diff --git a/CMakeLists.txt b/CMakeLists.txt
index c22ef9de727..dd0a8729c4e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -241,6 +241,9 @@ endif()
target_compile_definitions(WarpX PRIVATE
WARPX_PARSER_DEPTH=${WarpX_PARSER_DEPTH})
+target_compile_definitions(WarpX PRIVATE PULSAR)
+
+
# Warnings ####################################################################
#
diff --git a/Examples/Physics_applications/pulsar/Pulsar_scale_RstarPhysical.ipynb b/Examples/Physics_applications/pulsar/Pulsar_scale_RstarPhysical.ipynb
new file mode 100644
index 00000000000..f87d443abb6
--- /dev/null
+++ b/Examples/Physics_applications/pulsar/Pulsar_scale_RstarPhysical.ipynb
@@ -0,0 +1,1392 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Overview\n",
+ "\n",
+ "This a notebook that inspects the results of a WarpX simulation.\n",
+ "\n",
+ "# Instruction\n",
+ "\n",
+ "Enter the path of the data you wish to visualize below. Then execute the cells one by one, by selecting them with your mouse and typing `Shift + Enter`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import statements\n",
+ "import yt ; yt.funcs.mylog.setLevel(50)\n",
+ "import numpy as np\n",
+ "import scipy.constants as scc\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#Define the Physical Constants, normalizations, and the scale-down parameter, r_scale, for the pulsar. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "######################\n",
+ "# physical constants #\n",
+ "######################\n",
+ "c = 299792458.0 # speed of light \n",
+ "q_e = 1.602176634e-19 # elementary charge\n",
+ "me=9.10938356*np.power(10.,-31) # electron mass\n",
+ "epsilon=8.8541878128*np.power(10.,-12) # permittivity of free space\n",
+ "mu_o = 4*3.14*1e-7 # permeability\n",
+ "pi = 3.14159265358979323846\n",
+ "SolarMass = 2e30\n",
+ "G = 6.674e-11 # gravitational constant\n",
+ "\n",
+ "#############################################################################\n",
+ "# Parameters for a real Pulsar #\n",
+ "# (Table 7 of J.Petri's article on Theory of Pulsar Magnetosphere and Wind) #\n",
+ "#############################################################################\n",
+ "Omega_real = 6283\n",
+ "B_real = 7.4E4\n",
+ "R_real = 12000\n",
+ "n_real = 6.9e16\n",
+ "omega_pe_real = (n_real*q_e*q_e/(me*epsilon))**0.5 #plasma frequency\n",
+ "SkinDepth_real = c/omega_pe_real \n",
+ "Mstar = 1.4*SolarMass\n",
+ "Rstar_skinDepth_real = 6e5\n",
+ "\n",
+ "##################\n",
+ "# Normalizations #\n",
+ "##################\n",
+ "Rstar_skinDepth = 6e0 # Ratio of radius of star to the skin Depth \n",
+ " # For a real star, this ratio is 6e5\n",
+ "exponent = np.arange(0,6,1) \n",
+ "Factor = np.array(10**exponent)\n",
+ "Rstar_skinDepth = np.array(6*Factor) \n",
+ "\n",
+ "RLC_Rstar = 4 # Ratio of light cylinder (where the particle speed ~ c) to Rstar\n",
+ " # For a pulsar with 1ms period, this ratio is 4. \n",
+ " # i.e., This ratio sets the omega value, since Omega*RLC = c\n",
+ "\n",
+ "# Choose skindepth as the free parameter for a choice of normalizations given above\n",
+ "# The skin depth below is computed from the number density for a real pulsar\n",
+ "# Keeping the SkinDepth constant across all the scales in our scaling study\n",
+ "#SkinDepth = 0.02\n",
+ "\n",
+ "# Since skin depth is maintained across the scales, and RLC/Rstar is also maintained \n",
+ "# lets define the decrease in the scale by comparing the value of \n",
+ "# (Rstar/skinDepth)/(Rstar_real/skinDepth_real)\n",
+ "#r_scale = Rstar_skinDepth/Rstar_skinDepth_real\n",
+ "\n",
+ "Rstar = np.ones(6)*12000\n",
+ "SkinDepth = Rstar/Rstar_skinDepth\n",
+ "r_scale = Rstar_skinDepth_real/Rstar_skinDepth"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "##################################################################\n",
+ "# Derive other physical parameters from the above normalizations #\n",
+ "##################################################################\n",
+ "\n",
+ "# 1. Lorentz Boost (dimensionless) #\n",
+ "# Note that in Alex Chen's paper, gamma_o ~ (1/4)*normalized_values.\n",
+ "# Instead here we have pi/2, since that leads to the closes gamma_o \n",
+ "# value for a real 1ms pulsar. \n",
+ "gamma_o = (pi/2)*(Rstar_skinDepth)**2/RLC_Rstar \n",
+ "\n",
+ "\n",
+ "gamma_real = (pi/2)*6e5**2/RLC_Rstar;\n",
+ "gamma_scaling = gamma_o/(gamma_real) # This is to see how the gamma value \n",
+ " # decreases due to decrease in the ratio of R_star to skin depth\n",
+ "\n",
+ "\n",
+ "\n",
+ "# 3. Light cylinder location (m)\n",
+ "RLC = Rstar*RLC_Rstar\n",
+ "# 4. Angular Frequency (rad/s)\n",
+ "# Omega remains constant\n",
+ "Omega = c/RLC\n",
+ "# 5. Period (s)\n",
+ "# Period remains constant\n",
+ "Period = 2*3.14/Omega\n",
+ "# Moment of inertia for a sphere = (2/5)MR^2 (kg.m^2)\n",
+ "# Note that when the Rstar is scaled by r_scale, \n",
+ "# Mstar decreases as r_scale^3. Thus Mstar*r_scale**3 is the \n",
+ "# mass of the scaled down star.\n",
+ "# I remains constant across all scales\n",
+ "I = (2/5)*(Mstar)*Rstar**2\n",
+ "# 6. Rotation induced potential drop from pote to equator (V)\n",
+ "# Reference: Alex Chen's 2014 paper\n",
+ "# Scales as 1/r_scale**2\n",
+ "phi_o = gamma_o * (me/q_e) * c**2\n",
+ "\n",
+ "# Braking is the rate of slow-down of the spin. \n",
+ "# It is not relevant for the scaling. \n",
+ "# However, 1e-15 is the P_dot for a pulsar with P=1s\n",
+ "# and the rate of slowdown decreases proportional to the period. \n",
+ "# So we were to compare two stars with same radius, but different Omega\n",
+ "# then we can use Braking to determine the magnetic field strength at the surface as follows\n",
+ "# Bo = (3*mu_o*c*c*c/(32*pi*pi*pi))**0.5 * (I*Braking*Period)**0.5/(Rstar**3) \n",
+ "# (Reference : Table 7 of Jetri's article)\n",
+ "# Remains constant across all scales\n",
+ "Braking = 1e-15*Period\n",
+ "\n",
+ "# 7. Magnetic field strength (Tesla)\n",
+ "# Bo decreases as ~ 1/r_scale**2\n",
+ "Bo = phi_o/(Rstar*Rstar*Omega)\n",
+ "\n",
+ "# 8. Volume charge density (C/m^3) for uniform magnetization insize the star \n",
+ "# Refer to Table 7 of Petri's article \n",
+ "# Since Omega increases as r_scale and Bo decreases as r_scale\n",
+ "# The product of Omega*Bo remains constant across all scales. \n",
+ "# Thus, rho_e decreases as (1/r_scale**2)\n",
+ "rho_e = 2*epsilon*Omega*Bo #(Not adding the negative sign here)\n",
+ "\n",
+ "# 9. Electron number density (#/m^3)\n",
+ "# ne decreases as (1/r_scale**2)\n",
+ "ne = rho_e/q_e\n",
+ "# 9a. plasma frequency \n",
+ "# decreases as (1/r_scale)\n",
+ "omega_pe = (ne*q_e*q_e/(me*epsilon))**0.5 #plasma frequency\n",
+ "# 10. Magnetic moment (Am^2)\n",
+ "# decreases as (1/r_scale**2)\n",
+ "magnetic_moment = Bo*Rstar*Rstar*Rstar*4*pi/(mu_o)\n",
+ "# 11. E-field (V/m)\n",
+ "# Efield decreases as (1/r_scale**2)\n",
+ "E = Omega*Bo*Rstar\n",
+ "\n",
+ "\n",
+ "#########################################\n",
+ "# How do the energies and forces scale? #\n",
+ "#########################################\n",
+ "# 12. Magnetic Energy (J)\n",
+ "# Magnetic energy decreases as (1/r_scale**4)\n",
+ "magnetic_energy = (4*pi/3)*Bo**2*Rstar**3/(2*mu_o)\n",
+ "\n",
+ "# 13. Gravitational Potential Energy (J)\n",
+ "# G.pot energy remains constant \n",
+ "GP_energy = (3/5)*G*Mstar*Mstar/(Rstar)\n",
+ "\n",
+ "# 14. Gravitational to Electric force (dimensionless)\n",
+ "# This ratio increases as r_scale**2\n",
+ "G_EForce = G*Mstar*me/(Rstar*Rstar*q_e*E)\n",
+ "\n",
+ "# 15. Rotational kinetic energy \n",
+ "# Rot. KE remains constant\n",
+ "rotational_KE = (1/2)*I*Omega**2\n",
+ "\n",
+ "# 16. From (12) and (13), we know the B.energy scales as (1/r_scale**4) and GP energy is constant \n",
+ "# Thus the ratio of GP_energy and B_energy increases as r_scale**4\n",
+ "GB_energy_ratio = GP_energy/magnetic_energy\n",
+ "\n",
+ "# 17. Rate of change of Omega, or angular acceleration decreases as 1/r_scale**4\n",
+ "Omega_dot = Bo*Bo*Rstar**6*Omega**3/(I) * (32*pi/(3*mu_o*c**3))* (1/(4*pi*pi))\n",
+ "\n",
+ "# 18. Torque decreases as 1/r_scale**4\n",
+ "Torque = I * Omega_dot\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Input parameters for WarpX for geometry scaled down by r_scale = [1.e-05 1.e-04 1.e-03 1.e-02 1.e-01 1.e+00]\n",
+ "Lorentz factor, (gamma_o) = [1.41371669e+01 1.41371669e+03 1.41371669e+05 1.41371669e+07\n",
+ " 1.41371669e+09 1.41371669e+11]\n",
+ "Rstar (m) [12000. 12000. 12000. 12000. 12000. 12000.]\n",
+ "Omega (rad/s) [6245.67620833 6245.67620833 6245.67620833 6245.67620833 6245.67620833\n",
+ " 6245.67620833]\n",
+ "Bo (Tesla) [8.03230942e-06 8.03230942e-04 8.03230942e-02 8.03230942e+00\n",
+ " 8.03230942e+02 8.03230942e+04]\n",
+ "ne (/m^3) [5.54482989e+06 5.54482989e+08 5.54482989e+10 5.54482989e+12\n",
+ " 5.54482989e+14 5.54482989e+16]\n",
+ "Size of the domain, (m) [360000. 360000. 360000. 360000. 360000. 360000.]\n",
+ "Minimum cell size (m) [2.e+03 2.e+02 2.e+01 2.e+00 2.e-01 2.e-02]\n",
+ "timestep (s) [3.76386776e-06 3.76386776e-07 3.76386776e-08 3.76386776e-09\n",
+ " 3.76386776e-10 3.76386776e-11]\n",
+ "Numver of cells assuming unif. grid, [1.8e+02 1.8e+03 1.8e+04 1.8e+05 1.8e+06 1.8e+07]\n"
+ ]
+ }
+ ],
+ "source": [
+ "############################################################\n",
+ "# Print all the values that will be used as input in WarpX #\n",
+ "############################################################\n",
+ "print(\"Input parameters for WarpX for geometry scaled down by r_scale = \", Rstar_skinDepth/Rstar_skinDepth_real)\n",
+ "print(\"Lorentz factor, (gamma_o) = \", gamma_o)\n",
+ "print(\"Rstar (m)\", Rstar)\n",
+ "print(\"Omega (rad/s)\", Omega)\n",
+ "print(\"Bo (Tesla)\", Bo)\n",
+ "print(\"ne (/m^3)\", ne)\n",
+ "print(\"Size of the domain, (m)\", 30*Rstar)\n",
+ "print(\"Minimum cell size (m)\", SkinDepth)\n",
+ "print(\"timestep (s)\", 0.5*omega_pe**-1) # Or may be cfl criterion\n",
+ "print(\"Numver of cells assuming unif. grid, \", 30*Rstar/SkinDepth)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Gravitational force to E-force : [1.22562947e-02 1.22562947e-04 1.22562947e-06 1.22562947e-08\n",
+ " 1.22562947e-10 1.22562947e-12]\n",
+ "Gravitational energy to B-energy : [1.40727411e+38 1.40727411e+34 1.40727411e+30 1.40727411e+26\n",
+ " 1.40727411e+22 1.40727411e+18]\n",
+ "B energy, (J) [1.85906071e+08 1.85906071e+12 1.85906071e+16 1.85906071e+20\n",
+ " 1.85906071e+24 1.85906071e+28]\n",
+ "Gravitational potential energy, (J) [2.616208e+46 2.616208e+46 2.616208e+46 2.616208e+46 2.616208e+46\n",
+ " 2.616208e+46]\n",
+ "Rotational Kinetic Energy (J) [3.14564313e+45 3.14564313e+45 3.14564313e+45 3.14564313e+45\n",
+ " 3.14564313e+45 3.14564313e+45]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#############################################################\n",
+ "# Print ratios of G.Pot.Energy/B_energy and G.Force/E_force #\n",
+ "#############################################################\n",
+ "print(\"Gravitational force to E-force : \", G_EForce)\n",
+ "print(\"Gravitational energy to B-energy : \",GB_energy_ratio)\n",
+ "\n",
+ "\n",
+ "# Print dimensional values of energies\n",
+ "print(\"B energy, (J)\",magnetic_energy);\n",
+ "print(\"Gravitational potential energy, (J)\", GP_energy)\n",
+ "print(\"Rotational Kinetic Energy (J)\", rotational_KE )\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
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+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
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+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
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+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
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+ "mpl.figure.prototype._init_canvas = function() {\n",
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+ " var nav_element = $('');\n",
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+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('');\n",
+ "\n",
+ " var fmt_picker = $('');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('![](\"')
');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('');\n",
+ " var button = $('');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
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+ "data": {
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\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot Gravitational force to electric force -- should be constant across all scales\n",
+ "\n",
+ "plt.plot(Rstar_skinDepth/6e5,G_EForce,'ro')\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"G.Force/E.Force\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"G_Eforce_scaling.png\",bbox_inches='tight')\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# plot Gravitational to magnetic energy -- should be constant across all scales\n",
+ "\n",
+ "plt.plot(Rstar_skinDepth/6e5,GB_energy_ratio,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"G.Energy/B.energy\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"G_Benergy_scaling.png\",bbox_inches='tight')\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Trends of all energies\n",
+ "plt.plot(Rstar_skinDepth/6e5,magnetic_energy,color=\"red\",ls='--',lw=2,marker=\"^\",markersize=8)\n",
+ "plt.plot(Rstar_skinDepth/6e5,GP_energy,color=\"blue\",lw=2,marker='o',markersize=8,markerfacecolor='blue')\n",
+ "plt.plot(Rstar_skinDepth/6e5,rotational_KE,color=\"purple\",lw=2,marker='s',markersize=8,markeredgecolor='purple')\n",
+ "plt.plot(Rstar_skinDepth/6e5,Torque,color=\"cyan\",lw=2,marker='>',markersize=8,markeredgecolor='purple')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Energy (J)\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.legend([\"Magnetic\",\"G. Potential\",\"Rotational KE\",\"Torque\"],loc=2,fontsize='small',frameon=\"false\",borderpad=0.1)\n",
+ "plt.ylim([1e0,1e90])\n",
+ "plt.savefig(\"Energy_scaling.png\",bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# plot Gravitational to magnetic energy -- should be constant across all scales\n",
+ "plt.plot(Rstar_skinDepth/6e5,Bo/Bo[5],color=\"blue\",lw=2,marker='s',markersize=8)\n",
+ "plt.ylabel(\"Normalized B. field\",color=\"blue\")\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.yticks(color=\"blue\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.twinx()\n",
+ "plt.plot(Rstar_skinDepth/6e5,Omega/Omega[5],color=\"red\",lw=2,marker='o',markersize=8)\n",
+ "plt.ylabel(\"Normalized \\u03A9\",color=\"red\")\n",
+ "plt.yticks(color=\"red\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.ylim([0,2])\n",
+ "plt.savefig(\"Normalized_B_Omega.png\",bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(Rstar_skinDepth/6e5,gamma_o,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Normalized G.Energy/B.energy\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"G_Benergy_scaling.png\",bbox_inches='tight')\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(Rstar_skinDepth/6e5,Omega_dot,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Omega_dot\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"G_Benergy_scaling.png\",bbox_inches='tight')\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(Rstar_skinDepth/6e5,Torque,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Torque\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"Torque.png\",bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(Rstar_skinDepth/6e5,phi_o,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Normalized G.Energy/B.energy\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"phi_o.png\",bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.020230407144897423"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "SkinDepth_real"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([48000., 48000., 48000., 48000., 48000., 48000.])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "RLC\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "140.625"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "36000/256"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "particle_weight = 140.625**3*5.544e6/64"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "240896701812.74414"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "particle_weight"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.0010055, 0.0010055, 0.0010055, 0.0010055, 0.0010055, 0.0010055])"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Period"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([48000., 48000., 48000., 48000., 48000., 48000.])"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "RLC\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.9"
+ },
+ "widgets": {
+ "state": {
+ "11d243e9f5074fe1b115949d174d59de": {
+ "views": [
+ {
+ "cell_index": 6
+ }
+ ]
+ }
+ },
+ "version": "1.2.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Examples/Physics_applications/pulsar/analysis_pulsar_vis.py b/Examples/Physics_applications/pulsar/analysis_pulsar_vis.py
new file mode 100644
index 00000000000..3694a1f91e5
--- /dev/null
+++ b/Examples/Physics_applications/pulsar/analysis_pulsar_vis.py
@@ -0,0 +1,54 @@
+import yt
+import os
+import glob
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-f", "--plotfile", type=str, help="Path to single input plotfile to visualize.")
