHCjbF582YxsHbr1g1jx44VeadiY2OFC4Wecfv27UI03bx5M9m+omuePn0qBtH169fHW7b69esnRA4Nsmp3DtVNz0PiIjVokB8xYoR4NhIn5A68dOlS/PnJkydj7969WLFihWjPxYsXxfOTBYP6Vs2kSZPE/en4oEGD0KdPH1y5ckWc69q1qxACVIdCoRCuKBIkSenUqRMeP36M48eP4/Tp0+IYhWAkheqbN28eZs+eHd9PO3fuFAIt4TOpuXXrlnCHUZ/Re0PPQM9JwoSOkThTQ/vkEvzSZ5U+cyVLlhR9rKZJkyZCdNNn5Ev9nh6wyGG+Cfpw62nqic1GP3kTaRGzIhh6Zii2GBuhVvhL/BhyFpP2mWNz38oZ8kXOMOkFCZziU09keoe6/tYEelrp+7X84sUL7Nq1C6dOnRIDFkEWBzU0qJYvXx6zZs2KP7Zu3Tph9aFrXVxcUo2FIYvQpk2b0KhRI3GMrCj58uWLL/Pq1SshTD58+BA/2JEQoUGaBkv1fWNiYrBy5UoULFhQ7JMg+e2338RrEiwUR9miRYv482TxSQ6yApE1hQbShO6bdu3aYfjw4cLCQwLuawZriishCxLdnywtZMUgQaK2ZJE4JEFI1hN1/5J75p9//kkkKGbOnBm/T1YWcuVQDApZh+geJBiLFi0qzqvFX1KobdRGDQ2NVN1TJIZGjx4tnqNWrVri2LZt20RMDInIlFxXZDFKWC+JQxJk1EYSh2RBIgFGou5LUIwQiaqEUMwQxePQucyA3VVMhlE7X210KtJJvJ5iaYYRWlvx2u0Z9t/34F5nmEyCBiT6VZ7cr3eCflGTJUYd10KbeqAlgZIadJ6sKurBXW01KVKkSPz+3bt3hWWMxFLCe1y4cCFR/eTCUQsYglxf3t7e8XWSGCErQMuWLbF48eJErqy0QAM0WVdIwKn7hQZsqvdLkIAjYUPihVx5ZAEiyxNB7hgSKlQmYftI+CXtv9KlSydqH6Fu45gxY4SgICE6Z86cL/b9lyDR0qhRI/GsBFnvyLJCFp6vgSxoJKjI+kZQ/5EFRu3e+hLJCUj6PGTWD1225DAZys8Vf8aNTzfgHuyOBZZ6mPVpNcYcskVdFyuY6icfXc8wWQ25jciqkhX3Tfc6vxCwqlKphHD4888/PzunHohT4ksxGer6SWSRmKK/CUkYLJzUNUODYML6yepCLiOyAJHbhVxEZJ2qWrUq0gqJCHKjkVWJBusGDRqkKQM5WW9IrFFsDcWZTJ06VcT+kIuH2kccOXJExBwlJOksp4RtVA/y6uupPrKyUD3Hjh3DtGnThHuPYmS+la5du2LkyJEihoasOCVKlBDuyq+BrC4k7Kj/27ZtK+pJ66w5sgjduHEj0bGAgABhtUtq4cko2JLDZCi6GrqYU2uOyKd1Sl8Pwcav0TDqJGYdfco9z2Rvd6yWRqZvGfHrtlSpUmIgJctJcpCr6smTJ+KXeaFChRJt5KJJDSpDA/f169cTDWLk5lJDbh2aZUMWi6T1f+1sIKprwoQJuHr1qoj1oAE3pYGZ7plcX1BsCQW+0rUUE5NWyJpBVhYKMH748KGIMSIXVfHixYWYIXdT0vaRy+9rIGsXuZhISJGgUMcUpbV9yVlhIiMjhTCk9pIlKzXovUyuXhKHFP9DwdckUOjZ0gJZ+Ch+KKHVjdpG/UWB1ZkBixwmwylhUQJDyg4Vr2eZm6KPzjZcvvMA1175ce8zTAZD4oWm7dKATjNvyG1BFgmK0yFoDRMKpP3pp59EMO/r16/FQETlvzSQkiWmb9++IpaEZg7RgEbun4QxHzRwk0WBgp8pjoPuTxYQshwdPXo0TW2ga0jckLuFZlTR85GQSikuh9pMQoRmL1GANQ3MCQdscgdR29JqJTl8+LAI2iYXF92fXFEkHMktR1YeijEicULxSORmunfvHpYtWyb20wIFe1MMEr0vVD8FI1MfpdY+6hN6HmofxR8lh76+Plq3bo0pU6aIYGyyFKUG1UvvI8XLkFhVQ89BFjMKSKfPSVqmsxONGzcWIpAsQdQnVDf1FQWQZ9Y6dSxymEyhT8k+KGdZFmFyOWZZ6WOm5mpM2vsQUbGZP4OFYfIaFFxMU56HDBki4m1okKGAWYKCgWlQpUGfYl7IQkIuDpqxk1yAalJoBk/t2rXRqlUrYemoWbPmZ7/SySJBIufnn38WwoDKkhsjrZYOitd59uyZCB4m0TRgwAAhCgYOHJhseWof3YesNhSbop7BRNAgTVYZGvDTuvgjBeOSQKNp6DTgU4A0BVOT+4f4/fffhQuLZjLReerHQ4cOiQUm0wK58Wg6O/URtY8Co3/44QcxVTw5qB9oBhTFxlD76FlSomvXriL2iIKP8+fPn+pz0MwvcgHS+6IOrFZDYpbir77G+kXtIvcb9TNN+ad2kXWJZtFlFjIpLU7VXEReXvE4q/kQ8gHtD7ZFWGwERvoH4rlvZ9jV7Y/RjVKevcEwGU1uXPGYSZn3798LiwVZSshVx6QNmhlGMUI07T8jSe8Vj9mSw2Qa+QzzYUKVSeL1MlNjdNDbjr3nb8LNO5XVlhmGYdIBcllR3Ay5XMj1wgInbdAyASQIKXiZAr9zGixymEylVcFWaJS/IWJlMvxuZYDJin8wce9DqFR5yqDIMN8FDdYJpysn3eh8TrpPZkAuK5pJRbO8yN2UEFrYL7V25mWGDRsmXJC0BEFSVxWtn5NSn9G57AC7q5hMJzAyEO32t4Z3lD86BYcgyLMDKv44HJ0qpe4vZpiMICe6q2gFYJrdkxLkjqG4k5xyn6yGAn89PFJev4tmSjGfQzPmUkrPQO4kdULRr4ETdDI5HhMdE/xeezYGnhqInUaGmBe2EwuOlEeDYtawMEhb9lyGycuQsMiMgTez7pPV0GyhvNDO9IZEzLcImcyE3VVMllDdrjq6FY2bzjjH0gDDpeX4/dATfjcYhmGYdINFDpNljKwwGoUMHOCnocBp649QPNqBiy98+B1hGIZh0gUWOUyWoaOhgzn1FkETcpzT10N5s134a+8FRETz2jkMwzDM98Mih8lSKFP5iPIjxesl5vroGL0YS878tyQ8wzAMw3wrLHKYLKdHyV6oZFYCEXI5Dll7wevKejzzTD5in2EYhmFyjMihaXuUNMzc3Fws3U0ZYmkdg9SgRHO0bDhN93R2dv5szQMmZyGXyTGz3iIYyrXwUEcbDua7Mfff87x2DsN8I3Xr1sWoUaPyzP0pgzeNHbmVDRs2iNQSX4ISvFJ+srTi5OSU5oziOZUsFTmUAIzyWVDmU0ot7+rqKnJnpPZm0noWzZo1E3k4KOHXxIkTxSqMe/bsydRnZ9IXWwNbTK42XbzeaKqHGkF/Yuv1lNfnYBgmZSjPEuVTyitQ0kdK/qiGkoRSjqTcQqdOnRJldk9J1FG2b8p5lVZu3bol8oB9q0hKC5QHjYwSlHk8K4Rolq7iRFloKRFYwnTypCxTg6w2lGRMrT4pGdrt27dFwi9KWsbkXJoVaokLr4/i6KfL2GPjhxInV8Kr5AxYG+WMBdoYJrtgZmaGvERuX5mY1vFJS+ZvGxubr6rX0tISGQ2lx6SVkikhK2WGz1OWnIMHD4ossR06dBALClHW09WrV6d6zbVr10T69oRQxlcSOpSbJCmUgp5WZEy4MdmXSXX/hI1CH+81NaFpvhd/7buQ1Y/EMDmOpO4i+h785ZdfxI9K+kVduHBhrF27Nv48WdHJQk5CwdraGt27d4evr2+a7kXZzCl7Nl1ra2srrPFJoezVdH97e3vo6+ujSpUqOH/+/GfumBMnTogfrlQXZdkmy4QaKl+5cmVxPZUlL8Dbt28/s2zQ640bN+LAgQPCMkEbXUsZxClFQUIo8zf1x9mzZ7/YzuXLl4t+ozAJ6iPK6p5wIJ87d64InyAxUqZMGezevTvRs9NzkLWJxjwKzahevTqeP38eX4YyhVNWcUNDQ7FaMFk/aFxL2D/q15SdnMqr20fHklpiqlWrhl9//TVRG3x8fITn5Ny5c5+5q9QGhjZt2oh6aJ9Wu6ZM9OrnUEN5rChFRlryey9ZsgRDhw4VfZMVZKnIef36NVasWCE+OPThplwX5HratGlTitd4enqKD1hCaJ+WH0/uP+Xs2bNF1lL1Rv/JmeyLkZaRiM+RScAhIx1YfZqBM66eWf1YTF6DvryjwzJ/S8Og8S2QCKEM0jTgPH36VFjE1ZYPEhKUl4hEAg1mx48fh5eXFzp27JimuseNGycGzX379uHkyZNiQE8aV9m7d2+RO4qegX7N0w9bEjEvX76MLxMeHi4s8ps3b8bFixdFXixyQxH0/U7uJ3pOup5+7JKbhQbjpNA19OxqkUQbCYp+/fph27ZtQvCp2bp1K+zs7IS4SA3qFxqbfvvtNyFMqI9q164df37y5MnCI0Hj2ZMnTzB69GgRa0rxowmZNGmSEIFUH60mnTAXVNeuXZEvXz7hQqL+I4FCgiQ519XPP/+MEiVKxLePjiWF6tu+fXsiIbJz504xXlI/JoXuS1A7qE7aJ6HTsGHDRN4WdRlyCSbX/9mNLHVXqVQqoWpnzZol9smSQx8Q+qDQf8qUSNqx6jcxuQ6fMGECxowZE79PlhwWOtmbyvbV0Mu5Jda/OYR9VoHwPrAQVQvOgr52zs+Rw+QQYsKBWXaZf9+JHwEt/XStkmI5du3ahVOnTokBi0j4q5q+bykjt/p7mFi3bp34nqRrXVxcUs1QTRYh+mHaqFEjcYysKDRYq3n16pUYbD98+CAEhVqIkFCgwVJ9X7LEk/gqWLCg2CerC4kK9fd2UFAQWrRoEX+eLD7JQeKNrCkkZhK6byicYfjw4cLCoxZwaR2sSXCRBYnuT5YWsmLQeKW2ZC1cuFBYg8h6ou7fy5cv459//kkkKGbOnBm/TyKmefPmIlcTWYfoHiQYixYtKs7Tj//koLZRG0kkpeaeIuEzevRo8RwUw0qQyOvSpYuwzqTkuiKLUcJ6SRySAYLaSFYvsiDdv39fxH3lBLLUkkOmzeLFiyc6Rh/c1DLbUueTNSdpkjB6w2mGVlLoTSHTX8KNyf4MqzEdLpqmCFAo4GtyAP8cvpLVj8QwORIakBQKRbK/3gmyGpAlJmEGafVASwIlNeg8uaLUg7s6HqhIkSLx+3fv3hU/REksJbwHWTkS1k8uHLWAUY8P9N2urpPECIUmtGzZEosXL07kykoLNBaQdYUEnLpfaMCmer8ECTgSNiReyJVHFiCyPKldfSRUqEzC9pHwS9p/pUuXTtQ+Qt1G+jFOgoKE6Jw5c77Y91+CREujRo3Es6on7ZAFjCw8XwNZ0Gh8JUsdQf1Hlq8vxc9mF7L0pzH5VBP6JAn65UAfppSg/0yHDh1KdIxMpGQRSs60x+RMtBRa+LPJanQ61B7X9bRg//pXPHp/DKUcvjyNkmG+G029OKtKVtw3nflSwCpZ1Ek40ESQpKgH4pRIS0wG1U8ii8QU/U1IwmDhpN/fZF1JWD9ZXchlRBYgcruQi4isU1WrVkVaIRFBbjmyKtFg3aBBg1THGzVkvSGxRq44Gm+mTp0qYn/IpUPtI44cOSJijpIKq4QkbKPaeqS+nuojKwvVQ7ONp02bJtx7FCPzrXTt2lXMbqIYGrLikIuL4oW+Bi0tLSHsqP/btm0r6slJ086z1JJDprTr168Lc6Wbm5vovFWrVokgpYTupoSuKzKbUbAZqV7yLdMHlcylat8tk3soZF4Eo4vFvfdHLIKxa/cfiFXGfSEwTIZCAxC5jTJ7y4AYh1KlSomBNGl8iBpyVVGYAP0yp0zcCTdy0aQGlaGBm77HEy4NknC6M7l1lEqlsFgkrf9rZwNRXTQmXL16FSVLlhRjRkoDM90zub6gH8Q0wYWuTRgT8yXImkFWFgowprggCsolFxV5I0jMkAciafu+NjSCrF00LpKQIkGRNBbmS+1LzgoTGRkphCG1lyxZqUHvZXL1kjg8ffq0CL4mtyI9W04hS0VOpUqVhAmM/LX0gaV1HUghJjSnkUkyofuqQIECOHr0qFDUpMjpGgqm4+njuZMulX9GZW0bRMrlcNU/gm1n//syZRjmy5B46dmzpxjQaeYNuS3o+5PidAj6Uenv74+ffvoJN2/eFBNCaJCl8l8aSMkS07dvXxFLQjOHHj9+LNw/CWM+aOCm73T6sUpxHHR/soCQ5Yi+y9MCXUPihtwt9COXno+EVEpxOdRmEiLkKaAJKQln3tKATe4galtarSSHDx8W4wy5uOj+5Ioi4UhuObLy0I9sEicUj0RuJlrDbdmyZWI/LURERIgYJHpfqH4K0qY+Sq191Cf0PNS+hMHUCdHX10fr1q0xZcoUYRQgS1FqUL30PlJICIlVNfQcZDEbP368+JykZTq7GjJg0HNSndROek0buTkzBSmPERQURPZP8ZfJGXgFe0jV15WUSm4oKY1eVFn64B+W1Y/E5CIiIiIkV1dX8Te3UKdOHWnkyJHx+9S20aNHS7a2tpKWlpZUqFAhad26dfHnX7x4IbVp00YyMTGRdHV1paJFi0qjRo2SVCrVF+8VEhIidevWTdLT05Osra2luXPnfnb/6OhoaerUqZKTk5Okqakp2djYiPs9fPhQnF+/fr1kbGycqN59+/aJ72rC09NT+vHHH+Of39HRUdSnVCrF+WnTpkllypSJv9bb21tq1KiRZGBgIOo4d+5couelZx0yZEia+/PSpUuiTaampqJ/SpcuLe3cuTP+PPXT4sWLpSJFioj2WVpaSk2aNJEuXLggztP96TkCAgLir7l375449ubNGykqKkrq3Lmz5ODgINpnZ2cnDRs2LP4zmbR/IiMjpXbt2on3i+qg8wS9pn5LyJEjR8Tx2rVrf9Yu6sdFixbF7x88eFB8NjQ0NMS5hKxdu1bUc/PmTelroH6j65Ju1O6v/f/4LeO3jP5BHoKi9GkqOUXqcxByzuHEvTUY+3Ax5JKENhGNMW3QghwxfZHJ/pA5n34Vk5WYZrkwuZv3798LiwVZSshVx6QNmhlGMUKPHj1CVv1//JbxO8tzVzFMWmhSrh+aaDtBJZPhiuZxHL1xgzuOYZg0Qy4rCn0glwu5XljgpA1aJoAEIQUvU+B3ToNFDpNjmN56E2yUMnhqKnD84lA8cvfK6kdimCyBBuuE05WTbqktw5Ed75MZUJwLzaSiWV5JkzpfunQp1XbmZYYNG4aaNWuKJQiSBmrTRKCU+ozOZQfYXcXkKG48/heDb0zHzI0qyBUyfOw2CD17D4dczq4rJu+4q2gFYJrdkxLkjqHZQDnlPlkNBcR6eHikeJ5mSjGfQzPmUkqVRO4kSteU1e4qFjlMjmP/usFw+Os89KKBaA3gejVLNP1jLxytLbL60ZgcSE4UOQyTW4nkmBwmr/NjnxWw2rgI7o4KaMUCtS/54PFPtbFte85ZoIphGIbJeDgmh8mROJRriiZHH+BD+1KI1AScP0ooPvMfLBtSE54BPln9eAzDMEw2gEUOk2ORKxRo9Mcu2G5aDg8HBbRjAZuXfui4pz7WX1yV1Y/HMAzDZDEscpgcj025emhw4iG82pTF/sZAgCaw8M1S9FjbGB8C32f14zEMwzBZBIscJlcgk8tRd/Z2LOyyD+1CdaEhSShz7gNudmyCDftmQCVxziuGYZi8BoscJldhaV8U0wZfx0ypKRo8UKHYOwllpu7A3NHV8NznaVY/HsNkCnXr1sWoUaPyzP0pgzflMsytbNiwASYmJl8sR6vAU36ytOLk5JSjMop/CyxymFxp1WnWez7MVi6Hn60COjFA6+PBeNCtHZbt+wVRyuST2TFMboESYVLy4rwCJcikxJJqKEkoZeDOLXTq1ClRZveURB0ltP7hhx/SXO+tW7cwYMCAbxZJX+LBgwcioSdlY6eknpToc/HixchMcv4qTgyTAvlr1Iftibu48nMPmJ27j1JvgYhphzD70hk0/3kZKtlX5b5jciVmZmbIS+T2lYlJIKQl87eNjc1X1WtpaYmMhFaXpnts2bJFCJ2rV68KUaVQKMRKypkBW3KYXI2mlhbqLt0BxaLFCLKWQzcaaH42HKMO9MPkM2MQFBWU1Y/IMBnuLoqKisIvv/wiBhptbW0ULlwYa9eujT/v6uqKZs2aCaFgbW2N7t27w9fXN033CgsLQ48ePcS1tra2WLBgwWdloqOjxf3t7e2hr6+PKlWq4Pz585+5Y06cOCF+7VNdTZs2FZYJNVS+cuXK4noqW6NGDbx9+/Yzywa93rhxIw4cOCAsE7TRtfXr1/9sYPXz8xP9cfbs2S+2c/ny5aLfaMFI6qP27dvHn6M813PnzoWzs7MQI2XKlMHu3bsTPTs9B1mbKlasCD09PVSvXh3Pnz9PZPWoV68eDA0NxWq+FSpUwO3btxP1j/r1jBkzRHl1++hYUktMtWrV8OuvvyZqg4+PDzQ1NXHu3LnP3FX0mmjTpo2oh/ZptWu5XB7/HGoojxWlyPhSfm9KA7FkyRKREoL6plu3bujdu7ewNGYWLHKYPEGJRk1Q8uhteNQphUc1YxGsL8OBD6fQclcjHH915Iv/WZm8BX0ewmPCM33LqM8hiRDKIE0DztOnT0XuJrXlg4QEDUIkEmgwO378OLy8vNCxY8c01T1u3DgxaO7btw8nT54UAzr9gk8IDWyUO4qe4eHDh+jQoYMQMS9fvowvEx4ejvnz52Pz5s24ePGiyItFbih1eglyP9Fz0vXXrl0TFgEajJNC19Czq0USbSQo+vXrh23btgnBp2br1q2ws7MT4iI1qF8oOeVvv/0mhAn1Ue3atePPT548GevXr8eKFSvw5MkTjB49WgzoFy5cSFTPpEmThAik+igdRsJcUF27dkW+fPmEC4n6jwQKCZLkXFc///wzSpQoEd8+OpYUqm/79u2JPlM7d+4UAo36MSl0X4LaQXXSPgmdhg0bimMJoX1yCSbX/1+CUjJkpqWR3VVMnsFAXxcN/9mFC9duYNL5YdhuEQLTtyr4bR+HX7pvxeiW82FnYJfVj8lkAyJiI1BlW5VMv++NLjegp6mXrnVSLMeuXbtw6tQpMWAR9KtaDQ3MlJF71qxZ8cfWrVsnrD50rYuLS6oZqskitGnTJjRq1EgcIysKDdZqXr16JQbbDx8+CEGhFiIkFGiwVN+XsoST+CpYsKDYJ6sLiQp1ziIaHFu0aBF/niw+yUHijawpJGYSum/atWuH4cOHCwuPWsCldbAmwUUWJLo/WVrIilGuXLl4S9bChQuFNYisJ+r+vXz5Mv75559EgmLmzJnx+yRimjdvLtIYkHWI7kGCsWjRouI8WY2Sg9pGbSSRlJp7ioTP6NGjxXPUqlVLHCOR16VLF2GdScl1RRajhPWSOKRkm9RGsnqRBen+/fvfZI0hcUqfxSNHjiCzYEsOk+eoU60K6g6+gNbBzTD8RCzKv5bQac49zJveFJsfb4RSpczqR2SYdIMGJIqBSO7XO0FWA7LEJMwgrR5oSaCkBp0nV5R6cCfoV3qRIkXi9+/evSusCSSWEt6DrBwJ6ycXjlrAEOT6ogSQ6jpJjDRp0gQtW7YUwasJXVlpgQZosq6QgFP3Cw3YVO+XIAFHwobEC7nyyAJElie1q4+ECpVJ2D4Sfkn7r3Tp0onaR6jbOGbMGCEoSIjOmTPni33/JUi0NGrUSDwrQfnZSGSQhedrIAsaCSqy1BHUf2T5Uru30gpZuFq3bo2pU6fGC+LMgC05TJ7ExkQPvUYvwEHzinBcPRP6fioMOByDO0//xPCuBzGt9XJY61tn9WMyWYSuhq6wqmTFfdO9zi8ErKpUKiEc/vzzz8/OqQfilEiLe43qJ5FFYor+JiRhsHBS1wxZVxLWT1YXchmRBYjcLuQiIutU1appn0BAIoLccmRVosG6QYMGQrx8CbLekFgjVxy55GigptgfculQ+wiyTlDMUVJhlZCEbVRbj9TXU31kZaF6jh07hmnTpgn3HsXIfCtdu3bFyJEjRQwNWXHIxUXxQl+DlpaWEHbU/23bthX1fO20cxKCFBPVv39/8b5lJixymDyLXC7Dj7264Em9hng1oT8K3H+OCq8A5wWuGP++BQZ0X4zqdtWz+jGZLIAGoPR2G2UVpUqVEgMpWU7U7qqEkKtqz5494pc5/WL/GgoVKiQG7uvXryN//vziWEBAgHBzqS1H5NZRKpXCYqF2m3wrVBdtEyZMENYjGnCTEzk0MNM9k+sLCvxdvXq1uJYG/7RCfUP9RxsJEHLrkIuKrBIkZsjdlJK1LK2QtYs2cjPR1GsSFsmJnJTal5wVZuDAgUIYUntJrKQGvZfJ1UvisGTJkiL4mtyKJHa+xoJDAqdnz57CXZfZsLuKyfOUcLRCw417cX3wOMQYA6ZhwM8bQ/HbjgFYfm8Zu6+YHA2JFxpgKMiVZt6Q24IsEhQbQQwdOhT+/v5iUL158yZev34trBVU/ksDKVli+vbtK2JJaObQ48ePhfsnYcwHDdpkUaDgZ4rjoPuTBYQsR0ePHk1TG+gaEjbkbqEZVfR8JKRSisuhNlOAMgUJ0ywxGpgTDtjkDqK2pdVKcvjwYRG0TS4uuj+5okg4kluOrDwUY0TChOKRyM107949LFu2TOynhYiICBGDRO8L1U9B2tRHqbWP+oSeh9qXMJg6Ifr6+sJFNGXKFBFwTpai1KB66X309PQUYlUNPQeJyfHjx4vPSVqms6sFDrm2SAiSO47qpY1meWUaUh4jKCiI7J/iL8Mk5fClh9K1BuWlQ20KSSXXl5BKbigp9T3WW/IJ9+HOyqVERERIrq6u4m9uoU6dOtLIkSPj96lto0ePlmxtbSUtLS2pUKFC0rp16+LPv3jxQmrTpo1kYmIi6erqSkWLFpVGjRolqVSqL94rJCRE6tatm6SnpydZW1tLc+fO/ez+0dHR0tSpUyUnJydJU1NTsrGxEfd7+PChOL9+/XrJ2Ng4Ub379u0T39WEp6en9OOPP8Y/v6Ojo6hPqVSK89OmTZPKlCkTf623t7fUqFEjycDAQNRx7ty5RM9LzzpkyJA09+elS5dEm0xNTUX/lC5dWtq5c2f8eeqnxYsXS0WKFBHts7S0lJo0aSJduHBBnKf703MEBATEX3Pv3j1x7M2bN1JUVJTUuXNnycHBQbTPzs5OGjZsWPxnMmn/REZGSu3atRPvF9VB5wl6Tf2WkCNHjojjtWvX/qxd1I+LFi2K3z948KD4bGhoaIhzCVm7dq2o5+bNm2nuN3pf6JqkW9K60/r/8VvGbxn9gzwERekbGxuLSH1ai4BhknLnjS8ubJiOggb7MNPCBPIoGaw0TDG1xSJUtKnIHZbLoKBR+lVcoEABMcuFyd28f/9eWCzIUkKuOiZtkKuJYoQePXqErPr/+C3jN8fkMEwSKhSwgNXIuViyxgUr38/Dhyv6MA3wxR9+vdGy2Sj0Ltkbchl7ehkmJ0EuK5qRRVO3yfXCAidt0DIB5Oqi+KWcmCqEv6kZJhkczPQwZcRg7NGfhYL+MlgEA9M3x+LW5oUYfmY4AiMDud+YLIOCXBNOV0660fmcdJ/MgOJcaCYVzfKi9XgScunSpVTbmZcZNmwYatasKYKqEy5eSND6OSn1GZ3LDrC7imFSIVapwtwdF9Fo9WgYeEaKYweqyHCumR3m1V+I0pb/rXvB5ExyoruKVgCmJfdT4ltmSmXlfbIaCvz18PBIdRYZ8zk0Y45cSMlB7iQrKytktbuKRQ7DfAEKW1t/0Q1mC4ehyPO4X673C8iw7EctDKo1Fl2Ldf2m5c2Z7EFOFDkMk1uJTGeRw+4qhvkCJGD61CkMwzmbcKBaPUAhoewbCcP2RePPW39izPkxCIkO4X5kGIbJZrDIYZg00qCYNVrNnIOlPwyEZARolw+BhiTh9LvT6HS4E576PeW+zMHksYmmDJMn/h+yyGGYr6C4nRGmTx2Mad0WQ64oiU0fvWAXEwuNl+/Q/UhX7Hq+iwfLHIZ6qX11LiKGYbIOyoVGJE0B8q3k/IgxhslkrIx0sGlQA4zZaQa7Z+uw8cEe+J41xZ2CKsyL/A13ve9iatWpuSYtQG6HvkxpiX51okRKFMkxVgyT+dAq0rQaMv0fTK+Adg48Zphv/g8pYf7J54jZught71wAVDJ8sAD+bKeAXgFnLKyzEIVMeVZGTjGR03LzgYG8NADDZCWUEoSCjik/V1J4dlUa4BWPmfTm39vvcWTtFvxyfRNkEUC4NrDoRzleuOhhctUpaFWwFXd6DoHyGSXMc8QwTOZC4iZh7rOEsMhJAyxymIzg+ms/TFt1HLMvzYOuXxRUMglb6ypwqIoMbV3aYULlCdDR4OnJDMMw3wpPIWeYLKKqszlWjv0Rv7acizdOtpBLMnQ/p0KV5xL2vtyLrke7wj0o5UXVGIZhmPSHZ1cxTDpRwEIfu0bUxeY2M3GsTBXo5I9BPyMfmKkkvAh4gc5HOuOe9z3ub4ZhmEyCRQ7DpCMmelrY2LcKvDqMQffyE2EUYYN/P3xEpZAo2LmHYvDpwXjs+5j7nGEYJhNgkcMw6YyWhhxz2pVC9x/qoF3MdFyPqoTpB6Px25ZYuDwNwcBTA/HM/xn3O8MwTAbDIodhMgBaZ2VgnYL4q1t1TJKGwU2ZDwqVDGP3KWH9OggDTg6AW4Ab9z3DMEwGwiKHYTKQJiVssH1IbUyuNwV+NkbQjJVh8q5Y6Hn4o9/JfhyMzDAMk4GwyGGYDKakvTG2DKyBn2tNQriZNnSjZJi+PRbw9kXfk33xPuQ9vwcMwzAZAIschskEnC0N8HffmhhUfQJiDDVgHEpCR4lwXy/0P9kfn0I/8fvAMAyTzrDIYZhMopKTGSZ3q4nB1cZApSuDdZAKFbxV8Aj1EK4r7/C43EkMwzBM+sAih2EykdZl7dGlTS2MrTYEUl1NTJZ7wl4JvAt5Jyw6fhF+/H4wDMOkEyxyGCaTGVK3IErXr42ehhMQHWuBNR89UDgIeB34Cv1P9UdgJCeJZBiGSQ9Y5DBMFkwv/6NNSRQtXAjdon+FhrcRZq6NRr9zEl4GvMTA0wMRHB3M7wvDMMx3wiKHYbIATYUcy7qWh65VISzzawlEydH4hgodbkpw9XPFkNNDEBYTxu8NwzDMd8Aih2GyCCMdTazrXQknirfGqRIVxbEOZ5Ro/ESGBz4PMPTMUETERvD7wzAM842wyGGYLMTeRBfrelXCyuJdcKeQizjW91A0qr6W4Y7XHYw4OwJRyih+jxiGYb4BFjkMkw0WC1zapTymlewHNwd7yCQZRu+JRomPclz/dB1jzo9BjDImqx+TYRgmx8Eih2GyAQ2KWWN661IYXW44vKxNIYuVYdKZUOjINHDxw0WMuzgOMSoWOgzDMF8DixyGySZ0r+aEXrULY1ClsfBwsUKJqn5Y/MkTmjIFzrw7g0mXJkGpUmb1YzIMw+QYWOQwTDZiYrNiqFvGEf2Kj8MhjTqoHhGORd5+0FbJcMz9GKZdnQaVpMrqx2QYhskRsMhhmGyEXC7Dok5lUcbBFGMi++KKvCKKX5OwZmMUdGJlOPDqAP64/gckScrqR2UYhsn2sMhhmGyGrpYCa3pUhJ2ZAcb5dYfPCyNoe8qx4t9oKFTAvy/+xdxbc1noMAzDfAEWOQyTDbE01Mb6XpURamqL6VV6AwpA312GFYeUgCRhy9Mt+OvuXyx0GIZhUoFFDsNkUwpZGWBVj4q4Z10Cf1VsD8gAE1cJf5+L+2+77vE6rHywMqsfk2EYJtvCIodhsjFVnc0xt31pnLCtik1lm4hjVjdiMP+mtni9/MFyrH20NoufkmEYJnvCIodhsjltyuXD6IYu2O7YCMdKVBXH8p8Jxa8BZuI1ua3+uvMXQqNDs/hJGYZhshcschgmBzCiQSG0K58PSwq1w02XkrCpGoaugfcxWDu/OL/28Vo03dsUax6tQXhMeFY/LsMwTLZAJuWxuajBwcEwNjZGUFAQjIyMsvpxGCbNRMeq0Gv9TVx95YcOBg8xVzkXkFQ4Wb49lql88CbojShnqm2KPiX7oFPRTtDV0OUeZhgmz47fbMlhmByCloYcK7pVQGErA/wbWhqLdIZBGSlHsUVnsTPYCbOrToOjkSMCogKw4M4C/LDnB2x23YzI2MisfnSGYZgsgS05DJPDeO8fjjbLr8I3NArbHy2AyatPkCkkmBVXwXjQEBzLlw8rH6+BR6iHKG+pa4l+pfqhnUs7aCviApYZhmHygiWHRQ7D5EAevA9Ep1XXoB0ahNWPVsHwwydxXK6pgkUFTRgM+xWHDXXwz6NV+BQWd85azxoDSg9Am0JtoKnQzOIWMAzDfB0scjKokxgmO3LyiScGbrkDSSVhqqkPap9aixgPP3FOQ1cJq9rG0Bv6O/apgrDq0Wp4hXuJc7b6thhYeiBaFWoFTTmLHYZhcgYscjKokxgmu7L+yhvMOOQqXtsZamGW7ivY716DWP8wWJQIgWWpEKBAbUTVm4g9YW/E7CufCB9R3t7AXoidlgVbQkOukcUtYRiGSR0WOWmARQ6T2zjy8BNmHX0Kj8AIsV/RVh9TY++jRKGPUDxcDyijEeGnCVW+mlD0mol//e4KseMf6S/K5zfMj0FlBqFZgWZQyBVZ3BqGYZjkYZGTBljkMLmRyBgl1l5+g+Xn3BAWrRTHWpaxw8TqerC5swju808h0l8L+rZRsOpcF6qOU7DL44JIDUGzsQgnIycMKTsEjR0bs9hhGCbbwSIngzqJYXIK3sGRmH/yOf6984HyeEJbQ46BVezR4eJahB45CaiolAQjp2hY9m6P2Gajsc39CDY82YCgqCBRRyGTQhhcZjAaOjaEXMarTDAMkz1gkZNBncQwOY3HHkH4/bArbryJc0lZGWpjUhkDVDi0HCEXbsUVkkswLaKExcB+iKzdF9vc9gqxExIdIk67mLoIN1adfHWgpdDKyuYwDMOARU4aYJHD5BVoMfMTTzwx6+gzvPOPS/VQ0t4I0wrLYb1+LsIevBDH7KoGwLi0OVBvIoKLNcOWZ9vFIoKhMXG5sPQ09FDDvoYQO7Xy1YKZTlzOLIZhmMwkx4mc6dOnY8aMGYmOWVtbw9PTM9ny58+fR7169T47/vTpUxQtWjRN92SRw+Q1omKV2HjVHUvPuCEkKlYc+6GkDX4xD4T2vlWwLfIQspCP4nikzAVabacgpHBtbHTdhH1u++Ab4RtflwwylLEsgzoOdVA3X10UNCkImUyWZW1jGCbvEJwTRc7u3btx+vTp+GMKhQKWlpapipznz58naiCVp+vSAoscJq9CKyQvPPUCO26+g0oCtBRy9K7phGE17WH4cANUZxfAbY825AoJlnWtYTR8LiTHqnD1c8X59+dx4cMFPPN/lqjOfAb5UNehrhA9Fawq8CKDDMNkGDlS5Ozfvx/3799PU3m1yAkICICJick33ZNFDpPXeeYZjD8OP8VltzgLjYWBFn5uXAStND7i46DBiA2Oy3WlbRIDy6aFYNB2AGRFGgPaBvAM88SF9xdw/sN53Px0E9Gq6Ph6DTQN/nNr2deCic63/R9lGIbJNSJn3rx54qG1tbVRpUoVzJo1C87OzqmKHCcnJ0RGRqJ48eKYPHlysi6slGCRwzBx8Tpnn3lj5pGneO0bJrqkqI0hpjZ0hsvZHfBbtxGqyDjXlkJbCYN8sbBoXhZa1dsDLk0BPTOEx4Tj2qdrQvSQlUe97g5Bs7LKWpaNt/IUMCrAbi2GYbKnyDl48OBXP0yjRo2gq6ubapljx44hPDwcLi4u8PLywh9//IFnz57hyZMnMDc3/6w8uakuXryIChUqICoqCps3b8bKlSuF+Kldu3ay96BytCXsJAcHB55dxTAAomNV2HL9Lf46/QLB/xc1DYtZY2JNW+hvWYbA/YehiogRxwu18oSmngqQKRBtVBmKMs2gqNgeMLKDSlLhke+jeCvPy4CXifqXFhwksVPPoR7KWpXldBIMw2QfkSOXf91aGRSI+PLlyxQtMikRFhaGggUL4pdffsGYMWPSdE3Lli3F/VISYskFNxM8hZxh/iMgLBqLz7zE5utvoVRJ0FTI0KOaE4bXcoKm6wNEXDsDi/IawNNDgNdjvLtghjBPbehZRsOwhCUMm7aAZs0ugHlBUR9lQFdbeG563kSsKk5AEYZahqhpXxMdXTqiok1FfhsYhsl6kUMznqysrNJUqaGhIR48ePDVIkdtASpUqBBWrFiRpvIzZ87Eli1bxAyr5GBLDsOkHTfvEPxx5CnOP4/Lb2Wsq4nmpW3RopQtqjibQyGXQfJ5CffufRHpHldGjbZpNAxdDGHYsCF0GnYHbErTLx6ExYTh6serInj50odL8Sssk0trbMWx6FasG7uyGIbJEJGTpqx8PXv2/KLrKSHdunX7poX2SJCQWKlVq1aar7l37x5sbW1TPE+xPrQxDPNlClkZYkPvyjj/3FuIHTfvUGy78U5sFKD8Q0lbIXoqHb0ApccHhBzdj9CjBxH+4gOiArQQdSMK4e574fhqNWCSHyjWCnouzdDIsT4aOTaCUqUUbq3tz7bj6JujmHtrLl4FvsKkKpN4ZhbDMOlOlgYejx07Vrib8ufPD29vbxGTc+HCBTx69AiOjo6YMGECPDw8sGnTJlH+r7/+EkHHJUqUQHR0tLDgzJkzB3v27EHbtm3TdE8OPGaYtBGrVOHaaz8cfvAJx594Iuj/sTmEpaE2mpW0QfPSdqjoaApVYABCTx5ByOE9MLCNgKnxAyA2AjERcrw5bgmD/DIY1igP/ZbdIS/SEJJCE5tcN2HB7QWQIKGSTSUsrLOQZ2QxDJN7Zld17txZBBL7+vqKtW6qVq2K33//XcyaInr16gV3d3cRWEzMnTsXq1atEsKHLEskdkgINWvWLM33ZJHDMF9PjFKFK26+IuM5raKsDlImrI20hYWnZRlblHMwhVwuA6LDgVdnELBpNTz3/ReELFOooG+ngmHlYjDsNgaXDWT45eIvCI8NF8HJSxsshbPx17u5GYbJ/QRntMg5deoULl++jDp16qB+/fpCoMyePVu4mbp3747evXsju8Mih2G+f0YWCZ7DDz/hpKsnQhIIHltjHTQrFefSKudgAsTGIvzWDYTs24rQy9cRExC3Bg+hoaeEw/RBeFerBYafGY6PYR9hqGmI+XXno7pddX6bGIbJPJFDriESMaVLl8aLFy+wdOlSjB49Gu3btxdrbtB07q1bt4r97AyLHIZJ35QRl1744sijTzjl6oXQ/6eNIOxNdNGsVJxLq0w+47jyrq4I2bcZQYePQRURAaeGvtBqMQ5+Vfpj1PnRuO9zHwqZAuMrj8dPRX/it4phmMwROeXKlRMiZ8SIEThz5oyIpaGZTSR0iIULF2Lv3r3C0pOdYZHDMBlDZIwSF1/4CMFz2tULYdHK+HP5THX/P0vLTiQJVQUGIubAb9BxXxdXoGw3RDebixk3Z+Hgq7jlIDoV6YRfK/8KDXma5kcwDJPLCc5IkWNgYCACggsUKCD2tbS0cPv2bWHZUS/UV6NGDRFfk51hkcMwmSN4aBo6CZ4zT70QnkDw5DfTE4KncyUHOL7ZBRwZg9CPmoiUFYXZwoNY/2oPFt9dLAKSq9pWxfw682GsHWcJYhgm7xL8DSInzav8aWpqihlNamhaNgkfNSR6IiIivvaZGYbJhehoKtC0pA2W/lQOdyY3woqu5YWw0dVU4J1/OFacf4UWSy7jhlkrxDT+Bx5XzOBz3gdePeqij30TLKq3CLoaurj+6Tq6He2Gt8Fvs7pJDMPkQNIscmiBPkq5oIZmOKmtOsSrV6+QL1++9H9ChmFyNLpaCvxQyhbLupTHnSkN8XeXcijrYIKQqFj0WHcTF03qwHJwL8qohcDHUXjfqRHqqUyx6YdNsNazhnuwO7oc6SISgjIMw2SIyJk4cSJMTU3j98lUROkU1JDrqmPHjl91c4Zh8hZ6WhpoUdoOOwZURcNiVoiKVWHA5ju4UL0b8v05DTINIOw98LZrZxR68xI7WuxAaYvSCI4OxsBTA7H7xe6sbgLDMDmILF0nJyvgmByGyT5r74zf/RB773mI/Wkti6Ozxge8HzwIynAVNPWVcPh9FKTGfTD16lQce3NMlKM0EJQOQiFXZHELGIbJNTE5DMMw6YmmQo75HcqgV3UnsT/jkCtWBlvDcfc+aJppIyZMgcCVs6FzbTn+rDkHw8oOE+W2PN2CYWeHITQ6lN8QhmFShUUOwzBZBq2OTBac0Q1dxD5lQp/9OAb5D56GeQMXWJUNBs7MgOzozxhYsi8W1FkAHYUOLntcFgHJ70Pe87vHMEyKsMhhGCZLodi+kQ0LY0arEmJ/w1V3jD/zAaZL9kHW7E8qAenmOgRPa45GttWxoekGWOla4VXQK3Q90hV3vO7wO8gwTLKwyGEYJlvQs7oTFncuCw25DPvueWDQ5juIrNAfUoeN8LxnBo/d7+DVozaKa5phW/NtKG5eHAFRAeh3sh/2u+3P6sdnGCYbwiKHYZhsQ+uy9ljVowK0NeQ488wbPdbeREjBZtBu2EdMMQ94EIEPnRrAMsBHWHQaOTZCrCoWU65MwcLbC6FU/bfoIMMwzDfNrrp586bIDO7t7Q2VSpXoHKV3yM7w7CqGyf7cfOOPvhtuibV0itsaYWOfytA+sQMfp82BpAR0LFRwWLYU8tINseLBCqx8sFJcV9ehLubUmgN9Tf2sbgLDMDktCzkxa9YsTJ48GUWKFIG1tXWitXLo9dmzZ5GdYZHDMDmDJx+D0HPdTfiGRqOAhT42960MsyfX8WHYMCgjJWgaKOEwexy0G/XH0ddHhTUnWhWNwqaF0atEL5HJ3ELXIqubwTBMThI5JGz+/PNP9OpFK5TmPFjkMEzO4Y1vGLqtuQGPwAjYGOkIoZM/8D3e9/wJMYHR0NSLRcHFwyGrNQIPfR5ixNkR8Iv0i7++qFlR1LCrgRr2NVDWsiw0FZpZ2h6GYbK5yLG1tcXFixdRuHBh5ERY5DBMzsIzKBLd197AS+9QmOppYn3vyiipHYUP3X+EueMrGNpFAVUGA01mwjvSD9ufbccVjyt46v80UT16GnqobFs5XvQ4GDpkWZsYhsmmImfu3Ln4+PEj/vrrL+REWOQwTM4jICwavTbcwoP3gdDTUmB1j4qoXsAUsut/A6emijKxDk2h0WMDoKkr9n0jfHHt4zVc+XhF/PWP9E9UZ37D/ELskOipZFMJepp6WdI2hmGykcihQOPmzZvjxYsXKF68uMhOnpC9e/ciO8Mih2FyJqFRsRi4+TauuPlBSyHHkp/KiUzneLQb0ZuHwv2EMYxLGsNq5VHIDC0TXauSVMKyc9XjqhA9D7wfIFaKjT+vKddEeavyqG5fXYgeF1OXRPGGDMPkEZEzdOhQrF27FvXq1fss8JhYv349sjMschgm5xIVq8TI7fdx/Ikn5DJgTtvS6FjJAQErZsNz8SZRxtBZDquh/aBVqzNgZJtsPZQS4obnDeHWuvrxKjxC4/JnqbHUtRSBy2TpqWZbDSY6JpnSPoZhsljkGBoaYseOHcKakxNhkcMwOZtYpQqT9j3GzttxKR0mNSuG/rWdEbRtNT79sRDS/1e10DSIhUEhQxjUrAaDNn0A23KUR+Kz+ugr8G3wW2HhIdFzy/MWIpWR8edlkKGkRUkhepo5N4OzsXPmNZZhmMwVOY6Ojjhx4gSKFi2KnAiLHIbJ+dDX1pxjz/DPxddif0jdghjXpAgirpyFz/yZCH/xCfi/2NExjUaBJr6AvhVQuDFiLatDo2IrQNsw2bqjlFG463VXCB4SPm6BbvHntBXaWFxvsbDwMAyTC0UOuaOOHz8u/urp5bxAPRY5DJN7WHH+Ff48/ky87lIlP35vXRIKuQzK0DCEnz+B0GO7oa3pCzMrVyA6BKoYGZ7vs4GWvhL6RS1gULsO9Jr3hNyuWIr38AzzFIHLlDrirvddEb9DiULr5a+XiS1lGCY4M0ROuXLl8OrVK/FLysnJ6bPA47t372brd4JFDsPkLrbffIeJ+x6Bvsmal7bFoo5loaWRxC0VGw28u4rwY9vwdtmleCsPIVOooO+gAYNKpWHQshM0yzcDNLQ+u0+MMgbjL43HqbenoCHTwJzac9DEqUkmtJBhmEwTOTNmzEj1/LRp05CdYZHDMLmPIw8/YdTOe4hRSqhV2ALTWhZHQUuDZGdIKUNCEHZiL0JPHETYveeIDf0v35V1+SCYlVIABetD5VQfssKNIDO1iz9PebImXZ6Eo2+OQi6T448af6BlwZaZ1k6GycsEZ4bIyemwyGGY3MnFFz4YuPkOImLiRIuzhT4albBG4+I2KOdgAjlNx0oCff1FPbyD0INbEHrlOuwq+kBLw1ecC3ilB+97RtBz1INB1fIwaNUFmqXrQympMOPaDOxz2yeCkqdVm4Z2Lu0yvb0Mk9cIZpGTMZ3EMEzO4OGHQCw89QJX3fwQrfzPJ2VpqI1GxUnwWKNaQXNoayiSr4ASDn+8B7w4jo9L/0XQ06hEp/ULaCPf5sOAuR1m3ZiFnc93iuMTq0zET0V/ytjGMUweJzijRI6ZmZlY/M/CIm3J7vLnz49Lly6JmVjZDRY5DJP7CYmMwYUXPjj5xAvnnnmLbOZqDLQ1ULeIJRqXsEG9IpYw1Ek+n5WkUiHy9kWEHdqG0Ot3EfEhFJBk0MunCYddJyAztcH82/OxyTVufZ6fK/yMXiVzZk4/hsnTIkcul2Pjxo2i8rTw008/4dGjR3B2zn7rSbDIYZi8RXSsCtde++HkE0+ccvWCd8h/1hlNhQzVC1qgcQlrNCpmDSsjnRTriTh/EO+G/yJmaJmU1oXtpnOQtI2w9N5SrH60WpQZVnYYBpYZmCntYpi8RnBGipyvxc3NjUUOwzDZCpVKwoMPgTjxxEuInte+YYnOl8tvgiYlbIRby9nS4LPrw0/vhdeMCXCo7g2NwhWAbnsBHSOserhKiB2if6n+GF5uOKeFYJh0hmNyMqiTGIbJnbh5h+Kkq6dwa91/H5joXCErAyF2yK1V2t44PnBZ+vgAsk2tgMhAwKEqpK67IdMxxMYnG4X7iuhevDvGVRzHQodh0hEWORnUSQzD5H68giNx0jXOwnPtlR9iVf8ZuW2MdETgcqdKDihpbxwXnLyxNQKfRSMsOB/stp6BTM8YO57twMwbM8U1HV06YlLVSWKqOcMw3w+LnAzqJIZh8hZBETE4/9xbiJ7zz7wRFh03LZ2yn2/oXQnVC1kg5v4pvOoyHJJKBqMShrDbcgYyXUPse7kP065OgwQJrQu2xozqM6CQpzCbi2GYNMMiJ4M6iWGYvEtkjFJYdtZefoPLbr7Q11JgW/+qKONgguDty+Hx2xIx68q4jDFsN52FTFsPh18fxuTLk6GUlPihwA+YWXOmSAfBMEzmjt9sR2UYhkkFHU0F6hW1wpqeFVG9oLmw6vRafxNu3iEw+mkI7CcMpikcCHoQBK8+DSHFRKKFcwvMqzNPpH849uYYxl0YJ9JCMAyTubDIYRiGSaPYWdWjIsrkM0ZAeAy6rbmJDwHhMOoxEnZj+1FIMgLuBMC7XyNIMVFo5NgIi+otEhacM+/OYNT5USLDOcMw2VzkUILOyZMni/VwvL29xTHKTP7kyZP0fj6GYZhsAy0kuL53ZTHzyjM4Et3X3oRvaBSM+46F7ageooz/DV+Ezm4HKGNQ16Eu/q7/N3QUOrj44SKGnRmG8JjwrG4Gw+QZvlrkXLhwAaVKlcKNGzewd+9ehIaGiuMPHz7M9sk5GYZhvhczfS1s7lsZ9ia6eOMbhp7rbiI4MgYmgybCZkhHmBcPh4HyErCnH6CMRXX76ljecDl0NXRx/dN1DD49GGExidfnYRgmm4icX3/9FX/88QdOnToFLS2t+OP16tXDtWvX0vv5GIZhsh22xrpC6Jjra+HJx2D023hbBCibjpgBqzmrIVNoAq77Ie0dCKiUqGRTCasarYKBpgHuet/FgFMDEBwdnNXNYJhcz1eLHErX0KZNm8+OW1paws/PL72ei2EYJltDKyJv7FMZhtoauPnGH8O23UUMJQV1aQx03ASVSgPvl5+F37jWQuiUtSqLNU3WwFjbGA99HqLfiX4IpAUFGYbJPiLHxMQEnz59+uz4vXv3YG9vn17PxTAMk+2hhQFp1pW2hhynn3pj/O6HInUEijZDsPVQhH3SgfeRV/Cf0FZkOC9hXgJrG6+FmY4Znvo/Re8TveEb4ZvVzWCYXMtXi5wuXbpg/Pjx8PT0FEuWq1QqXLlyBWPHjkWPHnGBdwzDMHmFKs7mWNalPBRyGfbe88DvR1xBKQGNh8yAeZvaoozXgRcImNoRkCQUMSuC9U3Ww1LXEm6Bbuh9vDe8wryyuhkMkyv5apEzc+ZM5M+fX1htKOi4ePHiqF27NqpXry5mXDEMw+Q1Gha3xvwOpcXr9VfcsfSsm/gRaDlrJcyaVxPHPXc/RuBvPwmh42zijA1NN8BW3xbuwe7odbwXPoR8yOJWMEzuI01ZyFOaRk4uKrLklCtXDoULF0ZOgFc8Zhgmo1h/5Q1mHHIVr39vXQLdqzkJq47XyB4IOHlbLBpo370SjCZsAmQyfAz9iL4n+uJD6AeYapuKdXUqWFfgN4hhkoHTOqQBFjkMw2QkC0+9wJIzL0nD4K9OZdG6rD0klQqeQ7sg8NwDKLRUKPh7GyhazRZCxzvcG8PPDoernys05BqYUnUK2hZuy28Sw2SFyKHiu3fvxrlz58RCgGTJSQitnZOdYZHDMExGQt+R0w8+wcZrb6Ehl2F1j4oiLQQJHa+R3WCMk9A1jwFqjgEaTBVCJyI2AlOuTMEJ9xOiju7Fu2NMhTFC9DAMk4m5q0aOHInu3bvjzZs3MDAwEDdMuDEMw+RlKBZnWssSaF3WDrEqCYO33sEtd3/I5HLYLN0G3W6z4gpeXgjVqT/ES1oocF7teRhadqjY3+y6GcPODkNIdEhWNoVhcjxfbckxMzPDli1b0KxZM+RE2JLDMExmQGvmDNx8B2efecNQRwM7B1RDcbv///q8tgwR26fhwyUz2A1uAf0BC+OvO+l+EpMuT0KkMhIFjAtgaf2lcDRy5DeNyfMEZ4Ylh27g7Oyc5zubYRgmNTQVcjG1vJKTKUIiY9Fj3U24+/4/nUO1ofAPqobYCAXeLzmC8PXj469r7NQYm37YBGs9a7wJeoMuR7qIdBAMw3w9Xy1ypk+fjhkzZiAiIuIbbscwDJN30NVSYE3PSihmayQSeXZbewNewZHinO2q3dAvbg8pVo73C/YjfM0YMb2cKGZeDDta7EBpy9Ii/cOgU4Ow49mOLG4Nw+QBd1V4eDjatm0rFgB0cnKCpqZmovN3795FdobdVQzDZDY+IVHosPIq3P3C4WJtgF0Dq8FETwuqiAi879wM4c89IZNLsOtSCkYTtgGU+wpAlDIKM67OwKHXh8R+pyKdML7yeGjKE3/vMkxeIDgzZld17NhRzKxq3749rK2tRZBdQrJ7JnIWOQzDZAXv/cPRfuVVeAVHoVx+E2ztVwV6WhpQhYfDo19HhN59RXOzYNXQGmbzDkKmGzeRg76i1z9Zj7/u/AUJEqrYVMGCugtEDiyGyUsEZ4bI0dfXx4kTJ1CzZk3kRFjkMAyTVbzwCkHHf64hMDwGtQpb/D/vlQKSUgmvcYMQcPQyDOwika+9LWTddgNGdvHXnnt3Dr9e+hXhseFwMHTA3/X/FisnM0xeITgzAo8dHBzSXDnDMAzzHy7WhljfqxL0tBS49NIXY3Y+gFIlQaZQwHrBKtj+MhD2DTUg834CrGkIeD2Jv7Ze/nrY3Gwz7A3s8T7kPboe7YpLHy5x9zJMeoqcBQsW4JdffoG7u/vXXsowDJPnKZffFP90rwBNhQxHHn3C5P2PhUuKXP8mfUZBPvg0YOECKcgDvqNbIvbOwfg+czF1wbbm21DeqjxCY0LFWjqbnmwS1zMMkw7uKlNTUxF8HBsbCz09vc8Cj/39/ZGdYXcVwzDZgaOPPmHYtrtQScCQugXxS9Oi/52MCIDf6ObwPh8ATQMlHH4bBu1mI+JPxyhj8MeNP7D3ZdwK820KtcHkqpOhpdDKiqYwTO6Jydm4cWOq53v27InsDIschmGyC9tvvsOEvY/E65ENCmNUw8LxkzmiXj7H+x6dEBMQBYWWEvlGtoBenwUiDQRBX91bn27FvNvzoJJUwrqzsO5CmOuaZ2mbGCaj4ASdGdRJDMMwGcU/F15h9rFn4nX3qo6Y3qoEFPI4IRPr7Y333dsg8i2lhZBg91PJuCnmGv9ZbK54XMG4C+MQEhMCO307LKm/BEXMivAbxuQ6MkXkvHv3LtXz+fPnR3aGRQ7DMNmNTdfcMe3gE7EWYPPStljYsYyYdUWIKeZ9OyL03v+nmDewgtl8mmJuEn/966DXGH5mON6FvBN5sObUmoP6+etnYYsYJoeKHLlc/tnaOAlRKpXIzrDIYRgmO3L44UeM3nkfMUoJNQtZYGX3CjDQjstCHjfFfDACjl6ib204dzWC9vC9gHG++OuDooLw84WfcePTDcggw4jyI9C3ZN9Uv68ZJieRKSLnwYMHifZjYmJw7949LFy4EDNnzhSrIWdnWOQwDJNdufzSFwM230Z4tBKl8xmL6ebmBtriHH1V+y+eCcWTzTCx9wQMbYEuuwDb0vHXx6hiMPfmXOx4HpcCorlzc8yoPgPairg6GCYnk6UxOUeOHMG8efNw/vx5ZGdY5DAMk515+CEQvdbfgn9YNJwt9LGxT2U4mOn9VyDwHbC1A+DzDNFRhpC3WwqNim0S1bHz2U7MvjkbSkmJomZFMaLcCNS0r8lWHSZHk6Ui5+XLlyhbtizCwv6fZTebwiKHYZjszmufUHRfexMegRGwNtIWQqeoTYIv9YhAxK7/Ce7r3MSuw4wh0G4+OlEd5LYi9xW5sYhSFqUwqMwg1LKvxWKHyZFkisihmySELv/06ZPITv7s2TPcv38f2RkWOQzD5AQ8gyLRY90NvPAKhZGOBtb1qoSKTmbx56Nfu+Fd1w6ICYiEQkuFfMObQK/f4vgp5oRvhC/WP16PXc93IVIZl/28uHlxDCo9CHUd6rLYYXIUWRZ4TFVQuocdO3agWrVqyM6wyGEYJqcQGB6Nvhtv487bAGhryLG8a3k0KGYdfz7Wxwfvu9EUc7+4KeadisNo0o5EU8zVYmfjk43Y+XwnImIjxLFiZsUwsMxA1Heoz2KHyRFkisi5cOHCZ6LH0tIShQoVgoZG3EyA7AyLHIZhchIR0UoM3XYXZ595i/Vz/mxXGu0r/DerSkwx798ZoXdein2reuYwW3AIMj3Tz+ryj/QXYmf7s+3xYqeIaRHhxqIp53LZV2f6YZhMgxcDzKBOYhiGyUpilCr8uucR9tz9IPYnNiuKAbULxp8XU8x/GYyAI3EJO61r6cBs3kHAxCHZ+gIiA7DJdRO2Pd0mspoThU0LY2DpgWjk2IjFDpO3RM7Bg/8liPsSrVq1QnaGRQ7DMDkR+qqmlZFXXXwt9gfWdsavPxSNdzWJKeZLZiFw22Y41veChrkV0JWmmJdJsU4KSlaLHUr4SRQyKRQvdhTyuAUJGSZXixxySaWpMpmMFwNkGIbJpDQQ7crnw5/tSkFD8d93tMrrFeR7ugHeroCmPlQtV0JeOvUfnyR2tjzdgq2uW0V6CMLZ2FmInSZOTVjsMNkCdldlUCcxDMNkJ/69/R6/7n0EpUpCg6JW+LtLeehqJbC6RAYBO7sj4PQt+D42hEW9fDAZ+SdkjpVSrTc4Olgk/dzsuhkh0XFip4BxAQwoPQBNnZpCQ5794y6Z3EtwVq6Tk1NgkcMwTG7gtKuXCEiOilWhoqMp1vasBGM9zfjzqvBQvGlaD9HecW4oTf1YWDRwhvGIPyHLVzbVukngkAuLXFkkfAhHI0chdpoVaMZih8ndIodmWM2fPx9Pnz4VLqpixYph3LhxqFWrFrI7LHIYhskt3HL3R98NtxAcGYsi1oZi0UAbY53486rISASuXw7ftRugDI35T+w0KQLj4fMgsy2Wav2h0aFiJtZG143xiwrmN8yP/qX7i5QRmvL/RBXDZMfx+6vnC27ZsgUNGzaEnp4eRowYgWHDhkFXVxcNGjTAtm3bvqouWkCQRFLCzcbG5osCq0KFCtDR0YGzszNWrlz5tU1gGIbJFVRyMsOuQdVgZaiN514haLfiqlgtWY1cRwdmg8eg0KUbsBraGwp9DcSEaeDT3lfwGtAE2DcI8I8LZE4OAy0DIWhOtDuBUeVHwVTbVGQ6n3JlClrta4Uld5fg6ser8dPRGSa78dWWHLLaDBgwAKNHJ15CnBJ0rl69Wlh3vkbk7N69G6dPn44/plAoxLo7yfHmzRuULFkS/fv3x8CBA3HlyhUMGTIE27dvR7t27dJ0T7bkMAyT23jvH44e627ijW8YzPS1sKF3JZTOZ/JZOVpTJ+CfhfDbvBP5a3+EjkksIFNAWawT5A1/hczMMdX7hMeEiwUFNzzZINbcUUOxOqUtSqOSTSWxlbEsAx2N/yxKDJNj3FXa2tp48uSJWPwvIW5ubkKAREbGLR2eVpGzf//+NKeCGD9+vJjOnlBIDRo0SGRGv3btWprqYJHDMExuxDc0Cr3X38IjjyDoaynwT/eKqFnYItmyquhoyH0eAedmAW6n8emmMSL8tGHRujIMh8yFzMT+i2Ln1NtTIj/WTc+b8Ar3SnSe3FgkdBKKHi1F4lWYGeZr+Zbx+6tD5Sl9w5kzZz4TOXSMzn1LYk87OzshnqpUqYJZs2YJN1RykJBp3LhxomNNmjTB2rVrERMTA03Nz/3DUVFRYksp9xbDMExuwMJAG9sHVMXAzbdxxc0PvTfcxKJOZdGitN1nZeVaWoB9BaDbHqieX0DIwSFQhqvgsekutA/WhUXbGjAcPBcyQ6tk76WnqYfWhVqLjX4nfwj5IMQObbc8b8Enwge3vW6LbcWDFdBWaMeLnso2lUWyUE0Fx/MwGc9XW3JWrFiBUaNGoU+fPqhevbqIo7l8+TI2bNiAxYsXCzdSWjl27BjCw8Ph4uICLy8v/PHHHyLJJ1mKzM3NPytP5Xr16oWJEyfGH7t69Spq1KiBjx8/wtbWNllr0YwZMz47zlPIGYbJjUTFKjFm5wMcefRJ5Oqc3Lw4eld3glyeOOdgQpTBwfBf8jv8dx2BKjpuSNA2U8KyfV0YDJwNmf7n38cpQUPK2+C3QvDc9rwt/vpF+iUqo6PQQVmrskLwkPApYVGCg5iZ7DO7at++fViwYEG820g9u6p169b4HsLCwlCwYEH88ssvGDNmTLIip3fv3pgwYUL8MYrLqVmzpsiEnlzQcnKWHLI4schhGCa3QuvnTDv4GFuuvxP7xW2NMKFZUdQqbJn6dYGB8F80A/77TsSLHevKUTDrMwCoOhjQMf7qZ6Eh5k3QG2HhEcLH63aieB5CV0MX5a3Ko6JNRSF8KFM6r8nD5Mp1cho1aiRcYWQxSkrt2rVRrlw5YTFKKLg6duwoLELJuauSwjE5DMPkBeirff0Vdyw69QIhUbHiWK3CFhjftChK2qcuVmL9A+C/YAqCjp+Fc+OPUGhLgI4JlOUGQV5nGGQ6ht/1XG6BbkL0iM3rVvz0dDU2+jZY3mC5yKfFMJkqcsiS0q1bN9SvXz8+Z0p6QRYXsuTQ7K2pU6cmG3h86NAhuLq6xh8bPHiwCFzmwGOGYZjP8Q+Lxt9n3bD5ujtilHFf9z+WtcPPjYvAwUwv1S6TIiMhe3UMODcbks9zvD1rDsg0YdH9R+j3nAaZVurXpwWVpMLLgJeJLD20GCFNV1/deDWKmBXht5XJPJFDCThPnjwpYmY6d+4sBA9ZV76FsWPHomXLlsifPz+8vb1FTA6tg/Po0SM4OjoKt5SHhwc2bdqUaAo5xf3QNHISNjS7iqeQMwzDpM47v3DMP/kcBx98FPtaCjl6VHPEsPqFYKL3hZlPKiWiT/2D12OWQlLGHdK1lmBSrwL0G7WGZqXmgJZ+urwFZNUZeGognvg9gbG2MVY1WiXcVwwTnFnuqsDAQOzatUss/nfp0iUUKVJEiJ0uXbrAyckpzfWQSLp48SJ8fX3F2jhVq1bF77//juLF4z7QFGTs7u6O8+fPx19DIojW6KHgZJqVRdYdEjpphd1VDMPkZR59CMLsY09x9VVcMLCRjgaG1CuEXtWdoKOZetbxGM+P8Js9HoGnb8eLHULLUAnLBnYwatYccK4P2JUFviODOaWSGHxqMB76PoShliFWN1otgpOZvE1wVsTkfPjwQVhS1q1bJ6aDx8bG+X6zKyxyGIbJ69DX/oUXPphz7BmeecYl4rQz1sHoRi5oWz4fFKnMxCJiPN4jcOVshN24i4j3QYAEONT2g4Fd3CSPiBBThAQXgH6N2tBt3Bly26IQU72+AkopMfj0YNz3uQ9DTUOsbLQSpS1Lf0ermZxOposcWpvmyJEjItUD/TUzMxPupewMixyGYZj/ZmHtu+eBhSef42NQ3EKuRW0MMf6HoqjrYpmmuEtlSAjCzx6Cvlkg5B5XgDeX4H1DBb9nccHJMoUEPVs59EsXhF7dxtCp1xkyw9RneakJiwnDkNNDcNf7LvQ19bGy4Uox9ZzJmwRnlsg5d+6ccFXt2bMHSqUSbdu2RdeuXUUwslz+1emwMhUWOQzDMImJjFFi41V3/H3ODSGRcdb46gXNMeGHYiiV7yunjStjEbp/A4IPHUDY4zeIDU3g16LUPVoqFOhhAc0yDQDnekD+aoCmTqqrKw87O0wEJutp6GF5w+WoYF2B38I8SHBmiJx8+fLBz89PrDRMwoYChylZZk6BRQ7DMEzyBIZHY9k5N2y8+hbRSpU41rKMHcY1LoL85l8/k4qGl+hnjxF2bCfCrl5B+HNPyORKFP7RK9575fXQFCotG+hXqQS9ph2gUaQmkOTHMiUAHXF2BK5/ui7W1FnWYJlYRJDJWwRnhshZtWoVOnToAFNTU+REWOQwDMN8OeHnwlMvsP++B2iE0FTI0K2qI4bXLywSgH4rUkwMYl48hFasG/D6PKRX5/ByiwrKyP+ClLXNVDCq7ALz2dsg0/1vPZ7I2EiMOjcKVz5eESsmL22wFFVtq/JbmYcIzg2LAWY0LHIYhmHSxmOPIPx5/BkuvfQV+4baGhhUtyD61CgAXa1vnz2lRoqNRdjRnQg7cwRh954iyvu/BM/GpYxhu/UcZFq68ceilFEYfW40LnlcEvmwFtdbjBr2NfjtzCMEs8jJmE5iGIbJy1x66YPZR5/B9VNcgmMbI5qJVRg/lrOHtsb3ix01sZ6fELxtKbxW7wUkGayb2MNs4TEgQTLPaGU0fj7/M85/OA8tuRYW1VuE2vlqp9szMNkXFjkZ1EkMwzB5HZVKwoEHHph/4gU8AiPEMT0tBaoXtEDdIpZiy2f6/SsgE0Hr5yN4ywrYV/OFvEwboO0aQKERfz5GGYNxF8fhzLszIrHnwroLUdehbrrcm8m+sMjJoE5iGIZh/puJteX6W6y6+BreIf8lPyYKWuqjbhErIXgqFzD7LiuP9OIUZDt+AlQxkEp3Blovh0zxX30xqhiMvzgep96eEsk859eejwaODfhtysUEs7sqYzqJYRiG+dyyQ+4rWlTw/HNv3H0XKNbdUaOrSVYe8/9beay+mCcrWZ4ehrSzB3zu60GWvywsFx9KtKhgrCoWEy9NxDH3Y9CQaeDP2n+isVNjfqtyKcEscjKmkxiGYZjUCQqPwWU3XyF4SPgktfI4k5XH5T8rz5dSSKgJ2zEP76avE6+tWhSH+bzdnwmdKVem4PDrw1DIFJhTaw6aFmjKb1cuJJhFTsZ0EsMwDJN2aNIuWXnOP/fBhec+uPMuIJGVR0dTjmrOZOWJEz2O5qkn9/SdPgQ+O86J19bty8Hs962JhI5SpcTUq1Nx8NVByGVyzKw5Ey2cW/BblstgkZNBncQwDMN8O0ERMbiSwMrjFZzYylPAQh91XOKCl6s6mydr5fH5tTd8918Xr226VofplLWJzqskFWZcm4G9L/dCBhl+r/E7WhdqzW9bLoJFTgZ1EsMwDJN+Vh5KCkpWHhI9d94GIDaBlUdbQ47GJWwwu20pGGhrJLrOe3RX+B+/R3uw69sAxuOWfSZ0/rj+B/598a8QOtOrT0fbwm35rcslsMjJoE5iGIZhMobgyBhcFVYeEj0+8AyOWxCwZiELrOtVCVoa8kRCx2tIewScc6WlbFFwfj9oNR+bqD4qM+vGLOx4vkPsT602FR1cOvDblwtgkZNBncQwDMNkPCRQrr32Q7+NtxEerRR5sxZ3Kgu5/L/4G0mlgueA1tCNuQWTAhFAq6VA+R6f1TP31lxsebpF7E+sMhE/Ff2J38I8OH5n75ThDMMwTJ5BJpOJxQVXdqsADbkMhx58xG+HXYVoiS8jl8N29UGYdOkbd+DgCEj3d3xWzy+VfkGvEr3EPll2trjGCR4mb8Eih2EYhslW1HaxxIKOZcTrDVfdRWb0RNDMqsZ/AJX6ITZCBvchkxG2c0GSIjKMqTAGfUvGiaE/b/2JjU82Zl4jmGwBixyGYRgm29G6rD2mtiguXs8/+QLbb777XOj8MA++PhUR6a+J97+tRvj+FUmKyDCy/EgMKD0grp7b87Hk7hIx5ZzJG7DIYRiGYbIlfWoWwJC6BcXrSfse4fhjz8QF5HJYLTsA/UImkJQyvJ/8FyKOrf9M6AwvNxxDyg4R+6sfrUb/U/3hHe6deQ1hsgwWOQzDMEy2ZVyTIuhYMR9olvmIHfdw/bVfovNyXT3k23ECek4GUMXK8W78HESe2f5ZPYPLDMbsWrOhq6GLW5630OFQB1zxuJKJLWGyAhY5DMMwTLaFLDGz2pRCw2LWiI5Vof/G23D9GJyojNzACA67TkE3nx5U0XK8GzMdUZf3f1YXrYK8q8UuFDEtAv9Ifww6PQh/3flLpIZgcicschiGYZhsjYZCjr+7lEMlJ1OERMWi5/qbeO8fnqiM3MgEDruOQcdGG8ooOT5N+AXSx/uf1eVk7IStzbeiU5FOYn/t47Xoc6IPPMOSuMKYXAGLHIZhGCbbQ6ke1vSohCLWhvAJiUL3tTfgG5o4PYTCzAr5/z0KQxc92Ff1gWxzG8D76Wd1aSu0MbnqZMyvMx8Gmga4530P7Q+1x/n35zOxRUxmwCKHYRiGyREY62liU9/KsDfRhbtfOHqvv4XQqMSuJoWlHfLtOgfNwmWBCH9gYytIns+Tra+JUxPsarkLJcxLICgqCMPPDheLCMYoYzKpRUxGwyKHYRiGyTFYG+lgc9/KMNPXwiOPIAzcfBtRsUmmhOsYAd32ANalEPw0GG/a/IhYt7vJ1udg6IDNP2xGt2LdxP5m183ocawHPoR8yIzmMBkMixyGYRgmR+FsaYD1vSpBT0uBK25+GLPrAZQJknwK9Myg6rwL3o/NERUAvO3eFbHuT5KtT1OhifGVx2NJvSUw0jLCY7/H6HioI069PZU5DWIyDBY5DMMwTI6jjIMJ/uleAZoKGY48/IQZh54kSv9AyE3tkH/jZmjoA9EBwLtuHaD0eJlinfXy18PulrtRxrIMQmJCMOb8GMy8PhNRysSxP0zOgUUOwzAMkyOpVZjSP5QVix9vuvYWS88mSf8AQKtYBeRfuwYKXSDKV4J7u1aIun4kxTptDWyxvun6+HQQlM2829FueBv8NkPbwmQMLHIYhmGYHEurMnaY9v/0DwtPvcC2G+8+n01Vtgbyr1oeZ9EJBNz7jUHIP5NTrFNTrolRFUZhRcMVMNU2xTP/Z8J9deR1yuKIyZ6wyGEYhmFyNL1qFMCweoXE68n7Kf3Dp8/K6FSqhwIHjkA3v75YGTny1HqRwRyxKbuiatrXxO5Wu1HRuiLCY8Px66VfMe3qNETERmRoe5j0g0UOwzAMk+P5ubELOldy+H/6h/ufpX8gNPI5w/HQFdh0rwGLEmHA3Y3AuqZAUMozqaz0rLCm8RoMKjMIMsiw9+VedDnSBa8CX2Vwi5j0gEUOwzAMkyvSP/zxY0k0Lv5f+ocnH4M+L6etDdNJayDrvhvQNYXq3T186Fgfkae2pFi3Qq7A0LJDsbrxaljoWsAt0A0/HfkJ+932fxbszGQvWOQwDMMwuSb9w5KfyqGyk1lc+od1t/DOL3H6h3gKNQQGnIfPG2eEvFXAfdQfCJo3BEhFtFSxrYJ/W/6LarbVhMtqypUpmHR5EsJjUrgHk+WwyGEYhmFyVfqH1T0roqiNoUj70H3dDZEGIllMnWCx+Bj0i5hBUsrwce05ePaqAynEP8X6yZKzstFKjCg3AnKZHIdeH0Knw53w3D/5VZWZrIVFDsMwDJOrMNbVxKY+lZHPVBdv/cLRa/1NhEQmn6pBYWENhz0XYPFjVbEfcMMHb1vVQszTaynWT+Kmf+n+WNdknYjZcQ92R9ejXXHS/WSGtYn5NljkMAzDMLkOK5H+oQrM9bXw5GMwBm6+83n6h/8j09CA5Zz1yPfbaMg1gYhPKrh36YWIA3+neo8K1hXE4oG17GuJBQPHXhiLjU82cpxONoJFDsMwDJMrKWChjw29K0NfS4Grr/wwZmcy6R8SYNhxAJx2bIGWhSYklQSNy1OBk1MAZeIkoAkx1THF0vpL8VPRnyBBwvzb8zHn5hwoVckLKiZzYZHDMAzD5FpK5TPGP90rxqV/ePQJ4/59kKJFh9AuUQEFjl5G/hGNoKmvBK4uATb/CCnIM9XZVxMqT8DYimPF/rZn2zD6/GheTycbwCKHYRiGydXULGyBRZ3KQi4D9t7zQLc1N0RQckrIjYyg03cZ0H49oKmPkMu34N68HqJvH091CnvPEj0xv858aMm1cO79OfQ90Rd+EZ+v18NkHixyGIZhmFxPi9J2WN+7Mgx1NHDLPQCt/76CZ57BqV9Usi2k3qfg9dAckb6Ae5+RCF0/LdVp5k2cmoj1dIy1jfHI95HIe+Ue5J7+DWLSBIschmEYJk9Qx8US+4bUgJO5HjwCI9Bu+VWccvVK9RqZXQk47twPHTs9KKPleD93J3xHt4AUnfLaOOWty2PzD5thb2CPD6Ef0O1YN9zzvpcBLWK+BIschmEYJs9QyMoA+4fWQPWC5giLVmLA5ttYcf5VqjOiNAu4wPHoFRjXcAEkGXyOv4ZH22pQfnBN8ZoCxgWwtdlWlDQviaCoIPQ70Y+nmGcBLHIYhmGYPIWJnhY29qmMrlXyC8/Tn8ef4ed/HyAyJuWAZLmODmzX7IfNsC6AXEKIWzTc27WB8mHKmcnNdc2xtsla1HWoi2hVNE8xzwJY5DAMwzB5Dk2FHDPblMLvrUtAIZdh710PdFl9PeXVkf8fXGw6bAqcVi2GhoEc+lbhUOztClycB6hUyV6jp6mHv+r+hc5FOvMU8yxAJuWx7GLBwcEwNjZGUFAQjIyMsvpxGIZhmCzm8ktfDNl6B8GRsbAz1hFpIUrYGad6TaznRyiu/AHZo61iX+nYFPLOqyHTTX5coaF2k+smsY4OUd+hPubUngNdDd0MaFHuJPgbxm+25DAMwzDI61PMKU7H2UIfH4Mi0X7FNRx/nPK6OISGjR1k7ZYDLRdDkmnhw/pb+NilLqQg7zRNMT/7/qyI0/GPTDlPFvP9sMhhGIZh8jzOlgZi5lWtwhaIiFFi0JY7WHbO7cspGir0QmT1JQj30ULw0wi879gAKu/XaZpi/tD3IU8xz2BY5DAMwzAMJfbU08T6XpXQq7qT6I95J55j1