diff options
9 files changed, 832 insertions, 1057 deletions
diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-2-Electrostatic-Potential-and-Capacitance.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-2-Electrostatic-Potential-and-Capacitance.ipynb new file mode 100644 index 0000000..b46447e --- /dev/null +++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-2-Electrostatic-Potential-and-Capacitance.ipynb @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 2 \n", + "# ELECTROSTATIC POTENTIAL AND CAPACITANCE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 2.1; Page No 55" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Potential is: 89877.4243799 Volts\n", + "Work done is: 0.00017975484876 Joules\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "import math\n", + "# Given Data\n", + "def pot(Q,r1):\n", + " e=1e-7\n", + " Q = Q*e # value of charge at p\n", + " r1 = r1/100.0 # distance from point p\n", + " eps = 8.854e-12 \n", + " q1 = 2e-9 # value of charge at infinity\n", + "# Formula to calculate potential V = (1/4(*pi*eps))*(Q/r)\n", + " v = (1/(4*math.pi*eps))*(Q/r1)\n", + " print \"Potential is: \",v,\"Volts\"\n", + "# Formula to calculate work done W = qV\n", + " w = q1*v\n", + " print \"Work done is: \",w,\"Joules\"\n", + "\n", + "interact(pot,Q=(1,9),r1=(1,9))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 2.5; Page No 65" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Electrosatic Potential Energy is: -1.24829756083 Joules\n", + "Work done is: 1.24829756083 Joules\n", + "Net Electrostatic Energy is: -1.24829756083 Joules\n" + ] + } + ], + "source": [ + "import math\n", + "from ipywidgets import interact\n", + "#Give Data\n", + "\n", + "def potential(q1,q2):\n", + " e=1e-6\n", + " q1 = q1*e # value of charge 1\n", + " q2 = q2*e # value of charge 2\n", + " r1 = 0.09 # distance between charges\n", + " r2 = -0.09\n", + " r = r1 - r2 \n", + " eps = 8.854e-12\n", + "# Formula to calculate electrostaic potential energy U = (1/(4*pi*eps))*((q1*q2)/r)\n", + " u = (1/(4*math.pi*eps))*((q1*q2)/r)\n", + " print \"Electrosatic Potential Energy is: \",u,\"Joules\"\n", + "# Formula to claculate work done W = U2 - U1\n", + " u1 = u\n", + " u2 = 0\n", + " w = u2-u1\n", + " print \"Work done is: \",w,\"Joules\"\n", + "# Formula to calculate net electrostatic energy (q1*(V)*r1) + (q2*(V)*r2) + ((q1q2)/4*pi*eps*r12)\n", + " A = 9e+5\n", + " e = ((A*q1)/r1)+((A*q2)/r1)+u\n", + " print \"Net Electrostatic Energy is: \",e,\"Joules\"\n", + "interact(potential,q1=(1,9),q2=(-9,-1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 2.9; Page No 79" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Charge on capacitors C1,C2,C3 is: 0.000833333333333 Coloumb\n", + "Charge on capacitor C4 is: 0.0025 Coloumb\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "# Given Data\n", + "\n", + "def capacitor(c1,c2,c3,c4):\n", + " e=1e-6\n", + " c1 = c1 * e # value of capacitors C1,C2,C3,C4\n", + " c2 = c2 * e\n", + " c3 = c3 * e\n", + " c4 = c4 * e\n", + "# Formula to calculate capacitance when connected in parallel is (1/c) = (1/c1)+(1/c2)+(1/c3)\n", + " c = (1/c1)+(1/c2)+(1/c3)\n", + " c = 1/c \n", + "# Formula to calculate capacitance when connected in series is c = c1 + c2 + c3\n", + "# Calculate equivalent capacitance \n", + " c_new = c + c4\n", + "# Formula to calculate potential difference is V = Q/C\n", + "# the potential difference across AB is Q/C1\n", + "# the potential difference across BC is Q/C2\n", + "# the potential difference across CD is Q/C3\n", + " v = 500\n", + "# Charge on each capacitor is \n", + " q = v * c\n", + " q1 = v * c4\n", + " print \"Charge on capacitors C1,C2,C3 is: \",q,\"Coloumb\"\n", + " print \"Charge on capacitor C4 is: \",q1,\"Coloumb\"\n", + "\n", + "interact(capacitor,c1=(1,9),c2=(1,9),c3=(1,9),c4=(1,9))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 2.10; Page No 82" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Charge is: 5.4e-08 Coloumb\n", + "Energy stored by capacitor is: 2.7e-06 Joules\n", + "Electrostatic Energy is: 1.35e-06 Joules\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "# Given Data\n", + "\n", + "def energy(c):\n", + " e = 1e-12\n", + " c = c * e # value of capacitor \n", + " v = 100 # voltage of battery\n", + "# Formula to find charge is Q = C*V\n", + " q = c * v\n", + " print \"Charge is: \",q,\"Coloumb\"\n", + "# Formula to calculate energy stored by capacitor (1/2) C*V^2 = (1/2) Q*V\n", + " e = (q * v)/2\n", + " print \"Energy stored by capacitor is: \",e,\"Joules\"\n", + "# Electrostatic Energy\n", + "# By charge conservation, Q′ = Q/2. This implies V′ = V/2\n", + " e1 = (q * v)/4\n", + " print \"Electrostatic Energy is: \",e1,\"Joules\"\n", + "interact(energy,c=(100,1000,10))" + ] + } + ], + "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": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-3-Current-Electricity.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-3-Current-Electricity.ipynb new file mode 100644 index 0000000..1b4647a --- /dev/null +++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-3-Current-Electricity.ipynb @@ -0,0 +1,200 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 3\n", + "# CURRENT ELECTRICITY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 3.1; Page No 99" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drift Speed is: 0.00110243055556 m s^-1\n" + ] + } + ], + "source": [ + "# Given Data\n", + "e = 1.6e-19 # charge of electron\n", + "A = 1.0e-7 # cross-sectional area\n", + "I = 1.5 # current in copperwire\n", + "# Formula to calculate Drift Speed is v(d)=(I/neA)\n", + "n = (6.0e23/63.5)*9.0e6\n", + "# Calculate Drift Speed\n", + "v = 1.5/(n*e*A)\n", + "print \"Drift Speed is: \",v,\"m s^-1\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 3.3; Page No 105" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The steady temperature of the heating element is: 847.248418092 degree celsius\n" + ] + } + ], + "source": [ + "# Given Data\n", + "V = 230 # supply voltage\n", + "I = 2.68 # current in toaster\n", + "r1 = 75.3 # resistance at room temperature\n", + "t1 = 27 # room temperature\n", + "alpha = 1.70e-4 # Temperature coefficient\n", + "# Formula to calculate Resistance is R=V/I\n", + "r2 = V/I\n", + "r2 = round(r2,1)\n", + "t = (r2 - r1)/(r1 * alpha)\n", + "t2 = t + t1\n", + "print \"The steady temperature of the heating element is:\",t2,\"degree celsius\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 3.4; Page No 105" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperature of the bath is: 345.652173913 degree celsius\n" + ] + } + ], + "source": [ + "# Given Data\n", + "r0 = 5 # resistance at ice point\n", + "r100 = 5.23 # resistance at steam point\n", + "rt = 5.795 # resistance at hot bath\n", + "# Formula to calculate temperature is t=((rt - r0)/(r100 - r0)* 100)\n", + "t = ((rt - r0)/(r100 - r0))*100\n", + "print \"Temperature of the bath is:\",t,\"degree celsius\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 3.5; Page No 112" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Equivalent Resistance of the network is: 7 ohm\n", + "Voltage drop across AB is: 14 volts\n", + "Voltage drop across BC is: 2 volts\n", + "Voltage drop across CD is: 8 volts\n" + ] + } + ], + "source": [ + "\n", + "# Given Data\n", + "\n", + "V = 16 # voltage of battery\n", + "r = 1 # internal resistance\n", + "# Equivalent resistance for two 4ohm resistance in parallel\n", + "r1 = (4 * 4)/(4 + 4)\n", + "# Equivalent resistance for 12ohm and 6ohm resistance in parallel\n", + "r2 = (12 * 6)/(12 + 6)\n", + "# Equivalent resistance for r1, r2 and 1ohm resistance in series\n", + "r3 = 1\n", + "R = r1 + r2 + r3\n", + "print \"Equivalent Resistance of the network is: \",R,\"ohm\"\n", + "# Formula to calculate current is I = V/(R+r)\n", + "I = V/(R + r)\n", + "# I1 is the current in one of the 4 ohm resistors and I2 the current in the other 4 ohm resistor\n", + "# I1 * 4 = I2 * 4 i.e. I1 = I2\n", + "# Here I1 + I2 = I\n", + "# Therefore I1 = I2 = 1A\n", + "I1 = 1\n", + "I2 = 1\n", + "# I3 is the current in the 12 ohm resistor, and I4 in the 6 ohm resistor\n", + "# I3 * 12 = I4 * 6 i.e. I4 = 2 * I3\n", + "# Here I3 + I4 = 2A\n", + "# Therefore I3 = (2/3)A and I4 = (4/3)A\n", + "I3 = 2/3\n", + "I4 = 4/3\n", + "# voltage drop across AB is Vab = I * R\n", + "Vab = I * R\n", + "print \"Voltage drop across AB is: \",Vab,\"volts\"\n", + "# voltage drop across BC is Vbc = I * r3\n", + "Vbc = I * r3\n", + "print \"Voltage drop across BC is: \",Vbc,\"volts\"\n", + "# voltage drop across CD is Vcd = I * r2\n", + "Vcd = I * r2\n", + "print \"Voltage drop across CD is: \",Vcd,\"volts\"" + ] + } + ], + "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": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-4-Moving-Charges-and-Magnetism.