<jupyter_start><jupyter_text>Curve Fitting: Temperature as a function of month of the year<jupyter_code>temp_max = np.array([17,19,21,28,33,37,37,31,23,19,18,23])
temp_min = np.array([-62,-59,-56,-46,-32,-18,-9,-13,-25,-46,-52,-58])
months = np.arange(12)
plt.plot(months,temp_max,'ro')
plt.plot(months,temp_min,'bo')
plt.xlabel("Month")
plt.ylabel("Min and Max Temperature")<jupyter_output><empty_output><jupyter_text>Topic: Signal Processing  Knowledge of Fourier Series  1. From SciPy we can use stats module to perform statistical distribution plotting.<jupyter_code>from scipy import signal
t = np.linspace(0,5,100)
t<jupyter_output><empty_output><jupyter_text>Creating a sine wave:<jupyter_code>x = np.sin(t)
x<jupyter_output><empty_output><jupyter_text>resampling:<jupyter_code>x_resamp = signal.resample(x,25)
x_resamp
plt.plot(t,x)
plt.plot(t[::4],x_resamp,'ko')<jupyter_output><empty_output><jupyter_text>t[::4] 4 steps: total dots = 2525 x 4 = 100, the value that was given to the linspace  'ko' : black<jupyter_code>plt.plot(t,x)
plt.plot(t[::4],x_resamp,'ko')<jupyter_output><empty_output><jupyter_text>.detrended  Remove linear trend along axis from data.<jupyter_code>x_detrended = signal.detrend(x)
plt.plot(t,x)
plt.plot(t,x_detrended)<jupyter_output><empty_output>