{ "cells": [ { "cell_type": "markdown", "id": "a5965ac5-04d1-4124-a895-61b1630c1e17", "metadata": {}, "source": [ "# Walktrough: Cross-Compound Analysis\n", "\n", "In this tutorial, we compare the **geometrical space explored during photoinduced dynamics** for related molecular systems: \n", "- the Retinal system (**I02**)\n", "- and the compound **I01**, methyleneimmonium ($\\mathrm{CH_2NH_2^+}$)\n", "\n", "We demonstrate how to **select matching substructures** across different molecules in order to define **comparable, geometry-based descriptors**. These descriptors are then processed and analyzed using **principal component analysis (PCA)** to reduce the dimensionality from six internal coordinates to two, enabling clear and intuitive visualization of the sampled configuration space.\n", "\n", "> **Important**: \n", "> Note, the data of the tutorial is provided as a shnitsel-style NetCDF file for I02 (`./test_data/shnitsel/traj_I02.nc`) and imported from SHARC output files for I01 (`./test_data/sharc/traj_I01_v3.0_triplets/`) to illustrate that we can compare existing datasets to new datasets during analysis." ] }, { "cell_type": "code", "execution_count": 1, "id": "ed48b01a-61ca-49b0-aa11-bfe4b0948ac5", "metadata": {}, "outputs": [], "source": [ "# from IPython.display import display\n", "import matplotlib.pyplot as plt\n", "\n", "import shnitsel as st\n", "import shnitsel.xarray" ] }, { "cell_type": "markdown", "id": "32a558f2-8077-49c8-9561-830082f0e1b7", "metadata": {}, "source": [ "## 1) Loading and Annotating Molecular Trajectory Data\n", "\n", "In the first step, we load the trajectory data of methyleneimmonium (**I01**), retinal (**I02**):\n", "\n", "1. Read the trajectory datasets from disk.\n", "2. Clean the inputs to eliminate physically unreasonable data.\n", "3. Merge the data from the different compounds into a common tree that the further analysis will be based on" ] }, { "cell_type": "code", "execution_count": 2, "id": "68af0e84", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ce9d97f5f6c84a16a575387f4f7592db", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3 [00:00\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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_level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'3\\\\\\': ABCMeta(_name=\\\\\\'3\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 94kB\\\\nDimensions:              (time: 104, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 832B 0.0 0.5 1.0 1.5 ... 50.5 51.0 51.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts  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\\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-32476\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', 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\\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00004\\\\\\', \\\\\\'trajid\\\\\\': 4, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'4\\\\\\': ABCMeta(_name=\\\\\\'4\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 85kB\\\\nDimensions:              (time: 94, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 752B 0.0 0.5 1.0 1.5 ... 45.5 46.0 46.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 443\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 1kB ...\\\\n    forces               (time, state, atom, direction) float32 47kB ...\\\\n    atXYZ                (time, atom, direction) float32 16kB -4.518 ... -1.171\\\\n    dip_perm             (time, state, direction) float32 3kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 3kB ...\\\\n    socs                 (time, full_statecomb) complex128 9kB ...\\\\n    phases               (time, state) float32 1kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 752B 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 752B 1.465 ... 1.149\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 443\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00006\\\\n    trajid:                 6\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          215286057\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bbb150>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, 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\\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00006\\\\\\', \\\\\\'trajid\\\\\\': 6, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'5\\\\\\': ABCMeta(_name=\\\\\\'5\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 402kB\\\\nDimensions:              (time: 448, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 222.5 223.0 223.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 450\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 5kB ...\\\\n    forces               (time, state, atom, direction) float32 226kB ...\\\\n    atXYZ                (time, atom, direction) float32 75kB -4.122 ... -0.6151\\\\n    dip_perm             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trajectory_id:          1257393886\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bc3970>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 450, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-26082\\\\\\', 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3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 219.5 220.0 220.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 444\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               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{\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00008\\\\n    trajid:                 8\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          470449612\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bc35b0>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 444, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', 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\\\\\\'7\\\\\\': ABCMeta(_name=\\\\\\'7\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 71kB\\\\nDimensions:              (time: 78, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 624B 0.0 0.5 1.0 1.5 ... 37.5 38.0 38.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 356\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 936B ...\\\\n    forces               (time, state, atom, direction) float32 39kB ...\\\\n    atXYZ                (time, atom, direction) float32 13kB -4.22 ... -1.146\\\\n    dip_perm             (time, state, direction) float32 3kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 3kB ...\\\\n    socs                 (time, full_statecomb) complex128 7kB ...\\\\n    phases               (time, state) float32 936B ...\\\\n    energy_filtranda     (energy_criterion, time) float32 624B 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 624B 1.42 ... 1.22\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 356\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00009\\\\n    trajid:                 9\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          1337730505\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bc31f0>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 356, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-29045\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', 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\\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00009\\\\\\', \\\\\\'trajid\\\\\\': 9, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'8\\\\\\': ABCMeta(_name=\\\\\\'8\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 116kB\\\\nDimensions:              (time: 128, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 1kB 0.0 0.5 1.0 1.5 ... 62.5 63.0 63.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 447\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 2kB ...\\\\n    forces               (time, state, atom, direction) float32 65kB ...\\\\n    atXYZ                (time, atom, direction) float32 22kB -4.286 ... -0.69\\\\n    dip_perm             (time, state, direction) float32 5kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 5kB ...\\\\n    socs                 (time, full_statecomb) complex128 12kB ...\\\\n    phases               (time, state) float32 2kB ...\\\\n    energy_filtranda     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state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 442\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 5kB ...\\\\n    forces               (time, state, atom, direction) float32 222kB ...\\\\n    atXYZ                (time, atom, direction) float32 74kB -4.307 ... 3.564\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\n    socs                 (time, full_statecomb) complex128 42kB ...\\\\n    phases               (time, state) float32 5kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 4kB 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 4kB 1.449 ... 1.186\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 442\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00012\\\\n    trajid:                 12\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          1088618441\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bded40>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 442, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-2750\\\\\\', \\\\\\'ezero\\\\\\': 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             statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 223.0 223.5 224.0\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 451\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) 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\\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00013\\\\n    trajid:                 13\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          1300430345\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bde700>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 451, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'31118\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\'}, \\\\\\'output.dat\\\\\\': {\\\\\\'SHARC_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'method\\\\\\': \\\\\\'0\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'2\\\\\\', \\\\\\'maxmult\\\\\\': \\\\\\'1\\\\\\', \\\\\\'nstates_m\\\\\\': \\\\\\'3\\\\\\', \\\\\\'natom\\\\\\': \\\\\\'14\\\\\\', \\\\\\'dtstep\\\\\\': \\\\\\'20.670686894780374\\\\\\', \\\\\\'nsteps\\\\\\': \\\\\\'2000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.27812829999999\\\\\\', \\\\\\'write_overlap\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_grad\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_nacdr\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property1d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property2d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'n_property1d\\\\\\': \\\\\\'1\\\\\\', 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\\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00013\\\\\\', \\\\\\'trajid\\\\\\': 13, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'11\\\\\\': ABCMeta(_name=\\\\\\'11\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 398kB\\\\nDimensions:              (time: 443, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 220.0 220.5 221.0\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 445\\\\n    t_max               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\\\\\\'max_ts\\\\\\': 450, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-29587\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': 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state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 449\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 2kB ...\\\\n    forces               (time, state, atom, direction) float32 103kB ...\\\\n    atXYZ                (time, atom, direction) float32 34kB -4.269 ... -1.398\\\\n    dip_perm             (time, state, direction) float32 7kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 7kB ...\\\\n    socs                 (time, full_statecomb) complex128 20kB ...\\\\n    phases               (time, state) float32 2kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 2kB 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 2kB 1.502 ... 1.13\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 449\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00018\\\\n    trajid:                 18\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          2056194826\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bf3c40>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 449, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-15866\\\\\\', \\\\\\'ezero\\\\\\': 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\\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-15866\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00018\\\\\\', \\\\\\'trajid\\\\\\': 18, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'14\\\\\\': ABCMeta(_name=\\\\\\'14\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 5kB\\\\nDimensions:              (time: 5, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 40B 0.0 0.5 1.0 1.5 2.0\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 335\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 60B ...\\\\n    forces               (time, state, atom, direction) float32 3kB ...\\\\n    atXYZ                (time, atom, direction) float32 840B -4.484 ... 0.5728\\\\n    dip_perm             (time, state, direction) float32 180B ...\\\\n    dip_trans            (time, statecomb, direction) float32 180B ...\\\\n    socs                 (time, full_statecomb) complex128 480B ...\\\\n    phases               (time, state) float32 60B ...\\\\n    energy_filtranda     (energy_criterion, time) float32 40B 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 40B 1.44 ... 1.267\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 335\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00019\\\\n    trajid:                 19\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          36705956\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bf3600>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 335, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'16691\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\'}, \\\\\\'output.dat\\\\\\': {\\\\\\'SHARC_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'method\\\\\\': \\\\\\'0\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'2\\\\\\', \\\\\\'maxmult\\\\\\': \\\\\\'1\\\\\\', \\\\\\'nstates_m\\\\\\': \\\\\\'3\\\\\\', \\\\\\'natom\\\\\\': \\\\\\'14\\\\\\', \\\\\\'dtstep\\\\\\': \\\\\\'20.670686894780374\\\\\\', \\\\\\'nsteps\\\\\\': \\\\\\'2000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.27812829999999\\\\\\', \\\\\\'write_overlap\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_grad\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_nacdr\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property1d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property2d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'n_property1d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'n_property2d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'laser\\\\\\': \\\\\\'0\\\\\\'}, \\\\\\'output.lis\\\\\\': {\\\\\\'nsteps\\\\\\': 335, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'t_max\\\\\\': 167.0}, \\\\\\'output.log\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'16691\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00019\\\\\\', \\\\\\'trajid\\\\\\': 19, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'15\\\\\\': ABCMeta(_name=\\\\\\'15\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 392kB\\\\nDimensions:              (time: 437, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 3kB 0.0 0.5 1.0 ... 217.0 217.5 218.0\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 439\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 5kB ...\\\\n    forces               (time, state, atom, direction) float32 220kB ...\\\\n    atXYZ                (time, atom, direction) float32 73kB -4.368 ... 1.94\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\n    socs                 (time, full_statecomb) complex128 42kB ...\\\\n    phases               (time, state) float32 5kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 3kB 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 3kB 1.414 ... 1.133\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 439\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00020\\\\n    trajid:                 20\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          759924551\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08c11f80>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 439, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'15722\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\'}, \\\\\\'output.dat\\\\\\': {\\\\\\'SHARC_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'method\\\\\\': \\\\\\'0\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'2\\\\\\', \\\\\\'maxmult\\\\\\': \\\\\\'1\\\\\\', \\\\\\'nstates_m\\\\\\': \\\\\\'3\\\\\\', \\\\\\'natom\\\\\\': \\\\\\'14\\\\\\', \\\\\\'dtstep\\\\\\': \\\\\\'20.670686894780374\\\\\\', \\\\\\'nsteps\\\\\\': \\\\\\'2000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.27812829999999\\\\\\', \\\\\\'write_overlap\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_grad\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_nacdr\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property1d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property2d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'n_property1d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'n_property2d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'laser\\\\\\': \\\\\\'0\\\\\\'}, \\\\\\'output.lis\\\\\\': {\\\\\\'nsteps\\\\\\': 439, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'t_max\\\\\\': 219.0}, \\\\\\'output.log\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'15722\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00020\\\\\\', \\\\\\'trajid\\\\\\': 20, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\')}, _attrs={\\\\\\'DataTree_Level\\\\\\': \\\\\\'CompoundGroup\\\\\\', \\\\\\'compound_info\\\\\\': {\\\\\\'compound_name\\\\\\': \\\\\\'I02\\\\\\'}}, _parent=<class \\\\\\'shnitsel.data.tree.tree.ShnitselDBRoot[Trajectory]\\\\\\'> [{\\\\\\'level\\\\\\': \\\\\\'ShnitselDBRoot\\\\\\', \\\\\\'children\\\\\\': \\\\\\'2: {\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\': \\\\\\\\\\\\\\'...\\\\\\\\\\\\\\', \\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\': \\\\\\\\\\\\\\'ABCMeta(_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=None, _children={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=1, _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 917kB\\\\\\\\\\\\\\\\nDimensions:                 (time: 88, state: 13, atom: 6, direction: 3,\\\\\\\\\\\\\\\\n                             statecomb: 78, full_statecomb: 156,\\\\\\\\\\\\\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/27)\\\\\\\\\\\\\\\\n  * time                    (time) float64 704B 0.0 0.5 1.0 ... 42.5 43.0 43.5\\\\\\\\\\\\\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * atom                    (atom) int64 48B 0 1 2 3 4 5\\\\\\\\\\\\\\\\n  * direction               (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\\\\\\\\\\\\\n    ...                      ...\\\\\\\\\\\\\\\\n    delta_t                 float64 8B 0.5\\\\\\\\\\\\\\\\n    max_ts                  int64 8B 90\\\\\\\\\\\\\\\\n    t_max                   float64 8B 100.0\\\\\\\\\\\\\\\\n    charge                  float64 8B 1.0\\\\\\\\\\\\\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy                  (time, state) float32 5kB -94.4 -94.06 ... -93.89\\\\\\\\\\\\\\\\n    forces                  (time, state, atom, direction) float32 82kB -0.06...\\\\\\\\\\\\\\\\n    atXYZ                   (time, atom, direction) float32 6kB -0.005705 ......\\\\\\\\\\\\\\\\n    nacs                    (time, statecomb, atom, direction) float32 494kB ...\\\\\\\\\\\\\\\\n    dip_perm                (time, state, direction) float32 14kB -0.06869 .....\\\\\\\\\\\\\\\\n    dip_trans               (time, statecomb, direction) float32 82kB 0.07728...\\\\\\\\\\\\\\\\n    socs                    (time, full_statecomb) complex128 220kB 0j ... -4...\\\\\\\\\\\\\\\\n    phases                  (time, state) float32 5kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\\\\\\\\\\\\\n    energy_filtranda        (energy_criterion, time) float32 704B 0.0 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda        (length_criterion, time) float32 704B 1.324 ... 1...\\\\\\\\\\\\\\\\nAttributes: (12/15)\\\\\\\\\\\\\\\\n    input_format:                 sharc\\\\\\\\\\\\\\\\n    _shnitsel_setup_for_cleanup:  True\\\\\\\\\\\\\\\\n    completed:                    False\\\\\\\\\\\\\\\\n    input_type:                   dynamic\\\\\\\\\\\\\\\\n    input_format_version:         3.0\\\\\\\\\\\\\\\\n    num_singlets:                 4\\\\\\\\\\\\\\\\n    ...                           ...\\\\\\\\\\\\\\\\n    theory_basis_set:             cc-pVDZ\\\\\\\\\\\\\\\\n    est_level:                    CASSCF\\\\\\\\\\\\\\\\n    misc_input_settings:          {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\\\\\\\\\\\\\n    trajectory_id:                1\\\\\\\\\\\\\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0832..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=2, _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 327kB\\\\\\\\\\\\\\\\nDimensions:                 (time: 31, state: 13, atom: 6, direction: 3,\\\\\\\\\\\\\\\\n                             statecomb: 78, full_statecomb: 156,\\\\\\\\\\\\\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/27)\\\\\\\\\\\\\\\\n  * time                    (time) float64 248B 0.0 0.5 1.0 ... 14.0 14.5 15.0\\\\\\\\\\\\\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * atom                    (atom) int64 48B 0 1 2 3 4 5\\\\\\\\\\\\\\\\n  * direction               (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\\\\\\\\\\\\\n    ...                      ...\\\\\\\\\\\\\\\\n    delta_t                 float64 8B 0.5\\\\\\\\\\\\\\\\n    max_ts                  int64 8B 108\\\\\\\\\\\\\\\\n    t_max                   float64 8B 100.0\\\\\\\\\\\\\\\\n    charge                  float64 8B 1.0\\\\\\\\\\\\\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy                  (time, state) float32 2kB -94.39 -94.06 ... -93.92\\\\\\\\\\\\\\\\n    forces                  (time, state, atom, direction) float32 29kB 0.058...\\\\\\\\\\\\\\\\n    atXYZ                   (time, atom, direction) float32 2kB -0.003343 ......\\\\\\\\\\\\\\\\n    nacs                    (time, statecomb, atom, direction) float32 174kB ...\\\\\\\\\\\\\\\\n    dip_perm                (time, state, direction) float32 5kB 0.02128 ... ...\\\\\\\\\\\\\\\\n    dip_trans               (time, statecomb, direction) float32 29kB -0.0009...\\\\\\\\\\\\\\\\n    socs                    (time, full_statecomb) complex128 77kB 0j ... 0.7...\\\\\\\\\\\\\\\\n    phases                  (time, state) float32 2kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\\\\\\\\\\\\\n    energy_filtranda        (energy_criterion, time) float32 248B 0.0 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda        (length_criterion, time) float32 248B 1.339 ... 1...\\\\\\\\\\\\\\\\nAttributes: (12/15)\\\\\\\\\\\\\\\\n    input_format:                 sharc\\\\\\\\\\\\\\\\n    _shnitsel_setup_for_cleanup:  True\\\\\\\\\\\\\\\\n    completed:                    False\\\\\\\\\\\\\\\\n    input_type:                   dynamic\\\\\\\\\\\\\\\\n    input_format_version:         3.0\\\\\\\\\\\\\\\\n    num_singlets:                 4\\\\\\\\\\\\\\\\n    ...                           ...\\\\\\\\\\\\\\\\n    theory_basis_set:             cc-pVDZ\\\\\\\\\\\\\\\\n    est_level:                    CASSCF\\\\\\\\\\\\\\\\n    misc_input_settings:          {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\\\\\\\\\\\\\n    trajectory_id:                2\\\\\\\\\\\\\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0835..