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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:
 Lin and Miranda (2008)
This method, described in Lin and Miranda (2008), estimates the maximum inelastic displacement of an existing structure based on the maximum elastic displacement response of its equivalent linear system without the need of iterations, based on the strength ratio. The equivalent linear system has a longer period of vibration and a higher viscous damping than the original system. The estimation of these parameters is based on the strength ratio $R$.
Note
Step1:  Load capacity curves
In order to use this methodology, it is necessary to provide one (or a group) of capacity curves, defined according to the format described in the RMTK manual.
Please provide the location of the file containing the capacity curves using the parameter capacity_curves_file.
Step2:  Load ground motion records
Please indicate the path to the folder containing the ground motion records to be used in the analysis through the parameter gmrs_folder.
Note
Step3:  Load damage state thresholds
Please provide the path to your damage model file using the parameter damage_model_file in the cell below.
The damage types currently supported are
Step4:  Obtain the damage probability matrix
Step5:  Fit lognormal CDF fragility curves
The following parameters need to be defined in the cell below in order to fit lognormal CDF fragility curves to the damage probability matrix obtained above
Step6:  Plot fragility functions
The following parameters need to be defined in the cell below in order to plot the lognormal CDF fragility curves obtained above
Step7:  Save fragility functions
The derived parametric fragility functions can be saved to a file in either CSV format or in the NRML format that is used by all OpenQuake input models. The following parameters need to be defined in the cell below in order to save the lognormal CDF fragility curves obtained above
Step8:  Obtain vulnerability function
A vulnerability model can be derived by combining the set of fragility functions obtained above with a consequence model. In this process, the fractions of buildings in each damage state are multiplied by the associated damage ratio from the consequence model, in order to obtain a distribution of loss ratio for each intensity measure level. 
The following parameters need to be defined in the cell below in order to calculate vulnerability functions using the above derived fragility functions
Step9:  Plot vulnerability function
Step10:  Save vulnerability function
The derived parametric or nonparametric vulnerability function can be saved to a file in either CSV format or in the NRML format that is used by all OpenQuake input models. The following parameters need to be defined in the cell below in order to save the lognormal CDF fragility curves obtained above
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Python Code:
import lin_miranda_2008
from rmtk.vulnerability.common import utils
%matplotlib inline 
Explanation: Lin and Miranda (2008)
This method, described in Lin and Miranda (2008), estimates the maximum inelastic displacement of an existing structure based on the maximum elastic displacement response of its equivalent linear system without the need of iterations, based on the strength ratio. The equivalent linear system has a longer period of vibration and a higher viscous damping than the original system. The estimation of these parameters is based on the strength ratio $R$.
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
capacity_curves_file = "../../../../../../rmtk_data/capacity_curves_Sa-Sd.csv"
capacity_curves = utils.read_capacity_curves(capacity_curves_file)
utils.plot_capacity_curves(capacity_curves)
Explanation: Load capacity curves
In order to use this methodology, it is necessary to provide one (or a group) of capacity curves, defined according to the format described in the RMTK manual.
Please provide the location of the file containing the capacity curves using the parameter capacity_curves_file.
End of explanation
gmrs_folder = "../../../../../../rmtk_data/accelerograms"
gmrs = utils.read_gmrs(gmrs_folder)
minT, maxT = 0.1, 2.0
utils.plot_response_spectra(gmrs, minT, maxT)
Explanation: Load ground motion records
Please indicate the path to the folder containing the ground motion records to be used in the analysis through the parameter gmrs_folder.
Note: Each accelerogram needs to be in a separate CSV file as described in the RMTK manual.
The parameters minT and maxT are used to define the period bounds when plotting the spectra for the provided ground motion fields.
End of explanation
damage_model_file = "../../../../../../rmtk_data/damage_model.csv"
damage_model = utils.read_damage_model(damage_model_file)
Explanation: Load damage state thresholds
Please provide the path to your damage model file using the parameter damage_model_file in the cell below.
The damage types currently supported are: capacity curve dependent, spectral displacement and interstorey drift. If the damage model type is interstorey drift the user can provide the pushover curve in terms of Vb-dfloor to be able to convert interstorey drift limit states to roof displacements and spectral displacements, otherwise a linear relationship is assumed.
