Page maintained by Ke-Sen Huang.
If you have additions or changes, send an e-mail.
Note that when possible I link to the page containing the link to the actual PDF or PS of the preprint.
I prefer this as it gives some context to the paper and avoids possible copyright problems with direct linking.
Thus you may need to search on the page to find the actual document.
Eurographics Digital Library: Computer Graphics Forum Volume 35 - Issue 2 (EG 2016 Proceedings)
Eurographics Digital Library Link for the paper
Paper Abstract
Author Preprint
Paper Video
Paper Presentation
Paper Images
Paper Data
Demo Program or Source Code
Related Links
Changelog
FP1 Shape M & M
- Shapes In a Box: Disassembling 3D Objects for Efficient Packing and Fabrication
-
Marco Attene
- Fast and Robust Inversion-Free Shape Manipulation
-
Tiantian Liu,
Ming Gao,
Lifeng Zhu,
Eftychios Sifakis,
Ladislav Kavan
- Building Construction Sets by Tiling Grammar Simplification
-
Javor Kalojanov,
Michael Wand,
Philipp Slusallek
FP2 Editing, Sketch & Drawing
- Structure-Adaptive Shape Editing for Man-made Objects
-
Qiang Fu,
Xiaowu Chen,
Xiaoyu Su,
Jia Li,
Hongbo Fu
- SmartCanvas: Context-inferred Interpretation of Sketches for Preparatory Design Studies
-
Youyi Zheng,
Han Liu,
Julie Dorsey,
Niloy J. Mitra
- Shading Curves: Vector-Based Drawing With Explicit Gradient Control
-
Henrik Lieng,
Flora P. Tasse,
Jiri Kosinka,
Neil A. Dodgson
FP3 Fabrication
- Buoyancy Optimization for Computational Fabrication
-
Lingfeng Wang,
Emily Whiting
- Improved Surface Quality in 3D Printing by Optimizing the Printing Direction
-
Weiming Wang,
Cedric Zanni,
Leif Kobbelt
- Saliency-Preserving Slicing Optimization for Effective 3D Printing
-
Weiming Wang,
Haiyuan Chao,
Jing Tong,
Zhouwang Yang,
Xin Tong,
Hang Li,
Xiuping Liu,
Ligang Liu
FP4 Image Editing & Processing
- Generalized Diffusion Curves: An Improved Vector Representation for Smooth-Shaded Images
-
Stefan Jeschke
- Generalized As-Similar-As-Possible Warping with Applications in Digital Photography
-
Renjie Chen,
Craig Gotsman
- Automatic Portrait Segmentation for Image Stylization
-
Xiaoyong Shen,
Aaron Hertzmann,
Jiaya Jia,
Sylvain Paris,
Brian Price,
Eli Shechtman,
Ian Sachs
FP5 Character Animation
- Motion Grammars for Character Animation
-
Kyunglyul Hyun,
Kyungho Lee,
Jehee Lee
- Animation Setup Transfer for 3D Characters
-
Quentin AVRIL,
Donya Ghafourzadeh,
Srinivasan Ramachandran,
Sahel Fallahdoust,
Sarah Ribet,
Olivier Dionne,
Martin de Lasa,
Eric Paquette
- Character contact re-positioning under large environment deformation
-
Steve Tonneau*,
Al-Ashqar Rami*,
Julien Pettre,
Taku Komura,
Nicolas Mansard
(* Joint first authors)
FP6 HDR Imaging
- An Objective Deghosting Quality Metric for HDR Images
-
Okan Tarhan Tursun,
Ahmet Oguz Akyuz,
Aykut Erdem,
Erkut Erdem
- Convolutional Sparse Coding for High Dynamic Range Imaging
-
Ana Serrano,
Felix Heide,
Diego Gutierrez,
Gordon Wetzstein,
Belen Masia
- Evaluation of Tone Mapping Operators for HDR video under different ambient luminance levels
-
M. Melo, M. Bessa,
Kurt Debattista,
Alan Chalmers
FP7 Terrains & Fluids
- Large Scale Terrain Generation from Tectonic Uplift and Fluvial Erosion
-
Guillaume Cordonnier,
Jean Braun,
Marie-Paule Cani,
Bedrich Benes,
Eric Galin,
Adrien Peytavie,
Eric Guerin
- Sparse representation of terrains for procedural modeling
-
Eric Guerin,
Julie Digne,
Eric Galin,
Adrien Peytavie,
- Stable and fast fluid-solid coupling for incompressible SPH
-
X. Shao, Z. Zhou,
Nadia Magnenat-Thalmann,
W. Wu
FP8 Data-Driven Images
- DeepProp: Extracting Deep Features from a Single Image for Edit Propagation
-
Yuki Endo,
Satoshi Iizuka,
Yoshihiro Kanamori,
Jun Mitani
- Data-Driven Automatic Cropping using Semantic Composition Search
-
Armin Samii,
Zhe Lin,
Radomir Mech
- Smooth Image Sequences for Data-driven Morphing
-
Hadar Averbuch-Elor,
Daniel Cohen-Or,
Johannes Kopf
FP9 Fluid Simulation
- Boundary detection in particle-based fluids
-
Marcos Sandim,
Douglas Cedrim,
Luis Gustavo Nonato,
Paulo Pagliosa,
Afonso Paiva
- Narrow Band FLIP for Liquid Simulations
-
Florian Ferstl,
Ryoichi Ando,
Chris Wojtan,
Rudiger Westermann,
Nils Thuerey
- A Practical Method for High-Resolution Embedded Liquid Surfaces
-
Ryan Goldade,
Christopher Batty,
Chris Wojtan
FP10 Global illumination
- Stylized Caustics: Progressive Rendering of Animated Caustics
-
Tobias Gunther,
Kai Rohmer,
Christian Rossl,
Thorsten Grosch,
Holger Theisel
- Specular Lobe-Aware Filtering and Upsampling for Interactive Indirect Illumination
-
Yusuke Tokuyoshi
- Global Illumination Using Well-Separated Pair Decomposition
-
Norbert Bus,
Nabil H. Mustafa,
Venceslas Biri
FP11 Meshes
- CPH: a compact representation for hierarchical meshes generated by primal refinement
-
Lionel Untereiner,
Pierre Kraemer,
David Cazier,
Dominique Bechmann
- Structure-Aware Mesh Decimation
-
David Salinas,
Florent Lafarge,
Pierre Alliez
- Multi-Resolution Meshes for Feature-Aware Hardware Tessellation
-
Thibaud Lambert,
Pierre Benard,
Gael Guennebaud (Inria Bordeaux)
FP12 Rendering Techniques
- Regularizing Image Reconstruction for Gradient-Domain Rendering with Feature Patches
-
Marco Manzi,
Delio Vicini,
Matthias Zwicker
- A Practical and Controllable Hair and Fur Model for Production Path Tracing
-
Jen-Yuan Chiang,
Benedikt Bitterli,
Chuck Tappan,
Brent Burley
- SAH guided spatial split partitioning for fast BVH construction
-
Per Ganestam,
Michael Doggett
FP13 Curves & Surfaces
- Geometric Flows of Curves in Shape Space for Processing Motion of Deformable Objects
-
Christopher Brandt,
Christoph von Tycowicz,
Klaus Hildebrandt
- A Multi-sided Bezier Patch with a Simple Control Structure
-
Tamas Varady,
Peter Salvi,
Gyorgy Kariko
- Adapting Feature Curve Networks to a Prescribed Scale
-
Anne Gehre,
Isaak Lim,
Leif Kobbelt
FP14 Shadows
- Compressed Multiresolution Hierarchies for High-Quality Precomputed Shadows
-
Leonardo Scandolo,
Pablo Bauszat,
Elmar Eisemann
- Filtered Stochastic Shadow Mapping Using a Layered Approach
-
Magnus Andersson,
Jon Hasselgren,
Jacob Munkberg,
Tomas Akenine-Moller
- Filtering Multilayer Shadow Maps for Accurate Soft Shadows
-
Kai Selgrad,
Carsten Dachsbacher,
Quirin Meyer,
Marc Stamminger
FP15 Faces & Motion
- Blendforces: A Dynamic Framework for Facial Animation
-
Vincent Barrielle,
Nicolas Stoiber,
Cedric Cagniart
- Example-Based Retargeting of Human Motion to Arbitrary Mesh Models
-
Ufuk Celikcan,
lker O. Yaz,
Tolga Capin
- Near-Instant Capture of High-Resolution Facial Geometry and Reflectance
(TR)
-
Graham Fyffe,
Paul Graham,
Borom Tunwattanapong,
Abhijeet Ghosh,
Paul Debevec
FP16 Reflectance, Video & Display
- Mobile Surface Reflectometry
-
Jeremy Riviere,
Pieter Peers,
Abhijeet Ghosh
- Line-Drawing Video Stylization
-
Nir Ben-Zvi,
Jose Bento Ayres Pereira,
Moshe Mahler,
Jessica Hodgins,
Arik Shamir
- Reducing Lateral Visual Biases in Displays
-
Inbar Huberman,
Raanan Fattal
FP17 Shape Collections & Clouds
- Autocorrelation Descriptor for Efficient Co-alignment of 3D Shape Collections
-
Melinos Averkiou,
Vladimir G. Kim,
Niloy J. Mitra
- Detection of Geometric Temporal Changes in Point Clouds
-
Gianpaolo Palma,
Paolo Cignoni,
Tamy Boubekeur,
Roberto Scopigno
- Consistent Partial Matching of Shape Collections via Sparse Modeling
-
L. Cosmo, E. Rodola, A. Albarelli, F. Memoli,
Daniel Cremers
FP18 Deformable & Soft Objects
- Learning 3D Articulation and Deformation using 2D Images
-
Angjoo Kanazawa,
Shahar Kovalsky,
Ronen Basri,
David W. Jacobs
- Effect of Low-level Visual Cues in Perception of Deformation
-
Donghui Han,
John Keyser
- Modeling and Estimation of Energy-Based Hyperelastic Objects
-
Eder Miguel,
David Miraut,
Miguel A. Otaduy
FP19 Texturing & Compression
- Geometry and Attribute Compression for Voxel Scenes
-
Bas Dado,
Timothy R. Kol,
Pablo Bauszat,
Jean-Marc Thiery,
Elmar Eisemann
- VBTC: GPU-Friendly Variable Block Size Texture Encoding
-
Pavel Krajcevski,
Abhinav Golas,
Karthik Ramani,
Michael C. Shebanow,
Dinesh Manocha
- Seamless, Static Multi-texturing of 3D Meshes
-
R. Pages, D. Berjon, F. Moran, N. Garcia
FP20 Geometric Modeling
- Space-Time Co-Segmentation of Point Cloud Sequences
-
Qing Yuan,
Guiqing Li,
Kai Xu,
Hui Huang
- Convolution Filtering of Continuous Signed Distance Fields for Polygonal Meshes
-
Mathieu Sanchez, Oleg Fryazinov, Pierre-Alain Fayolle,
Alexander Pasko
- Anisotropic Diffusion Descriptors
-
Davide Boscaini,
Jonathan Masci,
Emanuele Rodola,
Michael M. Bronstein,
Daniel Cremers
FP21 Visualization
- A Visualisation Tool used to Develop New Photon Mapping Techniques
-
B. Spencer, M. W. Jones, I. S. Lim
- General Projective Maps for Multidimensional Data Projection
-
Dirk Joachim Lehmann,
Holger Theisel
- Inertial Steady 2D Vector Field Topology
-
Tobias Gunther,
Holger Theisel
FP22 Video
- Multisampling Compressive Video Spectroscopy
-
Daniel S. Jeon,
Inchang Choi,
Min H. Kim
- Seamless Video Stitching from Hand-held Camera Inputs
-
Kaimo Lin,
Shuaicheng Liu,
Loong-Fah Cheong,
Bing Zeng
- Interactive Videos: Plausible Video Editing using Sparse Structure Points
-
Chia-Sheng Chang,
Hung-Kuo Chu,
Niloy J. Mitra
FP23 Images & Geometry
- Single Image Weathering via Exemplar Propagation
-
Satoshi Iizuka,
Yuki Endo,
Yoshihiro Kanamori,
Jun Mitani
- Probably Approximately Symmetric: Fast Rigid Symmetry Detection with Global Guarantees
-
Simon Korman,
Roee Litman,
Shai Avidan,
Alex Bronstein
- Trivariate Biharmonic B-Splines
-
Fei Hou,
Hong Qin,
Aimin Hao
- Partial Shape Matching using Transformation Parameter Similarity
-
Paul Guerrero,
Thomas Auzinger,
Michael Wimmer,
Stefan Jeschke
FP24 Cloth & Animation
- CAMA: Contact-Aware Matrix Assembly with Unified Collision Handling for GPU-based Cloth Simulation
-
Min Tang,
Huamin Wang,
Le Tang, Ruofeng Tong,
Dinesh Manocha
- Dexterous Manipulation of Cloth
-
Yunfei Bai,
Wenhao Yu,
C. Karen Liu
(*co-first author)
- Data-guided Model Predictive Control Based on Smoothed Contact Dynamics
-
Daseong Han, Haegwang Eom,
Junyong Noh,
Joseph Shin
- Emotion analysis and classification: understanding the performers¡¦ emotions using the LMA entities
-
Andreas Aristidou,
Panayiotis Charalambous,
Yiorgos Chrysanthou
kesen.huang@gmail.com


