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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:
 PLUMOLOGY
vis
Step1:  Reading PLUMED output
We read a file in PLUMED output format
Step2:  We can also specify certain columns using regular expressions, and also specify the stepping
Step3:  Lets read some MetaD hills files
Step4:  The separate files are horizontally concatenated into one dataframe
Step5:  Analysis
Lets compute 1D histograms of our collective variables
Step6:  We also have a SketchMap representation of our trajectory and can visualize it as a free-energy surface
Step7:  We should probably clip it to a reasonable range first
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Python Code:
from plumology import vis, util, io
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: PLUMOLOGY
vis: Visualization and plotting functions
util: Various utilities and calculation functions
io: Functions to read certain output files and an HDF interface
End of explanation
data = io.read_plumed('data.dat')
data.head()
Explanation: Reading PLUMED output
We read a file in PLUMED output format:
End of explanation
data = io.read_plumed('data.dat', columns=[r'p.i\d', 'peplen'], step=10)
data.head()
Explanation: We can also specify certain columns using regular expressions, and also specify the stepping:
End of explanation
hills = io.read_all_hills(['HILLS.0', 'HILLS.1'])
Explanation: Lets read some MetaD hills files:
End of explanation
hills.head()
Explanation: The separate files are horizontally concatenated into one dataframe:
End of explanation
dist, ranges = util.dist1D(data, nbins=50)
_ = vis.dist1D(dist, ranges)
Explanation: Analysis
Lets compute 1D histograms of our collective variables:
End of explanation
sm_data = io.read_plumed('colvar-red1.dat')
Explanation: We also have a SketchMap representation of our trajectory and can visualize it as a free-energy surface:
End of explanation
clipped_data = util.clip(sm_data, ranges={'cv1': (-50, 50), 'cv2': (-50, 50)})
edges = util.dist1D(clipped_data, ret='edges')
dist = util.dist2D(clipped_data, nbins=50, weight_name='ww')
fes = util.free_energy(dist, kbt=2.49)
_ = vis.dist2D(fes, edges)
Explanation: We should probably clip it to a reasonable range first:
End of explanation
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