sys.path.append('Analysis/')
from AverageShape import PlotAvgShape
N = 1000
S = 100
sample_size = 50
rel = False
fig = plt.figure()
title = 'log(%N)'
""" 1. The Broken Stick Model constrained by N and S """
ax = fig.add_subplot(3, 3, 1)
RACsample = Models.SimBrokenStick(N, S, sample_size, rel)
fig = HeatMap.RACHeatMap(fig, RACsample)
fig = PlotAvgShape(fig, RACsample)
plt.title('Broken Stick', fontsize=13)
plt.xlabel('Rank')
#plt.ylabel('Abundance')
plt.ylabel(title)
print 'finished broken stick\n'
""" 2. The Log-normal (75/25) constrained by N and S """
ax = fig.add_subplot(3, 3, 2)
RACsample = Models.SimLogNormFloat(N, S, sample_size, rel)
fig = HeatMap.RACHeatMap(fig, RACsample)
fig = PlotAvgShape(fig, RACsample)
def fig2(SADModels):
'''Heat map of one site, observed v heat from each model'''
# Still working...
#1. get obs for one site 2. put into each model 3. heat map
fig = plt.figure()
for i, model in enumerate(SADModels):
fig.add_subplot(2, 2, i + 1)
obs_pred_data = import_obs_pred_data(mydir + '/Results/' + model +
'.txt')
obs = ((obs_pred_data["obs"]))
site = ((obs_pred_data["site"]))
obs_data = []
for sites in np.unique(site):
obs_data.append(obs[sites == site])
OBSSad = obs_data[10]
OBSSad = map(int, OBSSad.tolist())
N = sum(OBSSad)
S = len(OBSSad)
sample_size = 100
print OBSSad
if model == 'SimBrokenStick':
prdSADs = Models.SimBrokenStick(N, S, sample_size)
HeatMap.RACHeatMap(fig, prdSADs)
plt.plot(np.log(AverageShape.AvgShape(prdSADs)),
color='lime',
label='Predicted',
lw=2)
elif model == 'SimLogNormInt':
prdSADs = Models.SimLogNormInt(N, S, sample_size)
HeatMap.RACHeatMap(fig, prdSADs)
plt.plot(np.log(AverageShape.AvgShape(prdSADs)),
color='lime',
label='Predicted',
lw=2)
elif model == 'SimpleRandomFraction':
prdSADs = Models.SimpleRandomFraction(N, S, sample_size)
HeatMap.RACHeatMap(fig, prdSADs)
plt.plot(np.log(AverageShape.AvgShape(prdSADs)),
color='lime',
label='Predicted',
lw=2)
elif model == 'SimParetoInt':
prdSADs = Models.SimParetoInt(N, S, sample_size)
HeatMap.RACHeatMap(fig, prdSADs)
plt.plot(np.log(AverageShape.AvgShape(prdSADs)),
color='lime',
label='Predicted',
lw=2)
plt.title(model)
plt.plot(np.log(OBSSad), color='m', lw=3, label="Observed", alpha=1.0)
leg = plt.legend(loc=1, prop={'size': 8})
leg.draw_frame(False)
plt.text(10, 8, 'N =' + str(N) + ', S =' + str(S), fontsize=8)
plt.xlim(0, S + 2)
plt.xlabel('Rank in abundance', fontsize=12)
plt.ylabel('log(abundance)', fontsize=12)
plt.subplots_adjust(wspace=.35, hspace=.35)
plt.savefig(
'/Users/Nathan_Hillis/GitHub/SADModels/Projects/Mar2015meeting/HeatMaps_OneSite.png',
dpi=600,
bbox_inches='tight',
pad_inches=0.03)
plt.show()
return
Well it was a nice try but I ran out of memory.'''
SADModels = ['SimBrokenStick', 'SimLogNormInt', 'SimpleRandomFraction',
'SimParetoInt']
N = 40000
S = 100
sample_size = 100
fig = plt.figure()
for i, model in enumerate(SADModels):
fig.add_subplot(2, 2, i+1)
if model == 'SimBrokenStick':
prdSADs = Models.SimBrokenStick(N, S, sample_size)
HeatMap.RACHeatMap(fig, prdSADs)
plt.plot(np.log(AverageShape.AvgShape(prdSADs)), color = 'lime', label = 'Predicted', lw = 2)
print 'BS'
elif model == 'SimLogNormInt':
prdSADs = Models.SimLogNormInt(N, S, sample_size)
HeatMap.RACHeatMap(fig, prdSADs)
plt.plot(np.log(AverageShape.AvgShape(prdSADs)), color = 'lime', label = 'Predicted', lw = 2)
print 'SLN'
elif model == 'SimpleRandomFraction':
prdSADs = Models.SimpleRandomFraction(N, S, sample_size)
HeatMap.RACHeatMap(fig, prdSADs)
plt.plot(np.log(AverageShape.AvgShape(prdSADs)), color = 'lime', label = 'Predicted', lw = 2)
print 'RandFrac'
