semvec/pypi-packages huggingface dataset:
InputDriverFTC 0.3
Pathing library for FTC robotics.
Keywords: python,FTC,autonomous,robotics


# InputDriverFTC

A pathing library to help you visualize Coyote Beta's x/y piecewise position functions over time.

### Example of How to Use:

```python
import math
import time

from InputDriverFTC.FieldDisplay import FieldDisplay

# Everything is in meters
robot = FieldDisplay(ROBOT_INIT_X=3.35, ROBOT_INIT_Y=0.88, ROBOT_INIT_HEADING=180, ROBOT_SIZE=0.406)


def get_x(t):
   return -t*0.5
def get_y(t):
    return 0.25*math.sin(t)

def get_heading(t):
    if t < 3.4:
        return 180
    elif t < 4.4:
        return 50*math.cos(math.pi*t-4.4)+130
    return 80


if __name__ == "__main__":
    timing = 0
    while robot.running:
        robot.set_motion(x=get_x(timing), y=get_y(timing), heading=get_heading(timing))
        time.sleep(0.01)
        timing += 0.01

        robot()

```
useful-prints 0.0.0
Useful Prints
Keywords: python


# useful_prints
**Needs to_precision by :**
- **William Rusnack github.com/BebeSparkelSparkel linkedin.com/in/williamrusnack williamrusnack@gmail.com**
- **Eric Moyer github.com/epmoyer eric@lemoncrab.com**
- **Thomas Hladish https://github.com/tjhladish tjhladish@utexas.edu**
**I am not able to add it to the requirements so please visit [Bitbucket](https://bitbucket.org/william_rusnack/to-precision/src/master/) or [GitHub](https://github.com/BebeSparkelSparkel/to-precision)**

Functions:
- pretty_print_dict
- list_as_fitted_table_string
- pretty_print_list
- pretty_print_sym_double_list
- progress_printer
- main_and_sub_progress_printer


## Links
[GitHub](https://github.com/ICreedenI/useful_prints) | [PyPI](https://pypi.org/project/useful-prints/)
scada-data-analysis 1.0.7
A set of tools for enabling wind energy data analysis
Requires Python: >=3.6
Keywords: power-curve-filtering,scada-data-analytics,wind-energy

This library helps lower the entry barrier for domain experts
in the wind energy industry to leverage advanced analytics and methodologies
developed in related scientific research.

Modules are implemented for different routine and non-routine analysis of time series
data collected from operating wind turbines. Practicing wind engineers and analysts
can build on fundamental modules as part of a larger project development.

### Library installation
- Clone the github repo to have access to example notebooks and public data.
- Next, pip install the library

```
# Clone github repo
git clone https://github.com/abbey2017/wind-energy-analytics.git

# Pip install library
pip install scada-data-analysis
```

### Current modules
- Iterative power curve filter (returns normal and abnormal datapoints)
- Expected power estimator (returns expected power based on operational data)

### Usage of power curve filter
```
import pandas as pd

from scada_data_analysis.modules.power_curve_preprocessing import PowerCurveFiltering

# Load turbine scada data
df = pd.read_csv('path\to\data')

# Instantiate power curve filtering class
pc_filter = PowerCurveFiltering(turbine_label='Wind_turbine_name', windspeed_label='Ws_avg',
                                power_label='P_avg', data=df, cut_in_speed=3, bin_interval=0.5,
                                z_coeff=2.5, filter_cycle=5, return_fig=True, image_path='..\images')

# Process raw scada data
normal_df, abnormal_df = pc_filter.process()
```

### Usage of expected power estimator
```
import pandas as pd

from scada_data_analysis.modules.expected_power import ExpectedPower

# Load turbine scada data
df = pd.read_csv('path\to\data')

# Instantiate expected power estimator
power_model = ExpectedPower(turbine_label='Wind_turbine_name', windspeed_label='Ws_avg',
                            power_label='P_avg', method='binning', kind='linear')

# Fit estimator based on training data
power_model = power_model.fit(train_df)

# Estimate expected power based on operation power curve
pred_df = power_model.predict(test_df)
```