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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Create Some Fake Entires That demonstrates Plotting
In the constructor, give a title, number of total datasets that you want to plot side by side, a list of methods for which you are plotting results. logit_y will logit transforms the y axis for emphasis on low and high end part of the metric. eval_type simply is for naming purposes, and will appear in y-axis titles.
In the following block we just create fake SNR and metrics just fo demonstration purposes.
If you want to compute SNR of a templates (np.ndarray of shape (# time samples, # channels, # units)) just call main_channels(templates).
Step1:  Generate SNR vs Metric
Change save_to to a file path to save it to file.
Step2:  Generate the curve plots
Similar to the other plot, give path to file in save_to for saving to file.
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Python Code:
plot = ChristmasPlot('Fake', n_dataset=3, methods=['yass', 'kilosort', 'spyking circus'], logit_y=True, eval_type="Accuracy")
for method in plot.methods:
    for i in range(plot.n_dataset):
        x = (np.random.rand(30) - 0.5) * 10
        y = 1 / (1 + np.exp(-x + np.random.rand()))
        plot.add_metric(x, y, dataset_number=i, method_name=method)
Explanation: Create Some Fake Entires That demonstrates Plotting
In the constructor, give a title, number of total datasets that you want to plot side by side, a list of methods for which you are plotting results. logit_y will logit transforms the y axis for emphasis on low and high end part of the metric. eval_type simply is for naming purposes, and will appear in y-axis titles.
In the following block we just create fake SNR and metrics just fo demonstration purposes.
If you want to compute SNR of a templates (np.ndarray of shape (# time samples, # channels, # units)) just call main_channels(templates).
End of explanation
plot.generate_snr_metric_plot(save_to=None)
Explanation: Generate SNR vs Metric
Change save_to to a file path to save it to file.
End of explanation
plot.generate_curve_plots(save_to=None)
Explanation: Generate the curve plots
Similar to the other plot, give path to file in save_to for saving to file.
End of explanation
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