class WINC – wifi shield driver¶
The WINC
class is used for controlling the wifi shield.
Example usage:
import network
wlan = network.WINC()
wlan.connect("SSID", "KEY")
wlan.ifconfig()
Constructors¶
- class network.WINC([mode=MODE_STATION])¶
Creates a winc driver object and connects to the wifi shield which uses I/O pins P0, P1, P2, P3, P6, P7, and P8.
mode
controls the mode the WINC module works in:network.WINC.MODE_STATION
The module connects to an access point as a client. This is the default mode.
network.WINC.MODE_AP
The module will create an AP (Access Point) and accept connections from a client.
Note
The start_ap() method must be called after setting AP mode to configure the AP.
Also, the WINC1500 has some limitations in its AP implementation:
Only one client can connect at a time.
Only OPEN or WEP security are supported.
There’s a bug in the WiFi Module FW, when the client disconnects any bound sockets are lost (they just stop working). As a workaround, set a timeout for the server socket to force it to raise an exception and then reopen it (See the example script).
network.WINC.MODE_FIRMWARE:
This mode enables WiFi module firmware update.
Note
mode
can also benetwork.STA_IF
(station aka client, connects to upstream WiFi access points) and andnetwork.AP_IF
(access point, allows other WiFi clients to connect). Availability of the methods below depends on interface type. For example, only STA interface mayWLAN.connect()
to an access point.Methods¶
- active([is_active])¶
Activate (“up”) or deactivate (“down”) network interface, if boolean argument is passed. Otherwise, query current state if no argument is provided. Most other methods require active interface.
- connect(ssid[, key=None[, security=WPA_PSK]])¶
Connect to a wifi network with ssid
ssid
using keykey
with securitysecurity
.After connecting to the network use the
usocket
module to open TCP/UDP ports to send and receive data.Note
This method takes a little while to return.
- start_ap(ssid[, key=None[, security=OPEN[, channel=1]]])¶
When running in AP mode this method must be called after creating a WINC object to configure and start the AP .
ssid: The AP SSID (must be set).
key: The AP encryption key. A Key is required only if security is WEP.
security: AP security mode (only OPEN or WEP are supported).
channel: WiFi channel, change this if you have another AP running at the same channel.
- disconnect()¶
Disconnect from the wifi network.
- isconnected()¶
Returns True if connected to an access point and an IP address has been obtained.
- connected_sta()¶
This method returns a list containing the connected client’s IP adress.
- wait_for_sta(timeout)¶
This method blocks and waits for a client to connect. If timeout is 0 this will block forever. This method returns a list containing the connected client’s IP adress.
- ifconfig([ip_addr, subnet_addr, gateway_addr, dns_addr])¶
Returns a tuple containing:
[0]: IP Address String (XXX.XXX.XXX.XXX)
[1]: Subnet Address String (XXX.XXX.XXX.XXX)
[2]: Gateway String (XXX.XXX.XXX.XXX)
[3]: DNS Address String (XXX.XXX.XXX.XXX)
While connected to the network.
You may optionally pass a tuple/list of the ip_addr, subnet_addr, gateway_addr, and dns_addr strings in ipv4 (XXX.XXX.XXX.XXX) format to set a static IP address versus an address obtained through DHCP (which happens by default).
Example usage:
wlan = network.WINC() wlan.ifconfig(('<IP_ADDRESS>', '<IP_ADDRESS>', '<IP_ADDRESS>', '<IP_ADDRESS>')) wlan.connect(SSID, key=KEY, security=wlan.WPA_PSK)
- netinfo()¶
Returns a tuple containing:
[0]: RSSI - received signal strength indicator (int)
[1]: Authorization Type (see constants)
[2]: Set Service Identifier String (SSID)
[3]: MAC Address String (XX:XX:XX:XX:XX:XX) (BSSID)
[4]: IP Address String (XXX.XXX.XXX.XXX)
While connected to the network.
- scan()¶
Returns a list containing:
[0]: Channel Number (int)
[1]: RSSI - received signal strength indicator (int)
[2]: Authorization Type (see constants)
[3]: MAC Address String (XX:XX:XX:XX:XX:XX) (BSSID)
[4]: Set Service Identifier String (SSID)
You don’t need to be connected to call this.
- rssi()¶
Returns the received signal strength indicator (int) of the currently connected network.
- fw_version()¶
Returns a tuple containing the wifi shield firmware version number.
