def xavier (self): self.dork = self.maximus(self.mylist1) self.pork = self.maximus(self.mylist2) base = 0.0 mars = statistics(self.pork[0]) venus = statistics(self.pork) base = mars.mymean(self.pork[0]) statbase = mars.mymean(self.pork[0]) i = 0 temp = 0.0 temp1 = 0.0 temp2 = 0.0 slop = 0.0 while i < len(self.pork): # might need to make this into a switch statement while j < len(self.pork[i]): temp = self.pork[i][j] j = j + 1 if (temp > base+8.0): #need to change the value for threshhold temp1 = venus.standardev(self.pork[i]) if(temp1 > statbase*2): #checks the standard deviation for the subset temp2 = venus.rootmenasquare(self.pork[i]) #checks the rootmean value to determine if physical if(temp2 > 84.0): #physical else: slope = self.hitler(i) # since its not physical check the slop of best fit line if (slope > ): #do something here to determine if hte slope is steep enough to classify as emotion else: #do nothing or return to start elif (venus.mymean(self.pork[i]) < base): base = venus.mymean(self.pork[i]) else: # do nothing
def xavier (self): self.dork = self.maximus(self.mylist1) self.pork = self.maximus(self.mylist2) base = 0.0 mars = statistics(self.pork[0]) venus = statistics(self.pork) base = mars.mymean(self.pork[0]) statbase = mars.mymean(self.pork[0])
def calculateDiffs(pcTarget, pcSource, sourceTree):
mind = 1000
maxd = 0
sumd = 0.0
sumd2 = 0.0
nCorrect = 0
nTotal = len(np.asarray(pcTarget.points))
for j in range(nTotal):
[k, idx, _] = sourceTree.search_knn_vector_3d(pcTarget.points[j], 1)
p0 = np.asarray(pcTarget.points)[j, :]
p1 = np.asarray(pcSource.points)[idx[0], :]
d = np.linalg.norm(p0 - p1)
sumd += d
sumd2 += d*d
if mind > d:
mind = d
if maxd < d:
maxd = d
if thre > d:
nCorrect += 1
stas = statistics()
stas.min = mind
stas.max = maxd
stas.avg = sumd / nTotal
stas.stddev = np.sqrt(sumd2 / nTotal - ((sumd/nTotal)*(sumd/nTotal)))
stas.numCorrect = nCorrect
stas.n = nTotal
return stas
def make_tree(filename="sample.123.321.dump.gz"):
#Print statistics at the end of the program
printstat = False
stat = statistics(1234)
#Create binary tree root
root = node(socket.inet_aton("0.0.0.0"), 0, [], None, False, 0)
dump = Parser(filename)
loop = True
#Loop over the input file, reading one line at a time.
while loop:
try:
line = dump.next()
except:
print "EOF"
break
#Parsing dumps
if line[0] == 'D':
for entry in line[3]:
fromSet = False
time = entry[-1]
act = 'A' #Action: A|W
revoked = None
mask = line[2]
prefix = int2ip(line[1])
if act == "W":
revoked = 'revoked'
else:
revoked = None
origin = parseASpath(entry, stat)
new_node = node(socket.inet_aton(prefix), mask, origin, revoked, fromSet, time)
addAction(act, root, new_node, stat)
#Parsing updates
elif line[0] == 'U':
for entry in line[3]:
time = entry[-1]
fromSet = False
act = entry[0] #Action: A|W
revoked = None
mask = entry[2]
prefix = int2ip(entry[1])
if act == "W":
revoked = 0
else:
revoked = None
origin = parseASpath(entry, stat)
new_node = node(socket.inet_aton(prefix), mask, origin, revoked, fromSet, time)
addAction(act,root, new_node, stat)
#Unknown type (neither dump nor update)
else:
print "Unknown message type " + line[0]
return root