M77qQYkE7qNfoLDnMmQKSSEvY3Fu47NoXR/kKYp5u9D3qP7se64732f34MMgEUOwzAMw/wfDYUc01uVwMw2JaEhl+HA/Y/otOo6vIMjU+0jg9bdkH/ZAsi1gQhP4G2XToh9cjHVKeZbmm0RU8wDowLF6sin3p7i9yGdYZHDMAzDMEnoWsURm/pWhrGuJh68D0TrZVfw2CMo1X7Sq9scjhvWQaEnQ5S/DO69+iP6xoEUy1voWiSaYv7z+Z+x6ckmfi/SERY5DMMwDJMM1Qta4MDQGihoqY9PFJC88iqOPfqUal/plKsGpx27oGmsAWUkoNo5GHiecs6rpFPM592ex1nM0xEWOQzDMAyTAk4W+tg3tIZICREZo8LgrXex+PTLVAOStVxKwnHvYeTvVhg6RmHAji7Ag52pZjGfWGVifBbzrU+34ucLP3MW83SARQ7DMAzDpIKRjibW9qyIPjUKiP1Fp19g+PZ7qQYka9o7QnfsPqB0J0BSInzVMISumZBiefUU83l15kFTrokz787wFPN0gEUOwzAMw6QhIHlqy+KY07aUCEg+/PATOv5zDV6pBSQrNIEfVyKqQDe8v2iG9wv3IXj+wFSzmDd1aiqmmBtpGcVPMX8b/Jbfn2+ERQ7DMAzDpJHOlfNjS78qMNXTxMMPQWj192U8/BCYyigrh1bnhdAv7QyoZPBYcwEBUzqlmAaCqGBdQcy8Uk8x73OiDzzDUl+ckEkeFjkMwzAM8xVUdTbHgaE1UdjKAF7BUeiw8hoOPfiYYnmZtjbsNx2GSb3StAfP3Y/gO/IHSDFRX5xi7mzsDO9wbww5MwQh0SH8Pn0lLHIYhmEY5ivJb66HvUOqo14RS0TFqkSMzqZr7imWlykUsFm+A+Ztaot9n1Pv4N2/AaSosFSnmK9ouEL8fRnwEmPOj0GMKobfq6+ARQ7DMAzDfAOGOppY07MSeteIWyF56oEnWHPpdarBxVaz/4FVnx/Fvv91PwT+3AiICEjxGjsDOyxrsEwk8rz+6TqmX53+5VQTTDwschiGYRjmG1HIZZjaojiG1iso9v848hQrL7xK9RrzX2bD9uc+0LeLhbHpE2B9cyAk5Zib4ubFsaDOAihkChx8dRArHqzg9yuNsMhhGIZhmO+ALDRjGxfByAaFxf6cY8/w99mXqV5j0n8cHLbuhdzYGvB+AmltY6g+Pk2xfK18tTCp6iTxmkTOvpf7+D1LAyxyGIZhGCYdhM7oRi4Y29hF7M8/+QKLTr1I1bUksy0F9DkOycQR3ucC8LZjG8Q+u5pi+Q4uHdC/VH/x+rdrv+GqR8plmThY5DAMwzBMOjGsfmH8+kNR8XrxmZeYf/J56jE0Zs5Qtt6BoLeGiPSV4W3P3oi5fTTF4sPLDUcL5xaIlWIx+vxoPPN/xu9dKrDIYRiGYZh0ZFCdgpjcvJh4vezcK8w+9ixVoaNRoDgct22FhqEC0UFyuA8YjahzW1O0GP1W/TdUtqmM8NhwDD09lNfQSQUWOQzDMAyTzvSr5YzfWpcQr1ddfI3fDrumKnS0i5eD0+4D0DLXQmy4HG9H/YaIA0uSLaup0MSieotQyKQQvCO8Mfj0YARHB/N7mAwschiGYRgmA+hRzQmz2pQSr9dfcceUA4+hUqUsdDQdC8Jx3wno2OlDGSXHu0nLELZpWrJlKe3D8gbLYalrCbdAN4w5NwYxSl5DJykschiGYRgmg+hSJT/mtisNmQzYcv0dJu57lKrQ0bCyQf79Z6FX0AyqWBmU51cCl/9Ktqytga1YQ0dPQw83PG9g2tVpvIZOEljkMAzDMEwG0rGSAxZ0KAO5DNhx6z1+2fMQylSEjsLICA57zsBhaAMY5Y8ETk8DniQ/ZbyYeTEsrLtQrKFz6PUh/H3/7wxsSc6DRQ7DMAzDZDBty+fDok5lxeKBu+98wM+77iNWmXKSTrmODgyGLwOqDhH7yh1DEPv4fLJla9jXwNRqU8XrVQ9XYc+LPRnUipwHixyGYRiGyQRal7XH0p/KQUMuw/77HzFq533EpCJ0BI1+R5RpLbgfN8D7IYOh8n2XbLG2hdtiYOmB4vXv13/HZY/LGdGEHAeLHIZhGIbJJJqVssWyruWhqZDh8MNPGL7tHqJjUxE6Cg3IfpiD2GgNRHoDn/r/CCk6PNmiQ8sORauCraCUlPj5/M946pfyCsp5BRY5DMMwDJOJNClhg5XdKkBLIcfxJ54YsvUuomKVKZbXcimJfHP/AGQSgp9GwH9cWyCZ6ei0hs70atNRxbaKWENnyJkh+Bj6EXmZbCNyZs+eLd6gUaNGpVjm/PnzokzS7dkzXvGRYRiGyTk0KGaN1T0rQltDjtNPvTBo8x1ExqQsdPSbtIP1kC7itfcJd4T8PTLZcmINnbpxa+j4RvhiyOkheXoNnWwhcm7duoVVq1ahdOnSaSr//PlzfPr0KX4rXDguKRrDMAzD5BTquFhiXa9K0NGU49xzH/TfdBsR0SkLHbPhU2FSvwzZbPDxnxOIOrE62XKGWoZY0XAFrPSs8CroFUadG4VoZTTyIlkuckJDQ9G1a1esXr0apqamabrGysoKNjY28ZtCocjw52QYhmGY9KZGIQts6F0ZeloKXHrpiz4bbiE8OjbF8jaLN0OvoDlUsXJ4zpoLeD5Kvpy+jVgsUF9TH7c8b2HKlSl5cg2dLBc5Q4cORfPmzdGwYcM0X1OuXDnY2tqiQYMGOHfuXKplo6KiEBwcnGhjGIZhmOxCVWdzbOxTGfpaClx77Yde624hNCp5oSPT1IT9pv0wKmkI+2q+wLbOQKh3smWLmBXBwjoLoSHTwNE3R7H03lLkNbJU5OzYsQN3794V8ThpgYQNubX27NmDvXv3okiRIkLoXLx4McVrqG5jY+P4zcHBIR1bwDAMwzDfTyUnM2zuVwWG2hq46e6PHmtvIDgy+TQNGuYWsN98Ahr2zkDwB2BnNyA2Ktmy1e2rY1r1uNQQqx+txr8v/s1Tb5dMyiL71fv371GxYkWcPHkSZcqQjxGoW7cuypYti7/+Sn4J6+Ro2bKlCD4+ePBgipYc2tSQJYeETlBQEIyMjNKhJQzDMAyTPjz8EIhua0jgxKKMgwk29akMY13N5Av7ugFr6iPQNRqSXWWYzj5AU6ySLbri/gosf7AccpkcS+svRe18tXPcW0bjNxkrvmb8zjJLzp07d+Dt7Y0KFSpAQ0NDbBcuXMCSJUvEa6Uy5eCrhFStWhUvX75M8by2trbojIQbwzAMw2RHSuczwbb+VWGip4kH7wPRdc11BIanEDRsUQhhLhPx6aYpPA+8QNiaX1Ksd1CZQWhdsDVUkgpjL4zFE78nyAtkmcghN9OjR49w//79+I0sOxSETK/TGkx879494cZiGIZhmNxASXtjbO9fFWb6WnjsEYxe62+luI6OXpuBMK5eDJBk+LD0IKLPbUq2nEwmE26rarbVEBEbgaGnh8Ij1AO5nSwTOYaGhihZsmSiTV9fH+bm5uI1MWHCBPTo0SP+GnJj7d+/X1hunjx5Is5TfM6wYcOyqhkMwzAMk+4UszXCjgFVhavq/vtAzDjkmqJ4sVm+DbqOxlBFy/H+1z+gfHUr2bKack2RzNPF1AV+kX4YfHowPoV+ytXvXpbPrkoNWgPn3bv/8nRER0dj7NixYj2dWrVq4fLlyzhy5Ajatm2bpc/JMAzDMOmNi7UhFncuK8Jstt14h12336eYzDPfxn3QMFQgOkgBj0E9IQV7JVvWQMtATC231rPGm6A3+PHAj9jxbIdwY+VGsizwOCcFLjEMwzBMVrHkzEssPPUCWhpy7B1cXbizkiPizjW87dkHUixgVskI1usvARpayZZ9H/wek65Mwj3ve2K/vFV5zKg+A07GTsiu5KjAY4ZhGIZhvsyweoXQoKiVSOQ5cPMdBIQlH4isW6Ea7KaOAyBBHvEB0uHRyea4IhyMHLCh6Qb8WvlX6Gro4q73XbQ/1B7rH69HrCrlxQhzGixyGIZhGCYbI5fLsLBTWTia68EjMAIjdtyDUpW8eDHq2AfOyyfDslQ4ZPe3ANdXpFyvTI6uxbpiX+t9IiA5ShmFhXcWotvRbnju/xy5ARY5DMMwDJPNoQBkylxOea4o/cOiUy9SLKtdvxvQeKZ4rTo6CTHXd6dat72BPf5p9A9+q/6byHtF08s7H+6MZfeXIUaZ/IKEOQUWOQzDMAyTQ2ZczWkbl8j673NuOPnEM+XCVQcjxrkD3p42w/vRE6F6ez/VummWVpvCbbC/9X7Ud6iPWCkWKx+sRMfDHfHIJ/n8WDkBFjkMwzAMk0P4sZw9elWPCw7+edcDvPENS74gTcmqNwUx0TqIClDg46CukEJ9v1g/ZS7/q95fmFdnHsx0zOAW6IZux7ph/q35Yn2dnAaLHIZhGIbJQUxsVgwVHU0REhWLQZvvpJi1XNPBEfn+XgaZHAh5o4LviFZAGtxPZNVp6tRUWHVaOLcQ08s3um5Eu4PtREbznASLHIZhGIbJQdBU8uVdy8PSUBvPvUIwfs8jpLQajF61urAZH7dgru/VAATP6pbijKukmOqYYnat2VjWYJmw8LwPeY8+J/rg92u/IzQ6FDkBFjkMwzAMk8OwMtIRQkdDLsOhBx+x7op7imVNeg6F2Y/1xOuPOx4gYucfX3UvSuZJVp0OLh3E/q4Xu8Qighc/XER2h0UOwzAMw+RAKjmZYVLzYuL1rKNPceO1X4plrWYuhX6p/JCUMnxavBGS25mvuhfNuppabSrWNl4LB0MHeIV7YeiZoZhwaQICIwORXWGRwzAMwzA5FApCbl3WTqybM3TbPXgFRyZbTqZQwH7dbhiWskS+Gv6Q7e4N+Lp99f0q21bGnlZ70KN4D7HOzuHXh9H6QGuccD+RosssK2GRwzAMwzA5FAoSnt22FIraGMI3NApDtt4VKyMnh8LQEPm2n4JWsYpAZBCkrR0gBX746nvSCsnjKo3D5h82o6BxQfhH+mPshbEYfX40fMJ9kJ1gkcMwDMMwORg9LQ2xUKChjgbuvA3AzCPJZywXaGgDnbYCxg4IvOWJ9+3qIeb82jQHIyektGVp7Gq5CwNLD4SGTANn3p0RVp19L/dlG6sOixyGYRiGyeE4WehjUcey4vXGa2+x714qFhoDS6g67ITPY1OEeWjg9Yi5CJ7aEgj3/+r7aim0MKzcMOxosQPFzYsjJDoEU69OxaDTgxAQGYCshkUOwzAMw+QCGha3xvD6hcTrCXsfwfVjcIpl5flKwHHnPujkN4cqWg6Pf1/Bo0M1KO/s/aZ7FzErgq3NtmJ0hdHQkmvBJ8IHBpoGyGpkUnaxKWXjVO0MwzAMkxOgAOTeG27h4gsf5DfTw6FhNWGsp5lieSkmBr7zpsN3815KXg4NXSVsu1aCwfB/AO1vEynuQe6IVkXDxdQFWT1+syWHYRiGYXIJCrkMSzqXRT5TXbzzD8eonfegSiFjOSHT1ITlxJlw2roJWpYGiI1Q4P3aO4ieWx14e+2bnsHJ2CndBc63wiKHYRiGYXIRJnpaIhBZW0OOc899sOTsyy9eo1u+EgqcvAjT1vVhUU4OLeVbYP0PwKmpQGwUcioschiGYRgml1HS3hh//FhSvF585iXOPfP+4jVyXV3Y/LkMFmuvAmW7kjMLUUeXwbt3VUjv7yEnwiKHYRiGYXIhHSo6oGuV/GJ2+Mgd9/DOLzxN18l0TYAfl0Nqvxkfb1rA71Yk3nToiMjtkwGVEjkJFjkMwzAMk0uZ2rI4yjqYIDgyFgO33EFEdNpFiqxkK5iPnwmFngaiAjXg/vtu+A2qAcnny+6v7AKLHIZhGIbJpWhrKLCiW3mY62vh6adgTNqXcsby5DBq1R7OJ87CoEJhSCoZvC8G4W27Zog+suCbFhDMbFjkMAzDMEwuxtZYF0u7lINcBuy954HN199+1fUalpbIt+UAbCePgVxLhghvDbwevwqRC5oDwZ+QnWGRwzAMwzC5nOoFLfDrD0XF698OueLOW/+vzpFl0q0/Chw5Ab0i9tAxUUI7+AqwvCrweA+yKyxyGIZhGCYP0L+WM5qXskWsShKJPL1Dks9YnhpaDg7Iv+8k8q3fBpl9WSAyEKqdfRDyR5tvSguR0bDIYRiGYZg8gEwmw5/tS6OQlQG8gqMwbNs9RMV+/WwpmVwOjcKVgX6ngTrj4fPIGB+2PINHh+pQ3juI7ASLHIZhGIbJIxhox2Usp7833/ij3YqreO0T+m2VKTSBehMhr9QNkAHBr2R43WcsQhf0AKLDkB3g3FXJQJHnsbGxUCpz1noADJPX0dTUhEKhyOrHYJhsz+WXvhi2/S4Cw2Ogp6XAjFYl0L5CPmHt+RYibt/Ax9FDEe0TJ26c2mpBd8IJwNAmS3NXschJQnR0ND59+oTw8LQtmsQwTPaBvqDz5csHA4Osz37MMNmdT0ERGL3zPq6/joulaVnGDjPblISRTsoJPVNDFREB76ljEPv4POw7u0DWYz/9p0y352WR852dpFKp8PLlS/FL0NLSElpaWt+sahmGyVzIAuvj4yN+oBQuXJgtOgyTxqzlKy+8wsJTL8RrSuy5uHM5VHA0xbcihfpBBiVgYIX05FtEjka6PkEOh6w4JHQcHBygp6eX1Y/DMMxXQj9O3N3dERMTwyKHYdKYtXxovUKoVtBcpH547x+Bjv9cw+iGhTG4biFx/muRGZgju8CBx8l1ipy7hWFyImx5ZZhvo3x+UxwZUQutytgJi878ky/Qdc114dLKyfBozjAMwzAMKBZnceeymN+hjAhGplidHxZfwoknnjm2d1jkMNn+l/n+/fu/6prp06fD2tr6m65NifPnz4v6AgMDkV2pW7cuRo0alWX3d3Jywl9//ZVl92cY5vuRyWRiltXh4TVRyt5YzL4auPkOJu9/hMiYnDfjmEVOLuL9+/fo27cv7OzsRNC0o6MjRo4cCT8/v8/Kurm5oU+fPsifPz+0tbVhb2+PBg0aYOvWrWL6fELOnTuHFi1aiHgHHR0dFCxYEJ06dcLFixc/EwGmpqaIjEy8iubNmzfFucxwJTx9+hQzZszAP//8I2bJ/fDDDxlynw0bNsDExCRL6khJcO3duxe///47sopbt25hwIABWXZ/hmHSD2dLA+wZXB0DajuL/S3X36HV35fx3DMkR3Uzi5xcwuvXr1GxYkW8ePEC27dvFyJm5cqVOHPmDKpVqwZ/f/9EoqN8+fJCECxbtgyPHz/G4cOHheiha548eRJfdvny5UL8mJubY+fOneKazZs3o3r16hg9evRnz2FoaIh9+/YlOrZu3TohpjKDV69eib+tW7eGjY2NEHB5BTMzM9H/WQWJYA7YZ5jcg5aGHBObFcOmPpVhYaCNF16haPn3ZWy65v5VmcyzFCmPERQURO+M+JuUiIgIydXVVfzNaTRt2lTKly+fFB4enuj4p0+fJD09PWnQoEFiX6VSScWKFZMqVKggKZXKZOuiMsTbt28lTU1NafTo0amWI86dOyf6dfLkyVLDhg3jj9PzGBsbS1OmTBHnU+PFixdSrVq1JG1tbfGMJ0+eFNfs27cvvsyHDx+kjh07SiYmJpKZmZnUqlUr6c2bN+LctGnTRPmEG3Hz5k3xTObm5pKRkZFUu3Zt6c6dO/F10vVU9t69e/HHAgICxDFqV8L20XH164Qb3Ts57t+/L9WtW1cyMDCQDA0NpfLly0u3bt1KtY7NmzeL94eusba2ln766SfJy8sr0bMm3Hr27CnO1alTRxo5cmT8vSMjI6Vx48aJz4WWlpZUqFAhac2aNSn2/7Jly0QZ6n8rKyupXbt28eeo7qFDh4qN3k/q+0mTJiX6DDg6OkqLFi2K36dnW716tfTjjz9Kurq6ou4DBw7En1+/fr2oKyH0Xif8nFCflClTRlq5cqVoB9XTvn178T4kR07+P8ww2RmfkEip57obkuP4w2Lru+GW5BcalW3G75RgS04aUYWHp7xFRaW9bBJXTkrlvgay0pw4cQJDhgyBrq5uonNkzejatauwwtC4c//+fWGNGTt2bIqzyNRupT179oipuL/88kuq5RLSvXt3XLp0Ce/evYuvg2I1yHKUGjR1v23btmLa7/Xr14VFafz48YnK0Pon9erVEwu9kavs8uXL4nXTpk3F9H9q0/r160VZclXRRoSEhKBnz57iuahuWkOlWbNm4vi3QFYsij2hdRrU96F7Jwf1PS1OR66cO3fu4NdffxWr8qZWB7WF3E4PHjwQMUVv3rxBr169xDla3oD6lHj+/Lm4bvHixcneu0ePHtixYweWLFki3nPq05QWybt9+zZGjBiB3377TdR7/Phx1K5dO1GZjRs3QkNDAzdu3BB1Llq0CGvWrEm1r8h12LFjRzx8+FD0OfVHQqtiWiCr5K5du3Do0CHxXPQZHjp06FfVwTDM90GWnHU9K2FKi+LQUshx+qkXflh8EVdf+SI7w+vkpJHn5SukeE6/Tm3k/+ef+P0XNWpCikh+2p1epUpw3Lwpft+tQUMoAwI+K1fs2dO0PppYwJAETLFixZI9T8cDAgLEQmnkziKKFCkSf97b2xvOznF+V2Lu3LlCMFFZGoRJKKmhAZYEg5pr166hVKlS8ftWVlYiDobiTaZOnSpcVeQG+xL/a+9OgKMo9j+AN0cCBCgMlyQkkAhJLIwxKJpEgxCecrwgYsACpMRYwgM1wF9QC6/CwuKIJR7g88ADUQsBSyijCAgaEAWRSuAZoQAJlzFCDJCDoCFA/+vbz9m3u5ndbGJ2dmb2+6laskczMz09vfPbPma2bNmiTsS4xgmCAliwYIHLmBqcsBGY4cSqBVgIajCuBeNUhg4d6hjj4rzNQ4YMcVkXxutg7NC2bdvUWKPGwngnXJAK2+C8Hj0I9h577DFx9dVXq9cIsDSeluG8v1AuCChuuukmce7cORWkoFtK29eexvSg7BAYbN68Wdx2222OZXnbzvbt26v9gS4vjOfq37+/SxoEWAhssM04foqKitTrKVOmeFwugrMJEyY4ynPp0qWquxSBqa8wxgsBlnZcYBmZmZli8eLFDe5/Imo+LVu2EA+kx4qU2M5ixqo94sjvNWLi27vEQ4P7iP+7LV6EtDJfu4n5toiandZ36tzy4vwc423w6xgPnDTRkqCXDoYNG6bSrV+/XtTU1Oje3wsnaQQ5GCeEIAi/3huCAAfjdrQTGWAskTO0hOBXPU7CONlrJ3ycBLWxOHoQxE2bNk3Ex8erwAIPBAxaa5M/zZo1S0yePFkFGosWLfK6nZo9e/aoMUUINJBXzJqCxmwvygitYoMGDfIp/e23367Wh0AIrXEYgO5+a5PU1FSX4wHlgwDb2z3ekpKSHM8RRCE/KI/G0Dsu0PKHFiciMl5iz05q9tX4G6MFTi//zi8Wd7+xU5w4bb7bIbElx0cJhQWeP3S7IWD8d996TuvWRdT3qy3i7+rbt686+ezfv1+MHj263ucHDhxQLRddu3Z1tCTgveTk5L82v5VaBqA7QoO0uHz2yZMnHb+YEVggrXM6d+iWmDp1qprpdccdd6ggqiF6g9jcAyyc2G644QZ1AtYb9OqtNQGtWOgewokcg5FxotSCOa3bznkb0E3XHDCd/Z577lFB4YYNG8TcuXNVi9Rdd92lmx6BI1qk8Pjwww9VvhDcILh0Dj4b4t5t2RAEH4WFhapF7Msvv1StcNh2dLP9nVlk6JpzL1OUo7bf3cvdl/2uHRe88B9R4ISFthaLxiSJgXHdxJy1P4q9v1SIfy7Zru59dWdyT9MUDVtyfN1RYWGeH24zeLymbdvWp7SNgSACv8QxE+oPt24yBCgICjDlGycFdEGg6+SFF15wnGw8GTt2rDpJ5ebmNmp7EDShNQAnTF+6qqBfv37qZF5aWup4D61AzjCuBy0H6KZBoOX8QOuMJxiLg/EmCL6uueYaFeSUl5fXC5C0MTxaS0hDXVa+3qUeLUiYiYbgAeOOtHFDestA8IltQ6vPwIEDVVm5t3zg/4G39aMLEeWLLjlfIXBFixO6KzGGBl2HX3/9teNzjGdypo1vaupdv7HfMS4KgZ23/a53XCBAwn4losDKTIoQG2YOVPe6Old7UcxctVfMXvMf9dwMGOTYxKuvvipqa2vVL34MysU1czBIE8EProEzf/58lQ6BDk6yaOq/5ZZbRF5engoc0AqEgalo8dBOWugmwLgHDGzFOBxcLwcnPvzixzgR8HSCw8BZLAvb4wucXDHOA4NlMeAWgclTTz3lkgbdXmiNQlcOPseAXJzEcS2gkpISj8tGEIRp7+gSw6BZLMe5pQPP0RWDwAL7Afvv6aef9rq9GEyNLi9M0UdQonfXegScOTk5Ktg7fvy4+O6771TLiDZ2Sm8Z2OcIYjDuBN19KB/3a9+gNQrliGn/2MdYht72ocwQZGqDl7EdGKejB8tCmSLIwLa+//77KkhyHruFYwrdbzh2cJkCbCP2fVOlpKSoKedPPvmk6oZcuXKl6uZ0h2szIS/acYGAFYOZOR6HyByiwsPE6n+lihn/iBO41dUnhSVi5JLtoqzKdaJNQMggY9cp5HDs2DGZnZ0te/TooaZ+R0dHy+nTp8vy8vJ6aQ8ePKimHmNabuvWrdVUXkytfvPNN2VdXZ1L2s2bN8sRI0aoacNIi2nNmBa8ceNGRxrnKdZ63KcG68E2paenq+nO8fHxavnuU8gxJX7SpEmya9euaqrzVVddJadMmeIoT731FBYWygEDBqj0cXFx8uOPP6433RnlnpqaqqYoJycnO6av600h12BaPqale5pCXltbK8ePH6/KAXmKjIyUOTk5LseX3jJWrlwpY2Ji1PampaXJvLy8elPc582bp8q5RYsWHqeQYz2Y/h8REeGYQv7uu+/q7vvt27er/x8eHq72QVJSkly9erXjc3z20EMPqe3FNHykmzNnToNTyJ3LDnCcYeq4Bp9ju9q2bStHjhwply1bpjuF/LXXXlP7D+mysrLkmTNndPNh9TpMZHW7jpyWaQu2yHvf2SUvXfrf90OgppC3wD8iiHi7VTsGsOIXb2xsrPr1SET/hcHPGMNl9G0bMC4ILVENdR9qWIeJAq/yfJ24ePmy6NKhjWHnb0848JiIiIiaTacw1wkHgcQxOURERGRL7K5ywqZuImtjHSayr6omdFexJYeIiIhsiUEOERER2RKDHB1BNuGMyDZYd4nIGYMcnUvQ613YjYjMT7v1RVOvwkxE9sIp5E7wxYj79GiX0cfVWHl/HCJrwBWacQVo1Ftv91YjouDBbwI32qXiG3unZCIKPNzTCrfG4I8TIgIGOW7w5RgREaFuAtlcd6ImImPgvl/aXeWJiBjkeOm6Yr8+ERGRdfEnDxEREdkSgxwiIiKyJQY5REREZEutg/ViYbgHBhEREVmDdt5uzEU/gy7Iqa6uVn+jo6MDvSlERETUhPM4btTpi6C7CzkuGFZaWio6duzY7NfSQJSJ4OmXX37x+Q6pVmL3/AVDHpk/62MZWpvdy8+feUS4ggAnMjLS50tFBF1LDnZMVFSUX9eBQrXrwRsM+QuGPDJ/1scytDa7l5+/8uhrC46GA4+JiIjIlhjkEBERkS0xyGlGbdq0EXPnzlV/7cju+QuGPDJ/1scytDa7l5/Z8hh0A4+JiIgoOLAlh4iIiGyJQQ4RERHZEoMcIiIisiUGOURERGRLQRnkLFy4UNx4443qqsfdu3cXo0ePFgcPHnRJg/HYzz77rLqyYrt27cTgwYPFvn37XNLU1taK6dOni65du4r27duLUaNGiZKSEpc0Z8+eFffee6+6gBEeeF5RUeF1+3xZtxnyd+zYMfHAAw+I2NhYtYw+ffqoEfUXLlzwun3Z2dnqatPOj9TUVNPlD2JiYupt65w5c/xafkbmcevWrfXypz12795t+jJctmyZeh8XHMM26NUtK9fBhvIXqDpoZB4DVQ+Nyp+V6+CZM2fU90tCQoIICwsTvXr1EjNmzBCVlZUBr4POCwo6w4YNk8uXL5c//fST3Lt3r8zMzJS9evWS586dc6RZtGiR7Nixo/zkk09kUVGRHDdunIyIiJBVVVWONNOmTZM9e/aUmzdvloWFhTIjI0Ned9118uLFi440w4cPl4mJiXLHjh3qgecjR470un2+rNsM+duwYYPMzs6WmzZtksXFxfLTTz+V3bt3l7Nnz/a6fffdd5/aL7/99pvjcfr0aZ/yZmT+oHfv3nLevHku21pdXe3X8jMyj7W1tS55w2Py5MkyJiZGXr582fRl+NJLL8mFCxeqB77Ozp49W29dVq6DDeUvUHXQyDwGqh4alT8r18GioiKZlZUl8/Ly5OHDh+VXX30l4+Li5JgxYwJeBzVBGeS4KysrUwfftm3b1GscWD169FA7WfPnn3/KTp06yTfeeEO9rqiokCEhIXLVqlWONL/++qts2bKl3Lhxo3q9f/9+tdzvv//ekWbnzp3qvQMHDuhuiy/rNkv+9Dz//PMyNjbW6/agct55552yufgzf/hyxZeUr/xRfv7Oo7MLFy6okyROKGYvQ2f5+fm6JxAr10Ff8meWOujvPJqhHhpVhlatg5o1a9bI0NBQWVdXZ4o6GJTdVe60prXOnTurv0ePHhUnT54UQ4cOdaTBRY0GDRokduzYoV4XFBSIuro6lzRoVktMTHSk2blzp2qaS0lJcaRBcyLe09K482XdZsmfp3Vp6/EGTbRoIo2PjxdTpkwRZWVlTcqbEfnLzc0VXbp0EcnJyWL+/PleuwL8UX5G5FGTl5cnysvLVVO42cvQF1aug00ViDqordefeQx0PTSqDK1eBysrK1XXXOvWrU1RB4PuBp3u0Jo1a9YskZ6err78ATsXrrzySpe0eH38+HFHmtDQUBEeHl4vjfb/8RcHoDu8p6Vx58u6zZI/d8XFxWLp0qVi8eLFXrdpxIgR4u677xa9e/dWB/MzzzwjhgwZok7Kjb1Cpr/zN3PmTHH99derdD/88IN44okn1Da//fbbhpSfEXl09s4774hhw4apOwibvQx9YeU62BSBqING5DHQ9dDIMrRyHTx9+rR47rnnxNSpU01TB4M+yMnJyRE//vij+Pbbb+vtHAzkcj8Q3N9z555GL70vy2nKugORP01paakYPny4qnSTJ0/2uoxx48Y5nqNCDRgwQFXU9evXi6ysLGGm/D3yyCOO50lJSepLduzYsY5flf4uPyPLEAOSN23aJNasWdPgNpm5DBtahtXroCeBqoNG5DHQ9dCoMrRyHayqqhKZmZmiX79+avC7t2V4W05T1u1NUHdXYVQ4mgbz8/NFVFSU4/0ePXqov+5RJpoCtcgSadBcilHj3tKcOnWq3np///33ehFqY9Ztlvw5f7lmZGSItLQ0NZOgsSIiIlTl/Pnnn02ZP2fa7IXDhw/7vfyMzuPy5cvVCQMzsKxQhr6wch1sjEDVQSPzGKh6aGT+rFoHq6urVYDdoUMHsW7dOhESEmKaOhiUA48xqOnhhx+WkZGR