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diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-6-Electromagnetic-Induction.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-6-Electromagnetic-Induction.ipynb new file mode 100644 index 0000000..5da3948 --- /dev/null +++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-6-Electromagnetic-Induction.ipynb @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Chapter - 6: Electromagnetic Induction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 6.2 Page No: 208" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Induced EMF is 107978.459441 mV\n", + "Induced current is 15425.494206 mA\n" + ] + } + ], + "source": [ + "from math import cos, pi\n", + "from ipywidgets import interact\n", + "\n", + "###################### Given Data ###############################\n", + "square_loop_side = 0.1 # in meters\n", + "resistance = 0.5 # in ohm\n", + "B = 0.10 # magnetic field itensity in tesla\n", + "theta = 45 # orientation of B in north-east direction(in degrees)\n", + "\n", + "##################### Calculation ###############################\n", + "def emf_and_current(square_loop_side, resistance, B, theta):\n", + " A = square_loop_side**2 # Surface area in square-meter\n", + " flux_initial = B*A*cos(theta*pi/180) # in weber\n", + " flux_final = 0 \n", + " flux_change = flux_initial - flux_final\n", + " time_change = 0.7 # in second\n", + " induced_emf = (flux_change / time_change)*1000 # in milli-volt\n", + " induced_current = induced_emf / resistance # in milli-ohm\n", + "\n", + " ##################### Display Results ###########################\n", + " print \"Induced EMF is %f mV\" %(induced_emf)\n", + " print \"Induced current is %f mA\" %(induced_current)\n", + " \n", + "interact(emf_and_current, square_loop_side=(0.1,10,0.1), resistance=(0.1,10,0.1), B=(0.01,10,0.01), theta=(1,180,1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 6.3 Page no: 209" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Induced EMF is -4.569223 V\n", + "Induced current is -0.091384 A\n" + ] + } + ], + "source": [ + "from math import pi, cos\n", + "from ipywidgets import interact\n", + "\n", + "############################ Given Data ###############################\n", + "coil_radius = 0.1 # in meters\n", + "N = 500 # turns in the coil\n", + "resistance = 2 # in ohm\n", + "B = 3.0e-5 # magnetic field itensity in tesla\n", + "theta_initial = 0 # orientation of coil perpendicular to B (in degrees)\n", + "theta_final = 180\n", + "\n", + "########################### Calculation ###############################\n", + "def emf_and_current(coil_radius, N, resistance, B, theta_initial, theta_final):\n", + " A = pi*coil_radius**2 # Surface area in square-meter\n", + " flux_initial = B * A * cos(theta_initial * pi/180) # in weber\n", + " flux_final = B * A * cos(theta_final * pi/180) # in weber \n", + " flux_change = flux_initial - flux_final\n", + " time_change = 0.25 # in second\n", + " induced_emf = N * (flux_change / time_change) # in volt\n", + " induced_current = induced_emf / resistance # in ohm\n", + "\n", + " ########################### Display Results ###########################\n", + " print \"Induced EMF is %f V\" %(induced_emf)\n", + " print \"Induced current is %f A\" %(induced_current)\n", + " \n", + "interact(emf_and_current, coil_radius=(0.1,10,0.1), N=(1,1000,1), resistance=(1,100,1), B=(1e-5,10e-5,1e-5), theta_initial=(0,180,1), theta_final=(0,180,1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 6.7 Page no: 215" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Induced EMF in volt: 36.0118780394\n" + ] + } + ], + "source": [ + "from math import pi\n", + "from ipywidgets import interact\n", + "\n", + "############################ Given Data ###############################\n", + "rod_length = 0.1 # in meters\n", + "rpm = 120 # in the rev/s\n", + "R = 0.5 # spoke length(radius of wheel) in m\n", + "B = 0.4e-4 # magnetic field itensity in tesla\n", + "\n", + "########################### Calculation ###############################\n", + "def emf(rod_length, rpm, R, B):\n", + " frequency = rpm / 60\n", + " angular_frequency = (2*pi*frequency)\n", + " induced_emf = 0.5*rod_length*angular_frequency*B*R**2 # in volt\n", + "\n", + " ########################### Display Results ###########################\n", + " print \"Induced EMF in