Daniel Rothenberg (darothen@mit.edu)
Joint Program Student Luncheon, July 16, 2015
def get_git_versioning():
""" Returns the currently checked out commit shortname. """
return subprocess.check_output(
['git', 'rev-parse', '--short', 'HEAD']
).strip()
print("Current HEAD git commit: ", str(get_git_versioning()))
Current HEAD git commit: b'3fde6fc'
Fetch git commit at any time, attach it as metadata in whatever figure or output file you create
But why should you care about this?
!git log -2
commit d667821e910c1104ce48bd80fc2dec29be01f612 Author: Daniel Rothenberg <darothen@mit.edu> Date: Thu Jul 16 09:59:45 2015 -0400 Unified naming scheme for notebooks commit 383aaa394614be2ee78a6943bc0a720bb742d98d Author: Daniel Rothenberg <darothen@mit.edu> Date: Thu Jul 16 09:55:07 2015 -0400 updated global radiative forcing (PD - PI) change calculations
Oh no, I broke something!
!git diff ee8a2e9 ebe1ce2 marc_aie/convert.py
diff --git a/marc_aie/convert.py b/marc_aie/convert.py index ed7d01a..4120eab 100644 --- a/marc_aie/convert.py +++ b/marc_aie/convert.py @@ -109,12 +109,12 @@ def create_master(var, data_dict=None, new_fields=["PS", ]): if isinstance(var, str): assert data_dict is not None acts, aers = decompose_multikeys(list(data_dict.keys())) - new_fields.append(var) + new_fields += var elif isinstance(var, Var): data_dict = var.data acts = var.cases['act'] aers = var.cases['aer'] - new_fields.append(var.varname) + new_fields += var.varname else: raise ValueError("`var` must be a Var or a string.")
conda
¶virtualenv
)Docker
¶environment.yml
name: marc_aie
channels:
- unidata
- scitools
dependencies:
- cartopy>=0.12
- ipython>=3.2.0
- ipython-notebook
- matplotlib
- netcdf4
- numpy
- python=3.4
- seaborn
- xlrd
- xray>=0.5
Create environment based on environment.yml
cd [my_repo_dir]
conda env create
Activate environment
source activate marc_aie
!conda info -e
# conda environments: # cartopy_py3 /Users/daniel/anaconda/envs/cartopy_py3 ipython-dev /Users/daniel/anaconda/envs/ipython-dev iris_1_7_2 /Users/daniel/anaconda/envs/iris_1_7_2 marc_aie * /Users/daniel/anaconda/envs/marc_aie marc_aie2 /Users/daniel/anaconda/envs/marc_aie2 pymc3_test /Users/daniel/anaconda/envs/pymc3_test python3 /Users/daniel/anaconda/envs/python3 scipy-tutorial /Users/daniel/anaconda/envs/scipy-tutorial vispy_test /Users/daniel/anaconda/envs/vispy_test root /Users/daniel/anaconda
!conda list
# packages in environment at /Users/daniel/anaconda/envs/marc_aie: # binstar 0.11.0 py34_0 defaults cartopy 0.13.0 np19py34_0 https://conda.binstar.org/unidata/osx-64/cartopy-0.13.0-np19py34_0.tar.bz2 certifi 14.05.14 py34_0 defaults clyent 0.3.4 py34_0 defaults curl 7.38.0 0 http://repo.continuum.io/pkgs/free/osx-64/curl-7.38.0-0.tar.bz2 freetype 2.5.2 2 defaults geos 3.4.2 4 https://conda.binstar.org/unidata/osx-64/geos-3.4.2-4.tar.bz2 hdf5 1.8.14 0 defaults ipython 3.2.1 py34_0 defaults ipython-notebook 3.2.1 py34_0 defaults jinja2 2.7.3 py34_1 defaults jpeg 8d 2 https://conda.binstar.org/r/osx-64/jpeg-8d-2.tar.bz2 jsonschema 2.4.0 py34_0 defaults libnetcdf 4.3.2 1 defaults libpng 1.6.17 0 defaults libsodium 0.4.5 0 http://repo.continuum.io/pkgs/free/osx-64/libsodium-0.4.5-0.tar.bz2 libtiff 4.0.2 1 http://repo.continuum.io/pkgs/free/osx-64/libtiff-4.0.2-1.tar.bz2 markupsafe 0.23 py34_0 defaults matplotlib 1.4.3 np19py34_3 defaults mistune 0.6 py34_0 defaults mock 1.0.1 py34_0 defaults netcdf4 1.1.8 np19py34_0 defaults nose 1.3.7 py34_0 defaults numpy 1.9.2 py34_0 defaults openssl 1.0.1k 1 http://repo.continuum.io/pkgs/free/osx-64/openssl-1.0.1k-1.tar.bz2 owslib 0.9.0 py34_0 https://conda.binstar.org/unidata/osx-64/owslib-0.9.0-py34_0.tar.bz2 pandas 0.16.2 np19py34_0 defaults pillow 2.9.0 py34_0 defaults pip 7.1.0 