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-4-Moving-Charges-and-Magnetism.ipynb new file mode 100644 index 0000000..15b9ae8 --- /dev/null +++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-4-Moving-Charges-and-Magnetism.ipynb @@ -0,0 +1,324 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 4\n", + "# MOVING CHARGES AND MAGNETISM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.1; Page No 136" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "magnitude of magnetic field is: 0.784 Tesla\n" + ] + } + ], + "source": [ + "# Given Data\n", + "from ipywidgets import interact\n", + "def magnetic(m,I,l):\n", + " # m = mass of wire\n", + " g = 9.8 # gravity\n", + " # I = current in wire\n", + " # l = length of wire\n", + "# Formula to calculate magnitude of magnetic field B = m*g/I*l\n", + " B = (m * g)/(I * l)\n", + " print \"magnitude of magnetic field is: \",B,\"Tesla\"\n", + "interact(magnetic,m=(0.1,1),I=(1,2,0.1),l=(1,10,0.5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.3; Page No 139" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Radius of the path of an electron is: 0.28125 meters\n", + "Frequency is: 16976527.2631 Hz\n", + "Energy is: 4.05e-16 Joules\n", + "Energy is: 2.53125 Kev\n" + ] + } + ], + "source": [ + "import math as mp\n", + "# Given Data\n", + "m = 9e-31 # mass of electron\n", + "v = 3e+7 # speed of electron\n", + "q = 1.6e-19 # charge on electron\n", + "B = 6e-4 # magnetic field\n", + "Kev = 1.6e-19 # 1 eV = 1.6 × 10^–19 J\n", + "# Radius of the path of an electron is r = m*v/q*B\n", + "r = (m * v)/(q * B)\n", + "print\"Radius of the path of an electron is: \",r,\"meters\"\n", + "# Frequency is f = v/(2*pi*r)\n", + "f = (v/(2 * mp.pi * r))\n", + "print \"Frequency is: \",f,\"Hz\"\n", + "# Energy is (1/2)*m*v*v\n", + "E = (m*v*v)/2\n", + "print \"Energy is: \",E,\"Joules\"\n", + "E1 = (E / Kev)/1000\n", + "print \"Energy is: \",E1,\"Kev\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.4; Page No 142" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "kinetic energy (in MeV) of the proton beam produced by the accelerator is: 7.10611516878 Mev\n" + ] + } + ], + "source": [ + "import math\n", + "# Given Data\n", + "f = 10e6 # frequency of cyclotron\n", + "r = 0.6 # radius of \"dees\"\n", + "Mp = 1.67e-27 # mass of proton\n", + "Mev = 1.67e-13 # 1 MeV = 1.6 × 10^–13 J\n", + "q = 1.6e-19 # charge of electron\n", + "# Magnetic field is calculated by B = (2*pi*m*f)/q\n", + "B = (2 * math.pi * Mp * f)/q\n", + "# Final velocity of protons is v = r * 2*pi * v\n", + "v = r * 2 * math.pi * f\n", + "# Energy of protons \n", + "E1 = (Mp * v * v)/2\n", + "E2 = (E1 / Mev)\n", + "print \"kinetic energy (in MeV) of the proton beam produced by the accelerator is: \",E2,\"Mev\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.7; Page No 147" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Magnetic field at the centre of the coil is: 0.0009375 Tesla\n" + ] + } + ], + "source": [ + "import math\n", + "from ipywidgets import interact\n", + "# Given Data\n", + "def field(N,I,R):\n", + " u0 = 12.5e-7\n", + " # N = No of turn of coil\n", + " # I = current carried by coil\n", + " R = R/100.0 # R = radius of coil\n", + "# Magnetic field at the centre of the coil is B = (u0*N*I)/(2*R)\n", + " B = (u0 * N * I)/(2 * R)\n", + " print \"Magnetic field at the centre of the coil is: \",B,\"Tesla\"\n", + "interact(field,N=(100,500,10),I=(1,2,0.1),R=(10,50,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.9; Page No 154" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Magnetic field inside the solenoid is: 0.00098 Tesla\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "# Given Data\n", + " # l = length of solenoid\n", + " # N = No of turns\n", + " # I = Current in coil\n", + "def solenoid(l,N,I): \n", + " u0 = 12.5e-7\n", + "# number of turns per unit length is n = N/l\n", + " n = N/l\n", + "# magnitude of the magnetic field inside