def find_nearest_time(look_for, target_list):
# 格式化查询数据
look_for = time2secs(format_time(look_for))
target_list = [
time2secs(format_time(each_item)) for each_item in target_list
]
return (secs2time(find_closest(look_for, target_list)))
'Marathon'
]
hello_msg = "Welcome to the Marathon Club's App"
quit_msg = "Quitting the Marathon Club's App."
row_data = {}
with open('/sdcard/sl4a/scripts/PaceData.csv') as paces:
column_headings = paces.readline().strip().split(',')
column_headings.pop(0)
for each_line in paces:
row = each_line.strip().split(',')
row_label = row.pop(0)
inner_dict = {}
for i in range(len(column_headings)):
inner_dict[format_time(row[i])] = column_headings[i]
row_data[row_label] = inner_dict
app = android.Android()
def status_update(msg, how_long=2):
app.makeToast(msg)
time.sleep(how_long)
def do_dialog(title, data, func, ok='Select', notok='Quit'):
app.dialogCreateAlert(title)
func(data)
app.dialogSetPositiveButtonText(ok)
if notok:
target_data = [time2secs(t) for t in target_data]
return(secs2time(find_closest(look_for, target_data)))
data_dict = {}
with open('PaceData.csv') as pacedata:
headings_list = pacedata.readline().strip().split(',')
headings_list.pop(0)
for each_line in pacedata:
row_data = each_line.strip().split(',')
row_name = row_data.pop(0)
inner_dict = {}
for i in range(len(headings_list)):
inner_dict[format_time(row_data[i])] = headings_list[i]
data_dict[row_name] = inner_dict
distance_run = input('Enter the distance attempted: ')
recorded_time = input('Enter the recorded time: ')
predicted_distance = input('Enter the distance you want a prediction for: ')
closest_time = find_nearest_time(format_time(recorded_time), data_dict[distance_run])
closest_column_heading = data_dict[distance_run][closest_time]
prediction = [k for k in data_dict[predicted_distance].keys()
if data_dict[predicted_distance][k] == closest_column_heading]
print('The predicted time running ' + predicted_distance + ' is: ' + prediction[0] + '.')
# 定义字典信息
row_data = {}
# 获取个数据
with open("PaceData.csv") as paces:
# 先获取标题
column_headings = paces.readline().strip().split(",")
# 去掉第一个数据
column_headings.pop(0)
# 循环获取数据
for each_line in paces:
row = each_line.strip().split(",")
row_label = row.pop(0)
# 定义内部字典
inner_dict = {}
for i in range(len(row)):
inner_dict[tm2secs2tm.format_time(row[i])] = column_headings[i]
# 将数据与列标题关联
row_data[row_label] = inner_dict
# 提示数据输入
distance_run = input("Enter the distance attempted:")
recorded_time = input("Enter the recorded time:")
predicted_distance = input("Enter the distance you want a prediction for:")
closest_time = find_nearest_time(recorded_time, row_data[distance_run])
closest_column_heading = row_data[distance_run][closest_time]
prediction = [
k for k in row_data[predicted_distance].keys()
if row_data[predicted_distance][k] == closest_column_heading
]
'13.1mi', '25k', '30k', 'Marathon' ]
hello_msg = "Welcome to the Marathon Club's App"
quit_msg = "Quitting the Marathon Club's App."
row_data = {}
with open('/sdcard/sl4a/scripts/PaceData.csv') as paces:
#打开一个特定文件夹，得到并预处理CVS数据
column_headings = paces.readline().strip().split(',')
column_headings.pop(0)
for each_line in paces:
row = each_line.strip().split(',')
row_label = row.pop(0)
inner_dict = {}
for i in range(len(column_headings)):
inner_dict[format_time(row[i])] = column_headings[i]
row_data[row_label] = inner_dict
app = android.Android()
#创建android应用对象，并包含你的辅助函数
def status_update(msg, how_long=2):
app.makeToast(msg)
time.sleep(how_long)
def do_dialog(title, data, func, ok='Select', notok='Quit'):
app.dialogCreateAlert(title)
func(data)
app.dialogSetPositiveButtonText(ok)
if notok:
app.dialogSetNegativeButtonText(notok)
app.dialogShow()
distances = ['2mi', '5k', '5mi', '10k', '15k', '10mi', '20k', '13.1mi', '25k', '30k', 'Marathon']
hello_msg = "Welcome to the Marathon Club's App"
quit_msg = "Quitting the Marathon Club's App."
row_data = {}
with open('PaceData.csv') as paces:
column_headings = paces.readline().strip().split(',')
column_headings.pop(0)
for each_line in paces:
row = each_line.strip().split(',')
row_label = row.pop(0)
inner_dict = {}
for i in range(len(column_headings)):
inner_dict[format_time(row[i])] = column_headings[i] # associate the column heading with the time value from the row
row_data[row_label] = inner_dict
app = android.Android()
status_update(hello_msg)
resp = do_dialog("Pick a distance", distances, app.dialogSetSingleChoiceItems)
if resp['which'] in ('positive'):
distance_run = app.dialogGetSelectedItems().result[0]
distance_run = distances[distance_run]
recorded_time = app.dialogGetInput("Enter a " + distance_run + " time:", "Use HH:MM:SS format:").result
closest_time = find_nearest_time(format_time(recorded_time), row_data[distance_run])
closest_column_heading = row_data[distance_run][closest_time]
resp = do_dialog("Pick a distance to predict", distances, app.dialogSetSingleChoiceItems)
if resp['which'] in ('positive'):
where = [time2secs(t) for t in target_data]
res = find_closest(what, where)
return(secs2time(res))
row_data = {}
with open('PaceData.csv') as paces:
column_headings = paces.readline().strip().split(',')
column_headings.pop(0)
for each_line in paces:
row = each_line.strip().split(',')
row_label = row.pop(0)
inner_dict = {}
for i in range(len(column_headings)):
inner_dict[format_time(row[i])] = column_headings[i]
row_data[row_label] = inner_dict
distance_run = eval(input('Enter the distance attempted: '))
recorded_time = eval(input('Enter the recorded time: '))
predicted_distance = eval(input('Enter the distance you want a prediction for: '))
closest_time = find_nearest_time(format_time(recorded_time), row_data[distance_run])
closest_column_heading = row_data[distance_run][closest_time]
prediction = [k for k in list(row_data[predicted_distance].keys())
if row_data[predicted_distance][k] == closest_column_heading]
print(('The predicted time running ' + predicted_distance + ' is: ' + prediction[0] + '.'))
import time
from tm2secs2tm import format_time
row_data = {}
with open("data/PaceData.csv") as paces:
column_headings = paces.readline().strip().split(",")
column_headings.pop(0)
for each_line in paces:
row = each_line.strip() .split(",")
row_label = row.pop(0)
inner_data = {}
for each_column in column_headings:
#inner_data[each_column] = row.pop(0)
inner_data[format_time(row.pop(0))] = each_column
row_data[row_label] = inner_data
from find_it import find_closest
from tm2secs2tm import time2secs, secs2time
def find_nearest_time(look_for, target_data):
look_for_value = time2secs(look_for)
where_to_find = [time2secs(t) for t in target_data]
target_value = find_closest(look_for_value, where_to_find)
return secs2time(target_value)
distance_run = "20k"#input("Enter the distance attempted: ")
recorded_time = "59:59"#input("Enter the recorded time: ")
predicted_distance = "Marathon"#input("Enter the distance you want a prediction for: ")
closest_time = find_nearest_time(format_time(recorded_time), row_data[distance_run])