tropy.plotting_tools package¶

Submodules¶

tropy.plotting_tools.bmaps module¶

tropy.plotting_tools.colormaps module¶

tropy.plotting_tools.colormaps.colorname_based_cmap(colname, start_col=None, final_col=None, reverse=False)¶

A matplotlib colormap is constructed based on one color’

cmap = colorname_based_cmap(colname,: start_col = None, final_col = None, reverse = False)

colname : supported colorname as basis for colormap start_col : color at one end of the colormap final_col : color at the other end of the colormap reverse: option if order is reversed

cmap: resulting colormap

tropy.plotting_tools.colormaps.dwd_sfmap()¶

tropy.plotting_tools.colormaps.enhanced_colormap(vmin=200.0, vmed=240.0, vmax=300.0)¶

tropy.plotting_tools.colormaps.enhanced_wv62_cmap(vmin=200.0, vmed1=230.0, vmed2=240.0, vmax=260.0)¶

tropy.plotting_tools.colormaps.nice_cmaps(cmap_name)¶

tropy.plotting_tools.compare module¶

tropy.plotting_tools.compare.compare(*args, **kwargs)¶

The function compare can be used to fastly compare several 2d fields.

The coordinates of the x and y axis are needed. An arbitrary number of fields is plotted using the function shaded. Corresponding option keywords can be used.

tropy.plotting_tools.compare.compare_shaded(x, y, *args, **kwargs)¶

The function compare can be used to fastly compare several 2d fields.

The coordinates of the x and y axis are needed. An arbitrary number of fields is plotted using the function shaded. Corresponding option keywords can be used.

tropy.plotting_tools.histograms module¶

Module for histogram calculations and plotting.

Histogram calculations include * partial normalization to get marginal distributions * conditional averages, standard deviations and percentiles

Histogram plotting is for * conditioned histograms with averages or percentiles

tropy.plotting_tools.histograms.ave_sig_from_hist(xe, ye, h)¶

Use output from the numpy 2d histogram routine to calculate y-mean and standard deviations along y direction.

Parameters

xe (np.array) – x-part of histogram grid (edge values)
ye (np.array) – y-part of histogram grid (edge values)
h (np.array) – frequency of occurence

Returns

yave (np.array) – mean of y weigthed by h along y-direction
ysig (np.array) – standard deviation of y weigthed by h along y-direction

tropy.plotting_tools.histograms.axis_average_from_hist3d(bins3d, h, axis=0)¶

Calculates the average of a 3-dim histogram along a selected axis.

Parameters

bins3d (list of 3dim np.array) – mesh of bin edges
h (np.array) – absolute histogram counts
axis ({0, 1}, optional) – axis along which the calculations are peformed

Returns

ave – conditional average field

Return type

np.array

tropy.plotting_tools.histograms.conditioned_hist(h, axis=0)¶

Make conditioned histogram.

Prob per bin (not PDF!!!).

Parameters: h (np.array) – absolute frequency of occurence
Returns: “normalized” frequency
Return type: np.array

tropy.plotting_tools.histograms.hist2d_scatter(x, y, bins=200, **kwargs)¶

A 2d histogram is constructed and displayed as scatter plot.

Parameters

x (np.array) – 1st data vector
y (np.array) – 2nd data vector

Returns

hxy (np.array) – frequency of occurence
xs (np.array) – x-part of histogram grid (edge values)
ys (np.array) – y-part of histogram grid (edge values)

tropy.plotting_tools.histograms.hist3d(v1, v2, v3, bins)¶

A wrapper for 3d histogram binning.

It additionally generates the average bin values with the same 3d shape as histogram itself.

Parameters

v1 (np.array) – 1st data vector
v2 (np.array) – 2nd data vector
v3 (np.array) – 3rd data vector
bins (list of 3 np.arrays) – bins argument passed to the np.histogramdd function

Returns

bins3d (list of 3dim np.array) – mesh of bin edges
h (np.array) – absolute histogram counts

tropy.plotting_tools.histograms.max_from_hist(xe, ye, h)¶

Use output from the numpy 2d histogram routine to calculate y-positions where maximum occurences are located.

Parameters

xe (np.array) – x-part of histogram grid (edge values)
ye (np.array) – y-part of histogram grid (edge values)
h (np.array) – frequency of occurence

Returns

ymax – y-position where h is maximal along y-direction

Return type

np.array

tropy.plotting_tools.histograms.percentiles_from_hist(xe, ye, h, p=[25, 50, 75], axis=0, sig=0)¶

Use output from the numpy 2d histogram routine to calculate percentiles of the y variable based on relative occurence rates.

