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Free Book: A Whirlwind Tour of Python
 According to the author: "A Whirlwind Tour of Python is a fast-paced introduction to essential features of the Python language, aimed at researchers and developers who are already familiar with programming in another language. The material is particularly designed for those who wish to use Python for data science and/or scientific programming, and in this capacity serves as an introduction to my longer book, The Python Data Science Handbook." About the author: Jake VanderPlas Jake VanderPlas is a long-time user and developer of the Python scientific stack. He currently works as an interdisciplinary research director at the University of Washington, conducts his own astron‐ omy research, and spends time advising and consulting with local scientists from a wide range of fields. He is a co-author of the Python-powered text Statistics, Data Mining, and Machine Learning in Astronomy, and has presented many tutorials on the subject. He occasionally blogs about Python, Machine Learning, Visualization, and related topics at http://jakevdp.github.io. Links: |
Free Book: Think Python
 Think Python is an introduction to Python programming for beginners. It starts with basic concepts of programming, and is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression. Larger pieces, like recursion and object-oriented programming are divided into a sequence of smaller steps and introduced over the course of several chapters. About the Author
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Enthusiast Birding Lenses
Birding enthusiasts do it for fun, not for money. Therefore, they are always on the lookout for affordable equipment. Here is a list of affordable (<RM10k) birding lenses:
The Sony 200-600
Notes:
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R Result Analysis - Simple Reporting
Given a CSV file with the first 3 lines like this: $ head altera.csv name,matric,tests,final,mile1,mile4,prog,sect STUDENT 1,MATRIC1,9.1,31.5,14.25,15,2SKEE,1 STUDENT 2,MATRIC2,12.35,22.5,13,9.25,2SKEE,1 Read it and generate the total and grade columns. For CLO analysis, failure are not counted. > my=read.csv("altera.csv") > my$total <- round(my$tests + my$final +my$mile1 + my$mile4) >
my$grade <- ifelse(my$total < 30, "E", ifelse(my$total<35,
"D-", ifelse(my$total<40, "D", ifelse(my$total<45, "D+",
ifelse(my$total<50, "C-", ifelse(my$total<55,"C",
ifelse(my$total<60, "C+", ifelse(my$total<65, "B-",
ifelse(my$total<70, "B", ifelse(my$total<75, "B+",
ifelse(my$total<80, "A-", ifelse(my$total<90, "A",
"A+")))))))))))) > table(my$grade) A A- A+ B B- B+ C C- C+ D+ E 73 42 48 42 40 42 22 4 25 5 1 The frame is filtered to remove failures. > table(x$grade) A A- A+ B B- B+ C C- C+ D+ 73 42 48 42 40 42 22 4 25 5 Columns clo1, clo2 and clo3 reports the students' achievement on the course learning outcomes normalized to 1. > x$clo1 <- (x$tests+x$final)/70 > x$clo2 <- x$mile1/15 > x$clo3 <- x$mile4/15 > options(digits=2) > head(x,2) name matric tests final mile1 mile4 prog sect total grade clo1 clo2 clo3 1 STUDENT1 MATRIC1 9.1 32 14 15.0 2SKEE 1 70 B+ 0.58 0.95 1.00 2 STUDENT2 MATRIC2 12.3 22 13 9.2 2SKEE 1 57 C+ 0.50 0.87 0.62 CLO results for different sections: use the last 3 columns. > aggregate(x,by=list(x$sect),mean) Group.1 name matric tests final mile1 mile4 prog sect total grade clo1 clo2 clo3 1 1 NA NA 12 30 14 14 NA 1 70 NA 0.61 0.91 0.93 2 2 NA NA 12 31 14 14 NA 2 71 NA 0.62 0.94 0.90 3 3 NA NA 13 30 15 13 NA 3 71 NA 0.62 1.00 0.85 4 4 NA NA 12 29 15 14 NA 4 70 NA 0.58 0.99 0.94 5 5 NA NA 13 36 13 9 NA 5 71 NA 0.70 0.86 0.60 6 6 NA NA 15 40 14 13 NA 6 82 NA 0.79 0.90 0.85 7 7 NA NA 12 33 12 12 NA 7 68 NA 0.63 0.80 0.78 8 8 NA NA 11 31 12 12 NA 8 66 NA 0.60 0.82 0.80 9 9 NA NA 14 35 14 13 NA 9 77 NA 0.70 0.95 0.89 10 10 NA NA 14 35 14 14 NA 10 77 NA 0.70 0.94 0.96 11 11 NA NA 