test

gti.py

@@ -0,0 +1,222 @@
+#!/usr/bin/env python
+"""
+НАЗВАНИЕ:
+
+ gti.py
+ 
+ 
+НАЗНАЧЕНИЕ:
+
+   Cкрипт для создания интервалов хорошего времени по данным из файла, сбинированного по 60 минут программой create_binned_filed.py (на основе оригинального файла lc_orig_60-120.csv, выгруженного с СРГ L2 монитора https://monitor.srg.cosmos.ru/ )
+     Скрипт аппроксимирует кривую блеска полиномом, чтобы убрать синусоидальный тренд,  ищет времена вспышек и возвращает интервалы времени без вспышек в офрмате секунд с начала 1 января 2000 года (Москва) (файл gti_sec.csv) и в формате дестичного года (файл gti_dyear.csv).
+
+ВЫЗОВ:
+
+   conda activate
+   ./gti.py
+
+
+УПРАВЛЕНИЕ:
+
+   Название файла надо вставить внутрь скрипта (ищите lc_bin_60min_60-120.csv)
+ 
+ПАРАМЕТРЫ:
+
+   N/A
+
+
+ВЫВОД:
+   Файл с графиком monitor.png   записывается в текущую директорию.
+   Файл с интервалами времени gti.csv записывается в текущую директорию.
+  
+
+ИСТОРИЯ:
+	   Филиппова Екатерина, ИКИ РАН, kate@cosmos.ru
+   Сентябрь, 2025
+   
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib import ticker
+import pandas as pd
+import datetime
+import dateutil
+from scipy.optimize import curve_fit
+from scipy.ndimage import gaussian_filter1d
+from scipy import signal
+from scipy.stats import median_abs_deviation
+import astropy.io.fits as pf
+from astropy.time import Time
+
+
+def polynomial_func(x, *coefficients):
+    """General polynomial function"""
+    return sum(coef * x**i for i, coef in enumerate(coefficients))
+
+def detect_outbursts_threshold(times, fluxes, threshold=2.0, min_duration=3):
+    """
+    Detect outbursts using a threshold-based approach.
+    
+    Parameters:
+    times: array of time values
+    fluxes: array of flux values
+    threshold: number of standard deviations above median to consider as outburst
+    min_duration: minimum number of consecutive points to consider as outburst
+    
+    Returns:
+    outburst_intervals: list of (start_time, end_time) tuples
+    """
+    # Calculate median and standard deviation
+    median_flux = np.median(fluxes)
+    mad = median_abs_deviation(fluxes)
+    print("median_flux",median_flux,"mad",mad)
+    
+    # Create a threshold value
+    threshold_value = median_flux + threshold * mad
+    
+    # Identify points above threshold
+    above_threshold = fluxes > threshold_value
+    
+    # Find consecutive points above threshold
+    outburst_indices = []
+    in_outburst = False
+    start_idx = None
+    
+    for i, is_above in enumerate(above_threshold):
+        if is_above and not in_outburst:
+            in_outburst = True
+            start_idx = i
+        elif not is_above and in_outburst:
+            in_outburst = False
+            # Check if the outburst duration meets the minimum requirement
+            if i - start_idx >= min_duration:
+                outburst_indices.append((start_idx, i-1))
+            start_idx = None
+    
+    # Handle case where outburst continues until end of data
+    if in_outburst and len(fluxes) - start_idx >= min_duration:
+        outburst_indices.append((start_idx, len(fluxes)-1))
+    
+    # Convert indices to time intervals
+    outburst_intervals = [(times[start], times[end]) for start, end in outburst_indices]
+    
+    return outburst_intervals, median_flux,mad
+
+
+
+fig, ax = plt.subplots(figsize=(9, 5), dpi=100)
+ax.grid(visible=True,linestyle='dotted', )
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(1/12))
+ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.2))
+ax.tick_params(axis="both", width=1, labelsize=14)
+
+plt.ylim([-2,21])
