Source Data for figures

Main text
=========

fig.1, fig.3
see the example data and analysis at 
https://github.com/SMADynamics/BFM_radial_fluct


fig4a.txt
3 dictionaries
{occurrences, radius bins (nm), bead diameter (nm)}

fig4b.txt
3 dictionaries
{occurrences, s_min (nm), bead diameter (nm)}

fig4c.txt
3 dictionaries
{occurrences, gamma_phi (pN nm s), bead diameter (nm)}

fig4d.txt
3 dictionaries
{occurrences, gamma_theta (pN nm s), bead diameter (nm)}


fig5b.txt:
list of python dictionaries
each dict is the measurement of one motor with keys
torque (pN nm) : np.array
EI (Nm**2) : np.array
bead size (nm) : int
(fig.5a is obtained binning the points of fig.5b)

fig5c.txt
one python dictionary with keys
theta (rad), EI (Nm**2), torque (pN nm)


Supplementary Information
=========================

figS3a.txt
list of python dictionaries with keys:
torque (pN nm) , theta (rad), torque_error, theta_error, bead_diameter (nm)
each dict is the measurement of one motor

figS3b.txt
list of python dictionaries with keys:
speed (Hz) , L (um), bead_diameter (nm)
each dict is the measurement of one motor


figS9.txt
list of python dictionaries with keys:
torque (pN nm), theta (rad), EI (Nm**2), sigma_omega (Hz), omega (Hz), bead diameter (nm) 


figS10.txt
list of dictionaries with keys:
torque_ccw (pN nm), EI_ccw (n m**2), torque_cw (pN nm), EI_cw (n m**2)
each dict is the measurement of one motor