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Horizon’s settings tend to fall into three categories:
What follows is an overview of the Horizon and OpenStack-specific settings and a few notes on the Django-related settings.
Note
Prior to the Essex release of Horizon there were settings which controlled whether features such as Object Storage/Swift or Networking/Neutron would be enabled in the OpenStack Dashboard. This code has long since been removed and those pre-Essex settings have no impact now.
In Essex and later, the Service Catalog returned by the Identity Service after a user has successfully authenticated determines the dashboards and panels that will be available within the OpenStack Dashboard. If you are not seeing a particular service you expected make sure your Service Catalog is configured correctly.
The following options are available in order to configure/customize the behavior of your Horizon installation. All of them are contained in the HORIZON_CONFIG dictionary.
Warning
In OpenStack Dashboard configuration, we suggest NOT to use this setting. Please specify the order of dashboard using the Pluggable Settings.
Both the pluggable dashboard mechanism (OpenStack Dashboard default) and this setting dashboard configure the order of dashboards and the setting dashboard precedes the pluggable dashboard mechanism. Specifying the order in two places may cause confusion. Please use this parameter only when the pluggable config is not used.
New in version 2012.1(Essex).
Default: None
Horizon Dashboards are automatically discovered in the following way:
By default, dashboards defined by openstack_dashboard/local/enabled are displayed first in the alphabetical order of the config files, and then the remaining dashboards discovered by traversing INSTALLED_APPS are displayed in the alphabetical order.
If a list of dashboard slugs is provided in this setting, the supplied ordering is applied to the list of discovered dashboards. If the list of dashboard slugs is shorter than the number of discovered dashboards, the remaining dashboards are appended in the default order described above.
The dashboards listed must be in a Python module which is included in the INSTALLED_APPS list and on the Python path.
Warning
In OpenStack Dashboard configuration, we suggest NOT to use this setting. Please specify the order of dashboard using the Pluggable Settings.
The default dashboard can be configured via both the pluggable dashboard mechanism (OpenStack Dashboard default) and this setting default_dashboard, and if both are specified, the setting by the pluggable dashboard mechanism will be used. Specifying the default dashboard in two places may cause confusion. Please use this parameter only when the pluggable config is not used.
New in version 2012.1(Essex).
Default: None
The slug of the dashboard which should act as the first-run/fallback dashboard whenever a user logs in or is otherwise redirected to an ambiguous location.
New in version 2012.1(Essex).
Default: settings.LOGIN_REDIRECT_URL
This can be either a literal URL path (such as the default), or Python’s dotted string notation representing a function which will evaluate what URL a user should be redirected to based on the attributes of that user.
New in version 2012.1(Essex).
Default: 10
The maximum number of simultaneous AJAX connections the dashboard may try to make. This is particularly relevant when monitoring a large number of instances, volumes, etc. which are all actively trying to update/change state.
New in version 2012.1(Essex).
Default: 2500
How frequently resources in transition states should be polled for updates, expressed in milliseconds.
New in version 2013.2(Havana).
Defaults: {'delay': [3000], 'fade_duration': [1500], 'types': []}
If provided, will auto-fade the alert types specified. Valid alert types include: [‘alert-success’, ‘alert-info’, ‘alert-warning’, ‘alert-error’] Can also define the delay before the alert fades and the fade out duration.
New in version 2012.2(Folsom).
Default: None
If provided, a “Help” link will be displayed in the site header which links to the value of this settings (ideally a URL containing help information).
New in version 2012.1(Essex).
 Based on these exception categories, Horizon will handle the exception and display a message to the user.
New in version 2014.2(Kilo).
Default: "static"
Controls how bootstrap backdrop element outside of modals looks and feels. Valid values are "true" (show backdrop element outside the modal, close the modal after clicking on backdrop), "false" (do not show backdrop element, do not close the modal after clicking outside of it) and "static" (show backdrop element outside the modal, do not close the modal after clicking on backdrop).
New in version 2015.1(Kilo).
Default: False
Setting this to True will disable the reveal button for password fields, including on the login form.
New in version 2012.1(Essex).
Default: {'regex': '.*', 'help_text': _("Password is not accepted")}
A dictionary containing a regular expression which will be used for password validation and help text which will be displayed if the password does not pass validation. The help text should describe the password requirements if there are any.
This setting allows you to set rules for passwords if your organization requires them.
New in version 2013.1(Grizzly).
Default: "off"
Controls whether browser autocompletion should be enabled on the login form. Valid values are "on" and "off".
New in version 2013.1(Grizzly).
Default: True
Enable or disable simplified floating IP address management.
“Simple” floating IP address management means that the user does not ever have to select the specific IP addresses they wish to use, and the process of allocating an IP and assigning it to an instance is one-click.
The “advanced” floating IP management allows users to select the floating IP pool from which the IP should be allocated and to select a specific IP address when associating one with an instance.
Note
Currently “simple” floating IP address management is not compatible with Neutron. There are two reasons for this. First, Neutron does not support the default floating IP pool at the moment. Second, a Neutron floating IP can be associated with each VIF and we need to check whether there is only one VIF for an instance to enable simple association support.
Default: []
A list of AngularJS modules to be loaded when Angular bootstraps. These modules are added as dependencies on the root Horizon application horizon.
Default: []
A list of javascript source files to be included in the compressed set of files that are loaded on every page. This is needed for AngularJS modules that are referenced in angular_modules and therefore need to be include in every page.
New in version 2015.1(Kilo).
Default: []
A list of javascript spec files to include for integration with the Jasmine spec runner. Jasmine is a behavior-driven development framework for testing JavaScript code.
The following settings inform the OpenStack Dashboard of information about the other OpenStack projects which are part of this cloud and control the behavior of specific dashboards, panels, API calls, etc.
New in version 2015.1(Kilo).
Default: ['openstack_auth.urls']
A list of modules from which to collate authentication URLs from. The default option adds URLs from the django-openstack-auth module however others will be required for additional authentication mechanisms.
New in version 2012.1(Essex).
Default: 1000
The maximum number of objects (e.g. Swift objects or Glance images) to display on a single page before providing a paging element (a “more” link) to paginate results.
New in version 2012.2(Folsom).
Default: 20
Similar to API_RESULT_LIMIT. This setting controls the number of items to be shown per page if API pagination support for this exists.
New in version 2012.1(Essex).
Default: None
A list of tuples which define multiple regions. The tuple format is ('http://{{ keystone_host }}:5000/v2.0', '{{ region_name }}'). If any regions are specified the login form will have a dropdown selector for authenticating to the appropriate region, and there will be a region switcher dropdown in the site header when logged in.
If you do not have multiple regions you should use the OPENSTACK_HOST and OPENSTACK_KEYSTONE_URL settings instead.
New in version 2013.2(Havana).
Default: "AUTO"
This setting specifies the type of in-browser console used to access the VMs. Valid values are "AUTO"``(default), ``"VNC", "SPICE", "RDP", "SERIAL", and None. None deactivates the in-browser console and is available in version 2014.2(Juno). "SERIAL" is available since 2015.1(Kilo).
New in version 2015.1(Kilo).
Default: 512 * 1024
This setting specifies the size of the chunk (in bytes) for downloading objects from Swift. Do not make it very large (higher than several dozens of Megabytes, exact number depends on your connection speed), otherwise you may encounter socket timeout. The default value is 524288 bytes (or 512 Kilobytes).
New in version 2015.1(Kilo).
Default: 35
This setting enables you to change the default number of lines displayed for the log of an instance. Valid value must be a positive integer.
New in version 2013.2(Havana).
Default: {'key':'ram'}
When launching a new instance the default flavor is sorted by RAM usage in ascending order. You can customize the sort order by: id, name, ram, disk and vcpus. Additionally, you can insert any custom callback function. You can also provide a flag for reverse sort. See the description in local_settings.py.example for more information.
This example sorts flavors by vcpus in descending order:
CREATE_INSTANCE_FLAVOR_SORT = {
'key':'vcpus',
'reverse': True,
}
New in version 2015.1(Kilo).
Default: "themes/default"
This setting tells Horizon to use a directory as a custom theme.
By default, this directory will serve as the static root of the theme and the entire contents of the directory will be served up at /static/custom. If you wish to include content other than static files in a theme directory, but do not wish that content to be served up, then you can create a sub directory named static. If the theme folder contains a sub-directory with the name static, then static/custom/static` will be used as the root for the content served at /static/custom.
The static root of the theme folder must always contain a _variables.scss file and a _styles.scss file. These must contain or import all the bootstrap and horizon specific variables and styles which are used to style the GUI. For example themes, see: /horizon/openstack_dashboard/themes/
Horizon ships with one alternate theme based on Google’s Material Design. To use the alternate theme, set your CUSTOM_THEME_PATH to themes/material.
New in version 8.0.0(Liberty).
Default: "themes/default"
This setting allows Horizon to collect an additional theme during static collection and be served up via /static/themes/default. This is useful if CUSTOM_THEME_PATH inherits from another theme (like ‘default’).
If DEFAULT_THEME_PATH is the same as CUSTOM_THEME_PATH, then collection is skipped and /static/themes will not exist.
New in version 2015.1(Kilo).
Default: 30
This setting sets the maximum number of items displayed in a dropdown. Dropdowns that limit based on this value need to support a way to observe the entire list.
New in version 2015.1(Kilo).
Default: False
This setting will display an ‘Admin Password’ field on the Change Password form to verify that it is indeed the admin logged-in who wants to change the password.
New in version 2013.1(Grizzly).
Default: None
A list of dictionaries to add optional categories to the image fixed filters in the Images panel, based on project ownership.
Each dictionary should contain a tenant attribute with the project id, and optionally a text attribute specifying the category name, and an icon attribute that displays an icon in the filter button. The icon names are based on the default icon theme provided by Bootstrap.
Example: [{'text': 'Official', 'tenant': '27d0058849da47c896d205e2fc25a5e8', 'icon': 'icon-ok'}]
Note
Since the Kilo release, the Bootstrap icon library (e.g. ‘icon-ok’) has been replaced with Font Awesome (e.g. ‘fa-check’).
New in version 2014.2(Juno).
Default: []
A list of image custom property keys that should not be displayed in the Update Metadata tree.
This setting can be used in the case where a separate panel is used for managing a custom property or if a certain custom property should never be edited.
New in version 2013.2(Havana).
Default:
{
"data-processing": 1.1,
"identity": 2.0,
"volume": 2
}
Overrides for OpenStack API versions. Use this setting to force the OpenStack dashboard to use a specific API version for a given service API.
Note
The version should be formatted as it appears in the URL for the service API. For example, the identity service APIs have inconsistent use of the decimal point, so valid options would be “2.0” or “3”. For example:
OPENSTACK_API_VERSIONS = {
"data-processing": 1.1,
"identity": 3,
"volume": 2
}
New in version 2014.1(Icehouse).
Default: "False"
When set, enables the instance action “Retrieve password” allowing password retrieval from metadata service.
New in version 2012.1(Essex).
Default: "publicURL"
A string which specifies the endpoint type to use for the endpoints in the Keystone service catalog. The default value for all services except for identity is "publicURL" . The default value for the identity service is "internalURL".
New in version 2012.1(Essex).
Default: "<IP_ADDRESS>"
The hostname of the Keystone server used for authentication if you only have one region. This is often the only setting that needs to be set for a basic deployment.
New in version 2012.2(Folsom).
Default:
{
'can_set_mount_point': False,
'can_set_password': False,
'requires_keypair': False,
}
A dictionary containing settings which can be used to identify the capabilities of the hypervisor for Nova.
The Xen Hypervisor has the ability to set the mount point for volumes attached to instances (other Hypervisors currently do not). Setting can_set_mount_point to True will add the option to set the mount point from the UI.
Setting can_set_password to True will enable the option to set an administrator password when launching or rebuilding an instance.
Setting requires_keypair to True will require users to select a key pair when launching an instance.
New in version 2013.2(Havana).
Default:
{
'image_formats': [
('', _('Select format')),
('aki', _('AKI - Amazon Kernel Image')),
('ami', _('AMI - Amazon Machine Image')),
('ari', _('ARI - Amazon Ramdisk Image')),
('docker', _('Docker')),
('iso', _('ISO - Optical Disk Image')),
('qcow2', _('QCOW2 - QEMU Emulator')),
('raw', _('Raw')),
('vdi', _('VDI')),
('vhd', _('VHD')),
('vmdk', _('VMDK'))
]
}
Used to customize features related to the image service, such as the list of supported image formats.
New in version 2014.1(Icehouse).
Default:
{
"architecture": _("Architecture"),
"kernel_id": _("Kernel ID"),
"ramdisk_id": _("Ramdisk ID"),
"image_state": _("Euca2ools state"),
"project_id": _("Project ID"),
"image_type": _("Image Type")
}
Used to customize the titles for image custom property attributes that appear on image detail pages.
New in version 2013.1(Grizzly).
Default: True
If set to False, this setting disables local uploads to prevent filling up the disk on the dashboard server since uploads to the Glance image store service tend to be particularly large - in the order of hundreds of megabytes to multiple gigabytes.
Note
This will not disable image creation altogether, as this setting does not affect images created by specifying an image location (URL) as the image source.
New in version 2012.1(Essex).
Default: {'name': 'native', 'can_edit_user': True, 'can_edit_project': True}
A dictionary containing settings which can be used to identify the capabilities of the auth backend for Keystone.
If Keystone has been configured to use LDAP as the auth backend then set can_edit_user and can_edit_project to False and name to "ldap".
New in version 2013.2(Havana).
Default: "Default"
Overrides the default domain used when running on single-domain model with Keystone V3. All entities will be created in the default domain.
New in version 2011.3(Diablo).
Default: "_member_"
The name of the role which will be assigned to a user when added to a project. This value must correspond to an existing role name in Keystone. In general, the value should match the member_role_name defined in keystone.conf.
New in version 2015.1(Kilo).
Default: ["admin"]
The list of roles that have administrator privileges in this OpenStack installation. This check is very basic and essentially only works with keystone v2.0 and v3 with the default policy file. The setting assumes there is a common admin like role(s) across services. Example uses of this setting are:
- to rename the admin role to cloud-admin
- allowing multiple roles to have administrative privileges, like ["admin", "cloud-admin", "net-op"]
New in version 2013.2(Havana).
Default: False
Set this to True if running on multi-domain model. When this is enabled, it will require user to enter the Domain name in addition to username for login.
New in version 2011.3(Diablo).
Default: "http://%s:5000/v2.0" % OPENSTACK_HOST
The full URL for the Keystone endpoint used for authentication. Unless you are using HTTPS, running your Keystone server on a nonstandard port, or using a nonstandard URL scheme you shouldn’t need to touch this setting.
New in version 2015.1(Kilo).
Default: False
Enables keystone web single-sign-on if set to True. For this feature to work, make sure that you are using Keystone V3 and Django OpenStack Auth V1.2.0 or later.
New in version 2015.1(Kilo).
Default: "credentials"
Determines the default authentication mechanism. When user lands on the login page, this is the first choice they will see.
New in version 2015.1(Kilo).
Default:
(
("credentials", _("Keystone Credentials")),
("oidc", _("OpenID Connect")),
("saml2", _("Security Assertion Markup Language"))
)
This is the list of authentication mechanisms available to the user. It includes Keystone federation protocols such as OpenID Connect and SAML. The list of choices is completely configurable, so as long as the id remains intact. Do not remove the credentials mechanism unless you are sure. Once removed, even admins will have no way to log into the system via the dashboard.
New in version 2014.2(Juno).
Default: {'enable_backup': False}
A dictionary of settings which can be used to enable optional services provided by cinder. Currently only the backup service is available.
New in version 2013.1(Grizzly).
Default:
{
'enable_router': True,
'enable_distributed_router': False,
'enable_ha_router': False,
'enable_lb': True,
'enable_quotas': False,
'enable_firewall': True,
'enable_vpn': True,
'profile_support': None,
'supported_provider_types': ["*"],
'supported_vnic_types': ["*"],
'segmentation_id_range': {},
'enable_fip_topology_check': True,
'default_ipv4_subnet_pool_label': None,
'default_ipv6_subnet_pool_label': None,
}
A dictionary of settings which can be used to enable optional services provided by Neutron and configure Neutron specific features. The following options are available.
enable_router:
New in version 2014.2(Juno).
Default: True
Enable (True) or disable (False) the panels and menus related to router and Floating IP features. This option only affects when Neutron is enabled. If your Neutron deployment has no support for Layer-3 features, or you do not wish to provide the Layer-3 features through the Dashboard, this should be set to False.
enable_distributed_router:
New in version 2014.2(Juno).
Default: False
Enable or disable Neutron distributed virtual router (DVR) feature in the Router panel. For the DVR feature to be enabled, this option needs to be set to True and your Neutron deployment must support DVR. Even when your Neutron plugin (like ML2 plugin) supports DVR feature, DVR feature depends on l3-agent configuration, so deployers should set this option appropriately depending on your deployment.
enable_ha_router:
New in version 2014.2(Juno).
Default: False
Enable or disable HA (High Availability) mode in Neutron virtual router in the Router panel. For the HA router mode to be enabled, this option needs to be set to True and your Neutron deployment must support HA router mode. Even when your Neutron plugin (like ML2 plugin) supports HA router mode, the feature depends on l3-agent configuration, so deployers should set this option appropriately depending on your deployment.
enable_lb:
New in version 2013.1(Grizzly).
(Deprecated)
Default: True
Enables the load balancer panel. The load balancer panel will be enabled when this option is True and your Neutron deployment supports LBaaS. If you want to disable load balancer panel even when your Neutron supports LBaaS, set it to False.
This option is now marked as “deprecated” and will be removed in Kilo or later release. The load balancer panel is now enabled only when LBaaS feature is available in Neutron and this option is no longer needed. We suggest not to use this option to disable the load balancer panel from now on.
enable_quotas:
Default: False
Enable support for Neutron quotas feature. To make this feature work appropriately, you need to use Neutron plugins with quotas extension support and quota_driver should be DbQuotaDriver (default config).
enable_firewall:
(Deprecated)
Default: True
Enables the firewall panel. firewall panel will be enabled when this option is True and your Neutron deployment supports FWaaS. If you want to disable firewall panel even when your Neutron supports FWaaS, set it to False.
This option is now marked as “deprecated” and will be removed in Kilo or later release. The firewall panel is now enabled only when FWaaS feature is available in Neutron and this option is no longer needed. We suggest not to use this option to disable the firewall panel from now on.
enable_vpn:
(Deprecated)
Default: True
Enables the VPN panel. VPN panel will be enabled when this option is True and your Neutron deployment supports VPNaaS. If you want to disable VPN panel even when your Neutron supports VPNaaS, set it to False.
This option is now marked as “deprecated” and will be removed in Kilo or later release. The VPN panel is now enabled only when VPNaaS feature is available in Neutron and this option is no longer needed. We suggest not to use this option to disable the VPN panel from now on.
profile_support:
Default: None
This option specifies a type of network port profile support. Currently the available value is either None or "cisco". None means to disable port profile support. cisco can be used with Neutron Cisco plugins.
supported_provider_types:
New in version 2014.2(Juno).
Default: ["*"]
For use with the provider network extension. Use this to explicitly set which provider network types are supported. Only the network types in this list will be available to choose from when creating a network. Network types include local, flat, vlan, gre, and vxlan. By default all provider network types will be available to choose from.
Example: ['local', 'flat', 'gre']
supported_vnic_types:
New in version 2015.1(Kilo).
Default ['*']
For use with the port binding extension. Use this to explicitly set which VNIC types are supported; only those listed will be shown when creating or editing a port. VNIC types include normal, direct and macvtap. By default all VNIC types will be available to choose from.
Example ['normal', 'direct']
To disable VNIC type selection, set an empty list or None.
segmentation_id_range:
New in version 2014.2(Juno).
Default: {}
For use with the provider network extension. This is a dictionary where each key is a provider network type and each value is a list containing two numbers. The first number is the minimum segmentation ID that is valid. The second number is the maximum segmentation ID. Pertains only to the vlan, gre, and vxlan network types. By default this option is not provided and each minimum and maximum value will be the default for the provider network type.
Example: {'vlan': [1024, 2048], 'gre': [4094, 65536]}
enable_fip_topology_check:
Default: True
The Default Neutron implementation needs a router with a gateway to associate a FIP. So by default a topology check will be performed by horizon to list only VM ports attached to a network which is itself attached to a router with an external gateway. This is to prevent from setting a FIP to a port which will fail with an error. Some Neutron vendors do not require it. Some can even attach a FIP to any port (e.g.: OpenContrail) owned by a tenant. Set to False if you want to be able to associate a FIP to an instance on a subnet with no router if your Neutron backend allows it.
New in version 8.0.0(Liberty).
default_ipv4_subnet_pool_label:
New in version 8.0.0(Liberty).
Default: None (Disabled)
Neutron can be configured with a default Subnet Pool to be used for IPv4 subnet-allocation. Specify the label you wish to display in the Address pool selector on the create subnet step if you want to use this feature.
default_ipv6_subnet_pool_label:
New in version 8.0.0(Liberty).
Default: None (Disabled)
Neutron can be configured with a default Subnet Pool to be used for IPv6 subnet-allocation. Specify the label you wish to display in the Address pool selector on the create subnet step if you want to use this feature.
You must set this to enable IPv6 Prefix Delegation in a PD-capable environment.
New in version 2013.2(Havana).
Default: None
When unset or set to None the default CA certificate on the system is used for SSL verification.
When set with the path to a custom CA certificate file, this overrides use of the default system CA certificate. This custom certificate is used to verify all connections to openstack services when making API calls.
New in version 2012.2(Folsom).
Default: False
Disable SSL certificate checks in the OpenStack clients (useful for self-signed certificates).
New in version 2014.2(Juno).
Default: "md5"
The hash algorithm to use for authentication tokens. This must match the hash algorithm that the identity (Keystone) server and the auth_token middleware are using. Allowed values are the algorithms supported by Python’s hashlib library.
New in version 8.0.0(Liberty).
Default: True
Hashing tokens from Keystone keeps the Horizon session data smaller, but it doesn’t work in some cases when using PKI tokens. Uncomment this value and set it to False if using PKI tokens and there are 401 errors due to token hashing.
New in version 2013.2(Havana).
Default: {'identity': 'keystone_policy.json', 'compute': 'nova_policy.json'}
This should essentially be the mapping of the contents of POLICY_FILES_PATH to service types. When policy.json files are added to POLICY_FILES_PATH, they should be included here too.
New in version 2013.2(Havana).
Default: os.path.join(ROOT_PATH, "conf")
Specifies where service based policy files are located. These are used to define the policy rules actions are verified against.
New in version 2013.2(Havana).
Default: "1800"
Default: False
This setting notifies the Data Processing (Sahara) system whether or not automatic IP allocation is enabled. You would want to set this to True if you were running Nova Networking with auto_assign_floating_ip = True.
New in version 2013.2(Havana).
Default: []
Trove user and database extension support. By default, support for creating users and databases on database instances is turned on. To disable these extensions set the permission to something unusable such as [!].
New in version 2015.1(Kilo).
Default: "/"
Specifies the location where the access to the dashboard is configured in the web server.
For example, if you’re accessing the Dashboard via https://<your server>/dashboard, you would set this to "/dashboard/".
Note
Additional settings may be required in the config files of your webserver of choice. For example to make "/dashboard/" the web root in Apache, the "sites-available/horizon.conf" requires a couple of additional aliases set:
Alias /dashboard/static %HORIZON_DIR%/static
Alias /dashboard/media %HORIZON_DIR%/openstack_dashboard/static
Apache also requires changing your WSGIScriptAlias to reflect the desired path. For example, you’d replace / with /dashboard for the alias.
New in version 8.0.0(Liberty).
Default: <path_to_horizon>/static
The absolute path to the directory where static files are collected when collectstatic is run.
For more information see: https://docs.djangoproject.com/en/1.7/ref/settings/#static-root
New in version 8.0.0(Liberty).
Default: /static/
URL that refers to files in STATIC_ROOT.
By default this value is WEBROOT/static/.
This value can be changed from the default. When changed, the alias in your webserver configuration should be updated to match.
Note
The value for STATIC_URL must end in ‘/’.
This value is also available in the scss namespace with the variable name $static_url. Make sure you run python manage.py collectstatic and python manage.py compress after any changes to this value in settings.py.
For your convenience, a custom theme for only setting the static url has been provided see: "/horizon/openstack_dashboard/themes/webroot"
For more information see: https://docs.djangoproject.com/en/1.7/ref/settings/#static-url
New in version 8.0.0(Liberty).
Default: True
This setting can be used to defend against Clickjacking and prevent Horizon from being embedded within an iframe. Legacy browsers are still vulnerable to a Cross-Frame Scripting (XFS) vulnerability, so this option allows extra security hardening where iframes are not used in deployment. When set to true, a "frame-buster" script is inserted into the template header that prevents the web page from being framed and therefore defends against clickjacking.
For more information see: http://tinyurl.com/anticlickjack
Note
If your deployment requires the use of iframes, you can set this setting to False to exclude the frame-busting code and allow iframe embedding.
New in version 8.0.0(Liberty).
Default: []
Ignore all listed Nova extensions, and behave as if they were unsupported. Can be used to selectively disable certain costly extensions for performance reasons.
Warning
This is not meant to be anywhere near a complete list of settings for Django. You should always consult the upstream documentation, especially with regards to deployment considerations and security best-practices.
There are a few key settings you should be aware of for development and the most basic of deployments. Further recommendations can be found in the Deploying Horizon section of this documentation.
New in version 2013.2(Havana).
Default: ['localhost']
This list should contain names (or IP addresses) of the host running the dashboard; if it’s being accessed via name, the DNS name (and probably short-name) should be added, if it’s accessed via IP address, that should be added. The setting may contain more than one entry.
Note
ALLOWED_HOSTS is required. If Horizon is running in production (DEBUG is False), set this with the list of host/domain names that the application can serve. For more information see: https://docs.djangoproject.com/en/dev/ref/settings/#allowed-hosts
New in version 2011.2(Cactus).
Default: True
Controls whether unhandled exceptions should generate a generic 500 response or present the user with a pretty-formatted debug information page.
This setting should always be set to False for production deployments as the debug page can display sensitive information to users and attackers alike.
New in version 2012.1(Essex).
This should absolutely be set to a unique (and secret) value for your deployment. Unless you are running a load-balancer with multiple Horizon installations behind it, each Horizon instance should have a unique secret key.
Note
Setting a custom secret key: You can either set it to a specific value or you can let Horizon generate a default secret key that is unique on this machine, regardless of the amount of Python WSGI workers (if used behind Apache+mod_wsgi). However, there may be situations where you would want to set this explicitly, e.g. when multiple dashboard instances are distributed on different machines (usually behind a load-balancer). Either you have to make sure that a session gets all requests routed to the same dashboard instance or you set the same SECRET_KEY for all of them.
From horizon.utils import secret_key:
SECRET_KEY = secret_key.generate_or_read_from_file(
os.path.join(LOCAL_PATH, '.secret_key_store'))
The local_settings.py.example file includes a quick-and-easy way to generate a secret key for a single installation.
New in version 2013.1(Grizzly).
These three settings should be configured if you are deploying Horizon with SSL. The values indicated in the default local_settings.py.example file are generally safe to use.
When CSRF_COOKIE_SECURE or SESSION_COOKIE_SECURE are set to True, these attributes help protect the session cookies from cross-site scripting.
New in version 2015.1(Kilo).
A list of Django applications to be prepended to the INSTALLED_APPS setting. Allows extending the list of installed applications without having to override it completely.
Horizon allows dashboards, panels and panel groups to be added without modifying the default settings. Pluggable settings are a mechanism to allow settings to be stored in separate files. Those files are read at startup and used to modify the default settings.
The default location for the dashboard configuration files is openstack_dashboard/enabled, with another directory, openstack_dashboard/local/enabled for local overrides. Both sets of files will be loaded, but the settings in openstack_dashboard/local/enabled will overwrite the default ones. The settings are applied in alphabetical order of the filenames. If the same dashboard has configuration files in enabled and local/enabled, the local name will be used. Note, that since names of python modules can’t start with a digit, the files are usually named with a leading underscore and a number, so that you can control their order easily.
Before we describe the specific use cases, the following keys can be used in any pluggable settings file:
New in version 2014.1(Icehouse).
A dictionary of exception classes to be added to HORIZON['exceptions'].
New in version 2014.1(Icehouse).
A list of applications to be prepended to INSTALLED_APPS. This is needed to expose static files from a plugin.
New in version 2014.2(Juno).
A list of AngularJS modules to be loaded when Angular bootstraps. These modules are added as dependencies on the root Horizon application horizon.
New in version 2014.2(Juno).
A list of javascript source files to be included in the compressed set of files that are loaded on every page. This is needed for AngularJS modules that are referenced in ADD_ANGULAR_MODULES and therefore need to be included in every page.
New in version 2015.1(Kilo).
A list of javascript spec files to include for integration with the Jasmine spec runner. Jasmine is a behavior-driven development framework for testing JavaScript code.
New in version 8.0.0(Liberty).
A list of scss files to be included in the compressed set of files that are loaded on every page. We recommend one scss file per dashboard, use @import if you need to include additional scss files for panels.
New in version 8.0.0(Liberty).
If set to True, JavaScript files and static angular html template files will be automatically discovered from the static folder in each apps listed in ADD_INSTALLED_APPS.
JavaScript source files will be ordered based on naming convention: files with extension .module.js listed first, followed by other JavaScript source files.
JavaScript files for testing will also be ordered based on naming convention: files with extension .mock.js listed first, followed by files with extension .spec.js.
If ADD_JS_FILES and/or ADD_JS_SPEC_FILES are also specified, files manually listed there will be appended to the auto-discovered files.
New in version 2014.1(Icehouse).
If set to True, this settings file will not be added to the settings.
New in version 2014.2(Juno).
A dictionary of values that will replace the values in HORIZON_CONFIG.
New in version 2014.1(Icehouse).
The following keys are specific to registering a dashboard:
New in version 2014.1(Icehouse).
The slug of the dashboard to be added to HORIZON['dashboards']. Required.
New in version 2014.1(Icehouse).
If set to True, this dashboard will be set as the default dashboard.
To disable a dashboard locally, create a file openstack_dashboard/local/enabled/_40_dashboard-name.py with the following content:
DASHBOARD = '<dashboard-name>'
DISABLED = True
To add a Tuskar-UI (Infrastructure) dashboard, you have to install it, and then create a file openstack_dashboard/local/enabled/_50_tuskar.py with:
from tuskar_ui import exceptions
DASHBOARD = 'infrastructure'
ADD_INSTALLED_APPS = [
'tuskar_ui.infrastructure',
]
ADD_EXCEPTIONS = {
'recoverable': exceptions.RECOVERABLE,
'not_found': exceptions.NOT_FOUND,
'unauthorized': exceptions.UNAUTHORIZED,
}
New in version 2014.1(Icehouse).
The following keys are specific to registering or removing a panel:
New in version 2014.1(Icehouse).
The slug of the panel to be added to HORIZON_CONFIG. Required.
New in version 2014.1(Icehouse).
The slug of the dashboard the PANEL associated with. Required.
New in version 2014.1(Icehouse).
The slug of the panel group the PANEL is associated with. If you want the panel to show up without a panel group, use the panel group “default”.
New in version 2014.1(Icehouse).
If set, it will update the default panel of the PANEL_DASHBOARD.
New in version 2014.1(Icehouse).
If set to True, the PANEL will be removed from PANEL_DASHBOARD/PANEL_GROUP.
To add a new panel to the Admin panel group in Admin dashboard, create a file openstack_dashboard/local/enabled/_60_admin_add_panel.py with the following content:
PANEL = 'plugin_panel'
PANEL_DASHBOARD = 'admin'
PANEL_GROUP = 'admin'
ADD_PANEL = 'test_panels.plugin_panel.panel.PluginPanel'
To remove Info panel from Admin panel group in Admin dashboard locally, create a file openstack_dashboard/local/enabled/_70_admin_remove_panel.py with the following content:
PANEL = 'info'
PANEL_DASHBOARD = 'admin'
PANEL_GROUP = 'admin'
REMOVE_PANEL = True
To change the default panel of Admin dashboard to Instances panel, create a file openstack_dashboard/local/enabled/_80_admin_default_panel.py with the following content:
PANEL = 'instances'
PANEL_DASHBOARD = 'admin'
PANEL_GROUP = 'admin'
DEFAULT_PANEL = 'instances'
New in version 2014.1(Icehouse).
The following keys are specific to registering a panel group:
New in version 2014.1(Icehouse).
The slug of the panel group to be added to HORIZON_CONFIG. Required.
New in version 2014.1(Icehouse).
The slug of the dashboard the PANEL_GROUP associated with. Required.
To add a new panel group to the Admin dashboard, create a file openstack_dashboard/local/enabled/_90_admin_add_panel_group.py with the following content:
PANEL_GROUP = 'plugin_panel_group'
PANEL_GROUP_NAME = 'Plugin Panel Group'
PANEL_GROUP_DASHBOARD = 'admin'


