swim | Full stack application platform for building stateful | Architecture library
kandi X-RAY | swim Summary
kandi X-RAY | swim Summary
SwimOS is a full stack application platform for building stateful web services, streaming APIs, and real-time UIs.
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Top functions reviewed by kandi - BETA
- Split this node with the given version .
- Inflates the code table
- Runs reflection command .
- Get the state of this message .
- Computes Adler - 32 bits from the given buffer .
- This function is used to process the input block .
- Pull out the envelope .
- Remove a key from the cache .
- Do the actual write operation .
- Commit a chunk .
swim Key Features
swim Examples and Code Snippets
Lines of Code : 10
License : No License
Copydef swim(self, pos: int) -> None:
"""Restore queue's order by moving up current element until
parent element's priority is higer than current element.
Args:
pos (int): current element index
"""
def swim(self, pos: int) -> None:
"""Restore queue's order by moving up current element until
parent element's priority is lower than current element.
Args:
pos (int): current element index
"""
def swim(self, pos: int) -> None:
"""Restore queue's order by moving up current element until
parent element's priority is higher than current element.
Args:
pos (int): current element index
"""
Community Discussions
Trending Discussions on swim
QUESTION
I have a data frame with 3 columns I want to display using geom_raster where the x-axis, y-axis, and fill all contain categorial values. The goal with my original dataset is to show the overlapping data with fills and to show where no data is held for the x-axis. For some combinations of the x and y, multiple fills may apply, however, the geom_raster is only filling the cell on the last occurrence of the fill. Instead, I would like to stack all fills similar to a percent stacked barplot where if only one fill applies, then fill the entire cell with that color. However, if 3 fill values apply, then fill its stack equally among all three colors i.e. 33% each.
For my example dataset, I simulated 2 triathlon races between 26 people where they were assigned a speed of first, last, or second (156 individual events = 26 people x 3 events x 2 races). I then randomly assigned non-finishes by assigning NA values to 26 random places. I then create a raster that shows each person's place for each event for the combined races and it colors based on if they finished 'first', 'second', or 'last. This also results in empty (white) cells if they did not place for the event. With the x axis as the person and the y axis as the 3 events in a triathlon, this creates a raster with 78 cells. My goal for this minimum example, is to stack the fills of each cell based on their finishes for each race. Since this dataset contains 2 triathalon races, there are 2 possible fills for each cell. If a person places the same for a given event, then that should be the same color. If they place differently then the cell should be divided in 2 with one color for each place. Currently, the code fills the entire cell based on the last occurring value (e.g. their place for the second race). For example person 'A' placed second in biking for the first race and did not finish in the second race, placed first in run for the first race and last for the second race, and placed second in swimming for the first race and did not place in the last race. However, the raster is showing that person A did not finish for swimming, placed last for running, and did not finish for biking. How can I stack fills for cells in a geom_raster?
An important part I want in the code is to make sure the size of the cell is all the same. So if someone competed in 10 races they would have 10 possible fills for their cell, while someone competeing in 3 will only have 3. But the overall size of their cell should be the same. Or for example with the above dataset, if we removed the places were they did not finish, df %>% drop_na(), the cells with 2 values should be the same size as the cells with 1 value.
Reproducible Code
...ANSWER
Answered 2022-Apr-16 at 22:52What you are describing can't be created by using geom_raster, at least without a fair bit of data manipulation. I would use geom_col, with a position_fill, and make the y axis out of facets:
QUESTION
So, I have the following code
...ANSWER
Answered 2022-Mar-30 at 21:34If I understood you correctly try like following snippet:
QUESTION
I have a sample dataframe as below
...ANSWER
Answered 2022-Mar-29 at 18:47Remove the .vocab here in model_glove.vocab, this is not supported in the current version of gensim any more: Edit: also needs split() to iterate over words and not characters here.
QUESTION
I was recently tasked with 'exploding' each row in a given range with respect to the split value of one of the columns, i.e. going from
Name Interests Age John swimming, movies 31 Mary basketball 26 Richard football, music 21to:
Name Interest Age John swimming 31 John movies 31 Mary basketball 26 Richard football 21 Richard music 21It's a little similar to a Cartesian product, only one of the terms needs to be computed on the basis of the value in the Interests column. I eventually solved it using an Apps Script function, but I'm wondering if it could be easily solved using a regular formula.
Note that the input range in my case was a product of another formula (a QUERY(...), to be exact), so not necessarily contiguous or addressable within the spreadsheet.
Any ideas?
...ANSWER
Answered 2022-Mar-07 at 18:42You can use the custom "UNPIVOT" function found on this sheet. File>Make a Copy to grab the script. Also here on github.
