diff --git a/test.ipynb b/test.ipynb index f2e2af9..d5aefda 100644 --- a/test.ipynb +++ b/test.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Mac:47319] shmem: mmap: an error occurred while determining whether or not /var/folders/yh/dx7xl94n3g52ts3td8qcxjcc0000gn/T//ompi.Mac.501/jf.0/1258881024/sm_segment.Mac.501.4b090000.0 could be created.\n" + "[Mac:48946] shmem: mmap: an error occurred while determining whether or not /var/folders/yh/dx7xl94n3g52ts3td8qcxjcc0000gn/T//ompi.Mac.501/jf.0/1050607616/sm_segment.Mac.501.3e9f0000.0 could be created.\n" ] } ], @@ -32,7 +32,7 @@ "from numpy.linalg import inv\n", "import warnings\n", "\n", - "start_time = timer()\n" + "start_time = timer()" ] }, { @@ -50,9 +50,7 @@ ], "source": [ "# this cell mimicks an input file\n", - "fdf = sisl.get_sile(\n", - " \"./lat3_791/Fe3GeTe2.fdf\"\n", - ")\n", + "fdf = sisl.get_sile(\"./lat3_791/Fe3GeTe2.fdf\")\n", "# this information needs to be given at the input!!\n", "scf_xcf_orientation = np.array([0, 0, 1]) # z\n", "# list of reference directions for around which we calculate the derivatives\n", @@ -66,19 +64,28 @@ "]\n", "\n", "# human readable definition of magnetic entities\n", - "magnetic_entities=[dict(atom=3 ,l=2),\n", - " dict(atom=4 ,l=2),\n", - " dict(atom=5 ,l=2),\n", - " dict(atom=[3,4],)]\n", + "magnetic_entities = [\n", + " dict(atom=4, l=2),\n", + " dict(atom=5, l=2),\n", + " dict(atom=3, l=2),\n", + " dict(\n", + " atom=[3, 4],\n", + " ),\n", + "]\n", "\n", "# pair information\n", - "pairs=[dict(ai=0,aj=1,Ruc=np.array([0,0,0])), # isotropic should be -82 meV\n", - " dict(ai=1,aj=0,Ruc=np.array([0,0,0])),\n", - " dict(ai=0,aj=2,Ruc=np.array([0,0,0])), # these should all be around -41.9 in the isotropic part\n", - " dict(ai=1,aj=2,Ruc=np.array([0,0,0])),\n", - " dict(ai=0,aj=2,Ruc=np.array([-1,0,0])),\n", - " dict(ai=1,aj=2,Ruc=np.array([-1,0,0]))\n", - " ]\n", + "# these should all be around -41.9 in the isotropic part\n", + "# isotropic should be -82 meV\n", + "pairs = [\n", + " dict(ai=0, aj=1, Ruc=np.array([0, 0, 0])),\n", + " dict(ai=1, aj=0, Ruc=np.array([0, 0, 0])),\n", + " dict(\n", + " ai=0, aj=2, Ruc=np.array([0, 0, 0])\n", + " ),\n", + " dict(ai=1, aj=2, Ruc=np.array([0, 0, 0])),\n", + " dict(ai=0, aj=2, Ruc=np.array([-1, 0, 0])),\n", + " dict(ai=1, aj=2, Ruc=np.array([-1, 0, 0])),\n", + "]\n", "\n", "# Brilloun zone sampling and Green function contour integral\n", "kset = 20\n", @@ -96,15 +103,17 @@ "if rank == root_node:\n", " print(\"Number of nodes in the parallel cluster: \", size)\n", "\n", - "simulation_parameters = dict(path=\"Not yet specified.\",\n", - " scf_xcf_orientation=scf_xcf_orientation, \n", - " ref_xcf_orientations=ref_xcf_orientations,\n", - " kset=kset,\n", - " kdirs=kdirs, \n", - " ebot=ebot,\n", - " eset=eset, \n", - " esetp=esetp,\n", - " parallel_size=size)\n", + "simulation_parameters = dict(\n", + " path=\"Not yet specified.\",\n", + " scf_xcf_orientation=scf_xcf_orientation,\n", + " ref_xcf_orientations=ref_xcf_orientations,\n", + " kset=kset,\n", + " kdirs=kdirs,\n", + " ebot=ebot,\n", + " eset=eset,\n", + " esetp=esetp,\n", + " parallel_size=size,\n", + ")\n", "\n", "# digestion of the input\n", "# read in hamiltonian\n", @@ -122,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -213,46 +222,6 @@ "H_and_XCF_time = timer()" ] }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "84" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "NO" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(3, 81, 84, 84)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "XCF.shape" - ] - }, { "cell_type": "code", "execution_count": 4, @@ -264,22 +233,16 @@ " parsed = parse_magnetic_entity(dh, **mag_ent) # parse orbital indexes\n", " magnetic_entities[i][\"orbital_indeces\"] = parsed\n", " # calculate spin box indexes\n", - " magnetic_entities[i][\"spin_box_indeces\"] = blow_up_orbindx(\n", - " parsed\n", - " )\n", + " magnetic_entities[i][\"spin_box_indeces\"] = blow_up_orbindx(parsed)\n", " # calculate size for Greens function generation\n", - " spin_box_shape = len(\n", - " mag_ent[\"spin_box_indeces\"]\n", - " )\n", + " spin_box_shape = len(mag_ent[\"spin_box_indeces\"])\n", "\n", " mag_ent[\"energies\"] = [] # we will store the second order energy derivations here\n", "\n", " mag_ent[\"Gii\"] = [] # Greens function\n", " mag_ent[\"Gii_tmp\"] = [] # Greens function for parallelization\n", " # These will be the perturbed potentials from eq. 100\n", - " mag_ent[\"Vu1\"] = [\n", - " list([]) for _ in range(len(ref_xcf_orientations))\n", - " ]\n", + " mag_ent[\"Vu1\"] = [list([]) for _ in range(len(ref_xcf_orientations))]\n", " mag_ent[\"Vu2\"] = [list([]) for _ in range(len(ref_xcf_orientations))]\n", " for i in ref_xcf_orientations:\n", " # Greens functions for every quantization axis\n", @@ -294,12 +257,8 @@ "# in the 3 reference directions, because G_ij and G_ji are both needed\n", "for pair in pairs:\n", " # calculate size for Greens function generation\n", - " spin_box_shape_i = len(\n", - " magnetic_entities[pair[\"ai\"]][\"spin_box_indeces\"]\n", - " )\n", - " spin_box_shape_j = len(\n", - " magnetic_entities[pair[\"aj\"]][\"spin_box_indeces\"]\n", - " )\n", + " spin_box_shape_i = len(magnetic_entities[pair[\"ai\"]][\"spin_box_indeces\"])\n", + " spin_box_shape_j = len(magnetic_entities[pair[\"aj\"]][\"spin_box_indeces\"])\n", "\n", " pair[\"energies\"] = [] # we will store the second order energy derivations here\n", "\n", @@ -321,7 +280,7 @@ " np.zeros((eset, spin_box_shape_j, spin_box_shape_i), dtype=\"complex128\")\n", " )\n", "\n", - "site_and_pair_dictionaries_time = timer()\n" + "site_and_pair_dictionaries_time = timer()" ] }, { @@ -341,7 +300,7 @@ "kset = make_kset(dirs=kdirs, NUMK=kset) # generate k space sampling\n", "wkset = np.ones(len(kset)) / len(kset) # generate weights for k points\n", "kpcs = np.array_split(kset, size) # split the k points based on MPI size\n", - "kpcs[root_node] = tqdm(kpcs[root_node], desc='k loop', file=stdout)\n", + "kpcs[root_node] = tqdm(kpcs[root_node], desc=\"k loop\", file=stdout)\n", "\n", "k_set_time = timer()" ] @@ -352,7 +311,7 @@ "metadata": {}, "outputs": [], "source": [ - "# this will contain all the data needed to calculate the energy variations upon rotation\n", + "# this will contain the three hamiltonians in the reference directions needed to calculate the energy variations upon rotation\n", "hamiltonians = []\n", "\n", "# iterate over the reference directions (quantization axes)\n", @@ -366,15 +325,16 @@ " rot_H_XCF_uc = rot_H_XCF[uc_in_sc_idx]\n", "\n", " # obtain total Hamiltonian with the rotated exchange field\n", - " rot_H = hTRS + rot_H_XCF # equation 76\n", + " rot_H = (\n", + " hTRS + rot_H_XCF\n", + " ) # equation 76 #######################################################################################\n", "\n", " hamiltonians.append(\n", - " dict(orient=orient[\"o\"], H=rot_H, rotations=[])\n", + " dict(orient=orient[\"o\"], H=rot_H)\n", " ) # store orientation and rotated Hamiltonian\n", - " \n", - " for u in orient[\n", - " \"vw\"\n", - " ]: # these are the infinitezimal rotations (for now) perpendicular to the quantization axis\n", + "\n", + " # these are the infinitezimal rotations (for now) perpendicular to the quantization axis\n", + " for u in orient[\"vw\"]:\n", " Tu = np.kron(np.eye(NO, dtype=int), tau_u(u)) # section 2.H\n", "\n", " Vu1 = 1j / 2 * commutator(rot_H_XCF_uc, Tu) # equation 100\n", @@ -403,7 +363,7 @@ "Number of magnetic entities being calculated: 4\n", "We have to calculate the Greens function for three reference direction and we are going to calculate 15 energy integrals per site.