This document provides an outline for a Python programming workshop for ArcGIS. It discusses using Python to access ArcGIS commands, attribute tables, and geometries for geoprocessing. It covers Python data types like lists and tuples, string processing, functions, and accessing attribute tables using search cursors. Exercises demonstrate selecting features by attributes and location using cursors and layers.
Programing for problem solving ( airline reservation system)Home
The software we used for this project is Dev C++.
Dev-C++ is a free IDE for Windows that uses either MinGW or TDM-GCC as the underlying compiler.
Also, this software is quite handy as compared with others as a result used by beginners.
CIRCUIT 2015 - Glimpse of perceptual diffICF CIRCUIT
Rachel Ingles - ICFI
It is a presentation on how to use before and after page screenshots for testing and how the contrasts highlight the status of the build.
It is a talk given by Vivian Zhang, CTO of SupStat Inc which is a leading Data Analytic consulting firm based in New York City, Shanghai and Beijing. NYC Open Data meetup are honored to host this event on Mar 24th,2014. You can find more information at www.meetup.com/nyc-open-data and www.nycopendata.com
This is an intermediate conversion course for C++, suitable for second year computing students who may have learned Java or another language in first year.
Type Casting C# - Lec4 (Workshop on C# Programming: Learn to Build)Jannat Ruma
This is provided as a course material in the Workshop named "Workshop on C# Programming: Learn to Build" organized by East West University Computer Programming Club (EWUCoPC) & supported by Young Engineers Society (YES).
Programing for problem solving ( airline reservation system)Home
The software we used for this project is Dev C++.
Dev-C++ is a free IDE for Windows that uses either MinGW or TDM-GCC as the underlying compiler.
Also, this software is quite handy as compared with others as a result used by beginners.
CIRCUIT 2015 - Glimpse of perceptual diffICF CIRCUIT
Rachel Ingles - ICFI
It is a presentation on how to use before and after page screenshots for testing and how the contrasts highlight the status of the build.
It is a talk given by Vivian Zhang, CTO of SupStat Inc which is a leading Data Analytic consulting firm based in New York City, Shanghai and Beijing. NYC Open Data meetup are honored to host this event on Mar 24th,2014. You can find more information at www.meetup.com/nyc-open-data and www.nycopendata.com
This is an intermediate conversion course for C++, suitable for second year computing students who may have learned Java or another language in first year.
Type Casting C# - Lec4 (Workshop on C# Programming: Learn to Build)Jannat Ruma
This is provided as a course material in the Workshop named "Workshop on C# Programming: Learn to Build" organized by East West University Computer Programming Club (EWUCoPC) & supported by Young Engineers Society (YES).
Writing an essay in English is not easy, Essays can be structured in many different ways, but they all include your thesis and arguments, An essay is a piece of writing, usually from an author’s personal point of view. Essays are non-fiction but often subjective; while expository, they can also include narrative.
3 ključna problema svakodnevnog poslovanja! Pitali smo poduzetnikeHrvatski Telekom
Pitali smo Vas poduzetnike, koja su to 3 ključna problema svakodnevnog poslovanja! Dobili smo odgovore za koje vjerujemo da postoje načini kako ih otkloniti ili barem ublažiti. Zato iskušajte neke od ovih mogućih rješenja!
Data Migrations in the App Engine DatastoreRyan Morlok
Data migration is a core problem when dealing with web frameworks. Rails and Django have their own built-in migration tools to help you manage data, but with Google Cloud Datastore, things are bit more manual. This presentation walks through several techniques and Python examples that leverage deferred tasks or map reduce to keep the data for your app consistent with the state of your code.
Educational Objectives After successfully completing this assignmen.pdfrajeshjangid1865
Educational Objectives: After successfully completing this assignment, the student should be
able to accomplish the following:
Use a loop structure to read user input of unknown size through std::cin and store it in an array.
Use conditional branching to selectively perform computational tasks.
Declare (prototype) and define (implement) functions.
Declare and define functions with arguments of various types, including pointers, references,
const pointers, and const references.
Call functions, making appropriate use of the function arguments and their types.
Make decisions as to appropriate function call parameter type, from among: value, reference,
const reference, pointer, and const pointer.
Create, edit, build and run multi-file projects using the Linux/Emacs/Make environment
announced in the course organizer.
Operational Objectives: Create a project that computes the mean and median of a sequence of
integers received via standard input.
Deliverables: Files: stats.h, stats.cpp, main.cpp, makefile, log.txt. Note that these files constitute
a self-contained project.
Assessment Rubric: The following will be used as a guide when assessing the assignment:
Please self-evaluate your work as part of the development process.
Background
Given a finite collection of n numbers:
The mean is the sum of the numbers divided by n, and
The median is the middle value (in case n is odd) or the average of the two middle values (in
case n is even).
