由于自己对新知识的好奇，2014年被一位朋友吸引，当时遇到他时，他是在坚持用python进行实现自己的功能。同时用的是vim进行编写，而之前我一直是用 IDE ,2015年初，我开始了解python,首先我接触到的就是
import this
The Zen of Python
- **Python的原则** Beautiful is better than ugly. - **优美胜于丑陋（Python 以编写优美的代码为目标）** Explicit is better than implicit. - **明了胜于晦涩（优美的代码应当是明了的，命名规范，风格相似）** Simple is better than complex. - **简洁胜于复杂（优美的代码应当是简洁的，不要有复杂的内部实现）** Complex is better than complicated. - **复杂胜于凌乱（如果复杂不可避免，那代码间也不能有难懂的关系，要保持接口简洁）** Flat is better than nested. - **扁平胜于嵌套（优美的代码应当是扁平的，不能有太多的嵌套） ** Sparse is better than dense. - **间隔胜于紧凑（优美的代码有适当的间隔，不要奢望一行代码解决问题）** Readability counts. - **可读性很重要（优美的代码是可读的）** Special cases aren't special enough to break the rules. - **即便假借特例的实用性之名，也不可违背这些规则（这些规则至高无上）** Although practicality beats purity. Errors should never pass silently. - **不要包容所有错误，除非你确定需要这样做（精准地捕获异常，不写 except:pass 风格的代码）** Unless explicitly silenced. - **而是尽量找一种，最好是唯一一种明显的解决方案（如果不确定，就用穷举法）** In the face of ambiguity, refuse the temptation to guess. - **当存在多种可能，不要尝试去猜测** There should be one-- and preferably only one --obvious way to do it. - **而是尽量找一种，最好是唯一一种明显的解决方案（如果不确定，就用穷举法）** Although that way may not be obvious at first unless you're Dutch. - **虽然这并不容易，因为你不是 Python 之父（这里的 Dutch 是指 Guido ）** Now is better than never. Although never is often better than *right* now.
- **做也许好过不做，但不假思索就动手还不如不做（动手之前要细思量） ** If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea. - **如果你无法向人描述你的方案，那肯定不是一个好方案；反之亦然（方案测评标准）** Namespaces are one honking great idea -- let's do more of those! - **命名空间是一种绝妙的理念，我们应当多加利用（倡导与号召）** ---- by Tim Peters
Lookup Type PostGIS Oracle MySQL [7] SpatiaLite
bbcontains X X X
bboverlaps X X X
contained X X X
contains X X X X
contains_properly X
coveredby X X
covers X X
crosses X X
disjoint X X X X
distance_gt X X X
distance_gte X X X
distance_lt X X X
distance_lte X X X
dwithin X X
equals X X X X
exact X X X X
intersects X X X X
overlaps X X X X
relate X X X
same_as X X X X
touches X X X X
within X X X X
left X
right X
overlaps_left X
overlaps_right X
overlaps_above X
overlaps_below X
strictly_above X
strictly_below X
####我这里只关注一下对mysql的空间操作支持
按我们的需要我们选用 within
bbcontains
支持：PostGIS,MySQL,SpatiaLite
查询数据库中空间数据的bbox包含在指定的空间bbox内的数据。
数据库 操作
PostGIS poly ~ geom
MySQL MBRContains(poly,geom)
SpatiaLite MbrContains(poly,geom)
bboverlaps
支持：PostGIS,MySQL,SpatiaLite
查询数据库中空间数据的bbox与指定的空间bbox相交的数据。
数据库 操作
PostGIS poly && geom
MySQL MBROverlops(poly,geom)
SpatiaLite MbrOverlops(poly,geom)
contained
支持：PostGIS,MySQL,SpatiaLite
查询数据库中空间数据的bbox完全包含指定的空间bbox的数据。
数据库 操作
PostGIS poly @ geom
MySQL MBRWithin(poly,geom)
SpatiaLite MbrWithin(poly,geom)
from django.contrib.gis.geos import (Polygon,Point)
point = Point(130,39)
buffer=point.buffer(degree)
进行within查询
AppPoint.objects.filter(point__within=buffer)
问题
这里给的半径通常是米为km，但是这个构建buffer的方法需要的参数是一个度。
degree=l*180/(math.pi*6371)
##测试方法和数据
def get_point(point,r): EARTH_R=6378.137 buffer = point.buffer(r*180/(math.pi*EARTH_R)) aps=AppPoint.objects.filter(point__within=buffer) for ap in aps: print ap.point.json,(math.pi*EARTH_R*ap.point.distance(point)/180)
其中点与点间的距离方法distance在django中解释为：
Returns the distance between the closest points on this Geometry and the other. Units will be in those of the coordinate system of the Geometry.
from django.contrib.gis.geos import (Polygon,Point) import math point = Point(130,39) EARTH_R=6378.137 buffer = point.buffer(r*180/(math.pi*EARTH_R)) aps=AppPoint.objects.filter(point__within=buffer)
*Running a scheduler that will move scheduled jobs into queues when the time comes
*RQ Scheduler comes with a script rqscheduler that runs a scheduler process that polls Redis once every minute and move scheduled jobs to the relevant queues when they need to be executed:
scheduler.schedule( scheduled_time=datetime.now(), # Timefor first execution, in UTC timezone func=func, # Functionto be queued args=[arg1, arg2], # Arguments passed intofunctionwhen executed kwargs={'foo': 'bar'}, # Keyword arguments passed intofunctionwhen executed interval=60, # Timebefore the functioniscalled again, in seconds repeat=10 # Repeat this number of times (None means repeat forever) )
##使用方法
###初始化：
In [1]: from analytics.etl import analytics_etl In [2]: analytics_etl.init()