Microsoft Word - Python assignment.docx Python assignment: Write a Python code that executes the following tasks: • It reads a .txt file named “Bead trajectory.txt” that can be found in the ‘Final...

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Microsoft Word - Python assignment.docx Python assignment: Write a Python code that executes the following tasks: • It reads a .txt file named “Bead trajectory.txt” that can be found in the ‘Final Assignment’ section. The file contains the one dimensional trajectory of an optically trapped microsphere; i.e., two columns: [time, r-position]. The units are second and microns, respectively. • It asks for your date of birth (DOB); • It multiplies the second column by your DOB divided by 10^7 (e.g. if your DOB is 22/07/1995, then multiply by 2.2071995 or if your DOB is 02/12/1990, then multiply by 0.2121990). • It calculates the lag-times (?) for a finite set of timestamps within the explored time window: Where N is the number of points (i.e. the length) of the vectors in “Bead trajectory.txt” • It calculates the mean position of the particle and subtract it from the second column: • It calculates the variance (〈?!〉) of the new second column: Where �̅� is the mean value of r. • It calculates the normalised mean square displacement (NMSD, Π(?)) of the new second column as function of the lag-time: • It calculates the normalised position autocorrelation function (NPAF, ?(?)) of the new second column as function of the lag-time: • It exports/saves the calculated functions into a .cvs file; • Then you must draw a Log-Log plot of the NMSD versus lag-time and Lin-Log plot of the NPAF versus lag-time by using Microsoft Excel. Both the Python code and the excel file should be uploaded on Moodle.AAACGXicbVBNS8NAEN34WetX1KOXYBFaxJoVQS+C6MWTVLBWaGLYbDe6dLMJuxOhhPwML/4VLx4UxKOe/Dduaw5afTDw9r0ZduaFqeAaXPfTmpicmp6ZrcxV5xcWl5btldVLnWSKsjZNRKKuQqKZ4JK1gYNgV6liJA4F64T9k6HfuWNK80RewCBlfkxuJI84JWCkwN7xgGQBP/QiRWiOi7x+ts0bhaezOMDXuXkUdQhyucWLbQhkI7BrbtMdwflLcElqqEQrsN+9XkKzmEmggmjdxW4Kfk4UcCpYUfUyzVJC++SGdQ2VJGbaz0eHFc6mUXpOlChTEpyR+nMiJ7HWgzg0nTGBWz3uDcX/vG4G0YGfc5lmwCT9/ijKhAOJM0zJ6XHFKIiBIYQqbnZ16C0xEYHJsmpCwOMn/yWd3Sbea2J8vlc7Oi7zqKB1tIHqCKN9dIROUQu1EUX36BE9oxfrwXqyXq2379YJq5xZQ79gfXwBUAWfug== ⌧i = 1 (N � i) N�iX 1 (tn+i � tn) AAACDnicbZDLSgMxFIYz9VbrbdSlm2Ap1E2ZEUU3QtGNK6lgL9CpQybNtKFJZkgyQhnmCdz4Km5cKOLWtTvfxrSdhbb+EPj4zzmcnD+IGVXacb6twtLyyupacb20sbm1vWPv7rVUlEhMmjhikewESBFGBWlqqhnpxJIgHjDSDkZXk3r7gUhFI3GnxzHpcTQQNKQYaWP5dsULkExlduGFEuHUzdKbzFMJ9917Q7Kq/VRkR75ddmrOVHAR3BzKIFfDt7+8foQTToTGDCnVdZ1Y91IkNcWMZCUvUSRGeIQGpGtQIE5UL52ek8GKcfowjKR5QsOp+3siRVypMQ9MJ0d6qOZrE/O/WjfR4XkvpSJONBF4tihMGNQRnGQD+1QSrNnYAMKSmr9CPEQmF20SLJkQ3PmTF6F1XHNPa87tSbl+mcdRBAfgEFSBC85AHVyDBmgCDB7BM3gFb9aT9WK9Wx+z1oKVz+yDP7I+fwDlfpyh r̄ = 1 N NX 1 r(tn) AAACJ3icbZDLSgMxFIYz9VbrrerSTbAIdWGZlIJuCkU3rqSCtYXOdMikmTY0kxmSjFCGeRo3voobFxVEl76J6WWhrT8EPv5zDifn92POlLbtLyu3tr6xuZXfLuzs7u0fFA+PHlWUSEJbJOKR7PhYUc4EbWmmOe3EkuLQ57Ttj26m9fYTlYpF4kGPY+qGeCBYwAjWxvKKdYdjMeAUyl4VOnLGdSeQmKQoS+8yRyWhh3qGurKsvVRk5xeOj2UqM7dX9Yolu2LPBFcBLaAEFmp6xYnTj0gSUqEJx0p1kR1rN8VSM8JpVnASRWNMRnhAuwYFDqly09mZGTwzTh8GkTRPaDhzf0+kOFRqHPqmM8R6qJZrU/O/WjfRwZWbMhEnmgoyXxQkHOoITjODfSYp0XxsABPJzF8hGWKTkTbJFkwIaPnkVWhXK6hWQei+VmpcL/LIgxNwCsoAgUvQALegCVqAgGfwCibg3Xqx3qwP63PemrMWM8fgj6zvH11OpiE= hr2i = 1 N NX 1 [r(tn)� r̄]2 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 ⇧(⌧i) = h[r(t+ ⌧i)� r(t)]2i 2hr2i = 1 2hr2i(N � i) N�iX 1 [r(tn+i)� r(tn)]2 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