summaryrefslogtreecommitdiff
path: root/Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter5.ipynb
diff options
context:
space:
mode:
authorTrupti Kini2016-09-15 23:32:16 +0600
committerTrupti Kini2016-09-15 23:32:16 +0600
commit288d791ba890cb6162f57da7597b68b47f4d2538 (patch)
treeccfc22a8ee32e81701a64973eaf1ab2800a9f7ca /Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter5.ipynb
parentd54c6fae8ab793a084fb1a5bc353dba81b060e98 (diff)
downloadPython-Textbook-Companions-288d791ba890cb6162f57da7597b68b47f4d2538.tar.gz
Python-Textbook-Companions-288d791ba890cb6162f57da7597b68b47f4d2538.tar.bz2
Python-Textbook-Companions-288d791ba890cb6162f57da7597b68b47f4d2538.zip
Added(A)/Deleted(D) following books
M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/Chapter1.ipynb M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/Chapter10.ipynb M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/Chapter11.ipynb M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/Chapter12.ipynb M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/Chapter2.ipynb M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/Chapter3.ipynb M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/Chapter4.ipynb M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/Chapter5.ipynb M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/Chapter6.ipynb M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/Chapter7.ipynb M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/Chapter8.ipynb M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/screenshots/2.png M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/screenshots/3.png M BSc_First_Year_Physics_by_P._BalaBhaskar,_N._Srinivasa_Rao,_B._Sanjeeva_Rao/screenshots/4.png M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter01.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter02.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter03.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter04.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter05.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter10.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter11.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter12.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter13.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter14.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter15.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter16.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter17.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter18.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter19.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter20.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter21.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter22.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter6.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter7.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter8.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/Chapter9.ipynb M Basic_And_Applied_Thermodynamics_by_P._K._Nag/screenshots/16.11.png M Basic_And_Applied_Thermodynamics_by_P._K._Nag/screenshots/3.3.png M Basic_And_Applied_Thermodynamics_by_P._K._Nag/screenshots/7.10.png R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter1.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter1.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter10.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter10.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter13.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter13.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter14.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter14.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter15.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter15.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter2.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter2.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter3.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter3.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter4.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter4.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter5.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter5.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter6.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter6.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter7.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter7.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter8.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter8.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/chapter9.ipynb -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter9.ipynb R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/screenshots/Screenshot_(88).png -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/screenshots/Screenshot_(88).png R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/screenshots/Screenshot_(89).png -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/screenshots/Screenshot_(89).png R Basic_Electronics_(Electronics_Engineering)_by_J_B_Gupta/screenshots/Screenshot_(90).png -> Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/screenshots/Screenshot_(90).png M Basic_Electronics_by_Rakesh_Kumar_Garg,_Ashish_Dixit_&_Paban_Yadav/chapter1.ipynb M Basic_Electronics_by_Rakesh_Kumar_Garg,_Ashish_Dixit_&_Paban_Yadav/chapter2.ipynb M Basic_Electronics_by_Rakesh_Kumar_Garg,_Ashish_Dixit_&_Paban_Yadav/chapter3.ipynb M Basic_Electronics_by_Rakesh_Kumar_Garg,_Ashish_Dixit_&_Paban_Yadav/chapter4.ipynb M Basic_Electronics_by_Rakesh_Kumar_Garg,_Ashish_Dixit_&_Paban_Yadav/chapter5.ipynb M Basic_Electronics_by_Rakesh_Kumar_Garg,_Ashish_Dixit_&_Paban_Yadav/chapter6.ipynb M Basic_Electronics_by_Rakesh_Kumar_Garg,_Ashish_Dixit_&_Paban_Yadav/chapter7.ipynb M Basic_Electronics_by_Rakesh_Kumar_Garg,_Ashish_Dixit_&_Paban_Yadav/chapter8.ipynb M Basic_Electronics_by_Rakesh_Kumar_Garg,_Ashish_Dixit_&_Paban_Yadav/screenshots/DCLOADLINEchapter4.png M Basic_Electronics_by_Rakesh_Kumar_Garg,_Ashish_Dixit_&_Paban_Yadav/screenshots/DCLOADLineCH4.png M Basic_Electronics_by_Rakesh_Kumar_Garg,_Ashish_Dixit_&_Paban_Yadav/screenshots/TransferCharofnchmosfetCH8.png M Electrical_Machines_-_I_by_M._Verma_And_V._Ahuja/ch1.ipynb M Electrical_Machines_-_I_by_M._Verma_And_V._Ahuja/ch2.ipynb M Electrical_Machines_-_I_by_M._Verma_And_V._Ahuja/ch3.ipynb M Electrical_Machines_-_I_by_M._Verma_And_V._Ahuja/ch4.ipynb M Electrical_Machines_-_I_by_M._Verma_And_V._Ahuja/screenshots/EffiOfTrans4.png M Electrical_Machines_-_I_by_M._Verma_And_V._Ahuja/screenshots/starDeltaNdeltaStar4.png M Electrical_Machines_-_I_by_M._Verma_And_V._Ahuja/screenshots/voltageReg4.png M Engineering_Mechanics_by_A._K._Tayal/Chapter10.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter12.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter13.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter14.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter15.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter16.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter17.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter18.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter19.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter2.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter20.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter21.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter22.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter23.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter24.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter25.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter26.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter3.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter4.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter5.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter6.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter7.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter8.ipynb M Engineering_Mechanics_by_A._K._Tayal/Chapter9.ipynb M Engineering_Mechanics_by_A._K._Tayal/screenshots/1.png M Engineering_Mechanics_by_A._K._Tayal/screenshots/2.png M Engineering_Mechanics_by_A._K._Tayal/screenshots/3.png M Fundamentals_Of_Electronic_Devices_by_J._B._Gupta/Ch1.ipynb M Fundamentals_Of_Electronic_Devices_by_J._B._Gupta/Ch2.ipynb M Fundamentals_Of_Electronic_Devices_by_J._B._Gupta/Ch3.ipynb M Fundamentals_Of_Electronic_Devices_by_J._B._Gupta/Ch4.ipynb M Fundamentals_Of_Electronic_Devices_by_J._B._Gupta/Ch5.ipynb M Fundamentals_Of_Electronic_Devices_by_J._B._Gupta/Ch6.ipynb M Fundamentals_Of_Electronic_Devices_by_J._B._Gupta/Ch7.ipynb M Fundamentals_Of_Electronic_Devices_by_J._B._Gupta/Ch8.ipynb M Fundamentals_Of_Electronic_Devices_by_J._B._Gupta/README.txt M Fundamentals_Of_Electronic_Devices_by_J._B._Gupta/screenshots/6.1.png M Fundamentals_Of_Electronic_Devices_by_J._B._Gupta/screenshots/6.png M Fundamentals_Of_Electronic_Devices_by_J._B._Gupta/screenshots/7.png M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/Chapter_10_Heat_Exchangers.ipynb M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/Chapter_11_Mass_Transfer.ipynb M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/Chapter_1_Introduction.ipynb M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/Chapter_2_Steady_State_Conduction_One_Dimension.ipynb M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/Chapter_3_Steady_State_Conduction_Multiple_Dimension.ipynb M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/Chapter_4_Unsteady_State_Conduction.ipynb M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/Chapter_5_Principles_of_Convection.ipynb M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/Chapter_6_Empirical_and_Practical_Relations_for_Forced_Convection_Heat_Transfer.ipynb M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/Chapter_7_Natural_Convection_Systems.ipynb M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/Chapter_8_Radiation_Heat_Transfer.ipynb M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/Chapter_9_Condensation_and_Boiling_Heat_Transfer.ipynb M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/screenshots/9.1.png M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/screenshots/9.2.png M Heat_Transfer_(In_SI_Units)_by_J_P_Holman/screenshots/9.4.png M Heat_Transfer_Principles_And_Applications_by_Dutta/README.txt M Heat_Transfer_Principles_And_Applications_by_Dutta/ch10.ipynb M Heat_Transfer_Principles_And_Applications_by_Dutta/ch11.ipynb M Heat_Transfer_Principles_And_Applications_by_Dutta/ch2.ipynb M Heat_Transfer_Principles_And_Applications_by_Dutta/ch3.ipynb M Heat_Transfer_Principles_And_Applications_by_Dutta/ch4.ipynb M Heat_Transfer_Principles_And_Applications_by_Dutta/ch5.ipynb M Heat_Transfer_Principles_And_Applications_by_Dutta/ch6.ipynb M Heat_Transfer_Principles_And_Applications_by_Dutta/ch7.ipynb M Heat_Transfer_Principles_And_Applications_by_Dutta/ch8.ipynb M Heat_Transfer_Principles_And_Applications_by_Dutta/ch9.ipynb M Heat_Transfer_Principles_And_Applications_by_Dutta/screenshots/10.png M Heat_Transfer_Principles_And_Applications_by_Dutta/screenshots/5.png M Heat_Transfer_Principles_And_Applications_by_Dutta/screenshots/51.png M Heat_Transfer_in_SI_units_by_Holman/Chapter1.ipynb M Heat_Transfer_in_SI_units_by_Holman/Chapter10.ipynb M Heat_Transfer_in_SI_units_by_Holman/Chapter11.ipynb M Heat_Transfer_in_SI_units_by_Holman/Chapter2.ipynb M Heat_Transfer_in_SI_units_by_Holman/Chapter3.ipynb M Heat_Transfer_in_SI_units_by_Holman/Chapter4.ipynb M Heat_Transfer_in_SI_units_by_Holman/Chapter5.ipynb M Heat_Transfer_in_SI_units_by_Holman/Chapter6.ipynb M Heat_Transfer_in_SI_units_by_Holman/Chapter7.ipynb M Heat_Transfer_in_SI_units_by_Holman/Chapter8.ipynb M Heat_Transfer_in_SI_units_by_Holman/Chapter9.ipynb M Heat_Transfer_in_SI_units_by_Holman/README.txt M Heat_Transfer_in_SI_units_by_Holman/screenshots/9.1.png M Heat_Transfer_in_SI_units_by_Holman/screenshots/9.2.png M Heat_Transfer_in_SI_units_by_Holman/screenshots/9.4.png M Linear_Algebra_And_Its_Applications_by_G._Strang/CHAPTER1.ipynb M Linear_Algebra_And_Its_Applications_by_G._Strang/CHAPTER2.ipynb M Linear_Algebra_And_Its_Applications_by_G._Strang/CHAPTER3.ipynb M Linear_Algebra_And_Its_Applications_by_G._Strang/CHAPTER4.ipynb M Linear_Algebra_And_Its_Applications_by_G._Strang/CHAPTER5.ipynb M Linear_Algebra_And_Its_Applications_by_G._Strang/CHAPTER6.ipynb M Linear_Algebra_And_Its_Applications_by_G._Strang/CHAPTER7.ipynb M Linear_Algebra_And_Its_Applications_by_G._Strang/CHAPTER8.ipynb M Linear_Algebra_And_Its_Applications_by_G._Strang/screenshots/Ch5Eigenmatrix.png M Linear_Algebra_And_Its_Applications_by_G._Strang/screenshots/Ch5eigenVectors.png M Linear_Algebra_And_Its_Applications_by_G._Strang/screenshots/ch5eigenvaluematrix.png M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/Chapter1.ipynb M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/Chapter2.ipynb M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/Chapter3.ipynb M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/Chapter4.ipynb M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/Chapter5.ipynb M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/Chapter6.ipynb M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/Chapter7.ipynb M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/Chapter8.ipynb M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/Chapter9.ipynb M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/README.txt M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/screenshots/1.png M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/screenshots/2.png M OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/screenshots/8.png M Power_Electronics_Principles_and_Applications_by_Jacob/Chapter1.ipynb M Power_Electronics_Principles_and_Applications_by_Jacob/Chapter2.ipynb M Power_Electronics_Principles_and_Applications_by_Jacob/Chapter3.ipynb M Power_Electronics_Principles_and_Applications_by_Jacob/Chapter4.ipynb M Power_Electronics_Principles_and_Applications_by_Jacob/Chapter5.ipynb M Power_Electronics_Principles_and_Applications_by_Jacob/Chapter6.ipynb M Power_Electronics_Principles_and_Applications_by_Jacob/Chapter7.ipynb M Power_Electronics_Principles_and_Applications_by_Jacob/Chapter8.ipynb M Power_Electronics_Principles_and_Applications_by_Jacob/Chapter9.ipynb M Power_Electronics_Principles_and_Applications_by_Jacob/README.txt M Power_Electronics_Principles_and_Applications_by_Jacob/screenshots/4.png M Power_Electronics_Principles_and_Applications_by_Jacob/screenshots/5.png M Power_Electronics_Principles_and_Applications_by_Jacob/screenshots/6.png M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter10_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter11_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter12_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter13_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter14_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter15_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter16_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter17_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter18_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter19_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter1_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter20_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter21_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter22_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter23_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter24_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter25_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter26_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter2_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter6_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter7_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter8_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter9_6.ipynb M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/screenshots/chapter10_ac_load_line_5.png M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/screenshots/chapter18_clipping_ckt_output_6.png M Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/screenshots/chapter8_dc_load_line_6.png M Principles_of_Physics_by_F.J.Bueche/README.txt M Semiconductor_circuit_approximations_by_A.P._Malvino/ch10.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/ch11.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/ch12.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/ch14.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/ch16.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/ch2.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/ch3.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/ch4.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/ch5.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/ch6.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/ch7.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/ch8.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/ch9.ipynb M Semiconductor_circuit_approximations_by_A.P._Malvino/screenshots/ACloadLineChapter10.png M Semiconductor_circuit_approximations_by_A.P._Malvino/screenshots/DCandACloadlinechapter9.png M Semiconductor_circuit_approximations_by_A.P._Malvino/screenshots/Powerratingchapter9.png M Strength_Of_Materials_by_S_S_Bhavikatti/chapter_no.10_8.ipynb M Strength_Of_Materials_by_S_S_Bhavikatti/chapter_no.2_8.ipynb M Strength_Of_Materials_by_S_S_Bhavikatti/chapter_no.3_8.ipynb M Strength_Of_Materials_by_S_S_Bhavikatti/chapter_no.4_8.ipynb M Strength_Of_Materials_by_S_S_Bhavikatti/chapter_no.5_8.ipynb M Strength_Of_Materials_by_S_S_Bhavikatti/chapter_no.6_8.ipynb M Strength_Of_Materials_by_S_S_Bhavikatti/chapter_no.7_8.ipynb M Strength_Of_Materials_by_S_S_Bhavikatti/chapter_no.8_8.ipynb M Strength_Of_Materials_by_S_S_Bhavikatti/chapter_no.9_8.ipynb M Strength_Of_Materials_by_S_S_Bhavikatti/screenshots/BMD.JPG M Strength_Of_Materials_by_S_S_Bhavikatti/screenshots/SFD2.JPG M Strength_Of_Materials_by_S_S_Bhavikatti/screenshots/S_F_D_1.JPG A Theory_of_Alternating_Current_Machinery_by_A._S._Langsdorf/README.txt M sample_notebooks/AviralYadav/Chapter5.ipynb A sample_notebooks/RONAKBANSAL/chapter_1.ipynb
Diffstat (limited to 'Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter5.ipynb')
-rwxr-xr-xBasic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter5.ipynb1355
1 files changed, 1355 insertions, 0 deletions
diff --git a/Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter5.ipynb b/Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter5.ipynb
new file mode 100755
index 00000000..8ce58f25
--- /dev/null
+++ b/Basic_Electronics_Electronics_Engineering_by_J_B_Gupta/chapter5.ipynb
@@ -0,0 +1,1355 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#Chapter 5 , Diode Applications - DC Power Supplies and Waveshaping Circuits "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.1 , Page Number 140"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Idc = 1.957 A.\n",
+ "Irms = 3.074 A.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import math\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "VSrms = 10 #Supply voltage\n",
+ "VSmax = 10* 2**0.5 #Peak value of supply voltage (in volts)\n",
+ "RF = 0.3 #Forward resistance (in ohm)\n",
+ "RL = 2 #Load resistance (in ohm)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Imax = VSmax/(RL + RF) #Peak value of current in load (in Ampere)\n",
+ "Idc = Imax/math.pi #DC ouput current (in Ampere)\n",
+ "Irms = Imax/2 #RMS value of output current (in Ampere) \n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"Idc = \",round(Idc,3),\" A.\"\n",
+ "print \"Irms = \",round(Irms,3),\" A.\"\n",
+ "\n",
+ "#Slight variation due to higher precision."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.2 , Page Number 141"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Imax = 148.156 mA.\n",
+ "Idc = 47.16 mA.\n",
+ "Irms = 74.078 mA.\n",
+ "PIV = 311.127 V.\n",
+ "Load output voltage = 94.32 V.\n",
+ "DC output power = 4.448 W.\n",
+ "AC input power = 11.524 W.\n",
+ "Ripple factor = 1.21 .\n",
+ "Transformer utilisation factor = 0.2724 .\n",
+ "Rectification efficiency = 38.6 %.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import math\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "VSrms = 220.0 #Supply voltage\n",
+ "VSmax = 220.0 * 2**0.5 #Peak value of supply voltage (in volts)\n",
+ "RF = 100.0 #Forward resistance (in ohm)\n",
+ "RL = 2.0 * 10**3 #Load resistance (in ohm)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Imax = VSmax/(RL + RF) #Maximum value of current (in Ampere)\n",
+ "Idc = Imax/math.pi #DC ouput current (in Ampere)\n",
+ "Irms = Imax/2 #RMS value of output current (in Ampere) \n",
+ "PIV = VSmax #Peak inverse voltage (in volts)\n",
+ "Vdc = Idc*RL #Load output voltage (in volts)\n",
+ "Pdc = Idc**2 * RL #DC output power (in watt)\n",
+ "Pac = Imax**2/4*(RF + RL) #AC input power (in watt)\n",
+ "gamma = ((Irms/Idc)**2 - 1)**.5 #Ripple factor \n",
+ "TUF = 0.286/(1 + RF/RL) #Transformer utilisation factor\n",
+ "eeta = Pdc/Pac * 100 #Rectification efficiency\n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"Imax = \",round(Imax * 10**3,3),\" mA.\\nIdc = \",round(Idc * 10**3,2),\" mA.\\nIrms = \",round(Irms * 10**3,3),\" mA.\"\n",
+ "print \"PIV = \",round(PIV,3),\" V.\"\n",
+ "print \"Load output voltage = \",round(Vdc,2),\" V.\"\n",
+ "print \"DC output power = \",round(Pdc,3),\" W.\\nAC input power = \",round(Pac,3),\" W.\"\n",
+ "print \"Ripple factor = \",round(gamma,2),\".\"\n",
+ "print \"Transformer utilisation factor = \",round(TUF,4),\".\"\n",
+ "print \"Rectification efficiency = \",round(eeta,1),\"%.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.3 , Page Number 141"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Percentage voltage regulation : 4.76 %.\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Variables \n",
+ "\n",
+ "VNL = 44.0 #No load voltage (in volts)\n",
+ "VFL = 42.0 #Full load voltage (in volts) \n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Reg = (VNL - VFL)/VFL * 100 #Percentage voltage regulation \n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"Percentage voltage regulation : \",round(Reg,2),\" %.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.4 , Page Number 141"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DC output voltage : 4.4 V.\n",
+ "PIV : 17.0 V.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import math\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "RF = 10 #Forward resistance (in ohm)\n",
+ "IL = 100 * 10**-3 #Load current (in Ampere)\n",
+ "VSrms = 12 #RMS value of supply voltage (in volts)\n",
+ "VSmax = 12 * 2**0.5 #Maximum value of supply voltage (in volts)\n",
+ "Idc = 0.1 #DC current (in Ampere)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Vdc = VSmax/math.pi - Idc*RF #DC output voltage (in volts)\n",
+ "PIV = VSmax #Peak inverse voltage (in volts)\n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"DC output voltage : \",round(Vdc,1),\" V.\"\n",
+ "print \"PIV : \",round(PIV),\" V.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.5 , Page Number 146 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Peak value of current : 0.109 A.\n",
+ "Average value of current : 0.0694 A.\n",
+ "RMS value of current : 0.077 A.\n",
+ "Ripple factor : 0.483 .\n",
+ "Efficiency of rectifier : 73.82 %.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import math\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "VSmax = 60.0 #Maximum value of supply voltage (in volts)\n",
+ "RF = 50.0 #Forward resistance (in ohm)\n",
+ "RL = 500.0 #Load resistance (in ohm) \n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Imax = VSmax/(RL + RF) #Peak current (in Ampere)\n",
+ "Idc = 2*Imax/math.pi #Average current (in Ampere)\n",
+ "Irms = Imax/2**0.5 #RMS value of current (in Ampere)\n",
+ "r = ((Irms/Idc)**2 - 1)**0.5 #Ripple factor \n",
+ "n = 0.812/(1 + RF/RL)*100 #Efficiency of rectifier \n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"Peak value of current : \",round(Imax,3),\" A.\\nAverage value of current : \",round(Idc,4),\" A.\\nRMS value of current : \",round(Irms,3),\" A.\"\n",
+ "print \"Ripple factor : \",round(r,3),\".\"\n",
+ "print \"Efficiency of rectifier : \",round(n,2),\"%.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.6 , Page Number 147"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DC output voltage : 10.8 V.\n",
+ "DC load current : 108.0 mA.\n",
+ "PIV rating required : 33.94 V.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import math \n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "VSmax = 12 * 2**0.5 #Peak value of supply voltage (in volts)\n",
+ "RL = 100.0 #Load resistance (in ohm)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Idc = 2*VSmax/(RL*math.pi) #Average current (in Ampere)\n",
+ "Vdc = Idc * RL #Average voltage (in volts)\n",
+ "PIV = 2*VSmax #Peak inverse voltage (in volts)\n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"DC output voltage : \",round(Vdc,1),\" V.\"\n",
+ "print \"DC load current : \",round(Idc * 10**3),\" mA.\"\n",
+ "print \"PIV rating required : \",round(PIV,2),\" V.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.7 , Page Number 153 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Output DC voltage : 25.46 V.\n",
+ "Ripple fator : 0.0149 .\n"
+ ]
+ }
+ ],
+ "source": [
+ "import math\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "VLmax = VSmax = 40.0 #Peak value of supply voltage (in volts)\n",
+ "f = 50 #Frequency (in Hertz) \n",
+ "w = 2*math.pi*50 #Angular frequency (in rad/sec)\n",
+ "L = 2.0 #Inductance (in Henry)\n",
+ "C = 40 * 10**-6 #Capacitance (in Farad) \n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Vdc = 2*VSmax/math.pi #Average voltage (in bolts)\n",
+ "r = 1/(6*2**0.5*w**2*L*C) #Ripple factor\n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"Output DC voltage : \",round(Vdc,2),\"V.\"\n",
+ "print \"Ripple fator : \",round(r,4),\".\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.8 , Page Number 161"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Peak output voltage : 14.3 V.\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Variables \n",
+ "\n",
+ "Vo = 0.7 #Barrier potential (in volts)\n",