\n", "The shape of the Hamiltonian and the Greens function is 84x84.\n", - "k loop: 100%|██████████| 400/400 [01:50<00:00, 3.61it/s]\n" + "k loop: 100%|██████████| 400/400 [01:51<00:00, 3.60it/s]\n" ] } ], @@ -427,9 +387,8 @@ "# sampling the integrand on the contour and the BZ\n", "for k in kpcs[rank]:\n", " wk = wkset[rank] # weight of k point in BZ integral\n", - " for i, hamiltonian_orientation in enumerate(\n", - " hamiltonians\n", - " ): # iterate over reference directions\n", + " # iterate over reference directions\n", + " for i, hamiltonian_orientation in enumerate(hamiltonians):\n", " # calculate Greens function\n", " H = hamiltonian_orientation[\"H\"]\n", " HK, SK = hsk(H, ss, dh.sc_off, k)\n", @@ -463,7 +422,7 @@ " comm.Reduce(pair[\"Gij_tmp\"][i], pair[\"Gij\"][i], root=root_node)\n", " comm.Reduce(pair[\"Gji_tmp\"][i], pair[\"Gji\"][i], root=root_node)\n", "\n", - "green_function_inversion_time = timer()\n" + "green_function_inversion_time = timer()" ] }, { @@ -490,7 +449,7 @@ "[0 0 1]\n", "Quantization axis and perpendicular rotation directions:\n", "[1 0 0] --» [array([0, 1, 0]), array([0, 0, 1])]\n", - "[0 1 0] --» [array([0, 0, 1]), array([1, 0, 0])]\n", + "[0 1 0] --» [array([1, 0, 1]), array([0, 0, 1])]\n", "[0 0 1] --» [array([1, 0, 0]), array([0, 1, 0])]\n", "================================================================================\n", "number of k points: 20\n", @@ -512,49 +471,52 @@ "--------------------------------------------------------------------------------\n", "Atom1 Atom2 [i j k] d [Ang]\n", "--------------------------------------------------------------------------------\n", - "[3]Fe(2) [4]Fe(2) [0 0 0] d [Ang] Not yet.\n", - "Isotropic: -61.134994398006846\n", - "DMI: [-6.44078586e+00 7.51906171e+00 6.90431275e-04 9.74101032e-04\n", - " -5.49031203e-06]\n", - "Symmetric-anisotropy: [-9.32966923e-01 8.92579299e-04 -2.04258659e-06]\n", + "[4]Fe(2) [5]Fe(2) [0 0 0] d [Ang] Not yet.\n", + "Isotropic: -60.54974989595536\n", + "DMI: [-3.79945963e+00 4.35043554e+00 3.58990840e-03]\n", + "Symmetric-anisotropy: [-3.36380523e-01 6.52391161e-01 7.10682631e-02 4.74666547e+01\n", + " 3.63670088e-02]\n", + "\n", + "[5]Fe(2) [4]Fe(2) [0 0 0] d [Ang] Not yet.\n", + "Isotropic: -60.54974989595536\n", + "DMI: [ 3.79945963e+00 -4.35043554e+00 -3.58990840e-03]\n", + "Symmetric-anisotropy: [-3.36380523e-01 6.52391161e-01 7.10682631e-02 3.87657836e+01\n", + " 3.63670088e-02]\n", "\n", "[4]Fe(2) [3]Fe(2) [0 0 0] d [Ang] Not yet.\n", - "Isotropic: -61.134994398006846\n", - "DMI: [-6.44078586e+00 7.51906171e+00 6.90431275e-04 -8.11057566e-04\n", + "Isotropic: -60.893309223093574\n", + "DMI: [9.32966923e-01 6.31148875e-04 2.04258658e-06]\n", + "Symmetric-anisotropy: [-5.95741551e+00 7.27737654e+00 6.90431275e-04 4.72712896e+01\n", " -5.49031203e-06]\n", - "Symmetric-anisotropy: [ 9.32966923e-01 -8.92579299e-04 2.04258658e-06]\n", "\n", - "[3]Fe(2) [5]Fe(2) [0 0 0] d [Ang] Not yet.\n", - "Isotropic: -60.47084255793878\n", - "DMI: [-0.17431857 0.57352788 0.07106945 6.04526746 -0.0424978 ]\n", - "Symmetric-anisotropy: [3.78506176e+00 6.13838308e+00 3.59037036e-03]\n", + "[5]Fe(2) [3]Fe(2) [0 0 0] d [Ang] Not yet.\n", + "Isotropic: -60.55651225519789\n", + "DMI: [-3.78506176e+00 4.34049230e+00 -3.59037036e-03]\n", + "Symmetric-anisotropy: [-3.45657964e-01 6.59197575e-01 7.10694456e-02 4.73389872e+01\n", + " -4.24978017e-02]\n", "\n", - "[4]Fe(2) [5]Fe(2) [0 0 0] d [Ang] Not yet.\n", - "Isotropic: -60.46575240352053\n", - "DMI: [-0.16838554 0.56839367 0.07106826 -6.06303351 0.03636701]\n", - "Symmetric-anisotropy: [-3.79945963e+00 -6.15244494e+00 3.58990840e-03]\n", - "\n", - "[3]Fe(2) [5]Fe(2) [-1 0 0] d [Ang] Not yet.\n", - "Isotropic: -6.691813632755354\n", - "DMI: [-0.79119686 0.79044933 -0.031302 -7.59758103 0.03239586]\n", - "Symmetric-anisotropy: [ 5.95251705 -7.64859703 6.50501652]\n", + "[4]Fe(2) [3]Fe(2) [-1 0 0] d [Ang] Not yet.\n", + "Isotropic: -2.7142498730523856\n", + "DMI: [-5.95948398e-01 -2.17295487e-04 7.07077562e-06]\n", + "Symmetric-anisotropy: [-2.19746937e+00 2.29055415e+00 2.34394278e-03 3.47374083e+00\n", + " -6.09018501e-04]\n", "\n", - "[4]Fe(2) [5]Fe(2) [-1 0 0] d [Ang] Not yet.\n", - "Isotropic: -6.190626204103167\n", - "DMI: [ 0.19185206 0.28941842 -0.03129943 -4.21429122 -0.09833472]\n", - "Symmetric-anisotropy: [ 6.19414647 -4.23019689 6.50504332]\n", + "[5]Fe(2) [3]Fe(2) [-1 0 0] d [Ang] Not yet.\n", + "Isotropic: -6.625329589943322\n", + "DMI: [-5.95251705 -5.40837483 -6.50501652]\n", + "Symmetric-anisotropy: [-0.65822877 0.72396528 -0.031302 -0.22204756 0.03239586]\n", "\n", "================================================================================\n", "Runtime information: \n", - "Total runtime: 111.725750458\n", + "Total runtime: 112.01323325000001\n", "--------------------------------------------------------------------------------\n", - "Initial setup: 0.20263604099999988\n", - "Hamiltonian conversion and XC field extraction: 0.595 s\n", - "Pair and site datastructure creatrions: 0.011 s\n", - "k set cration and distribution: 0.019 s\n", - "Rotating XC potential: 0.210 s\n", - "Greens function inversion: 110.600 s\n", - "Calculate energies and magnetic components: 0.087 s\n" + "Initial setup: 0.1183263750000001\n", + "Hamiltonian conversion and XC field extraction: 0.558 s\n", + "Pair and site datastructure creatrions: 0.013 s\n", + "k set cration and distribution: 0.016 s\n", + "Rotating XC potential: 0.217 s\n", + "Greens function inversion: 111.009 s\n", + "Calculate energies and magnetic components: 0.082 s\n" ] } ], @@ -573,7 +535,7 @@ " storage.append(np.trapz(-1 / np.pi * np.imag(traced * cont.we)))\n", "\n", " # fill up the magnetic entities dictionary with the energies\n", - " mag_ent[\"energies\"].append(storage)\n", + " magnetic_entities[tracker][\"energies\"].append(storage)\n", "\n", " # iterate over the pairs\n", " for tracker, pair in enumerate(pairs):\n", @@ -595,66 +557,103 @@ "\n", " end_time = timer()\n", "\n", - " print(\"############################### GROGU OUTPUT ###################################\")\n", - " print(\"================================================================================\")\n", + " print(\n", + " \"############################### GROGU OUTPUT ###################################\"\n", + " )\n", + " print(\n", + " \"================================================================================\"\n", + " )\n", " print(\"Input file: \")\n", " print(simulation_parameters[\"path\"])\n", - " print(\"Number of nodes in the parallel cluster: \", simulation_parameters[\"parallel_size\"])\n", - " print(\"================================================================================\")\n", + " print(\n", + " \"Number of nodes in the parallel cluster: \",\n", + " simulation_parameters[\"parallel_size\"],\n", + " )\n", + " print(\n", + " \"================================================================================\"\n", + " )\n", " try:\n", " print(\"Cell [Ang]: \")\n", " print(simulation_parameters[\"geom\"].cell)\n", " except:\n", " print(\"Geometry could not be read.