Interp | Interpolation Add-in for EXCEL | Data Manipulation library

I am looking for a way to return the interpolated coordinate value of a variable at a query variable value. I think it is the opposite use to a normal interpolation/table-look up.

For example, here is a simple, normal interpolation, From this very good tutorial:

I have data.table as follows and I want to smooth the z-axis so that it creates a smooth surface when plotted on the x and y-axis.

I want to interpolate a 3D array along the first dimension.

In terms of data, it means I want to interpolated missing times in a geographic value, in other terms smoothing a bit this animation:

I do this by calling:

I want to compile this C code with the GNU C Compiler on Ubuntu without linking any standard libraries, having only the following code execute.

I would like to know if there is a Python functionality in either Numpy or SciPy that allows to shift arrays over non-uniform grids. I have created a minimal example to illustrate the procedure, but this does not seem to work in this minimal example:

I have a program where I want the user to choose a temperature (T_user), whatever he wants. Knowing that I have a temperature array: T=np.array([10,20,30,50,100,150,200]). I have found a way to get the index and closest value for T_user compared to the values in T. I then have X=np.array([1,2,3,4,5,6]) and Y=np.array([3,6,9,12,15,18]) using numpy.interp(). Now lets say that T_user=12 and that X,Y are respectively linked to T[0]=10 and T[1]=20 how can I create a new array interpolated with X,T arrays and "the distance"/ratio from T_user to T[0]=10 and T[1]=20. Let me know if this is not clear at all.

I have a numerical routine that I need to run to solve a certain equation, which contains a few nested four loops. I initially wrote this routine into Python, using numba.jit to achieve an acceptable performance. For large system sizes however, this method becomes quite slow, so I have been rewriting the routine into Fortran hoping to achieve a speed-up. However I have found that my Fortran version is much slower than the first version in Python, by a factor of 2-3.

I believe the bottleneck is a linear interpolation function that is called at each innermost loop. In the Python implementation I use numpy.interp, which seems to be pretty fast when combined with numba.jit. In Fortran I wrote my own interpolation function, which reads,

I am working with NASA-NEX-GDDP CMIP6 data. I currently have working code that individually opens and slices each file, however it takes days to download one variable for all model outputs and scenarios. My goal is to have all temperature and precipitation data for all models outputs and scenarios then apply climate indicators and make an ensemble with xclim.

I've recently found a code in StackOverflow which inherits the Text class to add -textvariable option to it (as Text widget originally has no -textvariable option) and another code which also inherits the Text class to add a scrollbar to it by default (I found it from source code of Tkinter).

Text with scrollbar: