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Indigo5684
2025-09-30 13:19:40 -05:00
parent 493a0a0c49
commit 6f572c320f
1 changed files with 44 additions and 38 deletions
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82
statics/physics/modern-phys-lab/counting/run.py
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@@ -10,11 +10,11 @@ def read_file(file: str) -> np.typing.ArrayLike:
all_data = {
'200': read_file('200'),
'100': read_file('100-1') + read_file('100-2'),
'40': sum([read_file(f'40-{i}') for i in range(1, 6)])
'100': np.hstack([read_file('100-1'), read_file('100-2')]),
'40': np.hstack([read_file(f'40-{i}') for i in range(1, 6)])
}
fig, axs = plt.subplots(len(all_data), 1, sharex='col')
fig, axs = plt.subplots(len(all_data), 1)
i = 0
for dwell_time in all_data:
@@ -42,48 +42,54 @@ for dwell_time in all_data:
times_dev_more_than_P = (np.abs(data - mean) > P).sum()
print(f'Found {times_dev_more_than_P} / {N} ({times_dev_more_than_P / N * 100:0.2f}%) samples deviating from the mean more than P {P:0.2f}')
ax = axs[i]
ax.set_xlabel('Run Count')
ax.set_ylabel('Events Measured')
ax.set_title('Raw Data')
ax.scatter(np.arange(1, len(data)+1), data, s=4)
ax.legend(['\n'.join([
f'Mean: {mean:0.2f}',
f'Sample Sd. Dev: {stdev:0.2f}',
f'P: {P:0.2f}',
f'Events > sigma: {times_dev_more_than_s} ({times_dev_more_than_s / N * 100:0.2f}%)',
f'Events > P: {times_dev_more_than_P} ({times_dev_more_than_P / N * 100:0.2f}%)'
])])
if True:
ax = axs[i]
ax.set_xlabel('Run Count')
ax.set_ylabel('Events Measured')
ax.set_title(f'Raw Data ({dwell_time} ms)')
ax.scatter(np.arange(1, len(data)+1), data, s=4)
ax.legend(['\n'.join([
f'Mean: {mean:0.2f}',
f'Sample Sd. Dev: {stdev:0.2f}',
f'Sqrt(Mean): {sigma:0.2f}',
f'P: {P:0.2f}',
f'Events > sigma: {times_dev_more_than_s} ({times_dev_more_than_s / N * 100:0.2f}%)',
f'Events > P: {times_dev_more_than_P} ({times_dev_more_than_P / N * 100:0.2f}%)'
])])
# ax = axs[i][1]
# ax.set_visible(False)
if False:
ax = axs[i]
# ax.set_title(f'Cumulative Average ({dwell_time}ms Dwell time)')
# ax.set_xlabel('After N Runs')
# ax.set_ylabel('Cumulative Average')
ax.set_title(f'Cumulative Average ({dwell_time}ms Dwell time)')
ax.set_xlabel('After N Runs')
ax.set_ylabel('Cumulative Average')
# sample_num = np.arange(1, N + 1)
# cumulative_average = np.cumsum(data) / sample_num
# ax.plot(sample_num, cumulative_average, label=f'Final Value: {cumulative_average[-1]:0.2f}')
# ax.legend()
sample_num = np.arange(1, N + 1)
cumulative_average = np.cumsum(data) / sample_num
error = 1 / np.sqrt(sample_num) # Not used, tiny
ax.plot(sample_num, cumulative_average, label=f'Final Value: {cumulative_average[-1]:0.2f}')
ax.legend()
if False:
ax = axs[i]
N, bins, patches = ax.hist(data, density=True, bins=16, label='Measured Data')
# ax = axs[i]
# N, bins, patches = ax.hist(data, density=True, bins=32, label='Measured Data')
fracs = N / N.max()
norm = colors.Normalize(fracs.min(), fracs.max())
for thisfrac, thispatch in zip(fracs, patches):
color = plt.cm.viridis(norm(thisfrac))
thispatch.set_facecolor(color)
# fracs = N / N.max()
# norm = colors.Normalize(fracs.min(), fracs.max())
# for thisfrac, thispatch in zip(fracs, patches):
# color = plt.cm.viridis(norm(thisfrac))
# thispatch.set_facecolor(color)
ax.set_xlabel('Event Count')
ax.set_ylabel('Frequency')
# ax.set_xlabel('Event Count')
# ax.set_ylabel('Frequency')
# linspace = np.arange(bins.min(), bins.max())
# gaussian = scipy.stats.norm.pdf(linspace, mean, stdev)
# ax.plot(linspace, gaussian, label='Fitted Gaussian')
# ax.legend()
linspace = np.arange(bins.min(), bins.max())
gaussian = scipy.stats.norm.pdf(linspace, mean, stdev)
ax.set_title(f'Distribution ({dwell_time}ms Dwell time)')
ax.plot(linspace, gaussian, label='Fitted Gaussian')
ax.legend()
i += 1
fig.tight_layout()
plt.show()