![\"Schematic\"](\"img/NanowireQuantumDotPurcellSweep.png\")
"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "6489e2c1",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import tidy3d as td\n",
"import tidy3d.web as web\n",
"from tidy3d.log import set_warn_once\n",
"\n",
"set_warn_once(True) # show each unique warning only once"
]
},
{
"cell_type": "markdown",
"id": "9c640320",
"metadata": {},
"source": [
"## Simulation Parameters\n",
"\n",
"We first define the geometry, material, and numerical parameters. All spatial dimensions are in micrometers. The nanowire consists of a straight hexagonal section of height $H$ and a long taper of height $H_{\\text{taper}}$ that narrows from a circumradius $R$ at the base to $R_{\\text{tip}}$ at the truncated tip. The wire stands on a 10 nm $\\text{SiO}_2$ layer over a 1 $\\mu m$ thick gold film."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "fd19fe2b",
"metadata": {},
"outputs": [],
"source": [
"# ============================================================\n",
"# USER PARAMETERS\n",
"# Tidy3D spatial units are micrometers.\n",
"# ============================================================\n",
"\n",
"wavelength0 = 0.90 # central vacuum wavelength [um]\n",
"\n",
"H = 2.0 # straight-section height [um]\n",
"H_taper=13.922 # taper height [um]\n",
"#H_taper=5.5\n",
"R = 0.1515 # base hexagon circumradius [um]\n",
"R_tip = 0.030 # truncated-tip circumradius: 30 nm [um]\n",
"\n",
"msize = 4.0 # lateral size of Au and oxide [um]\n",
"\n",
"t_gold = 1.00 # gold thickness: 1 um\n",
"t_oxide = 0.010 # oxide thickness: 10 nm [um]\n",
"\n",
"\n",
"monitor_above_tip = 1.500\n",
"\n",
"run_time = 2e-12 # 2 ps\n",
"\n",
"n_nanowire = 3.44 # nondispersive InP approximation\n",
"n_oxide = 1.45 # nondispersive SiO2 approximation\n",
"\n",
"xy_air_padding = 0.0\n",
"bottom_air_padding = 0.0\n",
"top_air_padding = 0.8\n",
"\n",
"monitor_span = msize # adjust independently if desired\n",
"\n",
"min_steps_per_wvl = 10"
]
},
{
"cell_type": "markdown",
"id": "c34f8d74",
"metadata": {},
"source": [
"Next, we compute the source frequency, define the materials, and set the z coordinates of the material interfaces. InP and $\\text{SiO}_2$ are modeled as nondispersive [Medium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium.html) objects, and gold uses the Johnson and Christy fit from the Tidy3D [material library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html). The top surface of the gold film is at $z = 0$."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "8062d229",
"metadata": {},
"outputs": [],
"source": [
"# ============================================================\n",
"# FREQUENCY\n",
"# ============================================================\n",
"\n",
"freq0 = td.C_0 / wavelength0\n",
"\n",
"# A moderately broadband pulse.\n",
"# Increase the denominator for a narrower source spectrum.\n",
"fwidth = freq0 / 10\n",
"\n",
"\n",
"# ============================================================\n",
"# MATERIALS\n",
"# ============================================================\n",
"\n",
"air = td.Medium(name=\"air\")\n",
"\n",
"nanowire_medium = td.Medium(\n",
" permittivity=n_nanowire**2,\n",
" name=\"InP\",\n",
")\n",
"\n",
"oxide_medium = td.Medium(\n",
" permittivity=n_oxide**2,\n",
" name=\"SiO2\",\n",
")\n",
"\n",
"gold_medium = td.material_library[\"Au\"][\"JohnsonChristy1972\"]\n",
"\n",
"\n",
"# ============================================================\n",
"# Z POSITIONS\n",
"#\n",
"# Gold top surface is defined as z = 0.\n",
"# ============================================================\n",
"\n",
"z_gold_min = -t_gold\n",
"z_gold_max = 0.0\n",
"\n",
"z_oxide_min = 0.0\n",
"z_oxide_max = t_oxide\n",
"\n",
"z_wire_min = z_oxide_max\n",
"z_base_max = z_wire_min + H\n",
"z_tip = z_base_max + H_taper\n",
"\n",
"\n",
"z_monitor = z_tip + monitor_above_tip"
]
},
{
"cell_type": "markdown",
"id": "b4099af3",
"metadata": {},
"source": [
"## Hexagonal Cross Section and Taper Angle\n",
"\n",
"The function below returns the vertices of a regular hexagon of a given circumradius, used as the base of the [PolySlab](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PolySlab.html) sections. In a `PolySlab`, the sidewall displacement occurs normal to each polygon edge, so the taper angle is set by the reduction of the apothem (the center to edge distance) rather than of the circumradius."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "af1579a8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Taper sidewall angle: 0.433 degrees\n"
]
}
],
"source": [