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=3, _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 824kB\\\\\\\\\\\\\\\\nDimensions:                 (time: 79, state: 13, atom: 6, direction: 3,\\\\\\\\\\\\\\\\n                             statecomb: 78, full_statecomb: 156,\\\\\\\\\\\\\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/27)\\\\\\\\\\\\\\\\n  * time                    (time) float64 632B 0.0 0.5 1.0 ... 38.0 38.5 39.0\\\\\\\\\\\\\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * atom                    (atom) int64 48B 0 1 2 3 4 5\\\\\\\\\\\\\\\\n  * direction               (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\\\\\\\\\\\\\n    ...                      ...\\\\\\\\\\\\\\\\n    delta_t                 float64 8B 0.5\\\\\\\\\\\\\\\\n    max_ts                  int64 8B 179\\\\\\\\\\\\\\\\n    t_max                   float64 8B 100.0\\\\\\\\\\\\\\\\n    charge                  float64 8B 1.0\\\\\\\\\\\\\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy                  (time, state) float32 4kB -94.38 -94.07 ... -93.79\\\\\\\\\\\\\\\\n    forces                  (time, state, atom, direction) float32 74kB 0.054...\\\\\\\\\\\\\\\\n    atXYZ                   (time, atom, direction) float32 6kB 0.02998 ... -...\\\\\\\\\\\\\\\\n    nacs                    (time, statecomb, atom, direction) float32 444kB ...\\\\\\\\\\\\\\\\n    dip_perm                (time, state, direction) float32 12kB -0.1124 ......\\\\\\\\\\\\\\\\n    dip_trans               (time, statecomb, direction) float32 74kB -0.1073...\\\\\\\\\\\\\\\\n    socs                    (time, full_statecomb) complex128 197kB 0j ... 18...\\\\\\\\\\\\\\\\n    phases                  (time, state) float32 4kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\\\\\\\\\\\\\n    energy_filtranda        (energy_criterion, time) float32 632B 0.0 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda        (length_criterion, time) float32 632B 1.302 ... 1...\\\\\\\\\\\\\\\\nAttributes: (12/15)\\\\\\\\\\\\\\\\n    input_format:                 sharc\\\\\\\\\\\\\\\\n    _shnitsel_setup_for_cleanup:  True\\\\\\\\\\\\\\\\n    completed:                    False\\\\\\\\\\\\\\\\n    input_type:                   dynamic\\\\\\\\\\\\\\\\n    input_format_version:         3.0\\\\\\\\\\\\\\\\n    num_singlets:                 4\\\\\\\\\\\\\\\\n    ...                           ...\\\\\\\\\\\\\\\\n    theory_basis_set:             cc-pVDZ\\\\\\\\\\\\\\\\n    est_level:                    CASSCF\\\\\\\\\\\\\\\\n    misc_input_settings:          {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\\\\\\\\\\\\\n    trajectory_id:                3\\\\\\\\\\\\\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0834..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\')}, _attrs={}, _parent=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.tree.tree.ShnitselDBRoot[Trajectory]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'> [{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ShnitselDBRoot\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'children\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': "2: {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}"}], _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'CompoundGroup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _group_info=None)\\\\\\\\\\\\\\'}\\\\\\'}], _level_name=\\\\\\'CompoundGroup\\\\\\', _group_info=None)\\', \\'I01\\': \\'ABCMeta(_name=\\\\\\'I01\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=None, _children={\\\\\\'1\\\\\\': ABCMeta(_name=1, _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 917kB\\\\nDimensions:                 (time: 88, state: 13, atom: 6, direction: 3,\\\\n                             statecomb: 78, full_statecomb: 156,\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\nCoordinates: (12/27)\\\\n  * time                    (time) float64 704B 0.0 0.5 1.0 ... 42.5 43.0 43.5\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * atom                    (atom) int64 48B 0 1 2 3 4 5\\\\n  * direction               (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\n    ...                      ...\\\\n    delta_t                 float64 8B 0.5\\\\n    max_ts                  int64 8B 90\\\\n    t_max                   float64 8B 100.0\\\\n    charge                  float64 8B 1.0\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy                  (time, state) float32 5kB -94.4 -94.06 ... -93.89\\\\n    forces                  (time, state, atom, direction) float32 82kB -0.06...\\\\n    atXYZ                   (time, atom, direction) float32 6kB -0.005705 ......\\\\n    nacs                    (time, statecomb, atom, direction) float32 494kB ...\\\\n    dip_perm                (time, state, direction) float32 14kB -0.06869 .....\\\\n    dip_trans               (time, statecomb, direction) float32 82kB 0.07728...\\\\n    socs                    (time, full_statecomb) complex128 220kB 0j ... -4...\\\\n    phases                  (time, state) float32 5kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\n    energy_filtranda        (energy_criterion, time) float32 704B 0.0 ... 0.0\\\\n    length_filtranda        (length_criterion, time) float32 704B 1.324 ... 1...\\\\nAttributes: (12/15)\\\\n    input_format:                 sharc\\\\n    _shnitsel_setup_for_cleanup:  True\\\\n    completed:                    False\\\\n    input_type:                   dynamic\\\\n    input_format_version:         3.0\\\\n    num_singlets:                 4\\\\n    ...                           ...\\\\n    theory_basis_set:             cc-pVDZ\\\\n    est_level:                    CASSCF\\\\n    misc_input_settings:          {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'...\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\n    trajectory_id:                1\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0832..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'2\\\\\\': ABCMeta(_name=2, _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 327kB\\\\nDimensions:                 (time: 31, state: 13, atom: 6, direction: 3,\\\\n                             statecomb: 78, full_statecomb: 156,\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\nCoordinates: (12/27)\\\\n  * time                    (time) float64 248B 0.0 0.5 1.0 ... 14.0 14.5 15.0\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * atom                    (atom) int64 48B 0 1 2 3 4 5\\\\n  * direction               (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\n    ...                      ...\\\\n    delta_t                 float64 8B 0.5\\\\n    max_ts                  int64 8B 108\\\\n    t_max                   float64 8B 100.0\\\\n    charge                  float64 8B 1.0\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy                  (time, state) float32 2kB -94.39 -94.06 ... -93.92\\\\n    forces                  (time, state, atom, direction) float32 29kB 0.058...\\\\n    atXYZ                   (time, atom, direction) float32 2kB -0.003343 ......\\\\n    nacs                    (time, statecomb, atom, direction) float32 174kB ...\\\\n    dip_perm                (time, state, direction) float32 5kB 0.02128 ... ...\\\\n    dip_trans               (time, statecomb, direction) float32 29kB -0.0009...\\\\n    socs                    (time, full_statecomb) complex128 77kB 0j ... 0.7...\\\\n    phases                  (time, state) float32 2kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\n    energy_filtranda        (energy_criterion, time) float32 248B 0.0 ... 0.0\\\\n    length_filtranda        (length_criterion, time) float32 248B 1.339 ... 1...\\\\nAttributes: (12/15)\\\\n    input_format:                 sharc\\\\n    _shnitsel_setup_for_cleanup:  True\\\\n    completed:                    False\\\\n    input_type:                   dynamic\\\\n    input_format_version:         3.0\\\\n    num_singlets:                 4\\\\n    ...                           ...\\\\n    theory_basis_set:             cc-pVDZ\\\\n    est_level:                    CASSCF\\\\n    misc_input_settings:          {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'...\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\n    trajectory_id:                2\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0835..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'3\\\\\\': ABCMeta(_name=3, _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 824kB\\\\nDimensions:                 (time: 79, state: 13, atom: 6, direction: 3,\\\\n                             statecomb: 78, full_statecomb: 156,\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\nCoordinates: (12/27)\\\\n  * time                    (time) float64 632B 0.0 0.5 1.0 ... 38.0 38.5 39.0\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * atom                    (atom) int64 48B 0 1 2 3 4 5\\\\n  * direction               (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\n    ...                      ...\\\\n    delta_t                 float64 8B 0.5\\\\n    max_ts                  int64 8B 179\\\\n    t_max                   float64 8B 100.0\\\\n    charge                  float64 8B 1.0\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy                  (time, state) float32 4kB -94.38 -94.07 ... -93.79\\\\n    forces                  (time, state, atom, direction) float32 74kB 0.054...\\\\n    atXYZ                   (time, atom, direction) float32 6kB 0.02998 ... -...\\\\n    nacs                    (time, statecomb, atom, direction) float32 444kB ...\\\\n    dip_perm                (time, state, direction) float32 12kB -0.1124 ......\\\\n    dip_trans               (time, statecomb, direction) float32 74kB -0.1073...\\\\n    socs                    (time, full_statecomb) complex128 197kB 0j ... 18...\\\\n    phases                  (time, state) float32 4kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\n    energy_filtranda        (energy_criterion, time) float32 632B 0.0 ... 0.0\\\\n    length_filtranda        (length_criterion, time) float32 632B 1.302 ... 1...\\\\nAttributes: (12/15)\\\\n    input_format:                 sharc\\\\n    _shnitsel_setup_for_cleanup:  True\\\\n    completed:                    False\\\\n    input_type:                   dynamic\\\\n    input_format_version:         3.0\\\\n    num_singlets:                 4\\\\n    ...                           ...\\\\n    theory_basis_set:             cc-pVDZ\\\\n    est_level:                    CASSCF\\\\n    misc_input_settings:          {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'...\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\n    trajectory_id:                3\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0834..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\')}, _attrs={}, _parent=<class \\\\\\'shnitsel.data.tree.tree.ShnitselDBRoot[Trajectory]\\\\\\'> [{\\\\\\'level\\\\\\': \\\\\\'ShnitselDBRoot\\\\\\', \\\\\\'children\\\\\\': \\\\\\'2: {\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\': \\\\\\\\\\\\\\'ABCMeta(_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=None, _children={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 343kB\\\\\\\\\\\\\\\\nDimensions:              (time: 382, state: 3, atom: 14, direction: 3,\\\\\\\\\\\\\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\\\\\\\\\\\\\n                          length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/25)\\\\\\\\\\\\\\\\n  * time                 (time) float64 3kB 0.0 0.5 1.0 ... 189.5 190.0 190.5\\\\\\\\\\\\\\\\n  * state                (state) int64 24B 1 2 3\\\\\\\\\\\\\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * direction            (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\\\\\\\\\\\\\n    ...                   ...\\\\\\\\\\\\\\\\n    delta_t              float64 8B 0.5\\\\\\\\\\\\\\\\n    max_ts               int64 8B 384\\\\\\\\\\\\\\\\n    t_max                float64 8B 1e+03\\\\\\\\\\\\\\\\n    charge               float64 8B 1.0\\\\\\\\\\\\\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy               (time, state) float32 5kB ...\\\\\\\\\\\\\\\\n    forces               (time, state, atom, direction) float32 193kB ...\\\\\\\\\\\\\\\\n    atXYZ                (time, atom, direction) float32 64kB -4.182 ... 3.616\\\\\\\\\\\\\\\\n    dip_perm             (time, state, direction) float32 14kB ...\\\\\\\\\\\\\\\\n    dip_trans            (time, statecomb, direction) float32 14kB ...\\\\\\\\\\\\\\\\n    socs                 (time, full_statecomb) complex128 37kB ...\\\\\\\\\\\\\\\\n    phases               (time, state) float32 5kB ...\\\\\\\\\\\\\\\\n    energy_filtranda     (energy_criterion, time) float32 3kB 0.0 0.1572 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda     (length_criterion, time) float32 3kB 1.455 ... 1.21\\\\\\\\\\\\\\\\nAttributes: (12/17)\\\\\\\\\\\\\\\\n    input_format:           sharc\\\\\\\\\\\\\\\\n    t_max:                  1000.0\\\\\\\\\\\\\\\\n    delta_t:                0.5\\\\\\\\\\\\\\\\n    max_ts:                 384\\\\\\\\\\\\\\\\n    completed:              False\\\\\\\\\\\\\\\\n    input_type:             dynamic\\\\\\\\\\\\\\\\n    ...                     ...\\\\\\\\\\\\\\\\n    misc_input_settings:    {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'"geom"...\\\\\\\\\\\\\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00001\\\\\\\\\\\\\\\\n    trajid:                 1\\\\\\\\\\\\\\\\n    DataTree_Level:         TrajectoryData\\\\\\\\\\\\\\\\n    trajectory_id:          1027217349\\\\\\\\\\\\\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bba480>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_format\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'t_max\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 1000.0, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'delta_t\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 0.5, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'max_ts\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 384, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'completed\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': False, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_type\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'dynamic\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_format_version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', 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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_dat_steps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajectory_input_path\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'retinal_tutorial/I02/TRAJ_00001\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajid\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 1, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataTree_Level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'TrajectoryData\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'__shnitsel_setup_for_cleanup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'14\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'dtstep\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'20.670686894780374\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nsteps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nsubsteps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'25\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ezero\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'-248.27812829999999\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_overlap\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_grad\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_nacdr\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_property1d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_property2d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'n_property1d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'n_property2d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'laser\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output.lis\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nsteps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 367, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'delta_t\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 0.5, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'t_max\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 183.0}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output.log\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'"geom"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'veloc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'external\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'velocfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'"veloc"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nstates\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'actstates\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'state\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2 mch\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'coeff\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'auto\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'rngseed\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'-28912\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ezero\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'-248.2781283000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'tmax\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1000.000000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'stepsize\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.500000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nsubsteps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'25\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'integrator\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'fvv\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'method\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'tsh\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'surf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'diagonal\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'coupling\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'overlap\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nogradcorrect\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ekincorrect\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'parallel_vel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'reflect_frustrated\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'none\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_scheme\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'edc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_param\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'hopping_procedure\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'grad_all\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'eselect\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.001000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'select_directly\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nospinorbit\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_grad\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_nacdr\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_overlap\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_format\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ascii\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_dat_steps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajectory_input_path\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'retinal_tutorial/I02/TRAJ_00002\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajid\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 2, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataTree_Level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'TrajectoryData\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'__shnitsel_setup_for_cleanup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 397kB\\\\\\\\\\\\\\\\nDimensions:              (time: 442, state: 3, atom: 14, direction: 3,\\\\\\\\\\\\\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\\\\\\\\\\\\\n                          length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/25)\\\\\\\\\\\\\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 219.5 220.0 220.5\\\\\\\\\\\\\\\\n  * state                (state) int64 24B 1 2 3\\\\\\\\\\\\\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * direction            (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\\\\\\\\\\\\\n    ...                   ...\\\\\\\\\\\\\\\\n    delta_t              float64 8B 0.5\\\\\\\\\\\\\\\\n    max_ts               int64 8B 444\\\\\\\\\\\\\\\\n    t_max                float64 8B 1e+03\\\\\\\\\\\\\\\\n    charge               float64 8B 1.0\\\\\\\\\\\\\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy               (time, state) float32 5kB ...\\\\\\\\\\\\\\\\n    forces               (time, state, atom, direction) float32 223kB ...\\\\\\\\\\\\\\\\n    atXYZ                (time, atom, direction) float32 74kB -4.284 ... 0.4159\\\\\\\\\\\\\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\\\\\\\\\\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\\\\\\\\\\\\\n    socs                 (time, full_statecomb) complex128 42kB ...\\\\\\\\\\\\\\\\n    phases               (time, state) float32 5kB ...\\\\\\\\\\\\\\\\n    energy_filtranda     (energy_criterion, time) float32 4kB 0.0 0.106 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda     (length_criterion, time) float32 4kB 1.444 ... 1.257\\\\\\\\\\\\\\\\nAttributes: (12/17)\\\\\\\\\\\\\\\\n    input_format:           sharc\\\\\\\\\\\\\\\\n    t_max:                  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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'hopping_procedure\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'grad_all\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'eselect\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.001000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'select_directly\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nospinorbit\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_grad\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_nacdr\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_overlap\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_format\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ascii\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_dat_steps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajectory_input_path\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'retinal_tutorial/I02/TRAJ_00006\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajid\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 6, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataTree_Level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'TrajectoryData\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'__shnitsel_setup_for_cleanup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'5\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'5\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 402kB\\\\\\\\\\\\\\\\nDimensions:              (time: 448, state: 3, atom: 14, direction: 3,\\\\\\\\\\\\\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\\\\\\\\\\\\\n                          length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/25)\\\\\\\\\\\\\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 222.5 223.0 223.5\\\\\\\\\\\\\\\\n  * state                (state) int64 24B 1 2 3\\\\\\\\\\\\\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * direction            (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' 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Size: 116kB\\\\\\\\\\\\\\\\nDimensions:              (time: 128, state: 3, atom: 14, direction: 3,\\\\\\\\\\\\\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\\\\\\\\\\\\\n                          length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/25)\\\\\\\\\\\\\\\\n  * time                 (time) float64 1kB 0.0 0.5 1.0 1.5 ... 62.5 63.0 63.5\\\\\\\\\\\\\\\\n  * state                (state) int64 24B 1 2 3\\\\\\\\\\\\\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * direction            (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\\\\\\\\\\\\\n    ...                   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1e+03\\\\\\\\\\\\\\\\n    charge               float64 8B 1.0\\\\\\\\\\\\\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy               (time, state) float32 60B ...\\\\\\\\\\\\\\\\n    forces               (time, state, atom, direction) float32 3kB ...\\\\\\\\\\\\\\\\n    atXYZ                (time, atom, direction) float32 840B -4.484 ... 0.5728\\\\\\\\\\\\\\\\n    dip_perm             (time, state, direction) float32 180B ...\\\\\\\\\\\\\\\\n    dip_trans            (time, statecomb, direction) float32 180B ...\\\\\\\\\\\\\\\\n    socs                 (time, full_statecomb) complex128 480B ...\\\\\\\\\\\\\\\\n    phases               (time, state) float32 60B ...\\\\\\\\\\\\\\\\n    energy_filtranda     (energy_criterion, time) float32 40B 0.0 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda     (length_criterion, time) float32 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mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'2427\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\'}, \\\\\\'output.dat\\\\\\': {\\\\\\'SHARC_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'method\\\\\\': \\\\\\'0\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'2\\\\\\', \\\\\\'maxmult\\\\\\': \\\\\\'1\\\\\\', \\\\\\'nstates_m\\\\\\': \\\\\\'3\\\\\\', \\\\\\'natom\\\\\\': \\\\\\'14\\\\\\', \\\\\\'dtstep\\\\\\': \\\\\\'20.670686894780374\\\\\\', \\\\\\'nsteps\\\\\\': \\\\\\'2000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.27812829999999\\\\\\', \\\\\\'write_overlap\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_grad\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_nacdr\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property1d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property2d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'n_property1d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'n_property2d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'laser\\\\\\': \\\\\\'0\\\\\\'}, \\\\\\'output.lis\\\\\\': {\\\\\\'nsteps\\\\\\': 444, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'t_max\\\\\\': 221.5}, \\\\\\'output.log\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'\"geom\"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'\"veloc\"\\\\\\', \\\\\\'nstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'2427\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': 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\\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\'}, \\\\\\'output.dat\\\\\\': {\\\\\\'SHARC_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'method\\\\\\': \\\\\\'0\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'2\\\\\\', \\\\\\'maxmult\\\\\\': \\\\\\'1\\\\\\', \\\\\\'nstates_m\\\\\\': \\\\\\'3\\\\\\', \\\\\\'natom\\\\\\': \\\\\\'14\\\\\\', \\\\\\'dtstep\\\\\\': \\\\\\'20.670686894780374\\\\\\', 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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_grad\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_nacdr\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_property1d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_property2d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'n_property1d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'n_property2d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'laser\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output.lis\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nsteps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 439, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'delta_t\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 0.5, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'t_max\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 219.0}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output.log\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\"geom\"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'veloc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'external\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'velocfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\"veloc\"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nstates\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'actstates\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'state\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2 mch\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'coeff\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'auto\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'rngseed\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'15722\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ezero\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'-248.2781283000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'tmax\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1000.000000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'stepsize\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.500000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nsubsteps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'25\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'integrator\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'fvv\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'method\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'tsh\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'surf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'diagonal\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'coupling\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'overlap\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nogradcorrect\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ekincorrect\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'parallel_vel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'reflect_frustrated\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'none\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_scheme\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'edc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_param\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'hopping_procedure\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'grad_all\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'eselect\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.001000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'select_directly\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nospinorbit\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_grad\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_nacdr\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_overlap\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_format\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ascii\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_dat_steps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajectory_input_path\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'retinal_tutorial/I02/TRAJ_00020\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajid\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 20, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataTree_Level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'TrajectoryData\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'__shnitsel_setup_for_cleanup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\')}, _attrs={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataTree_Level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'CompoundGroup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'compound_info\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'compound_name\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}}, _parent= [{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ShnitselDBRoot\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'children\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \"2: {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}\"}], _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'CompoundGroup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _group_info=None)\\\\\\\\\\\\\\', \\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\': \\\\\\\\\\\\\\'...\\\\\\\\\\\\\\'}\\\\\\'}], _level_name=\\\\\\'CompoundGroup\\\\\\', _group_info=None)\\'}'}]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from shnitsel.clean import sanity_check\n", "from shnitsel.data.tree.support_functions import tree_merge\n", "from pathlib import Path\n", "\n", "\n", "dt_retinal = st.io.read('test_data/shnitsel/traj_I02.nc')\n", "dt_retinal = sanity_check(dt_retinal)\n", "\n", "dt_I01 = st.io.read('./test_data/sharc/traj_I01_v3.0_triplets_nacs_socs/', parallel=False).set_compound_info('I01')\n", "dt_I01 = sanity_check(dt_I01)\n", "\n", "mc_tree = tree_merge(dt_retinal, dt_I01)\n", "mc_tree" ] }, { "cell_type": "markdown", "id": "b8cc4c1f", "metadata": {}, "source": [ "If we want to, we can inspect individual compounds in the tree by array-style access:" ] }, { "cell_type": "code", "execution_count": 3, "id": "11007340-fc86-4f53-9d73-ee112a0541fe", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCMeta(_name='I01', _dtype=<class 'shnitsel.data.dataset_containers.trajectory.Trajectory'>, _data=None, _children={'1': ABCMeta(_name=1, _dtype=<class 'shnitsel.data.dataset_containers.trajectory.Trajectory'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 917kB\n",
       "Dimensions:                 (time: 88, state: 13, atom: 6, direction: 3,\n",
       "                             statecomb: 78, full_statecomb: 156,\n",
       "                             energy_criterion: 2, length_criterion: 2)\n",
       "Coordinates: (12/27)\n",
       "  * time                    (time) float64 704B 0.0 0.5 1.0 ... 42.5 43.0 43.5\n",
       "  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
       "  * atom                    (atom) int64 48B 0 1 2 3 4 5\n",
       "  * direction               (direction) <U1 12B 'x' 'y' 'z'\n",
       "  * statecomb               (statecomb) object 624B MultiIndex\n",
       "  * full_statecomb          (full_statecomb) object 1kB MultiIndex\n",
       "    ...                      ...\n",
       "    delta_t                 float64 8B 0.5\n",
       "    max_ts                  int64 8B 90\n",
       "    t_max                   float64 8B 100.0\n",
       "    charge                  float64 8B 1.0\n",
       "    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\n",
       "    length_thresholds       (length_criterion) float64 16B 3.0 2.0\n",
       "Data variables:\n",
       "    energy                  (time, state) float32 5kB -94.4 -94.06 ... -93.89\n",
       "    forces                  (time, state, atom, direction) float32 82kB -0.06...\n",
       "    atXYZ                   (time, atom, direction) float32 6kB -0.005705 ......\n",
       "    nacs                    (time, statecomb, atom, direction) float32 494kB ...\n",
       "    dip_perm                (time, state, direction) float32 14kB -0.06869 .....\n",
       "    dip_trans               (time, statecomb, direction) float32 82kB 0.07728...\n",
       "    socs                    (time, full_statecomb) complex128 220kB 0j ... -4...\n",
       "    phases                  (time, state) float32 5kB -1.0 -1.0 -1.0 ... 1.0 1.0\n",
       "    energy_filtranda        (energy_criterion, time) float32 704B 0.0 ... 0.0\n",
       "    length_filtranda        (length_criterion, time) float32 704B 1.324 ... 1...\n",
       "Attributes: (12/15)\n",
       "    input_format:                 sharc\n",
       "    _shnitsel_setup_for_cleanup:  True\n",
       "    completed:                    False\n",
       "    input_type:                   dynamic\n",
       "    input_format_version:         3.0\n",
       "    num_singlets:                 4\n",
       "    ...                           ...\n",
       "    theory_basis_set:             cc-pVDZ\n",
       "    est_level:                    CASSCF\n",
       "    misc_input_settings:          {'input': {'printlevel': '2', 'geomfile': '...\n",
       "    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\n",
       "    trajectory_id:                1\n",
       "    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0832..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name='DataLeaf'), '2': ABCMeta(_name=2, _dtype=<class 'shnitsel.data.dataset_containers.trajectory.Trajectory'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 327kB\n",
       "Dimensions:                 (time: 31, state: 13, atom: 6, direction: 3,\n",
       "                             statecomb: 78, full_statecomb: 156,\n",
       "                             energy_criterion: 2, length_criterion: 2)\n",
       "Coordinates: (12/27)\n",
       "  * time                    (time) float64 248B 0.0 0.5 1.0 ... 14.0 14.5 15.0\n",
       "  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
       "  * atom                    (atom) int64 48B 0 1 2 3 4 5\n",
       "  * direction               (direction) <U1 12B 'x' 'y' 'z'\n",
       "  * statecomb               (statecomb) object 624B MultiIndex\n",
       "  * full_statecomb          (full_statecomb) object 1kB MultiIndex\n",
       "    ...                      ...\n",
       "    delta_t                 float64 8B 0.5\n",
       "    max_ts                  int64 8B 108\n",
       "    t_max                   float64 8B 100.0\n",
       "    charge                  float64 8B 1.0\n",
       "    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\n",
       "    length_thresholds       (length_criterion) float64 16B 3.0 2.0\n",
       "Data variables:\n",
       "    energy                  (time, state) float32 2kB -94.39 -94.06 ... -93.92\n",
       "    forces                  (time, state, atom, direction) float32 29kB 0.058...\n",
       "    atXYZ                   (time, atom, direction) float32 2kB -0.003343 ......\n",
       "    nacs                    (time, statecomb, atom, direction) float32 174kB ...\n",
       "    dip_perm                (time, state, direction) float32 5kB 0.02128 ... ...\n",
       "    dip_trans               (time, statecomb, direction) float32 29kB -0.0009...\n",
       "    socs                    (time, full_statecomb) complex128 77kB 0j ... 0.7...\n",
       "    phases                  (time, state) float32 2kB -1.0 -1.0 -1.0 ... 1.0 1.0\n",
       "    energy_filtranda        (energy_criterion, time) float32 248B 0.0 ... 0.0\n",
       "    length_filtranda        (length_criterion, time) float32 248B 1.339 ... 1...\n",
       "Attributes: (12/15)\n",
       "    input_format:                 sharc\n",
       "    _shnitsel_setup_for_cleanup:  True\n",
       "    completed:                    False\n",
       "    input_type:                   dynamic\n",
       "    input_format_version:         3.0\n",
       "    num_singlets:                 4\n",
       "    ...                           ...\n",
       "    theory_basis_set:             cc-pVDZ\n",
       "    est_level:                    CASSCF\n",
       "    misc_input_settings:          {'input': {'printlevel': '2', 'geomfile': '...\n",
       "    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\n",
       "    trajectory_id:                2\n",
       "    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0835..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name='DataLeaf'), '3': ABCMeta(_name=3, _dtype=<class 'shnitsel.data.dataset_containers.trajectory.Trajectory'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 824kB\n",
       "Dimensions:                 (time: 79, state: 13, atom: 6, direction: 3,\n",
       "                             statecomb: 78, full_statecomb: 156,\n",
       "                             energy_criterion: 2, length_criterion: 2)\n",
       "Coordinates: (12/27)\n",
       "  * time                    (time) float64 632B 0.0 0.5 1.0 ... 38.0 38.5 39.0\n",
       "  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
       "  * atom                    (atom) int64 48B 0 1 2 3 4 5\n",
       "  * direction               (direction) <U1 12B 'x' 'y' 'z'\n",
       "  * statecomb               (statecomb) object 624B MultiIndex\n",
       "  * full_statecomb          (full_statecomb) object 1kB MultiIndex\n",
       "    ...                      ...\n",
       "    delta_t                 float64 8B 0.5\n",
       "    max_ts                  int64 8B 179\n",
       "    t_max                   float64 8B 100.0\n",
       "    charge                  float64 8B 1.0\n",
       "    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\n",
       "    length_thresholds       (length_criterion) float64 16B 3.0 2.0\n",
       "Data variables:\n",
       "    energy                  (time, state) float32 4kB -94.38 -94.07 ... -93.79\n",
       "    forces                  (time, state, atom, direction) float32 74kB 0.054...\n",
       "    atXYZ                   (time, atom, direction) float32 6kB 0.02998 ... -...\n",
       "    nacs                    (time, statecomb, atom, direction) float32 444kB ...\n",
       "    dip_perm                (time, state, direction) float32 12kB -0.1124 ......\n",
       "    dip_trans               (time, statecomb, direction) float32 74kB -0.1073...\n",
       "    socs                    (time, full_statecomb) complex128 197kB 0j ... 18...\n",
       "    phases                  (time, state) float32 4kB -1.0 -1.0 -1.0 ... 1.0 1.0\n",
       "    energy_filtranda        (energy_criterion, time) float32 632B 0.0 ... 0.0\n",
       "    length_filtranda        (length_criterion, time) float32 632B 1.302 ... 1...\n",
       "Attributes: (12/15)\n",
       "    input_format:                 sharc\n",
       "    _shnitsel_setup_for_cleanup:  True\n",
       "    completed:                    False\n",
       "    input_type:                   dynamic\n",
       "    input_format_version:         3.0\n",
       "    num_singlets:                 4\n",
       "    ...                           ...\n",
       "    theory_basis_set:             cc-pVDZ\n",
       "    est_level:                    CASSCF\n",
       "    misc_input_settings:          {'input': {'printlevel': '2', 'geomfile': '...\n",
       "    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\n",
       "    trajectory_id:                3\n",
       "    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0834..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name='DataLeaf')}, _attrs={}, _parent=<class 'shnitsel.data.tree.tree.ShnitselDBRoot[Trajectory]'> [{'level': 'ShnitselDBRoot', 'children': '2: {\\'I02\\': \\'ABCMeta(_name=\\\\\\'I02\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=None, _children={\\\\\\'0\\\\\\': ABCMeta(_name=\\\\\\'0\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 343kB\\\\nDimensions:              (time: 382, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 3kB 0.0 0.5 1.0 ... 189.5 190.0 190.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 384\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 5kB ...\\\\n    forces               (time, state, atom, direction) float32 193kB ...\\\\n    atXYZ                (time, atom, direction) float32 64kB -4.182 ... 3.616\\\\n    dip_perm             (time, state, direction) float32 14kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 14kB ...\\\\n    socs                 (time, full_statecomb) complex128 37kB ...\\\\n    phases               (time, state) float32 5kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 3kB 0.0 0.1572 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 3kB 1.455 ... 1.21\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 384\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00001\\\\n    trajid:                 1\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          1027217349\\\\n    __mol:         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full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 3kB 0.0 0.5 1.0 ... 181.0 181.5 182.0\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 367\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 4kB ...\\\\n    forces    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 float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 5kB ...\\\\n    forces               (time, state, atom, direction) float32 223kB ...\\\\n    atXYZ                (time, atom, direction) float32 74kB -4.284 ... 0.4159\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\n    socs                 (time, full_statecomb) complex128 42kB ...\\\\n    phases               (time, state) float32 5kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 4kB 0.0 0.106 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 4kB 1.444 ... 1.257\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:             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\\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-9876\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', 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\\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'3\\\\\\': ABCMeta(_name=\\\\\\'3\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 94kB\\\\nDimensions:              (time: 104, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 832B 0.0 0.5 1.0 1.5 ... 50.5 51.0 51.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 106\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 1kB ...\\\\n    forces               (time, state, atom, direction) float32 52kB ...\\\\n    atXYZ                (time, atom, direction) float32 17kB -4.143 ... 0.9226\\\\n    dip_perm             (time, state, direction) float32 4kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 4kB ...\\\\n    socs                 (time, full_statecomb) complex128 10kB ...\\\\n    phases               (time, state) float32 1kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 832B 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 832B 1.443 ... 1.278\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 106\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00004\\\\n    trajid:                 4\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          1157718546\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bbba60>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 106, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-32476\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', 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\\\\\\'20.670686894780374\\\\\\', \\\\\\'nsteps\\\\\\': \\\\\\'2000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.27812829999999\\\\\\', \\\\\\'write_overlap\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_grad\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_nacdr\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property1d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property2d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'n_property1d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'n_property2d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'laser\\\\\\': \\\\\\'0\\\\\\'}, \\\\\\'output.lis\\\\\\': {\\\\\\'nsteps\\\\\\': 106, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'t_max\\\\\\': 52.5}, \\\\\\'output.log\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': 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\\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00004\\\\\\', \\\\\\'trajid\\\\\\': 4, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'4\\\\\\': ABCMeta(_name=\\\\\\'4\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 85kB\\\\nDimensions:              (time: 94, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 752B 0.0 0.5 1.0 1.5 ... 45.5 46.0 46.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 443\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 1kB ...\\\\n    forces               (time, state, atom, direction) float32 47kB ...\\\\n    atXYZ                (time, atom, direction) float32 16kB -4.518 ... -1.171\\\\n    dip_perm             (time, state, direction) float32 3kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 3kB ...\\\\n    socs                 (time, full_statecomb) complex128 9kB ...\\\\n    phases               (time, state) float32 1kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 752B 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 752B 1.465 ... 1.149\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 443\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00006\\\\n    trajid:                 6\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          215286057\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bbb150>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 443, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-22669\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\'}, \\\\\\'output.dat\\\\\\': {\\\\\\'SHARC_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'method\\\\\\': \\\\\\'0\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'2\\\\\\', \\\\\\'maxmult\\\\\\': \\\\\\'1\\\\\\', \\\\\\'nstates_m\\\\\\': \\\\\\'3\\\\\\', \\\\\\'natom\\\\\\': \\\\\\'14\\\\\\', \\\\\\'dtstep\\\\\\': \\\\\\'20.670686894780374\\\\\\', \\\\\\'nsteps\\\\\\': \\\\\\'2000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.27812829999999\\\\\\', \\\\\\'write_overlap\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_grad\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_nacdr\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property1d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property2d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'n_property1d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'n_property2d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'laser\\\\\\': \\\\\\'0\\\\\\'}, \\\\\\'output.lis\\\\\\': {\\\\\\'nsteps\\\\\\': 443, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'t_max\\\\\\': 221.0}, \\\\\\'output.log\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-22669\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00006\\\\\\', \\\\\\'trajid\\\\\\': 6, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'5\\\\\\': ABCMeta(_name=\\\\\\'5\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 402kB\\\\nDimensions:              (time: 448, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 222.5 223.0 223.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 450\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 5kB ...\\\\n    forces               (time, state, atom, direction) float32 226kB ...\\\\n    atXYZ                (time, atom, direction) float32 75kB -4.122 ... -0.6151\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\n    socs                 (time, full_statecomb) complex128 43kB ...\\\\n    phases               (time, state) float32 5kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 4kB 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 4kB 1.461 ... 1.172\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 450\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00007\\\\n    trajid:                 7\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          1257393886\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bc3970>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 450, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-26082\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\'}, \\\\\\'output.dat\\\\\\': {\\\\\\'SHARC_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'method\\\\\\': \\\\\\'0\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'2\\\\\\', \\\\\\'maxmult\\\\\\': \\\\\\'1\\\\\\', \\\\\\'nstates_m\\\\\\': \\\\\\'3\\\\\\', \\\\\\'natom\\\\\\': \\\\\\'14\\\\\\', \\\\\\'dtstep\\\\\\': \\\\\\'20.670686894780374\\\\\\', \\\\\\'nsteps\\\\\\': \\\\\\'2000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.27812829999999\\\\\\', \\\\\\'write_overlap\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_grad\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_nacdr\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property1d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property2d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'n_property1d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'n_property2d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'laser\\\\\\': \\\\\\'0\\\\\\'}, \\\\\\'output.lis\\\\\\': {\\\\\\'nsteps\\\\\\': 450, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'t_max\\\\\\': 224.5}, \\\\\\'output.log\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-26082\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00007\\\\\\', \\\\\\'trajid\\\\\\': 7, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'6\\\\\\': ABCMeta(_name=\\\\\\'6\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 397kB\\\\nDimensions:              (time: 442, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 219.5 220.0 220.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 444\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 5kB ...\\\\n    forces               (time, state, atom, direction) float32 223kB ...\\\\n    atXYZ                (time, atom, direction) float32 74kB -4.135 ... 1.728\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\n    socs                 (time, full_statecomb) complex128 42kB ...\\\\n    phases               (time, state) float32 5kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 4kB 0.0 0.2627 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 4kB 1.412 ... 1.233\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 444\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00008\\\\n    trajid:                 8\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          470449612\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bc35b0>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 444, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'2427\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', 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\\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'7\\\\\\': ABCMeta(_name=\\\\\\'7\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 71kB\\\\nDimensions:              (time: 78, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 624B 0.0 0.5 1.0 1.5 ... 37.5 38.0 38.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 356\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 936B ...\\\\n    forces               (time, state, atom, direction) float32 39kB ...\\\\n    atXYZ                (time, atom, direction) float32 13kB -4.22 ... -1.146\\\\n    dip_perm             (time, state, direction) float32 3kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 3kB ...\\\\n    socs                 (time, full_statecomb) complex128 7kB ...\\\\n    phases               (time, state) float32 936B ...\\\\n    energy_filtranda     (energy_criterion, time) float32 624B 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 624B 1.42 ... 1.22\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 356\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00009\\\\n    trajid:                 9\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          1337730505\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bc31f0>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 356, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-29045\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\'}, \\\\\\'output.dat\\\\\\': {\\\\\\'SHARC_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'method\\\\\\': \\\\\\'0\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'2\\\\\\', \\\\\\'maxmult\\\\\\': \\\\\\'1\\\\\\', \\\\\\'nstates_m\\\\\\': \\\\\\'3\\\\\\', \\\\\\'natom\\\\\\': \\\\\\'14\\\\\\', \\\\\\'dtstep\\\\\\': 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\\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00009\\\\\\', \\\\\\'trajid\\\\\\': 9, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'8\\\\\\': ABCMeta(_name=\\\\\\'8\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 116kB\\\\nDimensions:              (time: 128, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 1kB 0.0 0.5 1.0 1.5 ... 62.5 63.0 63.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 447\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 2kB ...\\\\n    forces               (time, state, atom, direction) float32 65kB ...\\\\n    atXYZ                (time, atom, direction) float32 22kB -4.286 ... -0.69\\\\n    dip_perm             (time, state, direction) float32 5kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 5kB ...\\\\n    socs                 (time, full_statecomb) complex128 12kB ...\\\\n    phases               (time, state) float32 2kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 1kB 0.0 0.3184 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 1kB 1.46 ... 1.224\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 447\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00010\\\\n    trajid:                 10\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          884240710\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bdf830>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 447, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-17512\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\'}, \\\\\\'output.dat\\\\\\': {\\\\\\'SHARC_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'method\\\\\\': \\\\\\'0\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'2\\\\\\', \\\\\\'maxmult\\\\\\': \\\\\\'1\\\\\\', \\\\\\'nstates_m\\\\\\': \\\\\\'3\\\\\\', \\\\\\'natom\\\\\\': \\\\\\'14\\\\\\', \\\\\\'dtstep\\\\\\': \\\\\\'20.670686894780374\\\\\\', \\\\\\'nsteps\\\\\\': \\\\\\'2000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.27812829999999\\\\\\', \\\\\\'write_overlap\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_grad\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_nacdr\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property1d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property2d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'n_property1d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'n_property2d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'laser\\\\\\': \\\\\\'0\\\\\\'}, \\\\\\'output.lis\\\\\\': {\\\\\\'nsteps\\\\\\': 447, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'t_max\\\\\\': 223.0}, \\\\\\'output.log\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-17512\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00010\\\\\\', \\\\\\'trajid\\\\\\': 10, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'9\\\\\\': ABCMeta(_name=\\\\\\'9\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 395kB\\\\nDimensions:              (time: 440, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 218.5 219.0 219.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 442\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 5kB ...\\\\n    forces               (time, state, atom, direction) float32 222kB ...\\\\n    atXYZ                (time, atom, direction) float32 74kB -4.307 ... 3.564\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\n    socs                 (time, full_statecomb) complex128 42kB ...\\\\n    phases               (time, state) float32 5kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 4kB 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 4kB 1.449 ... 1.186\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 442\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  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451\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00013\\\\n    trajid:                 13\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          1300430345\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bde700>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 451, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'31118\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', 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\\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'11\\\\\\': ABCMeta(_name=\\\\\\'11\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 398kB\\\\nDimensions:              (time: 443, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 220.0 220.5 221.0\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 445\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 5kB ...\\\\n    forces               (time, state, atom, direction) float32 223kB ...\\\\n    atXYZ                (time, atom, direction) float32 74kB -4.153 ... 2.578\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\n    socs                 (time, full_statecomb) complex128 43kB ...\\\\n    phases               (time, state) float32 5kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 4kB 0.0 0.1743 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 4kB 1.436 ... 1.133\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 445\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00014\\\\n    trajid:                 14\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          1644969181\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bdffb0>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 445, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-8375\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', 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\\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00014\\\\\\', \\\\\\'trajid\\\\\\': 14, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'12\\\\\\': ABCMeta(_name=\\\\\\'12\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 387kB\\\\nDimensions:              (time: 431, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 3kB 0.0 0.5 1.0 ... 214.0 214.5 215.0\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 450\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 5kB ...\\\\n    forces               (time, state, atom, direction) float32 217kB ...\\\\n    atXYZ                (time, atom, direction) float32 72kB -4.141 ... 1.27\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\n    socs                 (time, full_statecomb) complex128 41kB ...\\\\n    phases               (time, state) float32 5kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 3kB 0.0 0.0874 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 3kB 1.469 ... 1.181\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 450\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00017\\\\n    trajid:                 17\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          371103181\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bf2430>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 450, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-29587\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', 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True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00017\\\\\\', \\\\\\'trajid\\\\\\': 17, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'13\\\\\\': ABCMeta(_name=\\\\\\'13\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 184kB\\\\nDimensions:              (time: 204, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 2kB 0.0 0.5 1.0 ... 100.5 101.0 101.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 449\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 2kB ...\\\\n    forces               (time, state, atom, direction) float32 103kB ...\\\\n    atXYZ                (time, atom, direction) float32 34kB -4.269 ... -1.398\\\\n    dip_perm             (time, state, direction) float32 7kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 7kB ...\\\\n    socs                 (time, full_statecomb) complex128 20kB ...\\\\n    phases               (time, state) float32 2kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 2kB 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 2kB 1.502 ... 1.13\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 449\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00018\\\\n    trajid:                 18\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          2056194826\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bf3c40>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 449, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-15866\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\'}, \\\\\\'output.dat\\\\\\': {\\\\\\'SHARC_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'method\\\\\\': \\\\\\'0\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'2\\\\\\', \\\\\\'maxmult\\\\\\': \\\\\\'1\\\\\\', \\\\\\'nstates_m\\\\\\': \\\\\\'3\\\\\\', \\\\\\'natom\\\\\\': \\\\\\'14\\\\\\', \\\\\\'dtstep\\\\\\': \\\\\\'20.670686894780374\\\\\\', \\\\\\'nsteps\\\\\\': \\\\\\'2000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.27812829999999\\\\\\', \\\\\\'write_overlap\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_grad\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_nacdr\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property1d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property2d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'n_property1d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'n_property2d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'laser\\\\\\': 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\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 5kB\\\\nDimensions:              (time: 5, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 40B 0.0 0.5 1.0 1.5 2.0\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 335\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 60B ...\\\\n    forces               (time, state, atom, direction) float32 3kB ...\\\\n    atXYZ                (time, atom, direction) float32 840B -4.484 ... 0.5728\\\\n    dip_perm             (time, state, direction) float32 180B ...\\\\n    dip_trans            (time, statecomb, direction) float32 180B ...\\\\n    socs                 (time, full_statecomb) complex128 480B ...\\\\n    phases               (time, state) float32 60B ...\\\\n    energy_filtranda     (energy_criterion, time) float32 40B 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 40B 1.44 ... 1.267\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 335\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00019\\\\n    trajid:                 19\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          36705956\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08bf3600>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 335, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'16691\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\'}, \\\\\\'output.dat\\\\\\': {\\\\\\'SHARC_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'method\\\\\\': \\\\\\'0\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'2\\\\\\', \\\\\\'maxmult\\\\\\': \\\\\\'1\\\\\\', \\\\\\'nstates_m\\\\\\': \\\\\\'3\\\\\\', \\\\\\'natom\\\\\\': \\\\\\'14\\\\\\', \\\\\\'dtstep\\\\\\': \\\\\\'20.670686894780374\\\\\\', \\\\\\'nsteps\\\\\\': \\\\\\'2000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.27812829999999\\\\\\', \\\\\\'write_overlap\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_grad\\\\\\': \\\\\\'1\\\\\\', \\\\\\'write_nacdr\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property1d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'write_property2d\\\\\\': \\\\\\'0\\\\\\', \\\\\\'n_property1d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'n_property2d\\\\\\': \\\\\\'1\\\\\\', \\\\\\'laser\\\\\\': \\\\\\'0\\\\\\'}, \\\\\\'output.lis\\\\\\': {\\\\\\'nsteps\\\\\\': 335, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'t_max\\\\\\': 167.0}, \\\\\\'output.log\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'16691\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00019\\\\\\', \\\\\\'trajid\\\\\\': 19, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'15\\\\\\': ABCMeta(_name=\\\\\\'15\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 392kB\\\\nDimensions:              (time: 437, state: 3, atom: 14, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n  * time                 (time) float64 3kB 0.0 0.5 1.0 ... 217.0 217.5 218.0\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    delta_t              float64 8B 0.5\\\\n    max_ts               int64 8B 439\\\\n    t_max                float64 8B 1e+03\\\\n    charge               float64 8B 1.0\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\nData variables:\\\\n    energy               (time, state) float32 5kB ...\\\\n    forces               (time, state, atom, direction) float32 220kB ...\\\\n    atXYZ                (time, atom, direction) float32 73kB -4.368 ... 1.94\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\n    socs                 (time, full_statecomb) complex128 42kB ...\\\\n    phases               (time, state) float32 5kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 3kB 0.0 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 3kB 1.414 ... 1.133\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 439\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00020\\\\n    trajid:                 20\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          759924551\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c08c11f80>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 439, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'"veloc"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'15722\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', 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\\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00020\\\\\\', \\\\\\'trajid\\\\\\': 20, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\')}, _attrs={\\\\\\'DataTree_Level\\\\\\': \\\\\\'CompoundGroup\\\\\\', \\\\\\'compound_info\\\\\\': {\\\\\\'compound_name\\\\\\': \\\\\\'I02\\\\\\'}}, _parent=<class \\\\\\'shnitsel.data.tree.tree.ShnitselDBRoot[Trajectory]\\\\\\'> [{\\\\\\'level\\\\\\': \\\\\\'ShnitselDBRoot\\\\\\', \\\\\\'children\\\\\\': "2: {\\\\\\'I02\\\\\\': \\\\\\'...\\\\\\', \\\\\\'I01\\\\\\': \\\\\\'...\\\\\\'}"}], _level_name=\\\\\\'CompoundGroup\\\\\\', _group_info=None)\\', \\'I01\\': \\'...\\'}'}], _level_name='CompoundGroup', _group_info=None)