+parser.add_argument("-d", "--dir", type=str, help="Path to input plotfile directory to visualize.")
+
+args = parser.parse_args()
+
+def visualize(plt, annotate_particles=True):
+ ds = yt.load(plt)
+ sl = yt.SlicePlot(ds, 0, 'Ez', aspect=1) # Create a sliceplot object
+ if annotate_particles:
+ sl.annotate_particles(width=(2.e3, 'm'),ptype="plasma_p",col='b',p_size=5.0)
+ sl.annotate_particles(width=(2.e3, 'm'),ptype="plasma_e",col='r',p_size=5.0)
+ sl.annotate_streamlines("Ey", "Ez", plot_args={"color": "black"})
+ sl.annotate_grids() # Show grids
+ sl.annotate_sphere([90000.0, 90000.0, 90000.0], radius=12000.0,
+ circle_args={'color':'white', 'linewidth':2})
+ out_name = os.path.join(os.path.dirname(plt), "pulsar_{}.png".format(os.path.basename(plt)))
+ sl.save(out_name)
+
+if __name__ == "__main__":
+ yt.funcs.mylog.setLevel(50)
+ if args.dir:
+ failed = []
+ plotfiles = glob.glob(os.path.join(args.dir, "plt" + "[0-9]"*5))
+ for pf in plotfiles:
+ try:
+ visualize(pf)
+ except:
+ # plotfile 0 may not have particles, so turn off annotations if we first failed
+ try:
+ visualize(pf, annotate_particles=False)
+ except:
+ failed.append(pf)
+ pass
+
+ if len(failed) > 0:
+ print("Visualization failed for the following plotfiles:")
+ for fp in failed:
+ print(fp)
+ else:
+ print("No plots failed, creating a gif ...")
+ input_files = os.path.join(args.dir, "*.png")
+ output_gif = os.path.join(args.dir, "pulsar.gif")
+ os.system("convert -delay 20 -loop 0 {} {}".format(input_files, output_gif))
+
+ elif args.plotfile:
+ visualize(args.plotfile)
+ else:
+ print("Supply either -f or -d options for visualization. Use the -h option to see help.")
diff --git a/Examples/Physics_applications/pulsar/analysis_pulsar_viz.ipynb b/Examples/Physics_applications/pulsar/analysis_pulsar_viz.ipynb
new file mode 100644
index 00000000000..febea7950aa
--- /dev/null
+++ b/Examples/Physics_applications/pulsar/analysis_pulsar_viz.ipynb
@@ -0,0 +1,745 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Overview\n",
+ "\n",
+ "This a notebook that inspects the results of a WarpX simulation.\n",
+ "\n",
+ "# Instruction\n",
+ "\n",
+ "Enter the path of the data you wish to visualize below. Then execute the cells one by one, by selecting them with your mouse and typing `Shift + Enter`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import statements\n",
+ "import yt ; yt.funcs.mylog.setLevel(50)\n",
+ "import numpy as np\n",
+ "import scipy.constants as scc\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Read data in the simulation frame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot data with yt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('all', 'particle_cpu'),\n",
+ " ('all', 'particle_id'),\n",
+ " ('all', 'particle_momentum_x'),\n",
+ " ('all', 'particle_momentum_y'),\n",
+ " ('all', 'particle_momentum_z'),\n",
+ " ('all', 'particle_position_x'),\n",
+ " ('all', 'particle_position_y'),\n",
+ " ('all', 'particle_position_z'),\n",
+ " ('all', 'particle_weight'),\n",
+ " ('boxlib', 'Bx'),\n",
+ " ('boxlib', 'By'),\n",
+ " ('boxlib', 'Bz'),\n",
+ " ('boxlib', 'Ex'),\n",
+ " ('boxlib', 'Ey'),\n",
+ " ('boxlib', 'Ez'),\n",
+ " ('boxlib', 'divE'),\n",
+ " ('boxlib', 'jx'),\n",
+ " ('boxlib', 'jy'),\n",
+ " ('boxlib', 'jz'),\n",
+ " ('boxlib', 'part_per_cell'),\n",
+ " ('boxlib', 'rho'),\n",
+ " ('plasma_e', 'particle_cpu'),\n",
+ " ('plasma_e', 'particle_id'),\n",
+ " ('plasma_e', 'particle_momentum_x'),\n",
+ " ('plasma_e', 'particle_momentum_y'),\n",
+ " ('plasma_e', 'particle_momentum_z'),\n",
+ " ('plasma_e', 'particle_position_x'),\n",
+ " ('plasma_e', 'particle_position_y'),\n",
+ " ('plasma_e', 'particle_position_z'),\n",
+ " ('plasma_e', 'particle_weight'),\n",
+ " ('plasma_p', 'particle_cpu'),\n",
+ " ('plasma_p', 'particle_id'),\n",
+ " ('plasma_p', 'particle_momentum_x'),\n",
+ " ('plasma_p', 'particle_momentum_y'),\n",
+ " ('plasma_p', 'particle_momentum_z'),\n",
+ " ('plasma_p', 'particle_position_x'),\n",
+ " ('plasma_p', 'particle_position_y'),\n",
+ " ('plasma_p', 'particle_position_z'),\n",
+ " ('plasma_p', 'particle_weight')]"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "ds = yt.load( './diags/plt00010/' ) # Create a dataset object\n",
+ "filenum = 90\n",
+ "ds.field_list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/yt-3.7.dev0-py3.7-linux-x86_64.egg/yt/visualization/base_plot_types.py:222: MatplotlibDeprecationWarning: default base may change from np.e to 10. To suppress this warning specify the base keyword argument.\n",
+ " vmax=float(np.nanmax(data)))\n",
+ "/usr/local/lib/python3.7/dist-packages/yt-3.7.dev0-py3.7-linux-x86_64.egg/yt/units/yt_array.py:1417: RuntimeWarning: invalid value encountered in true_divide\n",
+ " out=out, **kwargs)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ 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\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "sl = yt.SlicePlot(ds, 0, 'Ey', aspect=1) # Create a sliceplot object\n",
+ "#sl.annotate_particles(width=(2.e3, 'm'),ptype=\"plasma_p\",col='b',p_size=5.0)\n",
+ "#sl.annotate_particles(width=(2.e3, 'm'),ptype=\"plasma_e\",col='r',p_size=5.0)\n",
+ "sl.annotate_streamlines(\"Ey\", \"Ez\", plot_args={\"color\": \"black\"})\n",
+ "sl.annotate_grids() # Show grids\n",
+ "sl.annotate_sphere([90000.0, 90000.0, 90000.0], radius=12000.0,\n",
+ " circle_args={'color':'white', 'linewidth':2})\n",
+ "sl.show() # Show the plot\n",
+ "#sl.save('./particle_in_ring_plt'+str(filenum)+'.png')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "YTFieldNotFound",
+ "evalue": "Could not find field '('plasma_p', 'particle_position_x')' in .",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m-----------------------------------------------\u001b[0m",
+ "\u001b[0;31mYTFieldNotFound\u001b[0mTraceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParticlePlot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'plasma_p'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'particle_position_x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'plasma_p'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'particle_position_y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'plasma_p'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'particle_momentum_x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"positron_plt\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilenum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\".png\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/yt-3.7.dev0-py3.7-linux-x86_64.egg/yt/visualization/particle_plots.py\u001b[0m in \u001b[0;36mParticlePlot\u001b[0;34m(ds, x_field, y_field, z_fields, color, *args, **kwargs)\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdd\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 510\u001b[0m \u001b[0mdd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 511\u001b[0;31m \u001b[0mx_field\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_determine_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_field\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 512\u001b[0m \u001b[0my_field\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_determine_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_field\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 513\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/yt-3.7.dev0-py3.7-linux-x86_64.egg/yt/data_objects/data_containers.py\u001b[0m in \u001b[0;36m_determine_fields\u001b[0;34m(self, fields)\u001b[0m\n\u001b[1;32m 1359\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mYTFieldNotParseable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfield\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1360\u001b[0m \u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1361\u001b[0;31m \u001b[0mfinfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_field_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1362\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfield\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDerivedField\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1363\u001b[0m \u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfield\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/yt-3.7.dev0-py3.7-linux-x86_64.egg/yt/data_objects/static_output.py\u001b[0m in \u001b[0;36m_get_field_info\u001b[0;34m(self, ftype, fname)\u001b[0m\n\u001b[1;32m 737\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_finfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfield_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 738\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_finfo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 739\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mYTFieldNotFound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 740\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 741\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_setup_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mYTFieldNotFound\u001b[0m: Could not find field '('plasma_p', 'particle_position_x')' in ."
+ ]
+ }
+ ],
+ "source": [
+ "p = yt.ParticlePlot(ds,('plasma_p', 'particle_position_x'),('plasma_p', 'particle_position_y'),('plasma_p', 'particle_momentum_x'))\n",
+ "p.show()\n",
+ "p.save(\"positron_plt\"+str(filenum)+\".png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "['electrons_plt90.png']"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p = yt.ParticlePlot(ds,('plasma_e', 'particle_position_y'),('plasma_e', 'particle_position_z'),('plasma_e', 'particle_momentum_z'))\n",
+ "#p = yt.ParticlePlot(ds,('plasma_p', 'particle_position_y'),('plasma_p', 'particle_position_z'),('plasma_p', 'particle_momentum_x'))\n",
+ "p.show()\n",
+ "p.save(\"electrons_plt\"+str(filenum)+\".png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def r_cl(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ "\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc))**0.5\n",
+ "\n",
+ " return r"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.add_field((\"index\",\"r_cl\"),function=r_cl,units='m',sampling_type=\"cell\")\n",
+ "#yt.SlicePlot(ds,'z','r_cl')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def rad(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ "\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ "\n",
+ " return r"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.add_field((\"index\",\"rad\"),function=rad,units='m',sampling_type=\"cell\")\n",
+ "#yt.SlicePlot(ds,'z','rad')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Store quantities in numpy arrays, and plot with matplotlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ex_external(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ " r_star = YTQuantity(12000, 'm')\n",
+ " omega = 6245.8\n",
+ " B = 8.03e-6\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ " c_theta = (data[\"z\"]-zc)/r\n",
+ " s_theta = data[\"r_cl\"]/r\n",
+ " c_phi = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
+ " s_phi = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
+ " r_ratio = r_star/r\n",
+ " r3 = r_ratio*r_ratio*r_ratio\n",
+ " Er = omega*r_star*B*r3*r_ratio*(1.0-3.0*c_theta*c_theta) \n",
+ " + (2.0/3.0)*omega*B*r_star*r_ratio*r_ratio\n",
+ " E_theta = (-1)*omega*B*r_star*r3*r_ratio*(c_theta*s_theta*2.0)\n",
+ " Ex_ext = Er*c_phi*s_theta + E_theta*c_phi*c_theta\n",
+ "\n",
+ " outside = np.zeros_like(data[\"r_cl\"])\n",
+ " outside[data[\"rad\"] > 12000] = 1.0\n",
+ "\n",
+ " \n",
+ " return outside.d*Ex_ext.d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ex_internal(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ " r_star = YTQuantity(12000, 'm')\n",
+ " omega = 6245.8\n",
+ " B = 8.03e-6\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ " c_theta = (data[\"z\"]-zc)/r\n",
+ " s_theta = data[\"r_cl\"]/r\n",
+ " c_phi = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
+ " s_phi = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
+ " r_ratio = r_star/r\n",
+ " r3 = r_ratio*r_ratio*r_ratio\n",
+ " Er = omega*r*r3*B*s_theta*s_theta\n",
+ " E_theta = (-1)*omega*B*r*r3*(c_theta*s_theta*2.0)\n",
+ " Ex_ext = Er*c_phi*s_theta + E_theta*c_phi*c_theta\n",
+ "\n",
+ " outside = np.zeros_like(data[\"r_cl\"])\n",
+ " outside[data[\"rad\"] <= 12000] = 1.0\n",
+ "\n",
+ " \n",
+ " return outside.d*Ex_ext.d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ey_external(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ " r_star = YTQuantity(12000, 'm')\n",
+ " omega = 6245.8\n",
+ " B = 8.03e-6\n",
+ "\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ " c_theta = (data[\"z\"]-zc)/r\n",
+ " s_theta = data[\"r_cl\"]/r\n",
+ " c_phi = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
+ " s_phi = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
+ " r_ratio = r_star/r\n",
+ " r3 = r_ratio*r_ratio*r_ratio\n",
+ " Er = omega*r_star*B*r3*r_ratio*(1.0-3.0*c_theta*c_theta) \n",
+ " + (2.0/3.0)*omega*B*r_star*r_ratio*r_ratio\n",
+ " E_theta = (-1)*omega*B*r_star*r3*r_ratio*(c_theta*s_theta*2.0)\n",
+ " Ey_ext = Er*s_phi*s_theta + E_theta*s_phi*c_theta\n",
+ "\n",
+ " outside = np.zeros_like(data[\"r_cl\"])\n",
+ " outside[data[\"rad\"] > 12000] = 1.0\n",
+ "\n",
+ " \n",
+ " return outside.d*Ey_ext.d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ey_internal(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ " r_star = YTQuantity(12000, 'm')\n",
+ " omega = 6245.8\n",
+ " B = 8.03e-6\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ " c_theta = (data[\"z\"]-zc)/r\n",
+ " s_theta = data[\"r_cl\"]/r\n",
+ " c_phi = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
+ " s_phi = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
+ " r_ratio = r_star/r\n",
+ " r3 = r_ratio*r_ratio*r_ratio\n",
+ " Er = omega*r*r3*B*s_theta*s_theta\n",
+ " E_theta = (-1)*omega*B*r*r3*(c_theta*s_theta*2.0)\n",
+ " Ey_ext = Er*s_phi*s_theta + E_theta*s_phi*c_theta\n",
+ " outside = np.zeros_like(data[\"r_cl\"])\n",
+ " outside[data[\"rad\"] <= 12000] = 1.0\n",
+ "\n",
+ " \n",
+ " return outside.d*Ey_ext.d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ez_external(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ " r_star = YTQuantity(12000, 'm')\n",
+ " omega = 6245.8\n",
+ " B = 8.03e-6\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ " c_theta = (data[\"z\"]-zc)/r\n",
+ " s_theta = data[\"r_cl\"]/r\n",
+ " c_phi = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
+ " s_phi = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
+ " r_ratio = r_star/r\n",
+ " r3 = r_ratio*r_ratio*r_ratio\n",
+ " Er = omega*r_star*B*r3*r_ratio*(1.0-3.0*c_theta*c_theta) \n",
+ " + (2.0/3.0)*omega*B*r_star*r_ratio*r_ratio\n",
+ " E_theta = (-1)*omega*B*r_star*r3*r_ratio*(c_theta*s_theta*2.0)\n",
+ " Ez_ext = Er*c_theta - E_theta*s_theta\n",
+ "\n",
+ " outside = np.zeros_like(data[\"r_cl\"])\n",
+ " outside[data[\"rad\"] > 12000] = 1.0\n",
+ "\n",
+ " \n",
+ " return outside.d*Ez_ext.d \n",
+ " #return outside.d*Er.d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ez_internal(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ " r_star = YTQuantity(12000, 'm')\n",
+ " omega = 6245.8\n",
+ " B = 8.03e-6\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ " c_theta = (data[\"z\"]-zc)/r\n",
+ " s_theta = data[\"r_cl\"]/r\n",
+ " c_phi = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
+ " s_phi = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
+ " r_ratio = r_star/r\n",
+ " r3 = r_ratio*r_ratio*r_ratio\n",
+ " Er = omega*r*r3*B*s_theta*s_theta\n",
+ " E_theta = (-1)*omega*B*r*r3*(c_theta*s_theta*2.0)\n",
+ " Ez_ext = Er*c_theta - E_theta*s_theta\n",
+ "\n",
+ " outside = np.zeros_like(data[\"r_cl\"])\n",
+ " outside[data[\"rad\"] <= 12000] = 1.0\n",
+ "\n",
+ " \n",
+ " #return outside.d*Ez_ext.d\n",
+ " return outside.d*Er.d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ex_total_ext(field,data):\n",
+ "\n",
+ " return data[\"Ex_internal\"] + data[\"Ex_external\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ey_total_ext(field,data):\n",
+ "\n",
+ " return data[\"Ey_internal\"] + data[\"Ey_external\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ez_total_ext(field,data):\n",
+ "\n",
+ " return data[\"Ez_internal\"] + data[\"Ez_external\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'Ez_external' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m-----------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Ex_external\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEx_external\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msampling_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cell\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Ey_external\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEy_external\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msampling_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cell\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Ez_external\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEz_external\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msampling_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cell\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Ex_internal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEx_internal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msampling_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cell\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Ey_internal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEy_internal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msampling_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cell\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'Ez_external' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "ds.add_field((\"Ex_external\"),function=Ex_external,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ey_external\"),function=Ey_external,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ez_external\"),function=Ez_external,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ex_internal\"),function=Ex_internal,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ey_internal\"),function=Ey_internal,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ez_internal\"),function=Ez_internal,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ex_total_ext\"),function=Ex_total_ext,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ey_total_ext\"),function=Ey_total_ext,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ez_total_ext\"),function=Ez_total_ext,units='',sampling_type=\"cell\")\n",
+ "\n",
+ "p = yt.SlicePlot(ds,'x','Ez_external')\n",
+ "p.annotate_streamlines(\"Ex_external\", \"Ez_external\", \n",
+ " plot_args={\"color\": \"black\"})\n",
+ "#p.annotate_particles(width=(2.e3, 'm'),ptype=\"plasma_p\",col='b',p_size=5.0)\n",
+ "#p.annotate_particles(width=(2.e3, 'm'),ptype=\"plasma_e\",col='r',p_size=5.0)\n",
+ "p.show()\n",
+ "p = yt.SlicePlot(ds,'x','Ez_internal')\n",
+ "p.annotate_streamlines(\"Ex_internal\", \"Ez_internal\", \n",
+ " plot_args={\"color\": \"black\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Read data back-transformed to the lab frame when the simulation runs in the boosted frame (example: 2D run)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# read_raw_data.py is located in warpx/Tools.\n",
+ "import os, glob\n",
+ "#import read_raw_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#species = 'beam'\n",
+ "#iteration = 1\n",
+ "#field = 'Ex'\n",
+ "\n",
+ "#snapshot = './lab_frame_data/' + 'snapshot' + str(iteration).zfill(5)\n",
+ "#header = './lab_frame_data/Header'\n",
+ "#allrd, info = read_raw_data.read_lab_snapshot(snapshot, header) # Read field data\n",
+ "#F = allrd[field]\n",
+ "#print( \"Available info: \", *list(info.keys()) )\n",
+ "#print(\"Available fields: \", info['field_names'])\n",
+ "#nx = info['nx']\n",
+ "#nz = info['nz']\n",
+ "#x = info['x']\n",
+ "#z = info['z']\n",
+ "#xbo = read_raw_data.get_particle_field(snapshot, species, 'x') # Read particle data\n",
+ "#ybo = read_raw_data.get_particle_field(snapshot, species, 'y')\n",
+ "#zbo = read_raw_data.get_particle_field(snapshot, species, 'z')\n",
+ "#uzbo = read_raw_data.get_particle_field(snapshot, species, 'uz')\n",
+ "\n",
+ "#plt.figure(figsize=(6, 3))\n",
+ "#extent = np.array([info['zmin'], info['zmax'], info['xmin'], info['xmax']])\n",
+ "#plt.imshow(F, aspect='auto', extent=extent, cmap='seismic')\n",
+ "#plt.colorbar()\n",
+ "#plt.plot(zbo, xbo, 'g.', markersize=1.)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Read back-transformed data with hdf5 format (example: 3D run)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "5e10*1400*1400"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "5e6*1400*1400"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "685708*1400*1400"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.7"
+ },
+ "widgets": {
+ "state": {
+ "11d243e9f5074fe1b115949d174d59de": {
+ "views": [
+ {
+ "cell_index": 6
+ }
+ ]
+ }
+ },
+ "version": "1.2.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Examples/Physics_applications/pulsar/inputs.corotating.3d.PM b/Examples/Physics_applications/pulsar/inputs.corotating.3d.PM
new file mode 100644
index 00000000000..9d7bc954bc8
--- /dev/null
+++ b/Examples/Physics_applications/pulsar/inputs.corotating.3d.PM
@@ -0,0 +1,120 @@
+# Description:
+#
+# This inputs file sets up a NS with these properties:
+# - E = -(omega*R)[cross]B inside the NS
+# - external E and B outside the star
+#
+# See the "Pulsar Setup" section at the end for the options
+#
+# This initializes the electrons and positrons with a corotating momentum function.