def process_books():
tale2cities_url = 'https://raw.githubusercontent.com/maigfrga/spark-streaming-book/'\
'master/data/books/tale2cities.txt'
hamlet_url = 'https://raw.githubusercontent.com/maigfrga/spark-streaming-book/'\
'master/data/books/hamlet.txt'
tale2cities = clean_book(create_text_rdd_from_url(tale2cities_url))
tale2cities = remove_stop_words(tale2cities)
tale2cities = exclude_popular_words(tale2cities, 10)
tale2cities.setName('A tale of two cities')
hamlet = clean_book(create_text_rdd_from_url(hamlet_url))
hamlet = remove_stop_words(hamlet)
hamlet = exclude_popular_words(hamlet, 10)
hamlet.setName('hamlet')
#report(tale2cities, hamlet)
statistics(tale2cities)
statistics(hamlet)
def hitler (self, k): min1 = min(self.pork[k-1]) mx1 = max(self.pork[k]) merged = [] merged = self.pork[k-1] + self.pork[k] jupiter = statistics(calcifer) calcifer = [] i = merged.index(min(merged)) while i <= merged.index(max(merged)): calcifer.append(merged[i]) i = i + 1 return jupiter.lineread(calcifer)
def hitler (self, k): min1 = min(self.pork[k-1]) mx1 = max(self.pork[k]) merged = [] merged = self.pork[k-1] + self.pork[k] jupiter = statistics(calcifer) calcifer = [] i = merged.index(min(merged)) while i <= merged.index(max(merged)): calcifer.append(merged[i]) i = i + 1 return jupiter.lineread(calcifer)
def post(self, request, *args, **kwargs):
# Code for POST requests
context = super(IndexView, self).get_context_data(**kwargs)
regions = Region.objects.all()
try:
for region in regions:
stat = statistics(region)
stat.fetch()
messages.success(request, 'Content synced successfully')
except Exception as e:
messages.error(
request,
'Some error occurred while syncing data (' + str(e) + ')')
return super(IndexView, self).render_to_response(context)
list_of_marks.append(mark)
name = values[0]
list_of_names.append(name)
highest = max(list_of_marks)
lowest = min(list_of_marks)
average = (sum(list_of_marks)) / (len(list_of_marks))
stats = (lowest, highest, average, top_students)
return stats
def print_results(stats):
"""Print the statistics given. The parameters 'stats' is a
tuple of the form returned by the 'statistics' function
above.
"""
(minimum, maximum, average, top_students) = stats
print("Minimum mark is: {:.2f}".format(minimum))
print("Maximum mark is: {:.2f}".format(maximum))
print("Average mark is: {:.2f}".format(average))
if len(top_students) == 1:
print("The top student: {}".format(top_students[0]))
else:
print("The top-equal students:\n {}".format(", ".join(top_students)))
data = statistics([('Angus McGurkinshaw', '25\n'),
('Thomas Albert Finkelstein III', '75\n'),
('Myrtle', '50\n')])
print_results(data)
if __name__ == '__main__':
# 默认使用 GBK 编码
encoding = 'gbk'
# 获取输入文件位置
file_path = get_input_path()
# 计算出输出文件位置
statistics_path = get_output_path(file_path, 'statistics.csv')
absence_path = get_output_path(file_path, 'absence.csv')
# 从文件名中获取统计表的起止日期
month, start, end = get_start_end(file_path)
# 根据起止日期生成工作日列表
workdays = work_calendar_for_period(2017, month, start, end)
# 统计出勤信息
statistics(file_path, workdays, encoding)
# 生成缺勤记录
find_absence(statistics_path, workdays, encoding)
# 删除上周四的统计
start_date = '2017/%s/%s' % (month, start)
if end - start == 7 and days_in_week(start_date):
print('\n%s 记录已删除' % start_date)
remove_records(statistics_path, start_date)
remove_records(absence_path, start_date)
f_list = os.listdir("./")
f_list.sort()
for cur_file in f_list:
ll = 0
try:
n_file = open("./" + cur_file, 'r')
except:
ll += 1
if ll == 0:
name = os.path.basename(n_file.name)
ns = name.split('-')
fg = name.split('_')
if (ns[0] == "psyncLog" and len(fg) == 1):
statistics(cur_file, 'final_file.csv')
if 0 in file_dict:
print("it's here though")
else:
file_dict[0].append(0.0)
if 0 in count_dict:
print("it's here")
else:
count_dict[0].append(0.0)
std_ar = []
for key, vt in count_dict.items():
sum = 0
sp = 0
for k in range(len(count_dict[key])):
sum = sum + count_dict[key][k]
def main():
system = 1 #default system == l3 = 1
#print(system)
argumentList = [
[{
"name": "a|b",
"sign": "+"
}], ### basics
[{
"name": "a|b",
"sign": "-"
}],
[{
"name": "~(a|b)",
"sign": "+"
}],
[{
"name": "~(a|b)",
"sign": "-"
}],
[{
"name": "~(~a|b)",
"sign": "-"
}],
[{
"name": "a|~b",
"sign": "-"
}],
[{
"name": "a&b",
"sign": "+"
}],
[{
"name": "a&b",
"sign": "-"
}],
[{
"name": "~(a&b)",
"sign": "+"
}],
[{
"name": "~(a&b)",
"sign": "-"
}],
[{
"name": "a>b",
"sign": "+"
}],
[{
"name": "a>b",
"sign": "-"
}],
[{
"name": "~(a>b)",
"sign": "+"
}],
[{
"name": "~(a>b)",
"sign": "-"
}],
[{
"name": "a&b&c",
"sign": "+"
}],
[{
"name": "a&b&c",
"sign": "-"
}],
[{
"name": "~(a&b&c)",
"sign": "+"
}],
[{
"name": "~(a&b&c)",
"sign": "-"
}],
[{
"name": "a|b|c",
"sign": "+"
}],
[{
"name": "a|b|c",
"sign": "-"
}],
[{
"name": "~(a|b|c)",
"sign": "+"
}],
[{
"name": "~(a|b|c)",
"sign": "-"
}],
[{
"name": "a&b>c",
"sign": "+"
}],
[{
"name": "a&b>c",
"sign": "-"
}],
[{
"name": "~(a>b&c)",
"sign": "+"
}],
[{
"name": "~(a>b&c)",
"sign": "-"
}],
]
preProgrammedList = [
[{
"name": "p&(q|~q)",
"sign": "+"
}, {
"name": "r",
"sign": "-"
}],
[{
"name": "p&~p",
"sign": "+"
}, {
"name": "q",
"sign": "-"
}], ### problems from the book
[{
"name": "p&q",
"sign": "+"
}, {
"name": "p",
"sign": "-"
}],
[{
"name": "p",
"sign": "+"
}, {
"name": "p|q",
"sign": "-"
}],
[{
"name": "p&(q|r)",
"sign": "+"
}, {
"name": "(p&q)|(p&r)",
"sign": "-"
}],
[{
"name": "p|(q&r)",
"sign": "+"
}, {
"name": "(p|q)&(p|r)",
"sign": "-"
}],
[{
"name": "~~p",
"sign": "+"
}, {
"name": "p",
"sign": "-"
}],
[{
"name": "(p&q)>r",
"sign": "+"
}, {
"name": "(p&~r)>q",
"sign": "-"
}],
[{
"name": "(p&~p)",
"sign": "+"
}, {
"name": "(p|~p)",
"sign": "-"
}],
[{
"name": "(p&~p)",
"sign": "+"
}, {
"name": "(q|~q)",
"sign": "-"
}],
[{
"name": "(p|q)",
"sign": "+"
}, {
"name": "(p&q)",
"sign": "-"
}],
[{
"name": "p",
"sign": "+"
}, {
"name": "~(p&~q)",
"sign": "+"
}, {
"name": "(q)",
"sign": "-"
}],
[{
"name": "(p&q)>r",
"sign": "+"
}, {
"name": "p>(~q|r)",
"sign": "-"
}]
]
preProgrammedList = [
[{
"name": "p&~p",
"sign": "+"
}, {
"name": "q",
"sign": "-"
}],
#[{"name": "p&~p", "sign": "+" },{"name": "q", "sign": "-" } ]
#[{"name": "p&(q|~q)", "sign": "+" },{"name": "r", "sign": "-" } ],
#[{"name": "(p&q)>r", "sign": "+" },{"name": "(p&~r)>q", "sign": "-" } ],
#[{"name": "(p|q)", "sign": "+" },{"name": "(p&q)", "sign": "-" } ]
]
