Commit 0cb254a2 authored by Remy Moll's avatar Remy Moll
Browse files

Last data-point. Sorry for the delay

parent 11f2e86f
*.npy
data/data_converted/
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
......
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{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# For plotting interactively:\n",
"%matplotlib ipympl\n",
"from ipywidgets import *\n",
"import matplotlib.pyplot as plt\n",
"\n",
"font = {'family' : 'normal',\n",
" 'weight' : 'normal',\n",
" 'size' : 15}\n",
"\n",
"plt.rc('font', **font)\n",
"\n",
"\n",
"import numpy as np\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6838ca332d1442fab8a732ec2d165e3c",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img 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' width=640.0/>\n",
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"voltage_1 = np.array([1900, 1975, 2000, 2010, 2015, 2020, 2030, 2050, 2075, 2100])\n",
"# We define 2020 as end of the plateau\n",
"count_1 = np.array([224, 685, 1200, 1938, 2322, 2388, 4788, 6588, 21124, 51420])\n",
"count_frequency_1 = count_1/120\n",
"\n",
"voltage_2 = np.array([1975, 2015, 2010, 2020, 2030, 2050, 2100])\n",
"count_2 = np.array([866, 1657, 1573, 2330, 3424, 10252, 64206])\n",
"# not actually used\n",
"count_frequency_2 = count_2/120\n",
"\n",
"new_voltage_2 = np.array([2075, 1975, 2015, 2010, 2020, 2030, 2050, 2100, 1900, 2000, 2040])\n",
"new_count_2 = np.array([5769, 866, 1435, 1331, 1583, 1977, 2944, 7691, 286, 1164, 2302])\n",
"new_count_frequency_2 = new_count_2/120\n",
"\n",
"\n",
"array_1 = np.vstack([voltage_1, count_frequency_1])\n",
"array_1.sort(axis=1)\n",
"\n",
"array_2 = np.vstack([voltage_2, count_frequency_2])\n",
"# WHICH ONE DO WE TAKE?\n",
"array_2.sort(axis=1)\n",
"\n",
"plt.figure()\n",
"# plt.scatter(voltage, count_frequency)\n",
"plt.plot(array_1[0], array_1[1], label=\"PMT 1\")\n",
"plt.plot(array_2[0], array_2[1], label=\"PMT 2\")\n",
"# plt.plot(new_voltage_2, new_count_frequency_2, \"+\", label=\"New PMT2\")\n",
"\n",
"plt.xlabel('Voltage [V]')\n",
"plt.ylabel('Counting Frequency [Hz]')\n",
"# plt.title(f'Counting Frequency vs PMT Voltage')\n",
"plt.legend(loc='best')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2000 20.5 $\\pm$ 0.2 & 15.2 $\\pm$ 0.1 & 8.2 $\\pm$ 0.05 & 6.8 $\\pm$ 0.025 & \n",
"2050 33.2 $\\pm$ 0.2 & 24.0 $\\pm$ 0.1 & 11.3 $\\pm$ 0.07 & 8.0 $\\pm$ 0.04 & \n",
"2100 65.0 $\\pm$ 0.6 & 41.0 $\\pm$ 0.5 & 17.0 $\\pm$ 0.2 & 11.7 $\\pm$ 0.1 & \n",
"2150 75.0 $\\pm$ 0.7 & 48.6 $\\pm$ 0.4 & 25.5 $\\pm$ 0.5 & 16.8 $\\pm$ 0.2 & \n",