Parameters

xe (np.array) – x-part of histogram grid (edge values)
ye (np.array) – y-part of histogram grid (edge values)
h (np.array) – frequency of occurence
p (list, optional, default = [25, 50, 75]) – list of percentile values to be calculated (in 100th)
axis ({0, 1}, optional) – axis along which the calculations are peformed
sig (float, optional, default = 0) –
signa of a Gaussian filter apllied to the histogram in advance

Should be non-negative.

Returns

yperc – list of arrays containing the percentiles

Return type

np.array

tropy.plotting_tools.histograms.plot_cond_hist(xe, ye, h, ax, axis=0, logscale=True, **kwargs)¶

Plot conditioned histogram (marginal distribution).

Parameters

xe (np.array) – x-part of histogram grid (edge values)
ye (np.array) – y-part of histogram grid (edge values)
h (np.array) – frequency of occurence
ax (plt.axes instance) – a current axes where the plot is placed in
axis ({0, 1}, optional) – axis along which the calculations are peformed
logscale ({True, False}, optional) – if histogram is plotted with logarithmic colorscale
**kwargs (dict) – further optional keywords passed to plt.pcolormesh

Returns

pcm

Return type

plt.pcolormesh instance

tropy.plotting_tools.histograms.plot_hist_median_and_intervals(xe, ye, h, ax)¶

Plot histogram with median and IQR. (axis = 1, fixed).

Parameters

xe (np.array) – x-part of histogram grid (edge values)
ye (np.array) – y-part of histogram grid (edge values)
h (np.array) – frequency of occurence
ax (plt.axes instance) – a current axes where the plot is placed in

Returns

ax – a current axes where the plot is placed in

Return type

plt.axes instance

tropy.plotting_tools.meta2png module¶

Module designed to write an Author Name and the Source Filename into the Meta-Data of an PNG file saved with matplotlib.

tropy.plotting_tools.meta2png.meta2png(fname, meta)¶

Saves meta data into image file.

Parameters

fname (str) – name of png file
meta (dict) – collection of extra meta data to be stored in image file

class tropy.plotting_tools.meta2png.pngsave(*args, **kwargs)¶

Bases: object

That class is designed to save pylab figures in png and add meta data.

Parameters

*args (list) – other positional arguments passed to plt.savefig
**kwargs (dict) –
other optional arguments passed to plt.savefig`

’author’ : Place yeur name into author keyword

’source’Specify your source filename if needed,
if not set, it tries to automatically find the filename

meta()¶: Set the meta data, esp. the Author name and Source file information.

tropy.plotting_tools.shaded module¶

A module to make shaded plots.

It contains a function for non-linear colormaps.

class tropy.plotting_tools.shaded.nlcmap(cmap, levels)¶

Bases: matplotlib.colors.LinearSegmentedColormap

A nonlinear colormap class.

Parameters

cmap (matplotlib.cmap) – a matplotlib colormap, e.g. matplotlib.cm.jet
levels (list or np.array) – list of values where different colors should appear

Notes

Derived from the matplotlib.colors.LinearSegmentedColormap

name = 'nlcmap'¶

tropy.plotting_tools.shaded.set_levs(nmax, depth, largest=5, sym=True, sign=True, zmax='None')¶

Makes a non-linear level set based on pre-defined numbers.

Parameters

nmax (int) – exponent of maximum number
depth (int) – number of iterations used down to scales of 10**(nmax - depth)
largest ({5, 8}, optional) – set the largest number in the base array either to 5 or 8
sym ({True, False}, optional) – switch if levels are symmetric around origin
sign ({True, False}, optional) – switches sign if negative
zmax (float, optional, default = 'none') – limiter for levels, |levs| > zmax are not allowed

Returns

levs – set of non-linear levels

Return type

np.array

tropy.plotting_tools.shaded.shaded(x, y, z, *args, **kwargs)¶

The function shaded is a wrapper for the pylab function contourf.

In addition to contourf, shaded can plot filled contours for which a non-linear colormap is used.

Parameters

x (np.array) – x-values passed to plt.contourf
y (np.array) – y-values passed to plt.contourf
z (np.array) – z-values passed to plt.contourf (color value)
*args (list) – other positional arguments passed to plt.contourf
**kwargs (dict) –
other optional arguments passed to plt.contourf

special keywords:
- ’levels’ : numpy array of color / contour levels
- ’lev_depth’ : gives the depth of an automatically generated level set i.e. an initial base (e.g. [1,2,3,5] ) that contains the maximum value of the field (e.g. 4.3) is downscaled by 10**-1, …, 10**-lev_depth.
  
  Example: Given lev_depth = 2, for a positive field with maximum 4.3 a level set [0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5] is generated.

Returns

Return type

pl.contourf instance