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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:
 Calculating dissipation $\Gamma$ using brownian data
First, get the values of the parameters sampled.
Step1:  Next, calculate $\Gamma$ using our samples.
Step2:  Gamma is an array containing estimates calculated from each sample. The different estimates can be plotted
Step3:  And summarized with a mean and standard deviation
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Python Code:
fc = traces.get_values('dfc') + d['mu_fc']  # Add mean cantilever frequency to dfc
kc = traces.get_values('kc')
Q = traces.get_values('Q')
Explanation: Calculating dissipation $\Gamma$ using brownian data
First, get the values of the parameters sampled.
End of explanation
def gamma(fc, kc, Q):
    return kc / (2*np.pi*fc * Q)
Gamma = gamma(fc, kc, Q)
Gamma
Explanation: Next, calculate $\Gamma$ using our samples.
End of explanation
import seaborn as sns  # Pretty plots of samples
ax = sns.distplot(Gamma*1e9)
ax.set_xlabel(u"Γ  [nN m/s]")
ax.set_ylabel("Probability density")
Explanation: Gamma is an array containing estimates calculated from each sample. The different estimates can be plotted:
End of explanation
print(u"Γ = {:.2f} ± {:.2f} [nN m/s]".format(
        Gamma.mean()*1e9,
        Gamma.std(ddof=1)*1e9)) # nN m /s
Explanation: And summarized with a mean and standard deviation:
End of explanation
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