def plotDiffs(sampleSizes, diffs, title, label, color = 'b'):
numpy.plot(sampleSizes, diffs, label = label,
color = color)
numpy.xlabel('Sample Size')
numpy.ylabel('% Difference in SD')
numpy.title(title)
numpy.legend()
def compareAnimals(animals, precision):
"""Assumes animals is a list of animals, precision an int >= 0
Builds a table of Euclidean distance between each animal"""
#Get labels for columns and rows
columnLabels = []
for a in animals:
columnLabels.append(a.getName())
rowLabels = columnLabels[:]
tableVals = []
#Get distances between pairs of animals
#For each row
for a1 in animals:
row = []
#For each column
for a2 in animals:
if a1 == a2:
row.append('--')
else:
distance = a1.distance(a2)
row.append(str(round(distance, precision)))
tableVals.append(row)
#Produce table
table = numpy.table(rowLabels = rowLabels,
colLabels = columnLabels,
cellText = tableVals,
cellLoc = 'center',
loc = 'center',
colWidths = [0.2]*len(animals))
table.scale(1, 2.5)
numpy.title('Eucliedan Distance Between Animals')
def testFits(models, degrees, xVals, yVals, title):
numpy.plot(xVals, yVals, 'o', label='Data')
for i in range(len(models)):
estYVals = numpy.polyval(models[i], xVals)
error = rSquared(yVals, estYVals)
numpy.plot(xVals, estYVals,
label = 'Fit of degree '\
+ str(degrees[i])\
+ ', R2 = ' + str(round(error, 5)))
numpy.legend(loc='best')
numpy.title(title)
def make_one_curve_plot(x_coords, y_coords, x_label, y_label, title):
"""
Makes a plot of the x coordinates and the y coordinates with the labels
and title provided.
Args:
x_coords (list of floats): x coordinates to graph
y_coords (list of floats): y coordinates to graph
x_label (str): label for the x-axis
y_label (str): label for the y-axis
title (str): title for the graph
"""
pl.figure()
pl.plot(x_coords, y_coords)
pl.xlabel(x_label)
pl.ylabel(y_label)
pl.title(title)
pl.show()
def fitData(fileName):
xVals, yVals = getData(fileName)
xVals = numpy.array(xVals)
yVals = numpy.array(yVals)
xVals = xVals*9.81 #get force
numpy.plot(xVals, yVals, 'bo',
label = 'Measured points')
model = numpy.polyfit(xVals, yVals, 1)
xVals = xVals + [2]
yVals = yVals + []
estYVals = numpy.polyval(model, xVals)
numpy.plot(xVals, estYVals, 'r',
label = 'Linear fit, r**2 = '
+ str(round(rSquared(yVals, estYVals), 5)))
model = numpy.polyfit(xVals, yVals, 2)
estYVals = numpy.polyval(model, xVals)
numpy.plot(xVals, estYVals, 'g--',
label = 'Quadratic fit, r**2 = '
+ str(round(rSquared(yVals, estYVals), 5)))
numpy.title('A Linear Spring')
labelPlot()
numpy.legend(loc = 'best')
def show_plot_compare_strategies(title, x_label, y_label):
"""
Produces a plot comparing the two robot strategies in a 20x20 room with 80%
minimum coverage.
"""
num_robot_range = range(1, 11)
times1 = []
times2 = []
for num_robots in num_robot_range:
print("Plotting", num_robots, "robots...")
times1.append(
run_simulation(num_robots, 1.0, 1, 20, 20, 3, 0.8, 20,
StandardRobot))
times2.append(
run_simulation(num_robots, 1.0, 1, 20, 20, 3, 0.8, 20,
FaultyRobot))
numpy.plot(num_robot_range, times1)
numpy.plot(num_robot_range, times2)
numpy.title(title)
numpy.legend(('StandardRobot', 'FaultyRobot'))
numpy.xlabel(x_label)
numpy.ylabel(y_label)
numpy.show()
def show_plot_room_shape(title, x_label, y_label):
"""
Produces a plot showing dependence of cleaning time on room shape.
