def plot2(df):
p = pyggplot.Plot(df).add_scatter("Y", "X")
p.width = 5
p.height = 2
return p
def plot(df):
return pyggplot.Plot(df).add_scatter("X", "Y")
def plot(df):
append("out/plot", "B")
return pyggplot.Plot(df).add_scatter("X", "Y")
def plot(df):
p = pyggplot.Plot(df["A"]).add_scatter("X", "Y")
p.width = 5
p.height = 1
return p
fwd['A'].append(forward_samples.count('A') / nSamples)
fwd['B'].append(forward_samples.count('B') / nSamples)
fwd['C'].append(forward_samples.count('C') / nSamples)
if __name__ == '__main__':
nSimulations = 10000
rand = RandomState(1122)
cftp = {'A': [], 'B': [], 'C': []}
fwd = {'A': [], 'B': [], 'C': []}
# couple_from_the_past parameters
nSamples = 1000
for simulation in range(nSimulations):
couple_engine(rand, cftp, fwd, nSamples)
cftp_data = pd.DataFrame.from_dict(cftp)
cftp_long = pd.melt(cftp_data, var_name="State", value_name="Proportion")
p = pyggplot.Plot(cftp_long)
p.add_boxplot(x='State', y='Proportion')
p.render("../../figures/cftp_simple_comparison.pdf")
fwd_data = pd.DataFrame.from_dict(fwd)
fwd_long = pd.melt(fwd_data, var_name="State", value_name="Proportion")
p = pyggplot.Plot(fwd_long)
p.add_boxplot(x='State', y='Proportion')
p.render("../../figures/fwd_simple_comparison.pdf")
def plot(df):
append('out/plot', 'B')
return pyggplot.Plot(df).add_scatter('X', 'Y')
def plot(df):
return pyggplot.Plot(df).add_scatter('X', 'Y')
