54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib import ticker
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import pandas as pd
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import datetime
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import dateutil
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fig, ax = plt.subplots(figsize=(9, 5), dpi=100)
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#ax.set(xlabel='time (s)', ylabel='voltage (mV)',
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# title='About as simple as it gets, folks')
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ax.grid(visible=True,linestyle='dotted', )
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#ax.grid()
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plt.xlim([2020,2025])
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plt.ylim([2,12])
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ax.xaxis.set_minor_locator(ticker.MultipleLocator(1/12))
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ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.2))
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ax.tick_params(axis="both", width=1, labelsize=14)
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for axis in ['top','bottom','left','right']:
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ax.spines[axis].set_linewidth(1)
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cl='black'
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df = pd.read_csv('default.csv',)
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tm=[]
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rate=[]
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for index, row in df.iterrows():
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dt = dateutil.parser.parse(row['timestamp'])
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day_of_year = dt.timetuple().tm_yday # returns 1 for January 1st
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tm.append(dt.year+(day_of_year/365))
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rate.append(float(row['value_60.0-120.0']))
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plt.plot(tm, rate, color=cl, linewidth=1, linestyle='solid')
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geminga_dt = datetime.date.fromisoformat('2023-04-16')
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day_of_year = geminga_dt.timetuple().tm_yday # returns 1 for January 1st
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geminga_tm=geminga_dt.year+(day_of_year/365)
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plt.axvline(x = geminga_tm, color = 'b', linewidth=3 , linestyle='dashed', label = 'Geminga scan')
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plt.ylabel('Count rate (counts s$^{-1}$)',fontsize=14, fontweight='normal')
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plt.xlabel('Year',fontsize=14, fontweight='normal')
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fig.savefig("monitor.png", bbox_inches='tight')
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plt.show()
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