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English original link "go, the Unwritten parts" published in 2017/05/22 author JBD is a member of the Go Language development team
Checking the execution path and current state of the program is a useful debugging tool. The core file contains a memory dump and state of a running process. It is primarily used as a post-mortem debug program. It ca
1.dumps ()1. Json.dumps ()To convert data from a dictionary (DIC) type to a string (str), an error occurs directly in writing the data of the Dict type to the JSON file, so the function needs to be used when writing the data.ImportJSON name= {'AA':'1111','BB':'2222','cc':'3333','DC':'4444'} jsobj=json.dumps (name)Print(name)Print(jsobj)Print(Type (name))Print(Type (jsobj))After the operation, the results are as follows;{'AA':'1111','cc':'3333','BB':'2
)Print (DATA2)f = open ('./tt.txt ', ' a ')Json.dump (DATA2,F)This generates a tt.txt file that holds the JSON-formatted data. The dumps also provides pritty print, formatted output. Json.load loading JSON format file The following is the JSON data read from the TXT file.f = open ('./tt.txt ', ' R ')hehe = Json.load (f)Print (hehe)Summarize:Json.dumps:dict to Str json.dump is to save Python data as JSONJson.loads:str turns into Dict json.load is to re
First, the Windows environment method1:cmd find the PID running the server containerJps-vCases:C:\users\administrator>jps-v4856 Bootstrap-djdk.tls.ephemeraldhkeysize=2048-djava.util.logging.config.file=d:\soft\apache-tomcat-7.0.69\conf\logging.properties-djava.util.logging.manageR=org.apache.juli.classloaderlogmanager-djava.endorsed.dirs=d:\soft\apache-tomcAt-7.0.69\endorsed-dcatalina.base=d:\soft\apache-tomcat-7.0.69-dcatalina.home=D:\soft\apache-tomcat-7.0.69-djava.io.tmpdir=d:\soft\apache-tom
(FileName, "w", encoding= ' Utf-8 '), Ensure_ascii=false)3, Json.loads ()Json.loads () is used to convert data of type STR to Dict.ImportJsonname= {'a':'Zhangsan','b':'Lisi','C':'Mawu','D':'Zhaoliu'}jsdumps=json.dumps (name) jsloads=json.loads (jsdumps)Print(Name,'type is:%s'%type (name))Print(Jsdumps,'type is:%s'%type (jsdumps))Print(Jsloads,'type is:%s'%type (jsloads))Result is{'a':'Zhangsan','b':'Lisi','C':'Mawu','D':'Zhaoliu'} type is:class 'Dict'>{"a":"Zhangsan","b":"Lisi","C":"Mawu","D":"
Null
JSON decodes the corresponding table for the Python type conversion:
JSON
Python
Object
Dict
Array
List
String
Str
Number (int)
Int
Number (real)
Float
True
True
False
False
Null
None
2, Json.dump () and Json.load () are mainly used to read and write JSON file
#JSON data exchange, cross-language data exchange. first JSON processing plus ' ' into a string # Json.dumps encapsulated into str,json.loads take out#Pickle is the python internal data exchange language#dic= ' {' name ': ' Cay '} ' write#f=open (' Dog.txt ', ' W ')#F.write (DIC)#Fread=open ("Dog.txt", ' R ') Read#Data=fread.read ()#Print (type (data))#data=eval (data)#Print (Data ("name")) Import jsondic={'name':'cat'}# must double quotation mark {"Name": "Cay"}, #JSON processing, first put {
Oracle Trace Files
Oracle trace files are grouped into three categories:
One is the background alarm log file that records the activity of the background process during startup, shutdown, and running of the database, such as table space creation,
[1] common MIME types (general type): hypertext markup language text. HTML text/htmlxml document. XML text/xmlxhtml document. XHTML application/XHTML + XML plain text. TXT text/plainrtf text. RTF application/rtfpdf document. PDF
distance between Y and C is equal to the ratio obtained by the length of CD. It is easy to understand it by using a mathematical expression:
In this way, each vertex from A to B corresponds to the unique Vertex on C to D. If there is an X, we can obtain an y.In addition, if X is not in [a, B], for example, x
Perspective Projection Transformation Well, with the above two theoretical knowledge, we will start to analyze this pivotal projection transformation. Here we use OpenGL's Perspective Pro
is not in [a, B], for example, x
Perspective Projection Transformation
With the above two theoretical knowledge, we began to analyze the pivot projection transformation, the main character of this analysis. Here we use OpenGL's Perspective Projection Transformation for analysis. Other APIs may have some differences, but the subject idea is similar and can be deduced similarly. After the transformation of the camera matrix, the vertex is transformed to the camera space. At this time, the polyg
multiplication while d3d uses row vector matrix multiplication.
(3) The Z range of OpenGL CVV is [-1, 1], and the Z range of d3d CVV is [0, 1].
These differences lead to the final differences between OpenGL and d3d Perspective Projection matrices.
D3dPivot projection matrix Derivation
Let's first look at the most basic perspective relationship diagram (the figure used at the beginning of the previous Art
1) Write the shell script:[email protected] cvv]# cat test1.sh#!/bin/sh/bin/date>>/home/cvv/test.logecho "Hello world!" by cvv54 ">>/home/cvv/test.logGive executable permission:[Email protected] cvv]# chmod 777 test1.sh2) Join the scheduled task queue with CrontabCreate a new cron file, write a program or command that
space enters CVV after perspective transformation. This transformation matrix actually completes two tasks:
1) projects a vertex from a 3D space to a 2D projection plane.
2) the 2D projection points on the projection plane are transformed to the CVV in the same cropping space by linear interpolation.
These transformations are all completed once through the perspective matrix (if you are not familiar with
facilitate computing .)5.22 BlinN-phong illumination model (illumination)5.22 model definition (rendering cube)5.23 transformation from model space to World Space (basic implementation of affine transformation)5.23 Gouraud coloring (gradient filling)5.23 texture definition (PNG texture temporarily)5.23 texture ing5.24 fluoroscopy correction5.24 interface correction (from triangle-based to vertex-based, and then considering batch processing of vertex and surface retrieval, laying the foundation
today.
In reality, the classification of the above-mentioned plane projection can continue to be subdivided. For example, the Perspective Projection can be divided into one drop point, two drop points, and three drop point perspective projection. Side projection can be further divided into scatter projection and oblique two-axis projection. Orthogonal projection can be divided into axial-side projection and multi-viewpoint orthogonal projection. If you are interested in this, you can refer to t
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