16 41 14 13 NA 11 83 NA 0.80 0.91 0.85 12 12 NA NA 12 32 14 13 NA 12 71 NA 0.63 0.91 0.87 13 13 NA NA 12 29 14 13 NA 13 68 NA 0.58 0.92 0.89 There were 50 or more warnings (use warnings() to see the first 50) CLO results for all sections: > mean(x$clo1) [1] 0.661 > mean(x$clo2) [1] 0.921 > mean(x$clo3) [1] 0.866 Find how many students achieved KPI of 0.4: [1] 314 > sum(x$clo2>.4) [1] 343 > sum(x$clo3>.4) [1] 329 The academic programs differ by the 5th character in the prog column. > mean(e$clo1) [1] 0.62 > mean(e$clo2) [1] 0.94 > mean(e$clo3) [1] 0.87 The SKEL and SKEM has 3 and 4 subgroups, respectively. The SKEL and SKEM subsets can be extracted using the subset or filter function. Here two more ways to perform the same operation s are shown. > l <- filter(x, x$prog=="1SKEL" | x$prog == "2SKEL" | x$prog=="4SKEL") > m = subset(x, substr(x$prog,5,5) == 'M) The filter function does not seem to work all the time. Both filter and subset have
subtle differences. Subset is part of the base R, filter is part the
dplyr package. After subsetting, find the means. > mean(m$clo2) [1] 0.92 etc... We can also perform subgroup analysis without first extracting a subset. We can just use the x dataframe directly. To find the mean for the variable 'clo1' for the 'SKEE' cohort, > options(digits=2) > mean(x$clo1) [1] 0.66 > mean(x$clo1['E'==substr(x$prog,5,5)]) [1] 0.62 > mean(x$clo1['L'==substr(x$prog,5,5)]) [1] 0.69 > mean(x$clo1['M'==substr(x$prog,5,5)]) [1] 0.68 etc.. Repeat for CLO2 and CLO3. All the numbers required for reporting are now ready for presentation. Need to study ggplot next. |
R Result Analysis - Preparing the Data
Given a CSV file with the first 3 lines like this: $ head altera.csv name,matric,tests,final,mile1,mile4,prog,sect STUDENT 1,MATRIC1,9.1,31.5,14.25,15,2SKEE,1 STUDENT 2,MATRIC2,12.35,22.5,13,9.25,2SKEE,1 Go into R, using Anaconda or Rstudio. Install dplyr (if not already installed) and import it. > install.packages(pkgs="dplyr") > library(dplyr) Read in the data file. > my=read.csv("altera.csv") Dump all data: > my Or just show the first 6 lines: > head(my) name matric tests final mile1 mile4 prog sect 1 student 1 MATRIC001 9.10 31.5 14.25 15.00 2SKEE 1 2 student 2 MATRIC002 12.35 22.5 13.00 9.25 2SKEE 1 3 student 3 MATRIC003 11.55 38.0 14.25 10.75 2SKEE 1 4 student 4 MATRIC004 11.60 28.0 14.25 10.75 2SKEE 1 5 student 5 MATRIC005 8.25 20.0 10.00 14.79 2SKEE 1 6 student 6 MATRIC006 15.30 38.5 15.00 15.00 2SKEE 1 Make a table or two: > table(my$prog) 1SKEE 1SKEL 1SKEM 2SKEE 2SKEL 2SKEM 3SKEM 4SKEL 4SKEM 3 8 4 128 126 72 1 1 1 > xtabs(~ prog + sect, my) sect prog 1 2 3 4 5 6 7 8 9 10 11 12 13 1SKEE 0 0 1 0 1 0 0 0 0 0 0 1 0 1SKEL 0 1 0 0 0 4 0 0 0 0 1 2 0 1SKEM 1 0 0 0 0 0 0 0 0 0 0 3 0 2SKEE 26 27 27 26 22 0 0 0 0 0 0 0 0 2SKEL 0 0 1 1 0 27 20 14 28 30 0 5 0 2SKEM 2 0 1 5 0 0 0 0 0 0 26 6 32 3SKEM 0 1 0 0 0 0 0 0 0 0 0 0 0 4SKEL 0 0 0 0 0 1 0 0 0 0 0 0 0 4SKEM 0 0 1 0 0 0 0 0 0 0 0 0 0 Create a total column > my$total <- my$tests + my$final +my$mile1 + my$mile4 > my$total <- round(my$total) > head(my,2) name matric tests final mile1 mile4 prog sect total 1 student 1 MATRIC001 9.10 31.5 14.25 15.00 2SKEE 1 70 2 student 2 MATRIC002 12.35 22.5 13.00 9.25 2SKEE 1 57 Create the passfail column: > my$passfail <- ifelse(my$total < 40, "fail", "pass") > head(my,2) name matric tests final mile1 mile4 prog sect total passfail1 student 1 MATRIC001 9.10 31.5 14.25 15.00 2SKEE 1 70 pass 2 student 2 MATRIC002 12.35 22.5 13.00 9.25 2SKEE 1 57 pass Create the grade column: > my$grade <- ifelse(my$total < 30, "E", ifelse(my$total<35, "D-", ifelse(my$total<40, "D", ifelse(my$total<45, "D+", ifelse(my$total<50, "C-", ifelse(my$total<55,"C", ifelse(my$total<60, "C+", ifelse(my$total<65, "B-", ifelse(my$total<70, "B", ifelse(my$total<75, "B+", ifelse(my$total<80, "A-", ifelse(my$total<90, "A", "A+")))))))))))) Delete the passfail column, where 10 is the