因為我的Focus MK3 JW原廠的大燈是全鹵素燈，而近光燈是用H7規格。在某些無路燈的路段總是會覺得鹵素燈不夠亮，就算是換了同是Philips出的號稱增強130%照射距離的夜勁光也還覺得有點暗，
所以很早就想換相對改HID單純些的LED大燈，也研究了一下市面上有那些LED H7車燈可選。
- Jan 30 Tue 2018 23:46
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Philips X-treme Ultinon超晶亮 H7 LED大燈 使用心得
因為我的Focus MK3 JW原廠的大燈是全鹵素燈，而近光燈是用H7規格。在某些無路燈的路段總是會覺得鹵素燈不夠亮，就算是換了同是Philips出的號稱增強130%照射距離的夜勁光也還覺得有點暗，
所以很早就想換相對改HID單純些的LED大燈，也研究了一下市面上有那些LED H7車燈可選。
- Mar 13 Mon 2017 00:26
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The old man under the moon
"不須作福不須求 用盡心機總未休 猶如行船陷險灘 亦似搭梯上雲端" 這詩或許就反映了現實吧 對現況的無能為力 也只能靜觀其變了…
- Aug 14 Thu 2014 23:58
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Linear regression using numpy
- Aug 05 Tue 2014 16:48
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Python簡易http sever
- Jul 31 Thu 2014 22:57
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list comprehension語法
[mapping expression for element in source if filtering-expression]
example:
example:
- Jul 31 Thu 2014 22:15
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Python 函數、基本檔案處理及list comprehension範例
def get_coach_data(filename):
try:
with open(filename) as f:
data = f.readline()
return (data.strip().split(','))
except IOError as ioerr:
print('File error: ' + str(ioerr))
return (None)
def sanitize(time_string):
if '-' in time_string:
splitter = '-'
elif ':' in time_string:
splitter = ':'
else:
return(time_string)
(mins, secs) = time_string.split(splitter)
return(mins + '.' + secs)
#with open('james.txt') as jaf: data = jaf.readline()
#james = data.strip().split(',')
james = get_coach_data('james.txt')
#clean_james = []
#for each_t in james:
# clean_james.append(sanitize(each_t))
#list comprehensions
#clean_james = [sanitize(each_t) for each_t in james]
#print(sorted(clean_james))
#james = sorted([sanitize(each_t) for each_t in james])
#unique_james = []
#for each_t in james:
# if each_t not in unique_james:
# unique_james.append(each_t)
#print(unique_james[0:3])
print(sorted(set([sanitize(each_t) for each_t in james]))[0:3])
try:
with open(filename) as f:
data = f.readline()
return (data.strip().split(','))
except IOError as ioerr:
print('File error: ' + str(ioerr))
return (None)
def sanitize(time_string):
if '-' in time_string:
splitter = '-'
elif ':' in time_string:
splitter = ':'
else:
return(time_string)
(mins, secs) = time_string.split(splitter)
return(mins + '.' + secs)
#with open('james.txt') as jaf: data = jaf.readline()
#james = data.strip().split(',')
james = get_coach_data('james.txt')
#clean_james = []
#for each_t in james:
# clean_james.append(sanitize(each_t))
#list comprehensions
#clean_james = [sanitize(each_t) for each_t in james]
#print(sorted(clean_james))
#james = sorted([sanitize(each_t) for each_t in james])
#unique_james = []
#for each_t in james:
# if each_t not in unique_james:
# unique_james.append(each_t)
#print(unique_james[0:3])
print(sorted(set([sanitize(each_t) for each_t in james]))[0:3])
- Apr 12 Sat 2014 12:08
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一些使用R統計分散csv檔資料函數
醒來寫了一下作業…
計算指定資料表欄位的mean
pollutantmean <- function(directory, pollutant, id = 1:332) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
## 'pollutant' is a character vector of length 1 indicating
## the name of the pollutant for which we will calculate the
## mean; either "sulfate" or "nitrate".
## 'id' is an integer vector indicating the monitor ID numbers
## to be used
## Return the mean of the pollutant across all monitors list
## in the 'id' vector (ignoring NA values)
dataSum <- numeric()
for(i in id)
{
dataPath <- sprintf("%s\\%03d.csv", directory, i)
data <- read.csv(dataPath)
dataSum <- c(dataSum, data[[pollutant]])
}
mean(dataSum, na.rm = TRUE)
}
計算指定資料表欄位的mean
pollutantmean <- function(directory, pollutant, id = 1:332) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
## 'pollutant' is a character vector of length 1 indicating
## the name of the pollutant for which we will calculate the
## mean; either "sulfate" or "nitrate".
## 'id' is an integer vector indicating the monitor ID numbers
## to be used
## Return the mean of the pollutant across all monitors list
## in the 'id' vector (ignoring NA values)
dataSum <- numeric()
for(i in id)
{
dataPath <- sprintf("%s\\%03d.csv", directory, i)
data <- read.csv(dataPath)
dataSum <- c(dataSum, data[[pollutant]])
}
mean(dataSum, na.rm = TRUE)
}
- Mar 08 Sat 2014 21:13
-
List stack behavior
>>> L = [1,2,3,4]
>>> while L:
front, L = L[0], L[1:]
print(front, L)
1 [2, 3, 4]
2 [3, 4]
3 [4]
4 []
>>>
>>> while L:
front, L = L[0], L[1:]
print(front, L)
1 [2, 3, 4]
2 [3, 4]
3 [4]
4 []
>>>
- Mar 08 Sat 2014 13:24
-
Python Pattern matching example
>>> import re
>>> match = re.match('Hello [ \t]*(.*)world', 'Hello Python world')
>>> match.group(1)
'Python '
>>> match = re.match('Hello [ \t]*(.*)world', 'Hello Python world')
>>> match.group(1)
'Python '
- Mar 06 Thu 2014 21:54
-
第一個python程式練習