"2200 144.0 $\\pm$ 1.0 & 93.5 $\\pm$ 0.5 & 49.3 $\\pm$ 0.3 & 31.6 $\\pm$ 0.2 & \n",
"2250 200.0 $\\pm$ 1.5 & 163.2 $\\pm$ 0.3 & 81.0 $\\pm$ 0.5 & 55.0 $\\pm$ 1.0 & \n",
"2300 357.0 $\\pm$ 1.5 & 269.0 $\\pm$ 2.5 & 167.0 $\\pm$ 1.5 & 117.5 $\\pm$ 1.5 & \n",
"2350 401.0 $\\pm$ 2.0 & 325.0 $\\pm$ 1.5 & 253.0 $\\pm$ 1.0 & 200.0 $\\pm$ 1.5 & \n",
"2400 544.0 $\\pm$ 3.0 & 450.0 $\\pm$ 10.0 & 350.0 $\\pm$ 10.0 & 280.0 $\\pm$ 20.0 & \n",
"2450 502.0 $\\pm$ 5.0 & 409.0 $\\pm$ 5.0 & 320.0 $\\pm$ 15.0 & 260.0 $\\pm$ 8.0 & \n",
"2500 545.0 $\\pm$ 5.0 & 480.0 $\\pm$ 5.0 & 385.0 $\\pm$ 8.0 & 304.0 $\\pm$ 10.0 & \n"
]
}
],
"source": [
"anode_voltage = np.array([2000, 2050, 2100, 2150, 2200, 2250, 2300, 2350, 2400, 2450, 2500])\n",
"\n",
"mean_peak_1 = np.array([20.5, 33.2, 65.0, 75.0, 144, 200, 357, 401, 544, 502, 545])\n",
"uncertainty_1 = np.array([0.2, 0.2, 0.6, 0.7, 1.0, 1.5, 1.5, 2, 3, 5, 5])\n",
"\n",
"mean_peak_2 = np.array([15.2, 24.0, 41.0, 48.6, 93.5, 163.2, 269, 325, 450, 409, 480])\n",
"uncertainty_2 = np.array([0.1, 0.1, 0.5, 0.4, 0.5, 0.3, 2.5, 1.5, 10, 5, 5])\n",
"\n",
"mean_peak_3 = np.array([8.2, 11.3, 17.0, 25.5, 49.3, 81.0, 167, 253, 350, 320, 385])\n",
"uncertainty_3 = np.array([0.05, 0.07, 0.2, 0.5, 0.3, 0.5, 1.5, 1, 10, 15, 8])\n",
"\n",
"mean_peak_4 = np.array([6.8, 8.0, 11.7, 16.8, 31.6, 55.0, 117.5, 200, 280, 260, 304])\n",
"uncertainty_4 = np.array([0.025, 0.04, 0.1, 0.2, 0.2, 1, 1.5, 1.5, 20, 8, 10])\n",
"\n",
"arrs = [mean_peak_1,mean_peak_2,mean_peak_3,mean_peak_4]\n",
"errs = [uncertainty_1, uncertainty_2, uncertainty_3, uncertainty_4]\n",
"for k in range(arrs[0].size):\n",
" print(anode_voltage[k], end=\" \")\n",
" for i in range(4):\n",
" print(\"{} $\\pm$ {} & \".format(arrs[i][k], errs[i][k]), end=\"\")\n",
" print(\"\")\n",
" \n",
"\n",
"# 578, 0.8; 523, 1; 423, 1.5;360, 0.8\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "153ce8f17e0e4530a7363791b025d6e4",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img 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' width=640.0/>\n",
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.errorbar(anode_voltage, mean_peak_1, yerr=uncertainty_1, label=\"Channel 1\")\n",
"plt.errorbar(anode_voltage, mean_peak_2, yerr=uncertainty_2, label=\"Channel 2\")\n",
"plt.errorbar(anode_voltage, mean_peak_3, yerr=uncertainty_3, label=\"Channel 3\")\n",
"plt.errorbar(anode_voltage, mean_peak_4, yerr=uncertainty_4, label=\"Channel 4\")\n",
"\n",
"plt.xlabel('Anode Voltage [V]')\n",
"plt.ylabel('Mean Peak Voltage [mV]')\n",
"# plt.title('Mean Peak Voltage per channel vs Anode Voltage')\n",
"plt.legend(loc='best')\n",
"plt.show()\n",
"\n",