Makes it faster than typing same joins over and over. Do tedious tasks, reduce user errors.]
Instructions belongs to recipes.
Grabs all the instructions for this particular recipe.
create a new instruction and attach it to this recipe.
Antying that changes our database schema
Database Schema - tables - column names - constraints - indexes
Model - represenation of a Table (or Relation)


WITNESS LIST State Affairs Committee May 6, 2003 - 8:00A HB 4 Committee Substitute FOR: Finley, Randall N. (Texas Hospital Association & Christus Health), Houston, TX Gadberry, Gavin (Texas Healthcare Association), Amarillo, TX Kiser, Roger (THCA & TMDA), Longview, TX Miller, Kendall (ICSC Tanglewood Corporation), Houston, TX Tucker, Jeffrey A. (Weingarten Realty Investors), Houston, TX Weekley, Richard W. (Texans for Lawsuit Reform), Houston, TX AGAINST: Girards, Jim (TTLA), Dallas, TX Hampton, Hartley (Texas Trial Lawyers Association), Austin, TX Kim, John H. (Autism Society of Texas), Houston, TX Moore, Edward (TTLA), Dallas, TX ON: Hine, Jim (Texas Department of Human Services), Austin, TX Montemayor, Jose (Texas Department of Insurance), Austin, TX Registering, but not testifying: On: Presley, Philip (Tx. Department of Insurance), Austin, TX


