What we did, learnt, how we build the data science system with Python and Django during the Antarctic Circumnavigation Expedition in 2016. Presented in Pycon-UK 2017.
This document provides a summary of a presentation on Python and its role in big data analytics. It discusses Python's origins and growth, key packages like NumPy and SciPy, and new tools being developed by Continuum Analytics like Numba, Blaze, and Anaconda to make Python more performant for large-scale data processing and scientific computing. The presentation outlines Continuum's vision of an integrated platform for data analysis and scientific work in Python.
The document discusses DNSSEC and efforts to secure the Domain Name System (DNS). It describes common DNS security threats like cache poisoning, where an attacker provides false data to a caching name server by guessing query IDs. The Kaminsky attack exploited a flaw that made it possible to guess IDs and poison caches without prior knowledge of the target domain's cache contents. DNSSEC aims to prevent such attacks by digitally signing DNS data to verify its integrity and authenticity.
This document discusses Linux performance analysis tools. It introduces tpoint, a tool for tracing Linux tracepoints. Some example one-liners are provided that demonstrate how to use tpoint to trace disk I/O and see the tasks and processes performing I/O. The document also summarizes ftrace, a Linux kernel tracing tool that can be used to analyze performance issues.
The document outlines an agenda for a conference on Apache Spark and data science, including sessions on Spark's capabilities and direction, using DataFrames in PySpark, linear regression, text analysis, classification, clustering, and recommendation engines using Spark MLlib. Breakout sessions are scheduled between many of the technical sessions to allow for hands-on work and discussion.
This document provides an overview of the history of computing including:
- Early counting methods like the abacus and mechanical counting devices.
- The development of punched card systems in the late 19th century.
- The creation of the Z3 computer in 1941 which used binary representation.
- The development of the Von Neumann architecture in the 1940s which is still used today.
- Moore's Law from 1965 which predicted computing power would double every 18 months.
Data Rescue and Preserving DR CapabilitiesChris Muller
Presentation at Best Practices Exchange conference in Harrisburg, PA. Enjoyable data rescue projects over the years and suggestions re IPDRC - Initiative to Preserve Data Rescue Capabilities.
Data science apps powered by Jupyter NotebooksNatalino Busa
Jupyter notebooks are transforming the way we look at computing, coding, and science. But is this the only "data scientist experience" that this technology can provide? In this presentation we will look at how to create interactive web applications for data exploration and machine learning. In the background this code is still powered by the well-understood and well-documented Jupyter Notebooks.
Code on github: https://github.com/natbusa/kernelgateway_demos
The document discusses the evolution of data storage and retrieval from oral traditions to modern databases integrated with the World Wide Web. It describes how early databases used file-based systems that had limitations in efficiency and usability. The development of relational databases and the ability to dynamically query databases from web servers enabled more powerful data-driven websites and applications. The integration of databases and client-side technologies like Flash further enhanced the interactivity and capabilities of websites and web applications.
This document provides a summary of a presentation on Python and its role in big data analytics. It discusses Python's origins and growth, key packages like NumPy and SciPy, and new tools being developed by Continuum Analytics like Numba, Blaze, and Anaconda to make Python more performant for large-scale data processing and scientific computing. The presentation outlines Continuum's vision of an integrated platform for data analysis and scientific work in Python.
The document discusses DNSSEC and efforts to secure the Domain Name System (DNS). It describes common DNS security threats like cache poisoning, where an attacker provides false data to a caching name server by guessing query IDs. The Kaminsky attack exploited a flaw that made it possible to guess IDs and poison caches without prior knowledge of the target domain's cache contents. DNSSEC aims to prevent such attacks by digitally signing DNS data to verify its integrity and authenticity.
This document discusses Linux performance analysis tools. It introduces tpoint, a tool for tracing Linux tracepoints. Some example one-liners are provided that demonstrate how to use tpoint to trace disk I/O and see the tasks and processes performing I/O. The document also summarizes ftrace, a Linux kernel tracing tool that can be used to analyze performance issues.
The document outlines an agenda for a conference on Apache Spark and data science, including sessions on Spark's capabilities and direction, using DataFrames in PySpark, linear regression, text analysis, classification, clustering, and recommendation engines using Spark MLlib. Breakout sessions are scheduled between many of the technical sessions to allow for hands-on work and discussion.