# NOT VALID IN L3 RM!!
preProgrammedList = [[{
"name": "p&~p",
"sign": "+"
}, {
"name": "q",
"sign": "-"
}], [{
"name": "p&(q|~q)",
"sign": "+"
}, {
"name": "r",
"sign": "-"
}], [{
"name": "(p&q)>r",
"sign": "+"
}, {
"name": "(p&~r)>q",
"sign": "-"
}], [{
"name": "(p|q)",
"sign": "+"
}, {
"name": "(p&q)",
"sign": "-"
}]]
# from pytableaux
preProgrammedList = [
[{
"name": "p&(q|~q)",
"sign": "+"
}, {
"name": "r",
"sign": "-"
}],
[{
"name": "p&~p",
"sign": "+"
}, {
"name": "q",
"sign": "-"
}], ### problems from the book
[{
"name": "p&q",
"sign": "+"
}, {
"name": "p",
"sign": "-"
}],
[{
"name": "p",
"sign": "+"
}, {
"name": "p|q",
"sign": "-"
}],
[{
"name": "p&(q|r)",
"sign": "+"
}, {
"name": "(p&q)|(p&r)",
"sign": "-"
}],
[{
"name": "p|(q&r)",
"sign": "+"
}, {
"name": "(p|q)&(p|r)",
"sign": "-"
}],
[{
"name": "~~p",
"sign": "+"
}, {
"name": "p",
"sign": "-"
}],
[{
"name": "(p&q)>r",
"sign": "+"
}, {
"name": "(p&~r)>q",
"sign": "-"
}],
[{
"name": "(p&~p)",
"sign": "+"
}, {
"name": "(p|~p)",
"sign": "-"
}],
[{
"name": "(p&~p)",
"sign": "+"
}, {
"name": "(q|~q)",
"sign": "-"
}],
[{
"name": "(p|q)",
"sign": "+"
}, {
"name": "(p&q)",
"sign": "-"
}],
[{
"name": "p",
"sign": "+"
}, {
"name": "~(p&~q)",
"sign": "+"
}, {
"name": "(q)",
"sign": "-"
}],
[{
"name": "(p&q)>r",
"sign": "+"
}, {
"name": "p>(~r|q)",
"sign": "-"
}],
[{
"name": "p|q",
"sign": "+"
}, {
"name": "~q",
"sign": "+"
}, {
"name": "p",
"sign": "-"
}],
[{
"name": "p|q",
"sign": "+"
}, {
"name": "p",
"sign": "+"
}, {
"name": "q",
"sign": "-"
}],
[{
"name": "q",
"sign": "+"
}, {
"name": "p|~p",
"sign": "-"
}],
[{
"name": "p>q",
"sign": "+"
}, {
"name": "~p",
"sign": "+"
}, {
"name": "q",
"sign": "-"
}],
[{
"name": "p&~p",
"sign": "+"
}, {
"name": "q",
"sign": "+"
}, {
"name": "p>p",
"sign": "-"
}],
[{
"name": "p>q",
"sign": "+"
}, {
"name": "p",
"sign": "+"
}, {
"name": "q",
"sign": "-"
}],
[{
"name": "p>q",
"sign": "+"
}, {
"name": "~q",
"sign": "+"
}, {
"name": "~p",
"sign": "-"
}],
[{
"name": "~(p|q)",
"sign": "+"
}, {
"name": "~p&~q",
"sign": "-"
}],
[{
"name": "~(p&q)",
"sign": "+"
}, {
"name": "~p|~q",
"sign": "-"
}],
[{
"name": "~p&~q",
"sign": "+"
}, {
"name": "~(p|q)",
"sign": "-"
}],
[{
"name": "~p|~q",
"sign": "+"
}, {
"name": "~(p&q)",
"sign": "-"
}],
[{
"name": "p>(p>q)",
"sign": "+"
}, {
"name": "p>q",
"sign": "-"
}],
[{
"name": "(p>(p>q))>p",
"sign": "+"
}, {
"name": "p>q",
"sign": "-"
}],
[{
"name": "(p>q)&(r>s)",
"sign": "+"
}, {
"name": "(p>s)|(r>q)",
"sign": "-"
}], # from advanced logic test set
[{
"name": "(p&q)>r",
"sign": "+"
}, {
"name": "p>(~q|r)",
"sign": "-"
}],
[{
"name": "(p&q)>r",
"sign": "+"
}, {
"name": "(p&~r)>~q",
"sign": "-"
}],
#[{"name": "(p>(p>q))>(p>q)", "sign": "+" }]
]
#preProgrammedList = [[{"name": "p>q", "sign": "-" } ]]
#preProgrammedList = [
#[{"name": "p&~p", "sign": "+" },{"name": "q", "sign": "-" } ]
#]
#pE = parsingExpression()
#system, argumentList = list(pE.userInput)