Note that to find the median of a collection of data, it is convenient to first sort the data, that is,
put the data in increasing (or non-decreasing) order. Then the median is just the middle datum in
the sorted sequence (or the average of the two middle data, if there are an even number).
One of the more intuitive sort algorithms is called Insertion Sort, which operates on an array
a[0..n-1] of elements. The idea is to \"insert\" the value of a[i] into the sub-array a[0..i-1] at the
largest possible index that results in the expanded sub-array a[0..i] sorted. We insert at the
highest possible index in order not to place the value ahead of any previously inserted elements
with the same value. The subarray a[0..i-1] is assumed to be sorted at the beginning of each
insertion step. The base case consists of a one-element array a[0..0], which is always sorted.
Here is a \"pseudocode\" description of the algorithm:
The inner loop copies all elements in a[0..i-1] up one index until the correct place for t is found.
Then put t in that place.
Procedural Requirements:
Begin a log file named log.txt. This should be an ascii text file in cop3330/proj1 with the
following header:
This file should document all work done by date and time, including all testing and test results.
Create and work within a separate subdirectory cop3330/proj1. Review the COP 3330 rules
found in Introduction/Work Rules.
Copy all of the files from LIB/proj1. These should include:
In addition you should have the script submit.sh in either your .bin or your.
Material ini digunakan untuk kelas teknologi pengenalan pemrograman dengan bahasa pengantar Python http://oo.or.id/py
Dipublikasikan dengan lisensi Atribusi-Berbagi Serupa Creative Commons (CC BY-SA) oleh oon@oo.or.id
Python Functions Tutorial | Working With Functions In Python | Python Trainin...Edureka!
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on Python Functions tutorial covers all the important aspects of functions in Python right from the introduction to what functions are, all the way till checking out the major functions and using the code-first approach to understand them better.
Agenda
Why use Functions?
What are the Functions?
Types of Python Functions
Built-in Functions in Python
User-defined Functions in Python
Python Lambda Function
Conclusion
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Writing an essay in English is not easy, Essays can be structured in many different ways, but they all include your thesis and arguments, An essay is a piece of writing, usually from an author’s personal point of view. Essays are non-fiction but often subjective; while expository, they can also include narrative.
3 ključna problema svakodnevnog poslovanja! Pitali smo poduzetnikeHrvatski Telekom
Pitali smo Vas poduzetnike, koja su to 3 ključna problema svakodnevnog poslovanja! Dobili smo odgovore za koje vjerujemo da postoje načini kako ih otkloniti ili barem ublažiti. Zato iskušajte neke od ovih mogućih rješenja!
Data Migrations in the App Engine DatastoreRyan Morlok
Data migration is a core problem when dealing with web frameworks. Rails and Django have their own built-in migration tools to help you manage data, but with Google Cloud Datastore, things are bit more manual. This presentation walks through several techniques and Python examples that leverage deferred tasks or map reduce to keep the data for your app consistent with the state of your code.
Educational Objectives After successfully completing this assignmen.pdfrajeshjangid1865
Educational Objectives: After successfully completing this assignment, the student should be
able to accomplish the following:
Use a loop structure to read user input of unknown size through std::cin and store it in an array.
Use conditional branching to selectively perform computational tasks.
Declare (prototype) and define (implement) functions.
Declare and define functions with arguments of various types, including pointers, references,
const pointers, and const references.
Call functions, making appropriate use of the function arguments and their types.
Make decisions as to appropriate function call parameter type, from among: value, reference,
const reference, pointer, and const pointer.
Create, edit, build and run multi-file projects using the Linux/Emacs/Make environment
announced in the course organizer.
Operational Objectives: Create a project that computes the mean and median of a sequence of
integers received via standard input.
Deliverables: Files: stats.h, stats.cpp, main.cpp, makefile, log.txt. Note that these files constitute
a self-contained project.
Assessment Rubric: The following will be used as a guide when assessing the assignment:
Please self-evaluate your work as part of the development process.
Background
Given a finite collection of n numbers:
The mean is the sum of the numbers divided by n, and
The median is the middle value (in case n is odd) or the average of the two middle values (in
case n is even).
Note that to find the median of a collection of data, it is convenient to first sort the data, that is,
put the data in increasing (or non-decreasing) order. Then the median is just the middle datum in
the sorted sequence (or the average of the two middle data, if there are an even number).
One of the more intuitive sort algorithms is called Insertion Sort, which operates on an array
a[0..n-1] of elements. The idea is to \"insert\" the value of a[i] into the sub-array a[0..i-1] at the
largest possible index that results in the expanded sub-array a[0..i] sorted. We insert at the
highest possible index in order not to place the value ahead of any previously inserted elements
with the same value. The subarray a[0..i-1] is assumed to be sorted at the beginning of each
insertion step. The base case consists of a one-element array a[0..0], which is always sorted.