volt:\", induced_emf\n", + "\n", + "interact(emf, rod_length=(0.1,10,0.1), rpm=(1,1000,1), R=(0.1,100,0.1), B=(0.1e-4, 0.1e-3, 0.1e-4)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 6.11 Page no: 226" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Maximum induced EMF is 244.742006 V\n" + ] + } + ], + "source": [ + "from math import pi\n", + "from ipywidgets import interact\n", + "\n", + "############################ Given Data ###############################\n", + "N = 100 # number of turns in the coil\n", + "A = 0.1 # surface area in square meter\n", + "f = 0.5 # rps\n", + "B = 0.01 # magnetic field itensity in tesla\n", + "\n", + " ########################### Calculation ###############################\n", + "def emf(N, A, f, B):\n", + " induced_emf = N*B*A*(2*pi*f) # in volt\n", + "\n", + " ########################### Display Results ###########################\n", + " print \"Maximum induced EMF is %f V\" %(induced_emf)\n", + " \n", + "interact(emf, N=(1,500,1),A=(0.01,0.5,0.01),f=(0.1,60,0.1),B=(0.001,0.1,0.001)) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-7-Alternating-current.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-7-Alternating-current.ipynb new file mode 100644 index 0000000..6c71758 --- /dev/null +++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-7-Alternating-current.ipynb @@ -0,0 +1,343 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter - 7: Alternating Current" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 7.1 Page no: 236" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bulb resistance in ohm: 128\n", + "peak voltage in volt: 271.529003976\n", + "rms current in ampere: 1.5\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "\n", + "###################### Given ################################\n", + "P = 100 # power rating of light bulb in watt\n", + "V = 220 # rms voltage in volt\n", + "\n", + "###################### Calculation ##########################\n", + "def bulb_R_and_Vm_and_Irms(P, V):\n", + " R = (V*V) / P # resistance of light bulb in ohm\n", + " V_m = (2**0.5) * V # peak voltage of the source in volt\n", + " I = P / float(V) # rms current through the bulb in ampere\n", + " print \"bulb resistance in ohm: \", R\n", + " print \"peak voltage in volt: \", V_m\n", + " print \"rms current in ampere: \", I\n", + "\n", + "\n", + "interact(bulb_R_and_Vm_and_Irms, P=(10,1000,1), V=(110,230,1)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 7.2 Page no: 239" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inductive reactance in ohm: 11.3222999235\n", + "rms current in ampere: 15.3679023851\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "from math import pi\n", + "\n", + "###################### Given ################################\n", + "L = 25e-3 # inductance in H\n", + "V = 220 # rms voltage in volt\n", + "f = 50 # source frequency in Hz\n", + "\n", + "###################### Calculation ##########################\n", + "def X_L_and_I_rms(L, V, f):\n", + " X_L = (2*pi*f) * L # inductive reactance in ohm\n", + " I = V / X_L # rms current in ampere\n", + "\n", + " ###################### Display Results ##################\n", + " print \"inductive reactance in ohm: \", X_L\n", + " print \"rms current in ampere: \", I\n", + "\n", + "interact(X_L_and_I_rms, L=(1e-3,100e-3,1e-3), V=(100,240,1), f=(45,65,1)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 7.4 Page no: 242" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "capacitive reactance in ohm: 39.2007248995\n", + "rms current in ampere: 3.41830413452\n", + "peak current in ampere: 4.83421206735\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "from math import pi, sqrt\n", + "\n", + "###################### Given ################################\n", + "C = 15e-6 # capacitance in farad\n", + "V = 220 # rms voltage in volt\n", + "f = 50 # source frequency in Hz\n", + "\n", + "###################### Calculation ##########################\n", + "def X_C_and_I_rms_and_I_m(C, V, f):\n", + "\n", + " X_C = float(1) / (2*pi*f * C) # capacitive reactance in ohm\n", + " I = V / X_C # rms current in ampere\n", + " I_m = sqrt(2)*I # peak current in ampere\n", + "\n", + "###################### Display Results ######################\n", + " print \"capacitive reactance in ohm: \", X_C\n", + " print \"rms current in ampere: \", I\n", + " print \"peak current in ampere: \", I_m\n", + " \n", + "interact(X_C_and_I_rms_and_I_m, C=(1e-6,100e-6,1e-6), V=(100,240,1), f=(45,65,1)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 7.6 Page no: 251" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "impedance in ohm: 140.272894279\n", + "rms current in ampere: 1.42579221045\n", + "voltage across R, in volt: 189.63036399\n", + "voltage across C, in volt: 63.5635512928\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "from math import pi, sqrt\n", + "\n", + "###################### Given ################################\n", + "R = 200 # resistance in ohm\n", + "C = 15e-6 # capacitance in farad\n", + "V = 220 # rms voltage in volt\n", + "f = 50 # source frequency in Hz\n", + "\n", + "###################### Calculation ##########################\n", + "def RC_ckt(R,C,V,f):\n", + " X_C = float(1) / (2*pi*f * C) # capacitive reactance in ohm\n", + " Z = sqrt(R*R + X_C*X_C)\n", + " I = V / Z # rms current in ampere\n", + " V_R = I*R # voltage across R, in volt\n", + " V_C = I*X_C # voltage across C, in volt\n", + "\n", + " ################## Display Results ######################\n", + " print \"impedance in ohm: \", Z\n", + " print \"rms current in ampere: \", I\n", + " print \"voltage across R, in volt: \", V_R\n", + " print \"voltage across C, in volt: \", V_C \n", + " \n", + "interact(RC_ckt, R=(1,1000,1), C=(1e-6,100e-6,1e-6), V=(100,240,5), f=(45,65,1)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 7.8 Page no: 253" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "impedance in ohm: 747.818503266\n", + "phase difference in degrees: 4.97605642003\n", + "rms current in ampere: 0.179656277344\n", + "power loss in the circuit: 24.0459016018\n", + "power factor: 0.996231032994\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "from math import *\n", + "\n", + "###################### Given ################################\n", + "V_m = 283 # peak voltage of sinusoidal wave in volt\n", + "f = 50 # source frequency in Hz\n", + "R = 3 # resistance in ohm\n", + "L = 25.48e-3 # inductance in henry\n", + "C = 796e-6 # capacitance in farad\n", + "\n", + "###################### Calculation ##########################\n", + "def RLC_ckt(V_m, R, L, C, f):\n", + " X_L = (2*pi*f) * L # inductive reactance in ohm\n", + " X_C = float(1) / (2*pi*f * C) # capacitive reactance in ohm\n", + " Z = sqrt(R*R + (X_L - X_C)**2) # impedance in ohm\n", + " phi = atan((X_C - X_L)/float(R))*180/pi # phase difference in degrees\n", + " I = V_m / (sqrt(2)*Z) # rms current in ampere\n", + " P = I*I*R # power loss in R, in watt\n", + " pf = cos(phi*pi/180) # power factor\n", + "\n", + " ###################### Display Results ######################\n", + " print \"impedance in ohm: \", Z\n", + " print \"phase difference in degrees: \", phi\n", + " print \"rms current in ampere: \", I\n", + " print \"power loss in the circuit: \", P\n", + " print \"power factor: \", pf\n", + "\n", + "interact(RLC_ckt, V_m=(150,340,1), R=(1,1000,1), L=(1e-3,100e-3,1e-3),C=(1e-6,100e-6,1e-6), V=(100,240,5), f=(45,65,1)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 7.9 Page no: 254" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "impedance in ohm: 307.649549475\n", + "phase difference in degrees: 20.0522148818\n", + "rms current in ampere: 0.652750267959\n", + "power loss in the circuit: 123.137961661\n", + "power factor: 0.939380540272\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "from math import pi, sqrt\n", + "\n", + "###################### Given ################################\n", + "V_m = 283 # peak voltage of sinusoidal wave in volt\n", + "f = 50 # source frequency in Hz\n", + "R = 3 # resistance in ohm\n", + "L = 25.48e-3 # inductance in henry\n", + "C = 796e-6 # capacitance in farad\n", + "\n", + "###################### Calculation ##########################\n", + "def resonant_ckt(V_m, R, L, C, f):\n", + " omega_resonant = 1 / sqrt(L*C)\n", + " f_resonant = omega_resonant / (2*pi)\n", + " X_C = float(1) / (2*pi*f * C) # capacitive reactance in ohm\n", + " Z = R # at resonance X_L = X_C ==> Z = R\n", + " V = V_m / sqrt(2) # rms voltage in volt\n", + " I = V / Z # rms current in ampere\n", + " P = I*I*R # power loss in R, in watt\n", + "\n", + " ###################### Display Results ######################\n", + " print \"resonant frequency in Hz: \", f_resonant\n", + " print \"impedance in ohm: \", Z\n", + " print \"rms current in ampere: \", I\n", + " print \"power loss in the circuit during resonance, in watt: \", P\n", + "\n", + "interact(RLC_ckt, V_m=(150,340,1), R=(1,1000,1), L=(1e-3,100e-3,1e-3),C=(1e-6,100e-6,1e-6), V=(100,240,5), f=(45,65,1)) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-8-Electromagnetic-waves.