#preProgrammedList = [[{'name': '(p&~p)', 'sign': '+'}, {'name': '(p|~p)', 'sign': '-'}]]
### in L3, D = 1
### in RM3, D = i, 1
#preProgrammedList = [
#[{"name": "(p&q)>r", "sign": "+" },{"name": "p>(~r|q)", "sign": "-" } ]
#]
#preProgrammedList = [[{"name": "p&(q|~q)", "sign": "+" },{"name": "r", "sign": "-" } ]]
#preProgrammedList = [[{"name": "p|q", "sign": "+" },{"name": "p", "sign": "+" },{"name": "q", "sign": "-" } ]]
#if len(argumentList) == 1:
#inOrder = (['p&~p'], [])
#preProgrammedList = [[{'name': '~(p|q)', 'sign': '+'}, {'name': '~(p&q)', 'sign': '-'}]]
argumentList = preProgrammedList
T1 = statistics()
T2 = statistics()
listOfTObjects = []
table = []
for argument in argumentList:
T1, tree1 = pros(1, T1, argument)
T2, tree2 = pros(2, T2, argument)
comp = comparisonTableaux(tree1, tree2)
l = 1
x = statContDeal(T1, comp.compare(), l) # l = 1 = small table
y = statContDeal(T2, comp.compare(), l)
table.append(x)
table.append(y)
x = T1.getListOfAll()
y = T2.getListOfAll()
listOfTObjects.append(x)
listOfTObjects.append(y)
### what do i want to show
# tableaux xx
# number of nodes
# height xx
# number of branches
# number of closed branches
# if not closed, then a countermodel with values for premises and conclusions
# for both trees, comparing:
# runtime
# edit distance xx
if l == 0:
table = tabulate(table,
headers=[
'logic', 'arguments', 'valid', 'height',
'number\nnodes', 'number\nbranches',
'rules\napplied', 'ratio\nclosed/open',
'edit\ndistance', 'runtime', 'countermodel',
'countermodel \ncheck'
],
tablefmt='grid')
if l == 1:
table = tabulate(table,
headers=[
'logic', 'arguments', 'valid', 'runtime',
'countermodel', 'countermodel \ncheck'
],
tablefmt='grid')
print(table)
plotting(listOfTObjects)
print("all prewritten arguments are resolved")
return 1
def acquire_information():
my_dict = {}
my_type = {}
regiontable = {}
typee = []
imy_dict = {}
imy_type = {}
iregiontable = {}
itypee = []
sum = 0
#start = 0
for i in range(7):
my_dict[i] = {}
imy_dict[i] = {}
num = 1
j1 = json.load(open('new.json'), strict=False)
content = list(j1.values())
for c in content:
area = {}
centroidx = {}
centroidy = {}
nodee = {}
R = {}
maxarea = 0
#information from each image
#if num <= 3:
xs = [r['shape_attributes']['all_points_x'] for r in c['regions']]
ys = [r['shape_attributes']['all_points_y'] for r in c['regions']]
type = [r['region_attributes']['type'] for r in c['regions']]
name = c['filename']
#print(c['filename'])
for i in range(0, len(xs)):
area[i] = get_area(xs[i], ys[i])
if area[i] > maxarea:
maxarea = area[i]
centroidx[i], centroidy[i] = get_centroid(xs[i], ys[i])
#print(type[i])
nodee[i] = nnode(type[i], area[i], centroidx[i], centroidy[i], i,
xs[i], ys[i])
#nodee[i].polytomask(name)
#print(nodee[3].area)
for i in range(0, len(xs)):
nodee[i].aver(maxarea)
nodee[i].contain(nodee)
#nodee[i].maskcontain(nodee)
typee = get_typee(nodee, typee)
