From 6f572c320fa37181d5db5912c56501c5fe141220 Mon Sep 17 00:00:00 2001
From: Indigo5684 <159226326+Indigo5684@users.noreply.github.com>
Date: Tue, 30 Sep 2025 13:19:40 -0500
Subject: [PATCH] ix

---
 .../physics/modern-phys-lab/counting/run.py   | 82 ++++++++++---------
 1 file changed, 44 insertions(+), 38 deletions(-)

diff --git a/statics/physics/modern-phys-lab/counting/run.py b/statics/physics/modern-phys-lab/counting/run.py
index 10e68e1..6f92e99 100644
--- a/statics/physics/modern-phys-lab/counting/run.py
+++ b/statics/physics/modern-phys-lab/counting/run.py
@@ -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()
\ No newline at end of file