[0]: Firmware Major Version Number (int)
[1]: Firmware Minor Version Number (int)
[2]: Firmware Patch Version Number (int)
[3]: Driver Major Version Number (int)
[4]: Driver Minor Version Number (int)
[5]: Driver Patch Version Number (int)
[6]: Hardware Revision Number - Chip ID (int)
- fw_dump(path)¶
Dumps the wifi shield firmware to a binary file at
path
. You must have put the module into firmware mode to use this.
- fw_update(path)¶
Programs the wifi shield with binary image found at
path
. You must have put the module into firmware mode to use this.
Constants¶
- OPEN¶
For connecting to an open wifi network.
- WEP¶
For connecting to a WEP based password protected network.
- WPA_PSK¶
For connecting to a WPA/PSK based password protected network.
- MODE_STA¶
Start in station mode (i.e. connect to a network).
- MODE_AP¶
Start in access point mode (i.e. become the network).
- MODE_FIRMWARE¶
Setup in firmware update mode.


class: center, middle # Types of Testing --- # Agenda * Look at various types of testing --- # Core Types Of Testing (People) --- ## WHITE BOX TESTING * Testing based on an analysis of internal workings, logic and structure of a piece of software. * Includes techniques such as testing a branch of code. * This is usually considered in house testing by developers or employees of the company. ## BLACK BOX TESTING * Testing without knowledge of the internal workings of the item being tested. * Testing from a user’s perspective. * Tests are usually functional. * This is usually considered outside of the company testing; alpha and beta tests are examples. --- # Core Modes of Testing (Process) ? Diagram for testing needed. --- # ACCEPTANCE TESTING * Testing to verify a product meets customer specified requirements. * customer usually does this type of testing on a product that is developed externally. --- # AUTOMATED TESTING * Utilizing a test tool to automate testing. * Reduces the testing time but requires maintenance and knowledge of the tool by a QA tester. --- # COMPATIBILITY TESTING * Testing to ensure compatibility of an application or Web site with different browsers, OSs, and hardware platforms. Compatibility testing can be performed manually or can be driven by an automated functional or regression test suite. * Considered important to make sure the software behaves the same across different devices. --- # CONFORMANCE TESTING * Verifying implementation conformance to industry standards such as 508. * Testing for quality factors such as portability, interoperability, and/or compatibility a standard defines. * Looks to make sure it meets accessibility requirements and/or federal, state, or local laws. --- # FUNCTIONAL TESTING * Validating an application/software/web site meets its specifications and correctly performs all its required functions. * This entails a series of tests which perform a feature by feature validation of behavior, using a wide range of normal and erroneous input data. This can involve testing of the product's user interface, APIs, database management, security, installation, networking, etc. * Testing can be performed on an automated or manual basis using black box or white box methodologies. * This is the most common form of testing. As you are making sure "functionality" exists for a given product. --- # INTEGRATION TESTING * Testing in which modules are combined and tested as a group. * Modules are typically code modules, individual applications, client and server applications on a network, etc. * Integration Testing follows unit testing and precedes system testing. * Testing that occurs after several parts are complete and they needed to be tested end to end. --- # LOAD TESTING * Load testing is a generic term covering Performance Testing and Stress Testing. --- # NEGATIVE TESTING * Validating an application/software/web site to validate error conditions * Does it behave like I expect it to when I attempt to break the piece of software of code. --- # PERFORMANCE TESTING * Performance testing can be applied to understand your application or WWW site's scalability, or to benchmark the performance in an environment of third party products such as servers and middleware for potential purchase. * This sort of testing is particularly useful to identify performance bottlenecks in high use applications. * Performance testing generally involves an automated test suite as this allows easy simulation of a variety of normal, peak, and exceptional load conditions. * How much resources does the program use with normal operations. --- # SMOKE TESTING * A quick-and-dirty test that the major functions of a piece of software work without bothering with finer details. * This testing is usually done right after a new build is received into a new test environment. * Testing one line of logic just built. Not testing other parts of the system just the one with the change. * Considered high risk because other parts could be affected by the change. * Examples of risk with smoke testing * https://www.youtube.com/watch?v=wj7P2DI_T0g * https://www.youtube.com/watch?v=KM5nzuz8JGw --- # STRESS TESTING * Testing conducted to evaluate a system or component at or beyond the limits