+ "Vinpeak = 15 #Peak input voltage (in volts)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Voutpeak = Vinpeak - Vo #Peak output voltage (in volts) \n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"Peak output voltage : \",Voutpeak,\" V.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.9 , Page Number 161"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Output voltage (rms value) : 1.27 V.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import math\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "R1 = 2.0 * 10**3 #Resistance1 (in ohm)\n",
+ "R2 = 1.0 * 10**3 #Resistance2 (in ohm)\n",
+ "Vinpeak = 10 #peak input voltage (in volts)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "RL = R1*R2/(R1+R2) #Load resistance (in ohm)\n",
+ "Voutpeak = Vinpeak*RL/(R2+RL) #Peak voltage across load resistance (in ohm)\n",
+ "Vrms = Voutpeak/math.pi #RMS value of output voltage (in volts)\n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"Output voltage (rms value) : \",round(Vrms,2),\" V.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.10 , Page Number 162 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Voutpeak : 8.0 V.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x6186270>"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEZCAYAAABoy7XpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x702b130>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import math\n",
+ "import numpy\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,title,xlabel,ylabel,ylim,xlim\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "R1 = 20.0 * 10**3 #Resistance1 (in ohm)\n",
+ "R2 = 10.0 * 10**3 #Resistance2 (in ohm)\n",
+ "Vinpeak = 20 #peak input voltage (in volts)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "RL = R1*R2/(R1+R2) #Load resistance (in ohm)\n",
+ "Voutpeak = Vinpeak*RL/(R2+RL) #Peak voltage across load resistance (in ohm)\n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"Voutpeak : \",Voutpeak,\"V.\"\n",
+ "\n",
+ "#Graph\n",
+ "\n",
+ "x = numpy.linspace(0,math.pi,100)\n",
+ "y = numpy.sin(x)\n",
+ "plot(x,8*y,'b')\n",
+ "ylim(0,9)\n",
+ "xlim(0,math.pi)\n",
+ "title(\"Output Waveform\")\n",
+ "xlabel(\"t ->\")\n",
+ "ylabel(\"-Vout->\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.11 , Page Number 162"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Peak output voltage : 10.0 V.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x6dc1210>"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEZCAYAAAB1mUk3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nICIiqBiIiAjw/wCL3MAebnEQAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7504050>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import math\n",
+ "import numpy\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,title,xlabel,ylabel,ylim,xlim\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "Vinpeak = 12.0 #Peak input voltage (in volts)\n",
+ "Vo = 0.7 #Barrier potential (in volts)\n",
+ "RS = 500 #Series resistance (in ohm)\n",
+ "RL = 2.5 * 10**3 #Load resistance (in ohm)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Voutpeak = Vinpeak*RL/(RS+RL) #Peak output voltage (in volts) \n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"Peak output voltage : \",Voutpeak,\" V.\"\n",
+ "\n",
+ "#Graph\n",
+ "\n",
+ "x = numpy.linspace(0,math.pi,100)\n",
+ "x1 = numpy.linspace(math.pi,2*math.pi,100)\n",
+ "x2 = numpy.linspace(math.pi,2*math.pi,100)\n",
+ "x3=numpy.linspace(0,8,100)\n",
+ "y2 = numpy.sin(x2)\n",
+ "y = numpy.sin(x)\n",
+ "plot(x,8*y,'b')\n",
+ "plot(x1,-0.7+x1-x1,'b')\n",
+ "plot(x2,12*y2,'--')\n",
+ "plot(x3,0+x3-x3,'k')\n",
+ "ylim(-13,9)\n",
+ "xlim(0,2*math.pi+1)\n",
+ "title(\"Output Waveform\")\n",
+ "xlabel(\"t ->\")\n",
+ "ylabel(\"-Vout->\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.12 , Page Number 163 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PIV of diode : 5 V.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x7d41190>"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEZCAYAAABoy7XpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7cb4d50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import math\n",
+ "import numpy\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,title,xlabel,ylabel,ylim,xlim\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "Vi = 10 #Input voltage (in volts)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Vo = Vi * 1.0/2 #Output voltage (in volts)\n",
+ "Vdc = 0.636 * Vo #DC output voltage (in volts)\n",
+ "PIV = 5 #Peak inverse voltage (in volts) \n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"PIV of diode : \",PIV,\"V.\"\n",
+ "\n",
+ "#Graph\n",
+ "\n",
+ "x = numpy.linspace(0,math.pi,100)\n",
+ "y = numpy.sin(x)\n",
+ "x1 = numpy.linspace(math.pi,2*math.pi,100)\n",
+ "y1 = numpy.sin(x1)\n",
+ "ylim(0,8)\n",
+ "xlim(0,2*math.pi)\n",
+ "plot(x,5*(y),'b')\n",
+ "plot(x1,-5*(y1),'b')\n",
+ "plot(x,0+x-x,'k')\n",
+ "plot(x1,0+x1-x1,'k')\n",
+ "plot(x,5+x-x,'--',color='g')\n",
+ "plot(x1,5+x1-x1,'--',color='g')\n",
+ "title(\"Output Waveform\")\n",
+ "xlabel(\"t ->\")\n",
+ "ylabel(\"-Vout->\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.13 , Page Number 164"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PIV rating of diode : 200 V.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x802a9d0>"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEZCAYAAABxbJkKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIhn2/7EYzCDf36DyAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x86934f0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import math\n",
+ "import numpy\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,title,xlabel,ylabel,ylim,xlim,annotate\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "Vimax = 200 #Peak input voltage (in volts)\n",
+ "R = 10 * 10**3 #Resistance (in ohm)\n",
+ "RL = 4 * 10**3 #Load resistance (in ohm)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Iimax = Vimax/R #Peak input current (in Ampere) \n",
+ "VLmax = Iimax * RL #Peak voltage across load (in volts) \n",
+ "PIV = 200 #Peak inverse voltage (in volts)\n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"PIV rating of diode :\",PIV,\"V.\"\n",
+ "\n",
+ "#Graph\n",
+ "\n",
+ "x1 = numpy.linspace(0,math.pi,100)\n",
+ "x2 = numpy.linspace(math.pi,2*math.pi,100)\n",
+ "x3 = numpy.linspace(2*math.pi,3*math.pi,100)\n",
+ "y1 = numpy.sin(x1)\n",
+ "y2 = numpy.sin(x2)\n",
+ "y3 = numpy.sin(x3)\n",
+ "plot(x1,80*(y1),'b')\n",
+ "plot(x2,(-1)*80*(y2),'b')\n",
+ "plot(x3,80*(y3),'b')\n",
+ "plot(x1/2,80+x1-x1,'--',color='g')\n",
+ "xlim(0,3*math.pi)\n",
+ "ylim(0,200)\n",
+ "annotate('80 V',xy=(0.5 ,80))\n",
+ "title(\"Output Voltage Waveform\")\n",
+ "xlabel(\"t ->\")\n",
