%matplotlib inline
from matplotlib import pyplot as plt
import pandas as pd

df = pd.DataFrame.from_csv('crb-talaya.csv')

# Remove all records except for those where breedingSiteID in (r1, r2, r3, r4)
breedingSiteIDlist = ['r1','r2','r3','r4']
df = df[df['breedingSiteID'].isin(breedingSiteIDlist)]
#df

grouped = df['beetles'].groupby(df['breedingSiteID'])
beetles = grouped.sum()
beetles

df['date'] = df.index
grouped = df['date'].groupby(df['breedingSiteID'])
days = grouped.max() - grouped.min()
days = days.astype('timedelta64[D]')
days

grouped.min()

grouped.max()

# Beetles trapped per day
trap_rate = beetles/(days + 1)
trap_rate

trap_rate.mean(), trap_rate.median()

rates

dfence_rates = list(trap_rate.values)
dfence_names = ['DF1', 'DF2', 'DF3', 'DF4']
dfence_x = [150, 250, 50, 350] # meters from corner of fence
dfence_color = len(dfence_rates) * ['red']

pan_rates = [0.017857, 0.416862, 0.491803, 0.215457, 0.306792]
pan_names = ['P27', 'P28', 'P29', 'P30', 'P31']
pan_x = [0, 100, 200, 300, 400]
pan_color = len(pan_rates) * ['green']

rates = dfence_rates + pan_rates
names = dfence_names + pan_names
x = dfence_x + pan_x
color = dfence_color + pan_color

plt.bar(x, rates, color=color, align='center', width=40)
plt.xticks(x, names)

pans = {'27': 0.017857, 
'28': 0.416862,
'29': 0.491803,
'30': 0.215457,
'31': 0.306792}

pan = pans.values()
dfence = trap_rate.values

plt.pyplot.boxplot([pan, dfence])





traps = {'r1', 'r2', 'r3', 'r4'}
pixels_per_cm = {'r1': 3.708, 'r2': 4.167, 'r3': 4.029, 'r4': 4.815}

# For each trapped beetle, this function returns x, y, and distance from
# the lure in centimeters, with the origin (0,0) at the lure.

def capture_points(trap, pixels_per_cm):
    filename = trap + '-Results.xls'
    df = pd.read_csv(filename, sep='\t', index_col=0 )
    nrows = df.shape[0]
    result = list()
    for i in range(2, nrows+1):
        x = (df.X[i] - df.X[1])/pixels_per_cm
        y = (df.Y[1] - df.Y[i])/pixels_per_cm
        distance = (x**2 + y**2)**0.5
        result.append({'trap': trap, 'x': x, 'y': y, 'distance': distance});
    return(result)    

b = list()
for trap in traps:
    a = capture_points(trap, pixels_per_cm[trap])
    b.extend(a)

df = pd.DataFrame(b)
#df

ax = df.plot(x = 'x',
          y = 'y', 
          style = 'o', 
          ylim = (-150, 150), 
          title = 'Location of trapped beetles with respect to lures at (0,0)')
ax.set_xlabel('x (cm)')
ax.set_ylabel('y (cm)')
ax.set_aspect('equal')

ax = df.hist(column='distance', bins=20)

df.distance.median()
@article {DeFence-traps,
	title = {DeFence Traps: Using Fish Netting as Novel CRB Pheromone Trap Deployed on Fence Lines},
	year = {2015},
	type = {CRB Technical Report},
	author = {Aubrey Moore and Roland Quitugua}
}