+plt.ylabel('Count rate (counts s$^{-1}$)',fontsize=14, fontweight='normal')
+plt.xlabel('Year',fontsize=14, fontweight='normal')
+
+
+for axis in ['top','bottom','left','right']:
+    ax.spines[axis].set_linewidth(1)
+
+cl='black'
+
+# Сюда надо вставить название файла, скачанного с https://monitor.srg.cosmos.ru/
+df = pd.read_csv('lc_bin_60min_60-120.csv',)
+
+tm=[]
+rate=[]
+error=[]
+for index, row in df.iterrows():
+    dt = dateutil.parser.parse(row['timestamp'])  
+    day_of_year = dt.timetuple().tm_yday+dt.timetuple().tm_hour/24+dt.timetuple().tm_min/60/24+dt.timetuple().tm_sec/60/60/24  # returns 1 for January 1st
+    is_leap = (dt.year % 4 == 0 and dt.year % 100 != 0) or (dt.year % 400 == 0)
+    days_in_year = 366 if is_leap else 365
+    tm.append(dt.year+(day_of_year/days_in_year))
+    rate.append(float(row['value_60.0-120.0']))
+    error.append(float(row['error_60.0-120.0']))
+
+rate=np.array(rate)
+tm=np.array(tm)
+tmp=np.where(rate>0,1,0)
+rate=np.compress(tmp,rate)
+tm=np.compress(tmp,tm)
+error=np.compress(tmp,error)
+
+tmp=np.where(rate < 4.3,1,0)
+tm_fit=np.compress(tmp,tm)
+rate_fit=np.compress(tmp,rate)
+
+# fit polinom of degree 
+degree = 6
+initial_guess = [1] * (degree + 1)  # Initial guess for coefficients
+#initial_guess = [-3.67265411e+08, 5.44675290e+05, -2.69261063e+02, 4.43698213e-02]
+initial_guess = [ -6.10911582e+07,  3.02005656e+04, 1.49297834e+01, 6.53394516e-09, -3.64859359e-06, -1.80368300e-09, 8.91651441e-13] 
+
+params, covariance = curve_fit(polynomial_func, tm_fit, rate_fit, p0=initial_guess)
+#print("curve_fit:",params)
+
+# Generate fitted curve
+y_fit = polynomial_func(tm, *params)
+
+rate_flat=rate-y_fit
+
+plt.plot(tm, rate, color=cl, linewidth=1, linestyle='solid',marker='.',label="Original data")
+#plt.plot(tm_fit, rate_fit, color=cl, marker="o")
+plt.plot(tm, y_fit, color="blue", linewidth=1, linestyle='solid', label=f'Degree {degree} fit')
+plt.plot(tm, rate_flat, color="red", linewidth=1, linestyle='solid',label="Residuals")
+
+smoothed_rate = gaussian_filter1d(rate_flat, sigma=2)
+plt.plot(tm, smoothed_rate, color=cl, linewidth=1, linestyle='solid',label="Smoothed resid")
+
+threshold=8.
+
+outbursts,median_flux,mad = detect_outbursts_threshold(tm, smoothed_rate, threshold=threshold, min_duration=1)
+for start, end in outbursts:
+    mask = (tm >= start) & (tm <= end)
+    plt.plot(tm[mask], rate[mask], 'm-', linewidth=2, label='Threshold outbursts' if start == outbursts[0][0] else "")
+    
+horline=np.ones(len(tm))*(median_flux + threshold * mad)
+plt.plot(tm,horline,color=cl, linewidth=1, linestyle='dashed',label="Threshold flux")
+
+
+print("Number of outburst:",np.size(outbursts))
+#print("Detected outburst intervals:", outbursts)
+
+tstart=[tm[0]]
+tstop=[]
+for start,end in outbursts:
+    if end != tm[-1]:
+        tstop.append(start)
+        tstart.append(end)
+    else:
+        tstop.append(start)
+
+if outbursts[-1][1] != tm[-1]:
+    tstop.append(tm[-1])
+
+    
+
+
+# plot GTI
+for i in np.arange(len(tstart)):
+    mask = (tm >= tstart[i]) & (tm <= tstop[i])
+#    plt.plot(tm[mask], np.ones(len(tm[mask]))*2, 'y-', linewidth=2, label='GTI')
+
+
+plt.legend()
+fig.savefig("outbursts.png", bbox_inches='tight')
+
+df = pd.DataFrame({'tstart': tstart,'tstop': tstop})
+df.to_csv('gti_dyear.csv', index=False)
+
+tstart_obj = Time(tstart, format='decimalyear')
+tstop_obj=Time(tstop,format='decimalyear')
+tstart_sec=(tstart_obj.mjd-51543.75)*86400
+tstop_sec=(tstop_obj.mjd-51543.75)*86400
+
+df = pd.DataFrame({'tstart': tstart_sec,'tstop': tstop_sec})
+df.to_csv('gti_sec.csv', index=False)
+
+print("Files gti_dyear.csv and gti_sec.csv are created")
+