semvec/pypi-packages huggingface dataset:
mvec 1.0.0
vectors of n dimensions
Requires Python: >=3.6

# mvec

a simple package for handling vectors of any size.

all* your favorite vector operations are here, and generalized to n-dimensional vectors. want to add three 4D vectors? dot product a 8D and 3D vector together? you can do it!

**\*disclaimer: i still haven't figured out how to implement the cross product in n dimensions, so right now, it only works on vectors <= 3 dimensions. sorry**

# examples
defining a vector is as simple as passing the constructor an n-dimensional array of scalar values!
```python
v = Vector([a, b, c,... y, z])
```
you can also create `Vector`s in other ways:
```python
# the vector between two points in n-dimensional space
v2 = Vector.fromTwoPoints([a₁, b₁, c₁,...], [a₂, b₂, c₂,...])

# for the special case of 3D vectors in a spherical coordinate system 
v3 = Vector.fromSpherical(r, θ, φ)
```

from there, you can do all sorts of fun unary and binary operations!
```python
v3 = v1.unitVector()

# returns the sector the vector is in, given as an array made up of either 1, 0, or -1
array = v1.sector()

# x(), y(), and z() are convenience methods to return the 1st, 2nd, or 3rd-dimensional component of a vector
f = v1.x()

v2 = v1.scale(7)

boolean = v1.parallel(v2)
boolean = v1.perpendicular(v2)

# returns the angle between v1 and v2
angle = v1.angleTo(v2)

v3 = v1.dot(v2)
v3 = v1.cross(v2)
```
and more!
you can also access various components of the vectors:
```python
# returns the components of a vector as a list
array = v1.toArray()

# returns the angle the vector makes from the positive axis in the i dimension (x = 0, y = 1, etc)
angle = v1.alphaRad(i)

```

you can also use built-in operators to do math with vectors and scalars!
```python
# v2 is a vector in the same direction as v1 but its length is 5 more
v2 = v1 + 5 
# v1 is now the difference between v1 and v2
v1 -= v2

v3 = v1 + v2

# equivalent to scaling v1 by 6 or 1/5 respectively
v2 = v1 * 6
v1 =/ 5

# compares magnitudes
v1 >= v2
v1 < v2
```

you can also iterate over the components of a vector:
```python
v1 = Vector([2, 4, 6])
for i in v1:
	print(i)
# 2
# 4
# 6
# >>>
```
metovhooks 0.1.8
My personal git hooks.

# Personal Git Hooks
This directory contains some scripts designed for use as [git hooks](https://git-scm.com/docs/githooks). They should have a pretty simple command line interface (see docstrings for details) so usage should be self-explanatory.

## Installation
You can install the entire hook package with `pip install metovhooks`.

## Usage
You can run the scripts manually in your shell. For example, `protect_branch master` will exit with code 1 if the current git branch is `master` and exit with 0 if not (if your shell does not display exit codes, you can check the code with `echo $?`).

Of course, it isn't very useful to run these manually. You can just type `git status` and see what the current branch is. But these scripts are intended to be used as git hooks. This is why the commands themselves are verbose - you'd be reading them much more often than typing them. However, you still have the option of just typing them manually -- a useful thing when troubleshooting.

If you're looking for a tool to help you manage git hooks, check out [yaghm](https://github.com/metov/yaghm). This repository also contains a yaghm config defining the hooks I use when developing it. This is optional; it won't actually do anything until you install yaghm and enable them.
ctf-stockobjects 0.0.4
A set of basic objects for storing and querying stock market company and sectors
Requires Python: >=3.6

# stockobjects
A simple set of objects for representing stock and sector objects in Python.

## Learning intention
1. Packaging my own reusable modules
1. Working towards mastery of OO in Python - more advance scoping, testing, inversion of control, dunder methods
1. More advanced testing approaches

## todo
1. More testing
1. Use mocking
1. Additional validation for dates and checking that exceptions are raised in all edge cases
1. Moving from dict to custom objects for quote collections so that I can use built-in methods via dunders and type hinting for how to interact (dicts provide no hints)

# Usage
## Data structure
SectorCollection object contains 0:m Sector objects
Sector object contains 0:m SectorQuote objects
Sector object contains 0:m Company objects
Company object contains 0:m CompanyQuote objects

## Examples
Instantiate the SectorCollection.  A better name (todo!) would be Market instead of SectorCollection
<br>
<code>my_collection = SectorCollection(name="My first collection")</code>

Instantiate a sector to add to the SectorCollection
<br>
<code>my_sector = Sector(sector_name="Fruit sector", sector_code="xbf")
my_collection.add_sector(my_sector)</code>

Instantiate a company and add it to the sector
<br>
<code>my_company = Company(company_name="Banana company", company_code="ban", sector_object=my_sector)
my_collection.get_sector(my_sector.sector_code).add_company(my_company)</code>

From here, instantiate SectorQuotes and Company quotes
And then start querying them
stockobjects-chris-t-fernando 0.0.4
A set of basic objects for storing and querying stock market company and sectors
Requires Python: >=3.6

# stockobjects
A simple set of objects for representing stock and sector objects in Python.

## Learning intention
1. Packaging my own reusable modules
1. Working towards mastery of OO in Python - more advance scoping, testing, inversion of control, dunder methods
1. More advanced testing approaches

## todo
1. More testing
1. Use mocking
1. Additional validation for dates and checking that exceptions are raised in all edge cases
1. Moving from dict to custom objects for quote collections so that I can use built-in methods via dunders and type hinting for how to interact (dicts provide no hints)

# Usage
## Data structure
SectorCollection object contains 0:m Sector objects
Sector object contains 0:m SectorQuote objects
Sector object contains 0:m Company objects
Company object contains 0:m CompanyQuote objects

## Examples
Instantiate the SectorCollection.  A better name (todo!) would be Market instead of SectorCollection
<br>
<code>my_collection = SectorCollection(name="My first collection")</code>

Instantiate a sector to add to the SectorCollection
<br>
<code>my_sector = Sector(sector_name="Fruit sector", sector_code="xbf")
my_collection.add_sector(my_sector)</code>

Instantiate a company and add it to the sector
<br>
<code>my_company = Company(company_name="Banana company", company_code="ban", sector_object=my_sector)
my_collection.get_sector(my_sector.sector_code).add_company(my_company)</code>

From here, instantiate SectorQuotes and Company quotes
And then start querying them
office365-REST-with-timeout 0.0.5
Office365 REST package with timeout added.
Requires Python: >=3.6

This is the same project as Office365-REST (https://github.com/vgrem/Office365-REST-Python-Client), but with the addition of a timeout_ variable in execute_query(), which gets passed down to requests.get. This prevents the execution from hanging indefinitely.

To execute a query with a timeout, simply add the following argument at the end:

```
>>> TIMEOUT=10
>>> my_query.execute_query(timeout_=TIMEOUT)
```

It also works with `execute_query_retry(timeout_=TIMEOUT)`.