Alexey Pechnikov

pgr_TSP() argument "end_id" doesn't work on MacOS and Ubuntu for PgRouting versions 2.6.2 ... 3.0.1

16

**Describe the bug** On MacOS in resulting routing sequence 1...N addresses the "end_id" node is N-1 sequential address instead of N. So we can't define the last address. **To Reproduce**...

ST7789 display connect to custom pins

15

I'm trying v1.18.8 to use ST7789 with custom pins: but it doesn't work: ``` import st7789 st7789.ST7789(135, 240, rotate180=False, slot=0, cs=5, dc=1) > ST7789(spi = 0, cs = 17, dc...

Garbage output

1

Is it possible to suppress the output? The library prints a lot of useless messages instead of some kind of the results indication: ``` make: Nothing to be done for...

Geo paths cannot be colorized

2

It works for 2 point lines only: ``` df = gpd.GeoDataFrame({'mission': ['A','B']}, geometry=[ shapely.geometry.LineString([(0,0), (1,0)]), shapely.geometry.LineString([(0,0), (1,1)]), ]) df.hvplot(geo=True, c='mission') ``` But it doesn't work for 3+ point lines: ```...

Cache receiving stalled

2

The same log line repeating indefinitely for 40+ minutes: ```Received 1653791515 of 1662180123 (99.5%)``` ``` - name: Cache dataset uses: actions/cache@v3 with: path: tests/S1A_Stack_CPGF_T173.tar.gz key: S1A_Stack_CPGF_T173.tar.gz restore-keys: S1A_Stack_CPGF_T173 ``` This...

I develop PyGMTSAR (Python InSAR) Sentinel-1 processor and time-series analyzer (SBAS, PSI, and beyond) that operates effectively on large Sentinel-1 SLC stacks with 200+ scenes using common hardware like an...

Module st7789 problems

9

1. Module st7789 exists only in [Version 1.18.8 - Beta Bonanza](https://github.com/pimoroni/pimoroni-pico/releases/tag/v1.18.8) but it doesn't work properly. 2. Module st7789 on GitHub documented as totally different one: https://github.com/pimoroni/st7789-python The examples like...

LinearRegression doesn't return lazy object

2

LinearRegression requires even longer time than sklearn version and it doesn't return a lazy object: ``` %%time from sklearn.pipeline import make_pipeline from dask_ml.linear_model import LinearRegression size = 1e6 X =...

dask_ml.linear_model.LinearRegression doesn't work for 1e7 elements (while it works for size = 1e6): ``` from sklearn.pipeline import make_pipeline from dask_ml.preprocessing import StandardScaler from dask_ml.linear_model import LinearRegression size = 1e7 X...

NaN values can't be used to fit a model and usually should be excluded. But dask_ml allows NaNs just to return wrong output: ``` from sklearn.pipeline import make_pipeline from dask_ml.preprocessing...