semvec/pypi-packages huggingface dataset:
DARt-py 0.0.1
A tool to estimate Instantaneous Reproduction Number(Rt) for the pandemic

A tool to estimate Instantaneous Reproduction Number(Rt)
========================================================

cal\_r() provide real-time estimation of time-varying distribution of Rt
and infected numbers from a range of epidemic observations (e.g., number
of onsets and confirmed cases).

Usage
-----

dart = DARt(GT,D\_s,Filename)

cal\_r()

Arguments
---------

GT: generation time distribution; GT[0] represents the probability that
the secondary infection case occurs 1 day after the primary infection.

D\_s: delay time distribution; D\_s[0] represents the probability that
an individual infected is observed after 1 day.

Filename: input file; this file should contain date and corresponding
observations, the date should be sorted from the oldest to the latest.
See 'Example\_input.csv'.

References:
-----------

This tool is described in the following paper:'Revealing the
Transmission Dynamics of COVID-19: A Bayesian Framework for R\_t
Estimation'

Examples
--------

::

    GT = [0, 0, 0.165720874545241, 0.226350757019051, 0.245007574714227, 0.213515210247327,
          0.149405583474155]  # 1:7; non-zero index 3:7
    D_s = np.array([0, 0, 0, 0, 0, 0, 0.0996906, 0.1130266, 0.1143032, 0.1069238, 0.0937167999999999])
    dart = DARt(GT=GT, D_s=D_s, filename='Example_input.csv')
    dart.cal_r()
json-extract 1.1.2
json提取器

>	我们在提取json串中的某个值得时候难免会碰到一层一层又一层的嵌套 想要提取要一层一层的找key 像剥洋葱一样
>今天安利一款暴力提取库 json_extract 

*极速上手教程*

**安装 json_extract**

> pip install json-extract

```json
{
    "animals": {
        "zoon": [
            {
                "name": "Peki",
                "sex": "girl",
                "age": 18
            },
            {
                "name": "George",
                "sex": "boy",
                "type":"zoon"

            }
        ],
        "people": [
            {
                "name": "Rufus",
                "sex": "girl",
                "weight":"88"
            },
            {
                "name": "Marty",
                "sex": "boy",
                "people": [
                            {
                                "name": "Rufus",
                                "type":"people",
                            },
                            {
                                "name": "Marty",
                                "sex": "？？？",
                                "age": "10"
                            }]
            }]

    }
    }

```

```python
response_json={}
from json_extract import GetValue2
getobj = GetValue2(response_json)

sex = getobj.get_values('sex')
print(sex)
# ['girl', 'boy', 'girl', 'boy']

deep = getobj.get_values("sex",deep=True)
# ['girl', 'boy', 'girl', 'boy', '？？？']

filte = getobj.get_values("sex",filters=True)
# ['girl', 'boy']

notexiste = getobj.get_values("asdfadfs",default='not existe')
# or
getobj.get_values("asdfadfs",'not existe')
# not existe

weight = getobj.get_values("weight")
# 88
weight_shell = getobj.get_values("weight",shell=True)
# ['88']

age = getobj.get_values("age")
# [18, '10']

age_int = getobj.get_values('age',ret_type=int)
# [18, 10]

age_str = getobj.get_values("age",ret_type=str)
# ['18', '10']




```


```text
:param key: Key value to be resolved 需要解析的key值
:param default: If the result is empty, the default value is none  如果结果为空 备用值 默认None
:param deep: Do you want to deeply resolve all keys? Default false 是否深度解析所有key 默认False
:param filters: Whether to de duplicate the result, the default is false 是否去重结果 默认False
:param shell: If the result is whether to remove the outer list shell, the default is false 如果结果为一个 是否去掉外面list壳子 默认False（去壳）
:param ret_type: The return result type can pass in 'int' or 'str' by default  返回结果类型 可传入'int'或'str' 默认原始
```

