### Install NYISOToolkit

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/README.md

Install the NYISOToolkit package using pip. This command installs directly from the GitHub repository.

```python
pip install git+https://github.com/m4rz910/NYISOToolkit#egg=nyisotoolkit
```

--------------------------------

### Generate Price PDFs

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/docs/tutorials/3. nyisovis pdfs.ipynb

Generates probability density functions for prices, with options for real-time data and cumulative distribution.

```python
for rtorda in [False, True]:
    for c in [False, True]:
        nv.price_pdf(rt=rtorda, cumulative=c)
```

--------------------------------

### Import Libraries

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/docs/tutorials/3. nyisovis pdfs.ipynb

Imports necessary libraries for data visualization and the nyisotoolkit.

```python
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

from nyisotoolkit import NYISOVis, NYISOData
```

--------------------------------

### Generate Demand PDFs

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/docs/tutorials/3. nyisovis pdfs.ipynb

Generates probability density functions for demand, with an option to plot the cumulative distribution.

```python
for c in [False, True]:
    nv.demand_pdf(cumulative=c)
```

--------------------------------

### Generate Price Delta PDFs

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/docs/tutorials/3. nyisovis pdfs.ipynb

Generates probability density functions for the difference in prices, with an option to plot the cumulative distribution.

```python
for c in [False, True]:
    nv.price_delta_pdf(cumulative=c)
```

--------------------------------

### Import Libraries and Generate Basic Plots

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/notebooks/simple.ipynb

Imports required libraries and calls the basic_plots function to generate visualizations. Set 'redownload' to True to force data redownload.

```python
import pytest
from datetime import datetime
import pytz
import matplotlib.pyplot as plt

from nyisotoolkit import NYISOVis
from nyisotoolkit.nyisovis.nyisovis import basic_plots, statistical_plots
from nyisotoolkit.nyisodata.utils import current_year

basic_plots({"redownload":False, 'year': 2024})
```

--------------------------------

### Construct NYISO Databases

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/README.md

Construct local databases for specified years and datasets. This function can redownload data, reconstruct databases, and optionally create CSV files.

```python
years = range(2022,2024)
datasets = ['load_h','interface_flows_5m']
NYISOData.construct_databases(years=years, datasets=datasets, redownload=True, reconstruct=True, create_csvs=False)
```

--------------------------------

### Calculate Covariance Between Renewables and Prices

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/docs/ideas/wind_and_solar_covariance_risk.ipynb

Fetches historical fuel mix and load-weighted LBMP data for a given year and time resolution (RT or DA). It calculates the energy generated by specified renewable sources and aligns it with the corresponding prices. Asserts that the indices of both datasets are compatible before returning them.

```python
from nyisotoolkit import NYISOData
from nyisotoolkit.nyisostat.nyisostat import table_load_weighted_price

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

def covariance(year, sources, rt):
    lw_lbmp = table_load_weighted_price(year=year, rt=rt).iloc[:,0] #convert to series
    fuel_mix = NYISOData(dataset="fuel_mix_5m", year=year).df.tz_convert('US/Eastern') # MW
    fuel_mix = (fuel_mix[sources].sum(axis="columns")*1/12) #MW->MWh
    if not rt:
        fuel_mix = fuel_mix.resample("H").sum()
    assert lw_lbmp.index.isin(fuel_mix.index).any(), "Indices are not the same"
    assert len(lw_lbmp.index)==len(fuel_mix.index), "Indices are not the same size"
    return fuel_mix, lw_lbmp
```

--------------------------------

### Import NYISOData Class

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/docs/tutorials/1. nyisodata example.ipynb

Import the necessary class from the nyisotoolkit library.

```python
from nyisotoolkit import NYISOData
```

--------------------------------

### Initialize NYISOVis

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/docs/tutorials/3. nyisovis pdfs.ipynb

Initializes the NYISOVis object for a specific year.

```python
nv = NYISOVis(year=2020)
```

--------------------------------

### Generate Carbon Free Timeseries Visualization

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/README.md

Initializes the NYISOVis object and generates a carbon-free energy timeseries plot. Figures are saved to a default directory, which can be customized.

```python
from nyisotoolkit import NYISOData, NYISOStat, NYISOVis
nv = NYISOVis(year='2021') #figures saved in nyisotoolkit/nyisovis/visualization folder by default.  
nv.fig_carbon_free_timeseries(f='D')  # daily (D) or monthy (M) frequency is recommended
print(f"Figures saved by default to: {nv.out_dir} \nYou can change this by passing a pathlib object to the out_dir parameter in the NYISOVis object initialization.")
```

--------------------------------

### Load Hourly Load Data

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/README.md

Load hourly load data for a specific year. The data is returned in UTC and can be converted to local time if needed.

```python
from nyisotoolkit import NYISOData, NYISOStat, NYISOVis
df = NYISOData(dataset='load_h', year='2024').df # year argument in local time, but returns dataset in UTC

#If you need to work with data in local time, then convert time zone
df = df.tz_convert('US/Eastern')
```

--------------------------------

### Fetch and Convert Load Data

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/docs/tutorials/1. nyisodata example.ipynb

Fetch historical load data for a given year and convert it to Eastern Time. This snippet shows how to initialize NYISOData with a dataset and year, access the DataFrame, and perform timezone conversion.

```python
df = NYISOData(dataset="load_h", year="2019").df #UTC
df = df.tz_convert("US/Eastern")
df.head(5)
```

--------------------------------

### Visualize Renewable Covariance Risk

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/docs/ideas/wind_and_solar_covariance_risk.ipynb

Generates scatter plots to visualize the covariance risk between renewable energy sources and electricity prices. It iterates through different renewable sources and time resolutions (RT/DA), setting appropriate plot titles and labels. Requires matplotlib and seaborn for plotting.

```python
def renewable_covariance(year, rt):
    scatter_kwargs = {"x":None, "y":None,
                      "hue":None, "style":None,
                      "size":None, "data":None, 
                      "palette":None,
                      "hue_order":None, "hue_norm":None,
                      "sizes":None, "size_order":None, "size_norm":None,
                      "markers":True, "style_order":None,
                      "x_bins":None, "y_bins":None, "units":None,
                      "estimator":None, "ci":95, "n_boot":1000,
                      "alpha":None, "x_jitter":None, "y_jitter":None,
                      "legend":'auto',
                      "ax":None}

    for source in ["Wind", "Other Renewables"]:
        da_rt = "RT" if rt else "DA"
        ax_kwargs = {"title":f'Covariance Risk of {source} ({year})',
                     "xlabel":f"{source} [MWh]","ylabel":f"State-wide Average {da_rt} Price [$/MWh]",
                     "xlim":None, "ylim":None}

        fuel_mix, lw_lbmp = covariance(year=year, sources=[source], rt=rt)
        scatter_kwargs.update({"x": fuel_mix.values,
                               "y": lw_lbmp.values})

        fig, ax = plt.subplots(figsize=(6,3), dpi=300)
        sns.scatterplot(**scatter_kwargs)
        ax.set(**ax_kwargs)

for rtorda in [False, True]:
    renewable_covariance(year=2019, rt=rtorda)
```

--------------------------------

### UserWarning from set_ticklabels

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/notebooks/simple.ipynb

This warning indicates a potential issue with tick label settings in matplotlib, often related to fixed tick locators.

```python
Output:
/home/m4rz177/Documents/NYISOToolkit/nyisotoolkit/nyisovis/nyisovis.py:395: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  cb1.ax.set_yticklabels([i for i in range(0,120,20)])
```

--------------------------------

### Basic Plot Output Figure Size

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/notebooks/simple.ipynb

Represents a matplotlib figure with a size of 900x1800 pixels and one axes.

```text
Result:
<Figure size 900x1800 with 1 Axes>
```

--------------------------------

### Basic Plot Output Figure Size

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/notebooks/simple.ipynb

Represents a matplotlib figure with a size of 1800x900 pixels and one axes.

```text
Result:
<Figure size 1800x900 with 1 Axes>
```

--------------------------------

### Basic Plot Output Figure Size

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/notebooks/simple.ipynb

Represents a matplotlib figure with a size of 1800x900 pixels and two axes.

```text
Result:
<Figure size 1800x900 with 2 Axes>
```

--------------------------------

### Basic Plot Output Figure Size

Source: https://github.com/m4rz910/nyisotoolkit/blob/master/notebooks/simple.ipynb

Represents a matplotlib figure with a size of 1800x1800 pixels and two axes.

```text
Result:
<Figure size 1800x1800 with 2 Axes>
```

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