column number starting from 1: Delete the passfail column, where 10 is the column number starting from 1: > my <- my[,-10] > head(my,2) 1 student 1 MATRIC001 9.10 31.5 14.25 15.00 2SKEE 1 70 B+ 2 student 2 MATRIC002 12.35 22.5 13.00 9.25 2SKEE 1 57 C+ Generate cross tables > xtabs(~ grade + sect, my) sect grade 1 2 3 4 5 6 7 8 9 10 11 12 13 A 6 7 6 7 3 10 4 2 5 6 8 4 5 A- 6 5 4 3 4 1 6 1 1 2 2 3 4 A+ 2 0 4 0 1 12 0 0 8 9 10 0 2 B 3 2 1 10 4 2 1 3 4 3 1 3 5 B- 2 4 1 3 3 1 3 5 1 3 3 5 6 B+ 2 7 6 4 4 2 1 1 5 5 2 1 2 C 3 1 2 1 2 3 3 1 1 1 0 0 4 C- 0 0 3 0 0 0 0 0 0 1 0 0 0 C+ 5 3 4 4 1 1 1 1 2 0 1 1 1 D+ 0 0 0 0 0 0 1 0 1 0 0 0 3 E 0 0 0 0 1 0 0 0 0 0 0 0 0 > xtabs(~ grade + prog, my) prog grade 1SKEE 1SKEL 1SKEM 2SKEE 2SKEL 2SKEM 3SKEM 4SKEL 4SKEM A 0 1 1 24 26 21 0 0 0 A- 2 0 1 20 12 6 1 0 0 A+ 0 0 0 7 28 12 0 1 0 B 0 2 1 18 13 8 0 0 0 B- 0 4 0 11 16 9 0 0 0 B+ 0 1 0 23 13 5 0 0 0 C 0 0 0 8 9 5 0 0 0 C- 0 0 0 2 1 0 0 0 1 C+ 0 0 1 15 6 3 0 0 0 D+ 0 0 0 0 2 3 0 0 0 E 1 0 0 0 0 0 0 0 0 For the previous table, you can also use: > table(my$grade, my$sect) Then the summary table: > table(my$grade) A A- A+ B B- B+ C C- C+ D+ E 73 42 48 42 40 42 22 4 25 5 1 Until next time. |
Free Book: Fast Lane to Python
This book aims to enable the reader to quickly acquire a Python foundation. The material particularly feel quite comfortable to anyone with background in an object-oriented programming (OOP) language such as C++ or Java.
Even if ones lack this background, they will still be able to read these sections, but will probably need to go through them more slowly than those who do know OOP. Some Linux knowledge would also be helpful, but it certainly is not required.
Python is used on Windows and Macintosh platforms too, not just Linux. So, most statements here made for the Linux context will also apply to Macs as well. The author acknowledged that programming is a personal, creative activity, so everyone has his/her own view.
It covers all essential Python knowledge. You can learn complete primary skills of Python fast and easily. The book includes practical examples for beginners and includes tests & answers for the college exam, the engineer certification exam, and the job interview exam.
About the Author
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My RAM
$ sudo dmidecode --type 17 # dmidecode 3.1 Getting SMBIOS data from sysfs. SMBIOS 2.7 present. Handle 0x002E, DMI type 17, 34 bytes Memory Device Array Handle: 0x002C Error Information Handle: Not Provided Total Width: 64 bits Data Width: 64 bits Size: 4096 MB Form Factor: DIMM Set: None Locator: DIMM0 Bank Locator: BANK0 Type: DDR3 Type Detail: Synchronous Unbuffered (Unregistered) Speed: 1600 MT/s Manufacturer: Kingston Serial Number: 6718B60 Asset Tag: AssetTagNum0 Part Number: 9905584-015.A00LF Rank: 2 Configured Clock Speed: 800 MT/s Handle 0x0030, DMI type 17, 34 bytes Memory Device Array Handle: 0x002C Error Information Handle: Not Provided Total Width: 64 bits Data Width: 64 bits Size: 4096 MB Form Factor: DIMM Set: None Locator: DIMM1 Bank Locator: BANK1 Type: DDR3 Type Detail: Synchronous Unbuffered (Unregistered) Speed: 1600 MT/s Manufacturer: Kingston Serial Number: 781856E Asset Tag: AssetTagNum1 Part Number: 99U5584-005.A00LF Rank: 2 Configured Clock Speed: 800 MT/s |
Shortest Verilog Code
Guess what it does? module smart( input [2:0] x, output [7:0] y); assign y = 1 << x; endmodule It's a 3:8 decoder! The following is how it's done, normally: module naive(sel, res); input [2:0] sel; output [7:0] res; reg [7:0] res; always @(sel or res) begin case (sel) 3'b000 : res = 8'b00000001; 3'b001 : res = 8'b00000010; 3'b010 : res = 8'b00000100; 3'b011 : res = 8'b00001000; 3'b100 : res = 8'b00010000; 3'b101 : res = 8'b00100000; 3'b110 : res = 8'b01000000; default : res = 8'b10000000; endcase end endmodule Now why would you want to do normal? |
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