sum, itypee, imy_type, imy_dict = deep_acquire_information1(
sum, nodee, itypee, imy_type, imy_dict)
nodeee = get_container(nodee)
R = get_relation(nodeee)
my_type = get_mytype(R, nodeee, my_type)
#print(R)
my_dict = statistics(nodeee, R, my_dict)
#if num == 1:
# break
num += 1
#print(typee)
#print("my_type:")
#print(my_type)
stypee = {}
istypee = {}
for c in content:
area = {}
centroidx = {}
centroidy = {}
nodee = {}
R = {}
maxarea = 0
#information from each image
#if num <= 3:
xs = [r['shape_attributes']['all_points_x'] for r in c['regions']]
ys = [r['shape_attributes']['all_points_y'] for r in c['regions']]
type = [r['region_attributes']['type'] for r in c['regions']]
for i in range(0, len(xs)):
area[i] = get_area(xs[i], ys[i])
if area[i] > maxarea:
maxarea = area[i]
centroidx[i], centroidy[i] = get_centroid(xs[i], ys[i])
#print(type[i])
nodee[i] = nnode(type[i], area[i], centroidx[i], centroidy[i], i,
xs[i], ys[i])
#print(nodee[3].area)
for i in range(0, len(xs)):
nodee[i].aver(maxarea)
nodee[i].contain(nodee)
for i in range(0, len(xs)):
if len(nodee[i].children) != 0:
print(nodee[i].type)
for j in range(len(nodee[i].children)):
print(nodee[(nodee[i].children)[j]].type)
print('/s')
iregiontable, istypee = deep_acquire_information2(
nodee, iregiontable, istypee, itypee, imy_type, imy_dict)
nodeee = get_container(nodee)
R = get_relation(nodeee)
regiontable = region_table1(R, nodeee, my_type, regiontable)
stypee = area_table1(nodee, typee, stypee)
#mytype = get_mytype(R,nodeee,my_type)
#print(R)
#my_dict = statistics(nodeee,R,my_dict)
num += 1
regiontable = region_table2(my_type, regiontable)
areatable = area_table2(typee, stypee)
iregiontable = region_table2(imy_type, iregiontable)
iareatable = area_table2(itypee, istypee)
typetable = type_table(my_dict)
itypetable = type_table(imy_dict)
#print(nodee[i].area)
#print(type)
#print(xs)
#print(ys)
#print(area)
#print(centroidx)
#print(centroidy)
#print(nodee[3].area)
#print(num/2)
#print(stypee)
#print(regiontable)
#print(areatable)
#print(iregiontable)
#print(iareatable)
#print("imy_type:")
#print(imy_type)
#print(typetable)
#print(sum)
#print(itypetable)
return typetable, regiontable, areatable, itypetable, iregiontable, iareatable
def statistics():
'''Calculates and prints out the descriptive statistics about a city and time period
specified by the user via raw input.
Args:
none.
Returns:
none.
'''
# Filter by city (Chicago, New York, Washington)
city = get_city()
#city = 'test.csv'
# Load city
city_file = [] # Reset variable each time to avoid running out of memory
print("\nLoading city (WARNING this could take up to 10 minutes)...")
start_time = time.time()
city_file = load_city(city)
print("{} records loaded, that took {} seconds.".format(
len(city_file),
time.time() - start_time))
# Filter by time period (month, day, none)
time_period = get_time_period()
print('\nCalculating the first statistic...')
