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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
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+++ 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-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
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-interactive-version.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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\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
new file mode 100644
index 0000000..30a8826
--- /dev/null
+++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Electric-field-lines-due-to-point-charges.ipynb
@@ -0,0 +1,117 @@
+{
+ "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": 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deqhWizJ9/jmklTp1INcMG0b0wQcIKMbGqoszZGfj+W3eTPTxx5BnunbFOT09Ua9duyJV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\nKIMBcZ7cXAzKPj5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VQhDws88s979yBUT+66941qNHi+tbK2LLFgjgt2/D7B43juXOWMAm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mw8r0cPBPR8fIzfRUdDsKxaBQvIx4fonXcghJYsQeDvzBm44nXrQtlWq4b3Vrbs051k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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
+}