Timers Technical Report (ENTER NAME AND NET ID HERE)

BME554L - Fall 2025 - Palmeri

Methods

Briefly describe what your did for each type of data acquisition (e.g., how did you capture the logging data and save data from the oscilloscope).

The code below is just starter / example code that you can use to help guide how you approach doing this analysis. Feel free to use other analysis approaches if you are more comfortable with them, as long as you can draw the same conclusions from your testing.

Data Analysis

# import useful packages for data analysis
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import pandas as pd

Logging Data

# import data from CSV or similar files
# DO NOT copy/paste raw data into the notebook directly (!); data should be independent on this notebook
timing_data = pd.read_csv('log_data.csv')

# consider showing a snippet of the raw data too
timing_data.head(20)

	Ideal Period (ms)	Measurement Period (ms)
0	1000	1005
1	1000	1003
2	1000	1001
3	1000	990
4	1000	995
5	500	490
6	500	495
7	500	497
8	500	492
9	500	491
10	2000	2100
11	2000	2000
12	2000	2300
13	2000	1900
14	2000	1950

# display a plot or table of the data, along with the nominal values
timing_data.groupby('Ideal Period (ms)')['Measurement Period (ms)'].describe() 

	count	mean	std	min	25%	50%	75%	max
Ideal Period (ms)
500	5.0	493.0	2.915476	490.0	491.0	492.0	495.0	497.0
1000	5.0	998.8	6.180615	990.0	995.0	1001.0	1003.0	1005.0
2000	5.0	2050.0	158.113883	1900.0	1950.0	2000.0	2100.0	2300.0

# calculate the 95% confidence interval for the mean timing interval
confidence_interval = timing_data.groupby('Ideal Period (ms)')['Measurement Period (ms)'].apply(
    lambda x: sp.stats.t.interval(0.95, len(x)-1, loc=np.mean(x), scale=sp.stats.sem(x))
)
print(confidence_interval)

Ideal Period (ms)
500      (489.3799584784563, 496.6200415215437)
1000    (991.1257530463614, 1006.4742469536385)
2000    (1853.675683852244, 2246.3243161477562)
Name: Measurement Period (ms), dtype: object

Oscilloscope Data

# import data from CSV or similar files
# DO NOT copy/paste raw data into the notebook directly (!); data should be independent on this notebook
timing_data = pd.read_csv('oscope_data.csv')

# consider showing a snippet of the raw data too
timing_data.head(20)

	Ideal Period (ms)	Measurement Period (ms)
0	1000	1005
1	1000	1003
2	1000	1001
3	1000	990
4	1000	995
5	500	490
6	500	495
7	500	497
8	500	492
9	500	491
10	2000	2100
11	2000	2000
12	2000	2300
13	2000	1900
14	2000	1950

# display a plot or table of the data, along with the nominal values
timing_data.groupby('Ideal Period (ms)')['Measurement Period (ms)'].describe() 

	count	mean	std	min	25%	50%	75%	max
Ideal Period (ms)
500	5.0	493.0	2.915476	490.0	491.0	492.0	495.0	497.0
1000	5.0	998.8	6.180615	990.0	995.0	1001.0	1003.0	1005.0
2000	5.0	2050.0	158.113883	1900.0	1950.0	2000.0	2100.0	2300.0

# calculate the 95% confidence interval for the mean timing interval
confidence_interval = timing_data.groupby('Ideal Period (ms)')['Measurement Period (ms)'].apply(
    lambda x: sp.stats.t.interval(0.95, len(x)-1, loc=np.mean(x), scale=sp.stats.sem(x))
)
print(confidence_interval)

Discussion

How do your logging-based data and oscilloscope data compare to the nominal timing intervals?

Which measurement approach is more accurate?

Is there any difference in the accuracy of the default, slowest and fastest blink rate accuracy / precision?