8tChQx4HPeXm5rqMgNcb1Ll69WpHmtLSUt2Bx7t27XKkweArXwZceVu3WfIHJSUlajT9+PHjXWYlNUZ5ebls06aNXLFiheny5+6zzz5T5Xf8+HG/lV8g8ojlYbBqQ7NyzFSGjRl4bMU66Ev+AlUHjc5jIOqh0fmzah2srKyUqampctCgQbKmpqbecgJVBzVBGeQ8+OCDamdt3brVZfrd+fPnHWkwqhtp1q5dq6avTZgwQXd6blRUlNyyZYuanjtkyBDdKeRJSUlqNDke1157bb2pcwkJCWo9jVm3GfKHmTp9+/ZV7+OL1nldnvKHaZ+oxJhGePToUVXx09LS1DRns+UP2/jiiy/KPXv2yCNHjqhgAV8Io0aN8mv5GZlHDT7Hlw6+kPSYtQzxf1A+b731ltr+b775Rr12nkpr5TrYUP4CVQeNzGOg6qGRx6hV62BVVZVMSUlRdQpTyJ2XE+jzYFAHOTiQ9B64ZoBzJDl37lwVTSI6vvXWW9WOdvbHH3/InJwc2blzZ9muXTtVaCdOnHBJgwN54sSJar4/HnjuHsk3Zd1myB+W52ldnvKHCjR06FDZrVs31cqA6zJgKqT7fjND/goKClQFRkVr27atqoRYpvuvleYuPyPzqMEXyM033+x1e8xYhvi8oeVYuQ42lL9A1UEj8xioemjkMWrVOpj/V+uU3gPBVyDroKbFXwsnIiIispWgHnhMRERE9sUgh4iIiGyJQQ4RERHZEoMcIiIisiUGOURERGRLDHKIiIjIlhjkEBERkS0xyCEiS9i6dau6OV9FRUWgN4WILIIXAyQiUxo8eLBITk4WL7/8snqNm42eOXNG3aCvqXd5JqLg0jrQG0BE5IvQ0FDH3YmJiHzB7ioiMp3s7Gyxbds28corr6hWGzzee+89l+4qvL7iiivE559/LhISEkRYWJgYO3asqKmpEStWrBAxMTEiPDxcTJ8+XVy6dMmxbLQIPf7446Jnz56iffv2IiUlRXWFEZH9sCWHiEwHwc2hQ4dEYmKimDdvnnpv37599dKdP39eLFmyRKxatUpUV1eLrKws9UDw88UXX4gjR46IMWPGiPT0dDFu3Dj1f+6//35x7Ngx9X8iIyPFunXrxPDhw0VRUZGIi4szPK9E5D8McojIdDp16qS6p9A6o3VRHThwoF66uro68frrr4s+ffqo12jJ+eCDD8SpU6dEhw4dRL9+/URGRobIz89XQU5xcbH46KOPRElJiQpw4NFHHxUbN24Uy5cvFwsWLDA4p0TkTwxyiMiyEARpAQ5gUDK6qRDgOL9XVlamnhcWFgoppYiPj3dZTm1trejSpYuBW05ERmCQQ0SWFRIS4vIaY3b03rt8+bJ6jr+tWrUSBQUF6q8z58CIiOyBQQ4RmRK6q5wHDDeH/v37q2WiZWfgwIHNumwiMh/OriIiU0K3065du9Qg4fLyckdrzN+BbqqJEyeKSZMmibVr14qjR4+K3bt3i9zcXDVQmYjshUEOEZkSBgSjSwmDh7t16yZOnDjRLMvFAGMEObNnz1ZTz0eNGqWCqejo6GZZPhGZB694TERERLbElhwiIiKyJQY5REREZEsMcoiIiMiWGOQQERGRLTHIISIiIltikENERES2xCCHiIiIbIlBDhEREdkSgxwiIiKyJQY5REREZEsMcoiIiMiWGOQQERGRsKP/B/G/w/eErRZhAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "artists = ds_all_density.sel(rgi_id=rgi_ids[0]).volume.plot(hue='experiment_filesuffix')\n",
+    "ax = artists[0].axes; leg1 = ax.get_legend(); leg1.set_title('experiment_filesuffix');\n",
+    "line = ds_default.sel(rgi_id=rgi_ids[0]).volume.plot(linestyle='--')\n",
+    "ax.legend(handles=[line[0]], labels=['OGGM default static spinup'], loc='lower left')\n",
+    "ax.add_artist(leg1) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wkwcx93l3eo",
+   "metadata": {},
+   "source": [
+    "Since we only varied `ice_density` in the settings and used the same default observations for all experiments, all runs start at the same volume at the RGI reference year. However, each run shows a different slope. Although all experiments were calibrated to match the same `ref_mb`, ice density enters the conversion between mass balance and volume, which leads to different volume trajectories even with identical mass balance parameters. We can confirm that the same mass balance parameters were used across all experiments:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "6f921a73-d2d4-4e9d-86f7-f4508895e01e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\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>rgi_id</th>\n",
+       "      <th>filesuffix</th>\n",
+       "      <th>melt_f</th>\n",
+       "      <th>prcp_fac</th>\n",
+       "      <th>temp_bias</th>\n",
+       "      <th>ice_density</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RGI60-11.00897</td>\n",
+       "      <td>_ice_density_sensitivity_0</td>\n",
+       "      <td>4.906857</td>\n",
+       "      <td>3.509332</td>\n",
+       "      <td>1.705036</td>\n",
+       "      <td>850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>RGI60-11.00897</td>\n",
+       "      <td>_ice_density_sensitivity_1</td>\n",
+       "      <td>4.906857</td>\n",
+       "      <td>3.509332</td>\n",
+       "      <td>1.705036</td>\n",
+       "      <td>900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>RGI60-11.00897</td>\n",
+       "      <td>_ice_density_sensitivity_2</td>\n",
+       "      <td>4.906857</td>\n",
+       "      <td>3.509332</td>\n",
+       "      <td>1.705036</td>\n",
+       "      <td>950</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           rgi_id                  filesuffix    melt_f  prcp_fac  temp_bias  \\\n",
+       "0  RGI60-11.00897  _ice_density_sensitivity_0  4.906857  3.509332   1.705036   \n",
+       "1  RGI60-11.00897  _ice_density_sensitivity_1  4.906857  3.509332   1.705036   \n",
+       "2  RGI60-11.00897  _ice_density_sensitivity_2  4.906857  3.509332   1.705036   \n",
+       "\n",
+       "   ice_density  \n",
+       "0          850  \n",
+       "1          900  \n",
+       "2          950  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "utils.compile_settings(gdirs[0], ['melt_f', 'prcp_fac', 'temp_bias', 'ice_density'],\n",
+    "                       filesuffix=all_created_settings_density)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83023172-51cc-4684-a1b5-f04ae44b1f4b",
+   "metadata": {},
+   "source": [
+    "However, the specific mass balance calculated from the dynamic run are all the same:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "0394f90a-0fb9-4f2a-a90e-1f2de48d2bb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7c41a94b2d50>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "artists = ds_all_density.sel(rgi_id=rgi_ids[0]).specific_mb.plot(hue='experiment_filesuffix')\n",
+    "ax = artists[0].axes; leg1 = ax.get_legend(); leg1.set_title('experiment_filesuffix');\n",
+    "line = ds_default.sel(rgi_id=rgi_ids[0]).specific_mb.plot(linestyle='--')\n",
+    "ax.legend(handles=[line[0]], labels=['OGGM default static spinup'], loc='best')\n",
+    "ax.add_artist(leg1) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "le7w1l599ee",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this tutorial we introduced the new `settings.yml` and `observations.yml` files in OGGM v1.7 and showed how they enable systematic sensitivity studies. Here is a recap of the key concepts:\n",
+    "\n",
+    "- **`settings.yml`** centralizes glacier-specific parameter storage and provides a nested lookup chain, falling back to `cfg.PARAMS` for global parameters and includes backwards compatibility for v1.6.\n",
+    "- **`observations.yml`** centralizes the observations used during calibration and assimilation, making it easy to trace which observations were used in any given run and to conduct observation perturbation experiments.\n",
+    "- **`entity_task` integration**: in any task that includes the arguments `settings_filesuffix` or `observations_filesuffix` the correct values can be accessed inside the task with `gdir.settings` or `gdir.observations`.\n",
+    "- **Global parameters** such as `ice_density` can be included in sensitivity studies by storing override values in `settings.yml`."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}