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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:
 Generation of capacity curves using point dispersion
This notebook can be used to generate many synthethic capacity curves (spectral acceleration versus spectrum displacement), starting from a median pushover curve and dispersion levels for a number of specific points on the capacity curve. The figure below presents a number of capacity curves generated from a median curve representative of a given building typology.
<img src="../../../../figures/capacity_curves_dispersion.png" width="350" align="middle">
Note
Step1:  Capacity curve generator
The various points of the capacity curve should be defined in the vectors Sa (spectral acceleration) and Sd (spectral displacement).
For each of these points, the variability is specified in the form of coefficients of variation, using the vectors Sa_cov and Sd_cov.
The distribution can be set to "normal" or "lognormal" and controls the distribution used for the sampling.
The parameters Sa_corr, Sd_corr, and Sa_Sd_corr control the correlation used for sampling points on. A correlation factor equal to 1.0 implies full correlation, whereas a value equal to 0.0 will lead to an independent sampling process.
The parameter truncation_level specifies the number of standard deviations on either side of the mean of the distribution that will be considered during the sampling process.
The number of capacity curves that will be generated is specified by the parameter no_capacity_curves.
Step2:  Include additional information
Additional information can be added to the capacity curves generated using the above method. 
After generating the required capacity curves by running the cells above, you can add the following information to the capacity curves
Step3:  Save capacity curves
Please specify below the path for the output file to save the capacity curves
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Python Code:
from rmtk.vulnerability.model_generator.point_dispersion import point_dispersion as pd
from rmtk.vulnerability.common import utils
%matplotlib inline 
Explanation: Generation of capacity curves using point dispersion
This notebook can be used to generate many synthethic capacity curves (spectral acceleration versus spectrum displacement), starting from a median pushover curve and dispersion levels for a number of specific points on the capacity curve. The figure below presents a number of capacity curves generated from a median curve representative of a given building typology.
<img src="../../../../figures/capacity_curves_dispersion.png" width="350" align="middle">
Note: To run the code in a cell:
Click on the cell to select it.
Press SHIFT+ENTER on your keyboard or press the play button (<button class='fa fa-play icon-play btn btn-xs btn-default'></button>) in the toolbar above.
End of explanation
Sa_means = [0.40, 0.40, 0.40, 0.40]
Sa_covs  = [0.20, 0.20, 0.20, 0.20]
Sd_means = [0.03, 0.05, 0.08, 0.1]
Sd_covs  = [0.20, 0.20, 0.20, 0.20]
distribution = "normal"
Sa_corr = 0.99999
Sd_corr = 0.99999
Sa_Sd_corr = 0.5
truncation_level = 1
no_capacity_curves = 50
capacity_curves = pd.generate_capacity_curves(Sa_means, Sa_covs, Sd_means, Sd_covs,
                                              distribution, no_capacity_curves, 
                                              Sa_corr, Sd_corr, Sa_Sd_corr, truncation_level)
utils.plot_capacity_curves(capacity_curves)
Explanation: Capacity curve generator
The various points of the capacity curve should be defined in the vectors Sa (spectral acceleration) and Sd (spectral displacement).
For each of these points, the variability is specified in the form of coefficients of variation, using the vectors Sa_cov and Sd_cov.
The distribution can be set to "normal" or "lognormal" and controls the distribution used for the sampling.
The parameters Sa_corr, Sd_corr, and Sa_Sd_corr control the correlation used for sampling points on. A correlation factor equal to 1.0 implies full correlation, whereas a value equal to 0.0 will lead to an independent sampling process.
The parameter truncation_level specifies the number of standard deviations on either side of the mean of the distribution that will be considered during the sampling process.
The number of capacity curves that will be generated is specified by the parameter no_capacity_curves.
End of explanation
gamma = 1.2
height = 3.0
elastic_period = 0.6
yielding_point_index = 1
capacity_curves = utils.add_information(capacity_curves, 'gamma', 'value', gamma)
capacity_curves = utils.add_information(capacity_curves, 'heights', 'value', height)
capacity_curves = utils.add_information(capacity_curves, 'periods', 'calculate', 1)
capacity_curves = utils.add_information(capacity_curves, 'yielding point', 'point', yielding_point_index)
Explanation: Include additional information
Additional information can be added to the capacity curves generated using the above method. 
After generating the required capacity curves by running the cells above, you can add the following information to the capacity curves:
The parameter gamma defines the modal participation factor
The parameter height defines the height of the structure
The parameter elastic_period defines the elastic period of the first mode of vibration
The parameter yielding_point_index defines the yielding point
End of explanation
output_file = "../../../../../rmtk_data/capacity_curves_point.csv"
utils.save_SdSa_capacity_curves(capacity_curves, output_file)
Explanation: Save capacity curves
Please specify below the path for the output file to save the capacity curves:
End of explanation
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