the solenoid is B = u0 *n *I\n", + " B = u0 * n * I\n", + " print\"Magnetic field inside the solenoid is:\",B,\"Tesla\"\n", + "interact(solenoid,l=(0.1,1,0.1),N=(100,500,10),I=(1,2,0.1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.11; Page No 159" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final torque is: 1104.46616728 N-cm\n", + "angular speed acquired by the coil is: 148.62477366 s^-1\n" + ] + } + ], + "source": [ + "import math\n", + "# Given Data\n", + "def torque(N,I,R):\n", + " u0 = 12.5e-7\n", + "#I = 3.2 # current in the coil\n", + "#R = 0.1 # radius of coil\n", + "#N = 100 # no of turns of coil\n", + " i = 0.1 # moment of inertia\n", + "# Magnetic field of the coil is B = (u0*N*I)/(2*R)\n", + " B = (u0 * N * I)/(2 * R)\n", + " B = B*1000\n", + "# Magnetic Moment is m = N*I*A\n", + " m = N*I*math.pi*R*R\n", + "# torque is given by τ = m*B*sinθ\n", + "# Initially, θ = 0. Thus, initial torque τi = 0. Finally, θ = π/2 (or 90º)\n", + "# Therefore sin(90)=1\n", + " t = m*B\n", + " print\"Final torque is: \",t,\"N-cm\" \n", + "# angular speed acquired by the coil when it has rotated by 90º is w = (2*m*B/i)^1/2\n", + " w = math.sqrt((2*m*B)/i)\n", + " print\"angular speed acquired by the coil is: \",w,\"s^-1\"\n", + "interact(torque,N=(100,500,10),I=(1,4,0.1),R=(0.1,2,0.1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.13; Page No 165" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current for series equivalent resistance is: 0.047619047619 Amp\n", + "Current for galvanometer converted to an ammeter is: 0.993379675604 Amp\n", + "Current for ideal ammeter with zero resistance 1.0 Amp\n" + ] + } + ], + "source": [ + "# Given Data\n", + "Rg = 60 # Galvanometer Resistance\n", + "Rs = 0.02 # Shunt Resistance\n", + "V = 3.0 # voltage of cell\n", + "R = 3\n", + "# Total Resistance in the circuit \n", + "R1 = Rg + R\n", + "# current for series equivalent resistance\n", + "I1 = V / R1\n", + "print \"Current for series equivalent resistance is: \",I1,\"Amp\"\n", + "# Resistance of the galvanometer converted to an ammeter is R = (Rg * Rs)/(Rg + Rs)\n", + "Rgs = (Rg * Rs)/(Rg + Rs)\n", + "R2 = Rgs + R\n", + "# Current for galvanometer converted to an ammeter \n", + "I2 = V / R2 \n", + "print \"Current for galvanometer converted to an ammeter is: \",I2,\"Amp\"\n", + "# For the ideal ammeter with zero resistance\n", + "I3 = V/R\n", + "print \"Current for ideal ammeter with zero resistance\",I3,\"Amp\"" + ] + } + ], + "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": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} 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 deleted file mode 100644 index 5da3948..0000000 --- a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-6-Electromagnetic-Induction.ipynb +++ /dev/null @@ -1,231 +0,0 @@ -{ - "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 deleted file mode 100644 index 6c71758..0000000 --- a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-7-Alternating-current.ipynb +++ /dev/null @@ -1,343 +0,0 @@ -{ - "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 deleted file mode 100644 index 82d193c..0000000 --- a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-8-Electromagnetic-waves.ipynb +++ /dev/null @@ -1,208 +0,0 @@ -{ - "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-interactive-version.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Electric-field-intensity-due-to-point-charges-interactive-version.ipynb deleted file mode 100644 index 7ae4fe5..0000000 --- a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Electric-field-intensity-due-to-point-charges-interactive-version.ipynb +++ /dev/null @@ -1,158 +0,0 @@ -{ - "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": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEACAYAAAC08h1NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBf8yFBO/AgUKFPzL+D/uDNSrjkenAAAAAABJRU5ErkJggg==\n", - "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 -} diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Electric-field-lines-due-to-point-charges.