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

Copyright (c) 2006-2007, Robert Hetland <hetland@tamu.edu> Release under MIT license.

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