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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:
 Alternatively, we can read the CSV file into a data frame.
Step1:  At first glance, we can identify two different parts in this time series
Step2:  There are about 10 pseudo-periods so one pseudo-period is $T \sim 2$ fs.
Note that angles are easier on the eye when no sharper than $70$ degrees.
Step3:  What about a scatter plot?
Step4:  Maybe add a 'fitting' sine (line) as a guide to the eye?
We may want to use an inset in order to visualize both time scales (pseudo-period $T$ in the main plot and decoherence duration $t$ in the inset).
Step5:  plotly offers a more intuitive syntax.
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Python Code:
df = pd.read_csv('data/coherence_timeseries.csv', header=None)
df.columns = ['time', 'signal']
df.head()
import matplotlib
%matplotlib inline
matplotlib.style.use('ggplot')
import matplotlib.pyplot as plt
plt.plot(df['time'], df['signal'])
plt.xlabel('time (fs)')
plt.ylabel('signal (a.u.)')
plt.title('Decoherence')
Explanation: Alternatively, we can read the CSV file into a data frame.
End of explanation
ts.loc[:20.0].plot()
Explanation: At first glance, we can identify two different parts in this time series: before and after roughly $t = 20$ fs.
The signal looks pseudo-periodic before $20$ fs, and noisy after $20$ fs.
End of explanation
ts.loc[:8].plot()
Explanation: There are about 10 pseudo-periods so one pseudo-period is $T \sim 2$ fs.
Note that angles are easier on the eye when no sharper than $70$ degrees.
End of explanation
df[:20].plot(kind='scatter', x='time', y='signal')
Explanation: What about a scatter plot?
End of explanation
# Main axes
plt.plot(df['time'][:20], df['signal'][:20])
plt.xlabel('time (fs)')
plt.ylabel('signal (a.u.)')
plt.title('Decoherence')
# This is an inset axes over the main axes
inset = plt.axes([.67, .65, .2, .2], axisbg='w')
inset.plot(df['time'][:120], df['signal'][:120])
plt.setp(inset, yticks=[])
Explanation: Maybe add a 'fitting' sine (line) as a guide to the eye?
We may want to use an inset in order to visualize both time scales (pseudo-period $T$ in the main plot and decoherence duration $t$ in the inset).
End of explanation
import plotly
plotly.__version__
import plotly.plotly as py
import plotly.graph_objs as pgo
periods_trace = pgo.Scatter(x=df['time'][:20], y=df['signal'][:20])
decoherence_trace = pgo.Scatter(x=df['time'][:120], y=df['signal'][:120], xaxis='x2', yaxis='y2')
data = pgo.Data([periods_trace, decoherence_trace])
layout = pgo.Layout(xaxis2=pgo.XAxis(domain=[0.6, 0.95], anchor='y2', zeroline=False),
                    yaxis2=pgo.YAxis(domain=[0.6, 0.95], anchor='x2', zeroline=False),
                    showlegend=False,
                    xaxis=pgo.XAxis(zeroline=False),
                    yaxis=pgo.YAxis(zeroline=False))
fig = pgo.Figure(data=data, layout=layout)
py.iplot(fig, filename = 'inset-two-timescales')
Explanation: plotly offers a more intuitive syntax.
End of explanation
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