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Electric-field-lines-due-to-point-charges.ipynb deleted file mode 100644 index 30a8826..0000000 --- a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Electric-field-lines-due-to-point-charges.ipynb +++ /dev/null @@ -1,117 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Electric field lines due to point charges" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### Code contributed by: Dr.Ajith Kumar" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Steps to run the file\n", - "* Find the line Charges (Value, x-cord, y-cord)\n", - "* Value indicates the charge type(+ve or -ve) and it's magnitude\n", - "* x-cord & y-cord indicates the position of the charge in 2D plane\n", - "* You may add or delete charges to the list `Q`. Choose charge type, it's magnitude and it's position in 2D plane\n", - "* For example: try adding `(-10, 3, 3)` to the list `Q` and run the file\n", - "* To change the color of the charge replace color-field 'r' in text function with 'k' for black, 'b' for blue, 'y' for yellow \n", - "* Similarly, you can change the color of field lines by changing color-field in streamplot function with appropriate color values" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "<matplotlib.figure.Figure at 0xa021cac>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Import pylab inline\n", - "%pylab inline\n", - "seterr(all='ignore') # ignore all floating point errors\n", - "\n", - "N = 101 # Number of points in x-coordinate and y-coordinate each\n", - "a, b = linspace(-5, 5, N), linspace(-5, 5, N) # Set evenly spaced points within the interval -5 and 5\n", - "xa, ya = meshgrid(a, b) # A meshgrid in the xy plane. xa contains the x-coordinates\n", - "Ex = zeros_like(xa) # 2D array to store the Ex and\n", - "Ey = zeros_like(ya) # Ey components\n", - "\n", - "Q = [(-15, 0, 3), (5, -2, -2), (5, 2, -2)] # Charges (Value, x-cord, y-cord)\n", - "\n", - "for q in Q: # mark charge locations\n", - " text(q[1], q[2], 'o', color = 'r', fontsize=15, va='center', ha='center')\n", - "\n", - "for i in range(N): # calculate Ex and Ey at each point in the grid, due to all charges\n", - " for j in range(N):\n", - " x, y = xa[i,j], ya[i,j]\n", - " for k in range(len(Q)): # sum over the charges, using equation from the text book\n", - " \n", - " Ex[i,j] += Q[k][0]*(x-Q[k][1])/ ((x-Q[k][1])**2+(y-Q[k][2])**2)**(1.5)\n", - " Ey[i,j] += Q[k][0]*(y-Q[k][2])/ ((x-Q[k][1])**2+(y-Q[k][2])**2)**(1.5)\n", - "\n", - "streamplot(xa, ya, Ex, Ey, color='b', density= 1.6) #plot the field lines using streamplot\n", - "show() # show the plot" - ] - }, - { - "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/Right_Hand_Rule.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Right_Hand_Rule.ipynb new file mode 100644 index 0000000..beb92fc --- /dev/null +++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Right_Hand_Rule.ipynb @@ -0,0 +1,82 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Right Hand Thumb Rule" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEZCAYAAACaWyIJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x3de3048>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Right Hand Thumb Rule\n", + "# Reference: University of Redlands:Computational Tutorials\n", + "%matplotlib inline\n", + "from pylab import *\n", + "# Set limits and number of points in grid\n", + "# value of xmax and ymax can be changed to change the direction of magnetic field lines and can have any value except zero\n", + "xmax = 4\n", + "xmin = -xmax\n", + "NX = 10\n", + "ymax = 4\n", + "ymin = -ymax\n", + "NY = 10\n", + "# Make grid and calculate vector components\n", + "x = linspace(xmin, xmax, NX)\n", + "y = linspace(ymin, ymax, NY)\n", + "X, Y = meshgrid(x, y)\n", + "S2 = X**2 + Y**2 # This is the radius squared\n", + "Bx , By= -Y/S2 , +X/S2\n", + "if(xmax>0 or ymax>0):\n", + " plot(0,0,'ro') # Red Dot to represent direction of current is out of the plane\n", + " QP = streamplot(X,Y,Bx,By,density=1.6)\n", + "else:\n", + "\tplot(0,0,'go') # Green Dot to represent direction of current is into the plane\n", + "\tQP = streamplot(-X,-Y,Bx,By,density=1.6)\n", + "# Set the left, right, bottom, top limits of axes\n", + "title('Magnetic Field for current')\n", + "xlabel('x (cm)'), ylabel('y (cm)')\n", + "show()" + ] + } + ], + "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": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} |