Here is a \"pseudocode\" description of the algorithm:
The inner loop copies all elements in a[0..i-1] up one index until the correct place for t is found.
Then put t in that place.
Procedural Requirements:
Begin a log file named log.txt. This should be an ascii text file in cop3330/proj1 with the
following header:
This file should document all work done by date and time, including all testing and test results.
Create and work within a separate subdirectory cop3330/proj1. Review the COP 3330 rules
found in Introduction/Work Rules.
Copy all of the files from LIB/proj1. These should include:
In addition you should have the script submit.sh in either your .bin or your.
Material ini digunakan untuk kelas teknologi pengenalan pemrograman dengan bahasa pengantar Python http://oo.or.id/py
Dipublikasikan dengan lisensi Atribusi-Berbagi Serupa Creative Commons (CC BY-SA) oleh oon@oo.or.id
Python Functions Tutorial | Working With Functions In Python | Python Trainin...Edureka!
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on Python Functions tutorial covers all the important aspects of functions in Python right from the introduction to what functions are, all the way till checking out the major functions and using the code-first approach to understand them better.
Agenda
Why use Functions?
What are the Functions?
Types of Python Functions
Built-in Functions in Python
User-defined Functions in Python
Python Lambda Function
Conclusion
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Property Based Testing is an process to build robust systems.
It facilitates a deeper understanding of the system under test. It can be used on any testing level: unit, integration or functional.
The presentation introduces how Property Based Testing works, how to use it with PHPUnit, and in what way it differentiates from example based tests.
It talks about strategies to find good properties to check for.
This presentation was built for the Meet-Magento conference 2020 in Mumbai.
Object Oriented Concepts required to know about the Android Application Programming are discussed over here. Get the most of the details about each and every concept of OO paradigm so you can use them very well in Android Application Programming.
Functions, Exception, Modules and Files
Functions: Difference between a Function and a Method, Defining a Function, Calling a Function, Returning Results from a Function, Returning Multiple Values from a Function, Functions are First Class Objects, Pass by Object Reference, Formal and Actual Arguments, Positional Arguments, Keyword Arguments, Default Arguments, Variable Length Arguments, Local and Global Variables, The Global Keyword, Passing a Group of Elements to a Function, Recursive Functions, Anonymous Functions or Lambdas (Using Lambdas with filter() Function, Using Lambdas with map() Function, Using Lambdas with reduce() Function), Function Decorators, Generators, Structured Programming, Creating our Own Modules in Python, The Special Variable __name__
Exceptions: Errors in a Python Program (Compile-Time Errors, Runtime Errors, Logical Errors),Exceptions, Exception Handling, Types of Exceptions, The Except Block, The assert Statement, UserDefined Exceptions, Logging the Exceptions
20%
Files: Files, Types of Files in Python, Opening a File, Closing a File, Working with Text Files Containing Strings, Knowing Whether a File Exists or Not, Working with Binary Files, The with Statement, Pickle in Python, The seek() and tell() Methods, Random Accessing of Binary Files, Random Accessing of Binary Files using mmap, Zipping and Unzipping Files, Working with Directories, Running Other Programs from Python Program
Handout from Hand Drawing for Planners. Alternately titled: “These Fifteen Easy Tricks Will Make You Ridiculously Confident with a Marker!”
Presenter: Jossie Ivanov, @jossie_sf
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
1. Python Programming for Arcgis 2
Daniel Sheehan
dsheehan@mit.edu, gishelp@mit.edu
9:30AM-12:30PM
January 15, 2015
This class was originally developed by David
Quinn and taught by David and Daniel in IAP
2010 and 2011.
2. Goals for the workshop (from yesterday)
• Learning enough Python to
– Access Arcgis commands through Python
– Access individual records from attribute table
– Acccess individual geometries for use in
geoprocessing
• Develop the ability to record and document
your geoprocessing
3. Slides available at …
• http://web.mit.edu/dsheehan/www/
dataForPythonIAP2015.zip (same data as
yesterday)
• http://web.mit.edu/dsheehan/www/
PythonProgrammingforArcgis2.pdf
4. Outline
• More data types
• Python functions
• Accessing Attribute Tables
• Select by attributes and location
5. Lists = []
An ordered set of elements enclosed in square brackets.
Zero based (first element is accessed by typing list[0]).