+# Based on the pulsar_type = "active" or "dead", particles are injected continuously or
+# until rho_GJ is reached
+
+#################################
+####### GENERAL PARAMETERS ######
+#################################
+max_step = 50000
+amr.n_cell = 128 128 128
+amr.max_grid_size = 128
+amr.blocking_factor = 128
+amr.max_level = 0
+geometry.coord_sys = 0 # 0: Cartesian
+geometry.is_periodic = 0 0 0 # Is periodic?
+geometry.prob_lo = 0.0 0.0 0.0
+geometry.prob_hi = 180000 180000 180000
+
+#################################
+############ NUMERICS ###########
+#################################
+#algo.maxwell_fdtd_solver = yee
+warpx.verbose = 1
+warpx.do_dive_cleaning = 0
+warpx.use_filter = 1
+warpx.cfl = .2
+warpx.do_pml = 1
+my_constants.pi = 3.141592653589793
+my_constants.dens = 5.544e6
+my_constants.xc = 90000
+my_constants.yc = 90000
+my_constants.zc = 90000
+my_constants.r_star = 12032
+my_constants.omega = 6245.676
+my_constants.c = 299792458.
+my_constants.B_star = 8.0323e-6 # magnetic field of NS (T)
+my_constants.dR = 1400
+my_constants.to = 2.e-4
+interpolation.nox = 3
+interpolation.noy = 3
+interpolation.noz = 3
+
+#################################
+############ PLASMA #############
+#################################
+particles.nspecies = 2
+particles.species_names = plasma_e plasma_p
+
+plasma_e.charge = -q_e
+plasma_e.mass = m_e
+plasma_e.injection_style = "NUniformPerCell"
+plasma_e.profile = parse_density_function
+plasma_e.density_function(x,y,z) = "( ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))<=(r_star)) * ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))>=(r_star-dR)) )*dens"
+plasma_e.num_particles_per_cell_each_dim = 4 4 4
+plasma_e.momentum_distribution_type = parse_momentum_function
+
+## Inject stationary pairs
+plasma_e.momentum_function_ux(x,y,z) = "0.0"
+plasma_e.momentum_function_uy(x,y,z) = "0.0"
+## uz is always 0 for the injection methods above
+plasma_e.momentum_function_uz(x,y,z) = "0.0"
+
+plasma_e.do_continuous_injection = 0
+plasma_e.density_min = 1E0
+
+plasma_p.charge = q_e
+plasma_p.mass = m_e
+plasma_p.injection_style = "NUniformPerCell"
+plasma_p.profile = parse_density_function
+plasma_p.density_function(x,y,z) = "( ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))<=(r_star)) * ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))>=(r_star-dR)) )*dens"
+plasma_p.num_particles_per_cell_each_dim = 4 4 4
+plasma_p.momentum_distribution_type = parse_momentum_function
+
+## Inject stationary pairs
+plasma_p.momentum_function_ux(x,y,z) = "0.0"
+plasma_p.momentum_function_uy(x,y,z) = "0.0"
+## uz is always 0 for the injection methods above
+plasma_p.momentum_function_uz(x,y,z) = "0.0"
+
+plasma_p.do_continuous_injection = 0
+plasma_p.density_min = 1E0
+
+diagnostics.diags_names = plt chk
+plt.diag_type = Full
+plt.period = 20
+plt.fields_to_plot = Ex Ey Ez Bx By Bz jx jy jz part_per_cell rho divE
+
+chk.format = checkpoint
+chk.diag_type = Full
+chk.period = 1000
+
+#################################
+######### PULSAR SETUP ##########
+#################################
+pulsar.pulsarType = "dead" # [dead/active]: sets particle injection type
+pulsar.omega_star = 6245.676 # angular velocity of NS (rad/s)
+pulsar.ramp_omega_time = 0.0 # time over which to ramp up NS angular velocity (s)
+ # if ramp_omega_time < 0, omega = omega_star for any t
+ # consistency requires ramp_omega_time = my_constants.to
+pulsar.center_star = 90000 90000 90000
+pulsar.R_star = 12032 # radius of NS (m)
+pulsar.B_star = 8.0323e-6 # magnetic field of NS (T)
+pulsar.dR = 1400 # thickness on the surface of the pulsar
+pulsar.Chi = 45
+ # consistency requires dR = my_constants.dR
+pulsar.verbose = 0 # [0/1]: turn on verbosity for debugging print statements
+pulsar.EB_external = 1 # [0/1]: to apply external E and B
+pulsar.E_external_monopole = 1 # [0/1]
+pulsar.max_ndens = 5.54e6 # max ndens == ndens used in initializing density
+pulsar.Ninj_fraction = 0.2 # fraction of sigma injected
+pulsar.rhoGJ_scale = 1e0 # scaling down of rho_GJ
+pulsar.damp_EB_internal = 1 # Damp internal electric field
+pulsar.damping_scale = 1000.0 # Damping scale factor for internal electric field
diff --git a/Examples/Physics_applications/pulsar/pulsar_vis.py b/Examples/Physics_applications/pulsar/pulsar_vis.py
new file mode 100644
index 00000000000..3694a1f91e5
--- /dev/null
+++ b/Examples/Physics_applications/pulsar/pulsar_vis.py
@@ -0,0 +1,54 @@
+import yt
+import os
+import glob
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-f", "--plotfile", type=str, help="Path to single input plotfile to visualize.")
+parser.add_argument("-d", "--dir", type=str, help="Path to input plotfile directory to visualize.")
+
+args = parser.parse_args()
+
+def visualize(plt, annotate_particles=True):
+ ds = yt.load(plt)
+ sl = yt.SlicePlot(ds, 0, 'Ez', aspect=1) # Create a sliceplot object
+ if annotate_particles:
+ sl.annotate_particles(width=(2.e3, 'm'),ptype="plasma_p",col='b',p_size=5.0)
+ sl.annotate_particles(width=(2.e3, 'm'),ptype="plasma_e",col='r',p_size=5.0)
+ sl.annotate_streamlines("Ey", "Ez", plot_args={"color": "black"})
+ sl.annotate_grids() # Show grids
+ sl.annotate_sphere([90000.0, 90000.0, 90000.0], radius=12000.0,
+ circle_args={'color':'white', 'linewidth':2})
+ out_name = os.path.join(os.path.dirname(plt), "pulsar_{}.png".format(os.path.basename(plt)))
+ sl.save(out_name)
+
+if __name__ == "__main__":
+ yt.funcs.mylog.setLevel(50)
+ if args.dir:
+ failed = []
+ plotfiles = glob.glob(os.path.join(args.dir, "plt" + "[0-9]"*5))
+ for pf in plotfiles:
+ try:
+ visualize(pf)
+ except:
+ # plotfile 0 may not have particles, so turn off annotations if we first failed
+ try:
+ visualize(pf, annotate_particles=False)
+ except:
+ failed.append(pf)
+ pass
+
+ if len(failed) > 0:
+ print("Visualization failed for the following plotfiles:")
+ for fp in failed:
+ print(fp)
+ else:
+ print("No plots failed, creating a gif ...")
+ input_files = os.path.join(args.dir, "*.png")
+ output_gif = os.path.join(args.dir, "pulsar.gif")
+ os.system("convert -delay 20 -loop 0 {} {}".format(input_files, output_gif))
+
+ elif args.plotfile:
+ visualize(args.plotfile)
+ else:
+ print("Supply either -f or -d options for visualization. Use the -h option to see help.")
diff --git a/GNUmakefile b/GNUmakefile
index e8ec31fcbdd..ca026424e92 100644
--- a/GNUmakefile
+++ b/GNUmakefile
@@ -35,6 +35,7 @@ USE_ASCENT_INSITU = FALSE
USE_OPENPMD = FALSE
WarpxBinDir = Bin
+PULSAR = TRUE
USE_PSATD = FALSE
USE_PSATD_PICSAR = FALSE
diff --git a/Source/Evolve/WarpXEvolve.cpp b/Source/Evolve/WarpXEvolve.cpp
index 76c7e687219..ba423aa08db 100644
--- a/Source/Evolve/WarpXEvolve.cpp
+++ b/Source/Evolve/WarpXEvolve.cpp
@@ -18,6 +18,9 @@
# include "FieldSolver/SpectralSolver/SpectralSolver.H"
#endif
+#ifdef PULSAR
+ #include "Particles/PulsarParameters.H"
+#endif
#include
#include
@@ -136,7 +139,94 @@ WarpX::Evolve (int numsteps)
// B : guard cells are NOT up-to-date
}
+#ifdef PULSAR
+ if (PulsarParm::damp_EB_internal) {
+ MultiFab *Ex, *Ey, *Ez;
+ MultiFab *Bx, *By, *Bz;
+ for (int lev = 0; lev <= finest_level; ++lev) {
+ Ex = Efield_fp[lev][0].get();
+ Ey = Efield_fp[lev][1].get();
+ Ez = Efield_fp[lev][2].get();
+ Bx = Bfield_fp[lev][0].get();
+ By = Bfield_fp[lev][1].get();
+ Bz = Bfield_fp[lev][2].get();
+ amrex::IntVect ex_type = Ex->ixType().toIntVect();
+ amrex::IntVect ey_type = Ey->ixType().toIntVect();
+ amrex::IntVect ez_type = Ez->ixType().toIntVect();
+ amrex::IntVect bx_type = Bx->ixType().toIntVect();
+ amrex::IntVect by_type = By->ixType().toIntVect();
+ amrex::IntVect bz_type = Bz->ixType().toIntVect();
+ amrex::GpuArray Ex_stag, Ey_stag, Ez_stag, Bx_stag, By_stag, Bz_stag;
+ for (int idim = 0; idim < 3; ++idim)
+ {
+ Ex_stag[idim] = ex_type[idim];
+ Ey_stag[idim] = ey_type[idim];
+ Ez_stag[idim] = ez_type[idim];
+ Bx_stag[idim] = bx_type[idim];
+ By_stag[idim] = by_type[idim];
+ Bz_stag[idim] = bz_type[idim];
+ }
+ auto geom = Geom(lev).data();
+#ifdef _OPENMP
+#pragma omp parallel if (Gpu::notInLaunchRegion())
+#endif
+ for ( MFIter mfi(*Ex, TilingIfNotGPU()); mfi.isValid(); ++mfi )
+ {
+ const Box& tex = mfi.tilebox( Ex->ixType().toIntVect() );
+ const Box& tey = mfi.tilebox( Ey->ixType().toIntVect() );
+ const Box& tez = mfi.tilebox( Ez->ixType().toIntVect() );
+
+ auto const& Exfab = Ex->array(mfi);
+ auto const& Eyfab = Ey->array(mfi);
+ auto const& Ezfab = Ez->array(mfi);
+
+ amrex::ParallelFor(tex, tey, tez,
+ [=] AMREX_GPU_DEVICE (int i, int j, int k)
+ {
+ PulsarParm::DampField(i, j, k, geom, Exfab, Ex_stag);
+ },
+ [=] AMREX_GPU_DEVICE (int i, int j, int k)
+ {
+ PulsarParm::DampField(i, j, k, geom, Eyfab, Ey_stag);
+ },
+ [=] AMREX_GPU_DEVICE (int i, int j, int k)
+ {
+ PulsarParm::DampField(i, j, k, geom, Ezfab, Ez_stag);
+ });
+ }
+ for ( MFIter mfi(*Bx, TilingIfNotGPU()); mfi.isValid(); ++mfi )
+ {
+ const Box& tex = mfi.tilebox( Bx->ixType().toIntVect() );
+ const Box& tey = mfi.tilebox( By->ixType().toIntVect() );
+ const Box& tez = mfi.tilebox( Bz->ixType().toIntVect() );
+
+ auto const& Bxfab = Bx->array(mfi);
+ auto const& Byfab = By->array(mfi);
+ auto const& Bzfab = Bz->array(mfi);
+
+ amrex::ParallelFor(tex, tey, tez,
+ [=] AMREX_GPU_DEVICE (int i, int j, int k)
+ {
+ PulsarParm::DampField(i, j, k, geom, Bxfab, Bx_stag);
+ },
+ [=] AMREX_GPU_DEVICE (int i, int j, int k)
+ {
+ PulsarParm::DampField(i, j, k, geom, Byfab, By_stag);
+ },
+ [=] AMREX_GPU_DEVICE (int i, int j, int k)
+ {
+ PulsarParm::DampField(i, j, k, geom, Bzfab, Bz_stag);
+ });
+ }
+ }
+ }
+ FillBoundaryE(guard_cells.ng_FieldSolver, IntVect::TheZeroVector());
+ FillBoundaryB(guard_cells.ng_FieldSolver, IntVect::TheZeroVector());
+#endif
+
+#ifdef WARPX_USE_PY
if (warpx_py_beforeEsolve) warpx_py_beforeEsolve();
+#endif
if (cur_time + dt[0] >= stop_time - 1.e-3*dt[0] || step == numsteps_max-1) {
// At the end of last step, push p by 0.5*dt to synchronize
@@ -151,7 +241,9 @@ WarpX::Evolve (int numsteps)
is_synchronized = true;
}
+#ifdef WARPX_USE_PY
if (warpx_py_afterEsolve) warpx_py_afterEsolve();
+#endif
for (int lev = 0; lev <= max_level; ++lev) {
++istep[lev];
@@ -175,6 +267,16 @@ WarpX::Evolve (int numsteps)
int num_moved = MoveWindow(move_j);
+#ifdef PULSAR
+ if (!rho_fp[0]) {
+ amrex::Print() << " no rho -- compute rho! \n";
+ }
+ else {
+ amrex::Print() << " rho is computed \n";
+ }
+ mypc->PulsarParticleRemoval();
+ mypc->PulsarParticleInjection();
+#endif
mypc->ApplyBoundaryConditions();
// Electrostatic solver: particles can move by an arbitrary number of cells
diff --git a/Source/Initialization/InjectorPosition.H b/Source/Initialization/InjectorPosition.H
index 2854ec6f8ba..f83ba97f69c 100644
--- a/Source/Initialization/InjectorPosition.H
+++ b/Source/Initialization/InjectorPosition.H
@@ -12,6 +12,10 @@
#include
#include
+#ifdef PULSAR
+ #include "Particles/PulsarParameters.H"
+#endif
+
// struct whose getPositionUnitBox returns x, y and z for a particle with
// random distribution inside a unit cell.
struct InjectorPositionRandom
@@ -41,14 +45,23 @@ struct InjectorPositionRegular
amrex::RandomEngine const&) const noexcept
{
using namespace amrex;
-
+#ifdef PULSAR
+#if (defined WARPX_DIM_3D) || (defined WARPX_DIM_RZ)
+ int const nx = ref_fac*ppc.x*std::cbrt(PulsarParm::Ninj_fraction);
+ int const ny = ref_fac*ppc.y*std::cbrt(PulsarParm::Ninj_fraction);
+ int const nz = ref_fac*ppc.z*std::cbrt(PulsarParm::Ninj_fraction);
+#else
+ amrex::Abort("pulsar sim does not support 2d yet");
+#endif // endif for 3D
+#else // else ifndef PULSAR
int const nx = ref_fac*ppc.x;
int const ny = ref_fac*ppc.y;
#if (defined WARPX_DIM_3D) || (defined WARPX_DIM_RZ)
int const nz = ref_fac*ppc.z;
#else
int const nz = 1;
-#endif
+#endif // 3D ifdef
+#endif // PULSAR ifdef
int const ix_part = i_part / (ny*nz); // written this way backward compatibility
int const iz_part = (i_part-ix_part*(ny*nz)) / ny;
int const iy_part = (i_part-ix_part*(ny*nz)) - ny*iz_part;
@@ -58,6 +71,17 @@ struct InjectorPositionRegular
(0.5_rt + iz_part) / nz
};
}
+
+#ifdef PULSAR
+ AMREX_GPU_HOST_DEVICE
+ amrex::XDim3
+ getppcInEachDim () const noexcept
+ {
+ using namespace amrex;
+ return XDim3{ppc.x, ppc.y, ppc.z};
+ }
+#endif
+
private:
amrex::Dim3 ppc;
};
@@ -123,6 +147,25 @@ struct InjectorPosition
};
}
+#ifdef PULSAR
+ AMREX_GPU_HOST_DEVICE
+ amrex::XDim3
+ getppcInEachDim () const noexcept
+ {
+ switch (type)
+ {
+ case Type::regular:
+ {
+ return object.regular.getppcInEachDim();
+ }
+ default:
+ {
+ return object.regular.getppcInEachDim();
+ }
+ };
+ }
+#endif
+
// bool: whether position specified is within bounds.
AMREX_GPU_HOST_DEVICE
bool
@@ -133,6 +176,14 @@ struct InjectorPosition
z < zmax and z >= zmin);
}
+#ifdef PULSAR
+ AMREX_GPU_HOST_DEVICE
+ bool
+ insidePulsarBounds (amrex::Real r, amrex::Real R_star, amrex::Real dR_star) const noexcept
+ {
+ return (r<=(R_star + dR_star) and r>=(R_star - dR_star));
+ }
+#endif
// bool: whether the region defined by lo and hi overaps with the plasma region
AMREX_GPU_HOST_DEVICE AMREX_FORCE_INLINE
bool
diff --git a/Source/Make.WarpX b/Source/Make.WarpX
index 02ddd453a15..4e73e4ca167 100644
--- a/Source/Make.WarpX
+++ b/Source/Make.WarpX
@@ -65,6 +65,10 @@ else
endif
endif
+ifeq ($(PULSAR),TRUE)
+ DEFINES += -DPULSAR
+endif
+
-include Make.package
include $(WARPX_HOME)/Source/Make.package
include $(WARPX_HOME)/Source/BoundaryConditions/Make.package
diff --git a/Source/Particles/CMakeLists.txt b/Source/Particles/CMakeLists.txt
index 248de496025..75ed5acf5e8 100644
--- a/Source/Particles/CMakeLists.txt
+++ b/Source/Particles/CMakeLists.txt
@@ -5,6 +5,7 @@ target_sources(WarpX
PhysicalParticleContainer.cpp
RigidInjectedParticleContainer.cpp
WarpXParticleContainer.cpp
+ PulsarParameters.cpp
)
add_subdirectory(Collision)
diff --git a/Source/Particles/ElementaryProcess/Ionization.H b/Source/Particles/ElementaryProcess/Ionization.H
index 5ebd5ed50dd..8257ff8a79d 100644
--- a/Source/Particles/ElementaryProcess/Ionization.H
+++ b/Source/Particles/ElementaryProcess/Ionization.H
@@ -13,7 +13,6 @@
#include "Particles/Gather/GetExternalFields.H"
#include "Particles/Gather/FieldGather.H"
#include "Particles/Pusher/GetAndSetPosition.H"
-
struct IonizationFilterFunc
{
const amrex::Real* AMREX_RESTRICT m_ionization_energies;
@@ -50,6 +49,11 @@ struct IonizationFilterFunc
int m_n_rz_azimuthal_modes;
amrex::Dim3 m_lo;
+#ifdef PULSAR
+ amrex::GpuArray m_problo;
+ amrex::GpuArray m_probhi;
+ amrex::Real m_cur_time;
+#endif
IonizationFilterFunc (const WarpXParIter& a_pti, int lev, int ngE,
amrex::FArrayBox const& exfab,
@@ -93,7 +97,11 @@ struct IonizationFilterFunc
m_ex_arr, m_ey_arr, m_ez_arr, m_bx_arr, m_by_arr, m_bz_arr,
m_ex_type, m_ey_type, m_ez_type, m_bx_type, m_by_type, m_bz_type,
m_dx_arr, m_xyzmin_arr, m_lo, m_n_rz_azimuthal_modes,
- m_nox, m_galerkin_interpolation);
+ m_nox, m_galerkin_interpolation
+#ifdef PULSAR
+ , m_problo, m_probhi, m_cur_time
+#endif
+ );
// Compute electric field amplitude in the particle's frame of
// reference (particularly important when in boosted frame).
diff --git a/Source/Particles/Gather/FieldGather.H b/Source/Particles/Gather/FieldGather.H
index 89b665bf03a..231a5bac2c1 100644
--- a/Source/Particles/Gather/FieldGather.H
+++ b/Source/Particles/Gather/FieldGather.H
@@ -12,7 +12,9 @@
#include "Particles/Gather/GetExternalFields.H"
#include "Particles/ShapeFactors.H"
#include "Utils/WarpX_Complex.H"
-
+#ifdef PULSAR
+ #include "Particles/PulsarParameters.H"
+#endif
#include
/**
@@ -59,7 +61,15 @@ void doGatherShapeN (const amrex::ParticleReal xp,
const amrex::GpuArray& dx,
const amrex::GpuArray& xyzmin,
const amrex::Dim3& lo,
- const long n_rz_azimuthal_modes)
+ const long n_rz_azimuthal_modes
+#ifdef PULSAR
+ ,
+ amrex::GpuArray const& dom_lo = {},
+ amrex::GpuArray const& dom_hi = {},
+ amrex::Real cur_time = 0._rt )
+#else
+ )
+#endif
{
using namespace amrex;
@@ -137,6 +147,20 @@ void doGatherShapeN (const amrex::ParticleReal xp,
int const j_by = ((by_type[0] == NODE) ? j_node_v : j_cell_v);
int const j_bz = ((bz_type[0] == NODE) ? j_node_v : j_cell_v);
+#ifdef PULSAR
+ // store staggering of ex-bz in GpuArray
+ amrex::GpuArray Ex_stag, Ey_stag, Ez_stag, Bx_stag, By_stag, Bz_stag;
+ for (int idim = 0; idim < 3; ++idim)
+ {
+ Ex_stag[idim] = ex_type[idim];
+ Ey_stag[idim] = ey_type[idim];
+ Ez_stag[idim] = ez_type[idim];
+ Bx_stag[idim] = bx_type[idim];
+ By_stag[idim] = by_type[idim];
+ Bz_stag[idim] = bz_type[idim];
+ }
+#endif
+
#if (AMREX_SPACEDIM == 3)
// y direction
const amrex::Real y = (yp-ymin)*dyi;
@@ -325,6 +349,32 @@ void doGatherShapeN (const amrex::ParticleReal xp,
for (int ix=0; ix<= depos_order - galerkin_interpolation; ix++){
Exp += sx_ex[ix]*sy_ex[iy]*sz_ex[iz]*
ex_arr(lo.x+j_ex+ix, lo.y+k_ex+iy, lo.z+l_ex+iz);
+#ifdef PULSAR
+ // Add the external field contribution for pulsar
+ int ii = lo.x + j_ex + ix;
+ int jj = lo.y + k_ex + iy;
+ int kk = lo.z + l_ex + iz;
+ amrex::Real xcell, ycell, zcell;
+ amrex::Real r, theta, phi;
+ amrex::Real Er, Etheta, Ephi;
+ amrex::Real ex_ext;
+ // compute cell coordinates, (x,y,z) corresponding to (ii,jj,kk)
+ // based on the staggered Ex location on the yee-grid
+ PulsarParm::ComputeCellCoordinates( ii, jj, kk, Ex_stag, dom_lo,
+ dx, xcell, ycell, zcell );
+ // Convert (x,y,z) to (r,theta,phi)
+ PulsarParm::ConvertCartesianToSphericalCoord( xcell, ycell, zcell,
+ dom_lo, dom_hi,
+ r, theta, phi);
+ // Compute External vacuum fields of the pulsar (Er, Etheta, Ephi)
+ PulsarParm::ExternalEFieldSpherical( r, theta, phi, cur_time,
+ Er, Etheta, Ephi);
+ // Convert (Er, Etheta, Ephi) to ex_ext
+ PulsarParm::ConvertSphericalToCartesianXComponent( Er, Etheta, Ephi,
+ r, theta, phi, ex_ext);
+ // add contribution of ex_ext to
+ Exp += sx_ex[ix]*sy_ex[iy]*sz_ex[iz]*ex_ext;
+#endif
}
}
}
@@ -334,6 +384,32 @@ void doGatherShapeN (const amrex::ParticleReal xp,
for (int ix=0; ix<=depos_order; ix++){
Eyp += sx_ey[ix]*sy_ey[iy]*sz_ey[iz]*
ey_arr(lo.x+j_ey+ix, lo.y+k_ey+iy, lo.z+l_ey+iz);
+#ifdef PULSAR
+ // Add the external field contribution for pulsar
+ int ii = lo.x + j_ey + ix;
+ int jj = lo.y + k_ey + iy;
+ int kk = lo.z + l_ey + iz;
+ amrex::Real xcell, ycell, zcell;
+ amrex::Real r, theta, phi;
+ amrex::Real Er, Etheta, Ephi;
+ amrex::Real ey_ext;
+ // compute cell coordinates, (x,y,z) corresponding to (ii,jj,kk)
+ // based on the staggered Ey location on the yee-grid
+ PulsarParm::ComputeCellCoordinates( ii, jj, kk, Ey_stag, dom_lo,
+ dx, xcell, ycell, zcell );
+ // Convert (x,y,z) to (r,theta,phi)
+ PulsarParm::ConvertCartesianToSphericalCoord( xcell, ycell, zcell,
+ dom_lo, dom_hi,
+ r, theta, phi);
+ // Compute External vacuum fields of the pulsar (Er, Etheta, Ephi)
+ PulsarParm::ExternalEFieldSpherical( r, theta, phi, cur_time,
+ Er, Etheta, Ephi);
+ // Convert (Er, Etheta, Ephi) to ey_ext
+ PulsarParm::ConvertSphericalToCartesianYComponent( Er, Etheta, Ephi,
+ r, theta, phi, ey_ext);
+ // add contribution of ex_ext to
+ Eyp += sx_ey[ix]*sy_ey[iy]*sz_ey[iz]*ey_ext;
+#endif
}
}
}
@@ -343,6 +419,32 @@ void doGatherShapeN (const amrex::ParticleReal xp,
for (int ix=0; ix<=depos_order; ix++){
Ezp += sx_ez[ix]*sy_ez[iy]*sz_ez[iz]*
ez_arr(lo.x+j_ez+ix, lo.y+k_ez+iy, lo.z+l_ez+iz);
+#ifdef PULSAR
+ // Add the external field contribution for pulsar
+ int ii = lo.x + j_ez + ix;
+ int jj = lo.y + k_ez + iy;
+ int kk = lo.z + l_ez + iz;
+ amrex::Real xcell, ycell, zcell;
+ amrex::Real r, theta, phi;
+ amrex::Real Er, Etheta, Ephi;
+ amrex::Real ez_ext;
+ // compute cell coordinates, (x,y,z) corresponding to (ii,jj,kk)
+ // based on the staggered Ez location on the yee-grid
+ PulsarParm::ComputeCellCoordinates( ii, jj, kk, Ez_stag, dom_lo,
+ dx, xcell, ycell, zcell );
+ // Convert (x,y,z) to (r,theta,phi)
+ PulsarParm::ConvertCartesianToSphericalCoord( xcell, ycell, zcell,
+ dom_lo, dom_hi,
+ r, theta, phi);
+ // Compute External vacuum fields of the pulsar (Er, Etheta, Ephi)
+ PulsarParm::ExternalEFieldSpherical( r, theta, phi, cur_time,
+ Er, Etheta, Ephi);
+ // Convert (Er, Etheta, Ephi) to ez_ext
+ PulsarParm::ConvertSphericalToCartesianZComponent( Er, Etheta, Ephi,
+ r, theta, phi, ez_ext);
+ // add contribution of ex_ext to
+ Ezp += sx_ez[ix]*sy_ez[iy]*sz_ez[iz]*ez_ext;
+#endif
}
}
}
@@ -352,6 +454,32 @@ void doGatherShapeN (const amrex::ParticleReal xp,
for (int ix=0; ix<= depos_order - galerkin_interpolation; ix++){
Bzp += sx_bz[ix]*sy_bz[iy]*sz_bz[iz]*
bz_arr(lo.x+j_bz+ix, lo.y+k_bz+iy, lo.z+l_bz+iz);
+#ifdef PULSAR
+ // Add the external field contribution for pulsar
+ int ii = lo.x + j_bz + ix;
+ int jj = lo.y + k_bz + iy;
+ int kk = lo.z + l_bz + iz;
+ amrex::Real xcell, ycell, zcell;
+ amrex::Real r, theta, phi;
+ amrex::Real Br, Btheta, Bphi;
+ amrex::Real bz_ext;
+ // compute cell coordinates, (x,y,z) corresponding to (ii,jj,kk)
+ // based on the staggered Bz location on the yee-grid
+ PulsarParm::ComputeCellCoordinates( ii, jj, kk, Bz_stag, dom_lo,
+ dx, xcell, ycell, zcell );
+ // Convert (x,y,z) to (r,theta,phi)
+ PulsarParm::ConvertCartesianToSphericalCoord( xcell, ycell, zcell,
+ dom_lo, dom_hi,
+ r, theta, phi);
+ // Compute External vacuum fields of the pulsar (Br, Btheta, Bphi)
+ PulsarParm::ExternalBFieldSpherical( r, theta, phi, cur_time,
+ Br, Btheta, Bphi);
+ // Convert (Br, Btheta, Bphi) to bz_ext
+ PulsarParm::ConvertSphericalToCartesianZComponent( Br, Btheta, Bphi,
+ r, theta, phi, bz_ext);
+ // add contribution of ex_ext to
+ Bzp += sx_bz[ix]*sy_bz[iy]*sz_bz[iz]*bz_ext;
+#endif
}
}
}
@@ -361,6 +489,32 @@ void doGatherShapeN (const amrex::ParticleReal xp,
for (int ix=0; ix<= depos_order - galerkin_interpolation; ix++){
Byp += sx_by[ix]*sy_by[iy]*sz_by[iz]*
by_arr(lo.x+j_by+ix, lo.y+k_by+iy, lo.z+l_by+iz);
+#ifdef PULSAR
+ // Add the external field contribution for pulsar
+ int ii = lo.x + j_by + ix;
+ int jj = lo.y + k_by + iy;
+ int kk = lo.z + l_by + iz;
+ amrex::Real xcell, ycell, zcell;
+ amrex::Real r, theta, phi;
+ amrex::Real Br, Btheta, Bphi;
+ amrex::Real by_ext;
+ // compute cell coordinates, (x,y,z) corresponding to (ii,jj,kk)
+ // based on the staggered By location on the yee-grid
+ PulsarParm::ComputeCellCoordinates( ii, jj, kk, By_stag, dom_lo,
+ dx, xcell, ycell, zcell );
+ // Convert (x,y,z) to (r,theta,phi)
+ PulsarParm::ConvertCartesianToSphericalCoord( xcell, ycell, zcell,
+ dom_lo, dom_hi,
+ r, theta, phi);
+ // Compute External vacuum fields of the pulsar (Br, Btheta, Bphi)
+ PulsarParm::ExternalBFieldSpherical( r, theta, phi, cur_time,
+ Br, Btheta, Bphi);
+ // Convert (Br, Btheta, Bphi) to by_ext
+ PulsarParm::ConvertSphericalToCartesianYComponent( Br, Btheta, Bphi,
+ r, theta, phi, by_ext);
+ // add contribution of by_ext to
+ Byp += sx_by[ix]*sy_by[iy]*sz_by[iz]*by_ext;
+#endif
}
}
}
@@ -370,6 +524,32 @@ void doGatherShapeN (const amrex::ParticleReal xp,
for (int ix=0; ix<=depos_order; ix++){
Bxp += sx_bx[ix]*sy_bx[iy]*sz_bx[iz]*
bx_arr(lo.x+j_bx+ix, lo.y+k_bx+iy, lo.z+l_bx+iz);
+#ifdef PULSAR
+ // Add the external field contribution for pulsar
+ int ii = lo.x + j_bx + ix;
+ int jj = lo.y + k_bx + iy;
+ int kk = lo.z + l_bx + iz;
+ amrex::Real xcell, ycell, zcell;
+ amrex::Real r, theta, phi;
+ amrex::Real Br, Btheta, Bphi;
+ amrex::Real bx_ext;
+ // compute cell coordinates, (x,y,z) corresponding to (ii,jj,kk)
+ // based on the staggered Bx location on the yee-grid
+ PulsarParm::ComputeCellCoordinates( ii, jj, kk, Bx_stag, dom_lo,
+ dx, xcell, ycell, zcell );
+ // Convert (x,y,z) to (r,theta,phi)
+ PulsarParm::ConvertCartesianToSphericalCoord( xcell, ycell, zcell,
+ dom_lo, dom_hi,
+ r, theta, phi);
+ // Compute External vacuum fields of the pulsar (Br, Btheta, Bphi)
+ PulsarParm::ExternalBFieldSpherical( r, theta, phi, cur_time,
+ Br, Btheta, Bphi);
+ // Convert (Br, Btheta, Bphi) to bx_ext
+ PulsarParm::ConvertSphericalToCartesianXComponent( Br, Btheta, Bphi,
+ r, theta, phi, bx_ext);
+ // add contribution of by_ext to
+ Bxp += sx_bx[ix]*sy_bx[iy]*sz_bx[iz]*bx_ext;
+#endif
}
}
}
@@ -406,9 +586,17 @@ void doGatherShapeN(const GetParticlePosition& getPosition,
amrex::FArrayBox const * const bzfab,
const long np_to_gather,
const std::array& dx,
- const std::array xyzmin,
+ const std::array& xyzmin,
const amrex::Dim3 lo,
- const long n_rz_azimuthal_modes)
+ const long n_rz_azimuthal_modes
+#ifdef PULSAR
+ ,
+ amrex::GpuArray const& dom_lo = {},
+ amrex::GpuArray const& dom_hi = {},
+ amrex::Real cur_time = 0._rt )
+#else
+ )
+#endif
{
amrex::GpuArray dx_arr = {dx[0], dx[1], dx[2]};
@@ -443,7 +631,13 @@ void doGatherShapeN(const GetParticlePosition& getPosition,
xp, yp, zp, Exp[ip], Eyp[ip], Ezp[ip], Bxp[ip], Byp[ip], Bzp[ip],
ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
- dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes);
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes
+#ifdef PULSAR
+ ,
+ dom_lo, dom_hi, cur_time);
+#else
+ );
+#endif
}
);
}
@@ -492,41 +686,73 @@ void doGatherShapeN (const amrex::ParticleReal xp,
const amrex::Dim3& lo,
const long n_rz_azimuthal_modes,
const int nox,
- const bool galerkin_interpolation)
+ const bool galerkin_interpolation
+#ifdef PULSAR
+ ,
+ amrex::GpuArray const& dom_lo = {},
+ amrex::GpuArray const& dom_hi = {},
+ amrex::Real cur_time = 0._rt )
+#else
+ )
+#endif
{
if (galerkin_interpolation) {
if (nox == 1) {
doGatherShapeN<1,1>(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
- dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes);
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes
+#ifdef PULSAR
+ , dom_lo, dom_hi, cur_time
+#endif
+ );
} else if (nox == 2) {
doGatherShapeN<2,1>(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
- dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes);
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes
+#ifdef PULSAR
+ , dom_lo, dom_hi, cur_time
+#endif
+ );
} else if (nox == 3) {
doGatherShapeN<3,1>(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
- dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes);
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes
+#ifdef PULSAR
+ , dom_lo, dom_hi, cur_time
+#endif
+ );
}
} else {
if (nox == 1) {
doGatherShapeN<1,0>(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
- dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes);
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes
+#ifdef PULSAR
+ , dom_lo, dom_hi, cur_time
+#endif
+ );
} else if (nox == 2) {
doGatherShapeN<2,0>(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
- dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes);
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes
+#ifdef PULSAR
+ , dom_lo, dom_hi, cur_time
+#endif
+ );
} else if (nox == 3) {
doGatherShapeN<3,0>(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
- dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes);
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes
+#ifdef PULSAR
+ , dom_lo, dom_hi, cur_time
+#endif
+ );
}
}
}
diff --git a/Source/Particles/Gather/GetExternalFields.H b/Source/Particles/Gather/GetExternalFields.H
index 4bf520be955..25607b7cc5c 100644
--- a/Source/Particles/Gather/GetExternalFields.H
+++ b/Source/Particles/Gather/GetExternalFields.H
@@ -3,12 +3,16 @@
#include "Particles/WarpXParticleContainer.H"
#include "Particles/Pusher/GetAndSetPosition.H"
+#ifdef PULSAR
+ #include "Particles/PulsarParameters.H"
+#endif
#include
#include
-enum ExternalFieldInitType { Constant, Parser };
+enum ExternalFieldInitType { Constant, Parser
+ };
/** \brief Base class for functors that assign external
* field values (E or B) to particles.