start_time = time.time()
# What is the most popular month for start time?
if time_period[0] == 'NONE':
# Call popular_month function and print the results
print('Most popular month: {}'.format(
popular_month(city_file, time_period)))
print("That took %s seconds." % (time.time() - start_time))
print("\nCalculating the next statistic...")
start_time = time.time()
# What is the most popular day of week (Monday, Tuesday, etc.) for start time?
if time_period[0] == 'NONE' or time_period[0] == 'MONTH':
# Call popular_day function and print the results
print('Most popular day of week: {}'.format(
popular_day(city_file, time_period)))
print("That took %s seconds." % (time.time() - start_time))
print("\nCalculating the next statistic...")
start_time = time.time()
# What is the most popular hour of day for start time?
print('Most popular hour of day: {}'.format(
popular_hour(city_file, time_period)))
print("That took %s seconds." % (time.time() - start_time))
print("\nCalculating the next statistic...")
start_time = time.time()
# What is the total trip duration and average trip duration?
trip_stats = trip_duration(city_file, time_period)
if trip_stats is not None:
print("Average trip duration: {}".format(trip_stats[1]))
print("Total trip duration: {}".format(trip_stats[0]))
else:
print("No trip data found.")
print("That took %s seconds." % (time.time() - start_time))
print("\nCalculating the next statistic...")
start_time = time.time()
# What is the most popular start station and most popular end station?
popular_station = popular_stations(city_file, time_period)
if popular_station is not None:
print("Most popular start station: \"{}\"".format(popular_station[0]))
print("Most popular end station: \"{}\"".format(popular_station[1]))
else:
print("No station data found.")
print("That took %s seconds." % (time.time() - start_time))
print("\nCalculating the next statistic...")
start_time = time.time()
# What is the most popular trip?
pop_trip = popular_trip(city_file, time_period)
if pop_trip is not None:
print("Most popular trip: \"{}\" to \"{}\"".format(
pop_trip[0], pop_trip[1]))
else:
print("No trip data found.")
print("That took %s seconds." % (time.time() - start_time))
print("\nCalculating the next statistic...")
start_time = time.time()
# What are the counts of each user type?
user_types = users(city_file, time_period)
for user in user_types:
print("{}s: {}".format(user, user_types[user]))
print("That took %s seconds." % (time.time() - start_time))
print("\nCalculating the next statistic...")
start_time = time.time()
# What are the counts of gender?
gender_types = gender(city_file, time_period)
if gender_types is None:
print("Gender info not in data file.")
else:
for genders in gender_types:
print("{}s: {}".format(genders, gender_types[genders]))
print("That took %s seconds." % (time.time() - start_time))
print("\nCalculating the next statistic...")
start_time = time.time()
# What are the earliest, most recent, and most popular birth years?
births = birth_years(city_file, time_period)
if births is None:
print("Birth year info not in data file.")
else:
print("Earliest birth year: {}".format(births[0]))
print("Most recent birth year: {}".format(births[1]))
print("Most popular birth year: {}".format(births[2]))
print("That took %s seconds." % (time.time() - start_time))
# Display five lines of data at a time if user specifies that they would like to
display_data(city_file, time_period)
# Restart?
restart = input('\nWould you like to restart? Type \'yes\' or \'no\'. ')
if restart.lower() == 'yes':
statistics()
print("Gender info not in data file.")
else:
for genders in gender_types:
print("{}s: {}".format(genders, gender_types[genders]))
print("That took %s seconds." % (time.time() - start_time))
print("\nCalculating the next statistic...")
start_time = time.time()
# What are the earliest, most recent, and most popular birth years?
births = birth_years(city_file, time_period)
if births is None:
print("Birth year info not in data file.")
else:
print("Earliest birth year: {}".format(births[0]))
print("Most recent birth year: {}".format(births[1]))
print("Most popular birth year: {}".format(births[2]))
print("That took %s seconds." % (time.time() - start_time))
# Display five lines of data at a time if user specifies that they would like to
display_data(city_file, time_period)
# Restart?
restart = input('\nWould you like to restart? Type \'yes\' or \'no\'. ')
if restart.lower() == 'yes':
statistics()
if __name__ == "__main__":
statistics()
#EOF