--- layout: page title: About header-img: "img/about-bg.png" ---
A student thats learning something new about life every single day


| CDEAsyncReturn |
class CDEAsyncReturn
CDEAsyncReturn is only used as a return object for CDEPointRatingsAPI. |
| CDEPoint |
class CDEPoint
Data class representing an individual waterbody found in Catchment Data Explorer: |
| Characteristic |
class Characteristic
Data class storing individual characteristic of a waterbody found in Catchment Data Explorer. |
| Classification |
class Classification
Data class storing individual group of a waterbody found in Catchment Data Explorer. |
| DischargePermitPoint |
class DischargePermitPoint
Data class storing permit details found in Electronic Public Register(ePR). Extends Point. |
| Measurement |
class Measurement
Data class storing individual measurement of a sampling point found in Water Quality Data Archive. |
| MyArea |
class MyArea
Data class storing details of the nearest waterbody(including characteristics), water quality sampling point and pollution permit holder |
| Point |
class Point : ClusterItem
Class used to represent points on the Google Map. Implements ClusterItem |
| RNAG |
class RNAG
Data class storing individual Reason for Not Achieving Good of a waterbody found in Catchment Data Explorer. |
| WIMSPoint |
class WIMSPoint : com.epimorphics.android.myrivers.data.Point
Data class storing water quality sample details found in Water Quality Data Archive(WIMS). Extends Point. |


bezik@bezik:~$
faketyper --demo
Loading module faketyper
...
After this operation your good taste in web design may be damaged.
Do you want to continue? [Y/n]
You are probably right.
- Just call faketyper.init() method
- You can pass several options like duration, or interval
- Check the documentation for detailed documentation.
- Have fun!
bezik@bezik:~$


Congress protest causes disruption
by Special CorrespondentTraffic on Wallajah Road in Triplicane was hit on Monday after Congress cadre carried out a protest against economic slowdown due to the policies of the Central government.
Tamil Nadu Congress Committee president K.S. Alagiri, leading the protest at the State Guest House near Chepauk, lambasted the BJP-led Central government for the slowdown of the economy, affecting all spheres of society. He faulted the Modi government for bringing demonetisation, which has led to financial distress, rise in unemployment, and inflation.
He said the Central government was interested only in getting back the education loans from poor students, instead of waiving a portion of the loans.
The Centre was more interested in sending opposition party leaders to jail, than fixing the problems of the economy, he charged.
A large number of partymen participated in the protest.


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Eat together
There is something profoundly satisfying about shairing a meal. Eating togather, breaking bread together, is one of the oldest and most fundamentally unifying of human experiences
Explore Menu
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| Drag a column header and drop it here to group by that column | |||||
|---|---|---|---|---|---|
| ProductID | Product name | Unit price | Quantity per unit | Units in stock | Discounted |
| 1 2 3 4 5 6 7 8 9 10 ... | 335104 items in 33511 pages | ||||
| 1 | Chai | $18.00 | 10 boxes x 20 bags | 39 | |
| 1 | Chai | $18.00 | 10 boxes x 20 bags | 39 | |
| 2 | Chang | $19.00 | 24 - 12 oz bottles | 17 | |
| 3 | Aniseed Syrup | $10.00 | 12 - 550 ml bottles | 13 | |
| 4 | Chef Antin's Cajun Seasoning | $22.00 | 46 - 6 oz jars | 53 | |
| 5 | Chef Anton's Gumbo Mix | $21.35 | 36 boxes | 0 | |
| 6 | Grandma's Boysenberry Spread | $25.00 | 12 - 8 oz jars | 120 | |
| 7 | Uncle Bob's Organic Dried Pears | $30.00 | 12 - 1 lb pkgs. | 15 | |
| 9 | Mishi Kobe Niku | $97.00 | 18 - 500 g pkgs. | 29 | |
| 10 | Ikura | $31.00 | 12 - 200 ml jars | 31 |


These are the first 8 lessons in the NeHe tutorials. Just click on the thumbnail image to go to the lesson itself.
Lesson 1
This quick lesson covers setting up your browser to run WebGL content.
Lesson 2
Your first polygons and a real working WebGL demo.
Lesson 3
Adding some color to the demo.
Lesson 4
Adding movement to the demo.
Lesson 5
Making the objects 3D.
Lesson 6
Mapping a texture to a spinning cube.
Lesson 7
Using filters
Lesson 8
Introduction to blending