py34_0 defaults proj4 4.9.1 1 https://conda.binstar.org/unidata/osx-64/proj4-4.9.1-1.tar.bz2 ptyprocess 0.4 py34_0 defaults pyepsg 0.2.0 py34_0 https://conda.binstar.org/unidata/osx-64/pyepsg-0.2.0-py34_0.tar.bz2 pygments 2.0.2 py34_0 defaults pyparsing 2.0.3 py34_0 defaults pyqt 4.11.3 py34_0 defaults pyshp 1.2.1 py34_0 https://conda.binstar.org/unidata/osx-64/pyshp-1.2.1-py34_0.tar.bz2 python 3.4.3 0 defaults python-dateutil 2.4.2 py34_0 defaults python.app 1.2 py34_4 defaults pytz 2015.4 py34_0 defaults pyyaml 3.11 py34_1 defaults pyzmq 14.7.0 py34_0 defaults qgrid 0.1.1 <pip> qt 4.8.6 3 defaults readline 6.2 2 <unknown> requests 2.7.0 py34_0 defaults scipy 0.15.1 np19py34_0 defaults seaborn 0.6.0 np19py34_0 defaults setuptools 18.0.1 py34_0 defaults shapely 1.5.8 np19py34_2 https://conda.binstar.org/unidata/osx-64/shapely-1.5.8-np19py34_2.tar.bz2 sip 4.16.5 py34_0 defaults six 1.9.0 py34_0 defaults sqlite 3.8.4.1 1 defaults terminado 0.5 py34_0 defaults tk 8.5.18 0 defaults tornado 4.2 py34_0 defaults xlrd 0.9.3 py34_0 defaults xray 0.5.1 np19py34_0 defaults xz 5.0.5 0 defaults yaml 0.1.6 0 defaults zeromq 4.0.5 1 https://conda.binstar.org/r/osx-64/zeromq-4.0.5-1.tar.bz2 zlib 1.2.8 1 https://conda.binstar.org/r/osx-64/zlib-1.2.8-1.tar.bz2
!binstar search -t conda cartopy
Using binstar api site https://api.anaconda.org Run 'binstar show <USER/PACKAGE>' to get more details: Packages: Name | Version | Package Types | Platforms ------------------------- | ------ | --------------- | --------------- ChrisBarker/cartopy | | conda | osx-64 : None asmeurer/cartopy | 0.11.1 | conda | linux-64 : None benbovy/cartopy | 0.11.2 | conda | linux-64, osx-64 : A library providing cartographic tools for python christophkeller/cartopy | 0.11.2 | conda | osx-64 : A library providing cartographic tools for python jklymak/cartopy | 1.6.0 | conda | osx-64 : None julienchastang/cartopy | 0.10.0 | conda | osx-64 : None krisvanneste/cartopy | 0.11.2 | conda | win-64 : None nbren12/cartopy | 1.6.0 | conda | linux-64, osx-64 : None shoyer/cartopy | 0.11.2 | conda | osx-64 : A library providing cartographic tools for python vsheremet/cartopy | 0.11.2 | conda | linux-32 : None Found 10 packages
Cartopy is a powerful wrapper for matplotlib enabling cartographic/geographic transformations of your data
import marc_aie as ma
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
v = ma.CESMVar("TS")
v.load_datasets()
v.apply(lambda ds: ds['TS'].mean("time"))
data = v.data['arg_comp', 'F2000']
print(data)
Loading TS (Surface temperature (radiative)) [K] from /Users/daniel/Desktop/MARC_AIE/work/arg_min_smax_F2000_TS.nc Loading TS (Surface temperature (radiative)) [K] from /Users/daniel/Desktop/MARC_AIE/work/arg_min_smax_F1850_TS.nc Loading TS (Surface temperature (radiative)) [K] from /Users/daniel/Desktop/MARC_AIE/work/arg_comp_F2000_TS.nc Loading TS (Surface temperature (radiative)) [K] from /Users/daniel/Desktop/MARC_AIE/work/arg_comp_F1850_TS.nc <xray.DataArray 'TS' (lat: 96, lon: 144)> array([[ 218.58482361, 218.5799408 , 218.60137939, ..., 218.58233643, 218.57707214, 218.59594727], [ 218.88679504, 218.29620361, 218.22875977, ..., 219.26359558, 219.11993408, 218.63648987], [ 218.0002594 , 218.05961609, 217.96510315, ..., 218.80046082, 218.55628967, 218.3144989 ], ..., [ 256.84292603, 256.97180176, 257.05990601, ..., 256.39889526, 256.53231812, 256.67669678], [ 256.02627563, 256.08166504, 256.13250732, ..., 255.80848694, 255.88676453, 255.96191406], [ 255.69245911, 255.72254944, 255.75584412, ..., 255.62876892, 255.6466217 , 255.66511536]], dtype=float32) Coordinates: * lat (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ... * lon (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...
