Unidata /
MetPy
| Conditions | 4 |
| Total Lines | 59 |
| Lines | 0 |
| Ratio | 0 % |
| Changes | 4 | ||
| Bugs | 1 | Features | 1 |
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
| 1 | # Copyright (c) 2008-2015 MetPy Developers. |
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| 17 | def plot_vad(filelist, storm_motion=None): |
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| 18 | '''INPUT: list of NEXRAD VAD files |
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| 19 | OUTPUT: color-coded wind barbs plotted with height for |
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| 20 | the times given in the list of files |
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| 21 | |||
| 22 | plotting a storm-relative VAD is optional |
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| 23 | (requires a storm motion vector)''' |
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| 24 | vad_pd = pd.DataFrame() |
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| 25 | for files in filelist: |
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| 26 | vad_f = Level3File(files) |
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| 27 | vad_ft = vad_f.tab_pages |
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| 28 | lst = np.arange(0, len(vad_ft)-2) |
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| 29 | vad_list = [pd.read_table(StringIO(vad_ft[i]), sep='\s+', header=1, |
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| 30 | skiprows=[2]) for i in lst] |
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| 31 | vad = pd.concat(vad_list, axis=0) |
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| 32 | vad['TIME'] = vad_f.metadata['vol_time'] |
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| 33 | # convert from altitude in feet to AGL in meters |
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| 34 | vad['AGL'] = ((vad['ALT']*100.)-vad_f.height)*0.3048 |
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| 35 | vad_pd = pd.concat([vad_pd, vad], axis=0) |
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| 36 | |||
| 37 | # Begin Figure |
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| 38 | fig = plt.figure(figsize=(12, 10)) |
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| 39 | ax = fig.add_subplot(111) |
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| 40 | |||
| 41 | uplot = vad_pd['U'] |
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| 42 | vplot = vad_pd['V'] |
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| 43 | time_plot = mpl.dates.date2num(vad_pd['TIME'].astype(dt.datetime)) |
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| 44 | |||
| 45 | # Subtract storm motion vector from winds for storm-relative VAD |
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| 46 | if storm_motion is not None: |
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| 47 | uplot, vplot = vad_pd['U']-storm_motion[0], vad_pd['V']-storm_motion[1] |
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| 48 | |||
| 49 | # Set color params |
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| 50 | c = np.sqrt(uplot**2 + vplot**2) # color by speed |
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| 51 | bounds = np.arange(0, 100, 1) # min speed, max speed, interval |
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| 52 | |||
| 53 | # plot wind barbs |
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| 54 | cmap = plt.cm.gnuplot |
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| 55 | norm = mpl.colors.BoundaryNorm(bounds, cmap.N) |
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| 56 | m = 2 # plot every mth wind barb |
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| 57 | b = plt.barbs(time_plot[::m], vad_pd['AGL'].iloc[::m], uplot.iloc[::m], |
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| 58 | vplot.iloc[::m], c[::m], cmap=cmap, norm=norm) |
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| 59 | |||
| 60 | # Assign tick labels for x-axis |
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| 61 | starttime, endtime = vad_pd['TIME'].iloc[0], vad_pd['TIME'].iloc[-1] |
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| 62 | ax.xaxis.set_major_formatter(mpl.dates.DateFormatter('%d/%H:%M UTC')) |
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| 63 | ax.set_xlim(starttime-dt.timedelta(minutes=2), endtime+dt.timedelta(minutes=2)) |
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| 64 | |||
| 65 | # Plot asthetics |
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| 66 | fs = 14 |
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| 67 | cbar = plt.colorbar(b, cmap=cmap, boundaries=bounds, norm=norm) # Colorbar |
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| 68 | cbar.set_label('Wind Speed (m/s)', fontsize=fs) |
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| 69 | yticks = np.arange(0, 10500, 500) |
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| 70 | plt.yticks(yticks) |
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| 71 | plt.ylim(-500, 10500) |
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| 72 | plt.xlabel('Time (UTC)', fontsize=fs) |
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| 73 | plt.ylabel('Altitude AGL (m)', fontsize=fs) |
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| 74 | plt.grid('on') |
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| 75 | plt.title(vad_f.siteID+' Velocity Azimuth Display', fontsize=fs+3) |
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| 76 |