This document provides an overview of the history of computing including:
- Early counting methods like the abacus and mechanical counting devices.
- The development of punched card systems in the late 19th century.
- The creation of the Z3 computer in 1941 which used binary representation.
- The development of the Von Neumann architecture in the 1940s which is still used today.
- Moore's Law from 1965 which predicted computing power would double every 18 months.
Data Rescue and Preserving DR CapabilitiesChris Muller
Presentation at Best Practices Exchange conference in Harrisburg, PA. Enjoyable data rescue projects over the years and suggestions re IPDRC - Initiative to Preserve Data Rescue Capabilities.
Data science apps powered by Jupyter NotebooksNatalino Busa
Jupyter notebooks are transforming the way we look at computing, coding, and science. But is this the only "data scientist experience" that this technology can provide? In this presentation we will look at how to create interactive web applications for data exploration and machine learning. In the background this code is still powered by the well-understood and well-documented Jupyter Notebooks.
Code on github: https://github.com/natbusa/kernelgateway_demos
The document discusses the evolution of data storage and retrieval from oral traditions to modern databases integrated with the World Wide Web. It describes how early databases used file-based systems that had limitations in efficiency and usability. The development of relational databases and the ability to dynamically query databases from web servers enabled more powerful data-driven websites and applications. The integration of databases and client-side technologies like Flash further enhanced the interactivity and capabilities of websites and web applications.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2lGNybu.
Stefan Krawczyk discusses how his team at StitchFix use the cloud to enable over 80 data scientists to be productive. He also talks about prototyping ideas, algorithms and analyses, how they set up & keep schemas in sync between Hive, Presto, Redshift & Spark and make access easy for their data scientists, etc. Filmed at qconsf.com..
Stefan Krawczyk is Algo Dev Platform Lead at StitchFix, where he’s leading development of the algorithm development platform. He spent formative years at Stanford, LinkedIn, Nextdoor & Idibon, working on everything from growth engineering, product engineering, data engineering, to recommendation systems, NLP, data science and business intelligence.
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Digital Preservation Best Practices: Lessons Learned From Across the PondBenoit Pauwels
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Digital Presentation Best Practices: Lessons Learned From Across the PondULB - Bibliothèques
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The computational requirements of next generation sequencing is placing a huge demand on IT organisations .
Building compute clusters is now a well understood and relatively straightforward problem. However, NGS sequencing applications require large amounts of storage, and high IO rates.
This talk details our approach for providing storage for next-gen sequencing applications.
Talk given at BIO-IT World, Europe, 2009.
Michael Weber - Rechenkraft.net - From Volunteers to ScientistsCitizenCyberlab
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- Open source is the only sustainable option going forward. SUSE Enterprise Storage provides an enterprise-grade version of Ceph for petabyte to exabyte storage needs at massive cost savings over traditional storage.
- Computing has swung between centralized and distributed models over time, and is swinging back to centralized cloud processing but will also grow at the edge with 5G and IoT. Open source projects like Ceph, OpenStack, and containers allow for this hybrid model.
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The document provides an overview of computer hardware components including input/output ports, storage devices, and data storage. It discusses the major components of a computer system including primary storage in RAM and secondary storage devices like hard disk drives. Data is stored and manipulated in binary format using bits and bytes. Common input/output ports for connecting devices like monitors, networking cables, and storage are also outlined.
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Joshua Hollander Principal Engineer, Protectwise
Josh is Principal Engineer at Protectwise. He is functional programming devotee, stream whisperer, big data wrangler and sees ""Monoids everywhere"". He holds a Masters Degree in Computer Science from the University of Colorado.
Hi There Please help me with 1 The python code AND .pdfgeetakannupillai1
Hi There ... Please help me with:
1) The python code , AND
2) EDA doc questions
Thanks in advance :)
The file from which to read from is automobile.txt and I uploaded images of the dataset which I
attached to this post as well.