"# ============================================================\n",
"# REGULAR HEXAGON\n",
"#\n",
"# R is the circumradius: distance from center to a vertex.\n",
"# rotation = pi/6 produces horizontal upper/lower facets.\n",
"# ============================================================\n",
"\n",
"def hexagon_vertices(\n",
" radius: float,\n",
" rotation: float = np.pi / 6,\n",
") -> list[tuple[float, float]]:\n",
" \"\"\"Return counter-clockwise vertices of a regular hexagon.\"\"\"\n",
" angles = rotation + np.arange(6) * np.pi / 3\n",
"\n",
" return [\n",
" (\n",
" radius * np.cos(angle),\n",
" radius * np.sin(angle),\n",
" )\n",
" for angle in angles\n",
" ]\n",
"\n",
"\n",
"base_vertices = hexagon_vertices(R)\n",
"\n",
"\n",
"# ============================================================\n",
"# TAPER ANGLE\n",
"#\n",
"# PolySlab sidewall displacement occurs normal to each polygon\n",
"# side. For a regular hexagon:\n",
"#\n",
"# apothem = circumradius * cos(pi/6)\n",
"#\n",
"# Therefore, the required reduction in apothem is used here,\n",
"# rather than directly using R - R_tip.\n",
"# ============================================================\n",
"\n",
"apothem_base = R * np.cos(np.pi / 6)\n",
"apothem_tip = R_tip * np.cos(np.pi / 6)\n",
"\n",
"taper_angle = np.arctan(\n",
" (apothem_base - apothem_tip) / H_taper\n",
")\n",
"\n",
"print(f\"Taper sidewall angle: {np.degrees(taper_angle):.3f} degrees\")"
]
},
{
"cell_type": "markdown",
"id": "03b3fb20",
"metadata": {},
"source": [
"## Simulation Set Up\n",
"\n",
"The function below builds the complete [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) for a given dot height. The quantum dot is modeled as a y-polarized [PointDipole](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PointDipole.html) placed on the wire axis. The monitors include a small closed [FluxMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxMonitor.html) box around the dipole that measures the total emitted power, a [FluxMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxMonitor.html) plane above the tip for the upward flux, a [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html) for the far-field radiation pattern, a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) cross section for field visualization, and a [FieldTimeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldTimeMonitor.html) to verify that the fields decay by the end of the simulation. [MeshOverrideStructure](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/discretization/_autosummary/tidy3d.MeshOverrideStructure.html) refinements resolve the 10 nm oxide layer, the nanowire cross section, and the dipole region. Since the dipole is centered and y-polarized, symmetry planes at $x = 0$ (even) and $y = 0$ (odd) reduce the computational cost by a factor of 4."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "19b558aa",
"metadata": {},
"outputs": [],
"source": [
"# ============================================================\n",
"# STRUCTURES\n",
"# ============================================================\n",
"def make_sim(dot_height):\n",
" z_dipole_above_mirror = dot_height\n",
" z_dipole = z_dipole_above_mirror\n",
" # Extend the gold film 2 um beyond the simulation bottom so it\n",
" # passes all the way through the PML instead of ending at the\n",
" # domain edge.\n",
" gold = td.Structure(\n",
" geometry=td.Box.from_bounds(\n",
" rmin=(-msize, -msize, z_gold_min - 2 * t_gold),\n",
" rmax=(msize, msize, z_gold_max),\n",
" ),\n",
" medium=gold_medium,\n",
" name=\"gold_substrate\",\n",
" )\n",
" \n",
" \n",
" oxide = td.Structure(\n",
" geometry=td.Box(\n",
" center=(\n",
" 0,\n",
" 0,\n",
" 0.5 * (z_oxide_min + z_oxide_max),\n",
" ),\n",
" size=(\n",
" 2*msize,\n",
" 2*msize,\n",
" t_oxide,\n",
" ),\n",
" ),\n",
" medium=oxide_medium,\n",
" name=\"oxide_layer\",\n",
" )\n",
" \n",
" \n",
" straight_section = td.Structure(\n",
" geometry=td.PolySlab(\n",
" vertices=base_vertices,\n",
" axis=2,\n",
" slab_bounds=(\n",
" z_wire_min,\n",
" z_base_max,\n",
" ),\n",
" sidewall_angle=0.0,\n",
" reference_plane=\"bottom\",\n",
" ),\n",
" medium=nanowire_medium,\n",
" name=\"straight_hexagonal_section\",\n",
" )\n",
" \n",
" \n",
" taper_section = td.Structure(\n",
" geometry=td.PolySlab(\n",
" vertices=base_vertices,\n",
" axis=2,\n",
" slab_bounds=(\n",
" z_base_max,\n",
" z_tip,\n",
" ),\n",
" sidewall_angle=taper_angle,\n",
" reference_plane=\"bottom\",\n",
" ),\n",
" medium=nanowire_medium,\n",
" name=\"hexagonal_taper\",\n",
" )\n",
" \n",
" \n",
" # ============================================================\n",