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PDM, Sds = lin_miranda_2008.calculate_fragility(capacity_curves, gmrs, damage_model)
Explanation: Obtain the damage probability matrix
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IMT = "Sd"
period = 2.0
damping_ratio = 0.05
regression_method = "max likelihood"
fragility_model = utils.calculate_mean_fragility(gmrs, PDM, period, damping_ratio, 
                                                 IMT, damage_model, regression_method)
Explanation: Fit lognormal CDF fragility curves
The following parameters need to be defined in the cell below in order to fit lognormal CDF fragility curves to the damage probability matrix obtained above:
1. IMT: This parameter specifies the intensity measure type to be used. Currently supported options are "PGA", "Sd" and "Sa".
2. period: This parameter defines the time period of the fundamental mode of vibration of the structure.
3. damping_ratio: This parameter defines the damping ratio for the structure.
4. regression_method: This parameter defines the regression method to be used for estimating the parameters of the fragility functions. The valid options are "least squares" and "max likelihood".
End of explanation
minIML, maxIML = 0.01, 2.00
utils.plot_fragility_model(fragility_model, minIML, maxIML)
Explanation: Plot fragility functions
The following parameters need to be defined in the cell below in order to plot the lognormal CDF fragility curves obtained above:
* minIML and maxIML: These parameters define the limits of the intensity measure level for plotting the functions
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taxonomy = "RC"
minIML, maxIML = 0.01, 2.00
output_type = "nrml"
output_path = "../../../../../../rmtk_data/output/"
utils.save_mean_fragility(taxonomy, fragility_model, minIML, maxIML, output_type, output_path)
Explanation: Save fragility functions
The derived parametric fragility functions can be saved to a file in either CSV format or in the NRML format that is used by all OpenQuake input models. The following parameters need to be defined in the cell below in order to save the lognormal CDF fragility curves obtained above:
1. taxonomy: This parameter specifies a taxonomy string for the the fragility functions.
2. minIML and maxIML: These parameters define the bounds of applicability of the functions.
3. output_type: This parameter specifies the file format to be used for saving the functions. Currently, the formats supported are "csv" and "nrml".
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cons_model_file = "../../../../../../rmtk_data/cons_model.csv"
imls = [0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 
        0.60, 0.70, 0.80, 0.90, 1.00, 1.20, 1.40, 1.60, 1.80, 2.00]
distribution_type = "lognormal"
cons_model = utils.read_consequence_model(cons_model_file)
vulnerability_model = utils.convert_fragility_vulnerability(fragility_model, cons_model, 
                                                            imls, distribution_type)
Explanation: Obtain vulnerability function
A vulnerability model can be derived by combining the set of fragility functions obtained above with a consequence model. In this process, the fractions of buildings in each damage state are multiplied by the associated damage ratio from the consequence model, in order to obtain a distribution of loss ratio for each intensity measure level. 
The following parameters need to be defined in the cell below in order to calculate vulnerability functions using the above derived fragility functions:
1. cons_model_file: This parameter specifies the path of the consequence model file.
2. imls: This parameter specifies a list of intensity measure levels in increasing order at which the distribution of loss ratios are required to be calculated.
3. distribution_type: This parameter specifies the type of distribution to be used for calculating the vulnerability function. The distribution types currently supported are "lognormal", "beta", and "PMF".
End of explanation
utils.plot_vulnerability_model(vulnerability_model)
Explanation: Plot vulnerability function
End of explanation
taxonomy = "RC"
output_type = "nrml"
output_path = "../../../../../../rmtk_data/output/"
utils.save_vulnerability(taxonomy, vulnerability_model, output_type, output_path)
Explanation: Save vulnerability function
The derived parametric or nonparametric vulnerability function can be saved to a file in either CSV format or in the NRML format that is used by all OpenQuake input models. The following parameters need to be defined in the cell below in order to save the lognormal CDF fragility curves obtained above:
1. taxonomy: This parameter specifies a taxonomy string for the the fragility functions.
3. output_type: This parameter specifies the file format to be used for saving the functions. Currently, the formats supported are "csv" and "nrml".
End of explanation
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