" # mean_peak_average = np.zeros_like(mean_peak[0])\n",
" # for mean_peak in mean_peak_list:\n",
" # return\n",
" \n",
" \n",
" \n",
" # #plt.figure(figsize=(12,8))\n",
" # plt.errorbar(voltage, mean_peak, yerr=uncertainty)\n",
" # #plt. # plt.legend(loc='best')\n",
" # plot(voltage,count)\n",
" # plt.xlabel('Anode Voltage [V]')\n",
" # plt.ylabel('Mean Peak Voltage [mV]')\n",
" # plt.title(f'Mean Peak Voltage Channel {number} vs Anode Voltage')\n",
" # plt.savefig(f\"Mean Peak Voltage Channel {number} vs Anode Voltage.pdf\", dpi=dpi_set)\n",
" # plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# plot_anode(anode_voltage, mean_peak_1, uncertainty_1, 1)\n",
"# plot_anode(anode_voltage, mean_peak_2, uncertainty_2, 2)\n",
"# plot_anode(anode_voltage, mean_peak_3, uncertainty_3, 3)\n",
"# plot_anode(anode_voltage, mean_peak_4, uncertainty_4, 4)\n"
]
}
],
"metadata": {
"interpreter": {
"hash": "69414746224b7fd1cf715ab6582df012d701881e796edf01afa66d1cf26450ce"
},
"kernelspec": {
"display_name": "Python 3.9.7 64-bit ('VEL': venv)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
%% Cell type:code id: tags:
``` python
# For plotting interactively:
%matplotlib ipympl
from ipywidgets import *
import matplotlib.pyplot as plt
font = {'family' : 'normal',
'weight' : 'normal',
'size' : 15}
plt.rc('font', **font)
import numpy as np
```
%% Cell type:code id: tags:
``` python
voltage_1 = np.array([1900, 1975, 2000, 2010, 2015, 2020, 2030, 2050, 2075, 2100])
# We define 2020 as end of the plateau
count_1 = np.array([224, 685, 1200, 1938, 2322, 2388, 4788, 6588, 21124, 51420])
count_frequency_1 = count_1/120
voltage_2 = np.array([1975, 2015, 2010, 2020, 2030, 2050, 2100])
count_2 = np.array([866, 1657, 1573, 2330, 3424, 10252, 64206])
# not actually used
count_frequency_2 = count_2/120
new_voltage_2 = np.array([2075, 1975, 2015, 2010, 2020, 2030, 2050, 2100, 1900, 2000, 2040])
new_count_2 = np.array([5769, 866, 1435, 1331, 1583, 1977, 2944, 7691, 286, 1164, 2302])
new_count_frequency_2 = new_count_2/120
array_1 = np.vstack([voltage_1, count_frequency_1])
array_1.sort(axis=1)
array_2 = np.vstack([voltage_2, count_frequency_2])
# WHICH ONE DO WE TAKE?
array_2.sort(axis=1)
plt.figure()
# plt.scatter(voltage, count_frequency)
plt.plot(array_1[0], array_1[1], label="PMT 1")
plt.plot(array_2[0], array_2[1], label="PMT 2")
# plt.plot(new_voltage_2, new_count_frequency_2, "+", label="New PMT2")
plt.xlabel('Voltage [V]')
plt.ylabel('Counting Frequency [Hz]')
# plt.title(f'Counting Frequency vs PMT Voltage')
plt.legend(loc='best')
plt.show()
```
%%%% Output: stream
findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.
%%%% Output: display_data
%% Cell type:code id: tags:
``` python
anode_voltage = np.array([2000, 2050, 2100, 2150, 2200, 2250, 2300, 2350, 2400, 2450, 2500])
mean_peak_1 = np.array([20.5, 33.2, 65.0, 75.0, 144, 200, 357, 401, 544, 502, 545])
uncertainty_1 = np.array([0.2, 0.2, 0.6, 0.7, 1.0, 1.5, 1.5, 2, 3, 5, 5])
mean_peak_2 = np.array([15.2, 24.0, 41.0, 48.6, 93.5, 163.2, 269, 325, 450, 409, 480])
uncertainty_2 = np.array([0.1, 0.1, 0.5, 0.4, 0.5, 0.3, 2.5, 1.5, 10, 5, 5])
mean_peak_3 = np.array([8.2, 11.3, 17.0, 25.5, 49.3, 81.0, 167, 253, 350, 320, 385])
uncertainty_3 = np.array([0.05, 0.07, 0.2, 0.5, 0.3, 0.5, 1.5, 1, 10, 15, 8])
mean_peak_4 = np.array([6.8, 8.0, 11.7, 16.8, 31.6, 55.0, 117.5, 200, 280, 260, 304])
uncertainty_4 = np.array([0.025, 0.04, 0.1, 0.2, 0.2, 1, 1.5, 1.5, 20, 8, 10])
arrs = [mean_peak_1,mean_peak_2,mean_peak_3,mean_peak_4]
errs = [uncertainty_1, uncertainty_2, uncertainty_3, uncertainty_4]
for k in range(arrs[0].size):
print(anode_voltage[k], end=" ")
for i in range(4):
print("{} $\pm$ {} & ".format(arrs[i][k], errs[i][k]), end="")
print("")
# 578, 0.8; 523, 1; 423, 1.5;360, 0.8
```
%%%% Output: stream
2000 20.5 $\pm$ 0.2 & 15.2 $\pm$ 0.1 & 8.2 $\pm$ 0.05 & 6.8 $\pm$ 0.025 &
2050 33.2 $\pm$ 0.2 & 24.0 $\pm$ 0.1 & 11.3 $\pm$ 0.07 & 8.0 $\pm$ 0.04 &
2100 65.0 $\pm$ 0.6 & 41.0 $\pm$ 0.5 & 17.0 $\pm$ 0.2 & 11.7 $\pm$ 0.1 &
2150 75.0 $\pm$ 0.7 & 48.6 $\pm$ 0.4 & 25.5 $\pm$ 0.5 & 16.8 $\pm$ 0.2 &
2200 144.0 $\pm$ 1.0 & 93.5 $\pm$ 0.5 & 49.3 $\pm$ 0.3 & 31.6 $\pm$ 0.2 &
2250 200.0 $\pm$ 1.5 & 163.2 $\pm$ 0.3 & 81.0 $\pm$ 0.5 & 55.0 $\pm$ 1.0 &
2300 357.0 $\pm$ 1.5 & 269.0 $\pm$ 2.5 & 167.0 $\pm$ 1.5 & 117.5 $\pm$ 1.5 &
2350 401.0 $\pm$ 2.0 & 325.0 $\pm$ 1.5 & 253.0 $\pm$ 1.0 & 200.0 $\pm$ 1.5 &
2400 544.0 $\pm$ 3.0 & 450.0 $\pm$ 10.0 & 350.0 $\pm$ 10.0 & 280.0 $\pm$ 20.0 &
2450 502.0 $\pm$ 5.0 & 409.0 $\pm$ 5.0 & 320.0 $\pm$ 15.0 & 260.0 $\pm$ 8.0 &
2500 545.0 $\pm$ 5.0 & 480.0 $\pm$ 5.0 & 385.0 $\pm$ 8.0 & 304.0 $\pm$ 10.0 &
%% Cell type:code id: tags:
``` python
plt.figure()
plt.errorbar(anode_voltage, mean_peak_1, yerr=uncertainty_1, label="Channel 1")
plt.errorbar(anode_voltage, mean_peak_2, yerr=uncertainty_2, label="Channel 2")
plt.errorbar(anode_voltage, mean_peak_3, yerr=uncertainty_3, label="Channel 3")
plt.errorbar(anode_voltage, mean_peak_4, yerr=uncertainty_4, label="Channel 4")
plt.xlabel('Anode Voltage [V]')
plt.ylabel('Mean Peak Voltage [mV]')
# plt.title('Mean Peak Voltage per channel vs Anode Voltage')
plt.legend(loc='best')
plt.show()
# mean_peak_average = np.zeros_like(mean_peak[0])
# for mean_peak in mean_peak_list:
# return
# #plt.figure(figsize=(12,8))
# plt.errorbar(voltage, mean_peak, yerr=uncertainty)
# #plt. # plt.legend(loc='best')
# plot(voltage,count)
# plt.xlabel('Anode Voltage [V]')
# plt.ylabel('Mean Peak Voltage [mV]')
# plt.title(f'Mean Peak Voltage Channel {number} vs Anode Voltage')
# plt.savefig(f"Mean Peak Voltage Channel {number} vs Anode Voltage.pdf", dpi=dpi_set)
# plt.show()
```
%%%% Output: display_data
%% Cell type:code id: tags:
``` python
# plot_anode(anode_voltage, mean_peak_1, uncertainty_1, 1)
# plot_anode(anode_voltage, mean_peak_2, uncertainty_2, 2)
# plot_anode(anode_voltage, mean_peak_3, uncertainty_3, 3)
# plot_anode(anode_voltage, mean_peak_4, uncertainty_4, 4)
```
......@@ -3,7 +3,7 @@ import matplotlib.pyplot as plt
#%% Measurements
voltage_1 = np.array([1900, 1975, 2000, 2010, 2015, 2020, 2030, 2050, 2075, 2100]) # We define 2020 as end of the plateau
count_1 = np.array([ 224, 685, 1200, 1938, 2322, 2388, 4788, 6588, 21124, 51420])
count_1 = np.array([224, 685, 1200, 1938, 2322, 2388, 4788, 6588, 21124, 51420])
count_frequency_1 = count_1/120
voltage_2 = np.array([1975, 2015, 2010, 2020, 2030, 2050, 2100])
......