" # SOURCE\n",
" #\n",
" # A +Ey and -Ey point dipole differ only by a global pi phase.\n",
" # This does not change intensity, flux, Purcell factor, or the\n",
" # required symmetry.\n",
" # ============================================================\n",
" \n",
" source_time = td.GaussianPulse(\n",
" freq0=freq0,\n",
" fwidth=fwidth,\n",
" remove_dc_component=True,\n",
" )\n",
" \n",
" dipole = td.PointDipole(\n",
" center=(0, 0, z_dipole),\n",
" polarization=\"Ey\",\n",
" source_time=source_time,\n",
" name=\"y_dipole\",\n",
" )\n",
" \n",
" \n",
" # ============================================================\n",
" # MONITORS\n",
" #\n",
" # The plane is 1.5 um above the truncated tip.\n",
" # ============================================================\n",
" # ============================================================\n",
" # FAR-FIELD PROJECTION MONITOR\n",
" #\n",
" # theta = 0 corresponds to propagation along +z.\n",
" # theta = pi/2 corresponds to grazing propagation.\n",
" # phi is measured azimuthally from +x toward +y.\n",
" # ============================================================\n",
" \n",
" theta_ff = np.linspace(0, np.pi / 2, 181)\n",
" phi_ff = np.linspace(0, 2 * np.pi, 361)\n",
" \n",
" ff_box_bottom = t_oxide + 0.050\n",
" ff_box_top = z_monitor\n",
" \n",
" ff_box_center_z = 0.5 * (ff_box_bottom + ff_box_top)\n",
" ff_box_span_z = ff_box_top - ff_box_bottom\n",
" \n",
" far_field_monitor = td.FieldProjectionAngleMonitor(\n",
" center=(0, 0, ff_box_center_z),\n",
" size=(monitor_span, monitor_span, ff_box_span_z),\n",
" freqs=[freq0],\n",
" name=\"far_field_box\",\n",
" theta=theta_ff,\n",
" phi=phi_ff,\n",
" custom_origin=(0, 0, z_dipole),\n",
" exclude_surfaces=(\"z-\",),\n",
" proj_distance=1000 * wavelength0,\n",
" far_field_approx=True,\n",
" interval_space=(2, 2, 2),\n",
" )\n",
" x_min, x_max = -3, 3\n",
" y_min, y_max = 0, 0\n",
" z_min = 0\n",
" z_max = z_tip \n",
" \n",
" field_monitor = td.FieldMonitor(\n",
" center=(\n",
" (x_min + x_max) / 2,\n",
" (y_min + y_max) / 2,\n",
" (z_min + z_max) / 2,\n",
" ),\n",
" size=(\n",
" x_max - x_min,\n",
" 0,\n",
" z_max - z_min,\n",
" ),\n",
" freqs=[freq0],\n",
" fields=[\n",
" \"Ex\",\n",
" \"Ey\",\n",
" \"Ez\",\n",
" \"Hx\",\n",
" \"Hy\",\n",
" \"Hz\",\n",
" ],\n",
" name=\"field_cross_sec\",\n",
" )\n",
" \n",
" flux_monitor = td.FluxMonitor(\n",
" center=(0, 0, z_monitor),\n",
" size=(monitor_span, monitor_span, 0),\n",
" freqs=[freq0],\n",
" normal_dir=\"+\",\n",
" name=\"upward_flux\",\n",
" )\n",
" purcell_monitor = td.FluxMonitor(\n",
" center=(0, 0, z_dipole),\n",
" size=(0.20, 0.20, 0.20),\n",
" freqs=[freq0],\n",
" name=\"dipole_power\",\n",
" )\n",
" \n",
" # Optional time-domain monitor for checking whether fields decay.\n",
" decay_monitor = td.FieldTimeMonitor(\n",
" center=(0, 0, z_monitor),\n",
" size=(0, 0, 0),\n",
" fields=[\"Ey\"],\n",
" start=0,\n",
" stop=run_time,\n",
" interval=10,\n",
" name=\"field_decay\",\n",
" )\n",
" \n",
" \n",
" # ============================================================\n",
" # SIMULATION DOMAIN\n",
" # ============================================================\n",
" \n",
" sim_x = msize + 2 * xy_air_padding\n",
" sim_y = msize + 2 * xy_air_padding\n",
" \n",
" z_sim_min = z_gold_min - bottom_air_padding\n",
" z_sim_max = z_monitor + top_air_padding\n",
" \n",
" sim_z = z_sim_max - z_sim_min\n",
" sim_center_z = 0.5 * (z_sim_min + z_sim_max)\n",
" \n",
" \n",
" # ============================================================\n",
" # MESH\n",
" #\n",
" # The 10 nm oxide requires explicit refinement in z. Here the\n",
" # oxide and nearby interfaces receive a 5 nm maximum cell size.\n",
" # ============================================================\n",
" \n",
" oxide_mesh_override = td.MeshOverrideStructure(\n",
" geometry=td.Box(\n",
" center=(0, 0, t_oxide / 2),\n",
" size=(msize, msize, t_oxide + 0.040),\n",
" ),\n",
" dl=(None, None, 0.005),\n",
" name=\"oxide_z_mesh\",\n",
" )\n",
" \n",
" \n",
" nanowire_mesh_override = td.MeshOverrideStructure(\n",
" geometry=td.Box(\n",
" center=(\n",
" 0,\n",
" 0,\n",
" 0.5 * (z_wire_min + z_tip),\n",
" ),\n",
" size=(\n",
" 2 * R + 0.2,\n",
" 2 * R + 0.2,\n",
" z_tip - z_wire_min + 0.1,\n",
" ),\n",
" ),\n",
" dl=(0.01, 0.01, 0.01),\n",
" name=\"nanowire_mesh\",\n",
" priority=1,\n",
" )\n",
"\n",
" dot_mesh_override = td.MeshOverrideStructure(\n",
" geometry=td.Box(\n",
" center=(0, 0, z_dipole),\n",
" size=(0.050, 0.050, 0.050),\n",
" ),\n",
" dl=(None, None, 0.005),\n",
" name=\"oxide_z_mesh\",\n",
" priority=2,\n",
