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swc-bb
swc-lessons
python-notebooks
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665cb2a3
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665cb2a3
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Sep 20, 2017
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Frank Hellmann
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# swc workshop 2017 Fall
## Open questions
Frank: formulate targets (Python specific)
Stefan/Marvin/Peter/Martin: how(+why) to integrate shell usage in later R sessions
?: prepare time series files
Stefan/Marvin: if + where to introduce tidyverse
## DAY 1 AM --- both
### Session AM1 (get started, know what we're doing these days)
-
intro
-
lightning talks participants
-
course outline
### Session AM2 (basic concepts, reasons why, basis for later use cases)
-
shell
-
git
Each shorter than spring 2017, will be picked up in later sessions
## DAY 1 PM --- Python
### Session PM1 Python Intro (basic overview)
-
python+anaconda+conda+jupyter+history
-
operators, data structures (tuples, lists, dicts, nparrays, panda.df, etc)
-
object types (string, number, boolean, complex, etc)
-
provided functions + methods, atrributes
-
control flows: loops + conditions
-
numpy
-
read file, simple plot (or in session PM2)
### Session PM2 Python (structuring code, defensive programming, using prepared ts files)
-
write function + module
-
put under version control
-
simple test (assert)
-
documentation
-
write script + call from bash
-
traceback, errors
not covered: closures, argpars, numba
## DAY 1 PM --- R
### Session PM1 R Intro (basic intro)
-
R+Rstudio+CRAN+history
-
operators, vector, df, object structures (vector,df,list,other), read file, subset df
-
object class (string, number, boolean, complex, etc)
-
simple plot
-
read help for functions
-
install.packages, library + fun vs pack::fun
### Session PM2 R (overview of code structuring)
-
conditions, loops,
*
apply
-
write function (with comments for documentation)
-
put under version control
-
simple test (testthat)
not covered: package with structured documentation with devtools/Roxygen/sinew (but link to https://github.com/brry/course#packs)
## DAY 2 AM --- both
### Session AM1: Time series, functions, plotting
-
reading + understanding documentation of functions
Global temperature average development 1750-2010
http://berkeleyearth.lbl.gov/auto/Global/Raw_TAVG_complete.txt
-
download file (optional: call shell with wget from python/R)
-
read, process
-
moving average
-
plot with uncertainty band
-
write function (exercise!)
### Session AM2:
using shell:
-
if possible: get list of available files in http://berkeleyearth.lbl.gov/auto/Regional/TAVG/Text/
-
with wget: Get all textfiles (country temp development) starting with B (or with >3 consonants or whatever)
-
apply function to several textfiles
Mix:
-
Python: APIs: numpy, pandas, xarray
-
R: Tidyverse?
-
BUFFER (previous sessions potentially too full)
## DAY 2 PM --- both
### Session PM1: higher dimensional (spatial) data, advanced plotting + cartography, application of aquired knowledge
gridded data Europe:
-
read netcdf file
-
plot map of one time slice
-
now compare plot with given cuteplot function by Joachim
-
extract time series Potsdam
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compare with global average from Berkeley
### Session PM2: Git in practice (optional if we ran out of time)
-
use git: final commit
-
create github/gitlab account + create repos + add remote
-
git push + inspect online
-
github/gitlab release
-
outro, feedback
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