\")\n", - " print(\"================================================================================\")\n", + " print(\n", + " \"================================================================================\"\n", + " )\n", " print(\"DFT axis: \")\n", " print(simulation_parameters[\"scf_xcf_orientation\"])\n", " print(\"Quantization axis and perpendicular rotation directions:\")\n", " for ref in ref_xcf_orientations:\n", " print(ref[\"o\"], \" --» \", ref[\"vw\"])\n", - " print(\"================================================================================\")\n", + " print(\n", + " \"================================================================================\"\n", + " )\n", " print(\"number of k points: \", simulation_parameters[\"kset\"])\n", " print(\"k point directions: \", simulation_parameters[\"kdirs\"])\n", - " print(\"================================================================================\")\n", + " print(\n", + " \"================================================================================\"\n", + " )\n", " print(\"Parameters for the contour integral:\")\n", " print(\"Ebot: \", simulation_parameters[\"ebot\"])\n", " print(\"Eset: \", simulation_parameters[\"eset\"])\n", " print(\"Esetp: \", simulation_parameters[\"esetp\"])\n", - " print(\"================================================================================\")\n", + " print(\n", + " \"================================================================================\"\n", + " )\n", " print(\"Atomic informations: \")\n", " print(\"\")\n", " print(\"\")\n", " print(\"Not yet specified.\")\n", " print(\"\")\n", " print(\"\")\n", - " print(\"================================================================================\")\n", + " print(\n", + " \"================================================================================\"\n", + " )\n", " print(\"Exchange [meV]\")\n", - " print(\"--------------------------------------------------------------------------------\")\n", + " print(\n", + " \"--------------------------------------------------------------------------------\"\n", + " )\n", " print(\"Atom1 Atom2 [i j k] d [Ang]\")\n", - " print(\"--------------------------------------------------------------------------------\")\n", + " print(\n", + " \"--------------------------------------------------------------------------------\"\n", + " )\n", " for pair in pairs:\n", " J_iso, J_S, D = calculate_exchange_tensor(pair)\n", " J_iso = J_iso * sisl.unit_convert(\"eV\", \"meV\")\n", " J_S = J_S * sisl.unit_convert(\"eV\", \"meV\")\n", " D = D * sisl.unit_convert(\"eV\", \"meV\")\n", - " \n", + "\n", " print(print_atomic_indices(pair, magnetic_entities, dh))\n", " print(\"Isotropic: \", J_iso)\n", " print(\"DMI: \", D)\n", " print(\"Symmetric-anisotropy: \", J_S)\n", " print(\"\")\n", - " \n", - " print(\"================================================================================\")\n", + "\n", + " print(\n", + " \"================================================================================\"\n", + " )\n", " print(\"Runtime information: \")\n", " print(\"Total runtime: \", end_time - start_time)\n", - " print(\"--------------------------------------------------------------------------------\")\n", + " print(\n", + " \"--------------------------------------------------------------------------------\"\n", + " )\n", " print(\"Initial setup: \", setup_time - start_time)\n", - " print(f\"Hamiltonian conversion and XC field extraction: {H_and_XCF_time - setup_time:.3f} s\")\n", - " print(f\"Pair and site datastructure creatrions: {site_and_pair_dictionaries_time - H_and_XCF_time:.3f} s\")\n", - " print(f\"k set cration and distribution: {k_set_time - site_and_pair_dictionaries_time:.3f} s\")\n", + " print(\n", + " f\"Hamiltonian