fig = plt.figure(figsize=(12, 5))
ax = fig.add_subplot(111, projection=ccrs.PlateCarree())
gp = ma.global_plot(data, ax=ax, cmap="cubehelix_r",
levels=np.linspace(210, 320, 23))
cb = plt.colorbar(orientation='horizontal')
_ = plt.title(v.long_name + " (%s)" % v.units)
xray
¶dask
out-of-core computation libraryprint(data)
<xray.Dataset> Dimensions: (act: 2, aer: 2, lat: 96, lev: 30, lon: 144, nbnd: 2, time: 12) Coordinates: * act (act) <U12 'arg_min_smax' 'arg_comp' * aer (aer) <U5 'F2000' 'F1850' * lev (lev) float64 3.643 7.595 14.36 24.61 38.27 54.6 72.01 87.82 ... * time (time) datetime64[ns] 2007-02-01 2007-03-01 2007-04-01 ... * lat (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ... * lon (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ... * nbnd (nbnd) int64 0 1 Data variables: hyam (lev) float64 0.003643 0.007595 0.01436 0.02461 0.03827 ... hybm (lev) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... P0 float64 1e+05 time_bnds (time, nbnd) float64 2.555e+03 2.586e+03 2.586e+03 2.614e+03 ... gw (lat) float64 0.0001367 0.001093 0.002185 0.003275 0.004361 ... PS (act, aer, time, lat, lon) float64 6.871e+04 6.871e+04 ... TS (act, aer, time, lat, lon) float64 239.3 239.3 239.3 239.3 ... Attributes: Conventions: CF-1.0 source: CAM case: arg_min_smax title: UNSET logname: darothen host: ys4032 Version: $Name$ revision_Id: $Id$ initial_file: /glade/p/cesm/cseg/inputdata/atm/cam/inic/fv/cami_0000-01-01_1.9x2.5_L30_c070703.nc topography_file: /glade/p/cesm/cseg/inputdata/atm/cam/topo/USGS-gtopo30_1.9x2.5_remap_c050602.nc history: Mon Jul 13 10:25:41 2015: ncatted -O -a years_omit,global,o,i,5 -a years_offset,global,o,i,0 -a git_commit,global,o,c,2ae4314 -a units,TS,o,c,K -a long_name,TS,o,c,Surface temperature (radiative) /Users/daniel/Desktop/MARC_AIE/work/arg_min_smax_F2000_TS_monthly.nc Mon Jul 13 10:25:41 2015: ncrcat -O -v TS,time_bnds,hyam,hybm,PS,P0,gw /Users/daniel/Desktop/MARC_AIE/F2000/arg_min_smax/arg_min_smax.cam2.h0.0008-01.nc /Users/daniel/Desktop/MARC_AIE/F2000/arg_min_smax/arg_min_smax.cam2.h0.0008-02.... nco_openmp_thread_number: 1 years_omit: 5 years_offset: 0 git_commit: 2ae4314
pd_minus_pi = lambda ds: ds.sel(aer='F2000') - ds.sel(aer='F1850')
a = (data['TS']
.pipe(pd_minus_pi)
.mean('lon')
.groupby('time.season')
.mean('time'))
print(a)
<xray.DataArray 'TS' (act: 2, season: 4, lat: 96)> array([[[ 4.61950867e-01, 5.59026612e-01, 4.55550123e-01, 2.36727326e-01, 3.18702133e-01, 3.07158188e-01, 4.00162096e-01, 2.57165096e-01, -2.51953690e-03, -1.69026445e-01, -2.49001574e-01, -2.79389205e-01, -1.92487787e-01, -1.28579458e-01, -8.24304510e-02, -5.55523766e-02, -3.27090511e-02, -1.18752939e-02, -1.07835840e-03, -7.05789637e-03, -1.06829890e-02, -9.91637618e-03, -1.87625885e-02, -2.38267404e-02, -3.81276872e-02, -2.91075530e-02, -8.74378063e-03, 5.13648987e-03, 2.97010210e-02, 8.00307945e-02, 1.22102243e-01, 1.57001919e-01, 1.46723571e-01, 1.78918768e-01, 1.59398679e-01, 1.16689329e-01, 8.40828507e-02, 3.72428894e-02, -4.74922039e-02, -8.98047553e-02, -9.58072521e-02, -7.10083290e-02, -4.92844758e-02, -2.74109311e-02, -1.85739729e-02, -1.87570784e-02, -1.69532917e-02, -2.33111205e-02, -3.27513659e-02, -3.97032279e-02, -5.51194085e-02, -1.36929265e-02, -6.68758816e-03, -3.77916407e-02, -3.64000532e-02, -4.79006591e-02, 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'arg_min_smax' 'arg_comp' * lat (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ... * season (season) object 'DJF' 'JJA' 'MAM' 'SON'
import seaborn as sns
sns.set(style='ticks')
with sns.color_palette('Paired'):
for seas in a.season:
for act in a.act:
d = a.sel(season=seas, act=act)
plt.plot(d.lat, d, label="%s - %s" %
(act.values, seas.values))
sns.despine(offset=10)
plt.legend(loc='best')
<matplotlib.legend.Legend at 0x1143fc0b8>