Calculation of Solar RV Variations¶

1. calculate solar velocities: calculate solar velocities and save to csv
2. get component lists: using pandas to get the lists of each relevant component
3. calculate RVs and save to csv: calculate RVs using optimized parameters

NOTE: this is purely an example but requires ground-based Solar RV measurements for fitting

In [1]:

import numpy as np
import pandas as pd

from sklearn.linear_model import LinearRegression

import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression

Get ground-based 'sun-as-a-star' RVs.

In [ ]:

# get csv with solar RVs
csv_file = 'file_name'

# create pandas dataframe
component_df = pd.read_csv(csv_file)

# get RV list
rv_sun = component_df.rv_sun.values

# get csv with solar RVs csv_file = 'file_name' # create pandas dataframe component_df = pd.read_csv(csv_file) # get RV list rv_sun = component_df.rv_sun.values

Read in velocities from csv.

In [2]:

# csv file with rv components
csv_file = 'file_name'

# csv file with rv components csv_file = 'file_name'

Get component lists.

In [ ]:

# create pandas dataframe
component_df = pd.read_csv(csv_file)

# create pandas dataframe component_df = pd.read_csv(csv_file)

In [4]:

# get velocities lists
v_phot = component_df.v_phot.values
v_conv = component_df.v_conv.values

# get velocities lists v_phot = component_df.v_phot.values v_conv = component_df.v_conv.values

Calculate scaling coefficient values -- using linear regression.

Setup data to plug into linear regression model/

In [ ]:

# components
X = np.zeros(shape=(len(rv_sun), 2))
X[:, 0] = v_phot
X[:, 1] = v_conv

# data we are fitting to
y = rv_sun

# components X = np.zeros(shape=(len(rv_sun), 2)) X[:, 0] = v_phot X[:, 1] = v_conv # data we are fitting to y = rv_sun

Use sklearn linear regression to get coefficient values.

In [ ]:

# apply regression
reg = LinearRegression().fit(X, y)
print('R^2 for prediction: ' + str(reg.score(X, y)))

# get scaling factors
A = reg.coef_[0]
B = reg.coef_[1]
RV0 = reg.intercept_

# print scaling factors
print("Scaling Factors:\n A:", A, "\nB:", B, "\nRV0:", RV0)

# apply regression reg = LinearRegression().fit(X, y) print('R^2 for prediction: ' + str(reg.score(X, y))) # get scaling factors A = reg.coef_[0] B = reg.coef_[1] RV0 = reg.intercept_ # print scaling factors print("Scaling Factors:\n A:", A, "\nB:", B, "\nRV0:", RV0)

Calculate the Model RV Variation using these coefficients.

In [ ]:

RV = A*v_phot + B*v_conv + RV0

RV = A*v_phot + B*v_conv + RV0

Add calculation to csv.

In [ ]:

component_df["rv_model"] = RV
component_df.to_csv(csv_file, index=False)

component_df["rv_model"] = RV component_df.to_csv(csv_file, index=False)