conversion and XC field extraction: {H_and_XCF_time - setup_time:.3f} s\"\n", + " )\n", + " print(\n", + " f\"Pair and site datastructure creatrions: {site_and_pair_dictionaries_time - H_and_XCF_time:.3f} s\"\n", + " )\n", + " print(\n", + " f\"k set cration and distribution: {k_set_time - site_and_pair_dictionaries_time:.3f} s\"\n", + " )\n", " print(f\"Rotating XC potential: {reference_rotations_time - k_set_time:.3f} s\")\n", - " print(f\"Greens function inversion: {green_function_inversion_time - reference_rotations_time:.3f} s\")\n", - " print(f\"Calculate energies and magnetic components: {end_time - green_function_inversion_time:.3f} s\")\n" + " print(\n", + " f\"Greens function inversion: {green_function_inversion_time - reference_rotations_time:.3f} s\"\n", + " )\n", + " print(\n", + " f\"Calculate energies and magnetic components: {end_time - green_function_inversion_time:.3f} s\"\n", + " )" ] }, { @@ -703,3365 +702,37 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "color": "gray", - "width": 1 - }, - "meta": {}, - "mode": "lines", - "name": "Bonds", - "opacity": 1, - "type": "scatter", - "x": [ - 1.8955401056909067, - 1.7957744506988202, - 1.6960087957067336, - 1.596243140714647, - 1.4964774857225605, - 1.396711830730474, - 1.2969461757383873, - 1.1971805207463007, - 1.0974148657542142, - 0.9976492107621276, - 0.8978835557700411, - 0.7981179007779544, - 0.698352245785868, - 0.5985865907937815, - 0.49882093580169484, - 0.39905528080960817, - 0.2992896258175217, - 0.19952397082543527, - 0.0997583158333486, - -0.000007339158738073337, - null, - 1.8955401056909067, - 1.895536242697267, - 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"visible": true, - "zeroline": false, - "zerolinecolor": "#ccc", - "zerolinewidth": 1 - } - }, - "xaxis": { - "color": "black", - "gridcolor": "#ccc", - "gridwidth": 1, - "linewidth": 1, - "mirror": true, - "showgrid": false, - "showline": true, - "ticklen": 5, - "ticks": "outside", - "ticksuffix": " ", - "visible": true, - "zeroline": false, - "zerolinecolor": "#ccc", - "zerolinewidth": 1 - }, - "yaxis": { - "color": "black", - "gridcolor": "#ccc", - "gridwidth": 1, - "linewidth": 1, - "mirror": true, - "showgrid": false, - "showline": true, - "ticklen": 5, - "ticks": "outside", - "ticksuffix": " ", - "visible": true, - "zeroline": false, - "zerolinecolor": "#ccc", - "zerolinewidth": 1 - } - } - }, - "xaxis": { - "scaleanchor": "y", - "scaleratio": 1, - "title": { - "text": "X axis [Ang]" - } - }, - "yaxis": { - "title": { - "text": "Y axis [Ang]" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import sisl.viz\n", - "dh.geometry.tile(2,1).plot(axes=\"xy\")" + "\n", + "dh.geometry.tile(2, 1).plot(axes=\"xy\")" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "xyz[-3:]: red, green, blue\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", + "\n", "coords = dh.xyz[-3:]\n", "\n", "\n", - "plt.figure(figsize=(15,5))\n", + "plt.figure(figsize=(15, 5))\n", "plt.subplot(131)\n", - "plt.scatter(coords[:,0], coords[:,2], color=[\"r\", \"g\", \"b\"])\n", + "plt.scatter(coords[:, 0], coords[:, 2], color=[\"r\", \"g\", \"b\"])\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"z\")\n", "plt.subplot(132)\n", - "plt.scatter(coords[:,1], coords[:,2], color=[\"r\", \"g\", \"b\"])\n", + "plt.scatter(coords[:, 1], coords[:, 2], color=[\"r\", \"g\", \"b\"])\n", "plt.xlabel(\"y\")\n", "plt.ylabel(\"z\")\n", "plt.subplot(133)\n", - "plt.scatter(coords[:,0], coords[:,1], color=[\"r\", \"g\", \"b\"])\n", + "plt.scatter(coords[:, 0], coords[:, 1], color=[\"r\", \"g\", \"b\"])\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "print(\"xyz[-3:]: red, green, blue\")"