Guillaume Calmettes
So far, we have explored tests of significance, confidence intervals, generalization, and causation for situations limited to the analysis of a single variable (categorical or quantitative), or to the comparison of two groups.
With linear regression, we've started to study more complex situations: the explanatory variable could actually get more than only two values, and we could make inference about an association of two quantitative variables.
Today, we will continue to study more general situations, and see how we can deal with situations in which we have to analyze more than two proportions.
We've came across the analysis of categorical variable(s), and we've seen that they can be presented/organized into contingency tables.
Single categorical variable
| Response variable |
|||
| Cat. #1 | |||
| Yes | 50 | ||
| No | 30 | ||
| Maybe | 20 |
Ex: Are the six colors of M&Ms equally likely in a pack?
Two categorical variables
| Response variable |
Explanatory variable | ||
| Cat. #1 | Cat. #2 | Cat. #3 | |
| Yes | 50 | 65 | 35 |
| No | 30 | 45 | 15 |
| Maybe | 20 | 10 | 10 |
Ex: Which treatment increases the survival rate of the patients?
Rock-Paper-Scissors (RPS) tournament
Let's consider that you want to enter a RPS competition (it does exist!), but you're looking to get an advantage and wonder if there is a pattern in the signs the other players tend to chose.
Do players equally use each hand sign? Or is one hand sign usually used more than the others?
Can we use statistics to help us win?
A study analyzed the distribution of hand signs used by a group of students during a total of 381 games. The results are shown in the table below:
| Rock | Paper | Scissors | Total | |
| Observed count | 107 | 124 | 150 | 381 |
| Observed proportion | 0.281 | 0.325 | 0.394 | 1 |
Is the proportion of each signs equal?
What is the probability of observing such a distribution
of proportions if there were an equal chance for the 3 hand signs to be played?
A study analyzed the distribution of hand signs used by a group of students during a total of 381 games. The results are shown in the table below:
| Rock | Paper | Scissors | Total | |
| Observed count | 107 | 124 | 150 | 381 |
| Observed proportion | 0.281 | 0.325 | 0.394 | 1 |
$H_0: \pi_\texclass{danger}{\mathrm{rock}} = \pi_\texclass{info}{\mathrm{paper}} = \pi_\texclass{warning}{\mathrm{scissor}}=\frac{1}{3}$
$H_a:$ at least one of the 3 $\pi_\mathrm{sign}$ is different from the others
When we were studying the distribution of a single categorical variable with only two possible outcomes, we were comparing our observed proportion of success/failure to a null hypothesis (the value of the null hypothesis depends on the situation).
| Head | Tail | Total | |
| Observed count | 10 | 6 | 16 |
| Observed proportion | 0.625 | 0.375 | 1 |
$H_0: \texclass{danger}{\pi_\mathrm{head}}=\frac{1}{2}$
When we were studying the distribution of a single categorical variable with only two possible outcomes, we were comparing our observed proportion of success/failure to a null hypothesis (the value of the null hypothesis depends on the situation).
| Correct | Incorrect | Total | |
| Observed count | 16 | 5 | 21 |
| Observed proportion | 0.76 | 0.24 | 1 |
$H_0: \texclass{warning}{\pi_\mathrm{correct}}=\frac{1}{3}$
Could we apply the same strategy here?
Couldn't we just test independently $H_0: \pi_\mathrm{sign}=\frac{1}{3}$ for all three possible outcomes?
| Rock | Paper | Scissors | Total | |
| Observed count | 107 | 124 | 150 | 381 |
| Observed proportion | 0.281 | 0.325 | 0.394 | 1 |
No! The more inferences are made, the more likely erroneous inferences are to occur (multiple testing problem).
We want to compare our data to a single distribution pattern ($\frac{1}{3}/\frac{1}{3}/\frac{1}{3}$), so we need to find a way to perform the analysis in only one testing.
Could we apply the same strategy here?
Couldn't we just test independently $H_0: \pi_\mathrm{sign}=\frac{1}{3}$ for all three possible outcomes?
| Rock | Paper | Scissors | Total | |
| Observed count | 107 | 124 | 150 | 381 |
| Observed proportion | 0.281 | 0.325 | 0.394 | 1 |
Comparing the distribution pattern of the three proportions to the theoretical distribution pattern:
How far the observed counts are from the expected (theoretical) counts if the null hypothesis ($\pi_\texclass{danger}{\mathrm{rock}} = \pi_\texclass{info}{\mathrm{paper}} = \pi_\texclass{warning}{\mathrm{scissor}}$) was true?
The expected counts are the amounts we should see in each category (cell) if the null hypothesis was true for that sample size.
For each cell, the expected count is the total sample size ($n_\mathrm{total}$) times the null proportion for each hand sign, $\pi_\mathrm{i}$.
| Rock | Paper | Scissors | Total | |
| Observed count | 107 | 124 | 150 | 381 |
| Observed proportion | 0.281 | 0.325 | 0.394 | 1 |
| Expected proportion | $\frac{1}{3}$ | $\frac{1}{3}$ | $\frac{1}{3}$ | 1 |
| Expected count | 127 | 127 | 127 | 381 |
We have the observed and expected counts, now what could we do with these?
calculate the difference between observed and expected
| Rock | Paper | Scissors | |
| Observed | 107 | 124 | 150 |
| Expected | 127 | 127 | 127 |
How could we combine those 3 differences into a single statistic of interest? Add them up? Average them?
What could we do so we don't get a sum of zero?
Rock: $d_1 = 107-127=-20$
Paper: $d_2 = 124-127=-3$
Scissors: $d_3 = 150-127=23$
$\sum_{i=1}^3d_i=-20+(-3)+23=0$
A statistic that is widely used to describe the difference in distribution patterns of categorical variable(s) is the square of the relative differences of each observed frequencies compared to the expected frequencies. This is known as the Chi-Squared statistic for comparing frequencies of a categorical variable.
$\chi^2$ summarizes the discrepancy between observed and expected frequencies:
It's kind of like calculating the residuals of a linear regression model but for an histogram (categories) instead!
How far is the observed distribution pattern of the observed frequencies from the theoretical distribution pattern?
When we calculate a $\chi^2$ statistic to compare distribution patterns of categorical variables, this is called a $\chi^2$ goodness of fit test.
| Rock | Paper | Scissors | |
| Observed | 107 | 124 | 150 |
| Expected | 127 | 127 | 127 |
$\chi^2=\frac{(107-127)^2}{127}+\frac{(124-127)^2}{127}+\frac{(150-127)^2}{127}$
$\chi^2=3.150+0.071+4.165$
$\chi^2=7.386$
What does a $\chi^2$ of 7.386 mean? Is it a large or a small value?
What values of $\chi^2$ would we observe for a multitude of samples coming from the null distribution?
We need to compare the $\chi^2$ that we calculated to the distribution of this statistic assuming $H_0$ is true.
How do we get this?
For equally likely choices:
Rock-Paper-Scissors $\chi^2$ randomization distribution
Note: $\chi^2$ will always be positive. Always use the right tail to find a p-value.
Observed $\chi^2$: 7.39
$\textrm{p-value} = 0.026$
We have strong evidence against the null hypothesis. At least one of the hand signs frequency can be considered statistically different to the expected frequency of this hand sign if the probability of choosing either hand sign was $\frac{1}{3}$.
If each of the expected counts are at least 10 (validity condition), AND if the null hypothesis is true, then the $\chi^2$ statistic follows a $\chi^2$–distribution, with degrees of freedom equal to:
For Rock-Paper-Scissors:
df = 3 – 1 = 2
Simulation
Theoretical distribution
We found that there is a difference in distribution pattern that can be considered statistically significant (p-value<0.05) ... now what's next?
No standard approach to follow, there are many different follow-up approaches.
We want to know if one of the hand sign (rock, paper, scissors) is used more often compared to the others so we could play the opposite sign more often to get an advantage.
Now that we know that there is a statistical difference in the distribution pattern of the signs, we could use pairwise analysis of difference in proportions to determine exactly which signs are more often played.
Paper vs Scissors
$d=-0.068$
pvalue=0.0583
Paper vs Rock
$d=0.045$
pvalue=0.2058
Rock vs Scissors
$d=-0.113$
pvalue=0.0010
A $\chi^2$ test for goodness-of-fit tests whether the distribution of a categorical variable is the same as some null hypothesized distribution.
Note that the null hypothesized proportions for each category do not have to be the same!
Ex: Are the six colors (green, orange, blue, red, yellow, brown) of M&Ms equally likely in a pack?
Does the grade distribution of the class follow the usual pattern?
A $\chi^2$ test for goodness-of-fit tests whether the distribution of a categorical variable is the same as some null hypothesized distribution.
Exactly the same approach:
In 1866, Gregor Mendel, the “father of genetics” published the results of his experiments on peas.
He found that his experimental distribution of peas closely matched the theoretical distribution predicted by his theory of genetics (involving alleles, and dominant and recessive genes)
Does the observed experimental distribution match the theoretical distribution predicted by genetics?
Let’s test these data against the null hypothesis of the proportions distribution based on the theory of genetics.
| Phenotype | Theoretical proportion | Observed count |
| Round, Yellow | $9/16$ | $315$ |
| Round, Green | $3/16$ | $108$ |
| Wrinkled, Yellow | $3/16$ | $101$ |
| Wrinkled, Green | $1/16$ | $32$ |
| Total | $1$ | $556$ |
Null Hypothesis:
$H_0: \pi_1=\frac{9}{16}, \pi_2=\frac{3}{16}, \pi_3=\frac{3}{16}, \pi_4=\frac{1}{16}$
Alternative Hypothesis:
$H_a:$ at least one $\pi_i$ is not as specified in $H_0$
What is the expected count for each pea category? ($n_\mathrm{total}\times\pi_i$)
| Phenotype | Theoretical proportion | Observed count | Expected count |
| Round, Yellow | $9/16$ | $315$ | $556\times9/16=\texclass{warning}{312.75}$ |
| Round, Green | $3/16$ | $108$ | $556\times3/16=\texclass{warning}{104.25}$ |
| Wrinkled, Yellow | $3/16$ | $101$ | $556\times3/16=\texclass{warning}{104.25}$ |
| Wrinkled, Green | $1/16$ | $32$ | $556\times1/16=\texclass{warning}{34.75}$ |
| Total | $1$ | $556$ | $556$ |
What is the calculated $\chi^2$ from these results?
$\chi^2=\frac{(315-312.75)^2}{312.75}+\frac{(108-104.25)^2}{104.25}+\frac{(101-104.25)^2}{104.25}+\frac{(32-34.75)^2}{34.75}$
$\chi^2=0.016+0.135+0.101+0.218$
$\chi^2=0.470$
Simulation
Theoretical distribution
Does this prove Mendel’s theory of genetics?
No! You should never accept the null hypothesis!!! A high p-value doesn’t “prove” or give evidence for anything. This p-value only suggests that the obtained distribution of pea phenotypes could have been observed if the null hypothesis was true.
The statistics behind a $\chi^2$ test easily extends to two categorical variables.
A $\chi^2$-test for association (often called a $\chi^2$-test for independence) tests for an association between two categorical variables.
| Soda | Age group | |||
| 15-25 | 26-40 | 41-55 | Total | |
| Coke | 54 | 45 | 69 | 168 |
| Pepsi | 19 | 41 | 38 | 98 |
| Sprite | 19 | 22 | 28 | 69 |
| Total | 92 | 108 | 135 | 335 |
Now we are interested to know if the proportions of preference for each soda is dependent of (associated to) the age group the person we asked the question to is in.
| Soda | Age group | |||
| 15-25 | 26-40 | 41-55 | Total | |
| Coke | 54 | 45 | 69 | 168 |
| Pepsi | 19 | 41 | 38 | 98 |
| Sprite | 19 | 22 | 28 | 69 |
| Total | 92 | 108 | 135 | 335 |
Null Hypothesis:
$H_0$: Soda preference is not associated with age
Alternative Hypothesis:
$H_a$: Soda preference is associated with age
| Soda | Age group | |||
| 15-25 | 26-40 | 41-55 | Total | |
| Coke | 54 | 45 | 69 | 168 |
| Pepsi | 19 | 41 | 38 | 98 |
| Sprite | 19 | 22 | 28 | 69 |
| Total | 92 | 108 | 135 | 335 |
Null Hypothesis:
$H_0$: Soda preference is not associated with age
Alternative Hypothesis:
$H_a$: Soda preference is associated with age
Exemple for coke and age group (15-25):
$\mathrm{Expected}_\mathrm{coke}^\textrm{(15-25)}=\frac{168\times92}{335}=46.14$
| Soda | Age group | |||
| 15-25 | 26-40 | 41-55 | Total | |
| Coke | 54 | 45 | 69 | 168 |
| Pepsi | 19 | 41 | 38 | 98 |
| Sprite | 19 | 22 | 28 | 69 |
| Total | 92 | 108 | 135 | 335 |
| Soda | Age group | |||
| 15-25 | 26-40 | 41-55 | Total | |
| Coke | 46.14 | 54.16 | 67.7 | 168 |
| Pepsi | 26.91 | 31.59 | 39.49 | 98 |
| Sprite | 18.95 | 22.24 | 27.81 | 69 |
| Total | 92 | 108 | 135 | 335 |
Note: The expected counts maintain row and column totals, but redistribute the counts as if there were no association.
How do the observed results compared to the scenario of no-association?
compute the $\chi^2$ from all the cells in the table
| Soda | Age group | |||
| 15-25 | 26-40 | 41-55 | Total | |
| Coke | 54 | 45 | 69 | 168 |
| Pepsi | 19 | 41 | 38 | 98 |
| Sprite | 19 | 22 | 28 | 69 |
| Total | 92 | 108 | 135 | 335 |
| Soda | Age group | |||
| 15-25 | 26-40 | 41-55 | Total | |
| Coke | 46.14 | 54.16 | 67.7 | 168 |
| Pepsi | 26.91 | 31.59 | 39.49 | 98 |
| Sprite | 18.95 | 22.24 | 27.81 | 69 |
| Total | 92 | 108 | 135 | 335 |
$\chi^2=\frac{(54-46.14)^2}{46.14}+\frac{(45-54.16)^2}{54.16}+\frac{(69-67.7)^2}{67.7}+...+\frac{(22-22.24)^2}{22.24}+\frac{(28-27.81)^2}{27.81}$
$\chi^2=8.10$ Is it a big or low $\chi^2$?
We need the $\chi^2$ distribution
For no-association condition:
For a 2-ways contingency table, the number of degrees of freedom can be obtained by:
$df=(\textrm{Number of rows} − 1)\times(\textrm{Number of columns} − 1)$
For the soda study:
$df = (3-1)\times(3-1) = 4$
| Soda | Age group | |||
| 15-25 | 26-40 | 41-55 | Total | |
| Coke | 46.14 | 54.16 | 67.7 | 168 |
| Pepsi | 26.91 | 31.59 | 39.49 | 98 |
| Sprite | 18.95 | 22.24 | 27.81 | 69 |
| Total | 92 | 108 | 135 | 335 |
Simulation
Theoretical distribution
p-value > 0.05, we do not have strong evidence against the null hypothesis of no-association.
Soda preference does not seem to be associated with age.
What if you find evidence of an association? How do we determine exactly where the association is?
Again, no standard approach to follow, several options are possible.
pairwise confidence intervals for the difference in proportions to determine exactly where the association lies.
| $\chi^2$ for goodness of fit | $\chi^2$ test of independance |
| Test hypothesis about shape of discrete distribution | Test hypothesis about relationship of categorical variables |
| Are the frequencies I observe for my categorical variable consistent with my theory? | Are the two categorical variables associated with one another in the population? |
Goodness-of-fit test and test of intependence are in fact the same approach (how different are my data from the theoretical distribution?).
$\chi^2$ is just another statistic of interest, and the null randomization distribution can be easily simulated.
The theoretical approach to determine the $\chi^2$ distribution requires the use of the correct degrees of freedom number of the contingency table you are studying. Remember that you would also need a count>10 for all the cells as validity condition of the theoretical approach.