This source diff could not be displayed because it is stored in LFS. You can view the blob instead.
......@@ -207,6 +207,7 @@ class ArrayLoader:
for f in files:
if fname_fuzzy in f:
matches.append(f)
if len(matches) > 1:
print("Warning: The measurement file was not unambiguous. Please specify more arguments")
return
......@@ -214,7 +215,10 @@ class ArrayLoader:
print("Warning: No data found.")
return
else:
fname = self.base_path + matches[0]
f = matches[0]
delay = int(f[f.find("_d") + 2 : f.find(".dat")])
fname = self.base_path + f
fname_converted = fname.replace(self.base_path, self.base_path + "data_converted/").replace(".dat", ".npy")
# change /data/... to /data/data_converted and change .dat to .npy
......
from os import device_encoding
import numpy as np
from scipy import signal
import decode
DECODER = decode.ArrayLoader("data/")
from . import decode
DECODER = decode.ArrayLoader("driftchamber_computations/data/")
......@@ -96,9 +95,18 @@ class Event:
return (good >= self.min_good_channels)# and ends_good
def recreate_muon_path(self, drift_velocity):
pass
def recreate_muon_path(self, drift_velocity, delta_v, minimum_times):
x_pos = []
x_err = []
for i,ch in enumerate(self.channels):
delta_t = ch.peak_time - minimum_times[i] # each channel has a different minimum time
x_pos.append(drift_velocity * delta_t)
if not ch.is_usable:
x_err.append(0.3)
else:
x_err.append(delta_v * delta_t)
return np.array(x_pos), np.array(x_err)
class Measurement:
......@@ -114,6 +122,7 @@ class Measurement:
self.events.append(Event(loaded[i,...], nbins, min_good_channels))
del loaded
self.n_events = len(self.events)
self.n_channels = self.events[0].n_channels
def filter_events(self):
......@@ -134,4 +143,37 @@ class Measurement:
else:
timings[j,i] = -1
return timings
\ No newline at end of file
return timings
def get_per_channel_histogram(self, nbins):
"""Returns the binned histograms as well as their bin-centers"""
timings = self.get_peak_timings()
hists = []
bin_centers = []
raw_datas = []
self._min_times = []
for i in range(timings.shape[0]): # usually 8
hist, bin_edges = np.histogram(timings[i,...], bins=nbins, density=True)#, stacked=True)
bin_center = (bin_edges[:-1] + bin_edges[1:]) / 2
self._min_times.append(bin_center[1]) # 0th element is 0
hists.append(hist)
bin_centers.append(bin_center)
raw_datas.append(timings[i,...])
return raw_datas, hists, bin_centers
@property
def per_channel_min_times(self):
try:
return self._min_times
except:
raise Exception("Please call get_per_channel_histogram first.")
def get_total_histogram(self, nbins):
hists, bin_centers = self.get_per_channel_histogram(nbins)
avg_hist = np.mean(hists, axis=0)
avg_bin_centers = np.mean(bin_centers, axis=0)
return avg_hist, avg_bin_centers
\ No newline at end of file
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