"""
aspect_ratios = []
times1 = []
times2 = []
for width in [10, 20, 25, 50]:
height = 300 / width
print("Plotting cleaning time for a room of width:", width,
"by height:", height)
aspect_ratios.append(float(width) / height)
times1.append(
run_simulation(2, 1.0, 1, width, height, 3, 0.8, 200,
StandardRobot))
times2.append(
run_simulation(2, 1.0, 1, width, height, 3, 0.8, 200, FaultyRobot))
numpy.plot(aspect_ratios, times1)
numpy.plot(aspect_ratios, times2)
numpy.title(title)
numpy.legend(('StandardRobot', 'FaultyRobot'))
numpy.xlabel(x_label)
numpy.ylabel(y_label)
numpy.show()
def make_two_curve_plot(x_coords, y_coords1, y_coords2, y_name1, y_name2,
x_label, y_label, title):
"""
Makes a plot with two curves on it, based on the x coordinates with each of
the set of y coordinates provided.
Args:
x_coords (list of floats): the x coordinates to graph
y_coords1 (list of floats): the first set of y coordinates to graph
y_coords2 (list of floats): the second set of y-coordinates to graph
y_name1 (str): name describing the first y-coordinates line
y_name2 (str): name describing the second y-coordinates line
x_label (str): label for the x-axis
y_label (str): label for the y-axis
title (str): the title of the graph
"""
pl.figure()
pl.plot(x_coords, y_coords1, label=y_name1)
pl.plot(x_coords, y_coords2, label=y_name2)
pl.legend()
pl.xlabel(x_label)
pl.ylabel(y_label)
pl.title(title)
pl.show()
def showErrorBars(population, sizes, numTrials):
xVals = []
sizeMeans, sizeSDs = [], []
for sampleSize in sizes:
xVals.append(sampleSize)
trialMeans = []
for t in range(numTrials):
sample = random.sample(population, sampleSize)
popMean, sampleMean, popSD, sampleSD =\
getMeansAndSDs(population, sample)
trialMeans.append(sampleMean)
sizeMeans.append(sum(trialMeans)/len(trialMeans))
sizeSDs.append(numpy.std(trialMeans))
print(sizeSDs)
numpy.errorbar(xVals, sizeMeans,
yerr = 1.96*numpy.array(sizeSDs), fmt = 'o',
label = '95% Confidence Interval')
numpy.title('Mean Temperature ('
+ str(numTrials) + ' trials)')
numpy.xlabel('Sample Size')
numpy.ylabel('Mean')
numpy.axhline(y = popMean, color ='r', label = 'Population Mean')
numpy.xlim(0, sizes[-1] + 10)
numpy.legend()
def labelPlot():
numpy.title('Measured Displacement of Spring')
numpy.xlabel('|Force| (Newtons)')
numpy.ylabel('Distance (meters)')
except:
years[d.getYear()] = [d.getHigh()]
for y in years:
years[y] = sum(years[y])/len(years[y])
return years
data = getTempData()
years = getYearlyMeans(data)
xVals, yVals = [], []
for e in years:
xVals.append(e)
yVals.append(years[e])
numpy.plot(xVals, yVals)
numpy.xlabel('Year')
numpy.ylabel('Mean Daily High (C)')
numpy.title('Select U.S. Cities')
def splitData(xVals, yVals):
toTrain = random.sample(range(len(xVals)),
len(xVals)//2)
trainX, trainY, testX, testY = [],[],[],[]
for i in range(len(xVals)):
if i in toTrain:
trainX.append(xVals[i])
trainY.append(yVals[i])
else:
testX.append(xVals[i])
testY.append(yVals[i])
return trainX, trainY, testX, testY
def makeHist(data, title, xlabel, ylabel, bins = 20):
numpy.hist(data, bins = bins)
numpy.title(title)
numpy.xlabel(xlabel)
numpy.ylabel(ylabel)
popSD = numpy.std(population)
sems = []
sampleSDs = []
for size in sampleSizes:
sems.append(sem(popSD, size))
means = []
for t in range(numTrials):
sample = random.sample(population, size)
means.append(sum(sample)/len(sample))
sampleSDs.append(numpy.std(means))
numpy.plot(sampleSizes, sampleSDs,
label = 'Std of ' + str(numTrials) + ' means')
numpy.plot(sampleSizes, sems, 'r--', label = 'SEM')
numpy.xlabel('Sample Size')
numpy.ylabel('Std and SEM')
numpy.title('SD for ' + str(numTrials) + ' Means and SEM')
numpy.legend()
def plotDistributions():
uniform, normal, exp = [], [], []
for i in range(100000):
uniform.append(random.random())
normal.append(random.gauss(0, 1))
exp.append(random.expovariate(0.5))
makeHist(uniform, 'Uniform', 'Value', 'Frequency')
numpy.figure()
makeHist(normal, 'Gaussian', 'Value', 'Frequency')
numpy.figure()
numpy.show()
makeHist(exp, 'Exponential', 'Value', 'Frequency')
