ngrid | It 's less '' for data | Data Manipulation library

Before you attempt to use res0.x, check res0.success. In this case, you'll find that it is False in each case. When res0.success is False, take a look at res0.message for information about why root failed.

During development and debugging, you might also consider getting the solver working for just one set of parameter values before you embed root in three nested loops. For example, here are a few lines from an ipython session (variables were defined in previous lines, not shown):

After some research, I found some approaches to transform a 2D Cartesian Gaussian into a polar Gaussian [1][2]. There are mainly 3 approaches with different python libraries: abel, OpenCV and Skimage. I tested abel and skimage libraries. To get the curved shape, you have either to move the Gaussian away of the centre from the original image or set the centre point in the transformation functions. In this case I did the first option. I prefer the skimage function (warp_polar), because the grid resolution can be adjusted in the function with the argument "output_shape".

In numpy, the multiplication symbol is always element-wise (like .* in Matlab). For matrix multiplication, use @.

Also, you can do exponentiation in Python with **.

Therefore, if you change the loop to the code below, it works.

That reference is out of date. The default number of dummy points is determined by default.n.tiling. This is an internal, undocumented function.

The source code for default.n.tiling can be consulted to find out the exact rules, but here is a sketch:

Currently the default minimum number of grid points in each dimension is the greater of

GridSearchCV.cv_results_ is a dictionary of numpy ndarrays (source). You are trying to cast 1 dictionary into 3 variables (params, avg_score and _). It probably worked in the past since grid_search.cv_results_ returned 3 objects, while current GridSearchCV.cv_results_ returns one dictionary.
It's very straight forward to convert the dictionary into a Pandas DataFrame.

cv_results_ is a dict that contains the results of the GridSearchCV. It looks something like this:

That means if you want to access it, you need to access it the same way you access a dictionary in Python:

For your question about using FV or CV:

• FV if it is a parameter value that can either be set to a fixed value or else adjusted by an optimizer to minimize an objective
• CV if it is a variable that is determined by solving equations. The CV is different than a regular Var or SV in that there are extra equations added to facilitate matching to available measurements.

From your description, it sounds like it should be an FV. As a first step in solving your problem, I recommend that you verify that you have the specifications correctly set by switching to m.options.IMODE=7 and solve. This will tell you if you have the same number of equations and variables and also initializes the model so that parameter estimation is faster.

If you use a CV then the squared error objective function is defined for you with m.options.EV_TYPE=2. You can also use m.Minimize() if you define the CV as a Var instead.

You need to store all city coordinates in order to display them on a single grid print. In the code below I changed a few things in order to hopefully address your problem.

• I have moved all city-related data into a structure
• Then all cities are initialized before the grid output
• When printing the grid, we have to search all cities if their coordinates match the current position, if so, we print the corresponding index.

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