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-8-Electromagnetic-waves.ipynb new file mode 100644 index 0000000..82d193c --- /dev/null +++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-8-Electromagnetic-waves.ipynb @@ -0,0 +1,208 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter - 8: Electromagnetic waves" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 8.1 Page no: 273" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "displacement current in ampere: 2.49999449999210e-7\n", + "magnetic flux density in tesla: 9.99997799996840e-14\n" + ] + }, + { + "data": { + "text/plain": [ + "<function __main__.id_and_B>" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from math import pi, exp\n", + "from sympy import *\n", + "from ipywidgets import interact\n", + "\n", + "###################### Given ################################\n", + "C = 1e-9 # capacitance in farad\n", + "R = 1e+6 # resistance in ohm\n", + "V = 2 # battery voltage, in volt \n", + "\n", + "###################### Calculation ##########################\n", + "def id_and_B(C, R, V):\n", + " tou = C*R\n", + " t, mu = symbols('t mu')\n", + " q_t = C*V*(1 - exp(-t/tou))\n", + "\n", + " ###### Theory: from textbook\n", + " ###### loop radius 0.5 m parallel to the plates passing P\n", + " ###### Flux phi = E * area of the loop\n", + " ###### phi = E * pi * (0.5)**2 = q_t / (4*ϵ)\n", + " ###### i_d = ϵ * diff(phi/t) = 0.25 * diff(q_t/t) \n", + "\n", + " i_d = 0.25*diff(q_t, t)\n", + " i_d = i_d.subs(t, 1e-3)\n", + "\n", + " ###### B*2*pi*0.5 = mu*(i_c + i_d) = mu(0 + i_d)\n", + " ###### B = mu*i_d / (2*pi*0.5)\n", + " B = mu*i_d / (2*pi*0.5)\n", + " B = B.subs(mu, 4*pi*1e-7)\n", + "\n", + " ###################### Display Results ######################\n", + " print \"displacement current in ampere: \", i_d\n", + " print \"magnetic flux density in tesla: \", B\n", + "\n", + "interact(id_and_B, C=(1e-5,10e-5,1e-5), R=(1,1e+7,10), V=(1,10,1)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 8.4 Page no: 279" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total energy falling on the surface in J: 2250000\n", + "total momentum in kg-m/s: 0.0075\n", + "average force exerted on the surface in N: 8.33333333333e-06\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "\n", + "###################### Given ################################\n", + "energy_flux = 18 # in W/cm^2\n", + "surface_area = 20 # in cm^2\n", + "time_span = 30 * 60 # in second\n", + "\n", + "###################### Calculation ##########################\n", + "def energy_and_momentum(energy_flux, surface_area, time_span_mins):\n", + " U = energy_flux * surface_area * time_span_mins*60 # total energy in J\n", + " c = 3e+8 # speed of light in m/s\n", + " p = U / c # total momentum in kg-m/s\n", + " F = p / (time_span_mins*60)\n", + "\n", + " ################## Display Results ######################\n", + " print \"total energy falling on the surface in J: \", U\n", + " print \"total momentum in kg-m/s: \", p\n", + " print \"average force exerted on the surface in N: \", F\n", + " \n", + "interact(energy_and_momentum, energy_flux=(1,100,1), surface_area=(1,100,1), time_span_mins=(1,60,1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 8.5 Page no: 279 " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rms electric field in E/m: 1.31983561753\n", + "peak electric field in E/m: 1.86652943041\n", + "strength of magnetic field in tesla: 4.39945205842e-09\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "from math import pi, sqrt\n", + "\n", + "###################### Given ################################\n", + "P = 100 # power rating of bulb in watt\n", + "r = 3 # distance of radiation from bulb in meter\n", + "bulb_efficiency = 2.5/100 # in percent\n", + "\n", + "###################### Calculation ##########################\n", + "def E_and_B(P, r, bulb_efficiency):\n", + " A = 4*pi*r*r # surface area in m^2\n", + " I = (P*bulb_efficiency/100) / A # intensity in W/m^2\n", + " E_rms = sqrt(I / (8.85e-12 * 3e+8))\n", + " E_peak = sqrt(2) * E_rms\n", + " c = 3e+8 # speed of light in m/s\n", + " B_rms = E_rms / c\n", + "\n", + " ###################### Display Results ######################\n", + " print \"rms electric field in E/m: \", E_rms\n", + " print \"peak electric field in E/m: \", E_peak\n", + " print \"strength of magnetic field in tesla: \", B_rms\n", + " \n", + "interact(E_and_B, P=(1,500,1), r=(1,100,1), bulb_efficiency=(1,100,0.1)) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/electric-field-intensity-due-to-point-charges.