+ "ylabel(\"-Vout->\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.14 , Page Number 165 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "During positive half , Output voltage Vo = 2 V when Vi < 2 V.\n",
+ "Output voltage Vo = Vi when Vi > 2 V.\n",
+ "During negative half , Output voltage Vo = 2 V.\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Variables \n",
+ "\n",
+ "Vo = 2 #Output voltage when Vi < 2 V (in volts)\n",
+ "#Vo1 = Vi #Output voltage when Vi > 2 V (in volts) \n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Vo = 2 #Output voltage during negative half (in volts)\n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"During positive half , Output voltage Vo = 2 V when Vi < 2 V.\\nOutput voltage Vo = Vi when Vi > 2 V.\"\n",
+ "print \"During negative half , Output voltage Vo = 2 V.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.15 , Page Number 166 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "During positive half , Output voltage : 5.0 V.\n",
+ "During negative half , Output voltage : -10 V.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(-12, 10)"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEZCAYAAAB4hzlwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFpxJREFUeJzt3XmYbHV95/H3R66iiGxiwuKVqygqM0bgMT6gAndciCtE\nZ3CLqNGYGY1LJjFBzTyCk5lBx8TJ6swkCkHDFtcBFxTUi6igQcCwXRGVsCMIgmvC8p0/zukfRdPd\nt/re7j5V975fz1NP1zl16pxv1+muz/n9fqfqpKqQJAngfkMXIEmaHIaCJKkxFCRJjaEgSWoMBUlS\nYyhIkhpDQRqR5O+S/PHQdUyKJE9N8p0kP05y6ND1aPkZCtqgJK9OclGSnya5Psn7k2y/iOdfmeTp\nS1jPvOtLsnuSO5I8ao7HPpHkvRtYffU3kqxNcvWmV7x4ST6X5A9HpndPcvc8835pGUv5r8BfVNVD\nqurUZdyOJoShoAUl+X3g3cDvA9sB+wN7AGckuf+YqykgS1jWvOurqmuBLwBHjM5PshPwHODvxlj/\nUta6sc4CDhqZPghYP8e8y6vqB8tYxyOASzfmiUm2WuJatAIMBc0ryXbA0cAbq+rzVXVXVf0z8GJg\nDfCKfrl7dbmMHmEn+TDdG8tpfRfEW5Os6Y9wX5fk2iTX9eHDxqxvjtKPZ1YoAC8FLqmqS5I8Psm6\nJLcmuTjJC2YtW0m2AT4L7NZv5/YkuyR5cpJz+udel+QvR8MxySFJvp3kR0n+OslZSV478vhrklya\n5JYkpyd5xDwv/9nAU0emnwb8GfCkkXkHAl/u1/vnSa5KcluS85I8rZ+/W5KfJdlxpIZ9k9w086Y9\nX01Jvgs8qn+tb09y/359pyb5Yd+t9Fsj6z06yUeTfDjJbcCr+9f5vyX5av86nppk5yQn9LV+I8ke\n87wGGoChoIU8BXgg8PHRmVX1U+AzwLNmZvW3+6iqI4CrgOf3XRB/MvLwWuDRwCHAkUmesYnrm/FJ\nYOcko2+qRwDH92/gpwGnAw8D3gSckGSvkWVTVT8Dng1c129nu6q6AbgTeAvwUOAA4BnAGwCS7Ax8\nBDgS2An4dr/MTHfUYcDbgRcCO9O98Z801+8JfAPYOskT++mDgDOAK5LsMzLvyyPLPxHYETgR+EiS\nB1TVdcA5wL8fWffLgY9U1V0L1VRVe3LPa71dVd0BnNzP2xX4D8D/SPLvRtZ9aL/u7YET+nkvpjuA\n2B3Ys6/ng/1rdBlw1DyvgQZgKGghOwM3V9Xdczx2A90b44yN6XJ5V1X9vKouBo4DXraJ6wOgqn5O\n9+b8SoAkjwH2o3uz3B94cFW9u6rurKovAZ+ate15a6iq86vqG1V1d99q+hvg4P7h5wIXV9Un+8f/\ngu51mvGfgGOq6tv9a3oMsE+S1XNs51+ArwMH911f21fV9+netA/q5z2erpuJqjqhqm7tt/s+YGvg\nsf3qTpz5/ZIEeEk/b1E19fOeAhxZVf9aVd8CPjDzOve+NjP2UFW/oAvE46rq+1V1O13r6/Kq+mJV\n3UW3n/a9zyuvwRgKWsjNdEfcc/2d7No/vilGB3GvAnbbxPWNOh44PMnWdK2E06vq5n4bsweP/3nc\nbSfZK8mn0g243wb8d+4Jx92Aa2Y9ZXR6D+DP+66nW4Ef9vN3n2dzX6ZrDTwN+Go/7ysj866uqplu\ntbf2XUA/6te9PV2oQ9fSOyDJLv1z766qr2xETbsBt/QtxRlXzVp29u8PcOPI/V8AP5g1ve2cv70G\nYShoIecA/8K9ux5Isi1d18oX+lk/BbYZWWSXWeuZ76t4HzHr/rWbuL5RXwVuAQ4DfoMuJACuA1b3\nR8wz9hjZ9uj659rO/6YbeH1030XyR9zzf3Qd8PCZBfttPHzkuVcBv11VO47cHlxV587zO3yZbtxg\ntJvoa3RjDQfRtxKSHAj8AXB4Ve1QVTsCt9G3dKrqVuDzdC2El3PvLqvF1HQdsFO//2c8gnsHwYb2\njV/LPOEMBc2rqm4D3gX8ZZJf6wca1wD/QHe0/eF+0QuB5ybZsT8a/d1Zq7qRri95tv+S5EFJ/g3w\nauCUTVzfaO0FfAj4n3RHzaf1D50L/Az4w/73WQs8n66vHLo30pnAuBF4aLoB9xnbAj8GfpbkccDr\nRx77DPCEJIclWQX8DvcOtP8DvCPJ3gBJtk9y+AK/xjl0YwSvoOs2mnmDv7mfNxMUD6Eb67g5yQOS\nvJPuTLFRJwKvogv4E0fmj11T3yr5GnBMkq2T/ArwGuDvF/gd4N7dcJNwZpcWYChoQVX1XuAdwJ/Q\nHX2eS9fd8ox+4BG6cPgWcCXdAO7J3PuI8Bi6ALg1ye+NzD8LuAI4E3hvVZ25ieub7UPAauCUmVr7\nny+gOz31JuCvgCOq6vKZX3lmW1W1nu6o+nv9mTm7AG+lO9q+nW484eSR5W8GDqcLopvp+vzPo2tt\nUVWfBN4DnNx3PV0E/Np8xfeD3ecB9+/HXWZ8mW6QfCYUTu9vl/ev2c/pWgCjTqUb1L++qi4a2cai\naqIbm1hD12r4OPDOqvrizOqYuyVQs+7PXsbWwwTJkBfZSXIs8DzgB1X1hH7eTnRHjHvQ/YG/uKp+\nNFiRWnJ9a+N7wKp5BrE3C/1YzNXAy6vqrKHrkcYxdEvhOLq+6VFvA86oqr3o+qzftuJVSRsp3ecU\ndugHuN/Rz55vzECaOIOGQlWdDdw6a/ah3DMoeDzw6ytalFbK5tplcABdl9hNdK3gX+9PL5WmwqDd\nR9C6Ek4b6T66tT97YubsjVtmpiVJy2vo7qMF9WeQbK5HlJI0cVYNXcAcbkyyS1XdkGRX7v1BFwCS\nGBSStBGqasHTgiexpXAq3fnU9D8/OddCVTVxt6OOOmrwGqzJmrbEuqxpvNs4Bg2FJCfRfRjmsUmu\nTvKbdF/T/KwklwNP76clSStg0O6jqprrS8gAnrmihUiSgMnsPppaa9euHbqE+7Cm8VjT+CaxLmta\nOoOfkroxktQ01i1JQ0pCTeFAsyRpIIaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1\nhoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIa\nQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmN\noSBJagwFSVJjKEiSGkNBktQYCpKkZtXQBcwnyZXA7cBdwB1V9eRhK5Kkzd/EhgJQwNqqumXoQiRp\nSzHp3UcZugBJ2pJMcigUcGaS85K8buhiJGlLMMndR0+tquuTPAw4I8n6qjp75sHk6JFF1/Y3TYuq\noSvQxort9ymyrr+NLzUF/51JjgJ+UlV/2k/XNNStuSWGwjRz/02vJFTVgrE+kd1HSbZJ8pD+/oOB\nQ4CLhq1KkjZ/k9p99MvAJ9K1U1cBJ1TV54ctSZI2f1PRfTSb3UfTze6H6eb+m15T230kSRqGoSBJ\nagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAk\nNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiS\nGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkZOxSS\nPCnJ1stZjCRpWGOFQpJdgXOAw5e3HEnSkMZtKbwaOB547fKVco8kz06yPsl3khy5EtuUJI0RCkkC\nHAG8Ddg6yZ7LWVCSrYC/Ap4N7A28LMnjl3ObkqTOOC2FtcBlVXUzK9NaeDJwRVVdWVV3ACcDhy3z\nNiVJjBcKrwWO7e+fDLw4yXKetbQ7cPXI9DX9PEnSMlu10INJdgT2B14JUFW3JTkXeB5w2jLVVOMs\n1PVqaTqV+2+quf82ZwuGQlXdCjx61rxXLGtFcC2wemR6NV1r4V6qxsoOTaDE/TfN3H/Ta5wwX1Q3\nUJLf3uhqxnce8Jgka5I8AHgJcOoKbFeStniLHRt4/bJUMaKq7gTeCHwOuBQ4paouW+7tSpIgi2kG\nJrmgqvZdxnrGraNsvk6vrvth6Cq0sdx/0ysJVbVgH9I4n1N41MjkoXPMkyRtJsbpPvrYzJ2qmjlV\n9CPLU44kaUjznn3Uf4p4b2D7JC8CQne66HbAA1emPEnSSlrolNS9gBcA2/c/Z/wYeN1yFiVJGsYG\nB5qTHFBV56xQPWNxoHm6OVA53dx/02ucgeZxQuG4WbMKoKpes2nlbTxDYbr5pjLd3H/Ta5xQWPAT\nzb1Pc89XTzwIeCFw3SbWJkmaQIv6nAJA/2V4X62qA5anpLFqsKUwxTzSnG7uv+m1JJ9TmMNewMM2\nriRJ0iTbYPdRkp9wT/dRATcCXg1NkjZDGwyFqtp2JQqRJA1vnIFmkhwGHETXUjirqpbrWgqSpAGN\n891H7wbeDFwCXAa8Ockxy12YJGnljfM5hYuAfarqrn56K+DCqnrCCtQ3X02efTTFPHtlurn/ptdS\nnX1UwA4j0zsw5iUzJUnTZZwxhWOA85Os66cPBt62bBVJkgYzb/dRkvcDJ1bVV5LsBvwqXQvhH6vq\n+hWsca7a7D6aYnY/TDf33/Ta1K+5uBx4bx8IpwAnVdUFS1mgJGmyjDPQvAZ4KfASYBvgRLqAuHy5\ni1ugJlsKU8wjzenm/pteS/ItqbNWuC9wHPCEqtpqE+vbaIbCdPNNZbq5/6bXUl2jeVWSQ5OcCJwO\nrAdetEQ1SpImyEIDzYfQdRs9D/gGcBJw9sh1mgdjS2G6eaQ53dx/02uTuo+SfJEuCD5WVbf0886v\nqv2WvNJFMhSmm28q0839N7026eyjqnr6XOvc5KokSRNrsddT+NtlqUKSNBEWfeW1SWD30XSz+2G6\nuf+m13JdeU2StJkyFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwF\nSVJjKEiSGkNBktQYCpKkZuJCIcnRSa5JckF/e/bQNUnSlmLey3EOqID3VdX7hi5EkrY0E9dS6Hkt\naEkawKSGwpuSfCvJB5PsMHQxkrSlGOQazUnOAHaZ46E/As4Fbuqn/xjYtapeO+v5XqN5inmN3+nm\n/pte41yjeZAxhap61jjLJfkAcNpcjx199NHt/tq1a1m7du1SlCZJm41169axbt26RT1nkJbCQpLs\nWlXX9/f/M/CrVfXyWcvYUphiHmlON/ff9JrYlsIGvCfJPnRnIX0f+I8D1yNJW4yJaymMw5bCdPNI\nc7q5/6bXOC2FST37SJI0AENBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgK\nkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwF\nSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaC\nJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUDBIKSQ5PckmSu5LsN+uxtyf5TpL1SQ4Zoj5J2lKtGmi7\nFwEvBP7v6MwkewMvAfYGdgfOTLJXVd298iVK0pZnkJZCVa2vqsvneOgw4KSquqOqrgSuAJ68osVJ\n0hZs0sYUdgOuGZm+hq7FIElaAcvWfZTkDGCXOR56R1WdtohV1RKVJEnagGULhap61kY87Vpg9cj0\nw/t595G1uWdiDfBIOOrgozh67dH3WfbodUfzrrPedZ/5Lj/U8kVyn8UkLbF169axbt26RT0nVcMd\niCf5EvDWqvpmP703cCLdOMLuwJnAo2tWkUlmz5IkbUASqmrBQ7KhTkl9YZKrgf2BTyf5LEBVXQr8\nA3Ap8FngDb77S9LKGbSlsLFsKUjS4k1sS0GSNJkMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEU\nJEmNoSBJagwFSVJjKEiSGkNBktQYCktosd9bvhKsaTzWNL5JrMualo6hsIQm8Y/AmsZjTeObxLqs\naekYCpKkxlCQJDVTe5GdoWuQpGm0oYvsTGUoSJKWh91HkqTGUJAkNVMXCkmenWR9ku8kOXLoegCS\nHJvkxiQXDV3LjCSrk3wpySVJLk7y5gmo6YFJvp7kwiSXJjlm6JpmJNkqyQVJThu6FoAkVyb5p76m\nbwxdD0CSHZJ8NMll/f7bfwJqemz/Gs3cbpuQv/W39/97FyU5McnWE1DTW/p6Lk7ylnkXrKqpuQFb\nAVcAa4D7AxcCj5+Aug4E9gUuGrqWkZp2Afbp728LfHtCXqtt+p+rgHOBpw1dU1/P7wEnAKcOXUtf\nz/eBnYauY1ZNxwOvGdl/2w9d06z67gdcD6weuI41wPeArfvpU4BXDVzTvwUuAh7Yv4+eAew517LT\n1lJ4MnBFVV1ZVXcAJwOHDVwTVXU2cOvQdYyqqhuq6sL+/k+Ay4Ddhq0Kqupn/d0H0P1x3jJgOQAk\neTjwXOADwIJnZqywiaklyfbAgVV1LEBV3VlVtw1c1mzPBL5bVVcPXMftwB3ANklWAdsA1w5bEo8D\nvl5Vv6iqu4CzgBfNteC0hcLuwOgOv6afpwUkWUPXkvn6sJVAkvsluRC4EfhSVV06dE3A/wL+ALh7\n6EJGFHBmkvOSvG7oYoBHAjclOS7J+Un+Nsk2Qxc1y0uBE4cuoqpuAf4UuAq4DvhRVZ05bFVcDByY\nZKd+vz0PePhcC05bKHj+7CIl2Rb4KPCWvsUwqKq6u6r2ofuDPCjJ2iHrSfJ84AdVdQETdGQOPLWq\n9gWeA/xOkgMHrmcVsB/w/qraD/gp8LZhS7pHkgcALwA+MgG17An8Ll030m7Atkl+Y8iaqmo98B7g\n88BngQuY5yBo2kLhWmD1yPRqutaC5pDk/sDHgL+vqk8OXc+ovuvh08CTBi7lKcChSb4PnAQ8PcmH\nBq6Jqrq+/3kT8Am6rtMhXQNcU1X/2E9/lC4kJsVzgG/2r9fQngR8rap+WFV3Ah+n+zsbVFUdW1VP\nqqqDgR/RjTPex7SFwnnAY5Ks6Y8MXgKcOnBNEylJgA8Cl1bVnw1dD0CSnZPs0N9/EPAsuiOWwVTV\nO6pqdVU9kq774YtV9coha0qyTZKH9PcfDBxCN0g4mKq6Abg6yV79rGcClwxY0mwvowv1SbAe2D/J\ng/r/w2cCg3eTJvml/ucjgBcyT1fbqpUsalNV1Z1J3gh8jm6Q8oNVddnAZZHkJOBg4KFJrgbeWVXH\nDVzWU4FXAP+UZOaN9+1VdfqANe0KHJ/kfnQHJB+uqi8MWM9cJqGL8peBT3TvJ6wCTqiqzw9bEgBv\nAk7oD8i+C/zmwPUALTifCUzC2AtV9a2+tXkeXRfN+cDfDFsVAB9N8lC6QfA3VNXtcy3k11xIkppp\n6z6SJC0jQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgLUKS7ZO8fug6pOViKEiLsyPwhnEWTLJd/0E9\naWr4BystzruBPfsLurxnA8seCKxPclSS1RtYVpoIfqJZWoQkewCfqqonjLn8Q4EjgFcBN9B9H9X/\n668HIk0cQ0FahP7aFKeNGwqznnsAcCzwr1X1xCUuTVoSdh9JSyTJG/pupfOT7Doyf+8k76W7nOXZ\nwG8NVqS0AbYUpEXou4O+WVVrxlh2P+Cv6b4p8wPAKSOXI5UmkqEgLVKSE4BfAT5TVUcusNzjgKqq\nOS9mIk0iQ0GS1DimIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJzf8HrlMfb6z5gHsA\nAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x8939390>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import math\n",
+ "import numpy\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,title,xlabel,ylabel,ylim,xlim\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "Vi = 10 #Input voltage (in volts)\n",
+ "V1 = 2.5 #Voltage (in volts)\n",
+ "Rnet = 3 * 10**3 #Net resistance (in ohm)\n",
+ "R1 = 2.0 * 10**3 #Resistance1 (in ohm)\n",
+ "R2 = 1.0 * 10**3 #Resistance2 (in ohm)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "I = (Vi - V1)/Rnet #Current (in Ampere)\n",
+ "Vo = I * (R2) + 2.5 #Output voltage positive half (in volts)\n",
+ "Voneg = -Vi #Output voltage negative half (in volts)\n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"During positive half , Output voltage : \",Vo,\"V.\"\n",
+ "print \"During negative half , Output voltage : \",Voneg,\"V.\" \n",
+ "\n",
+ "#Graph\n",
+ "\n",
+ "x = numpy.linspace(0,9,100) \n",
+ "x1 = numpy.linspace(0,3,100)\n",
+ "x2 = numpy.linspace(3,6,100)\n",
+ "x3 = numpy.linspace(6,9,100)\n",
+ "y1 = numpy.linspace(-10,5,100)\n",
+ "plot(x,0+x-x,'k')\n",
+ "plot(x1,-10+x1-x1,'--',color='g')\n",
+ "plot(x1,5-x1+x1,'b')\n",
+ "plot(x2,-10+x2-x2,'b')\n",
+ "plot(x3,5+x3-x3,'b')\n",
+ "plot(3+y1-y1,y1,'b')\n",
+ "plot(6+y1-y1,y1,'b')\n",
+ "title(\"Output Voltage Waveform\")\n",
+ "xlabel(\"t ->\")\n",
+ "ylabel(\"-Vout->\")\n",
+ "xlim(0,9)\n",
+ "ylim(-12,10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.16 , Page Number 166"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "During positive half , peak voltage : 12 V.\n",
+ "During negative half , peak voltage : -8 V.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x9864710>"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEZCAYAAAB1mUk3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x8f02ef0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import math\n",
+ "import numpy\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,title,xlabel,ylabel,ylim,xlim\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "V1 = 12 #Voltage1 (in volts)\n",
+ "V2 = 8 #Voltage2 (in volts) \n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Vopos = V1 #Peak Output voltage during positive half (in volts)\n",
+ "Voneg = -V2 #Peak Output voltage during negative half (in volts) \n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"During positive half , peak voltage : \",Vopos,\"V.\\nDuring negative half , peak voltage : \",Voneg,\"V.\"\n",
+ "\n",
+ "#Graph \n",
+ "\n",
+ "x = numpy.linspace(0,24,100)\n",
+ "x1 = numpy.linspace(0,2,100)\n",
+ "x2 = numpy.linspace(2,6,100)\n",
+ "x3 = numpy.linspace(6,8,100)\n",
+ "x4 = numpy.linspace(8,8+8.0/6,100)\n",
+ "x5 = numpy.linspace(8+8.0/6,12+8.0/6,100)\n",
+ "x6 = numpy.linspace(12+8.0/6,12+2*8.0/6,100)\n",
+ "x7 = numpy.linspace(12+2*8.0/6,14+2*8.0/6,100)\n",
+ "x8 = numpy.linspace(14+2*8.0/6,18+2*8.0/6,100)\n",
+ "x9 = numpy.linspace(18+2*8.0/6,20+2*8.0/6,100)\n",
+ "x10 = numpy.linspace(0,8+8.0/6,100)\n",
+ "plot(x,0+x-x,'k')\n",
+ "plot(x1,6*x1,'b')\n",
+ "plot(x2,12-x2+x2,'b')\n",
+ "plot(x3,12-6*(x3-6),'b')\n",
+ "plot(x4,-6*(x4-8),'b')\n",
+ "plot(x5,-8+x5-x5,'b')\n",
+ "plot(x6,-8+6*(x6-(12+8.0/6)))\n",
+ "plot(x7,6*(x7-(12+2*8.0/6)),'b')\n",
+ "plot(x8,12-x8+x8,'b')\n",
+ "plot(x9,12-6*(x9-(18+2*8.0/6)),'b')\n",
+ "plot(x10,-8+x10-x10,'--',color='g')\n",
+ "plot(x1,12+x1-x1,'--',color='g')\n",
+ "ylim(-20,15)\n",
+ "xlim(0,20+2*8.0/6)\n",
+ "title(\"Output Voltage Waveform\")\n",
+ "xlabel(\"t ->\")\n",
+ "ylabel(\"-Vout->\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.17 , Page Number 167 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Following is the output wave generated when Vinmax is changed to 60 V.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x9d5c490>"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEZCAYAAACNebLAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBU7NAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x9582910>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import math\n",
+ "import numpy\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,title,xlabel,ylabel,ylim,xlim\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "Vinmax1 = 25 #Peak input voltage1 (in volts)\n",
