I have a pandas data frame containing location data (x_m and y_m) and another variable represented by the color bar in the figure.
Sample figure showing the data points and a possible gradient arrow
How can I obtain the average gradient of all of the data points in my data set? I drew one of the possible solutions showing the gradient vector.
Thank you!
EDIT:
I ended up using scipy.interpolate.griddata, similar to what was done here: https://earthscience.stackexchange.com/questions/12057/how-to-interpolate-scattered-data-to-a-regular-grid-in-python
Related
I have a polydata file containing 3D coordinates of the mesh (as vtkPoints) and temperature at each point as an attribute. I want to plot the temperature as a slice plot (at three elevations) over the geometry. I managed to get the data slices at different elevation using vtkClipPolyData function. However, I am unable to find a good example showing how to interpolate the value at each of these points and plot the data. Really appreciate if someone can help me on this.
I tried to render the clipped data directly by increasing the point size from actor property,
actor.GetProperty().SetPointSize(5)
however, this gives a pixelated plot. See plot here
I want to extract data points from a graph that is taken from the specification of a Si-photodiode, as you can see from the graph, the graph shows the sensitivity of the diode in some spectral ranges, I need to get the value of the sensitivity at the wavelength of 336 nm, is there any way to do that in Python? I saw a video that describes the similar feature of Matlab: https://www.youtube.com/watch?v=9P3PVLjW0bw, it would be very useful if someone could give some tips or suggestions about how to do that in Python.
The dataset consist of 4000+ records. Here , trying to identify anomaly in 'duration' attribute. However, when the box plot is drown, can find that it is highly skewed. Tried to transform data, however results are not got. Attaching the boxplot below. How should we proceed in these cases.
Boxplot
What you could do is create a histogram of your plot and try to fit a distribution on your data. Suppose you were able to fit a standard normal distribution on your data, then you could read off anomalies in your data by checking the probability of the sample in your distribution. If this probability is smaller than a threshold probability p, then you could mark it as an anomoly.
I created a graph in MATLAB (see figure below) such that around every data point there is a data distribution plotted (grey area plots). The way I did it in MATLAB was to create a set of axes for every distribution curve and then plot the curves without showing those axes at every point of the data curve. I also used a command 'linkaxes' to set figure limits for all the curves at once.
I must say that this is far from an elegant solution and I had many troubles with saving this figure in the correct aspect ratio settings. All in all I couldn't find any other useful option in MATLAB.
Is there a more elegant solution for such types of graphs in Python? I am not that much interested in how to do the areas highlighted, but how to place a set of curves(distributions) exactly at the positions of the main data curve points.
Thank you!
I have a huge data set of time series data. In order to visualise the clustering in python, I want to plot time series graphs along with the dendrogram as shown below.
I tried to do it by using subgrid2plot() function in python by creating two subplots side by side. I filled first one with series graphs and second one with dendrograms. But once number of time series increased, it became blur.
Can someone suggest a nice way to plot this type of dendrogram? I have around 50,000 time series to cluster and visualise.
Convert data into JSON with json module of python and then use D3.js for graph ploting.
Check the Gallery from here where you can find dendrogram and time series graph