#!/usr/bin/env python
# coding: utf-8

# In[1]:


import sys
sys.path.append('../src')
from popgen import *
get_ipython().run_line_magic('matplotlib', 'inline')


# ## One dimension

# In[2]:


m = SteppingStone(gens=100)
m.mig = [0, 0.01, 0.05]
m.pop_size = 200
m.num_pops_x = 5
m.num_msats = 50
BasicView(m, [fst(), ExpHe()], ['mean'], with_model=True)
m.run()


# ## peripheral populations

# In[3]:


m = SteppingStone(gens=200)
m.mig = 0.02
m.pop_size = 300
m.num_pops_x = 10
m.num_msats = 5
MetaVsDemeView(m, ExpHe(), ExpHe(do_structured=True))
MetaVsDemeView(m, NumAlleles(), NumAlleles(do_structured=True))
m.run()


# ## Two dimensions

# In[4]:


m = SteppingStone(gens=100, two_d=True)
m.mig = [0, 0.01, 0.05]
m.pop_size = 200
m.num_pops_y = 2
m.num_pops_x = 5
m.num_msats = 50
BasicView(m, [fst(), ExpHe()], ['mean'], with_model=True)
m.run()


# ## Princinpal Components Analysis over time!

# In[5]:


m = SteppingStone(gens=60)
m.mig = [0.00, 0.01, 0.1]
m.pop_size = 100
m.num_pops_x = 10
m.num_msats = 50
IndividualView(m, PCA(), step=20)
m.run()


# In[6]:


m = SteppingStone(gens=600)
m.mig = [0.0, 0.01, 0.1]
m.pop_size = 100
m.num_pops_x = 10
m.num_msats = 50
IndividualView(m, PCA(), step=150)
m.run()


# ## PCA over time, 2D

# In[7]:


m = SteppingStone(gens=1200, two_d=True)
m.mig = [0.001, 0.01]
m.pop_size = 100
m.num_pops_x = 4
m.num_pops_y = 4
m.num_msats = 50
IndividualView(m, PCA(), step=300)
m.run()


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