I have a plot in which I have already plotted all my data and a "twined" axis, on which I'd like to use another scale, in this case dates. I also have a list of all the dates corresponding to each element of my data, and want to add an a scale for the dates to the twined axis.
For example, I have
ax2 = ax1.twinx()
and lists x_temporal_data, y_day_offsets, y_dates, all of the same length, and have already plotted the relationship between the first two with
ax1.plot(x_temporal_data, y_day_offsets)
and I just want to have a scale on ax2 for the dates in y_dates, since y_day_offsets and y_dates are "synonyms" for the same time information.
Is there a way to do this without "plotting" something I don't need to display (since all my data is already plotted). For example, I can get the dates to appear perfectly on ax2 with
ax2.plot(len(y_dates)*[some_random_out_of_xrange_value], y_dates)
but that seems like a hack: plotting nothing to "calibrate" the second axis.
Is there a better, more idiomatic way of accomplishing this?
Simply set the scale on the second y-axis to your liking with:
ax2.set_ylim([min(y_dates), max(y_dates)])
Related
I am using a line to estimate the slope of my graphs. the data points are in the same size. But look at these two pictures. the first one seems to have a larger slope but its not true. the second one has larger slope. but since the y-axis has different rate, the first one looks to have a larger slope. is there any way to fix the rate of y-axis, then I can see with my eye which one has bigger slop?
code:
x = np.array(list(range(0,df.shape[0]))) # = array([0, 1, 2, ..., 3598, 3599, 3600])
df1[skill]=pd.to_numeric(df1[skill])
fit = np.polyfit(x, df1[skill], 1)
fit_fn = np.poly1d(fit)
df['fit_fn(x)']=fit_fn(x)
df[['Hodrick-Prescott filter',skill,'fit_fn(x)']].plot(title=skill + date)
Two ways:
One, use matplotlib.pyplot.axis to get the axis limits of the first figure and set the second figure to have the same axis limits (using the same function) (could also use get_ylim and set_ylim, which are specific to the y-axis but require directly referencing the Axes object)
Two, plot both in a subplots figure and set the argument sharey to True (my preferred, depending on the desired use)
Given a tidy Pandas column with 4 or more columns, I want an otherwise very straightforward plot: two of the columns should be the x-y axes of a single figure, and one of the columns should index an Overlay of N Curve objects based on the x-y columns, and N Spread objects, using the final column as error. So if N=4 there should be 4 curves and four spreads. The curves and spreads with same index should be the same color, and the legend should attest to this.
Using table.to(hv.Curve,'col1','col2') I can get a Holomap for the curves, and with some effort I can do the same for the spread. If I then call .overlay() I get a nice figure for the curves including a legend, but when I do the same for the spread the legend vanishes. If I overlay the two, the legend likewise vanishes and the color cycle stops working, making all curves and spreads the same color. If I create a Holomap of curve*spread objects, then the colors match but the legend is still gone.
This seems like a very standard plot, but I can find very little in the Holoviews docs about pairing different Elements or controlling the legend.
This is a bit difficult to answer without any concrete code, for example I can't reproduce some of the issues you are describing. However the first issue is simply that show_legend is not enabled by default for the Spread elemen. In the case of plotting a Curve and Spread using .to and .overlay, here is what I can confirm works:
%%opts Spread [show_legend=True width=600] Overlay [legend_position='right']
df = pd.DataFrame({
'index': np.arange(100), 'y': np.random.randn(100).cumsum(),
'err': np.random.rand(100)+0.1, 'z': np.repeat(np.arange(10), 10)
})
ds = hv.Dataset(df)
ds.to(hv.Curve, 'index', 'y', 'z').overlay() * ds.to(hv.Spread, 'index', ['y', 'err']).overlay()
If I create a Holomap of curve*spread objects, then the colors match but the legend is still gone.
This is indeed a current limitation since we recommended against nesting objects in this way in the past, however I have just opened this PR which will allow this approach as well.
I make a plot with different subplots (using gridspec.GridSpec). Two subplots share the same x-axis (sharex=ax1 in the definition of the second subplot).
However, as one subplot shows the indices of the chronologically sorted data, and the second subplot shows the corresponding decades, I want seperate ticks and labels for the x-axes of both plots. This seems not possible, a unique set of labels and ticks are assigned to both subplots. Until now, I can only:
use different x-axes and thus assign two sets of ticks and labels.
In that case, the axes are not alligned although
ax1.set_xlim([start, stop]) are similarly defined for both subplots
use a common x-axis and one set of ticks and labels
I do not find a solution for this on the internet. Is someone able to help? Thank you in advance!
I'm trying to display 2D data with axis labels using both contour and pcolormesh. As has been noted on the matplotlib user list, these functions obey different conventions: pcolormesh expects the x and y values to specify the corners of the individual pixels, while contour expects the centers of the pixels.
What is the best way to make these behave consistently?
One option I've considered is to make a "centers-to-edges" function, assuming evenly spaced data:
def centers_to_edges(arr):
dx = arr[1]-arr[0]
newarr = np.linspace(arr.min()-dx/2,arr.max()+dx/2,arr.size+1)
return newarr
Another option is to use imshow with the extent keyword set.
The first approach doesn't play nicely with 2D axes (e.g., as created by meshgrid or indices) and the second discards the axis numbers entirely
Your data is a regular mesh? If it doesn't, you can use griddata() to obtain it. I think that if your data is too big, a sub-sampling or regularization always is possible. If the data is too big, maybe your output image always will be small compared with it and you can exploit this.
If you use imshow() with "extent" and "interpolation='nearest'", you will see that the data is cell-centered, and extent provided the lower edges of cells (corners). On the other hand, contour assumes that the data is cell-centered, and X,Y must be the center of cells. So, you need to be care about the input domain for contour. The trivial example is:
x = np.arange(-10,10,1)
X,Y = np.meshgrid(x,x)
P = X**2+Y**2
imshow(P,extent=[-10,10,-10,10],interpolation='nearest',origin='lower')
contour(X+0.5,Y+0.5,P,20,colors='k')
My tests told me that pcolormesh() is a very slow routine, and I always try to avoid it. griddata and imshow() always is a good choose for me.
The page is
"http://matplotlib.sourceforge.net/examples/pylab_examples/histogram_demo_extended.html"
Let's look at the y-axis, the numbers there do not make any sense, could we change it to something else that is meaningful?
Except the cumulative distribution plot, and the last one, the rest of the y-axes data show normalized histogram values with normed=1 keyword set (i.e., the are underneath the histogram equals to 1 as in the definition of a probability density function (PDF))
You can use yticks(), see this example.