" ], "text/plain": [ "ABCMeta(_name='I01', _dtype=, _data=None, _children={'1': ABCMeta(_name=1, _dtype=, _data=Trajectory(_raw_dataset= Size: 917kB\n", "Dimensions: (time: 88, state: 13, atom: 6, direction: 3,\n", " statecomb: 78, full_statecomb: 156,\n", " energy_criterion: 2, length_criterion: 2)\n", "Coordinates: (12/27)\n", " * time (time) float64 704B 0.0 0.5 1.0 ... 42.5 43.0 43.5\n", " * state (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\n", " * atom (atom) int64 48B 0 1 2 3 4 5\n", " * direction (direction) , _data=Trajectory(_raw_dataset= Size: 327kB\n", "Dimensions: (time: 31, state: 13, atom: 6, direction: 3,\n", " statecomb: 78, full_statecomb: 156,\n", " energy_criterion: 2, length_criterion: 2)\n", "Coordinates: (12/27)\n", " * time (time) float64 248B 0.0 0.5 1.0 ... 14.0 14.5 15.0\n", " * state (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\n", " * atom (atom) int64 48B 0 1 2 3 4 5\n", " * direction (direction) , _data=Trajectory(_raw_dataset= Size: 824kB\n", "Dimensions: (time: 79, state: 13, atom: 6, direction: 3,\n", " statecomb: 78, full_statecomb: 156,\n", " energy_criterion: 2, length_criterion: 2)\n", "Coordinates: (12/27)\n", " * time (time) float64 632B 0.0 0.5 1.0 ... 38.0 38.5 39.0\n", " * state (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\n", " * atom (atom) int64 48B 0 1 2 3 4 5\n", " * direction (direction) [{'level': 'ShnitselDBRoot', 'children': '2: {\\'I02\\': \\'ABCMeta(_name=\\\\\\'I02\\\\\\', _dtype=, _data=None, _children={\\\\\\'0\\\\\\': ABCMeta(_name=\\\\\\'0\\\\\\', _dtype=, _data=Trajectory(_raw_dataset= Size: 343kB\\\\nDimensions: (time: 382, state: 3, atom: 14, direction: 3,\\\\n statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n length_criterion: 2)\\\\nCoordinates: (12/25)\\\\n * time (time) float64 3kB 0.0 0.5 1.0 ... 189.5 190.0 190.5\\\\n * state (state) int64 24B 1 2 3\\\\n * atom (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n * direction (direction) , _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 384, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'\"geom\"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'\"veloc\"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'5199\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': 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2 3\\\\n * atom (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n * direction (direction) , _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 367, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'\"geom\"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'\"veloc\"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-28912\\\\\\', 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(12/25)\\\\n * time (time) float64 4kB 0.0 0.5 1.0 ... 219.5 220.0 220.5\\\\n * state (state) int64 24B 1 2 3\\\\n * atom (atom) int64 112B 0 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n * direction (direction) , _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 444, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'\"geom\"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'\"veloc\"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 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\\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'\"geom\"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'\"veloc\"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'15722\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\'}, \\\\\\'output.dat\\\\\\': {\\\\\\'SHARC_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'method\\\\\\': \\\\\\'0\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'2\\\\\\', \\\\\\'maxmult\\\\\\': \\\\\\'1\\\\\\', \\\\\\'nstates_m\\\\\\': \\\\\\'3\\\\\\', \\\\\\'natom\\\\\\': \\\\\\'14\\\\\\', \\\\\\'dtstep\\\\\\': \\\\\\'20.670686894780374\\\\\\', 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\\\\\\'rngseed\\\\\\': \\\\\\'15722\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', \\\\\\'tmax\\\\\\': \\\\\\'1000.000000\\\\\\', \\\\\\'stepsize\\\\\\': \\\\\\'0.500000\\\\\\', \\\\\\'nsubsteps\\\\\\': \\\\\\'25\\\\\\', \\\\\\'integrator\\\\\\': \\\\\\'fvv\\\\\\', \\\\\\'method\\\\\\': \\\\\\'tsh\\\\\\', \\\\\\'surf\\\\\\': \\\\\\'diagonal\\\\\\', \\\\\\'coupling\\\\\\': \\\\\\'overlap\\\\\\', \\\\\\'nogradcorrect\\\\\\': True, \\\\\\'ekincorrect\\\\\\': \\\\\\'parallel_vel\\\\\\', \\\\\\'reflect_frustrated\\\\\\': \\\\\\'none\\\\\\', \\\\\\'decoherence_scheme\\\\\\': \\\\\\'edc\\\\\\', \\\\\\'decoherence_param\\\\\\': \\\\\\'0.1\\\\\\', \\\\\\'hopping_procedure\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'grad_all\\\\\\': True, \\\\\\'eselect\\\\\\': \\\\\\'0.001000\\\\\\', \\\\\\'select_directly\\\\\\': True, \\\\\\'nospinorbit\\\\\\': True, \\\\\\'write_grad\\\\\\': True, \\\\\\'write_nacdr\\\\\\': True, \\\\\\'write_overlap\\\\\\': True, \\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00020\\\\\\', \\\\\\'trajid\\\\\\': 20, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\')}, _attrs={\\\\\\'DataTree_Level\\\\\\': \\\\\\'CompoundGroup\\\\\\', \\\\\\'compound_info\\\\\\': {\\\\\\'compound_name\\\\\\': \\\\\\'I02\\\\\\'}}, _parent= [{\\\\\\'level\\\\\\': \\\\\\'ShnitselDBRoot\\\\\\', \\\\\\'children\\\\\\': \"2: {\\\\\\'I02\\\\\\': \\\\\\'...\\\\\\', \\\\\\'I01\\\\\\': \\\\\\'...\\\\\\'}\"}], _level_name=\\\\\\'CompoundGroup\\\\\\', _group_info=None)\\', \\'I01\\': \\'...\\'}'}], _level_name='CompoundGroup', _group_info=None)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mc_tree['I01']" ] }, { "cell_type": "markdown", "id": "431ab5b1-1967-4579-9818-a46370f191aa", "metadata": {}, "source": [ "## 2) Computing Descriptors for common substructures\n", "\n", "In the next step, we **flag a common substructure** that all molecules share, namely the central double bond between an N and a C atom and the adjacent H atoms. This flagging identifies which substructure is considered in the subsequent analysis.\n", "\n", "### 2.1) Selecting a substructure\n", "\n", "There are two main strategies to select a substructure of interest for further processing:\n", "\n", "- provide a SMARTS string that fits the substructure in all compounds\n", "- identify the maximum common substructure automatically\n", "\n", "The following example will find the maximum common substructure shared between all compounds within the tree:" ] }, { "cell_type": "code", "execution_count": 4, "id": "21261419", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<class 'shnitsel.data.tree.tree.ShnitselDBRoot[Trajectory]'> [{'level': 'ShnitselDBRoot', 'children': '2: {\\'I02\\': \\'ABCMeta(_name=\\\\\\'I02\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=None, _children={\\\\\\'0\\\\\\': ABCMeta(_name=\\\\\\'0\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 178kB\\\\nDimensions:              (time: 382, state: 3, atom: 5, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/26)\\\\n  * time                 (time) float64 3kB 0.0 0.5 1.0 ... 189.5 190.0 190.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 40B 0 1 2 3 4\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb        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state: 3, atom: 5, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/26)\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 219.5 220.0 220.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 40B 0 1 2 3 4\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    max_ts               int64 8B 444\\\\n    t_max                float64 8B 1e+03\\\\n    charge               int64 8B 1\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\n    __mol                object 8B <rdkit.Chem.rdchem.Mol object at 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     (time: 442, state: 3, atom: 5, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/26)\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 219.5 220.0 220.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 40B 0 1 2 3 4\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\n    ...                   ...\\\\n    max_ts               int64 8B 444\\\\n    t_max                float64 8B 1e+03\\\\n    charge               int64 8B 1\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\n    __mol                object 8B <rdkit.Chem.rdchem.Mol object at 0x744c081...\\\\nData variables:\\\\n    energy               (time, state) float32 5kB ...\\\\n    forces               (time, state, atom, direction) float32 80kB ...\\\\n    atXYZ                (time, atom, direction) float32 27kB 3.688 ... 0.8613\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\n    socs                 (time, full_statecomb) complex128 42kB ...\\\\n    phases               (time, state) float32 5kB ...\\\\n    energy_filtranda     (energy_criterion, time) float32 4kB 0.0 0.2627 ... 0.0\\\\n    length_filtranda     (length_criterion, time) float32 4kB 1.412 ... 1.233\\\\nAttributes: (12/17)\\\\n    input_format:           sharc\\\\n    t_max:                  1000.0\\\\n    delta_t:                0.5\\\\n    max_ts:                 444\\\\n    completed:              False\\\\n    input_type:             dynamic\\\\n    ...                     ...\\\\n    misc_input_settings:    {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"...\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00008\\\\n    trajid:                 8\\\\n    DataTree_Level:         TrajectoryData\\\\n    trajectory_id:          470449612\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c081c3e70>, _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 444, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'"geom"\\\\\\', 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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=None, _children={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=1, _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 820kB\\\\\\\\\\\\\\\\nDimensions:                 (time: 88, state: 13, atom: 5, direction: 3,\\\\\\\\\\\\\\\\n                             statecomb: 78, full_statecomb: 156,\\\\\\\\\\\\\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/28)\\\\\\\\\\\\\\\\n  * time                    (time) float64 704B 0.0 0.5 1.0 ... 42.5 43.0 43.5\\\\\\\\\\\\\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * atom                    (atom) int64 40B 0 1 2 3 4\\\\\\\\\\\\\\\\n  * direction   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-93.89\\\\\\\\\\\\\\\\n    forces                  (time, state, atom, direction) float32 69kB -0.06...\\\\\\\\\\\\\\\\n    atXYZ                   (time, atom, direction) float32 5kB -0.005705 ......\\\\\\\\\\\\\\\\n    nacs                    (time, statecomb, atom, direction) float32 412kB ...\\\\\\\\\\\\\\\\n    dip_perm                (time, state, direction) float32 14kB -0.06869 .....\\\\\\\\\\\\\\\\n    dip_trans               (time, statecomb, direction) float32 82kB 0.07728...\\\\\\\\\\\\\\\\n    socs                    (time, full_statecomb) complex128 220kB 0j ... -4...\\\\\\\\\\\\\\\\n    phases                  (time, state) float32 5kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\\\\\\\\\\\\\n    energy_filtranda        (energy_criterion, time) float32 704B 0.0 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda        (length_criterion, time) float32 704B 1.324 ... 1...\\\\\\\\\\\\\\\\nAttributes: (12/15)\\\\\\\\\\\\\\\\n    input_format:                 sharc\\\\\\\\\\\\\\\\n    _shnitsel_setup_for_cleanup:  True\\\\\\\\\\\\\\\\n    completed:                    False\\\\\\\\\\\\\\\\n    input_type:                   dynamic\\\\\\\\\\\\\\\\n    input_format_version:         3.0\\\\\\\\\\\\\\\\n    num_singlets:                 4\\\\\\\\\\\\\\\\n    ...                           ...\\\\\\\\\\\\\\\\n    theory_basis_set:             cc-pVDZ\\\\\\\\\\\\\\\\n    est_level:                    CASSCF\\\\\\\\\\\\\\\\n    misc_input_settings:          {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\\\\\\\\\\\\\n    trajectory_id:                1\\\\\\\\\\\\\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0823..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=2, _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 293kB\\\\\\\\\\\\\\\\nDimensions:                 (time: 31, state: 13, atom: 5, direction: 3,\\\\\\\\\\\\\\\\n                             statecomb: 78, full_statecomb: 156,\\\\\\\\\\\\\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/28)\\\\\\\\\\\\\\\\n  * time                    (time) float64 248B 0.0 0.5 1.0 ... 14.0 14.5 15.0\\\\\\\\\\\\\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * atom                    (atom) int64 40B 0 1 2 3 4\\\\\\\\\\\\\\\\n  * direction               (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\\\\\\\\\\\\\n    ...                      ...\\\\\\\\\\\\\\\\n    max_ts                  int64 8B 108\\\\\\\\\\\\\\\\n    t_max                   float64 8B 100.0\\\\\\\\\\\\\\\\n    charge                  int64 8B 1\\\\\\\\\\\\\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\n    __mol                   object 8B <rdkit.Chem.rdchem.Mol object at 0x744c...\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy                  (time, state) float32 2kB -94.39 -94.06 ... -93.92\\\\\\\\\\\\\\\\n    forces                  (time, state, atom, direction) float32 24kB 0.058...\\\\\\\\\\\\\\\\n    atXYZ                   (time, atom, direction) float32 2kB -0.003343 ......\\\\\\\\\\\\\\\\n    nacs                    (time, statecomb, atom, direction) float32 145kB ...\\\\\\\\\\\\\\\\n    dip_perm                (time, state, direction) float32 5kB 0.02128 ... ...\\\\\\\\\\\\\\\\n    dip_trans               (time, statecomb, direction) float32 29kB -0.0009...\\\\\\\\\\\\\\\\n    socs                    (time, full_statecomb) complex128 77kB 0j ... 0.7...\\\\\\\\\\\\\\\\n    phases                  (time, state) float32 2kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\\\\\\\\\\\\\n    energy_filtranda        (energy_criterion, time) float32 248B 0.0 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda        (length_criterion, time) float32 248B 1.339 ... 1...\\\\\\\\\\\\\\\\nAttributes: (12/15)\\\\\\\\\\\\\\\\n    input_format:                 sharc\\\\\\\\\\\\\\\\n    _shnitsel_setup_for_cleanup:  True\\\\\\\\\\\\\\\\n    completed:                    False\\\\\\\\\\\\\\\\n    input_type:                   dynamic\\\\\\\\\\\\\\\\n    input_format_version:         3.0\\\\\\\\\\\\\\\\n    num_singlets:                 4\\\\\\\\\\\\\\\\n    ...                           ...\\\\\\\\\\\\\\\\n    theory_basis_set:             cc-pVDZ\\\\\\\\\\\\\\\\n    est_level:                    CASSCF\\\\\\\\\\\\\\\\n    misc_input_settings:          {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\\\\\\\\\\\\\n    trajectory_id:                2\\\\\\\\\\\\\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0823..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=3, _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 737kB\\\\\\\\\\\\\\\\nDimensions:                 (time: 79, state: 13, atom: 5, direction: 3,\\\\\\\\\\\\\\\\n                             statecomb: 78, full_statecomb: 156,\\\\\\\\\\\\\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/28)\\\\\\\\\\\\\\\\n  * time                    (time) float64 632B 0.0 0.5 1.0 ... 38.0 38.5 39.0\\\\\\\\\\\\\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * atom                    (atom) int64 40B 0 1 2 3 4\\\\\\\\\\\\\\\\n  * direction               (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\\\\\\\\\\\\\n    ...                      ...\\\\\\\\\\\\\\\\n    max_ts                  int64 8B 179\\\\\\\\\\\\\\\\n    t_max                   float64 8B 100.0\\\\\\\\\\\\\\\\n    charge                  int64 8B 1\\\\\\\\\\\\\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\n    __mol                   object 8B <rdkit.Chem.rdchem.Mol object at 0x744c...\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy                  (time, state) float32 4kB -94.38 -94.07 ... -93.79\\\\\\\\\\\\\\\\n    forces                  (time, state, atom, direction) float32 62kB 0.054...\\\\\\\\\\\\\\\\n    atXYZ                   (time, atom, direction) float32 5kB 0.02998 ... -...\\\\\\\\\\\\\\\\n    nacs                    (time, statecomb, atom, direction) float32 370kB ...\\\\\\\\\\\\\\\\n    dip_perm                (time, state, direction) float32 12kB -0.1124 ......\\\\\\\\\\\\\\\\n    dip_trans               (time, statecomb, direction) float32 74kB -0.1073...\\\\\\\\\\\\\\\\n    socs                    (time, full_statecomb) complex128 197kB 0j ... 18...\\\\\\\\\\\\\\\\n    phases                  (time, state) float32 4kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\\\\\\\\\\\\\n    energy_filtranda        (energy_criterion, time) float32 632B 0.0 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda        (length_criterion, time) float32 632B 1.302 ... 1...\\\\\\\\\\\\\\\\nAttributes: (12/15)\\\\\\\\\\\\\\\\n    input_format:                 sharc\\\\\\\\\\\\\\\\n    _shnitsel_setup_for_cleanup:  True\\\\\\\\\\\\\\\\n    completed:                    False\\\\\\\\\\\\\\\\n    input_type:                   dynamic\\\\\\\\\\\\\\\\n    input_format_version:         3.0\\\\\\\\\\\\\\\\n    num_singlets:                 4\\\\\\\\\\\\\\\\n    ...                           ...\\\\\\\\\\\\\\\\n    theory_basis_set:             cc-pVDZ\\\\\\\\\\\\\\\\n    est_level:                    CASSCF\\\\\\\\\\\\\\\\n    misc_input_settings:          {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\\\\\\\\\\\\\n    trajectory_id:                3\\\\\\\\\\\\\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0825..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\')}, _attrs={}, _parent=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.tree.tree.ShnitselDBRoot[Trajectory]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'> [{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ShnitselDBRoot\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'children\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': "2: {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}"}], _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'CompoundGroup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _group_info=GroupInfo(group_name="TrajectoryGroupingMetadata(delta_t_in_fs=0.5, input_format_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', input_format_version=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', est_level=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'CASSCF\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', theory_basis_set=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'cc-pVDZ\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', charge_in_e=1.0, num_states=13)", group_attributes={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'delta_t_in_fs\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 0.5, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_format_name\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_format_version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'est_level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'CASSCF\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'theory_basis_set\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'cc-pVDZ\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'charge_in_e\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 1.0, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'num_states\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 13}, grouped_properties=None))\\\\\\\\\\\\\\'}\\\\\\'}], _level_name=\\\\\\'CompoundGroup\\\\\\', _group_info=GroupInfo(group_name="TrajectoryGroupingMetadata(delta_t_in_fs=0.5, input_format_name=\\\\\\'sharc\\\\\\', input_format_version=\\\\\\'3.0\\\\\\', est_level=None, theory_basis_set=None, charge_in_e=1.0, num_states=3)", group_attributes={\\\\\\'delta_t_in_fs\\\\\\': 0.5, \\\\\\'input_format_name\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'est_level\\\\\\': None, \\\\\\'theory_basis_set\\\\\\': None, \\\\\\'charge_in_e\\\\\\': 1.0, \\\\\\'num_states\\\\\\': 3}, grouped_properties=None))\\', \\'I01\\': \\'ABCMeta(_name=\\\\\\'I01\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=None, _children={\\\\\\'1\\\\\\': ABCMeta(_name=1, _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 820kB\\\\nDimensions:                 (time: 88, state: 13, atom: 5, direction: 3,\\\\n                             statecomb: 78, full_statecomb: 156,\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\nCoordinates: (12/28)\\\\n  * time                    (time) float64 704B 0.0 0.5 1.0 ... 42.5 43.0 43.5\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * atom                    (atom) int64 40B 0 1 2 3 4\\\\n  * direction               (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\n    ...                      ...\\\\n    max_ts                  int64 8B 90\\\\n    t_max                   float64 8B 100.0\\\\n    charge                  int64 8B 1\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\n    __mol                   object 8B <rdkit.Chem.rdchem.Mol object at 0x744c...\\\\nData variables:\\\\n    energy                  (time, state) float32 5kB -94.4 -94.06 ... -93.89\\\\n    forces                  (time, state, atom, direction) float32 69kB -0.06...\\\\n    atXYZ                   (time, atom, direction) float32 5kB -0.005705 ......\\\\n    nacs                    (time, statecomb, atom, direction) float32 412kB ...\\\\n    dip_perm                (time, state, direction) float32 14kB -0.06869 .....\\\\n    dip_trans               (time, statecomb, direction) float32 82kB 0.07728...\\\\n    socs                    (time, full_statecomb) complex128 220kB 0j ... -4...\\\\n    phases                  (time, state) float32 5kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\n    energy_filtranda        (energy_criterion, time) float32 704B 0.0 ... 0.0\\\\n    length_filtranda        (length_criterion, time) float32 704B 1.324 ... 1...\\\\nAttributes: (12/15)\\\\n    input_format:                 sharc\\\\n    _shnitsel_setup_for_cleanup:  True\\\\n    completed:                    False\\\\n    input_type:                   dynamic\\\\n    input_format_version:         3.0\\\\n    num_singlets:                 4\\\\n    ...                           ...\\\\n    theory_basis_set:             cc-pVDZ\\\\n    est_level:                    CASSCF\\\\n    misc_input_settings:          {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'...\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\n    trajectory_id:                1\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0823..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'2\\\\\\': ABCMeta(_name=2, _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 293kB\\\\nDimensions:                 (time: 31, state: 13, atom: 5, direction: 3,\\\\n                             statecomb: 78, full_statecomb: 156,\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\nCoordinates: (12/28)\\\\n  * time                    (time) float64 248B 0.0 0.5 1.0 ... 14.0 14.5 15.0\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * atom                    (atom) int64 40B 0 1 2 3 4\\\\n  * direction               (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\n    ...                      ...\\\\n    max_ts                  int64 8B 108\\\\n    t_max                   float64 8B 100.0\\\\n    charge                  int64 8B 1\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\n    __mol                   object 8B <rdkit.Chem.rdchem.Mol object at 0x744c...\\\\nData variables:\\\\n    energy                  (time, state) float32 2kB -94.39 -94.06 ... -93.92\\\\n    forces                  (time, state, atom, direction) float32 24kB 0.058...\\\\n    atXYZ                   (time, atom, direction) float32 2kB -0.003343 ......\\\\n    nacs                    (time, statecomb, atom, direction) float32 145kB ...\\\\n    dip_perm                (time, state, direction) float32 5kB 0.02128 ... ...\\\\n    dip_trans               (time, statecomb, direction) float32 29kB -0.0009...\\\\n    socs                    (time, full_statecomb) complex128 77kB 0j ... 0.7...\\\\n    phases                  (time, state) float32 2kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\n    energy_filtranda        (energy_criterion, time) float32 248B 0.0 ... 0.0\\\\n    length_filtranda        (length_criterion, time) float32 248B 1.339 ... 1...\\\\nAttributes: (12/15)\\\\n    input_format:                 sharc\\\\n    _shnitsel_setup_for_cleanup:  True\\\\n    completed:                    False\\\\n    input_type:                   dynamic\\\\n    input_format_version:         3.0\\\\n    num_singlets:                 4\\\\n    ...                           ...\\\\n    theory_basis_set:             cc-pVDZ\\\\n    est_level:                    CASSCF\\\\n    misc_input_settings:          {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'...