Write the python code which will be used to Create an EDA report in which you explain your
visualisations, investigations and findings. The Code for the Analysis should be in a jupyter
notebook named automobile.ipynbThe instructions for this capstone project are found in the
Capstone Project II.ipynb file. Follow these instructions clearly and use the EDA_doc.odt file as a
template for creating a report to explain your visualisations, investigations and findings.Now that
you know how to Explore a Dataset, it's time for you to do it from start to end. Please find the
Automobile Dataset in your task folder. notebook named automobile.ipynb. Be creative
:)Introduction Summary of the data set DATA CLEANING # SUMMARY OF THE METHODS AND
VISUALISATIONS DONE DURING DATA CLEANING MISSING DATA # ANY MISSING DATA?
HOW DID YOU HANDLE IT DATA STORIES AND VISUALISATIONS # THIS IS THE BULK OF
THIS PROJECT. EXTRACT STORIES AND ASSUMPTIONS BASED ON VISUALISATIONS OF
THE DATA.
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Presentation (only available in Spanish at the moment) of the IT system prepared and used for the ACE expedition on a Russian ice breaker. Explains the problems, solutions, links to code, etc. with photos during the presentation.
Usamos esta presentación para exponer a alumnos de 9 a 12 años en Chile nuestra experiencia en la expedición ACE.
We used this presentation to explain in schools in Chile our experience in the ACE expedition.
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- Open source is the only sustainable option going forward. SUSE Enterprise Storage provides an enterprise-grade version of Ceph for petabyte to exabyte storage needs at massive cost savings over traditional storage.
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This document provides an overview of the SHEBANQ project, which provides tools for querying annotated Hebrew text data. It describes the data sources and contributors that have built up the underlying text corpus over many years. It also outlines the steps taken to make this data and related tools more accessible, including developing a website, depositing data in archives, running demonstration projects, and integrating the data and tools into broader research environments through additional projects and publications. The goal has been to facilitate wider use of this linguistic resource and foster more digital humanities and data science work based on its contents.
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Hi There Please help me with 1 The python code AND .pdfgeetakannupillai1
Hi There ... Please help me with:
1) The python code , AND
2) EDA doc questions
Thanks in advance :)
The file from which to read from is automobile.txt and I uploaded images of the dataset which I
attached to this post as well.
Write the python code which will be used to Create an EDA report in which you explain your
visualisations, investigations and findings. The Code for the Analysis should be in a jupyter
notebook named automobile.ipynbThe instructions for this capstone project are found in the
Capstone Project II.ipynb file. Follow these instructions clearly and use the EDA_doc.odt file as a
template for creating a report to explain your visualisations, investigations and findings.Now that
you know how to Explore a Dataset, it's time for you to do it from start to end. Please find the
Automobile Dataset in your task folder. notebook named automobile.ipynb. Be creative
:)Introduction Summary of the data set DATA CLEANING # SUMMARY OF THE METHODS AND
VISUALISATIONS DONE DURING DATA CLEANING MISSING DATA # ANY MISSING DATA?
HOW DID YOU HANDLE IT DATA STORIES AND VISUALISATIONS # THIS IS THE BULK OF
THIS PROJECT. EXTRACT STORIES AND ASSUMPTIONS BASED ON VISUALISATIONS OF
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Usamos esta presentación para exponer a alumnos de 9 a 12 años en Chile nuestra experiencia en la expedición ACE.
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Circumnavigating the Antarctic with Python and Django during ACE 2016
1. Python and Django during the Antarctic
Circumnavigation Expedition (ACE)
PyCon UK, 2017. Carles Pina i Estany (carles@pina.cat)
2. What do we do for work?
Carles
C++/Qt software engineer at Mendeley
Some Python (scripts at work, evenings and weekends, Python code Dojos, etc.)
Using GNU/Linux for a long time
Long time ago: system administrator / network administrator
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3. What do we do for work?
Carles
C++/Qt software engineer at Mendeley
Some Python (scripts at work, evenings and weekends, Python code Dojos, etc.)
Using GNU/Linux for a long time
Long time ago: system administrator / network administrator
Jen
Worked for 3.5 years at the British Antarctic Survey (BAS) as a data manager
Now works for the Swiss Polar Institute, following up on the data management of ACE
More into wildlife and science (especially birds) than programming
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4. What were our roles on the ship?
As a team we:
managed the data of all 22 science projects
set up the data management recording system (Django);
set up the data storage and back-ups;
liaised with scientists to back up data, record metadata in a database and create
metadata records;
telecommunications setup:
onboard network;
e-mail system;
file uploader / Dropbox equivalent;
connecting science equipment.