" )\n",
" \n",
" \n",
" grid_spec = td.GridSpec.auto(\n",
" wavelength=wavelength0,\n",
" min_steps_per_wvl=min_steps_per_wvl,\n",
" override_structures=[\n",
" oxide_mesh_override,\n",
" nanowire_mesh_override,\n",
" dot_mesh_override,\n",
" ],\n",
" )\n",
" \n",
" \n",
" # ============================================================\n",
" # SYMMETRY\n",
" #\n",
" # For a centered y-oriented electric dipole:\n",
" #\n",
" # x = 0 plane:\n",
" # Ey is tangential and even -> PMC/even symmetry -> +1\n",
" #\n",
" # y = 0 plane:\n",
" # Ey is normal and even -> PEC/odd symmetry -> -1\n",
" #\n",
" # Tuple order is (x, y, z).\n",
" # ============================================================\n",
" \n",
" symmetry = (1, -1, 0)\n",
" \n",
" \n",
" # ============================================================\n",
" # COMPLETE SIMULATION\n",
" # ============================================================\n",
" \n",
" sim = td.Simulation(\n",
" center=(0, 0, sim_center_z),\n",
" size=(sim_x, sim_y, sim_z),\n",
" \n",
" medium=air,\n",
" \n",
" structures=[\n",
" gold,\n",
" oxide,\n",
" straight_section,\n",
" taper_section,\n",
" ],\n",
" \n",
" sources=[dipole],\n",
" \n",
" monitors=[\n",
" field_monitor,\n",
" purcell_monitor,\n",
" far_field_monitor,\n",
" flux_monitor,\n",
" decay_monitor,\n",
" ],\n",
" \n",
" grid_spec=grid_spec,\n",
" \n",
" boundary_spec=td.BoundarySpec.all_sides(\n",
" boundary=td.PML()\n",
" ),\n",
" \n",
" symmetry=symmetry,\n",
" \n",
" run_time=run_time,\n",
" shutoff=1e-6,\n",
" )\n",
" return sim"
]
},
{
"cell_type": "markdown",
"id": "c8a7fbbd",
"metadata": {},
"source": [
"## Parameter Sweep\n",
"\n",
"We sweep the dot height from 1.25 to 1.55 $\\mu m$ in 60 steps, creating one simulation per height. Before uploading, `validate_pre_upload()` checks each simulation locally and catches setup problems early."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "00ea67f7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validating D_1.250...\n",
"Validating D_1.255...\n",
"Validating D_1.260...\n",
"Validating D_1.265...\n",
"Validating D_1.270...\n",
"Validating D_1.275...\n",
"Validating D_1.281...\n",
"Validating D_1.286...\n",
"Validating D_1.291...\n",
"Validating D_1.296...\n",
"Validating D_1.301...\n",
"Validating D_1.306...\n",
"Validating D_1.311...\n",
"Validating D_1.316...\n",
"Validating D_1.321...\n",
"Validating D_1.326...\n",
"Validating D_1.331...\n",
"Validating D_1.336...\n",
"Validating D_1.342...\n",
"Validating D_1.347...\n",
"Validating D_1.352...\n",
"Validating D_1.357...\n",
"Validating D_1.362...\n",
"Validating D_1.367...\n",
"Validating D_1.372...\n",
"Validating D_1.377...\n",
"Validating D_1.382...\n",
"Validating D_1.387...\n",
"Validating D_1.392...\n",
"Validating D_1.397...\n",
"Validating D_1.403...\n",
"Validating D_1.408...\n",
"Validating D_1.413...\n",
"Validating D_1.418...\n",
"Validating D_1.423...\n",
"Validating D_1.428...\n",
"Validating D_1.433...\n",
"Validating D_1.438...\n",
"Validating D_1.443...\n",
"Validating D_1.448...\n",
"Validating D_1.453...\n",
"Validating D_1.458...\n",
"Validating D_1.464...\n",
"Validating D_1.469...\n",
"Validating D_1.474...\n",
"Validating D_1.479...\n",
"Validating D_1.484...\n",
"Validating D_1.489...\n",
"Validating D_1.494...\n",
"Validating D_1.499...\n",
"Validating D_1.504...\n",
"Validating D_1.509...\n",
"Validating D_1.514...\n",
"Validating D_1.519...\n",
"Validating D_1.525...\n",
"Validating D_1.530...\n",
"Validating D_1.535...\n",
"Validating D_1.540...\n",
"Validating D_1.545...\n",
"Validating D_1.550...\n",
"All simulations validated successfully.\n"
]
}
],
"source": [
"# ============================================================\n",
"# MAKE A DICTIONARY OF SIMS\n",
"# ============================================================\n",
"dot_heights = np.linspace(1.250,1.550,60)\n",
"\n",
"simulations = {\n",
" f\"D_{dot_height:.3f}\": make_sim(dot_height)\n",
" for dot_height in dot_heights \n",
"}\n",
"# ============================================================\n",
"# LOCAL VALIDATION\n",
"# ============================================================\n",
"\n",
"for task_name, sim in simulations.items():\n",
" print(f\"Validating {task_name}...\")\n",
" sim.validate_pre_upload()\n",
"\n",
"print(\"All simulations validated successfully.\")"
]
},
{
"cell_type": "markdown",
"id": "ad51c5d7",
"metadata": {},
"source": [
"## Geometry Visualization\n",
"\n",
"Before running the sweep, we inspect the first simulation: the nanowire cross section, the hexagonal taper cross section, and the FDTD grid."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "d135ccfe",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"11:58:34 -03 Maximum FlexCredit cost: 155.929 for the whole batch. \n", "\n" ], "text/plain": [ "\u001b[2;36m11:58:34 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m155.929\u001b[0m for the whole batch. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "155.9289782898351\n" ] } ], "source": [ "# ============================================================\n", "# CREATE JOB AND ESTIMATE COST\n", "# ============================================================\n", "\n", "batch = web.Batch(simulations=simulations)\n", "\n", "print(batch.estimate_cost())" ] }, { "cell_type": "markdown", "id": "7afdf76e", "metadata": {}, "source": [ "## Running the Batch\n", "\n", "`Batch.run()` uploads all simulations, monitors their progress, and downloads the results when they complete." ] }, { "cell_type": "code", "execution_count": 21, "id": "a634f68e", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "640ef54d0c0b45f3a92a140c3bfd2503", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
11:58:42 -03 Started working on Batch containing 60 tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m11:58:42 -03\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m60\u001b[0m tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
12:00:36 -03 Maximum FlexCredit cost: 155.929 for the whole batch. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:00:36 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m155.929\u001b[0m for the whole batch. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Use 'Batch.real_cost()' to get the billed FlexCredit cost after \n", " completion. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after \n", "\u001b[2;36m \u001b[0mcompletion. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1f67c844407a44d0a9e715fa882e9259", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
12:21:37 -03 Batch complete. \n",
"\n"
],
"text/plain": [
"\u001b[2;36m12:21:37 -03\u001b[0m\u001b[2;36m \u001b[0mBatch complete. \n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ============================================================\n",
"# RUN\n",
"# ============================================================\n",
"\n",
"batch_data = batch.run(\n",
" path_dir=\"hexagonal_nanowire_on_gold\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c19f0d85",
"metadata": {},
"source": [
"The solver log warns about a dispersive medium (the gold) extending into the PML. This is expected and safe in this case: the gold film is optically thick, so the fields decay inside it and do not penetrate the PML region behind it."
]
},
{
"cell_type": "markdown",
"id": "14dcd3e7",
"metadata": {},
"source": [
"## Purcell Factor Calculation\n",
"\n",
"The Purcell factor is defined as $F_p = P_{\\text{device}} / P_{\\text{bulk}}$, where $P_{\\text{device}}$ is the power emitted by the dipole inside the structure and $P_{\\text{bulk}}$ is the power emitted by the same dipole in homogeneous InP, computed analytically as\n",
"\n",
"$$P_{\\text{bulk}} = \\frac{\\omega^2}{12 \\pi} \\frac{\\mu_0 n}{c}.$$\n",
"\n",
"When symmetry planes are present, dipole images are included for each plane, so $P_{\\text{bulk}}$ must be multiplied by $2^{2 N_{\\text{sym}}}$, where $N_{\\text{sym}}$ is the number of symmetry planes."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "ec9ddd02",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P_bulk = 2.680727e+04\n"
]
}
],
"source": [
"# ============================================================\n",
"# BULK DIPOLE POWER (ANALYTIC)\n",
"#\n",
"# Power emitted by the same dipole in homogeneous InP,\n",
"# corrected for the dipole images introduced by the\n",
"# symmetry planes.\n",
"# ============================================================\n",
"\n",
"P_bulk = ((2 * np.pi * freq0) ** 2 / (12 * np.pi)) * (td.MU_0 * n_nanowire / td.C_0)\n",
"P_bulk = P_bulk * 2 ** (2 * np.sum(np.abs(first_sim.symmetry)))\n",
"\n",
"print(f\"P_bulk = {P_bulk:.6e}\")"
]
},
{
"cell_type": "markdown",
"id": "5e3f64cf",
"metadata": {},
"source": [
"For each simulation in the batch, we take the dipole power from the flux box monitor and normalize it by the bulk reference."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "194daaf7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dot height = 1.250 µm, Purcell = 1.407922\n",
"Dot height = 1.255 µm, Purcell = 1.467918\n",
"Dot height = 1.260 µm, Purcell = 1.507342\n",
"Dot height = 1.265 µm, Purcell = 1.524628\n",