\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\n    trajectory_id:                2\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0823..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'3\\\\\\': ABCMeta(_name=3, _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 737kB\\\\nDimensions:                 (time: 79, state: 13, atom: 5, direction: 3,\\\\n                             statecomb: 78, full_statecomb: 156,\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\nCoordinates: (12/28)\\\\n  * time                    (time) float64 632B 0.0 0.5 1.0 ... 38.0 38.5 39.0\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * atom                    (atom) int64 40B 0 1 2 3 4\\\\n  * direction               (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\n    ...                      ...\\\\n    max_ts                  int64 8B 179\\\\n    t_max                   float64 8B 100.0\\\\n    charge                  int64 8B 1\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\n    __mol                   object 8B <rdkit.Chem.rdchem.Mol object at 0x744c...\\\\nData variables:\\\\n    energy                  (time, state) float32 4kB -94.38 -94.07 ... -93.79\\\\n    forces                  (time, state, atom, direction) float32 62kB 0.054...\\\\n    atXYZ                   (time, atom, direction) float32 5kB 0.02998 ... -...\\\\n    nacs                    (time, statecomb, atom, direction) float32 370kB ...\\\\n    dip_perm                (time, state, direction) float32 12kB -0.1124 ......\\\\n    dip_trans               (time, statecomb, direction) float32 74kB -0.1073...\\\\n    socs                    (time, full_statecomb) complex128 197kB 0j ... 18...\\\\n    phases                  (time, state) float32 4kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\n    energy_filtranda        (energy_criterion, time) float32 632B 0.0 ... 0.0\\\\n    length_filtranda        (length_criterion, time) float32 632B 1.302 ... 1...\\\\nAttributes: (12/15)\\\\n    input_format:                 sharc\\\\n    _shnitsel_setup_for_cleanup:  True\\\\n    completed:                    False\\\\n    input_type:                   dynamic\\\\n    input_format_version:         3.0\\\\n    num_singlets:                 4\\\\n    ...                           ...\\\\n    theory_basis_set:             cc-pVDZ\\\\n    est_level:                    CASSCF\\\\n    misc_input_settings:          {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'...\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\n    trajectory_id:                3\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0825..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\')}, _attrs={}, _parent=<class \\\\\\'shnitsel.data.tree.tree.ShnitselDBRoot[Trajectory]\\\\\\'> [{\\\\\\'level\\\\\\': \\\\\\'ShnitselDBRoot\\\\\\', \\\\\\'children\\\\\\': \\\\\\'2: {\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\': \\\\\\\\\\\\\\'ABCMeta(_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=None, _children={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\\\\\\\\\\\\\n    ...                   ...\\\\\\\\\\\\\\\\n    max_ts               int64 8B 384\\\\\\\\\\\\\\\\n    t_max                float64 8B 1e+03\\\\\\\\\\\\\\\\n    charge               int64 8B 1\\\\\\\\\\\\\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\n    __mol                object 8B <rdkit.Chem.rdchem.Mol object at 0x744c081...\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy               (time, state) float32 5kB ...\\\\\\\\\\\\\\\\n    forces               (time, state, atom, direction) float32 69kB ...\\\\\\\\\\\\\\\\n    atXYZ                (time, atom, direction) float32 23kB 3.795 ... 1.982\\\\\\\\\\\\\\\\n    dip_perm 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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_scheme\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'edc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_param\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'hopping_procedure\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'grad_all\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'eselect\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.001000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'select_directly\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nospinorbit\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_grad\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_nacdr\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_overlap\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_format\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ascii\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_dat_steps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajectory_input_path\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'retinal_tutorial/I02/TRAJ_00008\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajid\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 8, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataTree_Level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'TrajectoryData\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'__shnitsel_setup_for_cleanup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'7\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'7\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 37kB\\\\\\\\\\\\\\\\nDimensions:              (time: 78, state: 3, atom: 5, direction: 3,\\\\\\\\\\\\\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\\\\\\\\\\\\\n                          length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/26)\\\\\\\\\\\\\\\\n  * time                 (time) float64 624B 0.0 0.5 1.0 1.5 ... 37.5 38.0 38.5\\\\\\\\\\\\\\\\n  * state                (state) int64 24B 1 2 3\\\\\\\\\\\\\\\\n  * atom   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__mol                object 8B <rdkit.Chem.rdchem.Mol object at 0x744c081...\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy               (time, state) float32 5kB ...\\\\\\\\\\\\\\\\n    forces               (time, state, atom, direction) float32 81kB ...\\\\\\\\\\\\\\\\n    atXYZ                (time, atom, direction) float32 27kB 3.78 ... 2.426\\\\\\\\\\\\\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\\\\\\\\\\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\\\\\\\\\\\\\n    socs                 (time, full_statecomb) complex128 43kB ...\\\\\\\\\\\\\\\\n    phases               (time, state) float32 5kB ...\\\\\\\\\\\\\\\\n    energy_filtranda     (energy_criterion, time) float32 4kB 0.0 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda     (length_criterion, time) float32 4kB 1.444 ... 1.17\\\\\\\\\\\\\\\\nAttributes: (12/17)\\\\\\\\\\\\\\\\n    input_format:           sharc\\\\\\\\\\\\\\\\n    t_max:                  1000.0\\\\\\\\\\\\\\\\n    delta_t:                0.5\\\\\\\\\\\\\\\\n    max_ts:                 451\\\\\\\\\\\\\\\\n    completed:              False\\\\\\\\\\\\\\\\n    input_type:             dynamic\\\\\\\\\\\\\\\\n    ...                     ...\\\\\\\\\\\\\\\\n    misc_input_settings:    {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'"geom"...\\\\\\\\\\\\\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00013\\\\\\\\\\\\\\\\n    trajid:                 13\\\\\\\\\\\\\\\\n    DataTree_Level:         TrajectoryData\\\\\\\\\\\\\\\\n    trajectory_id:          1300430345\\\\\\\\\\\\\\\\n    __mol:                  <rdkit.Chem.rdchem.Mol object at 0x744c081ee700>, _is_multi_trajectory=False), _children={}, 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__mol                object 8B <rdkit.Chem.rdchem.Mol object at 0x744c082...\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy               (time, state) float32 2kB ...\\\\\\\\\\\\\\\\n    forces               (time, state, atom, direction) float32 37kB ...\\\\\\\\\\\\\\\\n    atXYZ                (time, atom, direction) float32 12kB 3.814 ... 0.9335\\\\\\\\\\\\\\\\n    dip_perm             (time, state, direction) float32 7kB ...\\\\\\\\\\\\\\\\n    dip_trans            (time, statecomb, direction) float32 7kB ...\\\\\\\\\\\\\\\\n    socs                 (time, full_statecomb) complex128 20kB ...\\\\\\\\\\\\\\\\n    phases               (time, state) float32 2kB ...\\\\\\\\\\\\\\\\n    energy_filtranda     (energy_criterion, time) float32 2kB 0.0 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda     (length_criterion, time) float32 2kB 1.502 ... 1.13\\\\\\\\\\\\\\\\nAttributes: (12/17)\\\\\\\\\\\\\\\\n    input_format:           sharc\\\\\\\\\\\\\\\\n    t_max:                  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theory_basis_set=\\\\\\'cc-pVDZ\\\\\\', charge_in_e=1.0, num_states=13)\", group_attributes={\\\\\\'delta_t_in_fs\\\\\\': 0.5, \\\\\\'input_format_name\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'est_level\\\\\\': \\\\\\'CASSCF\\\\\\', \\\\\\'theory_basis_set\\\\\\': \\\\\\'cc-pVDZ\\\\\\', \\\\\\'charge_in_e\\\\\\': 1.0, \\\\\\'num_states\\\\\\': 13}, grouped_properties=None))\\'}'}]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from shnitsel.bridges import to_mol\n", "from shnitsel.geo.analogs import extract_analogs\n", "analogs_tree = extract_analogs(mc_tree)\n", "assert analogs_tree is not None\n", "\n", "analogs_tree" ] }, { "cell_type": "markdown", "id": "94a821bf", "metadata": {}, "source": [ "We can also get a more in-detail look at the substructures identified by the analogs function:" ] }, { "cell_type": "code", "execution_count": 5, "id": "ce067f14", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(analogs_tree['I02/0/data'].mol)\n", "display(analogs_tree['I01/2/data'].mol)" ] }, { "cell_type": "markdown", "id": "387d96a6", "metadata": {}, "source": [ "In fact, the matching has found the greatest possible overlap between the two molecules including the charge center around the nitrogen atom, its double bond to a neighboring C atom and the surrounding H atoms.\n", "\n", "In the following, we demonstrate how this selection would also have been possible by providing a SMARTS yourself:" ] }, { "cell_type": "code", "execution_count": 6, "id": "a748b80d-939b-45d9-89c5-aa96b4de744b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<class 'shnitsel.data.tree.tree.ShnitselDBRoot[Trajectory]'> [{'level': 'ShnitselDBRoot', 'children': '2: {\\'I02\\': \\'ABCMeta(_name=\\\\\\'I02\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=None, _children={\\\\\\'0\\\\\\': ABCMeta(_name=\\\\\\'0\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 178kB\\\\nDimensions:              (time: 382, state: 3, atom: 5, direction: 3,\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\n                          length_criterion: 2)\\\\nCoordinates: (12/26)\\\\n  * time                 (time) float64 3kB 0.0 0.5 1.0 ... 189.5 190.0 190.5\\\\n  * state                (state) int64 24B 1 2 3\\\\n  * atom                 (atom) int64 40B 0 1 2 3 4\\\\n  * direction            (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb        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\\\\\\'output_format\\\\\\': \\\\\\'ascii\\\\\\', \\\\\\'output_dat_steps\\\\\\': \\\\\\'1\\\\\\', \\\\\\'version\\\\\\': \\\\\\'3.0\\\\\\'}}, \\\\\\'trajectory_input_path\\\\\\': \\\\\\'retinal_tutorial/I02/TRAJ_00020\\\\\\', \\\\\\'trajid\\\\\\': 20, \\\\\\'DataTree_Level\\\\\\': \\\\\\'TrajectoryData\\\\\\', \\\\\\'__shnitsel_setup_for_cleanup\\\\\\': True}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\')}, _attrs={\\\\\\'DataTree_Level\\\\\\': \\\\\\'CompoundGroup\\\\\\', \\\\\\'compound_info\\\\\\': {\\\\\\'compound_name\\\\\\': \\\\\\'I02\\\\\\'}}, _parent=<class \\\\\\'shnitsel.data.tree.tree.ShnitselDBRoot[Trajectory]\\\\\\'> [{\\\\\\'level\\\\\\': \\\\\\'ShnitselDBRoot\\\\\\', \\\\\\'children\\\\\\': \\\\\\'2: {\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\': \\\\\\\\\\\\\\'...\\\\\\\\\\\\\\', \\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\': \\\\\\\\\\\\\\'ABCMeta(_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=None, _children={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=1, _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 820kB\\\\\\\\\\\\\\\\nDimensions:                 (time: 88, state: 13, atom: 5, direction: 3,\\\\\\\\\\\\\\\\n                             statecomb: 78, full_statecomb: 156,\\\\\\\\\\\\\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/28)\\\\\\\\\\\\\\\\n  * time                    (time) float64 704B 0.0 0.5 1.0 ... 42.5 43.0 43.5\\\\\\\\\\\\\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * atom                    (atom) int64 40B 0 1 2 3 4\\\\\\\\\\\\\\\\n  * direction               (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\\\\\\\\\\\\\n    ...                      ...\\\\\\\\\\\\\\\\n    max_ts                  int64 8B 90\\\\\\\\\\\\\\\\n    t_max                   float64 8B 100.0\\\\\\\\\\\\\\\\n    charge                  int64 8B 1\\\\\\\\\\\\\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\n    __mol                   object 8B <rdkit.Chem.rdchem.Mol object at 0x744c...\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy                  (time, state) float32 5kB -94.4 -94.06 ... -93.89\\\\\\\\\\\\\\\\n    forces                  (time, state, atom, direction) float32 69kB -0.06...\\\\\\\\\\\\\\\\n    atXYZ                   (time, atom, direction) float32 5kB -0.005705 ......\\\\\\\\\\\\\\\\n    nacs                    (time, statecomb, atom, direction) float32 412kB ...\\\\\\\\\\\\\\\\n    dip_perm                (time, state, direction) float32 14kB -0.06869 .....\\\\\\\\\\\\\\\\n    dip_trans               (time, statecomb, direction) float32 82kB 0.07728...\\\\\\\\\\\\\\\\n    socs                    (time, full_statecomb) complex128 220kB 0j ... -4...\\\\\\\\\\\\\\\\n    phases                  (time, state) float32 5kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\\\\\\\\\\\\\n    energy_filtranda        (energy_criterion, time) float32 704B 0.0 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda        (length_criterion, time) float32 704B 1.324 ... 1...\\\\\\\\\\\\\\\\nAttributes: (12/15)\\\\\\\\\\\\\\\\n    input_format:                 sharc\\\\\\\\\\\\\\\\n    _shnitsel_setup_for_cleanup:  True\\\\\\\\\\\\\\\\n    completed:                    False\\\\\\\\\\\\\\\\n    input_type:                   dynamic\\\\\\\\\\\\\\\\n    input_format_version:         3.0\\\\\\\\\\\\\\\\n    num_singlets:                 4\\\\\\\\\\\\\\\\n    ...                           ...\\\\\\\\\\\\\\\\n    theory_basis_set:             cc-pVDZ\\\\\\\\\\\\\\\\n    est_level:                    CASSCF\\\\\\\\\\\\\\\\n    misc_input_settings:          {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\\\\\\\\\\\\\n    trajectory_id:                1\\\\\\\\\\\\\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0823..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=2, _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 293kB\\\\\\\\\\\\\\\\nDimensions:                 (time: 31, state: 13, atom: 5, direction: 3,\\\\\\\\\\\\\\\\n                             statecomb: 78, full_statecomb: 156,\\\\\\\\\\\\\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/28)\\\\\\\\\\\\\\\\n  * time                    (time) float64 248B 0.0 0.5 1.0 ... 14.0 14.5 15.0\\\\\\\\\\\\\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * atom                    (atom) int64 40B 0 1 2 3 4\\\\\\\\\\\\\\\\n  * direction               (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\\\\\\\\\\\\\n    ...                      ...\\\\\\\\\\\\\\\\n    max_ts                  int64 8B 108\\\\\\\\\\\\\\\\n    t_max                   float64 8B 100.0\\\\\\\\\\\\\\\\n    charge                  int64 8B 1\\\\\\\\\\\\\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\n    __mol                   object 8B <rdkit.Chem.rdchem.Mol object at 0x744c...\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy                  (time, state) float32 2kB -94.39 -94.06 ... -93.92\\\\\\\\\\\\\\\\n    forces                  (time, state, atom, direction) float32 24kB 0.058...\\\\\\\\\\\\\\\\n    atXYZ                   (time, atom, direction) float32 2kB -0.003343 ......\\\\\\\\\\\\\\\\n    nacs                    (time, statecomb, atom, direction) float32 145kB ...\\\\\\\\\\\\\\\\n    dip_perm                (time, state, direction) float32 5kB 0.02128 ... ...\\\\\\\\\\\\\\\\n    dip_trans               (time, statecomb, direction) float32 29kB -0.0009...\\\\\\\\\\\\\\\\n    socs                    (time, full_statecomb) complex128 77kB 0j ... 0.7...\\\\\\\\\\\\\\\\n    phases                  (time, state) float32 2kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\\\\\\\\\\\\\n    energy_filtranda        (energy_criterion, time) float32 248B 0.0 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda        (length_criterion, time) float32 248B 1.339 ... 1...\\\\\\\\\\\\\\\\nAttributes: (12/15)\\\\\\\\\\\\\\\\n    input_format:                 sharc\\\\\\\\\\\\\\\\n    _shnitsel_setup_for_cleanup:  True\\\\\\\\\\\\\\\\n    completed:                    False\\\\\\\\\\\\\\\\n    input_type:                   dynamic\\\\\\\\\\\\\\\\n    input_format_version:         3.0\\\\\\\\\\\\\\\\n    num_singlets:                 4\\\\\\\\\\\\\\\\n    ...                           ...\\\\\\\\\\\\\\\\n    theory_basis_set:             cc-pVDZ\\\\\\\\\\\\\\\\n    est_level:                    CASSCF\\\\\\\\\\\\\\\\n    misc_input_settings:          {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\\\\\\\\\\\\\n    trajectory_id:                2\\\\\\\\\\\\\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0376..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=3, _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 737kB\\\\\\\\\\\\\\\\nDimensions:                 (time: 79, state: 13, atom: 5, direction: 3,\\\\\\\\\\\\\\\\n                             statecomb: 78, full_statecomb: 156,\\\\\\\\\\\\\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/28)\\\\\\\\\\\\\\\\n  * time                    (time) float64 632B 0.0 0.5 1.0 ... 38.0 38.5 39.0\\\\\\\\\\\\\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\\\\\\\\\\\\\n  * atom                    (atom) int64 40B 0 1 2 3 4\\\\\\\\\\\\\\\\n  * direction               (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\\\\\\\\\\\\\n    ...                      ...\\\\\\\\\\\\\\\\n    max_ts                  int64 8B 179\\\\\\\\\\\\\\\\n    t_max                   float64 8B 100.0\\\\\\\\\\\\\\\\n    charge                  int64 8B 1\\\\\\\\\\\\\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\n    __mol                   object 8B <rdkit.Chem.rdchem.Mol object at 0x744c...\\\\\\\\\\\\\\\\nData 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1...\\\\\\\\\\\\\\\\nAttributes: (12/15)\\\\\\\\\\\\\\\\n    input_format:                 sharc\\\\\\\\\\\\\\\\n    _shnitsel_setup_for_cleanup:  True\\\\\\\\\\\\\\\\n    completed:                    False\\\\\\\\\\\\\\\\n    input_type:                   dynamic\\\\\\\\\\\\\\\\n    input_format_version:         3.0\\\\\\\\\\\\\\\\n    num_singlets:                 4\\\\\\\\\\\\\\\\n    ...                           ...\\\\\\\\\\\\\\\\n    theory_basis_set:             cc-pVDZ\\\\\\\\\\\\\\\\n    est_level:                    CASSCF\\\\\\\\\\\\\\\\n    misc_input_settings:          {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\\\\\\\\\\\\\n    trajectory_id:                3\\\\\\\\\\\\\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0378..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\')}, _attrs={}, _parent=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.tree.tree.ShnitselDBRoot[Trajectory]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'> [{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ShnitselDBRoot\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'children\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': "2: {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}"}], _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'CompoundGroup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _group_info=GroupInfo(group_name="TrajectoryGroupingMetadata(delta_t_in_fs=0.5, input_format_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', input_format_version=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', est_level=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'CASSCF\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', theory_basis_set=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'cc-pVDZ\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', charge_in_e=1.0, num_states=13)", group_attributes={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'delta_t_in_fs\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 0.5, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_format_name\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_format_version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'est_level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'CASSCF\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'theory_basis_set\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'cc-pVDZ\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'charge_in_e\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 1.0, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'num_states\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 13}, grouped_properties=None))\\\\\\\\\\\\\\'}\\\\\\'}], _level_name=\\\\\\'CompoundGroup\\\\\\', _group_info=GroupInfo(group_name="TrajectoryGroupingMetadata(delta_t_in_fs=0.5, input_format_name=\\\\\\'sharc\\\\\\', input_format_version=\\\\\\'3.0\\\\\\', est_level=None, theory_basis_set=None, charge_in_e=1.0, num_states=3)", group_attributes={\\\\\\'delta_t_in_fs\\\\\\': 0.5, \\\\\\'input_format_name\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'est_level\\\\\\': None, \\\\\\'theory_basis_set\\\\\\': None, \\\\\\'charge_in_e\\\\\\': 1.0, \\\\\\'num_states\\\\\\': 3}, grouped_properties=None))\\', \\'I01\\': \\'ABCMeta(_name=\\\\\\'I01\\\\\\', _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=None, _children={\\\\\\'1\\\\\\': ABCMeta(_name=1, _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 820kB\\\\nDimensions:                 (time: 88, state: 13, atom: 5, direction: 3,\\\\n                             statecomb: 78, full_statecomb: 156,\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\nCoordinates: (12/28)\\\\n  * time                    (time) float64 704B 0.0 0.5 1.0 ... 42.5 43.0 43.5\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * atom                    (atom) int64 40B 0 1 2 3 4\\\\n  * direction               (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\n    ...                      ...\\\\n    max_ts                  int64 8B 90\\\\n    t_max                   float64 8B 100.0\\\\n    charge                  int64 8B 1\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\n    __mol                   object 8B <rdkit.Chem.rdchem.Mol object at 0x744c...\\\\nData variables:\\\\n    energy                  (time, state) float32 5kB -94.4 -94.06 ... -93.89\\\\n    forces                  (time, state, atom, direction) float32 69kB -0.06...\\\\n    atXYZ                   (time, atom, direction) float32 5kB -0.005705 ......\\\\n    nacs                    (time, statecomb, atom, direction) float32 412kB ...\\\\n    dip_perm                (time, state, direction) float32 14kB -0.06869 .....\\\\n    dip_trans               (time, statecomb, direction) float32 82kB 0.07728...\\\\n    socs                    (time, full_statecomb) complex128 220kB 0j ... -4...\\\\n    phases                  (time, state) float32 5kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\n    energy_filtranda        (energy_criterion, time) float32 704B 0.0 ... 0.0\\\\n    length_filtranda        (length_criterion, time) float32 704B 1.324 ... 1...