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10. Why is ACE different?
Usually science expeditions focus on a small area of the Antarctic
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11. Why is ACE different?
Usually science expeditions focus on a small area of the Antarctic
Only other similar expedition was Discovery II in the 1950's
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12. Why is ACE different?
Usually science expeditions focus on a small area of the Antarctic
Only other similar expedition was Discovery II in the 1950's
Sampled and studied marine and terrestrial habitats
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13. Why is ACE different?
Usually science expeditions focus on a small area of the Antarctic
Only other similar expedition was Discovery II in the 1950's
Sampled and studied marine and terrestrial habitats
First expedition of the Swiss Polar Institute
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14. Why is ACE different?
Usually science expeditions focus on a small area of the Antarctic
Only other similar expedition was Discovery II in the 1950's
Sampled and studied marine and terrestrial habitats
First expedition of the Swiss Polar Institute
It was organised very quickly
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21. What is it?
Backup all the collected data
Keep a log of what's happening:
ultimate goal - to be able to describe where, when, by whom and for what reason the
data were collected
Keep a database of all samples collected
Create metadata records of all data sets so they can be discovered
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22. What is it?
Backup all the collected data
Keep a log of what's happening:
ultimate goal - to be able to describe where, when, by whom and for what reason the
data were collected
Keep a database of all samples collected
Create metadata records of all data sets so they can be discovered
Why?
Backup to avoid losing data and to archive it properly for the future
The data can be made publicly available after the 2 year embargo
Make data easier to discover and usable by others in the future
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23. We had many IT tasks
Setup the data storage (2 NAS, 64 TB, 2 UPS, etc.)
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24. We had many IT tasks
Setup the data storage (2 NAS, 64 TB, 2 UPS, etc.)
Create utilities to backup from USB hard disks and network-attached computers
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25. We had many IT tasks
Setup the data storage (2 NAS, 64 TB, 2 UPS, etc.)
Create utilities to backup from USB hard disks and network-attached computers
Create an intranet webpage for the data entry, utilities and to disseminate information on
board
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26. We had many IT tasks
Setup the data storage (2 NAS, 64 TB, 2 UPS, etc.)
Create utilities to backup from USB hard disks and network-attached computers
Create an intranet webpage for the data entry, utilities and to disseminate information on
board
Design and implement an e-mail system that works with an unstable 128 kbit connection
for 80 people
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27. We had many IT tasks
Setup the data storage (2 NAS, 64 TB, 2 UPS, etc.)
Create utilities to backup from USB hard disks and network-attached computers
Create an intranet webpage for the data entry, utilities and to disseminate information on
board
Design and implement an e-mail system that works with an unstable 128 kbit connection
for 80 people
Create a way to upload videos for the news (we had journalists on board)
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28. We had many IT tasks
Setup the data storage (2 NAS, 64 TB, 2 UPS, etc.)
Create utilities to backup from USB hard disks and network-attached computers
Create an intranet webpage for the data entry, utilities and to disseminate information on
board
Design and implement an e-mail system that works with an unstable 128 kbit connection
for 80 people
Create a way to upload videos for the news (we had journalists on board)
Download data (ice images, weather models) for the scientists
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29. We had many IT tasks
Setup the data storage (2 NAS, 64 TB, 2 UPS, etc.)
Create utilities to backup from USB hard disks and network-attached computers
Create an intranet webpage for the data entry, utilities and to disseminate information on
board
Design and implement an e-mail system that works with an unstable 128 kbit connection
for 80 people
Create a way to upload videos for the news (we had journalists on board)
Download data (ice images, weather models) for the scientists
Help scientists to connect different equipment (like snow flake counter)
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30. We had many IT tasks
Setup the data storage (2 NAS, 64 TB, 2 UPS, etc.)
Create utilities to backup from USB hard disks and network-attached computers
Create an intranet webpage for the data entry, utilities and to disseminate information on
board
Design and implement an e-mail system that works with an unstable 128 kbit connection
for 80 people
Create a way to upload videos for the news (we had journalists on board)
Download data (ice images, weather models) for the scientists
Help scientists to connect different equipment (like snow flake counter)
Fix a winch (software part)
17 / 45
31. We had many IT tasks
Setup the data storage (2 NAS, 64 TB, 2 UPS, etc.)