diff --git a/Source/Particles/Make.package b/Source/Particles/Make.package
index 578c389647b..31f49c1ec5e 100644
--- a/Source/Particles/Make.package
+++ b/Source/Particles/Make.package
@@ -3,6 +3,7 @@ CEXE_sources += WarpXParticleContainer.cpp
CEXE_sources += RigidInjectedParticleContainer.cpp
CEXE_sources += PhysicalParticleContainer.cpp
CEXE_sources += PhotonParticleContainer.cpp
+CEXE_sources += PulsarParameters.cpp
include $(WARPX_HOME)/Source/Particles/Pusher/Make.package
include $(WARPX_HOME)/Source/Particles/Deposition/Make.package
diff --git a/Source/Particles/MultiParticleContainer.H b/Source/Particles/MultiParticleContainer.H
index ed6c277fecf..a0e05c1003e 100644
--- a/Source/Particles/MultiParticleContainer.H
+++ b/Source/Particles/MultiParticleContainer.H
@@ -29,6 +29,9 @@
# include "Particles/ElementaryProcess/QEDInternals/QuantumSyncEngineWrapper.H"
#endif
+#ifdef PULSAR
+ #include "Particles/PulsarParameters.H"
+#endif
#include
#include
@@ -234,6 +237,12 @@ public:
std::unique_ptr > m_Ey_particle_parser;
std::unique_ptr > m_Ez_particle_parser;
+#ifdef PULSAR
+ std::string m_E_ext_particle_coord = "cartesian";
+ std::string m_B_ext_particle_coord = "cartesian";
+ void PulsarParticleInjection();
+ void PulsarParticleRemoval();
+#endif
#ifdef WARPX_QED
/**
* \brief Performs QED events (Breit-Wheeler process and photon emission)
diff --git a/Source/Particles/MultiParticleContainer.cpp b/Source/Particles/MultiParticleContainer.cpp
index cc274655dda..3b8c15ec52b 100644
--- a/Source/Particles/MultiParticleContainer.cpp
+++ b/Source/Particles/MultiParticleContainer.cpp
@@ -110,6 +110,16 @@ MultiParticleContainer::ReadParameters ()
m_E_ext_particle_s.end(),
m_E_ext_particle_s.begin(),
::tolower);
+
+#ifdef PULSAR
+ // co-ordinate system used to specify external fields
+ // is cartesian (default) or spherical
+ // For pulsar it is easier to provide (r,theta,phi) components
+ // and let the code do the conversion to cartesian
+ pp.query("E_ext_particle_coord", m_E_ext_particle_coord);
+ pp.query("B_ext_particle_coord", m_B_ext_particle_coord);
+#endif
+
// if the input string for B_external on particles is "constant"
// then the values for the external B on particles must
// be provided in the input file.
@@ -1378,3 +1388,23 @@ void MultiParticleContainer::CheckQEDProductSpecies()
}
#endif
+
+#ifdef PULSAR
+void
+MultiParticleContainer::PulsarParticleInjection()
+{
+ amrex::Print() << " pulsar injection on! \n";
+ for (auto& pc : allcontainers) {
+ pc->PulsarParticleInjection();
+ }
+}
+void
+MultiParticleContainer::PulsarParticleRemoval()
+{
+ amrex::Print() << " pulsar particle removal on! \n";
+ for (auto& pc : allcontainers) {
+ pc->PulsarParticleRemoval();
+ }
+
+}
+#endif
diff --git a/Source/Particles/PhotonParticleContainer.cpp b/Source/Particles/PhotonParticleContainer.cpp
index 9eee508f10e..c8d1c835d1a 100644
--- a/Source/Particles/PhotonParticleContainer.cpp
+++ b/Source/Particles/PhotonParticleContainer.cpp
@@ -132,6 +132,10 @@ PhotonParticleContainer::PushPX (WarpXParIter& pti,
const std::array& xyzmin = WarpX::LowerCorner(box, galilean_shift, gather_lev);
const Dim3 lo = lbound(box);
+#ifdef PULSAR
+ const auto problo = WarpX::GetInstance().Geom(lev).ProbLoArray();
+ const auto probhi = WarpX::GetInstance().Geom(lev).ProbHiArray();
+#endif
bool galerkin_interpolation = WarpX::galerkin_interpolation;
int nox = WarpX::nox;
@@ -172,7 +176,12 @@ PhotonParticleContainer::PushPX (WarpXParIter& pti,
ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
- nox, galerkin_interpolation);
+ nox, galerkin_interpolation
+#ifdef PULSAR
+ , problo, probhi, cur_time);
+#else
+ );
+#endif
}
getExternalE(i, Exp, Eyp, Ezp);
getExternalB(i, Bxp, Byp, Bzp);
diff --git a/Source/Particles/PhysicalParticleContainer.H b/Source/Particles/PhysicalParticleContainer.H
index 1e1307072df..036edf6d217 100644
--- a/Source/Particles/PhysicalParticleContainer.H
+++ b/Source/Particles/PhysicalParticleContainer.H
@@ -328,6 +328,11 @@ public:
PairGenerationFilterFunc getPairGenerationFilterFunc ();
#endif
+#ifdef PULSAR
+ virtual void PulsarParticleInjection () override;
+ virtual void PulsarParticleRemoval () override;
+#endif
+
protected:
std::string species_name;
std::unique_ptr plasma_injector;
diff --git a/Source/Particles/PhysicalParticleContainer.cpp b/Source/Particles/PhysicalParticleContainer.cpp
index 51a615234c4..160db317677 100644
--- a/Source/Particles/PhysicalParticleContainer.cpp
+++ b/Source/Particles/PhysicalParticleContainer.cpp
@@ -22,6 +22,9 @@
#include "Particles/Pusher/PushSelector.H"
#include "Particles/Gather/GetExternalFields.H"
#include "Utils/WarpXAlgorithmSelection.H"
+#ifdef PULSAR
+ #include "Particles/PulsarParameters.H"
+#endif
#include
#include
@@ -214,8 +217,11 @@ void PhysicalParticleContainer::InitData ()
// Init ionization module here instead of in the PhysicalParticleContainer
// constructor because dt is required
if (do_field_ionization) {InitIonizationModule();}
+#ifdef PULSAR
+#else
AddParticles(0); // Note - add on level 0
Redistribute(); // We then redistribute
+#endif
}
void PhysicalParticleContainer::MapParticletoBoostedFrame (
@@ -576,12 +582,23 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
Real t = WarpX::GetInstance().gett_new(lev);
Real density_min = plasma_injector->density_min;
Real density_max = plasma_injector->density_max;
+#ifdef PULSAR
+ const MultiFab& Ex_mf = WarpX::GetInstance().getEfield(lev,0);
+ const MultiFab& Ey_mf = WarpX::GetInstance().getEfield(lev,1);
+ const MultiFab& Ez_mf = WarpX::GetInstance().getEfield(lev,2);
+ const MultiFab& rho_mf = WarpX::GetInstance().getrho_fp(lev);
+ const Real dt = WarpX::GetInstance().getdt(0);
+ amrex::Real omega_check = PulsarParm::Omega(t);
+#endif
#ifdef WARPX_DIM_RZ
const long nmodes = WarpX::n_rz_azimuthal_modes;
bool radially_weighted = plasma_injector->radially_weighted;
#endif
+ int Nmax_particles = 0;
+ int valid_particles_beforeAdd = TotalNumberOfParticles();
+
MFItInfo info;
if (do_tiling && Gpu::notInLaunchRegion()) {
info.EnableTiling(tile_size);
@@ -590,6 +607,7 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
info.SetDynamic(true);
#pragma omp parallel if (not WarpX::serialize_ics)
#endif
+
for (MFIter mfi = MakeMFIter(lev, info); mfi.isValid(); ++mfi)
{
if (cost && WarpX::load_balance_costs_update_algo == LoadBalanceCostsUpdateAlgo::Timers)
@@ -636,12 +654,24 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
const int grid_id = mfi.index();
const int tile_id = mfi.LocalTileIndex();
-
const GpuArray overlap_corner
{AMREX_D_DECL(overlap_realbox.lo(0),
overlap_realbox.lo(1),
overlap_realbox.lo(2))};
-
+#ifdef PULSAR
+ const FArrayBox& Ex_fab = Ex_mf[mfi];
+ const FArrayBox& Ey_fab = Ey_mf[mfi];
+ const FArrayBox& Ez_fab = Ez_mf[mfi];
+ const FArrayBox& rho_fab = rho_mf[mfi];
+ const Dim3 Ex_lo = lbound(mfi.tilebox());
+ const Dim3 Ey_lo = lbound(mfi.tilebox());
+ const Dim3 Ez_lo = lbound(mfi.tilebox());
+ amrex::Array4 const& ex_arr = Ex_fab.array();
+ amrex::Array4 const& ey_arr = Ey_fab.array();
+ amrex::Array4 const& ez_arr = Ez_fab.array();
+ amrex::Array4 const& rho_arr = rho_fab.array();
+ const Real q_pm = this->charge;
+#endif
// count the number of particles that each cell in overlap_box could add
Gpu::DeviceVector counts(overlap_box.numPts(), 0);
Gpu::DeviceVector offset(overlap_box.numPts());
@@ -657,9 +687,29 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
lo.z = applyBallisticCorrection(lo, inj_mom, gamma_boost, beta_boost, t);
hi.z = applyBallisticCorrection(hi, inj_mom, gamma_boost, beta_boost, t);
-
- if (inj_pos->overlapsWith(lo, hi))
+#ifdef PULSAR
+ amrex::Real xc = PulsarParm::center_star[0];
+ amrex::Real yc = PulsarParm::center_star[1];
+ amrex::Real zc = PulsarParm::center_star[2];
+ // Find cell-center
+ amrex::Real x, y, z;
+ x = overlap_corner[0] + i*dx[0] + 0.5*dx[0];
+ y = overlap_corner[1] + j*dx[1] + 0.5*dx[1];
+ z = overlap_corner[2] + k*dx[2] + 0.5*dx[2];
+ // radius of the cell-center
+ amrex::Real rad = std::sqrt( (x-xc)*(x-xc) + (y-yc)*(y-yc) + (z-zc)*(z-zc));
+ // is cell-center inside the pulsar ring
+ if (inj_pos->insidePulsarBounds( rad,PulsarParm::R_star,
+ PulsarParm::dR_star*1.5) )
{
+ auto index = overlap_box.index(iv);
+ const amrex::XDim3 ppc_per_dim = inj_pos->getppcInEachDim();
+ pcounts[index] = int(ppc_per_dim.x*std::cbrt(PulsarParm::Ninj_fraction))
+ * int(ppc_per_dim.y*std::cbrt(PulsarParm::Ninj_fraction))
+ * int(ppc_per_dim.z*std::cbrt(PulsarParm::Ninj_fraction));
+ }
+#else
+ if (inj_pos->overlapsWith(lo, hi)) {
auto index = overlap_box.index(iv);
if (lrefine_injection) {
Box fine_overlap_box = overlap_box & amrex::shift(lfine_box, shifted);
@@ -674,13 +724,14 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
}
#if (AMREX_SPACEDIM != 3)
amrex::ignore_unused(k);
-#endif
+#endif // end if amrex space dim
+#endif // End pulsar ifdef
});
// Max number of new particles. All of them are created,
// and invalid ones are then discarded
int max_new_particles = Scan::ExclusiveSum(counts.size(), counts.data(), offset.data());
-
+ Nmax_particles += max_new_particles;
// Update NextID to include particles created in this function
Long pid;
#ifdef _OPENMP
@@ -757,7 +808,7 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
amrex::ParallelForRNG(overlap_box,
[=] AMREX_GPU_DEVICE (int i, int j, int k, amrex::RandomEngine const& engine) noexcept
{
- IntVect iv = IntVect(AMREX_D_DECL(i, j, k));
+ amrex::IntVect iv = amrex::IntVect(AMREX_D_DECL(i, j, k));
const auto index = overlap_box.index(iv);
for (int i_part = 0; i_part < pcounts[index]; ++i_part)
{
@@ -812,16 +863,27 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
// the next generated particle.
// include ballistic correction for plasma species with bulk motion
- const Real z0 = applyBallisticCorrection(pos, inj_mom, gamma_boost,
+ amrex::Real z0 = applyBallisticCorrection(pos, inj_mom, gamma_boost,
beta_boost, t);
if (!inj_pos->insideBounds(xb, yb, z0)) {
p.id() = -1;
continue;
}
+#ifdef PULSAR
+ //amrex::Print() << " old xyz : " << xb << " " << yb << " " << z0 << "\n";
+ amrex::Real xc = PulsarParm::center_star[0];
+ amrex::Real yc = PulsarParm::center_star[1];
+ amrex::Real zc = PulsarParm::center_star[2];
+ amrex::Real rad = std::sqrt( (xb-xc)*(xb-xc) + (yb-yc)*(yb-yc) + (z0-zc)*(z0-zc));
+ if (!inj_pos->insidePulsarBounds(rad,PulsarParm::R_star,PulsarParm::dR_star)) {
+ //convert x, y, z to r, theta, phi;
+ p.id() = -1;
+ continue;
+ }
+#endif
u = inj_mom->getMomentum(pos.x, pos.y, z0, engine);
dens = inj_rho->getDensity(pos.x, pos.y, z0);
-
// Remove particle if density below threshold
if ( dens < density_min ){
p.id() = -1;
@@ -918,10 +980,161 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
amrex::HostDevice::Atomic::Add( &(*cost)[mfi.index()], wt);
}
}
-
+ amrex::Print() << " newly added particles : " << TotalNumberOfParticles()-valid_particles_beforeAdd << " total max particles " << Nmax_particles<< "\n";
// The function that calls this is responsible for redistributing particles.
