Now that we have the data processed and as a result of those we are left with text files for the times, longitudes and latitudes of sunspots, which are direct measurements from the data, we are ready to obtain indirect measurements from them, such as angular speeds and everything that may be acquired from the data sets text files.
These text files with the data are stored as follows (paths are relative to the location of this notebook):
results
├── set1
│ ├── set1_latitudes_0.txt
│ ├── set1_latitudes_1.txt
│ ├── set1_longitudes_0.txt
│ ├── set1_longitudes_1.txt
│ └── set1_times.txt
├── set2
│ ├── set2_latitudes_0.txt
│ ├── set2_latitudes_1.txt
│ ├── set2_longitudes_0.txt
│ ├── set2_longitudes_1.txt
│ └── set2_times.txt
...
10 directories, 32 files
Where the number after the "_" character means the index of spots, in case the data was acquired for more than 1 spot per image/data set.
In [1]:
# basic python modules and tools
import numpy as np
import matplotlib.pyplot as plt
In [57]:
# Extracting data from saved files
setstr = "set9" # name of the directory for the set
longitudes_0 = np.loadtxt("results/"+setstr+"/"+setstr+"_longitudes_0.txt")
#longitudes_1 = np.loadtxt("results/"+setstr+"/"+setstr+"_longitudes_1.txt") # set1
latitudes_0 = np.loadtxt("results/"+setstr+"/"+setstr+"_latitudes_0.txt")
#latitudes_1 = np.loadtxt("results/"+setstr+"/"+setstr+"_latitudes_1.txt") # set1
times = np.loadtxt("results/"+setstr+"/"+setstr+"_times.txt")
In [58]:
# Plot longitudes and latitudes over time
%matplotlib inline
plt.figure(figsize=(16,9))
plt.title("Longitudes")
plt.grid(True)
plt.plot(times, longitudes_0, '*')
#plt.plot(times, longitudes_1, '+') #set1
plt.figure(figsize=(16,9))
plt.title("Latitudes")
plt.grid(True)
plt.plot(times,latitudes_0)
# plt.plot(times,latitudes_1) #set1
Out[58]:
In [59]:
# Linear Fitting longitude variations (assumed constant speed)
# First spot
velocity_0, initial_long_0 = np.polyfit(times[0:], longitudes_0[0:], 1)
Y_0 = velocity_0*times[0:]+initial_long_0
plt.figure(figsize=(12,7))
plt.grid(True)
plt.title(r"Longitude variation with time")
plt.xlabel(r"t (s)")
plt.ylabel(r"$\theta$ (arcsec)")
plt.plot(times,longitudes_0, '+')
plt.plot(times[0:], Y_0)
# Second spot (optional) set1
#velocity_1, initial_long_1 = np.polyfit(times[0:], longitudes_1[0:], 1)
#Y_1 = velocity_1*times[0:]+initial_long_1
#plt.figure(figsize=(12,7))
#plt.grid(True)
#plt.title(r"Longitude variation with time")
#plt.xlabel(r"t (s)")
#plt.ylabel(r"$\theta$ (arcsec)")
#plt.plot(times,longitudes_1, '+')
#plt.plot(times[0:], Y_1)
Out[59]:
In [60]:
velocity_0
Out[60]:
In [11]:
velocity_1
Out[11]:
In [12]:
np.average(latitudes_0)
Out[12]:
In [13]:
np.average(latitudes_1)
Out[13]:
In [14]:
np.std(latitudes_0)
Out[14]:
In [15]:
np.std(latitudes_1)
Out[15]:
In [16]:
# To be run only once to store all values
velocities = []
latitudes = []
In [61]:
# Storing latitudes and velocities values
velocities.append(velocity_0)
#velocities.append(velocity_1) # set1
latitudes.append(np.average(latitudes_0))
#latitudes.append(np.average(latitudes_1)) # set1
In [62]:
velocities
Out[62]:
In [63]:
latitudes
Out[63]:
In [65]:
plt.plot(latitudes, velocities, '*')
Out[65]:
In [ ]: