Jakecob Vanderplas Sketch Path Filter
got into the core of matplotlib 1.3.
Now it is easy to produce plots with the XKCD style with just the following:
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import matplotlib.pyplot as plt
plt.xkcd() #<- That's it!
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You will need the latest development version of matplotlib. (If you use OpenSUSE 12.3 just add my custom repository, here is a list of the packages available).
And here is my XKC(T)D profile ;-)
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import gsw
import numpy as np
from oceans.sw_extras import gamma_GP_from_SP_pt
from ctd import DataFrame, Series, movingaverage, rosette_summary, lp_filter
cast = DataFrame.from_cnv('./data/CTD_001.cnv.gz', compression='gzip')
hours = cast['timeS'].max() / 60. / 60.
lon, lat = cast.longitude.mean(), cast.latitude.mean()
cast = cast[cast['pumps']] # True for good values.
cast = cast[~cast['flag']] # True for bad values blame SBE!
keep = set(['t090C', 'c0S/m', 'sbeox0Mm/Kg', 'dz/dtM'])
cast = cast.drop(keep.symmetric_difference(cast.columns), axis=1) # Remove unwanted columns.
cast['dz/dtM'] = movingaverage(cast['dz/dtM'], window_size=48)
cast['sbeox0Mm/Kg'] = movingaverage(cast['sbeox0Mm/Kg'], window_size=48)
kw = dict(sample_rate=24.0, time_constant=0.15)
cast.index = lp_filter(cast.index, **kw)
downcast, upcast = cast.split()
downcast = downcast.press_check() # Remove pressure reversals.
downcast = downcast[downcast['dz/dtM'] >= 0.25] # Threshold velocity.
kw = dict(n1=2, n2=20, block=150)
downcast = downcast.apply(Series.despike, **kw)
downcast = downcast.apply(Series.bindata, **dict(delta=1.))
downcast = downcast.apply(Series.interpolate)
pmax = max(cast.index)
if pmax >= 500:
window_len = 21
elif pmax >= 100:
window_len = 11
else:
window_len = 5
kw = dict(window_len=window_len, window='hanning')
downcast = downcast.apply(Series.smooth, **kw)
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fig, ax = downcast['t090C'].plot()
ax.set_title("XKC(T)D")
ax.set_xlabel("Temperature")
ax.set_ylabel("Pressure [dbar]")
fig.set_size_inches((5, 7))
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HTML(html)
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This post was written as an IPython notebook. It is available for download or as a static html.
python4oceanographers by Filipe Fernandes is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://ocefpaf.github.io/.