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Tailoring the TinyOS Build Environment
Last updated 08 Aug 2005 |
When building TinyOS applications, it is useful to define certain build parameters that are specific to your needs. Such parameters include the operating frequency (mica2/mica2dot), the Group ID, different component libraries, etc. This section describes how to tailor your TinyOS build environment to suit your needs.
| The Makelocal file |
The Makerules file in the tools/make directory manages many aspects of the build process for TinyOS applications. While it is possible to edit this file to make changes, such practice is not advisable since 1) the Makerules file is not intuitive, 2) changing this file may break the build process, 3) the changes may not portable to other users.
To allow for tailoring the build environment, the Makerules file looks for a Makelocal file located in the tools/make directory and includes it in the build process. Changes made to the Makelocal file can be easily reversed (just delete/rename the file) and can be distributed to other users.
To use this feature, create a Makelocal file in the tools/make directory
which contains your custom definitions using standard makefile
syntax. For example:
# Makelocal File
PFLAGS += -I%T/../beta/MyBetaCode
DEFAULT_LOCAL_GROUP = 0x33
PFLAGS += -DCC1K_DEF_FREQ=916700000
EPRB=myprogrammer.foo.com
The above Makelocal file adds a search path for code in ../../beta/MyCode, defines the default Group ID (0x33) and manual frequency setting (916.7 MHz) for the mica2 series motes, and sets the default programmer as an EPRB with hostname 'myprogrammer.foo.com'


--- title: "Days 9-12: Building a spam classifier in Python" created: !!timestamp '2019-01-23 12:00:00' --- {% mark excerpt -%} I love taking courses. For me it's like travelling to a new country, but with a great guide - all that lovely unexplored territory, but you know you can navigate it without anything going too badly wrong. {%- endmark %} Of the courses I've taken, by far my favourite has been [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)'s [Machine Learning course on Coursera](https://www.coursera.org/learn/machine-learning/). It took me a little while to decide to do it because the programming assignments are in [Octave](https://www.gnu.org/software/octave/), which is not a language I'm ever likely to use professionally. However, I'm so glad I did, because he explains the concepts so clearly, and the difficulty level was just right given I last studied maths in 1996! To consolidate what I've learned, I want to repeat some of the exercises in Python, starting with the spam classifier because it's related to a [competition on Kaggle](https://www.kaggle.com/c/quora-insincere-questions-classification/) that I think could be interesting. To classify emails as spam or not spam, both the original assignment and my Python version followed these steps: 1. Preprocess the email e.g. lowercasing and stemming 2. Convert the email to a bag of words 3. Train a support vector machine for classification ### Emails as bags of words I know this is step 2, but I want to explain it first because I think the preprocessing step will make more sense if we know what's going to happen next. Say we have 4 texts like this:
1. I am Sam. Sam I am. 2. That Sam-I-am! That Sam-I-am! I do not like that Sam-I-am! 3. Do you like green eggs and ham? 4. I do not like green eggs and ham. I do not like them, Sam-I-am.If we were trying to group them, we could say, for example, that texts 3 and 4 are about "green eggs and ham", or that only text 3 does not mention "Sam I am". If texts 1 and 2 were labelled as spam, a system could learn to classify texts as "not spam" if they contain "green eggs and ham", or as "spam" if they mostly contain "Sam I am". Since machine learning systems generally work by manipulating numbers not words, to make a system like this we convert the text into a matrix of features where each column is a feature (i.e. one of the words in the corpus), each row is a text, and each cell is set to 1 if the feature appears in that text.
| | I | am | Sam | That | do | not | like | you | green | eggs | and | ham | them | | --------------------------------------------------------------- | --- | --- | --- | ---- | --- | --- | ---- | --- | ----- | ---- | --- | --- | ---- | | I am Sam. Sam I am | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | That Sam-I-am! That Sam-I-am! I do not like that Sam-I-am! | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | | Do you like green eggs and ham? | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | | I do not like green eggs and ham. I do not like them, Sam-I-am. | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 |Remember that matrix multiplication works like this:
[ x ] [ a b c ] * [ y ] = [ ax + by + cz ] [ d e f ] [ z ] [ dx + ey + fz ]So if
[ a b c ]is a row of ones and zeroes representing a single text, and
[ax + by + cz]is 1 if the text is spam and 0 if it is not, a system can learn values for x, y and z (called the *model*) so that the texts are classified correctly. (It's actually a little bit more complicated than that, but I'll get to that later.) ### Preprocessing the emails In the table above, I made a number of assumptions, for example that "That" and "that" were the same word, and that "Sam-I-am" is the same as "Sam I am". Preprocessing the emails avoids the need for such assumptions by standardising the text before building the feature matrix. The spam classifier uses the following preprocessing steps: * lowercasing * stripping HTML tags * replace all URLS with "httpaddr" * replace all email addresses with "emailaddr" * replace all numbers with "number" * replace common currency signs (e.g. $, £) with "currency" (the original assignment used only $) * [word stemming](https://en.wikipedia.org/wiki/Stemming) * remove non-alphabetic characters ### Training a support vector machine The system I outlined above (where the matrix multiplation was solved for x, y and z) is [linear regression](https://en.wikipedia.org/wiki/Linear_regression). It's the easiest to explain, but it's not normally used for classification since it predicts a continuous-valued output and we want 0s and 1s. A better system would be [logistic regression](https://en.wikipedia.org/wiki/Logistic_regression), where a [sigmoid function](https://en.wikipedia.org/wiki/Sigmoid_function) is used instead of multiplication to restrict the output range. Logistic regression works well if there's a single straight-line boundary between data in one class and data in the other, like this. {% mark image -%} ![Straight boundary through 2 classes of data)]([[!!img/straight-boundary.png]]) {%- endmark %} But what if it looks more like this? {% mark image -%} ![Wiggly boundary through 2 classes of data)]([[!!img/wiggly-boundary.png]]) {%- endmark %} That's where the [support vector machine](https://en.wikipedia.org/wiki/Support-vector_machine) comes in. With the SVM, it's still possible to choose a linear boundary (but one that will be less sensitive to outliers) but also to draw other lines through the data based on the choice of *kernel*. This is mathematically complex stuff, but Python's [scikit-learn](https://scikit-learn.org/stable/) library makes it really simple:
from sklearn.svm import SVC model = SVC() model.fit(X_train, y_train) y_pred = model.predict(X_test)When trained on the 20021010_spam and 20030228_easy_ham_2 [SpamAssassin data sets](http://spamassassin.apache.org/old/publiccorpus/), even a toy project like this predicts spam with 98% accuracy, which I find incredible. (Not my doing; full credit to Andrew Ng for the original version and to the developers of scikit-learn for making it so easy to convert to Python.) The full source is available on [GitHub](https://github.com/hmcc/spam-classifier). --- Images based on [https://commons.wikimedia.org/w/index.php?curid=14941564](https://commons.wikimedia.org/w/index.php?curid=14941564) by By Alisneaky - Own work, CC0


--- layout: post title: Trophy Slots for 1.12.2 date: '2017-12-07' author: Lomeli12 categories: - minecraft tags: - mods - update ---
After an extremely long hiatus from modding, during which I've had two jobs (IT for a short while and now programming apps and robots for schools), gone to two seperate RTXs, went from never having visiting San Diego to traveling there nearly every week, I've finally had the change to update arguably my most well known and popular mods, Trophy Slots.
Not only is the mod now playable on Minecraft 1.12.2, but trading with Villagers for trophies, which was dropped in 1.8 due to the entire trading system on Forge requiring a rewrite that kept being delayed, is now back. The mod now relies on the Advancement system that replaced the old achievement system in 1.12, but otherwise works the same.
You can download it over on CurseForge or Mods.io!