}
+//void
+//PhysicalParticleContainer::AssignExternalFieldOnParticles(WarpXParIter& pti,
+// RealVector& Exp, RealVector& Eyp, RealVector& Ezp,
+// RealVector& Bxp, RealVector& Byp, RealVector& Bzp, int lev)
+//{
+// const long np = pti.numParticles();
+// /// get WarpX class object
+// auto & warpx = WarpX::GetInstance();
+// /// get MultiParticleContainer class object
+// auto & mypc = warpx.GetPartContainer();
+// if (mypc.m_E_ext_particle_s=="constant" ||
+// mypc.m_E_ext_particle_s=="default") {
+// Exp.assign(np,mypc.m_E_external_particle[0]);
+// Eyp.assign(np,mypc.m_E_external_particle[1]);
+// Ezp.assign(np,mypc.m_E_external_particle[2]);
+// }
+// if (mypc.m_B_ext_particle_s=="constant" ||
+// mypc.m_B_ext_particle_s=="default") {
+// Bxp.assign(np,mypc.m_B_external_particle[0]);
+// Byp.assign(np,mypc.m_B_external_particle[1]);
+// Bzp.assign(np,mypc.m_B_external_particle[2]);
+// }
+// if (mypc.m_E_ext_particle_s=="parse_e_ext_particle_function") {
+// const auto GetPosition = GetParticlePosition(pti);
+// Real* const AMREX_RESTRICT Exp_data = Exp.dataPtr();
+// Real* const AMREX_RESTRICT Eyp_data = Eyp.dataPtr();
+// Real* const AMREX_RESTRICT Ezp_data = Ezp.dataPtr();
+// ParserWrapper<4> *xfield_partparser = mypc.m_Ex_particle_parser.get();
+// ParserWrapper<4> *yfield_partparser = mypc.m_Ey_particle_parser.get();
+// ParserWrapper<4> *zfield_partparser = mypc.m_Ez_particle_parser.get();
+// Real time = warpx.gett_new(lev);
+// amrex::ParallelFor(pti.numParticles(),
+// [=] AMREX_GPU_DEVICE (long i) {
+// ParticleReal x, y, z;
+// GetPosition(i, x, y, z);
+// Exp_data[i] = (*xfield_partparser)(x, y, z, time);
+// Eyp_data[i] = (*yfield_partparser)(x, y, z, time);
+// Ezp_data[i] = (*zfield_partparser)(x, y, z, time);
+// });
+// }
+// if (mypc.m_B_ext_particle_s=="parse_b_ext_particle_function") {
+// const auto GetPosition = GetParticlePosition(pti);
+// Real* const AMREX_RESTRICT Bxp_data = Bxp.dataPtr();
+// Real* const AMREX_RESTRICT Byp_data = Byp.dataPtr();
+// Real* const AMREX_RESTRICT Bzp_data = Bzp.dataPtr();
+// ParserWrapper<4> *xfield_partparser = mypc.m_Bx_particle_parser.get();
+// ParserWrapper<4> *yfield_partparser = mypc.m_By_particle_parser.get();
+// ParserWrapper<4> *zfield_partparser = mypc.m_Bz_particle_parser.get();
+// Real time = warpx.gett_new(lev);
+// amrex::ParallelFor(pti.numParticles(),
+// [=] AMREX_GPU_DEVICE (long i) {
+// ParticleReal x, y, z;
+// GetPosition(i, x, y, z);
+// Bxp_data[i] = (*xfield_partparser)(x, y, z, time);
+// Byp_data[i] = (*yfield_partparser)(x, y, z, time);
+// Bzp_data[i] = (*zfield_partparser)(x, y, z, time);
+// });
+// }
+//
+//#ifdef PULSAR
+// if (PulsarParm::EB_external == 1) {
+// const auto GetPosition = GetParticlePosition(pti);
+// Real* const AMREX_RESTRICT Exp_data = Exp.dataPtr();
+// Real* const AMREX_RESTRICT Eyp_data = Eyp.dataPtr();
+// Real* const AMREX_RESTRICT Ezp_data = Ezp.dataPtr();
+// Real* const AMREX_RESTRICT Bxp_data = Bxp.dataPtr();
+// Real* const AMREX_RESTRICT Byp_data = Byp.dataPtr();
+// Real* const AMREX_RESTRICT Bzp_data = Bzp.dataPtr();
+// Real time = warpx.gett_new(lev);
+// amrex::ParallelFor(pti.numParticles(),
+// [=] AMREX_GPU_DEVICE (long i) {
+// ParticleReal x, y, z;
+// GetPosition(i, x, y, z);
+// PulsarParm::PulsarEBField(x, y, z,
+// Exp_data[i],Eyp_data[i],Ezp_data[i],
+// Bxp_data[i],Byp_data[i],Bzp_data[i],time);
+// });
+// }
+//#endif
+//
+//}
+//
+//
+//
+//void
+//PhysicalParticleContainer::FieldGather (int lev,
+// const amrex::MultiFab& Ex,
+// const amrex::MultiFab& Ey,
+// const amrex::MultiFab& Ez,
+// const amrex::MultiFab& Bx,
+// const amrex::MultiFab& By,
+// const amrex::MultiFab& Bz)
+//{
+// const std::array& dx = WarpX::CellSize(lev);
+//
+// BL_ASSERT(OnSameGrids(lev,Ex));
+//
+// MultiFab* cost = WarpX::getCosts(lev);
+//
+//#ifdef _OPENMP
+//#pragma omp parallel
+//#endif
+// {
+// for (WarpXParIter pti(*this, lev); pti.isValid(); ++pti)
+// {
+// Real wt = amrex::second();
+//
+// const Box& box = pti.validbox();
+//
+// auto& attribs = pti.GetAttribs();
+//
+// auto& Exp = attribs[PIdx::Ex];
+// auto& Eyp = attribs[PIdx::Ey];
+// auto& Ezp = attribs[PIdx::Ez];
+// auto& Bxp = attribs[PIdx::Bx];
+// auto& Byp = attribs[PIdx::By];
+// auto& Bzp = attribs[PIdx::Bz];
+//
+// const long np = pti.numParticles();
+//
+// // Data on the grid
+// const FArrayBox& exfab = Ex[pti];
+// const FArrayBox& eyfab = Ey[pti];
+// const FArrayBox& ezfab = Ez[pti];
+// const FArrayBox& bxfab = Bx[pti];
+// const FArrayBox& byfab = By[pti];
+// const FArrayBox& bzfab = Bz[pti];
+//
+// //
+// // Field Gather
+// //
+// int e_is_nodal = Ex.is_nodal() and Ey.is_nodal() and Ez.is_nodal();
+// FieldGather(pti, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
+// &exfab, &eyfab, &ezfab, &bxfab, &byfab, &bzfab,
+// Ex.nGrow(), e_is_nodal,
+// 0, np, lev, lev);
+//
+// if (cost) {
+// const Box& tbx = pti.tilebox();
+// wt = (amrex::second() - wt) / tbx.d_numPts();
+// Array4 const& costarr = cost->array(pti);
+// amrex::ParallelFor(tbx,
+// [=] AMREX_GPU_DEVICE (int i, int j, int k) noexcept
+// {
+// costarr(i,j,k) += wt;
+// });
+// }
+// }
+// }
+//}
+
void
PhysicalParticleContainer::Evolve (int lev,
const MultiFab& Ex, const MultiFab& Ey, const MultiFab& Ez,
@@ -960,7 +1173,6 @@ PhysicalParticleContainer::Evolve (int lev,
tmp_particle_data[t_lev][index][i].resize(np);
}
}
-
#ifdef _OPENMP
#pragma omp parallel
#endif
@@ -973,7 +1185,6 @@ PhysicalParticleContainer::Evolve (int lev,
FArrayBox filtered_Ex, filtered_Ey, filtered_Ez;
FArrayBox filtered_Bx, filtered_By, filtered_Bz;
-
for (WarpXParIter pti(*this, lev); pti.isValid(); ++pti)
{
if (cost && WarpX::load_balance_costs_update_algo == LoadBalanceCostsUpdateAlgo::Timers)
@@ -983,7 +1194,6 @@ PhysicalParticleContainer::Evolve (int lev,
Real wt = amrex::second();
const Box& box = pti.validbox();
-
auto& attribs = pti.GetAttribs();
auto& wp = attribs[PIdx::w];
@@ -1056,7 +1266,6 @@ PhysicalParticleContainer::Evolve (int lev,
const long np_gather = (cEx) ? nfine_gather : np;
int e_is_nodal = Ex.is_nodal() and Ey.is_nodal() and Ez.is_nodal();
-
//
// Gather and push for particles not in the buffer
//
@@ -1419,6 +1628,12 @@ PhysicalParticleContainer::PushP (int lev, Real dt,
const std::array& dx = WarpX::CellSize(std::max(lev,0));
+#ifdef PULSAR
+ const auto problo = WarpX::GetInstance().Geom(lev).ProbLoArray();
+ const auto probhi = WarpX::GetInstance().Geom(lev).ProbHiArray();
+ amrex::Real cur_time = WarpX::GetInstance().gett_new(lev);
+#endif
+
#ifdef _OPENMP
#pragma omp parallel
#endif
@@ -1429,7 +1644,6 @@ PhysicalParticleContainer::PushP (int lev, Real dt,
box.grow(Ex.nGrow());
const long np = pti.numParticles();
-
// Data on the grid
const FArrayBox& exfab = Ex[pti];
const FArrayBox& eyfab = Ey[pti];
@@ -1501,7 +1715,11 @@ PhysicalParticleContainer::PushP (int lev, Real dt,
ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
- nox, galerkin_interpolation);
+ nox, galerkin_interpolation
+#ifdef PULSAR
+ , problo, probhi, cur_time
+#endif
+ );
}
// Externally applied E-field in Cartesian co-ordinates
getExternalE(ip, Exp, Eyp, Ezp);
@@ -1795,7 +2013,6 @@ PhysicalParticleContainer::PushPX (WarpXParIter& pti,
const auto getPosition = GetParticlePosition(pti, offset);
auto setPosition = SetParticlePosition(pti, offset);
-
const auto getExternalE = GetExternalEField(pti, offset);
const auto getExternalB = GetExternalBField(pti, offset);
@@ -1810,6 +2027,10 @@ PhysicalParticleContainer::PushPX (WarpXParIter& pti,
const std::array& xyzmin = WarpX::LowerCorner(box, galilean_shift, gather_lev);
const Dim3 lo = lbound(box);
+#ifdef PULSAR
+ const auto problo = WarpX::GetInstance().Geom(lev).ProbLoArray();
+ const auto probhi = WarpX::GetInstance().Geom(lev).ProbHiArray();
+#endif
bool galerkin_interpolation = WarpX::galerkin_interpolation;
int nox = WarpX::nox;
@@ -1883,7 +2104,11 @@ PhysicalParticleContainer::PushPX (WarpXParIter& pti,
ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
- nox, galerkin_interpolation);
+ nox, galerkin_interpolation
+#ifdef PULSAR
+ , problo, probhi, cur_time
+#endif
+ );
}
// Externally applied E-field in Cartesian co-ordinates
getExternalE(ip, Exp, Eyp, Ezp);
@@ -2073,3 +2298,44 @@ PhysicalParticleContainer::getPairGenerationFilterFunc ()
}
#endif
+
+#ifdef PULSAR
+void PhysicalParticleContainer::PulsarParticleInjection() {
+
+ AddPlasma( 0 );
+}
+
+void PhysicalParticleContainer::PulsarParticleRemoval() {
+ int lev = 0;
+ // Remove Particles From inside sphere
+#ifdef _OPENMP
+#pragma omp parallel
+#endif
+ {
+#ifdef _OPENMP
+ int thread_num = omp_get_thread_num();
+#else
+ int thread_num = 0;
+#endif
+ for (WarpXParIter pti(*this, lev); pti.isValid(); ++pti)
+ {
+ const auto GetPosition = GetParticlePosition(pti);
+ Real xc = PulsarParm::center_star[0];
+ Real yc = PulsarParm::center_star[1];
+ Real zc = PulsarParm::center_star[2];
+ ParticleType* pp = pti.GetArrayOfStructs()().data();
+ amrex::ParallelFor(pti.numParticles(),
+ [=] AMREX_GPU_DEVICE (long i) {
+ ParticleReal x, y, z;
+ GetPosition(i, x, y, z);
+ Real r = std::sqrt((x-xc)*(x-xc)
+ + (y-yc)*(y-yc)
+ + (z-zc)*(z-zc));
+ if (r < (PulsarParm::R_star )) {
+ pp[i].id() = -1;
+ }
+ });
+ }
+ }
+}
+#endif
diff --git a/Source/Particles/PulsarParameters.H b/Source/Particles/PulsarParameters.H
new file mode 100644
index 00000000000..70533368868
--- /dev/null
+++ b/Source/Particles/PulsarParameters.H
@@ -0,0 +1,227 @@
+#ifndef PULSAR_PARAMETERS_H
+#define PULSAR_PARAMETERS_H
+
+#include
+#include
+#include
+#include
+#include
+#include
+
+using namespace amrex;
+
+namespace PulsarParm
+{
+ extern std::string pulsar_type;
+
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real omega_star;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real ramp_omega_time;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real B_star;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real R_star;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real dR_star;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real damping_scale;
+ extern AMREX_GPU_DEVICE_MANAGED int EB_external;
+ extern AMREX_GPU_DEVICE_MANAGED int E_external_monopole;
+ extern AMREX_GPU_DEVICE_MANAGED
+ amrex::GpuArray center_star;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real max_ndens;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real Ninj_fraction;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real rhoGJ_scale;
+ extern AMREX_GPU_DEVICE_MANAGED int damp_EB_internal;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real Chi;
+
+ extern AMREX_GPU_DEVICE_MANAGED int verbose;
+
+ void ReadParameters();
+
+
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ amrex::Real Omega(const amrex::Real time)
+ {
+ amrex::Real omega = omega_star;
+ if (ramp_omega_time > 0.0 && time < ramp_omega_time) {
+ omega = omega_star * time / ramp_omega_time;
+ }
+
+ return omega;
+ }
+
+
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void ExternalEFieldSpherical (amrex::Real const r, amrex::Real const theta,
+ amrex::Real const phi, amrex::Real const time,
+ amrex::Real &Er, amrex::Real &Etheta, amrex::Real &Ephi)
+ {
+ amrex::Real omega = Omega(time);
+ amrex::Real r_ratio = R_star/r;
+ amrex::Real c_theta = std::cos(theta); // polar angle
+ amrex::Real s_theta = std::sin(theta); // polar angle
+ amrex::Real c_chi = std::cos(Chi); // oblique angle
+ amrex::Real s_chi = std::sin(Chi); // oblique angle
+ // The instantaneous phase, psi = phi - Omega*t
+ amrex::Real psi = phi - omega*time;
+ amrex::Real c_psi = std::cos(psi);
+ amrex::Real s_psi = std::sin(psi);
+ // E-field inside pulsar that corresponds to a dipole magnetization
+ // Eq 2.9(a-c) Jeromi Petri 2016
+ // corotating external field in the thin spherical ring where
+ // particles are introduced.
+ if ( r < R_star) {
+ amrex::Real r2 = r_ratio * r_ratio;
+ Er = B_star * omega * r2 * R_star *
+ ( c_chi * s_theta * s_theta -
+ s_chi * c_theta * s_theta * c_psi
+ );
+ Etheta = -B_star * omega * r2 * R_star *
+ ( 2._rt * s_theta * c_theta * c_chi +
+ 2._rt * s_chi * s_theta * s_theta * c_psi
+ );
+ Ephi = 0._rt; // no Ephi inside the pulsar for dipole magnetization for any Chi
+ }
+
+ // outside pulsar Eq 2.8(a)-2.8(c) of Jeromi Petri, 2016
+ if ( r >= (R_star) ) {
+ amrex::Real r4 = r_ratio*r_ratio*r_ratio*r_ratio;
+ amrex::Real r2 = r_ratio*r_ratio;
+ Er = B_star * omega * R_star * r4 *
+ ( c_chi * (1._rt - 3._rt * c_theta * c_theta) -
+ 3._rt * s_chi * c_theta * s_theta * c_psi
+ );
+ if (E_external_monopole == 1) {
+ Er += (2._rt/3._rt) * omega * B_star * R_star * r_ratio * r_ratio * c_chi;
+ }
+ Etheta = B_star * omega * R_star *
+ ( r2 * s_chi * (r2 * std::cos(2._rt*theta) - 1._rt) * c_psi -
+ r4 * c_chi * std::sin(2._rt*theta)
+ );
+ Ephi = B_star * omega * R_star * r2 * (1._rt - r2) * s_chi * c_theta * s_psi;
+ }
+ }
+
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void ExternalBFieldSpherical (amrex::Real const r, amrex::Real const theta,
+ amrex::Real const phi, amrex::Real const time,
+ amrex::Real &Br, amrex::Real &Btheta, amrex::Real &Bphi)
+ {
+ amrex::Real omega = Omega(time);
+ amrex::Real r_ratio = R_star/r;
+ amrex::Real r3 = r_ratio*r_ratio*r_ratio;
+ amrex::Real c_theta = std::cos(theta);
+ amrex::Real s_theta = std::sin(theta);
+ amrex::Real c_chi = std::cos(Chi);
+ amrex::Real s_chi = std::sin(Chi);
+ // The instantaneous phase, psi = phi - Omega*t
+ amrex::Real psi = phi - omega*time;
+ amrex::Real c_psi = std::cos(psi);
+ amrex::Real s_psi = std::sin(psi);
+ // The full dipole magnetic field as a funciton of (theta, phi, omega, t, and Chi)
+ // Eq 2.7(a) - 2.7(c) of Jeromi Petri, 2016 article
+ Br = 2.0_rt * B_star * r3 *
+ ( c_chi * c_theta + s_chi * s_theta * c_psi );
+ Btheta = B_star * r3 * ( c_chi * s_theta - s_chi * c_theta * c_psi );
+ Bphi = B_star * r3 * s_chi * s_psi;
+ }
+
+ namespace Spherical
+ {
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ amrex::Real r(int i, int j, int k, amrex::GeometryData const& geom, amrex::GpuArray const mf_ixType)
+ {
+ const auto domain_box = geom.Domain();
+ const auto domain_ilo = amrex::lbound(domain_box);
+ const auto domain_xlo = geom.ProbLo();
+ const auto domain_xhi = geom.ProbHi();
+ const auto domain_dx = geom.CellSize();
+
+ const amrex::Real x = domain_xlo[0] + (i ) * domain_dx[0] + (1.0 - mf_ixType[0])*domain_dx[0]*0.5;
+ const amrex::Real y = domain_xlo[1] + (j ) * domain_dx[1] + (1.0 - mf_ixType[1])*domain_dx[1]*0.5;
+ const amrex::Real z = domain_xlo[2] + (k ) * domain_dx[2] + (1.0 - mf_ixType[2])*domain_dx[2]*0.5;
+
+ const amrex::Real xc = 0.5 * (domain_xlo[0] + domain_xhi[0]);
+ const amrex::Real yc = 0.5 * (domain_xlo[1] + domain_xhi[1]);
+ const amrex::Real zc = 0.5 * (domain_xlo[2] + domain_xhi[2]);
+
+ const amrex::Real r = std::sqrt((x-xc)*(x-xc) + (y-yc)*(y-yc) + (z-zc)*(z-zc));
+
+ return r;
+ }
+ }
+
+ /** Compute Cartesian components corresponding to i, j, k based on the staggering,
+ ixType, and the domain_lo and cell size, dx.