Create utilities to backup from USB hard disks and network-attached computers
Create an intranet webpage for the data entry, utilities and to disseminate information on
board
Design and implement an e-mail system that works with an unstable 128 kbit connection
for 80 people
Create a way to upload videos for the news (we had journalists on board)
Download data (ice images, weather models) for the scientists
Help scientists to connect different equipment (like snow flake counter)
Fix a winch (software part)
...
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34. science-data-management
Jen said: Can you help me build a database?
I answered: yes! (and thought of MySQL and create table...)
Jen also meant a web system to enter data in the database!
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46. I knew:
Python
Flask
MySQL
SQLAlchemy
Mustache
But... How could I create the forms to enter data into the database?
I asked Fran... and he said...
Django!
(I thought... nooooo...)
Fran created an example... and I loved it!
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47. One week of Django
I went back home and really enjoyed learning Django:
Writing Django models is easier than MySQL create table (with foreign keys, etc.)
Easier to change the models and migrate
We needed authentication of users
Permissions for tables were useful
Templates are very well integrated with models
All the admin part that we used has a good default UI
Easy to extend
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48. One week of Django
I went back home and really enjoyed learning Django:
Writing Django models is easier than MySQL create table (with foreign keys, etc.)
Easier to change the models and migrate
We needed authentication of users
Permissions for tables were useful
Templates are very well integrated with models
All the admin part that we used has a good default UI
Easy to extend
And it has very good off-line documentation!
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50. Django models
class StorageCrate(models.Model):
name = models.CharField(max_length=255, unique=True)
location = models.CharField(max_length=255)
description = models.CharField(max_length=255, null=True, blank=True)
comment = models.TextField(null=True, blank=True)
It creates the forms automatically
If the model didn't exist: creates the tables in the database
If the model already existed: creates the migration code
Deals with foreign keys, relations, etc.
It creates relational database models like we would have done
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57. In retrospect
Django was a very good tool to do what we had to do:
Without Django we would have taken much longer!
Django didn't get in the way as much as I had thought
Django commands were very easy to implement and useful for accessing the models from
command line parameters, widely used for many of our scripts
Django developers are very nice and helpful! Thanks for the concise error messages:
main.Project.title: (fields.E120) CharFields must define a 'max_length' attribute.
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58. In retrospect
Django was a very good tool to do what we had to do:
Without Django we would have taken much longer!
Django didn't get in the way as much as I had thought
Django commands were very easy to implement and useful for accessing the models from
command line parameters, widely used for many of our scripts
Django developers are very nice and helpful! Thanks for the concise error messages:
main.Project.title: (fields.E120) CharFields must define a 'max_length' attribute.
Django documentation is very well written!
Admin forms were easy to adapt and change
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60. What is Django?
From the Django website:
Django makes it easier to build
better Web apps more quickly and
with less code.
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61. What is Django?
From the Django website:
Django makes it easier to build
better Web apps more quickly and
with less code.
Data manager thinks:
We need to build a database
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62. What is Django?
From the Django website:
Django makes it easier to build
better Web apps more quickly and
with less code.
Data manager thinks:
We need to build a database
Django for data managers?
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63. Very useful Django features for us
Admin forms were easy to adapt and change (we could work around something if it
wasn't possible)
commands are fantastic!
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64. Very useful Django features for us
Admin forms were easy to adapt and change (we could work around something if it
wasn't possible)
commands are fantastic!
Django things that were not so helpful...
Some modules use resources (CSS, js, etc.) from the Internet (e.g. debug module). This
didn't work for us
Hard to change (only via changing the template?) the main admin page
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65. pip and rsync
pip wasn't "bad connection friendly":
No progress bar
We didn't see how to easily see the URIs being download (e.g. apt-get --show-uris to
download them overnight)
When bad networking: error messages weren't clear
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66. pip and rsync
pip wasn't "bad connection friendly":
No progress bar
We didn't see how to easily see the URIs being download (e.g. apt-get --show-uris to
download them overnight)
When bad networking: error messages weren't clear
man rsync, --progress:
This option tells rsync to print information showing the
progress of the transfer. This gives a bored user something to
watch.