QUESTION
In PowerShell, I want to get a count of all sports that use balls. The challenge is that they're a level lower than I can access with a direct Where-Object query. Here's the hashtable:
...ANSWER
Answered 2022-Mar-03 at 21:40Using .where(..):
QUESTION
I am not sure how to best describe this (I am sure there is a more proper way of describing it).
I have a large dataset full of house details (eg. walls, bathrooms, bedrooms, etc.) that I need to analyze and rank based on their characteristics. I have created a ranking system with "4" being the best and "0" being the worst, for example, a house with 1 bedroom may get a "0" for their bedroom score but a house with a 3 bathrooms may get a "4" for their bathroom score.
Once I assocaite the ranks to all the characteristics, I plan on creating a weighted average to see which houses are the best.
How is the best way to do this? I need to do this about 20 times (for 20 characteristics) and so far this is the only way I know how to do it-- and it is quite tedious, especially if I ever need to go back and change anything.
Also, would be good to better understand how the df.loc function works, I was able to do make it work but I don't quite understand it.
...ANSWER
Answered 2022-Feb-26 at 21:24I'll do this for land_use, hope you get the idea.
See https://pandas.pydata.org/docs/reference/api/pandas.Series.map.html for more details
QUESTION
Let's assume we have a DataFrame(df) defined below in PySpark. And, how to use PySpark to get the duration between the first biking action and the last biking action within the same day. And save the results into a date framework including first_biking_timedeatails, last_biking_timedeatails, durations_bewteween_first_last, etc. Notes: there can be other actions between the first and last biking action. And, if there is only one biking action within a day, then we should not get the duration (since we will not able to do the calculation, such as date 3/3/18)
Below is the example result for the date 3/01/2018:
duration_03_01 = 13:12 (last biking time) - 5:12 (first biking time) = 8 hours
Sample df below:
timedeatils actions 3/1/18 5:12 Biking 3/1/18 6:12 Running 3/1/18 7:12 Swimming 3/1/18 8:12 Running 3/1/18 9:12 Swimming 3/1/18 10:12 Biking 3/1/18 11:12 Biking 3/1/18 12:12 Running 3/1/18 13:12 Biking 3/2/18 4:12 Biking 3/2/18 5:12 Swimming 3/2/18 6:12 Running 3/2/18 7:12 Biking 3/2/18 8:12 Running 3/3/18 4:16 Biking 3/4/18 5:13 Running 3/4/18 6:13 Biking 3/4/18 7:13 Running 3/4/18 8:13 Swimming 3/4/18 9:13 Running 3/4/18 10:13 Running 3/4/18 11:13 BikingSome of my code
...ANSWER
Answered 2022-Feb-25 at 10:37Your df:
QUESTION
Assume we have a DataFrame(df) below in PySpark. And, how to use PySpark to get the duration (in mins) between each biking event and the driving event? And, assume for each biking will have only one corresponding driving event; However, within a day there could be multiple "biking - driving" pairs. Eventually, store the final results into data frame, including biking__time, biking_event, driving_time, driving_event, and each_durations, etc)
Notes: there can be other events between biking and driving, such as a person can start from biking, running, swimming then driving.
One example refers to below table:
The duration of date 03/01/2018 between biking and driving is: 8:12 - 5:12 = 3 hours = 180 mins
TimeDetails Event 1 3/1/2018 5:12 Biking 2 3/1/2018 6:12 Swimming 3 3/1/2018 7:12 Hiking 4 3/1/2018 8:12 Driving 5 3/2/2018 9:12 Biking 6 3/2/2018 10:12 Swimming 7 3/2/2018 11:12 Swimming 8 3/2/2018 12:12 Driving 9 3/2/2018 13:12 SwimmingBelow is the sample output:
biking_time event_name1 driving_time event_name2 durations_inMins 1 3/1/2018 5:12 biking 3/1/2018 8:12 driving 180 2 3/2/2018 9:12 biking 3/2/2018 12:12 driving 180Below is some of my code:
...ANSWER
Answered 2022-Feb-25 at 10:28Your exemple (I added another day with a missing Driving record - the solution will now also handle that):
QUESTION
I'm trying to understand behavior of function based composition in JavaScript.
...ANSWER
Answered 2022-Feb-19 at 06:15MDN Documentation for object.assign shows you how to copy "accessors"
Here's your code that works as expected - the completeAssign function is based entirely on the code in that link
QUESTION
I would like to split a tensorflow2 dataset into two datasets such that one contains "features" and another one contains "labels".
In the dataset, each element is a python dictionary:
...ANSWER
Answered 2022-Feb-23 at 10:19IIUC, you can try something like this:
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