+ */
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void ComputeCellCoordinates ( int i, int j, int k,
+ amrex::GpuArray const mf_type,
+ amrex::GpuArray const domain_lo,
+ amrex::GpuArray const dx,
+ amrex::Real &x, amrex::Real &y, amrex::Real &z)
+ {
+ x = domain_lo[0] + i*dx[0] + (1.0_rt - mf_type[0]) * dx[0]*0.5;
+ y = domain_lo[1] + j*dx[1] + (1.0_rt - mf_type[1]) * dx[1]*0.5;
+ z = domain_lo[2] + k*dx[2] + (1.0_rt - mf_type[2]) * dx[2]*0.5;
+ }
+
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void ConvertCartesianToSphericalCoord ( amrex::Real const x, amrex::Real const y,
+ amrex::Real const z,
+ amrex::GpuArray const domain_lo,
+ amrex::GpuArray const domain_hi,
+ amrex::Real &r, amrex::Real &theta, amrex::Real &phi)
+ {
+ amrex::Real xc = (domain_lo[0] + domain_hi[0])/2.0;
+ amrex::Real yc = (domain_lo[1] + domain_hi[1])/2.0;
+ amrex::Real zc = (domain_lo[2] + domain_hi[2])/2.0;
+
+ r = std::sqrt( (x-xc)*(x-xc) + (y-yc)*(y-yc) + (z-zc)*(z-zc) );
+ theta = 0.0;
+ if (r > 0) theta = std::acos( (z-zc) / r );
+ phi = std::atan2( (y-yc), (x-xc) );
+ }
+
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void ConvertSphericalToCartesianXComponent (amrex::Real const F_r,
+ amrex::Real const F_theta, amrex::Real const F_phi,
+ amrex::Real const r, amrex::Real const theta,
+ amrex::Real const phi, amrex::Real & F_x)
+ {
+ F_x = F_r * std::sin(theta) * std::cos(phi)
+ + F_theta * std::cos(theta) * std::cos(phi);
+ }
+
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void ConvertSphericalToCartesianYComponent (amrex::Real const F_r,
+ amrex::Real const F_theta, amrex::Real const F_phi,
+ amrex::Real const r, amrex::Real const theta,
+ amrex::Real const phi, amrex::Real & F_y)
+ {
+ F_y = F_r * std::sin(theta) * std::sin(phi)
+ + F_theta * std::cos(theta) * std::sin(phi);
+ }
+
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void ConvertSphericalToCartesianZComponent (amrex::Real const F_r,
+ amrex::Real const F_theta, amrex::Real const F_phi,
+ amrex::Real const r, amrex::Real const theta,
+ amrex::Real const phi, amrex::Real & F_z)
+ {
+ F_z = F_r * std::cos(theta) - F_theta * std::sin(theta);
+ }
+
+
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void DampField(int i, int j, int k, amrex::GeometryData const& geom, amrex::Array4 const& Efield, amrex::GpuArray const mf_ixtype)
+ {
+ amrex::Real r = Spherical::r(i, j, k, geom, mf_ixtype);
+ if (r < R_star) {
+ // Damping function: Fd = tanh(damping_scale * (r / R_star - 1)) + 1
+ // for damping_scale >= 10 or so:
+ // Fd(0) ~ 0
+ // Fd(R_star) ~ 1
+ const amrex::Real Fd = std::tanh(damping_scale * (r / R_star - 1.0)) + 1.0;
+ Efield(i, j, k) = Efield(i, j, k) * Fd;
+ }
+ }
+}
+
+#endif
diff --git a/Source/Particles/PulsarParameters.cpp b/Source/Particles/PulsarParameters.cpp
new file mode 100644
index 00000000000..d685625aaf4
--- /dev/null
+++ b/Source/Particles/PulsarParameters.cpp
@@ -0,0 +1,64 @@
+#include "PulsarParameters.H"
+#include "WarpX.H"
+#include
+#include
+#include
+#include
+#include
+
+namespace PulsarParm
+{
+ std::string pulsar_type;
+
+ AMREX_GPU_DEVICE_MANAGED amrex::Real omega_star;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real ramp_omega_time = -1.0;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real B_star;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real R_star;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real dR_star;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real damping_scale = 10.0;
+ AMREX_GPU_DEVICE_MANAGED int EB_external = 0;
+ AMREX_GPU_DEVICE_MANAGED int E_external_monopole = 0;
+ AMREX_GPU_DEVICE_MANAGED
+ amrex::GpuArray center_star;
+ AMREX_GPU_DEVICE_MANAGED int damp_EB_internal = 0;
+ AMREX_GPU_DEVICE_MANAGED int verbose = 0;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real max_ndens;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real Ninj_fraction;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real rhoGJ_scale;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real Chi; // Obliquity of the pulsar
+
+ void ReadParameters() {
+ amrex::ParmParse pp("pulsar");
+ pp.query("pulsarType",pulsar_type);
+ pp.get("omega_star",omega_star);
+ amrex::Vector center_star_v(AMREX_SPACEDIM);
+ pp.getarr("center_star",center_star_v);
+ std::copy(center_star_v.begin(),center_star_v.end(),center_star.begin());
+ pp.get("R_star",R_star);
+ pp.get("B_star",B_star);
+ pp.get("dR",dR_star);
+ pp.query("verbose",verbose);
+ pp.query("EB_external",EB_external);
+ pp.query("E_external_monopole",E_external_monopole);
+ pp.query("damp_EB_internal",damp_EB_internal);
+ pp.query("damping_scale",damping_scale);
+ pp.query("ramp_omega_time",ramp_omega_time);
+ amrex::Print() << " Pulsar center: " << center_star[0] << " " << center_star[1] << " " << center_star[2] << "\n";
+ amrex::Print() << " Pulsar omega: " << omega_star << "\n";
+ amrex::Print() << " Pulsar B_star : " << B_star << "\n";
+ pp.get("max_ndens", max_ndens);
+ pp.get("Ninj_fraction",Ninj_fraction);
+ pp.get("rhoGJ_scale",rhoGJ_scale);
+ pp.get("Chi", Chi);
+ Chi = Chi * MathConst::pi / 180._rt;
+ amrex::Print() << " Oblique angle between B-axis and Omega-axis " << Chi << "\n";
+ amrex::Print() << " pulsar max ndens " << max_ndens << "\n";
+ amrex::Print() << " pulsar ninj fraction " << Ninj_fraction << "\n";
+ amrex::Print() << " pulsar rhoGJ scaling " << rhoGJ_scale << "\n";
+ amrex::Print() << " EB_external : " << EB_external << "\n";
+ auto & warpx = WarpX::GetInstance();
+ warpx.setplot_rho(true);
+
+ }
+
+}
diff --git a/Source/Particles/RigidInjectedParticleContainer.cpp b/Source/Particles/RigidInjectedParticleContainer.cpp
index b27b67dc9ef..c0c3451b576 100644
--- a/Source/Particles/RigidInjectedParticleContainer.cpp
+++ b/Source/Particles/RigidInjectedParticleContainer.cpp
@@ -389,6 +389,11 @@ RigidInjectedParticleContainer::PushP (int lev, Real dt,
if (do_not_push) return;
const std::array& dx = WarpX::CellSize(std::max(lev,0));
+#ifdef PULSAR
+ const auto problo = WarpX::GetInstance().Geom(lev).ProbLoArray();
+ const auto probhi = WarpX::GetInstance().Geom(lev).ProbHiArray();
+ amrex::Real cur_time = WarpX::GetInstance().gett_new(lev);
+#endif
#ifdef _OPENMP
#pragma omp parallel
@@ -482,6 +487,7 @@ RigidInjectedParticleContainer::PushP (int lev, Real dt,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
nox, galerkin_interpolation);
+
getExternalE(ip, Exp, Eyp, Ezp);
getExternalB(ip, Bxp, Byp, Bzp);
diff --git a/Source/Particles/WarpXParticleContainer.H b/Source/Particles/WarpXParticleContainer.H
index d2c1f380e85..75db9fcdb0e 100644
--- a/Source/Particles/WarpXParticleContainer.H
+++ b/Source/Particles/WarpXParticleContainer.H
@@ -307,6 +307,10 @@ public:
amrex::ParticleReal getCharge () const {return charge;}
//amrex::Real getMass () {return mass;}
amrex::ParticleReal getMass () const {return mass;}
+#ifdef PULSAR
+ virtual void PulsarParticleInjection () {};
+ virtual void PulsarParticleRemoval () {};
+#endif
int DoFieldIonization() const { return do_field_ionization; }
int DoQED() const {
diff --git a/Source/WarpX.cpp b/Source/WarpX.cpp
index 0f7e2b4db28..dff6eb60c43 100644
--- a/Source/WarpX.cpp
+++ b/Source/WarpX.cpp
@@ -16,6 +16,9 @@
#include "Utils/WarpXUtil.H"
#include "Utils/WarpXAlgorithmSelection.H"
#include "Utils/WarpXProfilerWrapper.H"
+#ifdef PULSAR
+#include "Particles/PulsarParameters.H"
+#endif
#include
#include
@@ -23,6 +26,7 @@
# include
#endif
+
#ifdef _OPENMP
# include
#endif
@@ -737,6 +741,10 @@ WarpX::ReadParameters ()
}
}
+#ifdef PULSAR
+ PulsarParm::ReadParameters();
+#endif
+
}
void
@@ -983,9 +991,13 @@ WarpX::AllocLevelMFs (int lev, const BoxArray& ba, const DistributionMapping& dm
#ifdef WARPX_USE_PSATD
const bool deposit_charge = do_dive_cleaning || (plot_rho && do_back_transformed_diagnostics)
|| update_with_rho || current_correction;
+#else
+#ifdef PULSAR
+ const bool deposit_charge = do_dive_cleaning || plot_rho;
#else
const bool deposit_charge = do_dive_cleaning || (plot_rho && do_back_transformed_diagnostics);
-#endif
+#endif // pulsar endif
+#endif // PSATD/FDTD endif
if (deposit_charge)
{
rho_fp[lev] = std::make_unique(amrex::convert(ba,rho_nodal_flag),dm,2*ncomps,ngRho);
@@ -1128,7 +1140,12 @@ WarpX::AllocLevelMFs (int lev, const BoxArray& ba, const DistributionMapping& dm
current_cp[lev][1] = std::make_unique(amrex::convert(cba,jy_nodal_flag),dm,ncomps,ngJ);
current_cp[lev][2] = std::make_unique(amrex::convert(cba,jz_nodal_flag),dm,ncomps,ngJ);
- if (do_dive_cleaning || (plot_rho && do_back_transformed_diagnostics)) {
+#ifdef PULSAR
+ if (do_dive_cleaning || (plot_rho) )
+#else
+ if (do_dive_cleaning || (plot_rho && do_back_transformed_diagnostics))
+#endif
+ {
rho_cp[lev] = std::make_unique(amrex::convert(cba,rho_nodal_flag),dm,2*ncomps,ngRho);
}
if (do_dive_cleaning)
diff --git a/Tools/Pulsar_scale_RstarPhysical.ipynb b/Tools/Pulsar_scale_RstarPhysical.ipynb
new file mode 100644
index 00000000000..d3afd8bed75
--- /dev/null
+++ b/Tools/Pulsar_scale_RstarPhysical.ipynb
@@ -0,0 +1,706 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Overview\n",
+ "\n",
+ "This a notebook that inspects the results of a WarpX simulation.\n",
+ "\n",
+ "# Instruction\n",
+ "\n",
+ "Enter the path of the data you wish to visualize below. Then execute the cells one by one, by selecting them with your mouse and typing `Shift + Enter`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import statements\n",
+ "import yt ; yt.funcs.mylog.setLevel(50)\n",
+ "import numpy as np\n",
+ "import scipy.constants as scc\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#Define the Physical Constants, normalizations, and the scale-down parameter, r_scale, for the pulsar. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "######################\n",
+ "# physical constants #\n",
+ "######################\n",
+ "c = 299792458.0 # speed of light \n",
+ "q_e = 1.602176634e-19 # elementary charge\n",
+ "me=9.10938356*np.power(10.,-31) # electron mass\n",
+ "epsilon=8.8541878128*np.power(10.,-12) # permittivity of free space\n",
+ "mu_o = 4*3.14*1e-7 # permeability\n",
+ "pi = 3.14159265358979323846\n",
+ "SolarMass = 2e30\n",
+ "G = 6.674e-11 # gravitational constant\n",
+ "\n",
+ "#############################################################################\n",
+ "# Parameters for a real Pulsar #\n",
+ "# (Table 7 of J.Petri's article on Theory of Pulsar Magnetosphere and Wind) #\n",
+ "#############################################################################\n",
+ "Omega_real = 6283\n",
+ "B_real = 7.4E4\n",
+ "R_real = 12000\n",
+ "n_real = 6.9e16\n",
+ "omega_pe_real = (n_real*q_e*q_e/(me*epsilon))**0.5 #plasma frequency\n",
+ "SkinDepth_real = c/omega_pe_real \n",
+ "Mstar = 1.4*SolarMass\n",
+ "Rstar_skinDepth_real = 6e5\n",
+ "\n",
+ "##################\n",
+ "# Normalizations #\n",
+ "##################\n",
+ "Rstar_skinDepth = 6e0 # Ratio of radius of star to the skin Depth \n",
+ " # For a real star, this ratio is 6e5\n",
+ "exponent = np.arange(0,6,1) \n",
+ "Factor = np.array(10**exponent)\n",
+ "Rstar_skinDepth = np.array(6*Factor) \n",
+ "\n",
+ "RLC_Rstar = 4 # Ratio of light cylinder (where the particle speed ~ c) to Rstar\n",
+ " # For a pulsar with 1ms period, this ratio is 4. \n",
+ " # i.e., This ratio sets the omega value, since Omega*RLC = c\n",
+ "\n",
+ "# Choose skindepth as the free parameter for a choice of normalizations given above\n",
+ "# The skin depth below is computed from the number density for a real pulsar\n",
+ "# Keeping the SkinDepth constant across all the scales in our scaling study\n",
+ "#SkinDepth = 0.02\n",
+ "\n",
+ "# Since skin depth is maintained across the scales, and RLC/Rstar is also maintained \n",
+ "# lets define the decrease in the scale by comparing the value of \n",
+ "# (Rstar/skinDepth)/(Rstar_real/skinDepth_real)\n",
+ "#r_scale = Rstar_skinDepth/Rstar_skinDepth_real\n",
+ "\n",
+ "Rstar = np.ones(6)*12000\n",
+ "SkinDepth = Rstar/Rstar_skinDepth\n",
+ "r_scale = Rstar_skinDepth_real/Rstar_skinDepth"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "##################################################################\n",
+ "# Derive other physical parameters from the above normalizations #\n",
+ "##################################################################\n",
+ "\n",
+ "# 1. Lorentz Boost (dimensionless) #\n",
+ "# Note that in Alex Chen's paper, gamma_o ~ (1/4)*normalized_values.\n",
+ "# Instead here we have pi/2, since that leads to the closes gamma_o \n",
+ "# value for a real 1ms pulsar. \n",
+ "gamma_o = (pi/2)*(Rstar_skinDepth)**2/RLC_Rstar \n",
+ "\n",
+ "\n",
+ "gamma_real = (pi/2)*6e5**2/RLC_Rstar;\n",
+ "gamma_scaling = gamma_o/(gamma_real) # This is to see how the gamma value \n",
+ " # decreases due to decrease in the ratio of R_star to skin depth\n",
+ "\n",
+ "\n",
+ "\n",
+ "# 3. Light cylinder location (m)\n",
+ "RLC = Rstar*RLC_Rstar\n",
+ "# 4. Angular Frequency (rad/s)\n",
+ "# Omega remains constant\n",
+ "Omega = c/RLC\n",
+ "# 5. Period (s)\n",
+ "# Period remains constant\n",
+ "Period = 2*3.14/Omega\n",
+ "# Moment of inertia for a sphere = (2/5)MR^2 (kg.m^2)\n",
+ "# Note that when the Rstar is scaled by r_scale, \n",
+ "# Mstar decreases as r_scale^3. Thus Mstar*r_scale**3 is the \n",
+ "# mass of the scaled down star.\n",
+ "# I remains constant across all scales\n",
+ "I = (2/5)*(Mstar)*Rstar**2\n",
+ "# 6. Rotation induced potential drop from pote to equator (V)\n",
+ "# Reference: Alex Chen's 2014 paper\n",
+ "# Scales as 1/r_scale**2\n",
+ "phi_o = gamma_o * (me/q_e) * c**2\n",
+ "\n",
+ "# Braking is the rate of slow-down of the spin. \n",
+ "# It is not relevant for the scaling. \n",
+ "# However, 1e-15 is the P_dot for a pulsar with P=1s\n",
+ "# and the rate of slowdown decreases proportional to the period. \n",
+ "# So we were to compare two stars with same radius, but different Omega\n",
+ "# then we can use Braking to determine the magnetic field strength at the surface as follows\n",
+ "# Bo = (3*mu_o*c*c*c/(32*pi*pi*pi))**0.5 * (I*Braking*Period)**0.5/(Rstar**3) \n",
+ "# (Reference : Table 7 of Jetri's article)\n",
+ "# Remains constant across all scales\n",
+ "Braking = 1e-15*Period\n",
+ "\n",
+ "# 7. Magnetic field strength (Tesla)\n",
+ "# Bo decreases as ~ 1/r_scale**2\n",
+ "Bo = phi_o/(Rstar*Rstar*Omega)\n",
+ "\n",
+ "# 8. Volume charge density (C/m^3) for uniform magnetization insize the star \n",
+ "# Refer to Table 7 of Petri's article \n",
+ "# Since Omega increases as r_scale and Bo decreases as r_scale\n",
+ "# The product of Omega*Bo remains constant across all scales. \n",
+ "# Thus, rho_e decreases as (1/r_scale**2)\n",
+ "rho_e = 2*epsilon*Omega*Bo #(Not adding the negative sign here)\n",
+ "\n",
+ "# 9. Electron number density (#/m^3)\n",
+ "# ne decreases as (1/r_scale**2)\n",
+ "ne = rho_e/q_e\n",
+ "# 9a. plasma frequency \n",
+ "# decreases as (1/r_scale)\n",
+ "omega_pe = (ne*q_e*q_e/(me*epsilon))**0.5 #plasma frequency\n",
+ "# 10. Magnetic moment (Am^2)\n",
+ "# decreases as (1/r_scale**2)\n",
+ "magnetic_moment = Bo*Rstar*Rstar*Rstar*4*pi/(mu_o)\n",
+ "# 11. E-field (V/m)\n",
+ "# Efield decreases as (1/r_scale**2)\n",
+ "E = Omega*Bo*Rstar\n",
+ "\n",
+ "\n",
+ "#########################################\n",
+ "# How do the energies and forces scale? #\n",
+ "#########################################\n",
+ "# 12. Magnetic Energy (J)\n",
+ "# Magnetic energy decreases as (1/r_scale**4)\n",
+ "magnetic_energy = (4*pi/3)*Bo**2*Rstar**3/(2*mu_o)\n",
+ "\n",
+ "# 13. Gravitational Potential Energy (J)\n",
+ "# G.pot energy remains constant \n",
+ "GP_energy = (3/5)*G*Mstar*Mstar/(Rstar)\n",
+ "\n",
+ "# 14. Gravitational to Electric force (dimensionless)\n",
+ "# This ratio increases as r_scale**2\n",
+ "G_EForce = G*Mstar*me/(Rstar*Rstar*q_e*E)\n",
+ "\n",
+ "# 15. Rotational kinetic energy \n",
+ "# Rot. KE remains constant\n",
+ "rotational_KE = (1/2)*I*Omega**2\n",
+ "\n",
+ "# 16. From (12) and (13), we know the B.energy scales as (1/r_scale**4) and GP energy is constant \n",
+ "# Thus the ratio of GP_energy and B_energy increases as r_scale**4\n",
+ "GB_energy_ratio = GP_energy/magnetic_energy\n",
+ "\n",
+ "# 17. Rate of change of Omega, or angular acceleration decreases as 1/r_scale**4\n",
+ "Omega_dot = Bo*Bo*Rstar**6*Omega**3/(I) * (32*pi/(3*mu_o*c**3))* (1/(4*pi*pi))\n",
+ "\n",
+ "# 18. Torque decreases as 1/r_scale**4\n",
+ "Torque = I * Omega_dot\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Input parameters for WarpX for geometry scaled down by r_scale = [1.e-05 1.e-04 1.e-03 1.e-02 1.e-01 1.e+00]\n",
+ "Lorentz factor, (gamma_o) = [1.41371669e+01 1.41371669e+03 1.41371669e+05 1.41371669e+07\n",
+ " 1.41371669e+09 1.41371669e+11]\n",
+ "Rstar (m) [12000. 12000. 12000. 12000. 12000. 12000.]\n",
+ "Omega (rad/s) [6245.67620833 6245.67620833 6245.67620833 6245.67620833 6245.67620833\n",
+ " 6245.67620833]\n",
+ "Bo (Tesla) [8.03230942e-06 8.03230942e-04 8.03230942e-02 8.03230942e+00\n",
+ " 8.03230942e+02 8.03230942e+04]\n",
+ "ne (/m^3) [5.54482989e+06 5.54482989e+08 5.54482989e+10 5.54482989e+12\n",
+ " 5.54482989e+14 5.54482989e+16]\n",
+ "Size of the domain, (m) [360000. 360000. 360000. 360000. 360000. 360000.]\n",
+ "Minimum cell size (m) [2.e+03 2.e+02 2.e+01 2.e+00 2.e-01 2.e-02]\n",
+ "timestep (s) [3.76386776e-06 3.76386776e-07 3.76386776e-08 3.76386776e-09\n",
+ " 3.76386776e-10 3.76386776e-11]\n",
+ "Numver of cells assuming unif. grid, [1.8e+02 1.8e+03 1.8e+04 1.8e+05 1.8e+06 1.8e+07]\n"
+ ]
+ }
+ ],
+ "source": [
+ "############################################################\n",
+ "# Print all the values that will be used as input in WarpX #\n",
+ "############################################################\n",
+ "print(\"Input parameters for WarpX for geometry scaled down by r_scale = \", Rstar_skinDepth/Rstar_skinDepth_real)\n",
+ "print(\"Lorentz factor, (gamma_o) = \", gamma_o)\n",
+ "print(\"Rstar (m)\", Rstar)\n",
+ "print(\"Omega (rad/s)\", Omega)\n",
+ "print(\"Bo (Tesla)\", Bo)\n",
+ "print(\"ne (/m^3)\", ne)\n",
+ "print(\"Size of the domain, (m)\", 30*Rstar)\n",
+ "print(\"Minimum cell size (m)\", SkinDepth)\n",
+ "print(\"timestep (s)\", 0.5*omega_pe**-1) # Or may be cfl criterion\n",
+ "print(\"Numver of cells assuming unif. grid, \", 30*Rstar/SkinDepth)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Gravitational force to E-force : [1.22562947e-02 1.22562947e-04 1.22562947e-06 1.22562947e-08\n",
+ " 1.22562947e-10 1.22562947e-12]\n",
+ "Gravitational energy to B-energy : [1.40727411e+38 1.40727411e+34 1.40727411e+30 1.40727411e+26\n",
+ " 1.40727411e+22 1.40727411e+18]\n",
+ "B energy, (J) [1.85906071e+08 1.85906071e+12 1.85906071e+16 1.85906071e+20\n",
+ " 1.85906071e+24 1.85906071e+28]\n",
+ "Gravitational potential energy, (J) [2.616208e+46 2.616208e+46 2.616208e+46 2.616208e+46 2.616208e+46\n",
+ " 2.616208e+46]\n",
+ "Rotational Kinetic Energy (J) [3.14564313e+45 3.14564313e+45 3.14564313e+45 3.14564313e+45\n",
+ " 3.14564313e+45 3.14564313e+45]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#############################################################\n",
+ "# Print ratios of G.Pot.Energy/B_energy and G.Force/E_force #\n",
+ "#############################################################\n",
+ "print(\"Gravitational force to E-force : \", G_EForce)\n",
+ "print(\"Gravitational energy to B-energy : \",GB_energy_ratio)\n",
+ "\n",
+ "\n",
+ "# Print dimensional values of energies\n",
+ "print(\"B energy, (J)\",magnetic_energy);\n",
+ "print(\"Gravitational potential energy, (J)\", GP_energy)\n",
+ "print(\"Rotational Kinetic Energy (J)\", rotational_KE )\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot Gravitational force to electric force -- should be constant across all scales\n",
+ "\n",
+ "plt.plot(Rstar_skinDepth/6e5,G_EForce,'ro')\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"G.Force/E.Force\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"G_Eforce_scaling.png\",bbox_inches='tight')\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot Gravitational to magnetic energy -- should be constant across all scales\n",
+ "\n",
+ "plt.plot(Rstar_skinDepth/6e5,GB_energy_ratio,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"G.Energy/B.energy\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"G_Benergy_scaling.png\",bbox_inches='tight')\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Trends of all energies\n",
+ "plt.plot(Rstar_skinDepth/6e5,magnetic_energy,color=\"red\",ls='--',lw=2,marker=\"^\",markersize=8)\n",
+ "plt.plot(Rstar_skinDepth/6e5,GP_energy,color=\"blue\",lw=2,marker='o',markersize=8,markerfacecolor='blue')\n",
+ "plt.plot(Rstar_skinDepth/6e5,rotational_KE,color=\"purple\",lw=2,marker='s',markersize=8,markeredgecolor='purple')\n",
+ "plt.plot(Rstar_skinDepth/6e5,Torque,color=\"cyan\",lw=2,marker='>',markersize=8,markeredgecolor='purple')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Energy (J)\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.legend([\"Magnetic\",\"G. Potential\",\"Rotational KE\",\"Torque\"],loc=2,fontsize='small',frameon=\"false\",borderpad=0.1)\n",
+ "plt.ylim([1e0,1e90])\n",
+ "plt.savefig(\"Energy_scaling.png\",bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot Gravitational to magnetic energy -- should be constant across all scales\n",
+ "plt.plot(Rstar_skinDepth/6e5,Bo/Bo[5],color=\"blue\",lw=2,marker='s',markersize=8)\n",
+ "plt.ylabel(\"Normalized B. field\",color=\"blue\")\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.yticks(color=\"blue\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.twinx()\n",
+ "plt.plot(Rstar_skinDepth/6e5,Omega/Omega[5],color=\"red\",lw=2,marker='o',markersize=8)\n",
+ "plt.ylabel(\"Normalized \\u03A9\",color=\"red\")\n",
+ "plt.yticks(color=\"red\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.ylim([0,2])\n",
+ "plt.savefig(\"Normalized_B_Omega.png\",bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(Rstar_skinDepth/6e5,gamma_o,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Normalized G.Energy/B.energy\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"G_Benergy_scaling.png\",bbox_inches='tight')\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(Rstar_skinDepth/6e5,Omega_dot,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Omega_dot\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"G_Benergy_scaling.png\",bbox_inches='tight')\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(Rstar_skinDepth/6e5,Torque,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Torque\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"Torque.png\",bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(Rstar_skinDepth/6e5,phi_o,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Normalized G.Energy/B.energy\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"phi_o.png\",bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.020230407144897423"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "SkinDepth_real"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([48000., 48000., 48000., 48000., 48000., 48000.])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "RLC\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "140.625"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "36000/256"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "particle_weight = 140.625**3*5.544e6/64"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "240896701812.74414"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "particle_weight"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.0010055, 0.0010055, 0.0010055, 0.0010055, 0.0010055, 0.0010055])"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Period"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([48000., 48000., 48000., 48000., 48000., 48000.])"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "RLC\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.2"
+ },
+ "widgets": {
+ "state": {
+ "11d243e9f5074fe1b115949d174d59de": {
+ "views": [
+ {
+ "cell_index": 6
+ }
+ ]
+ }
+ },
+ "version": "1.2.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Tools/Visualization_pulsar.ipynb b/Tools/Visualization_pulsar.ipynb
new file mode 100644
index 00000000000..3815de927fd
--- /dev/null
+++ b/Tools/Visualization_pulsar.ipynb
@@ -0,0 +1,394 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Overview\n",
+ "\n",
+ "This a notebook that inspects the results of a WarpX simulation.\n",
+ "\n",
+ "# Instruction\n",
+ "\n",
+ "Enter the path of the data you wish to visualize below. Then execute the cells one by one, by selecting them with your mouse and typing `Shift + Enter`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import statements\n",
+ "import yt ; yt.funcs.mylog.setLevel(50)\n",
+ "import numpy as np\n",
+ "import scipy.constants as scc\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Read data in the simulation frame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot data with yt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('all', 'particle_Bx'),\n",
+ " ('all', 'particle_By'),\n",
+ " ('all', 'particle_Bz'),\n",
+ " ('all', 'particle_Ex'),\n",
+ " ('all', 'particle_Ey'),\n",
+ " ('all', 'particle_Ez'),\n",
+ " ('all', 'particle_cpu'),\n",
+ " ('all', 'particle_id'),\n",
+ " ('all', 'particle_momentum_x'),\n",
+ " ('all', 'particle_momentum_y'),\n",
+ " ('all', 'particle_momentum_z'),\n",
+ " ('all', 'particle_position_x'),\n",
+ " ('all', 'particle_position_y'),\n",
+ " ('all', 'particle_position_z'),\n",
+ " ('all', 'particle_weight'),\n",
+ " ('boxlib', 'Bx'),\n",
+ " ('boxlib', 'By'),\n",
+ " ('boxlib', 'Bz'),\n",
+ " ('boxlib', 'Ex'),\n",
+ " ('boxlib', 'Ey'),\n",
+ " ('boxlib', 'Ez'),\n",
+ " ('boxlib', 'divE'),\n",
+ " ('boxlib', 'jx'),\n",
+ " ('boxlib', 'jy'),\n",
+ " ('boxlib', 'jz'),\n",
+ " ('boxlib', 'part_per_cell'),\n",
+ " ('boxlib', 'rho'),\n",
+ " ('plasma_e', 'particle_Bx'),\n",
+ " ('plasma_e', 'particle_By'),\n",
+ " ('plasma_e', 'particle_Bz'),\n",
+ " ('plasma_e', 'particle_Ex'),\n",
+ " ('plasma_e', 'particle_Ey'),\n",
+ " ('plasma_e', 'particle_Ez'),\n",
+ " ('plasma_e', 'particle_cpu'),\n",
+ " ('plasma_e', 'particle_id'),\n",
+ " ('plasma_e', 'particle_momentum_x'),\n",
+ " ('plasma_e', 'particle_momentum_y'),\n",
+ " ('plasma_e', 'particle_momentum_z'),\n",
+ " ('plasma_e', 'particle_position_x'),\n",
+ " ('plasma_e', 'particle_position_y'),\n",
+ " ('plasma_e', 'particle_position_z'),\n",
+ " ('plasma_e', 'particle_weight'),\n",
+ " ('plasma_p', 'particle_Bx'),\n",
+ " ('plasma_p', 'particle_By'),\n",
+ " ('plasma_p', 'particle_Bz'),\n",
+ " ('plasma_p', 'particle_Ex'),\n",
+ " ('plasma_p', 'particle_Ey'),\n",
+ " ('plasma_p', 'particle_Ez'),\n",
+ " ('plasma_p', 'particle_cpu'),\n",
+ " ('plasma_p', 'particle_id'),\n",
+ " ('plasma_p', 'particle_momentum_x'),\n",
+ " ('plasma_p', 'particle_momentum_y'),\n",
+ " ('plasma_p', 'particle_momentum_z'),\n",
+ " ('plasma_p', 'particle_position_x'),\n",
+ " ('plasma_p', 'particle_position_y'),\n",
+ " ('plasma_p', 'particle_position_z'),\n",
+ " ('plasma_p', 'particle_weight')]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ds = yt.load( './plotfiles/plt00075/' ) # Create a dataset object\n",
+ "ds.field_list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ 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\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "sl = yt.SlicePlot(ds, 2, 'Ez', aspect=1) # Create a sliceplot object\n",
+ "#sl.annotate_particles((10, 'Mpc'),ptype=\"plasma_p\",col='b',p_size=5.0)\n",
+ "#sl.annotate_particles((10, 'Mpc'),ptype=\"plasma_e\",col='r',p_size=5.0)\n",
+ "#sl.annotate_particles(width=(10.e-6, 'm'), p_size=2, ptype='beam', col='black')\n",
+ "#sl.annotate_grids() # Show grids\n",
+ "sl.show() # Show the plot\n",
+ "# sl.save('./toto.png')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Store quantities in numpy arrays, and plot with matplotlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.5/dist-packages/matplotlib/colors.py:1028: RuntimeWarning: invalid value encountered in less_equal\n",
+ " mask |= resdat <= 0\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ 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\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.5/dist-packages/matplotlib/colors.py:1028: RuntimeWarning: invalid value encountered in less_equal\n",
+ " mask |= resdat <= 0\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "['uniform_ring_50ppc.png']"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p = yt.ParticlePlot(ds,('plasma_p', 'particle_position_x'),('plasma_p', 'particle_position_y'),('plasma_p', 'particle_weight'))\n",
+ "\n",
+ "p.show()\n",
+ "p.save(\"uniform_ring_50ppc.png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.5/dist-packages/matplotlib/colors.py:1028: RuntimeWarning: invalid value encountered in less_equal\n",
+ " mask |= resdat <= 0\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.5/dist-packages/matplotlib/colors.py:1028: RuntimeWarning: invalid value encountered in less_equal\n",
+ " mask |= resdat <= 0\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "['uniform_ring_50ppc.png']"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p = yt.ParticlePlot(ds,('plasma_e', 'particle_position_x'),('plasma_e', 'particle_position_y'),('plasma_e', 'particle_weight'))\n",
+ "\n",
+ "p.show()\n",
+ "p.save(\"uniform_ring_50ppc.png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "\n",
+ "# Get field quantities\n",
+ "all_data_level_0 = ds.covering_grid(level=0,left_edge=ds.domain_left_edge, dims=ds.domain_dimensions)\n",
+ "Bx = all_data_level_0['boxlib', 'Ex'].v.squeeze()\n",
+ "Dx = ds.domain_width/ds.domain_dimensions\n",
+ "extent = [ds.domain_left_edge[ds.dimensionality-1], ds.domain_right_edge[ds.dimensionality-1],\n",
+ " ds.domain_left_edge[0], ds.domain_right_edge[0] ]\n",
+ "\n",
+ "# Get particle quantities\n",
+ "ad = ds.all_data()\n",
+ "x = ad['beam', 'particle_position_x'].v\n",
+ "z = ad['beam', 'particle_position_y'].v\n",
+ "\n",
+ "# Plot image\n",
+ "plt.figure()\n",
+ "plt.imshow(Bx, extent=extent)\n",
+ "plt.scatter(z,x,s=.1,c='k')\n",
+ "\n",
+ "# Print all available quantities\n",
+ "ds.field_list"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Read data back-transformed to the lab frame when the simulation runs in the boosted frame (example: 2D run)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# read_raw_data.py is located in warpx/Tools.\n",
+ "import os, glob\n",
+ "import read_raw_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "species = 'beam'\n",
+ "iteration = 1\n",
+ "field = 'Ex'\n",
+ "\n",
+ "snapshot = './lab_frame_data/' + 'snapshot' + str(iteration).zfill(5)\n",
+ "header = './lab_frame_data/Header'\n",
+ "allrd, info = read_raw_data.read_lab_snapshot(snapshot, header) # Read field data\n",
+ "F = allrd[field]\n",
+ "print( \"Available info: \", *list(info.keys()) )\n",
+ "print(\"Available fields: \", info['field_names'])\n",
+ "nx = info['nx']\n",
+ "nz = info['nz']\n",
+ "x = info['x']\n",
+ "z = info['z']\n",
+ "xbo = read_raw_data.get_particle_field(snapshot, species, 'x') # Read particle data\n",
+ "ybo = read_raw_data.get_particle_field(snapshot, species, 'y')\n",
+ "zbo = read_raw_data.get_particle_field(snapshot, species, 'z')\n",
+ "uzbo = read_raw_data.get_particle_field(snapshot, species, 'uz')\n",
+ "\n",
+ "plt.figure(figsize=(6, 3))\n",
+ "extent = np.array([info['zmin'], info['zmax'], info['xmin'], info['xmax']])\n",
+ "plt.imshow(F, aspect='auto', extent=extent, cmap='seismic')\n",
+ "plt.colorbar()\n",
+ "plt.plot(zbo, xbo, 'g.', markersize=1.)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Read back-transformed data with hdf5 format (example: 3D run)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import h5py\n",
+ "import matplotlib.pyplot as plt\n",
+ "f = h5py.File('HDF5_lab_frame_data/snapshot00003', 'r')\n",
+ "print( list(f.keys()) )\n",
+ "# plt.figure()\n",
+ "plt.imshow(f['Ey'][:,,:])"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
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