"Dot height = 1.270 µm, Purcell = 1.516392\n",
"Dot height = 1.275 µm, Purcell = 1.487461\n",
"Dot height = 1.281 µm, Purcell = 1.434381\n",
"Dot height = 1.286 µm, Purcell = 1.363479\n",
"Dot height = 1.291 µm, Purcell = 1.273250\n",
"Dot height = 1.296 µm, Purcell = 1.169655\n",
"Dot height = 1.301 µm, Purcell = 1.054108\n",
"Dot height = 1.306 µm, Purcell = 0.932519\n",
"Dot height = 1.311 µm, Purcell = 0.807763\n",
"Dot height = 1.316 µm, Purcell = 0.683838\n",
"Dot height = 1.321 µm, Purcell = 0.565789\n",
"Dot height = 1.326 µm, Purcell = 0.457001\n",
"Dot height = 1.331 µm, Purcell = 0.361717\n",
"Dot height = 1.336 µm, Purcell = 0.282629\n",
"Dot height = 1.342 µm, Purcell = 0.223002\n",
"Dot height = 1.347 µm, Purcell = 0.184572\n",
"Dot height = 1.352 µm, Purcell = 0.168912\n",
"Dot height = 1.357 µm, Purcell = 0.176630\n",
"Dot height = 1.362 µm, Purcell = 0.207228\n",
"Dot height = 1.367 µm, Purcell = 0.260496\n",
"Dot height = 1.372 µm, Purcell = 0.333511\n",
"Dot height = 1.377 µm, Purcell = 0.425201\n",
"Dot height = 1.382 µm, Purcell = 0.530375\n",
"Dot height = 1.387 µm, Purcell = 0.648082\n",
"Dot height = 1.392 µm, Purcell = 0.770889\n",
"Dot height = 1.397 µm, Purcell = 0.898345\n",
"Dot height = 1.403 µm, Purcell = 1.021574\n",
"Dot height = 1.408 µm, Purcell = 1.141229\n",
"Dot height = 1.413 µm, Purcell = 1.248013\n",
"Dot height = 1.418 µm, Purcell = 1.342607\n",
"Dot height = 1.423 µm, Purcell = 1.417494\n",
"Dot height = 1.428 µm, Purcell = 1.475826\n",
"Dot height = 1.433 µm, Purcell = 1.509801\n",
"Dot height = 1.438 µm, Purcell = 1.522528\n",
"Dot height = 1.443 µm, Purcell = 1.511137\n",
"Dot height = 1.448 µm, Purcell = 1.476930\n",
"Dot height = 1.453 µm, Purcell = 1.421451\n",
"Dot height = 1.458 µm, Purcell = 1.345774\n",
"Dot height = 1.464 µm, Purcell = 1.252828\n",
"Dot height = 1.469 µm, Purcell = 1.147580\n",
"Dot height = 1.474 µm, Purcell = 1.030137\n",
"Dot height = 1.479 µm, Purcell = 0.908843\n",
"Dot height = 1.484 µm, Purcell = 0.783521\n",
"Dot height = 1.489 µm, Purcell = 0.661923\n",
"Dot height = 1.494 µm, Purcell = 0.545201\n",
"Dot height = 1.499 µm, Purcell = 0.440072\n",
"Dot height = 1.504 µm, Purcell = 0.347761\n",
"Dot height = 1.509 µm, Purcell = 0.273340\n",
"Dot height = 1.514 µm, Purcell = 0.217969\n",
"Dot height = 1.519 µm, Purcell = 0.184615\n",
"Dot height = 1.525 µm, Purcell = 0.173829\n",
"Dot height = 1.530 µm, Purcell = 0.186449\n",
"Dot height = 1.535 µm, Purcell = 0.221937\n",
"Dot height = 1.540 µm, Purcell = 0.279112\n",
"Dot height = 1.545 µm, Purcell = 0.356188\n",
"Dot height = 1.550 µm, Purcell = 0.450247\n"
]
}
],
"source": [
"# ============================================================\n",
"# EXTRACT ACTUAL PURCELL FACTORS\n",
"# ============================================================\n",
"\n",
"purcells = []\n",
"\n",
"for dot_height, task_name in zip(\n",
" dot_heights,\n",
" simulations.keys(),\n",
"):\n",
"\n",
" sim_data = batch_data[task_name]\n",
"\n",
" P_device = float(\n",
" sim_data[\"dipole_power\"].flux.sel(\n",
" f=freq0,\n",
" method=\"nearest\",\n",
" )\n",
" )\n",
"\n",
" purcell = P_device / P_bulk\n",
" purcells.append(purcell)\n",
"\n",
" print(\n",
" f\"Dot height = {dot_height:.3f} µm, \"\n",
" f\"Purcell = {purcell:.6f}\"\n",
" )\n",
"\n",
"purcells = np.array(purcells)"
]
},
{
"cell_type": "markdown",
"id": "a522fcee",
"metadata": {},
"source": [
"## Results\n",
"\n",
"Finally, we plot the Purcell factor as a function of the dot height above the mirror. The oscillation reflects the standing wave formed between the emitter and the gold mirror."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "a9423d88",
"metadata": {},
"outputs": [
{
"data": {
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",
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"12:22:09 -03 Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:09 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.250: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:10 -03 Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:10 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.255: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.260: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:11 -03 Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:11 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.265: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.270: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:12 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:12 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.275: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.281: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:13 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:13 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.286: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:15 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:15 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.291: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:16 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:16 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.296: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.301: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:17 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:17 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.306: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.311: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:18 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:18 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.316: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:19 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:19 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.321: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.326: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:20 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:20 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.331: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.336: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:21 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:21 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.342: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:22 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:22 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.347: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:23 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:23 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.352: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:24 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:24 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.357: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.362: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:25 -03 Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:25 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.367: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.372: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:26 -03 Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:26 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.377: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.382: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:27 -03 Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:27 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.387: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:28 -03 Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:28 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.392: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:29 -03 Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:29 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.397: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:30 -03 Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:30 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.403: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:31 -03 Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:31 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.408: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:32 -03 Billed flex credit cost: 0.421. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:32 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.421\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.413: 0.421 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.418: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:33 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:33 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.423: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.428: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:34 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:34 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.433: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:35 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:35 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.438: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:36 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:36 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.443: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:37 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:37 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.448: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:38 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:38 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.453: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:39 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:39 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.458: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.464: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:40 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:40 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.469: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:41 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:41 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.474: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:42 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:42 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.479: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:43 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:43 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.484: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.489: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:44 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:44 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.494: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.499: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:45 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:45 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.504: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:46 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:46 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.509: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:47 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:47 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.514: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.519: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:48 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:48 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.525: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.530: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:49 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:49 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.535: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.540: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
12:22:50 -03 Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m12:22:50 -03\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.545: 0.427 FlexCredits\n" ] }, { "data": { "text/html": [ "
Billed flex credit cost: 0.427. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.427\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Note: the task cost pro-rated due to early shutoff was below the \n", " minimum threshold, due to fast shutoff. Decreasing the simulation \n", " 'run_time' should decrease the estimated, and correspondingly the \n", " billed cost of such tasks. \n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the \n", "\u001b[2;36m \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation \n", "\u001b[2;36m \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the \n", "\u001b[2;36m \u001b[0mbilled cost of such tasks. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "D_1.550: 0.427 FlexCredits\n", "\n", "Total actual cost: 25.519 FlexCredits\n" ] } ], "source": [ "total_cost = 0\n", "\n", "for task_name, job in batch.jobs.items():\n", " cost = web.real_cost(job.task_id)\n", " total_cost += cost\n", " print(f\"{task_name}: {cost:.3f} FlexCredits\")\n", "\n", "print(f\"\\nTotal actual cost: {total_cost:.3f} FlexCredits\")" ] } ], "metadata": { "applications": [ "Quantum emitters" ], "description": "This notebook demonstrates how to simulate the Purcell factor of a quantum dot embedded in a tapered nanowire on a gold mirror using Tidy3D.", "feature_image": "./img/NanowireQuantumDotPurcellSweep.png", "features": [ "Parameter sweep" ], "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "keywords": "Purcell factor, quantum dot, nanowire, Tidy3D, FDTD", "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" }, "title": "How to simulate the Purcell factor of a quantum dot in a nanowire on a gold mirror using Tidy3D | Flexcompute" }, "nbformat": 4, "nbformat_minor": 5 }