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List of Really Useful Free Tools For JavaScript Developers | W3Avenue - 0 views

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    List of Really Useful Free Tools For JavaScript Developers
pagetribe .

FUMSI - Data Visualisation: Tools and Examples - 0 views

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    The online visualisation of data continues to stretch in many directions with great effectiveness. Inspired by seminal influences, such as Edward Tufte, there's an ever growing stream of online and offline tools, projects, research and resources for visualising, interpreting and researching ultimately any type of data, for a vast range of uses.
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A tour of git: the basics - 0 views

shared by pagetribe . on 19 Feb 09 - Cached
  • ~ suffix
  • HEAD~
  • HEAD~2" refers to two commits back
  • ...19 more annotations...
  • refers to the previous commit
  • $ git log HEAD~3..
  • git show 13ed136b
  • git status" tells us that the current branch is "master"
  • It’s a little bit helpful to know that we’ve modified hello.c, but we might prefer to know exactly what changes we’ve made to it.
  • git diff
  • To set your name and email address, just use the following commands:
  • git config --global user.name "Your Name" git config --global user.email "you@example.com"
  • --author option to the “git commit”
  • a blank line, and then one or more paragraphs with supporting detail. Since many tools only print the first line of a commit message by default, it’s important that the first line stands alone.
  • git commit --amend
  • misspelling in it
  • It's worth emphasizing the value of minimal, independent commits. The smaller the changes are the more useful the history will be when actually using the history, not just viewing it.
  • Just run "git pull" everytime you want to pull in new changes that have landed in the upstream repository.
  • Again, you'll see that this precisely matches the final portion of the output from "git pull". Using "git fetch" and "git merge" let us achieve exactly what "git pull" did, but we were able to stop in the middle to examine the situation, (and we could have decided to reject the changes and not merge them---leaving our master branch unchanged).
  • For now, let's return back to the tip of the master branch by just checking it out again: $ git checkout master
  • $ git --bare init --shared The --shared option sets up the necessary group file permissions so that other users in my group will be able to push into this repository as well.
  • Now, generally the purpose of pushing to a repository is to have some "collaboration point" where potentially multiple people might be pushing or pulling.
  • git clone
pagetribe .

http://nltk.googlecode.com/svn/trunk/doc/book/ch01.html - 0 views

  • We can count how often a word occurs in a tex
  • Adding two lists creates a new list
  • count the occurrences of a particular word using text1.count('heaven')
  • ...18 more annotations...
  • By convention, m:n means elements m…n-1
  • A consequence of this last change is that the list only has four elements, and accessing a later value generates an error
  • We can join the words of a list to make a single string, or split a string into a list, as follows:
  • 'Monty Python'.split()
  • frequency distribution
  • frequency of each vocabulary item
  • find the 50 most frequent words
  • hese very long words are often hapaxes (i.e. unique) and perhaps it would be better to find frequently occurring long words.
  • Here are all words from the chat corpus that are longer than 7 characters, that occur more than 7 times:   >>> fdist5 = FreqDist(text5) >>> sorted([w for w in set(text5) if len(w) > 7 and fdist5[w] > 7]) ['#14-19teens', '#talkcity_adults', '((((((((((', '........', 'Question', 'actually', 'anything', 'computer', 'cute.-ass', 'everyone', 'football', 'innocent', 'listening', 'remember', 'seriously', 'something', 'together', 'tomorrow', 'watching'] >>>
  • The collocations() function does this for us
  • find bigrams that occur more often than we would expect based on the frequency of individual words.
  • fdist = FreqDist(samples) create a frequency distribution containing the given samples fdist.inc(sample) increment the count for this sample fdist['monstrous'] count of the number of times a given sample occurred fdist.freq('monstrous') frequency of a given sample fdist.N() total number of samples fdist.keys() the samples sorted in order of decreasing frequency for sample in fdist: iterate over the samples, in order of decreasing frequency fdist.max() sample with the greatest count fdist.tabulate() tabulate the frequency distribution fdist.plot() graphical plot of the frequency distribution fdist.plot(cumulative=True) cumulative plot of the frequency distribution fdist1 < fdist2 test if samples in fdist1 occur less frequently than in fdist2
  • it goes through each word in text1, assigning each one in turn to the variable w and performing the specified operation on the variable.
  • The above notation is called a "list comprehension"
  • [f(w) for ...] or [w.f() for ...],
  • Now that we are not double-counting words like This and this
  • by filtering out any non-alphabetic items:   >>> len(set([word.lower() for word in text1 if word.isalpha()]))
  • A collocation is a sequence of words which occur together unusually often. Thus red wine is a collocation, while the wine is not. A characteristic of collocations is that they are resistant to substitution with words that have similar senses — maroon wine sounds definitely odd.
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