# This is a list []
animals = [ 'dog ' , ' cat ' , 'horse ' , ' seal ' ]
print animals[3]
animals.append(‘owl’)
Lists are mutable
6. Tuples = ()
Zero base (first element of a non-empty tuple is
always tuple(0)
# This is a tuple
Countries = (‘Costa Rica’, ‘India’, ‘Abu Dhabi’)
# You cannot change a tuple (immutable)
7. String processing – slicing a string
# slicing a string
Name = ‘Massachusetts’
#Fenceposts – starting at index 1 and ending
# before index 2
Name[1:2]
# find the first 4 characters of Name
Name[:4]
8. Example: using length
len(Name) shows length of string
• Find the length of a string variable called
name with a value of ‘Massachusetts’
• Print the last 4 characters in the name string
9. Example Function 1
# Python uses dynamic typing (type is set when
a variable is assigned or argument is passed)
# Save in file called test.py
def print_value(argument):
# Print the passed argument
print argument
# default return is none for a function
10. Import ‘test.py’ - DIY
# Importing a script(that is stored in the same
folder, otherwise use full path)
import test
# print number from function
Answer = test.print_value(15)
11. Example Function 2
# Function will give an error if argument is not a
number
# Save this file as example.py
def change_value(number):
# Add documentation here
number += 8
return number
12. Import ‘example.py’ - DIY
# Importing a script (that is stored in the same
folder, otherwise use full path)
import example
# get new number from function
Answer = example.change_value(15)
print Answer
13. Exercise 1: Function to return shapefile
name
• Take in full file path string:
“C:Usersdsheehandesktopinterstatehigh
ways.shp”
• Return shapefile name:
“InterstateHighways.shp”
Should work for any full file path string, not one
case. You should have a second .py file to call the
function.
Examine python documentation http://doc.python.org;
in particular string.split(), len()
14. Warning: beware when copying from
slides
• Double quotes aren’t the same in PP as in IDLE
• Maintain indents so logic of the program
remains as intended
• And don’t open shapefiles with a cursor that
are open in Arcmap
15. Accessing the Attribute Table
def sumUpLength(shapefile):
# returns the length of all line segments in an attribute
table
import arcpy, os
sumLength = 0.0
try:
with arcpy.da.SearchCursor(shapefile, (“LENGTH”))
as cursor:
for row in cursor:
sumLength += row[0]
del cursor
except:
return -9999
return sumLength
16. Exercise 2
Call the function in the previous slide (called
accessAT.py) from a .py file. You should submit
a full path to the shapefile
(example -
“C:dsheehandesktopInterstateHighways.shp”)
as the argument to the function. Once this works,
add a where clause to the searchCursor function
to find only records with a length of 40 KM or
greater. Search help for Search Cursor and look for
AddFieldDelimiters.
18. Exercise 3 - cursor
We are working with a shapefile, stepping
through the atttribute table of a FEATURE
CLASS with the cursor:
FC =
"C:UsersdsheehanDesktopkenyaDistrict2.shp
"
with arcpy.da.SearchCursor(FC, ("DISTRICT")) as cursor:
for row in cursor:
print row[0]
del cursor
19. Exercise 3 – selecting using attributes
Selecting by layer requires converting from a FEATURE CLASS to
LAYER but uses the same shapefile but opened a second time.
before loop
arcpy.MakeFeatureLayer_management("C:Usersdsheehan
DesktopkenyaDistricts.shp", "lyr")
Inside loop
arcpy.SelectLayerByAttribute_management("lyr",
"NEW_SELECTION", ' "DISTRICT" = '' + row[0] + ''')
Confused by mix of quotes, single and double?
20. Exercise 3 – select by location
Using the Layer, not the feature class, all inside
the loop:
arcpy.SelectLayerByLocation_management
("lyr", "BOUNDARY_TOUCHES", "lyr")
And checking the number selected:
theCount = arcpy.GetCount_management("lyr")
print theCount
21. Exercise 3 - result
import arcpy, os
try:
with
arcpy.da.SearchCursor("C:UsersdsheehanDesktopPython2015currentkenyaDistricts.
shp", ("DISTRICT")) as cursor:
arcpy.MakeFeatureLayer_management("C:UsersdsheehanDesktopPython2015current
kenyaDistricts2.shp", "lyr")
for row in cursor:
arcpy.SelectLayerByAttribute_management("lyr", "NEW_SELECTION", ' "DISTRICT" = '' +
row[0] + ''')
arcpy.SelectLayerByLocation_management ("lyr", "BOUNDARY_TOUCHES", "lyr")
theCount = arcpy.GetCount_management("lyr")
print row[0]
print theCount
arcpy.SelectLayerByLocation_management ("lyr", "BOUNDARY_TOUCHES", "lyr")
theCount = arcpy.GetCount_management("lyr")
print theCount
del cursor
except:
print arcpy.GetMessages()
raise
22. Try this at home:
• Add code to test whether any of the nearby
polygons contain a specified district
• If not, select additional neighbors and test
• Count how many iterations before you find
the specified district
• You should do this inside a while loop