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Not getting the results you need? Email<EMAIL_ADDRESS>for help or to request a custom chart!
1 Import Data
Copy and paste cells from your spreadsheet into the text field below.
Swap rows and columnsVerify your data appears correct in this table. The first column will become labels for the horizontal axis. Remaining columns will be plotted on the vertical axis. (Except on bar charts.)
2 Chart Options
3 Vertical Axis Options
The space between the axis lines, e.g. 0.25, 5, 100.
4 Export
5 Add to Seamus
- Open the Seamus story editor and add an image by selecting "image" in the assets drawer.
- Click "browse" and locate the image you downloaded.
- Select the custom crop and expand the crop box to fit the entire image, including the title.
- Make sure "enlargement" is set to none, set the primary crop to "custom" and click "perform crop."
- Click "next" and add a source and caption for your chart. Always credit your data source!


Paragraph 1
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University of Applied Sciences And Arts, Fachhochschule Dortmund
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revision #build_version
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Script: matrix.csv | Result: PASSED | |
| Execution started: 2023.08.30 14:28:11 | Execution time: 1.203 sec | ||
| User: user | |||
| Description: | Constants: |
| Status | PASSED |
| Started | 14:28:11:434 |
| Finished | 14:28:11:935 |
| Wait before action | 500 |
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| Status | PASSED |
| Started | 14:28:11:424 |
| Finished | 14:28:12:599 |
| Description |


In the southern hemisphere, most countries (represented by Chile, Argentina, New Zealand, and Australia) appear to have passed their peak of influenza activity and have either returned to baseline levels or are experiencing focal activity in later affected areas; while a few others (represented by South Africa and Bolivia) continue to experience high levels of influenza activity.
Many countries in tropical regions (represented by Central America and tropical regions of Asia), continue to see increasing or sustained high levels of influenza activity with some countries reporting moderate strains on the healthcare system. In temperate areas of the northern hemisphere (represented by North America, Europe, and Central Asia), influenza and respiratory disease activity remains low overall, with some countries experiencing localized outbreaks. In Japan, the level of influenza activity has passed the seasonal epidemic threshold, signaling a very early beginning to the annual influenza season.
Pandemic H1N1 influenza virus continues to be the predominant circulating strain of influenza, both in the northern and southern hemisphere. Antiviral susceptibility testing has increased in several countries, confirming that pandemic H1N1 influenza virus remains sensitive to the antiviral oseltamivir, except for sporadic reports of oseltamivir resistant pandemic H1N1 virus detailed in the previous web update (No. 62).
The qualitative indicators monitor: the global geographic spread of influenza, trends in acute respiratory diseases, the intensity of respiratory disease activity, and the impact of the pandemic on health-care services.
The maps below display information on the qualitative indicators reported during weeks 29 to 33. Information is available for approximately 60 countries each week. Implementation of this monitoring system is ongoing and completeness of reporting is expected to increase over time.
The countries and overseas territories/communities that have newly reported their first pandemic (H1N1) 2009 confirmed case(s) since the last web update (No. 62) as of 23 August 2009 are:
Cameroon, Madagascar, and Mozambique.
*Given that countries are no longer required to test and report individual cases, the number of cases reported actually understates the real number of cases.


Identifying core values!
Written by Alex White on April 27, 2014.
Values I associate with the happiest, proudest, or most satisfied times of my life:
- Creativity
- Adventure
- Meaningful Work
- Close relationships
Creativity
Creativity is important to me because without it I feel like a spectator rather than a participant to life. With it I feel empowered and powerful.
In general I try to live up to these values
(Scale of 1-5)
- Creativity: 5
- Adventure: 4
- Meaningful Work: 5
- Close relationships: 5


| expand_grid {tidyr} | R Documentation |
expand_grid()
is heavily motivated by expand.grid()
.
Compared to expand.grid()
, it:
Produces sorted output (by varying the first column the slowest, rather than the fastest).
Returns a tibble, not a data frame.
Never converts strings to factors.
Does not add any additional attributes.
Can expand any generalised vector, including data frames.
expand_grid(..., .name_repair = "check_unique")
... |
Name-value pairs. The name will become the column name in the output. |
.name_repair |
Treatment of problematic column names:
This argument is passed on as |
A tibble with one column for each input in ...
. The output
will have one row for each combination of the inputs, i.e. the size
be equal to the product of the sizes of the inputs. This implies
that if any input has length 0, the output will have zero rows.
expand_grid(x = 1:3, y = 1:2) expand_grid(l1 = letters, l2 = LETTERS) # Can also expand data frames expand_grid(df = data.frame(x = 1:2, y = c(2, 1)), z = 1:3) # And matrices expand_grid(x1 = matrix(1:4, nrow = 2), x2 = matrix(5:8, nrow = 2))


This section lists configuration considerations relating to network partition detection.
The system uses a combination of member coordinators and system members, designated as lead members, to detect and resolve network partitioning problems.
Network partition detection works in all environments. Using multiple locators mitigates the effect of network partitioning. See Configuring Peer-to-Peer Discovery.
Network partition detection is enabled by default. The default setting in the
gemfire.properties
file isenable-network-partition-detection=true
Processes that do not have network partition detection enabled are not eligible to be the lead member, so their failure will not trigger declaration of a network partition.
All system members should have the same setting for
enable-network-partition-detection
. If they do not, the system throws aGemFireConfigException
upon startup.The property
enable-network-partition-detection
must be true if you are using either partitioned or persistent regions. If you create a persistent region andenable-network-partition-detection
to set to false, you will receive the following warning message:Creating persistent region {0}, but enable-network-partition-detection is set to false. Running with network partition detection disabled can lead to an unrecoverable system in the event of a network split."
Configure regions you want to protect from network partitioning with a scope setting of
DISTRIBUTED_ACK
orGLOBAL
. Do not useDISTRIBUTED_NO_ACK
scope. This prevents operations from being performed throughout the distributed system before a network partition is detected. Note: GemFire issues an alert if it detectsDISTRIBUTED_NO_ACK
regions when network partition detection is enabled:Region {0} is being created with scope {1} but enable-network-partition-detection is enabled in the distributed system. This can lead to cache inconsistencies if there is a network failure.
These other configuration parameters affect or interact with network partitioning detection. Check whether they are appropriate for your installation and modify as needed.
- If you have network partition detection enabled, the threshold percentage value for allowed membership weight loss is automatically configured to 51. You cannot modify this value. Note: The weight loss calculation uses round to nearest. Therefore, a value of 50.51 is rounded to 51 and will cause a network partition.
- Failure detection is initiated if a member’s
ack-wait-threshold
(default is 15 seconds) andack-severe-alert-threshold
(15 seconds) properties elapse before receiving a response to a message. If you modify theack-wait-threshold
configuration value, you should modifyack-severe-alert-threshold
to match the other configuration value. - If the system has clients connecting to it, the clients’
cache.xml
poolread-timeout
should be set to at least three times themember-timeout
setting in the server’sgemfire.properties
file. The default poolread-timeout
setting is 10000 milliseconds. - You can adjust the default weights of members by specifying the system property
gemfire.member-weight
upon startup. For example, if you have some VMs that host a needed service, you could assign them a higher weight upon startup.
By default, members that are forced out of the distributed system by a network partition event will automatically restart and attempt to reconnect. Data members will attempt to reinitialize the cache. See Handling Forced Cache Disconnection Using Autoreconnect.


Week 1 Independent Project (5/29/20)
Who is Tre Sonna?
age: 18
My epicodus class tasked me with reading and correcting the spacing and indetation on a cookie recipe.
My first real webpage project & favorite things project followed epicodus guidelines
Saying Hello to the world in many different world languanges
Second Favorite Things project remastered
Each Project was coded using GIt, CSS, & HTML
Tre Sonna was Born Niemel Ngatchou Sonna in Phoenix, Arizona. He was a 3 sport athlete all through Highschool playing Basketball, Soccer, and Football He has lived in 2 countries and 5 states (oregon, Indiana, Washington, and Arizona,) Tre hopes to grow up and work in either computer and web delopment or Real estate, Hopefully Both. I grew up without much, which is what really fuels my hunger, desire, and drive to be somebody.  I look Up to her because shes a testament of whatan individual can achieve in this world if they go out and strive for it. My education background is rather subtle, boucned around alot as a kid so i went to many different elementary,middle, and high schools, but i will be graduating from woodinville high school among the class of 2020. While in HIgh School i was a 3 sport varsity athlete playing Basketball , Football, and Track and Field at the Varsity Level. Currently though i wanted my life to take a route of Business and academia rather than sports and entertainment and while i do still frequently attend the gym on my spare time and go on runs as a hobby i also take alot of time to read and im actually very devoted in my religious faith as well.
I am Hoping to go through epicodus and gain the hands on expirience as well as not only learning how the world of coding works, but learning how to be able to really learn and absorb information to be able to innnovate. Im not in epicodus in terms of looking for work ( I am in the Long Run) but for right now this class is more to further my education giving me a vast amount of base knowledge before pursuing a degree at an accredited next level education institution. I would describe my personality as very outgoing, and in terms of education i tend to always be slow at picking up a concept but once i do i tend to really take off with it. as a learner please be patient with my progression at the start as i am trying genuinely to do the best i can. In addition to coding Tre dabbles in Real Estate as well as Selling insurnce, hes driven and looking to be successful as he continues to grow in life and in his career aspirations. as for athletcis and individual hobbies, I love sports. My 5 favorite sports in order are as listed
i Love sports becuse they are a fun recreational activity to watch as well as play, and i love playing sports because i use it as an outlet for my competitive spirit & will to be the best or try to be the best at everything i do. I also love the comradery and team feel u get whenapart of a team, It shows that in life the game and the overall objective is much bigger then one person.


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In creating my own blog, the most upsetting thing for me was that I had to start the css from scratch. Moreover, I wouldn't be able to save my content separate from the HTML file. Now, with Jekyll I can do that and much more.
I first came across Jekyll four months ago, but opted to use the Ghost blogging platform because I would have online access to a markdown editor. It was a great choice at the time -- Ghost got me addicted to writing in markdown.
In starting to blog for Dev Bootcamp, the thing I found most frustrating was that I had to specify dates, titles, and css multiple times over different HTML files (like specifying the date on the index.html
and post.html
).
With Jekyll, I now have the benefits of ruby inside the HTML to save time. Currently, this markdown file post.md
has the following head:
---
layout: post
title: switching to jekyll
---
switching to jekyll
================

10 October 2014 - San Francisco, CA

Now, if I want to the title to display across within the post and the homepage, I specify it in the title: (POST TITLE HERE)
. Then, it gets displayed in switching to jekyll
.
Likewise, given the way that the Jekyll files are set-up, the date is similarly displayed when entered here:
 (DATE HERE)

This is just one level of separation. We also have