\\\\nAttributes: (12/15)\\\\n    input_format:                 sharc\\\\n    _shnitsel_setup_for_cleanup:  True\\\\n    completed:                    False\\\\n    input_type:                   dynamic\\\\n    input_format_version:         3.0\\\\n    num_singlets:                 4\\\\n    ...                           ...\\\\n    theory_basis_set:             cc-pVDZ\\\\n    est_level:                    CASSCF\\\\n    misc_input_settings:          {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'...\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\n    trajectory_id:                1\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0823..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'2\\\\\\': ABCMeta(_name=2, _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 293kB\\\\nDimensions:                 (time: 31, state: 13, atom: 5, direction: 3,\\\\n                             statecomb: 78, full_statecomb: 156,\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\nCoordinates: (12/28)\\\\n  * time                    (time) float64 248B 0.0 0.5 1.0 ... 14.0 14.5 15.0\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * atom                    (atom) int64 40B 0 1 2 3 4\\\\n  * direction               (direction) <U1 12B \\\\\\'x\\\\\\' \\\\\\'y\\\\\\' \\\\\\'z\\\\\\'\\\\n  * statecomb               (statecomb) object 624B MultiIndex\\\\n  * full_statecomb          (full_statecomb) object 1kB MultiIndex\\\\n    ...                      ...\\\\n    max_ts                  int64 8B 108\\\\n    t_max                   float64 8B 100.0\\\\n    charge                  int64 8B 1\\\\n    energy_thresholds       (energy_criterion) float64 16B 0.7 1.0\\\\n    length_thresholds       (length_criterion) float64 16B 3.0 2.0\\\\n    __mol                   object 8B <rdkit.Chem.rdchem.Mol object at 0x744c...\\\\nData variables:\\\\n    energy                  (time, state) float32 2kB -94.39 -94.06 ... -93.92\\\\n    forces                  (time, state, atom, direction) float32 24kB 0.058...\\\\n    atXYZ                   (time, atom, direction) float32 2kB -0.003343 ......\\\\n    nacs                    (time, statecomb, atom, direction) float32 145kB ...\\\\n    dip_perm                (time, state, direction) float32 5kB 0.02128 ... ...\\\\n    dip_trans               (time, statecomb, direction) float32 29kB -0.0009...\\\\n    socs                    (time, full_statecomb) complex128 77kB 0j ... 0.7...\\\\n    phases                  (time, state) float32 2kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\n    energy_filtranda        (energy_criterion, time) float32 248B 0.0 ... 0.0\\\\n    length_filtranda        (length_criterion, time) float32 248B 1.339 ... 1...\\\\nAttributes: (12/15)\\\\n    input_format:                 sharc\\\\n    _shnitsel_setup_for_cleanup:  True\\\\n    completed:                    False\\\\n    input_type:                   dynamic\\\\n    input_format_version:         3.0\\\\n    num_singlets:                 4\\\\n    ...                           ...\\\\n    theory_basis_set:             cc-pVDZ\\\\n    est_level:                    CASSCF\\\\n    misc_input_settings:          {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'...\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\n    trajectory_id:                2\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0376..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\'), \\\\\\'3\\\\\\': ABCMeta(_name=3, _dtype=<class \\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 737kB\\\\nDimensions:                 (time: 79, state: 13, atom: 5, direction: 3,\\\\n                             statecomb: 78, full_statecomb: 156,\\\\n                             energy_criterion: 2, length_criterion: 2)\\\\nCoordinates: (12/28)\\\\n  * time                    (time) float64 632B 0.0 0.5 1.0 ... 38.0 38.5 39.0\\\\n  * state                   (state) int64 104B 1 2 3 4 5 6 7 8 9 10 11 12 13\\\\n  * atom                    (atom) int64 40B 0 1 2 3 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...\\\\n    dip_perm                (time, state, direction) float32 12kB -0.1124 ......\\\\n    dip_trans               (time, statecomb, direction) float32 74kB -0.1073...\\\\n    socs                    (time, full_statecomb) complex128 197kB 0j ... 18...\\\\n    phases                  (time, state) float32 4kB -1.0 -1.0 -1.0 ... 1.0 1.0\\\\n    energy_filtranda        (energy_criterion, time) float32 632B 0.0 ... 0.0\\\\n    length_filtranda        (length_criterion, time) float32 632B 1.302 ... 1...\\\\nAttributes: (12/15)\\\\n    input_format:                 sharc\\\\n    _shnitsel_setup_for_cleanup:  True\\\\n    completed:                    False\\\\n    input_type:                   dynamic\\\\n    input_format_version:         3.0\\\\n    num_singlets:                 4\\\\n    ...                           ...\\\\n    theory_basis_set:             cc-pVDZ\\\\n    est_level:                    CASSCF\\\\n    misc_input_settings:          {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'...\\\\n    trajectory_input_path:        test_data/sharc/traj_I01_v3.0_triplets_nacs...\\\\n    trajectory_id:                3\\\\n    __mol:                        <rdkit.Chem.rdchem.Mol object at 0x744c0378..., _is_multi_trajectory=False), _children={}, _attrs={}, _parent=..., _level_name=\\\\\\'DataLeaf\\\\\\')}, _attrs={}, _parent=<class \\\\\\'shnitsel.data.tree.tree.ShnitselDBRoot[Trajectory]\\\\\\'> [{\\\\\\'level\\\\\\': \\\\\\'ShnitselDBRoot\\\\\\', \\\\\\'children\\\\\\': \\\\\\'2: {\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\': \\\\\\\\\\\\\\'ABCMeta(_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=None, _children={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 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           (time) float64 3kB 0.0 0.5 1.0 ... 181.0 181.5 182.0\\\\\\\\\\\\\\\\n  * state                (state) int64 24B 1 2 3\\\\\\\\\\\\\\\\n  * atom                 (atom) int64 40B 0 1 2 3 4\\\\\\\\\\\\\\\\n  * direction            (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\\\\\\\\\\\\\n    ...                   ...\\\\\\\\\\\\\\\\n    max_ts               int64 8B 367\\\\\\\\\\\\\\\\n    t_max                float64 8B 1e+03\\\\\\\\\\\\\\\\n    charge               int64 8B 1\\\\\\\\\\\\\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\n    __mol 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length_filtranda     (length_criterion, time) float32 4kB 1.444 ... 1.257\\\\\\\\\\\\\\\\nAttributes: (12/17)\\\\\\\\\\\\\\\\n    input_format:           sharc\\\\\\\\\\\\\\\\n    t_max:                  1000.0\\\\\\\\\\\\\\\\n    delta_t:                0.5\\\\\\\\\\\\\\\\n    max_ts:                 444\\\\\\\\\\\\\\\\n    completed:              False\\\\\\\\\\\\\\\\n    input_type:             dynamic\\\\\\\\\\\\\\\\n    ...                     ...\\\\\\\\\\\\\\\\n    misc_input_settings:    {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'"geom"...\\\\\\\\\\\\\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00003\\\\\\\\\\\\\\\\n    trajid:                 3\\\\\\\\\\\\\\\\n    DataTree_Level:         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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_nacdr\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_property1d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_property2d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'n_property1d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'n_property2d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'laser\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output.lis\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nsteps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 106, 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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'reflect_frustrated\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'none\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_scheme\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'edc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_param\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'hopping_procedure\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'grad_all\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'eselect\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.001000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'select_directly\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nospinorbit\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_grad\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_nacdr\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_overlap\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_format\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ascii\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_dat_steps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajectory_input_path\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'retinal_tutorial/I02/TRAJ_00004\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajid\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 4, 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         (time) float64 752B 0.0 0.5 1.0 1.5 ... 45.5 46.0 46.5\\\\\\\\\\\\\\\\n  * state                (state) int64 24B 1 2 3\\\\\\\\\\\\\\\\n  * atom                 (atom) int64 40B 0 1 2 3 4\\\\\\\\\\\\\\\\n  * direction            (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\\\\\\\\\\\\\n    ...                   ...\\\\\\\\\\\\\\\\n    max_ts               int64 8B 443\\\\\\\\\\\\\\\\n    t_max                float64 8B 1e+03\\\\\\\\\\\\\\\\n    charge               int64 8B 1\\\\\\\\\\\\\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\n    __mol 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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_property1d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_property2d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'n_property1d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'n_property2d\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'laser\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output.lis\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nsteps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 444, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'delta_t\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 0.5, 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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_scheme\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'edc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_param\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'hopping_procedure\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'grad_all\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'eselect\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.001000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'select_directly\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nospinorbit\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_grad\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_nacdr\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_overlap\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_format\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ascii\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_dat_steps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajectory_input_path\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'retinal_tutorial/I02/TRAJ_00008\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajid\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 8, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataTree_Level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'TrajectoryData\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'__shnitsel_setup_for_cleanup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'7\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'7\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 37kB\\\\\\\\\\\\\\\\nDimensions:              (time: 78, state: 3, atom: 5, direction: 3,\\\\\\\\\\\\\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\\\\\\\\\\\\\n                          length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/26)\\\\\\\\\\\\\\\\n  * time                 (time) float64 624B 0.0 0.5 1.0 1.5 ... 37.5 38.0 38.5\\\\\\\\\\\\\\\\n  * state                (state) int64 24B 1 2 3\\\\\\\\\\\\\\\\n  * atom                 (atom) int64 40B 0 1 2 3 4\\\\\\\\\\\\\\\\n  * direction            (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\\\\\\\\\\\\\n    ...                   ...\\\\\\\\\\\\\\\\n    max_ts               int64 8B 356\\\\\\\\\\\\\\\\n    t_max                float64 8B 1e+03\\\\\\\\\\\\\\\\n    charge               int64 8B 1\\\\\\\\\\\\\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\n    __mol                object 8B <rdkit.Chem.rdchem.Mol object at 0x744c081...\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy               (time, 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   (length_criterion, time) float32 1kB 1.46 ... 1.224\\\\\\\\\\\\\\\\nAttributes: (12/17)\\\\\\\\\\\\\\\\n    input_format:           sharc\\\\\\\\\\\\\\\\n    t_max:                  1000.0\\\\\\\\\\\\\\\\n    delta_t:                0.5\\\\\\\\\\\\\\\\n    max_ts:                 447\\\\\\\\\\\\\\\\n    completed:              False\\\\\\\\\\\\\\\\n    input_type:             dynamic\\\\\\\\\\\\\\\\n    ...                     ...\\\\\\\\\\\\\\\\n    misc_input_settings:    {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'"geom"...\\\\\\\\\\\\\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00010\\\\\\\\\\\\\\\\n    trajid:                 10\\\\\\\\\\\\\\\\n    DataTree_Level:         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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'delta_t\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 0.5, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'t_max\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 220.5}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output.log\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'"geom"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'veloc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'external\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'velocfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'"veloc"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nstates\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'actstates\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'state\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2 mch\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'coeff\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'auto\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'rngseed\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'-2750\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ezero\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'-248.2781283000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'tmax\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1000.000000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'stepsize\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.500000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', 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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'reflect_frustrated\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'none\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_scheme\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'edc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_param\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'hopping_procedure\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'grad_all\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'eselect\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.001000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'select_directly\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nospinorbit\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_grad\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_nacdr\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_overlap\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_format\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ascii\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_dat_steps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajectory_input_path\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'retinal_tutorial/I02/TRAJ_00012\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajid\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 12, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataTree_Level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'TrajectoryData\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'__shnitsel_setup_for_cleanup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'10\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'10\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 209kB\\\\\\\\\\\\\\\\nDimensions:              (time: 449, state: 3, atom: 5, direction: 3,\\\\\\\\\\\\\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\\\\\\\\\\\\\n                          length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/26)\\\\\\\\\\\\\\\\n  * time                 (time) float64 4kB 0.0 0.5 1.0 ... 223.0 223.5 224.0\\\\\\\\\\\\\\\\n  * state                (state) int64 24B 1 2 3\\\\\\\\\\\\\\\\n  * atom                 (atom) int64 40B 0 1 2 3 4\\\\\\\\\\\\\\\\n  * direction            (direction) <U1 12B \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'x\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'y\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'z\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'\\\\\\\\\\\\\\\\n  * statecomb            (statecomb) object 24B MultiIndex\\\\\\\\\\\\\\\\n  * full_statecomb       (full_statecomb) object 48B MultiIndex\\\\\\\\\\\\\\\\n    ...                   ...\\\\\\\\\\\\\\\\n    max_ts               int64 8B 451\\\\\\\\\\\\\\\\n    t_max                float64 8B 1e+03\\\\\\\\\\\\\\\\n    charge               int64 8B 1\\\\\\\\\\\\\\\\n    energy_thresholds    (energy_criterion) float64 16B 0.7 1.0\\\\\\\\\\\\\\\\n    length_thresholds    (length_criterion) float64 16B 3.0 2.0\\\\\\\\\\\\\\\\n    __mol                object 8B <rdkit.Chem.rdchem.Mol object at 0x744c081...\\\\\\\\\\\\\\\\nData variables:\\\\\\\\\\\\\\\\n    energy               (time, state) float32 5kB ...\\\\\\\\\\\\\\\\n    forces               (time, state, atom, direction) float32 81kB ...\\\\\\\\\\\\\\\\n    atXYZ                (time, atom, direction) float32 27kB 3.78 ... 2.426\\\\\\\\\\\\\\\\n    dip_perm             (time, state, direction) float32 16kB ...\\\\\\\\\\\\\\\\n    dip_trans            (time, statecomb, direction) float32 16kB ...\\\\\\\\\\\\\\\\n    socs                 (time, full_statecomb) complex128 43kB ...\\\\\\\\\\\\\\\\n    phases               (time, state) float32 5kB ...\\\\\\\\\\\\\\\\n    energy_filtranda     (energy_criterion, time) float32 4kB 0.0 ... 0.0\\\\\\\\\\\\\\\\n    length_filtranda     (length_criterion, time) float32 4kB 1.444 ... 1.17\\\\\\\\\\\\\\\\nAttributes: (12/17)\\\\\\\\\\\\\\\\n    input_format:           sharc\\\\\\\\\\\\\\\\n    t_max:                  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length_filtranda     (length_criterion, time) float32 4kB 1.436 ... 1.133\\\\\\\\\\\\\\\\nAttributes: (12/17)\\\\\\\\\\\\\\\\n    input_format:           sharc\\\\\\\\\\\\\\\\n    t_max:                  1000.0\\\\\\\\\\\\\\\\n    delta_t:                0.5\\\\\\\\\\\\\\\\n    max_ts:                 445\\\\\\\\\\\\\\\\n    completed:              False\\\\\\\\\\\\\\\\n    input_type:             dynamic\\\\\\\\\\\\\\\\n    ...                     ...\\\\\\\\\\\\\\\\n    misc_input_settings:    {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'"geom"...\\\\\\\\\\\\\\\\n    trajectory_input_path:  retinal_tutorial/I02/TRAJ_00014\\\\\\\\\\\\\\\\n    trajid:                 14\\\\\\\\\\\\\\\\n    DataTree_Level:         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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'delta_t\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 0.5, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'t_max\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 224.5}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output.log\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'printlevel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'geomfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'"geom"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'veloc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'external\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'velocfile\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'"veloc"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nstates\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', 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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'reflect_frustrated\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'none\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_scheme\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'edc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'decoherence_param\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'hopping_procedure\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'grad_all\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'eselect\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'0.001000\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'select_directly\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nospinorbit\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_grad\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_nacdr\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_overlap\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_format\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ascii\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_dat_steps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajectory_input_path\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'retinal_tutorial/I02/TRAJ_00017\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajid\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 17, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataTree_Level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'TrajectoryData\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'__shnitsel_setup_for_cleanup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'), \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'13\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': ABCMeta(_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'13\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _dtype=<class \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'shnitsel.data.dataset_containers.trajectory.Trajectory\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'>, _data=Trajectory(_raw_dataset=<xarray.Dataset> Size: 95kB\\\\\\\\\\\\\\\\nDimensions:              (time: 204, state: 3, atom: 5, direction: 3,\\\\\\\\\\\\\\\\n                          statecomb: 3, full_statecomb: 6, energy_criterion: 2,\\\\\\\\\\\\\\\\n                          length_criterion: 2)\\\\\\\\\\\\\\\\nCoordinates: (12/26)\\\\\\\\\\\\\\\\n  * time     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int64 40B 0 1 2 3 4\\\\n * direction (direction) , _is_multi_trajectory=False), _children={}, _attrs={\\\\\\'input_format\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'t_max\\\\\\': 1000.0, \\\\\\'delta_t\\\\\\': 0.5, \\\\\\'max_ts\\\\\\': 450, \\\\\\'completed\\\\\\': False, \\\\\\'input_type\\\\\\': \\\\\\'dynamic\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'num_singlets\\\\\\': 3, \\\\\\'num_doublets\\\\\\': 0, \\\\\\'num_triplets\\\\\\': 0, \\\\\\'has_forces\\\\\\': True, \\\\\\'misc_input_settings\\\\\\': {\\\\\\'input\\\\\\': {\\\\\\'printlevel\\\\\\': \\\\\\'2\\\\\\', \\\\\\'geomfile\\\\\\': \\\\\\'\"geom\"\\\\\\', \\\\\\'veloc\\\\\\': \\\\\\'external\\\\\\', \\\\\\'velocfile\\\\\\': \\\\\\'\"veloc\"\\\\\\', \\\\\\'nstates\\\\\\': [3], \\\\\\'actstates\\\\\\': \\\\\\'3\\\\\\', \\\\\\'state\\\\\\': \\\\\\'2 mch\\\\\\', \\\\\\'coeff\\\\\\': \\\\\\'auto\\\\\\', \\\\\\'rngseed\\\\\\': \\\\\\'-26082\\\\\\', \\\\\\'ezero\\\\\\': \\\\\\'-248.2781283000\\\\\\', 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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'select_directly\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'nospinorbit\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_grad\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_nacdr\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'write_overlap\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_format\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ascii\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'output_dat_steps\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}}, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajectory_input_path\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'retinal_tutorial/I02/TRAJ_00020\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'trajid\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 20, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataTree_Level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'TrajectoryData\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'__shnitsel_setup_for_cleanup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': True}, _parent=..., _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataLeaf\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\')}, _attrs={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'DataTree_Level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'CompoundGroup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'compound_info\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'compound_name\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}}, _parent= [{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'ShnitselDBRoot\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'children\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \"2: {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I02\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'...\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'}\"}], _level_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'CompoundGroup\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', _group_info=GroupInfo(group_name=\"TrajectoryGroupingMetadata(delta_t_in_fs=0.5, input_format_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', input_format_version=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', est_level=None, theory_basis_set=None, charge_in_e=1.0, num_states=3)\", group_attributes={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'delta_t_in_fs\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 0.5, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_format_name\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_format_version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'est_level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': None, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'theory_basis_set\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': None, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'charge_in_e\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 1.0, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'num_states\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 3}, grouped_properties=None))\\\\\\\\\\\\\\', \\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\': \\\\\\\\\\\\\\'...\\\\\\\\\\\\\\'}\\\\\\'}], _level_name=\\\\\\'CompoundGroup\\\\\\', _group_info=GroupInfo(group_name=\"TrajectoryGroupingMetadata(delta_t_in_fs=0.5, input_format_name=\\\\\\'sharc\\\\\\', input_format_version=\\\\\\'3.0\\\\\\', est_level=\\\\\\'CASSCF\\\\\\', theory_basis_set=\\\\\\'cc-pVDZ\\\\\\', charge_in_e=1.0, num_states=13)\", group_attributes={\\\\\\'delta_t_in_fs\\\\\\': 0.5, \\\\\\'input_format_name\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'est_level\\\\\\': \\\\\\'CASSCF\\\\\\', \\\\\\'theory_basis_set\\\\\\': \\\\\\'cc-pVDZ\\\\\\', \\\\\\'charge_in_e\\\\\\': 1.0, \\\\\\'num_states\\\\\\': 13}, grouped_properties=None))\\'}'}]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from shnitsel.geo.analogs import extract_analogs\n", "smarts_share = '[#6](=[#7](-[#1])-[#1])-[#1]'\n", "tree_smarts_substruct = extract_analogs(mc_tree, smarts=smarts_share)\n", "tree_smarts_substruct" ] }, { "cell_type": "markdown", "id": "49b46133-2204-4777-a4e9-814a97e91fea", "metadata": {}, "source": [ "### 2.2) Calculate descriptors\n", "\n", "Upon creating the dataset from the matching substructures of all compounds, we can now compute descriptors. Note, the atom numbering was adjusted, so that we can use descriptors that are sensitive to permutation. Since we only searched for 4 atoms in common, the bats, i.e. bonds, angles torsion would only yield 6 descriptors ($3\\cdot 4 - 6 = 6$). Similarly, there are 6 pairwise distances in the molecule ($\\frac{1}{2}\\cdot 4\\cdot(4-1) = 6$).\n", "\n", "One could apply the seperate functions `subtract_combinations`, `center`, and `norm` to obtain the pairwise distances, subtract the mean and normalize the features. This is also wrapped in a pipeline within the `get_standardized_pairwise_dists` function provided in `shnitsel.analyze.generic`, which we will use in the following. Note, we have to transpose the resulting array, as the PCA requires the data in shape (frames, descriptors).\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "9b6d1bc6-78ee-4bd2-9115-08c8aff1be63", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/tpadmin/git/shnitsel-tools-official/shnitsel/data/traj_combiner_methods.py:491: FutureWarning: In a future version of xarray the default value for compat will change from compat='equals' to compat='override'. This change will result in the following ValueError: Cannot specify both coords='different' and compat='override'. The recommendation is to set compat explicitly for this case.\n", " frames = xr.concat(\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# first we perform PCA over pairwise distances for the whole A03 molecule\n", "from shnitsel.vis.plot import biplot_kde\n", "from shnitsel.analyze.pca import PCA\n", "\n", "I02_analog_stacked = analogs_tree[\"I02\"].as_stacked\n", "I02_analog_stacked.mol" ] }, { "cell_type": "code", "execution_count": 8, "id": "fb950770", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCMeta(_name='I02', _dtype=<class 'shnitsel.analyze.pca.PCAResult'>, _data=None, _children={'pca': ABCMeta(_name='pca', _dtype=<class 'shnitsel.analyze.pca.PCAResult'>, _data=<shnitsel.analyze.pca.PCAResult object at 0x744c082ff3e0>, _children={}, _attrs={}, _parent=..., _level_name='DataLeaf')}, _attrs={'DataTree_Level': 'CompoundGroup', 'compound_info': {'compound_name': 'I02'}}, _parent=None, _level_name='CompoundGroup', _group_info=GroupInfo(group_name="TrajectoryGroupingMetadata(delta_t_in_fs=0.5, input_format_name='sharc', input_format_version='3.0', est_level=None, theory_basis_set=None, charge_in_e=1.0, num_states=3)", group_attributes={'delta_t_in_fs': 0.5, 'input_format_name': 'sharc', 'input_format_version': '3.0', 'est_level': None, 'theory_basis_set': None, 'charge_in_e': 1.0, 'num_states': 3}, grouped_properties=None))
" ], "text/plain": [ "ABCMeta(_name='I02', _dtype=, _data=None, _children={'pca': ABCMeta(_name='pca', _dtype=, _data=, _children={}, _attrs={}, _parent=..., _level_name='DataLeaf')}, _attrs={'DataTree_Level': 'CompoundGroup', 'compound_info': {'compound_name': 'I02'}}, _parent=None, _level_name='CompoundGroup', _group_info=GroupInfo(group_name=\"TrajectoryGroupingMetadata(delta_t_in_fs=0.5, input_format_name='sharc', input_format_version='3.0', est_level=None, theory_basis_set=None, charge_in_e=1.0, num_states=3)\", group_attributes={'delta_t_in_fs': 0.5, 'input_format_name': 'sharc', 'input_format_version': '3.0', 'est_level': None, 'theory_basis_set': None, 'charge_in_e': 1.0, 'num_states': 3}, grouped_properties=None))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from shnitsel.geo.geocalc import get_bats\n", "\n", "selection_keys = ['bonds', 'angles', 'dihedrals', 'pyramids']\n", "# Calculate all BATs for all analogs:\n", "dt_analog_bats_all = get_bats(analogs_tree, selection_keys)\n", "\n", "# Get the data for just I02 as one dataset\n", "# I02_analog_bats = dt_analog_bats_all[\"I02\"]#.as_stacked\n", "# print(I02_analog_bats)\n", "\n", "# Calculate the PCA for I02\n", "pca_res_I02 = PCA(analogs_tree[\"I02\"], structure_selection=selection_keys)\n", "\n", "#Since we provided the inputs as a tree, the result is also a tree: \n", "pca_res_I02\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "2aa62dac", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum contributing features overall:\n", " dih(2,1,0,4) (weight: 0.3931517004966736) (Idxs: (2, 1, 0, 4)) \n", " dih(3,1,0,4) (weight: 0.532825767993927) (Idxs: (3, 1, 0, 4)) \n", " dist(0,1) (weight: 0.5414503812789917) (Idxs: (np.int64(0), np.int64(1))) \n", " angle(1,0,4) (weight: 0.5417289733886719) (Idxs: (1, 0, 4)) \n", " pyr(1,(0,2,3)) (weight: 0.9353857636451721) (Idxs: (1, (0, 2, 3))) \n", "\n", "\n", "Maximum contributing features to component 0 :\n", " angle(2,1,3) (weight: -0.12271258980035782) (Idxs: (2, 1, 3)) \n", " dist(0,1) (weight: 0.1403753012418747) (Idxs: (np.int64(0), np.int64(1))) \n", " dih(2,1,0,4) (weight: -0.2322123497724533) (Idxs: (2, 1, 0, 4)) \n", " dih(3,1,0,4) (weight: 0.347022145986557) (Idxs: (3, 1, 0, 4)) \n", " pyr(1,(0,2,3)) (weight: 0.8822901844978333) (Idxs: (1, (0, 2, 3))) \n", "\n", "Maximum contributing features to component 1 :\n", " pyr(1,(0,2,3)) (weight: 0.3106614649295807) (Idxs: (1, (0, 2, 3))) \n", " dih(2,1,0,4) (weight: 0.31724703311920166) (Idxs: (2, 1, 0, 4)) \n", " dih(3,1,0,4) (weight: -0.4043252766132355) (Idxs: (3, 1, 0, 4)) \n", " dist(0,1) (weight: -0.5229371786117554) (Idxs: (np.int64(0), np.int64(1))) \n", " angle(1,0,4) (weight: 0.541694700717926) (Idxs: (1, 0, 4)) \n", "\n", "\n" ] } ], "source": [ "# We can obtain the PCA result: \n", "pca_res_I02_unwrapped = pca_res_I02[\"/pca\"].data\n", "# And take a look at the most impactful features identified by PCA:\n", "display(I02_analog_stacked.mol)\n", "print(pca_res_I02_unwrapped.explain_loadings())\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "92d92c18", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We can also take a look at the visualized results:\n", "kde_data = biplot_kde(\n", " analogs_tree[\"I02\"], 1, (0, 2, 3), pca_data=pca_res_I02, num_bins=4, scatter_color_property=\"geo\"\n", ")" ] }, { "cell_type": "markdown", "id": "f0806555-685d-4593-ac63-cb065009d196", "metadata": {}, "source": [ "## 3) Compare Compounds\n", "\n", "We have started with different compounds, so it just seems reasonable to try and see, how the PCA of these different compounds compare. \n", "For this, we can simply apply the PCA to the combined tree and plot the analysis result, which will be performed on each component separately\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "42bba5c0", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "pca_res = PCA(analogs_tree, structure_selection=selection_keys)\n", "kde_plot = biplot_kde(\n", " analogs_tree, 1, (0, 2, 3), pca_data=pca_res, num_bins=4, scatter_color_property=\"geo\"\n", ")" ] }, { "cell_type": "markdown", "id": "0e1ae17d", "metadata": {}, "source": [ "Let us take a look at the loadings of the different PCA results:" ] }, { "cell_type": "code", "execution_count": 12, "id": "1d9ce203", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Results for entry /I02/pca/\n", "Maximum contributing features overall:\n", " dih(2,1,0,4) (weight: 0.3931517004966736) (Idxs: (2, 1, 0, 4)) \n", " dih(3,1,0,4) (weight: 0.532825767993927) (Idxs: (3, 1, 0, 4)) \n", " dist(0,1) (weight: 0.5414503812789917) (Idxs: (np.int64(0), np.int64(1))) \n", " angle(1,0,4) (weight: 0.5417289733886719) (Idxs: (1, 0, 4)) \n", " pyr(1,(0,2,3)) (weight: 0.9353857636451721) (Idxs: (1, (0, 2, 3))) \n", "\n", "\n", "Maximum contributing features to component 0 :\n", " angle(2,1,3) (weight: -0.12271258980035782) (Idxs: (2, 1, 3)) \n", " dist(0,1) (weight: 0.1403753012418747) (Idxs: (np.int64(0), np.int64(1))) \n", " dih(2,1,0,4) (weight: -0.2322123497724533) (Idxs: (2, 1, 0, 4)) \n", " dih(3,1,0,4) (weight: 0.347022145986557) (Idxs: (3, 1, 0, 4)) \n", " pyr(1,(0,2,3)) (weight: 0.8822901844978333) (Idxs: (1, (0, 2, 3))) \n", "\n", "Maximum contributing features to component 1 :\n", " pyr(1,(0,2,3)) (weight: 0.3106614649295807) (Idxs: (1, (0, 2, 3))) \n", " dih(2,1,0,4) (weight: 0.31724703311920166) (Idxs: (2, 1, 0, 4)) \n", " dih(3,1,0,4) (weight: -0.4043252766132355) (Idxs: (3, 1, 0, 4)) \n", " dist(0,1) (weight: -0.5229371786117554) (Idxs: (np.int64(0), np.int64(1))) \n", " angle(1,0,4) (weight: 0.541694700717926) (Idxs: (1, 0, 4)) \n", "\n", "\n", "Results for entry /I01/pca/\n", "Maximum contributing features overall:\n", " dist(1,3) (weight: 0.4587743580341339) (Idxs: (np.int64(1), np.int64(3))) \n", " angle(0,1,2) (weight: 0.4637525677680969) (Idxs: (0, 1, 2)) \n", " dih(3,1,0,4) (weight: 0.585440993309021) (Idxs: (3, 1, 0, 4)) \n", " dist(0,1) (weight: 0.5890254378318787) (Idxs: (np.int64(0), np.int64(1))) \n", " pyr(1,(0,2,3)) (weight: 0.6744986176490784) (Idxs: (1, (0, 2, 3))) \n", "\n", "\n", "Maximum contributing features to component 0 :\n", " dist(1,2) (weight: -0.1441391408443451) (Idxs: (np.int64(1), np.int64(2))) \n", " dih(3,1,0,4) (weight: 0.38052231073379517) (Idxs: (3, 1, 0, 4)) \n", " angle(0,1,2) (weight: -0.4624028205871582) (Idxs: (0, 1, 2)) \n", " pyr(1,(0,2,3)) (weight: 0.49887949228286743) (Idxs: (1, (0, 2, 3))) \n", " dist(0,1) (weight: 0.5835738182067871) (Idxs: (np.int64(0), np.int64(1))) \n", "\n", "Maximum contributing features to component 1 :\n", " dist(1,2) (weight: 0.270281583070755) (Idxs: (np.int64(1), np.int64(2))) \n", " dih(2,1,0,4) (weight: 0.34814438223838806) (Idxs: (2, 1, 0, 4)) \n", " dih(3,1,0,4) (weight: -0.4449089467525482) (Idxs: (3, 1, 0, 4)) \n", " dist(1,3) (weight: 0.4497644007205963) (Idxs: (np.int64(1), np.int64(3))) \n", " pyr(1,(0,2,3)) (weight: 0.45394670963287354) (Idxs: (1, (0, 2, 3))) \n", "\n", "\n" ] } ], "source": [ "for path, pca_data in pca_res.collect_data(with_path = True):\n", " print(f\"Results for entry {path}\")\n", " print(pca_data.explain_loadings())" ] }, { "cell_type": "markdown", "id": "9f35d9cc", "metadata": {}, "source": [ "We see that the pyramidalization around the center N-atom with index `1` is shared between both compounds. \n", "But the remaining parameters may look different. \n", "\n", "To visually compare the geometrical space exploration of the two compounds, we can now project the features for both components onto the same principal components. \n", "Let us use `I01` as the basis and then project the results for `I02` onto the same subspace.\n", "\n", "For this purpose, we can use the tree of BATs for all compounds and use the helper method `.project_array()` of the PCAResult class:" ] }, { "cell_type": "code", "execution_count": 13, "id": "be4aa83f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR:root:\"The 'units' attribute of the DataArray must be set and of type str.\"\n", "ERROR:root:\"The 'units' attribute of the DataArray must be set and of type str.\"\n" ] }, { "data": { "text/html": [ "
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input_format_name=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', input_format_version=\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', est_level=None, theory_basis_set=None, charge_in_e=1.0, num_states=3)\", group_attributes={\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'delta_t_in_fs\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 0.5, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_format_name\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_format_version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'est_level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': None, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'theory_basis_set\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': None, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'charge_in_e\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 1.0, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'num_states\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 3}, grouped_properties=None))\\\\\\\\\\\\\\', \\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\': \\\\\\\\\\\\\\'...\\\\\\\\\\\\\\'}\\\\\\'}], _level_name=\\\\\\'CompoundGroup\\\\\\', _group_info=GroupInfo(group_name=\"TrajectoryGroupingMetadata(delta_t_in_fs=0.5, input_format_name=\\\\\\'sharc\\\\\\', input_format_version=\\\\\\'3.0\\\\\\', est_level=\\\\\\'CASSCF\\\\\\', theory_basis_set=\\\\\\'cc-pVDZ\\\\\\', charge_in_e=1.0, num_states=13)\", group_attributes={\\\\\\'delta_t_in_fs\\\\\\': 0.5, \\\\\\'input_format_name\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'est_level\\\\\\': \\\\\\'CASSCF\\\\\\', \\\\\\'theory_basis_set\\\\\\': \\\\\\'cc-pVDZ\\\\\\', \\\\\\'charge_in_e\\\\\\': 1.0, \\\\\\'num_states\\\\\\': 13}, grouped_properties=None))\\'}'}]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Retrieve the PCA for I01\n", "pca_res_I01 = pca_res[\"I01/pca\"].data\n", "\n", "# Project all features onto the principal components for I01\n", "# The pca result offers the function `.project_array` to project an arbitrary array onto the results of the PCA pipeline\n", "\n", "# We\n", "I01_projected_features = dt_analog_bats_all.map_data(pca_res_I01.project_array)\n", "I01_projected_features" ] }, { "cell_type": "markdown", "id": "8172884c-2bbb-4bd3-afc4-f869206ca6fd", "metadata": {}, "source": [ "Now we just need to plot the data for the different compounds into a graph:" ] }, { "cell_type": "code", "execution_count": 14, "id": "4d371d79", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from shnitsel.analyze.hops import filter_data_at_hops\n", "from shnitsel.vis.colormaps import st_grey, st_yellow, st_orange\n", "\n", "fig, axs = plt.subplot_mosaic([['I01', 'I02']], layout='constrained')\n", "for ax_name, ax in axs.items():\n", " ax.set_title(ax_name)\n", " for compound_name, data in I01_projected_features.compounds.items():\n", " ax.plot(data.as_stacked.isel(PC=0), data.as_stacked.isel(PC=1), c='#ccc', rasterized=True)\n", "\n", " compound_data = I01_projected_features[ax_name].as_stacked\n", " if ax_name == 'I01':\n", " compound_color = st_yellow # if dih_end < 70 else '#2c3e50' if dih_end > 110 else 'gray'\n", " else:\n", " compound_color = st_grey\n", " ax.plot(compound_data.isel(PC=0), compound_data.isel(PC=1), c=compound_color, rasterized=True)\n", "\n", "\n", " hops_positions_21 = filter_data_at_hops(compound_data, \"2->1\")\n", " hops_positions_32 = filter_data_at_hops(compound_data, \"3->2\")\n", " ax.scatter(hops_positions_21.isel(PC=0), hops_positions_21.isel(PC=1), c=st_orange, s=1, zorder=10, rasterized=True)\n", " ax.scatter(hops_positions_32.isel(PC=0), hops_positions_32.isel(PC=1), c='#0D6F71', s=1, zorder=10, rasterized=True)\n", " \n", "fig.supxlabel('PC1')\n", "fig.supylabel('PC2')\n", "fig.savefig(\"./PCA_across_compounds_I01_I02_proj1.pdf\", dpi=300)" ] }, { "cell_type": "code", "execution_count": 15, "id": "4c24d3f6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR:root:\"The 'units' attribute of the DataArray must be set and of type str.\"\n", "ERROR:root:\"The 'units' attribute of the DataArray must be set and of type str.\"\n" ] }, { "data": { "text/html": [ "
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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_format_name\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'sharc\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'input_format_version\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'3.0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\', \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'est_level\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': None, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'theory_basis_set\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': None, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'charge_in_e\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 1.0, \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'num_states\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\': 3}, grouped_properties=None))\\\\\\\\\\\\\\', \\\\\\\\\\\\\\'I01\\\\\\\\\\\\\\': \\\\\\\\\\\\\\'...\\\\\\\\\\\\\\'}\\\\\\'}], _level_name=\\\\\\'CompoundGroup\\\\\\', _group_info=GroupInfo(group_name=\"TrajectoryGroupingMetadata(delta_t_in_fs=0.5, input_format_name=\\\\\\'sharc\\\\\\', input_format_version=\\\\\\'3.0\\\\\\', est_level=\\\\\\'CASSCF\\\\\\', theory_basis_set=\\\\\\'cc-pVDZ\\\\\\', charge_in_e=1.0, num_states=13)\", group_attributes={\\\\\\'delta_t_in_fs\\\\\\': 0.5, \\\\\\'input_format_name\\\\\\': \\\\\\'sharc\\\\\\', \\\\\\'input_format_version\\\\\\': \\\\\\'3.0\\\\\\', \\\\\\'est_level\\\\\\': \\\\\\'CASSCF\\\\\\', \\\\\\'theory_basis_set\\\\\\': \\\\\\'cc-pVDZ\\\\\\', \\\\\\'charge_in_e\\\\\\': 1.0, \\\\\\'num_states\\\\\\': 13}, grouped_properties=None))\\'}'}]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Retrieve the PCA for I02\n", "pca_res_I02 = pca_res[\"I02/pca\"].data\n", "\n", "# Project all features onto the principal components for I02\n", "# The pca result offers the function `.project_array` to project an arbitrary array onto the results of the PCA pipeline\n", "\n", "# We\n", "I02_projected_features = dt_analog_bats_all.map_data(pca_res_I02.project_array)\n", "I02_projected_features" ] }, { "cell_type": "code", "execution_count": 16, "id": "7fcd3fd9", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from shnitsel.analyze.hops import filter_data_at_hops\n", "from shnitsel.vis.colormaps import st_grey, st_yellow, st_orange\n", "\n", "fig, axs = plt.subplot_mosaic([['I01', 'I02']], layout='constrained')\n", "for ax_name, ax in axs.items():\n", " ax.set_title(ax_name)\n", " for compound_name, data in I02_projected_features.compounds.items():\n", " ax.plot(data.as_stacked.isel(PC=0), data.as_stacked.isel(PC=1), c=\"#ccc\", rasterized=True)\n", "\n", " compound_data = I02_projected_features[ax_name].as_stacked\n", " if ax_name == 'I01':\n", " compound_color = st_yellow # if dih_end < 70 else '#2c3e50' if dih_end > 110 else 'gray'\n", " else:\n", " compound_color = st_grey\n", " ax.plot(compound_data.isel(PC=0), compound_data.isel(PC=1), c=compound_color, rasterized=True)\n", "\n", " hops_positions_21 = filter_data_at_hops(compound_data, \"2->1\")\n", " hops_positions_32 = filter_data_at_hops(compound_data, \"3->2\")\n", " ax.scatter(hops_positions_21.isel(PC=0), hops_positions_21.isel(PC=1), c=st_orange, s=1, zorder=10, rasterized=True)\n", " ax.scatter(hops_positions_32.isel(PC=0), hops_positions_32.isel(PC=1), c='#0D6F71', s=1, zorder=10, rasterized=True)\n", " \n", "fig.supxlabel('PC1')\n", "fig.supylabel('PC2')\n", "fig.savefig(\"./PCA_across_compounds_I01_I02_proj2.pdf\", dpi=300)" ] }, { "cell_type": "markdown", "id": "69b0bf41", "metadata": {}, "source": [ "In either projection (to the PC of `I01` or `I02`), it becomes apparent that both compounds cover distinct regions of phase space with hops between different states notably happening in entirely different regions of phase space. \n" ] } ], "metadata": { "kernelspec": { "display_name": "shnitsel-tools-official (3.12.3)", "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }