footprint | 利用echarts展示旅行足迹 | Chart library

I have source (src) image(s) I wish to align to a destination (dst) image using an Affine Transformation whilst retaining the full extent of both images during alignment (even the non-overlapping areas).

I am already able to calculate the Affine Transformation rotation and offset matrix, which I feed to scipy.ndimage.interpolate.affine_transform to recover the dst-aligned src image.

The problem is that, when the images are not fuly overlapping, the resultant image is cropped to only the common footprint of the two images. What I need is the full extent of both images, placed on the same pixel coordinate system. This question is almost a duplicate of this one - and the excellent answer and repository there provides this functionality for OpenCV transformations. I unfortunately need this for scipy's implementation.

Much too late, after repeatedly hitting a brick wall trying to translate the above question's answer to scipy, I came across this issue and subsequently followed to this question. The latter question did give some insight into the wonderful world of scipy's affine transformation, but I have as yet been unable to crack my particular needs.

The transformations from src to dst can have translations and rotation. I can get translations only working (an example is shown below) and I can get rotations only working (largely hacking around the below and taking inspiration from the use of the reshape argument in scipy.ndimage.interpolation.rotate). However, I am getting thoroughly lost combining the two. I have tried to calculate what should be the correct offset (see this question's answers again), but I can't get it working in all scenarios.

Translation-only working example of padded affine transformation, which follows largely this repo, explained in this answer:

I am working on a large Pandas DataFrame which needs to be converted into dictionaries before being processed by another API.

The required dictionaries can be generated by calling the .to_dict(orient='records') method. As stated in the docs, the returned value depends on the orient option:

Returns: dict, list or collections.abc.Mapping

Return a collections.abc.Mapping object representing the DataFrame. The resulting transformation depends on the orient parameter.

For my case, passing orient='records', a list of dictionaries is returned. When dealing with lists, the complete memory required to store the list items, is reserved/allocated. As my dataframe can get rather large, this might lead to memory issues especially as the code might be executed on lower spec target systems.

I could certainly circumvent this issue by processing the dataframe chunk-wise and generate the list of dictionaries for each chunk which is then passed to the API. Furthermore, calling iter(df.to_dict(orient='records')) would return the desired generator, but would not reduce the required memory footprint as the list is created intermediately.

Is there a way to directly return a generator expression from df.to_dict(orient='records') instead of a list in order to reduce the memory footprint?

I am working on submitting an R package to CRAN. Right now I am trying to reduce the memory footprint of the package. Because this package deals with spatial data that has a very particular format, I want to include a properly formatted shapefile as an example. If I include the full-size original shapefile, there are no warnings (other than file size) in the R CMD checks. However, if I crop the file and include the cropped version in the package (in "inst/extdata") I get this warning:

The code calculates the minimum value at each row and picks the next minimum by scanning the nearby element on the same and the next row. Instead, I want the code to start with minimum value of the first row and then progress by scanning the nearby elements. I don't want it to calculate the minimum value for each row. The outputs are attached.

I am facing an issue with plotting points in a time series since I cannot identify the y-axis value. I have 2 datasets: one NetCDF file with satellite data (sea surface temperature), and another CSV file with storm track data (time, longitude, latitude, wind speed, etc.). I can plot the desired temperature time series for all storm track locations located in the ocean. However, I want to indicate the time of the storm footprint occurrence within each time series line. So, one line represents one location and the changing temperature over time, but I also want to show WHEN the storm occurred at that location.

This is my code so far (it works):

In this programming problem, the input is an n×m integer matrix. Typically, n≈ 10⁵ and m ≈ 10. The official solution (1606D, Tutorial) is quite imperative: it involves some matrix manipulation, precomputation and aggregation. For fun, I took it as an STUArray implementation exercise.

I have managed to implement it using STUArray, but still the program takes way more memory than permitted (256MB). Even when run locally, the maximum resident set size is >400 MB. On profiling, reading from stdin seems to be dominating the memory footprint:

Functions readv and readv.readInt, responsible for parsing integers and saving them into a 2D list, are taking around 50-70 MB, as opposed to around 16 MB = (10⁶ integers) × (8 bytes per integer + 8 bytes per link).

Is there a hope I can get the total memory below 256 MB? I'm already using Text package for input. Maybe I should avoid lists altogether and directly read integers from stdin to the array. How can we do that? Or, is the issue elsewhere?

I have the request which works well in postman:

and I'm trying to make it with Retrofit. In general file sizes will be >500MB that. I did such uploading method:

I have a struct defined that is used for messages sent across two different interfaces. One of them requires 32-bit alignment, but I need to minimize the space they take. Essentially I'm trying to byte-pack the structs, i.e. #pragma pack(1) but ensure that the resulting struct is a multiple of 32-bits long. I'm using a gcc arm cross-compiler for a 32-bit M3 processor. What I think I want to do is something like this:

I have a model that trains just fine on a single GPU. But I'm getting CUDA memory errors when I switch to Pytorch distributed data parallel (DDP). Specifically, the DDP model takes up twice the memory footprint compared to the model with no parallelism. Here is a minimal reproducible example: