Repeat | Cross-platform mouse/keyboard record/replay and automation hotkeys/macros creation, and more advance | Automation library
kandi X-RAY | Repeat Summary
kandi X-RAY | Repeat Summary
This runs on any platform that supports Java and is non [headless] AutoHotkey is written for Windows only, and AutoKey is only for Linux. Repeat works on Linux, Windows, and OSX. The written macro can be re-used cross platforms. The only limit to your hotkey power is your knowledge of the language you write your tasks in (e.g. Java, Python or C#). You don’t have to learn a new meta language provided by AutoHotkey. This allows you to leverage your expertise in the language chosen and/or the immense support from the internet.
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Top functions reviewed by kandi - BETA
- Extracts data from the configuration
- Parses the compiler settings
- Creates a RepeatsPeerServiceClient from a JSON node
- Parse ipc settings
- Extract data from a JSON node
- Parses the compiler settings
- Creates a RepeatsPeerServiceClient from a JSON node
- Parse ipc settings
- Process message
- Send a message to the given output stream
- Identify a processor
- Get the source code
- Handles a request to see if it is allowed or not
- Handles the allowed request
- Handles the request activation
- Handles a task action
- Process incoming request
- Convert the version information from the previous version to the JSON output
- Handles the request that is allowed by the client
- Override handleBackend
- Starts the server
- Handle incoming request
- Starts the launcher
- Returns the body source of the body
- Converts the state of the version to the activation state
- Adds a request to the backend page
- Main loop
- Handles a single run request
- Converts the previous version into a JSON node
Repeat Key Features
Repeat Examples and Code Snippets
Lines of Code : 3
License : No License
Copydef n_times_string(s, n):
return (s * n)
n_times_string('py', 4) #'pypypypy'
def repeat_with_axis(data, repeats, axis, name=None):
"""Repeats elements of `data`.
Args:
data: An `N`-dimensional tensor.
repeats: A 1-D integer tensor specifying how many times each element in
`axis` should be repeated. `len(re def repeat_elements(x, rep, axis):
"""Repeats the elements of a tensor along an axis, like `np.repeat`.
If `x` has shape `(s1, s2, s3)` and `axis` is `1`, the output
will have shape `(s1, s2 * rep, s3)`.
Args:
x: Tensor or variable.
def repeat(input, repeats, axis=None, name=None): # pylint: disable=redefined-builtin
"""Repeat elements of `input`.
See also `tf.concat`, `tf.stack`, `tf.tile`.
Args:
input: An `N`-dimensional Tensor.
repeats: An 1-D `int` Tensor. T Community Discussions
Trending Discussions on Repeat
QUESTION
I am working on a spatial search case for spheres in which I want to find connected spheres. For this aim, I searched around each sphere for spheres that centers are in a (maximum sphere diameter) distance from the searching sphere’s center. At first, I tried to use scipy related methods to do so, but scipy method takes longer times comparing to equivalent numpy method. For scipy, I have determined the number of K-nearest spheres firstly and then find them by cKDTree.query, which lead to more time consumption. However, it is slower than numpy method even by omitting the first step with a constant value (it is not good to omit the first step in this case). It is contrary to my expectations about scipy spatial searching speed. So, I tried to use some list-loops instead some numpy lines for speeding up using numba prange. Numba run the code a little faster, but I believe that this code can be optimized for better performances, perhaps by vectorization, using other alternative numpy modules or using numba in another way. I have used iteration on all spheres due to prevent probable memory leaks and …, where number of spheres are high.
ANSWER
Answered 2022-Feb-14 at 10:23Have you tried FLANN?
This code doesn't solve your problem completely. It simply finds the nearest 50 neighbors to each point in your 500000 point dataset:
QUESTION
I have an array of positive integers. For example:
...ANSWER
Answered 2022-Feb-27 at 22:44This problem has a fun O(n) solution.
If you draw a graph of cumulative sum vs index, then:
The average value in the subarray between any two indexes is the slope of the line between those points on the graph.
The first highest-average-prefix will end at the point that makes the highest angle from 0. The next highest-average-prefix must then have a smaller average, and it will end at the point that makes the highest angle from the first ending. Continuing to the end of the array, we find that...
These segments of highest average are exactly the segments in the upper convex hull of the cumulative sum graph.
Find these segments using the monotone chain algorithm. Since the points are already sorted, it takes O(n) time.
QUESTION
Haskell provides a convenient function forever that repeats a monadic effect indefinitely. It can be defined as follows:
ANSWER
Answered 2022-Feb-05 at 20:34The execution engine starts off with a pointer to your loop, and lazily expands it as it needs to find out what IO action to execute next. With your definition of forever, here's what a few iterations of the loop like like in terms of "objects stored in memory":
QUESTION
I wrote some code in https://github.com/p6steve/raku-Physics-Measure that looks for a Measure type in each maths operation and hands off the work to non-standard methods that adjust Unit and Error aspects alongside returning the new value:
...ANSWER
Answered 2021-Dec-30 at 03:53There are a few ways to approach this but what I'd probably do – and a generally useful pattern – is to use a subset to create a slightly over-inclusive multi and then redispatch the case you shouldn't have included. For the example you provided, that might look a bit like:
QUESTION
Is there a way to put text along a density line, or for that matter, any path, in ggplot2? By that, I mean either once as a label, in this style of xkcd: 1835, 1950 (middle panel), 1392, or 2234 (middle panel). Alternatively, is there a way to have the line be repeating text, such as this xkcd #930 ? My apologies for all the xkcd, I'm not sure what these styles are called, and it's the only place I can think of that I've seen this before to differentiate areas in this way.
Note: I'm not talking about the hand-drawn xkcd style, nor putting flat labels at the top
I know I can place a straight/flat piece of text, such as via annotate or geom_text, but I'm curious about bending such text so it appears to be along the curve of the data.
I'm also curious if there is a name for this style of text-along-line?
Example ggplot2 graph using annotate(...):
Above example graph modified with curved text in Inkscape:
Edit: Here's the data for the first two trial runs in March and April, as requested:
...ANSWER
Answered 2021-Nov-08 at 11:31Great question. I have often thought about this. I don't know of any packages that allow it natively, but it's not terribly difficult to do it yourself, since geom_text accepts angle as an aesthetic mapping.
Say we have the following plot:
QUESTION
This is my code:
...ANSWER
Answered 2021-Dec-21 at 00:17You may find this easier using gridExtra::grid.arrange().
QUESTION
On the pandas tag, I often see users asking questions about melting dataframes in pandas. I am gonna attempt a cannonical Q&A (self-answer) with this topic.
I am gonna clarify:
What is melt?
How do I use melt?
When do I use melt?
I see some hotter questions about melt, like:
pandas convert some columns into rows : This one actually could be good, but some more explanation would be better.
Pandas Melt Function : Nice question answer is good, but it's a bit too vague, not much expanation.
Melting a pandas dataframe : Also a nice answer! But it's only for that particular situation, which is pretty simple, only
pd.melt(df)Pandas dataframe use columns as rows (melt) : Very neat! But the problem is that it's only for the specific question the OP asked, which is also required to use
pivot_tableas well.
So I am gonna attempt a canonical Q&A for this topic.
Dataset:I will have all my answers on this dataset of random grades for random people with random ages (easier to explain for the answers :D):
...ANSWER
Answered 2021-Nov-04 at 09:34df.melt(...) for my examples, but your version would be too low for df.melt, you would need to use pd.melt(df, ...) instead.
Documentation references:
Most of the solutions here would be used with melt, so to know the method melt, see the documentaion explanation
Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.
And the parameters are:
Logic to melting:Parameters
id_vars : tuple, list, or ndarray, optional
Column(s) to use as identifier variables.
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
var_name : scalar
Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.
value_name : scalar, default ‘value’
Name to use for the ‘value’ column.
col_level : int or str, optional
If columns are a MultiIndex then use this level to melt.
ignore_index : bool, default True
If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary.
New in version 1.1.0.
Melting merges multiple columns and converts the dataframe from wide to long, for the solution to Problem 1 (see below), the steps are:
First we got the original dataframe.
Then the melt firstly merges the
MathandEnglishcolumns and makes the dataframe replicated (longer).Then finally adds the column
Subjectwhich is the subject of theGradescolumns value respectively.
This is the simple logic to what the melt function does.
I will solve my own questions.
Problem 1:Problem 1 could be solve using pd.DataFrame.melt with the following code:
QUESTION
I notice when I run the same code as my example over here but with a union or unionByName or unionAll instead of the join, my query planning takes significantly longer and can result in a driver OOM.
Code included here for reference, with a slight difference to what occurs inside the for() loop.
ANSWER
Answered 2021-Aug-16 at 17:48This is a known limitation of iterative algorithms in Spark. At the moment, every iteration of the loop causes the inner nodes to be re-evaluated and stacked upon the outer df variable.
This means your query planning process is taking O(exp(n)) where n is the number of iterations of your loop.
There's a tool in Palantir Foundry called Transforms Verbs that can help with this.
Simply import transforms.verbs.dataframes.union_many and call it upon the total set of dataframes you wish to materialize (assuming your logic will allow for it, i.e. one iteration of the loop doesn't depend upon the result of a prior iteration of the loop.
The code above should instead be modified to:
QUESTION
I'm looking for a way to store a small multidimensional set of data which is known at compile time and never changes. The purpose of this structure is to act as a global constant that is stored within a single namespace, but otherwise globally accessible without instantiating an object.
If we only need one level of data, there's a bunch of ways to do this. You could use an enum or a class or struct with static/constant variables:
ANSWER
Answered 2021-Sep-06 at 09:45How about something like:
QUESTION
Two similar ways to check whether a list contains an odd number:
...ANSWER
Answered 2021-Sep-06 at 05:17The first method sends everything to any() whilst the second only sends to any() when there's an odd number, so any() has fewer elements to go through.
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Install Repeat
Just download the [latest version](https://github.com/repeats/Repeat/releases/latest), put the jar in a separate directory, and run it with java. That’s it! You may need appropriate privileges since Repeat needs to listen to and/or control the mouse and keyboard.
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