+ "Vinmax2 = 60 #Peak input voltage2 (in volts)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Voutmax1 = 20 #Peak output voltage1 (in volts)\n",
+ "Voutmax2 = 10 #Peak output voltage2 (in volts)\n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"Following is the output wave generated when Vinmax is changed to 60 V.\"\n",
+ "\n",
+ "#Graph\n",
+ "\n",
+ "x = numpy.linspace(0,24,100)\n",
+ "x1 = numpy.linspace(0,2,100)\n",
+ "x2 = numpy.linspace(2,6,100)\n",
+ "x3 = numpy.linspace(6,8,100)\n",
+ "x4 = numpy.linspace(8,10,100)\n",
+ "x5 = numpy.linspace(10,14,100)\n",
+ "x6 = numpy.linspace(14,16,100)\n",
+ "x7 = numpy.linspace(0,10,100)\n",
+ "plot(x,0+x-x,'k')\n",
+ "plot(x1,10*x1,'b')\n",
+ "plot(x2,20-x2+x2,'b')\n",
+ "plot(x3,20-10*(x3-6),'b')\n",
+ "plot(x4,-10*(x4-8),'b')\n",
+ "plot(x5,-20+x5-x5,'b')\n",
+ "plot(x6,-20+10*(x6-14))\n",
+ "plot(x7,-20+x7-x7,'--',color='g')\n",
+ "xlim(0,16)\n",
+ "ylim(-22,22)\n",
+ "title(\"Output Voltage Waveform\")\n",
+ "xlabel(\"t ->\")\n",
+ "ylabel(\"-Vout->\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.18 , Page Number 168 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Output voltage Vout = Vin/2.\n",
+ "Voutmax = 5.0 V.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Annotation at 0xa9b53f0>"
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEZCAYAAAB/6SUgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQ7x1SgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0xafafd90>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import math\n",
+ "import numpy\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,title,xlabel,ylabel,ylim,xlim,annotate\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "Vinmax = 10.0 #Peak input voltage (in volts)\n",
+ "R1 = 5 * 10**3 #Resistance1 (in ohm)\n",
+ "R2 = 5 * 10**3 #Resistance2 (in ohm) \n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Vout = Vinmax/(R1 + R2)*R2 #Output voltage (in volts) \n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"Output voltage Vout = Vin/2.\"\n",
+ "print \"Voutmax = \",Vout,'V.'\n",
+ "\n",
+ "#Graph\n",
+ "\n",
+ "x = numpy.linspace(0,12,100) \n",
+ "x1 = numpy.linspace(0,3,100)\n",
+ "x2 = numpy.linspace(3,6,100)\n",
+ "x3 = numpy.linspace(6,9,100)\n",
+ "y1 = numpy.linspace(-5,0,100)\n",
+ "plot(x,0+x-x,'k')\n",
+ "plot(x1,0-x1+x1,'b')\n",
+ "plot(x2,-5+x2-x2,'b')\n",
+ "plot(x3,0+x3-x3,'b')\n",
+ "plot(3+y1-y1,y1,'b')\n",
+ "plot(6+y1-y1,y1,'b')\n",
+ "plot(9+y1-y1,y1,'b')\n",
+ "plot(x2,-2+x2-x2,'--',color='g')\n",
+ "title(\"Output Voltage Waveform\")\n",
+ "xlabel(\"t ->\")\n",
+ "ylabel(\"-Vout->\")\n",
+ "xlim(0,9)\n",
+ "ylim(-6,5)\n",
+ "annotate('T/2',xy=(4.25,-2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.19 , Page Number 168 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "OUtput voltage lies in range : -8 V to +6 V.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0xccf9450>"
+ ]
+ },
+ "execution_count": 92,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0xcbb18d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import math\n",
+ "import numpy\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,title,xlabel,ylabel,ylim,xlim,annotate\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "Vinmax = 10 #Peak input voltage (in volts)\n",
+ "V1 = 5.3 #Voltage source1 (in volts)\n",
+ "V2 = 7.3 #Voltage source2 (in volts) \n",
+ "R = 10.0 * 10**3 #Resistance (in ohm)\n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Vomax = 6 #Peak output voltage in positive half (in volts)\n",
+ "Vomin = -8 #Peak output voltage in negative half (in volts)\n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"OUtput voltage lies in range : -8 V to +6 V.\"\n",
+ "\n",
+ "#Graph \n",
+ "\n",
+ "x = numpy.linspace(0,24,100)\n",
+ "x1 = numpy.linspace(0,3,100)\n",
+ "x2 = numpy.linspace(3,5,100)\n",
+ "x3 = numpy.linspace(5,7,100)\n",
+ "x4 = numpy.linspace(7,10,100)\n",
+ "x5 = numpy.linspace(10,14,100)\n",
+ "x6 = numpy.linspace(14,15,100)\n",
+ "x7 = numpy.linspace(15,16,100)\n",
+ "x8 = numpy.linspace(16,20,100)\n",
+ "x9 = numpy.linspace(0,5,100)\n",
+ "x10 = numpy.linspace(0,14,100)\n",
+ "x11 = numpy.linspace(0,15,100)\n",
+ "\n",
+ "plot(x,0+x-x,'k')\n",
+ "plot(x1,2*x1,'b')\n",
+ "plot(x2,2*x2,'--',color='b')\n",
+ "plot(x3,10-2*(x3-5),'--',color='b')\n",
+ "plot(x4,6-2*(x4-7),'b')\n",
+ "plot(x5,-2*(x5-10),'b')\n",
+ "plot(x6,-8-2*(x6-14),'--',color='b')\n",
+ "plot(x7,-10+2*(x7-15),'--',color='b')\n",
+ "plot(x8,-8+2*(x8-16),'b')\n",
+ "\n",
+ "plot(x1,6-x1+x1,'--',color='g')\n",
+ "plot(x2,6-x2+x2,'k')\n",
+ "plot(x3,6-x3+x3,'k')\n",
+ "plot(x6,-8-x6+x6,'k')\n",
+ "plot(x7,-8-x7+x7,'k')\n",
+ "plot(x9,10+x9-x9,'--',color='g')\n",
+ "plot(x10,-8+x10-x10,'--',color='g')\n",
+ "plot(x11,-10+x11-x11,'--',color='g')\n",
+ "\n",
+ "xlim(0,20)\n",
+ "ylim(-12,12)\n",
+ "title(\"Output Voltage Waveform\")\n",
+ "xlabel(\"t ->\")\n",
+ "ylabel(\"-Vout->\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5.20 , Page Number 173 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Output voltage in positive half : 35 V.\n",
+ "Output voltage in negative half : -5 V.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(-10, 40)"
+ ]
+ },
+ "execution_count": 98,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEZCAYAAAB4hzlwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0xcbb1ad0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import math\n",
+ "import numpy\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,title,xlabel,ylabel,ylim,xlim\n",
+ "\n",
+ "#Variables \n",
+ "\n",
+ "Vin = 20 #Input voltage (in volts)\n",
+ "V1 = 5 #Battery voltage (in volts) \n",
+ "\n",
+ "#Calculation\n",
+ "\n",
+ "Voutp = (2*Vin - V1) #Output voltage in positive half (in volts)\n",
+ "Voutn = 0 - V1 #Output voltage in negative half (in volts) \n",
+ "\n",
+ "#Result\n",
+ "\n",
+ "print \"Output voltage in positive half : \",Voutp,\"V.\"\n",
+ "print \"Output voltage in negative half :\",Voutn,\"V.\"\n",
+ "\n",
+ "#Graph\n",
+ "\n",
+ "x = numpy.linspace(0,9,100) \n",
+ "x1 = numpy.linspace(0,3,100)\n",
+ "x2 = numpy.linspace(3,6,100)\n",
+ "x3 = numpy.linspace(6,9,100)\n",
+ "y1 = numpy.linspace(-5,35,100)\n",
+ "plot(x,0+x-x,'k')\n",
+ "plot(x1,-10+x1-x1,'--',color='g')\n",
+ "plot(x1,35-x1+x1,'b')\n",
+ "plot(x2,-5+x2-x2,'b')\n",
+ "plot(x3,35+x3-x3,'b')\n",
+ "plot(3+y1-y1,y1,'b')\n",
+ "plot(6+y1-y1,y1,'b')\n",
+ "title(\"Output Voltage Waveform\")\n",
+ "xlabel(\"t ->\")\n",
+ "ylabel(\"-Vout->\")\n",
+ "xlim(0,9)\n",
+ "ylim(-10,40)"
+ ]
+ }
+ ],
+ "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": "2.7.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
generated by cgit (git 2.34.1) at 2024-12-20 07:16:06 +0000