[✫传送门](https://github.com/TIM952597205/json_tool)
### End
digiformatter 0.5.7.2
Make your terminals look spicy.
Requires Python: >=3

# DigiFormatter
**Make your terminals look spicy.**

Oh, terminals. Lost to time, replaced by the ever-prevalent GUI. Why, in this age of nostalgia, do we restrict its growing reach to only synthwave music, 16-bit video games, and ａｅｓｔｈｅｔｉｃ video filters? Terminals are going to come back, I can feel it. And why not add a little pizzazz while we're at it? Unlike what hacker movies from the 2000's would like you to believe, terminals weren't all black and green. Special characters, colors, and overwriting made terminals a powerful and beautiful user interaction tool. And much like everything else in Python, now you can `import` it.
pybulletX 0.4.1
A Pythonic wrapper for pybullet

# pybulletX

[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
[![GitHubCI](https://github.com/facebookresearch/pybulletX/workflows/CI/badge.svg)](https://github.com/facebookresearch/pybulletX/actions)
[![CircleCI](https://circleci.com/gh/facebookresearch/pybulletX.svg?style=shield&circle-token=ad4f47a46ed4cc4ff976cdd2f79fcf7ef4494459)](https://circleci.com/gh/facebookresearch/pybulletX)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

The lightweight pybullet wrapper for robotics researchers.
Build robot simulation with less code.
Scale your research with less boilerplate.

## Examples
Here is an example of controlling Kuka arm with PyBulletX.

```python
import time

import numpy as np
import pybullet as p
import pybulletX as px

P_GAIN = 50
desired_joint_positions = np.array([1.218, 0.507, -0.187, 1.235, 0.999, 1.279, 0])

def main():
    px.init()

    robot = px.Robot("kuka_iiwa/model.urdf", use_fixed_base=True)
    robot.torque_control = True

    while True:
        time.sleep(0.01)

        error = desired_joint_positions - robot.get_states().joint_position
        actions = robot.action_space.new()
        actions.joint_torque = error * P_GAIN
        robot.set_actions(actions)

        p.stepSimulation()

if __name__ == "__main__":
    main()
```

Here is the same example but without PyBulletX.
```python
import time

import numpy as np
import pybullet as p
import pybullet_data

P_GAIN = 50
desired_joint_positions = np.array([1.218, 0.507, -0.187, 1.235, 0.999, 1.279, 0])

def main():
    p.connect(p.GUI)

    p.setAdditionalSearchPath(pybullet_data.getDataPath())
    p.loadURDF("plane.urdf")

    robot_id = p.loadURDF("kuka_iiwa/model.urdf", useFixedBase=True)

    num_dofs = 7
    joint_indices = range(num_dofs)

    # The magic that enables torque control
    p.setJointMotorControlArray(
        bodyIndex=robot_id,
        jointIndices=joint_indices,
        controlMode=p.VELOCITY_CONTROL,
        forces=np.zeros(num_dofs),
    )

    while True:
        time.sleep(0.01)

        joint_states = p.getJointStates(robot_id, joint_indices)
        joint_positions = np.array([j[0] for j in joint_states])
        error = desired_joint_positions - joint_positions
        torque = error * P_GAIN

        p.setJointMotorControlArray(
            bodyIndex=robot_id,
            jointIndices=joint_indices,
            controlMode=p.TORQUE_CONTROL,
            forces=torque,
        )

        p.stepSimulation()

if __name__ == "__main__":
    main()
```

The examples above are available in `examples/with_pybulletX.py` and `examples/without_pybulletX.py`.

## License
PyBulletX is licensed under [MIT License](LICENSE).
github-trending-languages 0.0.3
trending languages in gthub now
Requires Python: >=3.6

# Github trending language
## Required installation 
``` pip3 install -i https://test.pypi.org/simple/ github-trending-languages==0.0.2  #Windows```

``` python3 -m pip install -i https://test.pypi.org/simple/ github-trending-languages==0.0.2  #Linux```