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/electric-field-intensity-due-to-point-charges.ipynb new file mode 100644 index 0000000..7ae4fe5 --- /dev/null +++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/electric-field-intensity-due-to-point-charges.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Electric field intensity due to point charges" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Positive charge is indicated by blue circle. Negative charge is indicated by red circle\n", + "\n", + "Change the magnitudes of the charges using the sliders\n" + ] + }, + { + "data": { + "image/png": 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VrSqkulWVfrz0o8zIuJnXZtI2/21Ks4Go8m8832s+rb+3Xql6v6ZcWC5XVM9/3eFXGwLQ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nc0/MJwOhN8hgoqCkTWX15dTpW1lVRVW0XV/Gqq4ddQraCW+AI+CRgBMQzDyR5JoFZQS35JfrTX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\nP8din+4+1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/0PN6zKZI/INtld6EPsvr+bYACacmkKdTfrTrnluc3yiALn18608vpKicj6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\n95iaepnS3Q93S2x7LuEcNd5qzPhCnJKfQvU36TPW0l9xc4VcJ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+ "text/plain": [ + "<matplotlib.figure.Figure at 0xb8b854c>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\" References: \n", + " 1. dominodatalab: Building Interactive Dashboards with Jupyter\n", + " 2. Number Crunch blog: Visualizing streamlines\n", + " \n", + "\"\"\" \n", + "%pylab inline\n", + "from ipywidgets import *\n", + "\n", + "class charge:\n", + " \"\"\" This defines the type of charges (Positive or Negative) and their positions\n", + " in 2D plane.\n", + " \"\"\"\n", + " def __init__(self, q, pos): \n", + " self.q=q\n", + " self.pos=pos\n", + " \n", + "def E_point_charge(q, pos, x, y):\n", + " \"\"\"Calculates electric field intensity due to the single or two point charges\n", + " \n", + " Params:\n", + " q: charge value\n", + " x, y : points in 2D plane\n", + " \n", + " Returns:\n", + " Ex, Ey: Field intensity at points x and y respectively\n", + " \n", + " \"\"\"\n", + " Ex = q*(x-pos[0])/((x-pos[0])**2+(y-pos[1])**2)**(1.5)\n", + " Ey = q*(y-pos[1])/((x-pos[0])**2+(y-pos[1])**2)**(1.5) \n", + "\n", + " return Ex, Ey\n", + " \n", + "def E_total(x, y, charges):\n", + " \"\"\"Returns list of total electric field intensity at each 2D point in the plane\n", + " \n", + " Params:\n", + " x, y: points in 2D plane\n", + " charges: type of charge and has position of the charge\n", + " \n", + " Returns:\n", + " Field intensity at Ex and Ey as a list\n", + " \n", + " \"\"\"\n", + " Ex, Ey = 0, 0\n", + " for C in charges:\n", + " E = E_point_charge(C.q, C.pos, x, y)\n", + " Ex += E[0]\n", + " Ey += E[1]\n", + " return [ Ex, Ey ]\n", + "\n", + "def plot_field_lines(q1_value, q2_value):\n", + " \"\"\"Plots electric field lines using streamline plot\n", + " \n", + " Params:\n", + " q1_value, q2_value: magnitude of charges q1 and q2 with direction\n", + " \n", + " Returns:\n", + " Field lines of q1 and q2\n", + " \n", + " \"\"\"\n", + " charges = [ charge(q1_value, [-1, 0]), charge(q2_value, [1, 0]) ]\n", + " \n", + " for C in charges:\n", + " if C.q>0:\n", + " plot(C.pos[0], C.pos[1], 'bo', ms=8*sqrt(C.q))\n", + " if C.q<0:\n", + " plot(C.pos[0], C.pos[1], 'ro', ms=8*sqrt(-C.q))\n", + "\n", + " x0, x1 = -2, 2.5\n", + " y0, y1 = -1.5, 1.5\n", + " x = linspace(x0, x1, 100)\n", + " y = linspace(y0, y1, 100)\n", + "\n", + " x, y = meshgrid(x, y)\n", + "\n", + " Ex, Ey = E_total(x, y, charges)\n", + " streamplot(x, y, Ex, Ey, color='g')\n", + " draw()\n", + " \n", + " print \"Positive charge is indicated by blue circle. Negative charge is indicated by red circle\\n\"\n", + " print \"Change the magnitudes of the charges using the sliders\"\n", + "\n", + "interact(plot_field_lines, q1_value=(-20,20,0.1), q2_value=(-20,20,0.1)) \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} |