- #MULTIPLE COLORS IN ROSE DIAGRAM HOW TO#
- #MULTIPLE COLORS IN ROSE DIAGRAM PRO#
- #MULTIPLE COLORS IN ROSE DIAGRAM CODE#
Select Title & logo and specify the title, its position and its color. We will try with a scale of 250, it will be modified if it is too big or too small. This scale indicates the number of drawing points for each unit of the variable plotted. Then specify a scale in the text box named Scale for different plots (where different means that we are not producing a wind rose). Select Dirs & type tab, then select Data within the frame Wind rose or data, and specify a point size (20 in the example). We will accept wind directions within the range, wind speeds within the range (m/s), and concentration values within the range (ug/m3).īe sure to set the filter on valid data before loading the Excel file, because they are filtered while loading. Invalid data within the example file are written as -999, therefore we need to discard such values. Select the Not valid tab and specify the range of acceptability of the data within the Excel file. Open WindRose PRO, click Options and execute the following operations: To create a directional graphic with 3 variables we will use the example file sample_3v, which is composed by 4 columns: Date and time, concentration of an air pollutant, wind direction and wind speed. This kind of plot can be used in air quality studies for estimating the presence of important sources of specific pollutants.
#MULTIPLE COLORS IN ROSE DIAGRAM PRO#
"""A bar plot colored by a scalar sequence."""īottom = itertools.cycle(np.In a 3-variables plot WindRose PRO represents the third variable with circles of different colors which are placed at a radial distance given by the wind speed (or any other directional variable), and at an angular coordinate given by the wind direction (measured from North and increasing clockwise). Z = np.array() for i in range(1, idx.max() + 1)])Ĭoll = colored_bar(edges, counts, z=z, width=np.diff(edges), # Convert to 0-360, in case negative or >360 azimuths are passed in.Ĭounts, edges = np.histogram(azimuths, range=, bins=bins) Their y-value (the number of azimuths measurements in that bin).Īdditional keyword arguments are passed on to PatchCollection.Īzimuths = np.concatenate() String "count" is passed in, the displayed bars will be colored by if True, 0 and 180 are identical).Ī function to reduce the binned z values with. Whether or not to treat the observed azimuths as bi-directional The number of bins or a sequence of bin edges to use. Defaults to the current axes.īins: int or sequence of numbers (optional) circular histogram).Ī second, co-located variable to color the plotted rectangles by. Plt.xlabel('A rose diagram colored by a second variable')ĭef rose(azimuths, z=None, ax=None, bins=30, bidirectional=False, Plt.colorbar(coll, orientation='horizontal') If you want a "full-featured" rose diagram function, you might do something like this: import itertoolsĬoll = rose(azi, z=z, bidirectional=True) (Alternately, you could pass in the ListedColormap that you created in your example.) Then you'll get a discrete colormap with 5 intervals.
#MULTIPLE COLORS IN ROSE DIAGRAM CODE#
For example, in the code above, if you replace the line cmap = plt.get_cmap('cool') with: cmap = plt.get_cmap('cool', 5) If you want a discrete color map, it's easiest to just specify the number of intervals you'd like when you call plt.get_cmap. Width = itertools.cycle(np.atleast_1d(width))īottom = itertools.cycle(np.atleast_1d(bottom))įor x, y, h, w in zip(left, bottom, height, width):Ĭoll = PatchCollection(rects, array=z, **kwargs) the values you want to color by) as the array kwarg.įrom llections import PatchCollectionĪx = fig.add_subplot(111, projection='polar')Ĭoll = colored_bar(x, y, z, ax=ax, width=np.radians(10), cmap=cmap)ĭef colored_bar(left, height, z=None, width=0.8, bottom=0, ax=None, **kwargs): The easiest way is to use a PatchCollection and pass in your "z" (i.e.
#MULTIPLE COLORS IN ROSE DIAGRAM HOW TO#
I have played around with the code so much and I just can't figure out how to normalize the colorbar correctly. Ticks= for i in range(0, len(bounds), 2)],Īs you can see, the colorbar is not quite right. Norm = (values, cpt.N-1)Ĭax = fig.add_axes()Ĭb = (cax, cmap=cpt, # Add colorbar, make sure to specify tick locations to match desired ticklabelsĬpt = (colorlist)
![multiple colors in rose diagram multiple colors in rose diagram](https://m.media-amazon.com/images/I/710WSyjOWIL._AC_SX425_.jpg)
# project strike distribution as histogram barsĬolors.append(cm.jet(r_values, alpha=0.5)) # force square figure and square axes looks better for polar, IMOĪx = fig.add_axes(, polar=True) Here is a part of it: angle = radians(10.)įor i, item in enumerate(some_array_of_azimuth_directions): My code mimics a "rose diagram" projection which is essentially a bar chart on a polar projection. How would one add a colorbar to this plot? In this example the color is correlative to the radius of each bar.