In [1]:

    

import math
import numpy as np
%matplotlib inline
import matplotlib 
matplotlib.style.use('ggplot')
import matplotlib.pyplot as plt

# Htemp = Temporary habitat  
# Dcell = in a grid of diameter Dcell 
# Prain = increases with rainfall Prain and 
# tautemp = decays with a rate τtemp proportional to the evaporation rate driven by 
# Tk = temperature T (K) and 
# RH= humidity RH:
# Gas constant in units J K^{-1} mol^{-1}]]]]]]]]]]
# Htemp - Htemp(deltat/tautemp) = Prain * Ktemp * Dcell**2
# Htemp(1- deltat/tautemp) = Prain * Ktemp * Dcell**2

# H_temp = P_rain * K_temp * D_cell**2  / (1- delta_t/tau_temp)

yrindays = range(0,356)
Ktemp = [1.25e+9, 11.25e+9] #   1.25 × 109 for gambiae ss and 11.25 × 109 for arabiensis
ktempdecay = 0.05 # ktempdecay
Tkrange = np.arange(293,313,4)
R = 8.31451
RH = np.arange(0,1.0,0.2)
j = 0
dat = []
for Tk in Tkrange:
    tautemp = 1./(5.1e+11 * math.exp(-5628.1/Tk) * ktempdecay * math.sqrt(.018/(2.0*math.pi*R*Tk))*(1-RH))
    j +=1
    dat.append(tautemp)
    

deltat = np.arange(0,365,1)
Prainrange = np.random.uniform(0,15,800)
Htemp = []#0 # when there isn't rain

Prain = np.mean(Prainrange)
Dcell = 0.8e-4 #km, 1 km radius

for tautemp in dat:
    Htemp.append((Prain * Ktemp[0] * Dcell**2)/(1 - 1/tautemp))


    

In [45]:

    

# print Tk
# print RH
# print Htemp

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
i = 0 
for row in Htemp:
    ax.plot(Tkrange-273.15,row, label= ('humidity %.1g'%RH[i]))
    i +=1
ax.set_ylabel("Habitat Decay")
ax.set_xlabel("Temperateure (C)")
plt.legend(loc='upper right', shadow=True, fontsize='x-large')
# print Htemp[0][:]


    

    
Out[45]:





<matplotlib.legend.Legend at 0x109cdfc50>






    

In [27]:

    

import numpy as np

volume = 0.5     # range [0.0, 1.0]
fs = 1000       # sampling rate, Hz, must be integer
duration = 1.0   # in seconds, may be float
f = 440.0        # sine frequency, Hz, may be float

# generate samples, note conversion to float32 array
samples = (np.sin(2*np.pi*np.arange(fs*duration)*f/fs)).astype(np.float32)

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(range(0,len(samples)),samples)
plt.show()

import numpy as np

dt = 1./1000
time = np.arange(0., 6., dt)
frequency = np.sin(2*math.pi*time*1.)  # a 1Hz oscillation
waveform = np.sin(2*math.pi*time*1000)
plt.plot(time, waveform)
#Pxx, freqs, bins, im = plt.specgram(waveform, NFFT=4*1024, Fs=44100, noverlap=90, cmap=plt.cm.gist_heat) 
plt.show()


    

In [ ]:

    

rain