get_parameter_dataframe
.get_parameter_dataframe(
parameter, path, file
)
Initializes the seaweed model and returns the dataframe with the parameter for all the grid sections
Arguments
- parameter : the parameter to construct the dataframe for
- path : The path to the file
- file : The file name
Returns
- df : pandas.DataFrame
time_series_analysis
.time_series_analysis(
growth_df, n_clusters, global_or_US
)
Does time series analysis on the dataframe All the time serieses are clustered based on their overall shape using k-means Inspired by this article: https://www.kaggle.com/code/izzettunc/introduction-to-time-series-clustering/notebook
Arguments
- growth_df : pandas.DataFrame
- n_clusters : int - the number of clusters to use
Returns
- labels : list - the labels for each time series
- km : TimeSeriesKMeans - the k-means object
elbow_method
.elbow_method(
growth_df, max_clusters, global_or_US, scenario
)
Finds the optimal number of clusters using the elbow method https://predictivehacks.com/k-means-elbow-method-code-for-python/
Arguments
- growth_df : pandas.DataFrame
- max_clusters : int - the maximum number of clusters to try
Returns
None, just plots the elbow method and saves it
lme
.lme(
scenario
)
Calculates growth rate and all the factors for the lme and saves it in files appropriate for the plotting functions
Arguments
None
Returns
None
grid
.grid(
scenario, global_or_US, with_elbow_method = False
)
Calculates growth rate and all the factors for the grid and saves it in files appropriate for the plotting functions
Arguments
None
Returns
None