I'd say "it helps my impatience".
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67. pip and rsync
pip wasn't "bad connection friendly":
No progress bar
We didn't see how to easily see the URIs being download (e.g. apt-get --show-uris to
download them overnight)
When bad networking: error messages weren't clear
man rsync, --progress:
This option tells rsync to print information showing the
progress of the transfer. This gives a bored user something to
watch.
I'd say "it helps my impatience".
Python documentation
More comprehensive off-line documentation could have been better (we relied on examples
and source code)
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68. Django closing the circle
We wanted to thank Django somehow for all the fantastic help.
I thought of fixing at least one bug that we experienced during the expedition
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69. Django closing the circle
We wanted to thank Django somehow for all the fantastic help.
I thought of fixing at least one bug that we experienced during the expedition
Bug #28120 reported and fixed! (thanks for the quick code review, merge...)
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70. Python/Django ideas if you go on
an expedition
Take lots of code with you (e.g. Calibre, other projects, etc.). This was more useful than
books and documentation
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71. Python/Django ideas if you go on
an expedition
Take lots of code with you (e.g. Calibre, other projects, etc.). This was more useful than
books and documentation
Use an IDE like Pycharm that allows you to navigate code of your libraries easier
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72. Python/Django ideas if you go on
an expedition
Take lots of code with you (e.g. Calibre, other projects, etc.). This was more useful than
books and documentation
Use an IDE like Pycharm that allows you to navigate code of your libraries easier
Download the offline Django documentation: you will feel almost online!
36 / 45
73. Python/Django ideas if you go on
an expedition
Take lots of code with you (e.g. Calibre, other projects, etc.). This was more useful than
books and documentation
Use an IDE like Pycharm that allows you to navigate code of your libraries easier
Download the offline Django documentation: you will feel almost online!
Actually also download Wikipedia!
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75. Introduction
We had 2 Iridium systems
Initially one for calls, another one for Internet
This is, 128 kbits for (officialy) 80 people
The Iridium connections are very unstable
38 / 45
76. L. Wood, SaVi: satellite constellation visualization, First Annual CCSR Research Symposium
(CRS 2011), Centre for Communication Systems Research, 30 June 2011.
http://arxiv.org/abs/1204.3265
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79. Iridium
66 Iridium satellites
Iridium name: originally supposed to be 77 satellites: atomic number of Iridium
There are spare satellites for when there are problems
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80. Iridium
66 Iridium satellites
Iridium name: originally supposed to be 77 satellites: atomic number of Iridium
There are spare satellites for when there are problems
Signal on the north and south pole! But VERY slow and unstable: it connects and
disconnects because satellites are not geostationary
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81. Iridium
66 Iridium satellites
Iridium name: originally supposed to be 77 satellites: atomic number of Iridium
There are spare satellites for when there are problems
Signal on the north and south pole! But VERY slow and unstable: it connects and
disconnects because satellites are not geostationary
Satellites are around 780 Km above the Earth (GPS and Inmarsat around 20,000 Km)
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82. Iridium
66 Iridium satellites
Iridium name: originally supposed to be 77 satellites: atomic number of Iridium
There are spare satellites for when there are problems
Signal on the north and south pole! But VERY slow and unstable: it connects and
disconnects because satellites are not geostationary
Satellites are around 780 Km above the Earth (GPS and Inmarsat around 20,000 Km)
Sometimes the satellites are visible: Iridium flares!
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83. Iridium
66 Iridium satellites
Iridium name: originally supposed to be 77 satellites: atomic number of Iridium
There are spare satellites for when there are problems
Signal on the north and south pole! But VERY slow and unstable: it connects and
disconnects because satellites are not geostationary
Satellites are around 780 Km above the Earth (GPS and Inmarsat around 20,000 Km)
Sometimes the satellites are visible: Iridium flares!
They are launching new satellites
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88. Contact
Carles Pina i Estany (carles@pina.cat)
Jen Thomas (jenny.thomas@epfl.ch)
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Slides
Slides have been written using Markdown and rendered using remarkjs (https://remarkjs.com)
Slides: https://github.com/cpina/ace-it-presentation / https://cpina.github.io/ace-it-
presentation/
ACE: http://spi-ace-expedition.ch/
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