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swc-bb
swc-lessons
python-notebooks
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3fa80f13
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3fa80f13
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Sep 20, 2017
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Frank Hellmann
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# Python lesson outline
## Targets for the course
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Structure your code (functions and modules, everything else is out of scope)
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Think about possible errors
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document, document, document
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use version control
## Assumptions
90 minute blocks
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1 block shell
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1 block git
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4-5 blocks of python
## DAY 1
### Session AM1 Intro/Lightning talks
### Session AM2 Shell
### Session PM1 GIT
### Session PM2 Python Intro (basic overview)
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python+anaconda+conda+jupyter+history
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operators, data structures (tuples, lists, dicts, nparrays, panda.df, etc)
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object types (string, number, boolean, complex, etc)
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provided functions + methods, atrributes
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control flows: loops + conditions
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numpy
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read file, simple plot (or in session PM2)
## DAY 2 AM
### Session AM1 Python (structuring code, defensive programming, using prepared ts files)
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write function + module
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put under version control
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simple test (assert)
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documentation
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write script + call from bash
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traceback, errors
not covered: closures, argpars, numba
### Session AM2: Time series, functions, plotting
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reading + understanding documentation of functions
Global temperature average development 1750-2010
http://berkeleyearth.lbl.gov/auto/Global/Raw_TAVG_complete.txt
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download file (optional: call shell with wget from python/R)
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read, process
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moving average
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plot with uncertainty band
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write function (exercise!)
## DAY 2 PM
### Session PM1: higher dimensional (spatial) data, advanced plotting + cartography, application of aquired knowledge
gridded data Europe:
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read netcdf file
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plot map of one time slice
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now compare plot with given cuteplot function by Joachim
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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)
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use git: final commit
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create github/gitlab account + create repos + add remote
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git push + inspect online
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github/gitlab release
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outro, feedback
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