of its specified requirements to determine the load under which it fails and how. * A graceful degradation under load leading to non-catastrophic failure is the desired result. * Often Stress Testing is performed using the same process as Performance Testing but employing a very high level of simulated load. * Used to answer the important question of at which point will my program break. --- # REGRESSION TESTING * Similar in scope to a functional test, a regression test allows a consistent, repeatable validation of each new release of a product. * Such testing ensures reported product defects have been corrected for each new release and that no new quality problems were introduced in the maintenance process. * Though regression testing can be performed manually an automated test suite is often used to reduce the time and resources needed to perform the required testing. * As new functions are added you continue to do all the existing functional tests in a process called regression testing to make sure new features don't break existing tested functionality of the code. * You repeat all the existing functional testing until everything passes. --- # SYSTEM TESTING * Testing conducted on a complete, integrated system to evaluate the system's compliance with its specified requirements. * System testing falls within the scope of black box testing, and as such, should require no knowledge of the inner design of the code or logic. --- # UNIT TESTING * Functional and reliability testing in an development environment. * Producing tests for the behavior of components of a product to ensure their correct behavior prior to system integration. * This testing is usually conducted by the developer or development team. * Testing a unit or section (usually a specific function) within the code. --- # TYPES OF GAME TESTING --- # BALANCE TESTING * Testing to verify that gameplay is fair to both the human versus AI player, and single and multiplayer modes. * Making sure easy level is not too easy and hard level is not too hard. * Examples are testing weapons balance, and map balance. * https://shapeofplay.files.wordpress.com/2013/03/2forts.jpg * https://memeworld.funnyjunk.com/pictures/Dark+souls+1+heat+map_56292d_6536738.jpg * https://funnymemes.funnyjunk.com/large/pictures/d4/f7/d4f745_6536738.png --- # COMPATIBILITY TESTING * Same as above only on a game. --- # COMPLIANCE TESTING * Testing to verify games follows some established guidelines before it is allowed to be sold in stores. * Common compliance requirements are copyrighted brands and names are accompanied by their respective signs and certain warning message in regards to the memory card/hard drive formatting. --- # LOCALIZATION TESTING * Testing to verify a game can be converted from one region to another usually involves translation. * The same game can be played in US, Korea, Japan or Germany. * https://www.youtube.com/watch?v=jQE66WA2s-A --- # PLAYTESTING * Comparable to a user acceptance test with users from the general public. * Determining the fun factor, is the game entertaining and fun to play. --- # USABILITY TESTING * Testing to verify the game is easy to use. Not easy as in difficulty level. * The game is intuitive meaning user can figure out how to interact with other characters, how to aim, how to find your way and how to drive vehicles. * https://www.xbox.com/en-US/accessories/controllers/xbox-adaptive-controller --- .center[![Image]()] ---


Nicola regularly inspects the local networks for security issues. He uses a smart and aggressive program which takes control of computers on the network. This program attacks all connected computers simultaneously, then uses the captured computers for further attacks. Nicola started the virus program in the first computer and took note of the time it took to completely capture the network. We can help him improve his process by modeling and improving his inspections.
We are given information about the connections in the network and the security level for each computer. Security level is the time (in minutes) that is required for the virus to capture a machine. Capture time is not related to the number of infected computers attacking the machine. Infection start from the 0th computer (which is already infected). Connections in the network are undirected. Security levels are not equal to zero (except infected).
Information about a network is represented as a matrix NxN size, where N is a number of computers. If ith computer connected with jth computer, then matrix[i][j] == matrix[j][i] == 1, else 0. Security levels are placed in the main matrix diagonal, so matrix[i][i] is the security level for the ith computer.
You should calculate how much time is required to capture the whole network in minutes.
Input: Network information as a list of lists with integers.
Output: The total time of taken to capture the network as an integer.
Example:
capture([[0, 1, 0, 1, 0, 1], [1, 8, 1, 0, 0, 0], [0, 1, 2, 0, 0, 1], [1, 0, 0, 1, 1, 0], [0, 0, 0, 1, 3, 1], [1, 0, 1, 0, 1, 2]]) == 8 capture([[0, 1, 0, 1, 0, 1], [1, 1, 1, 0, 0, 0], [0, 1, 2, 0, 0, 1], [1, 0, 0, 1, 1, 0], [0, 0, 0, 1, 3, 1], [1, 0, 1, 0, 1, 2]]) == 4 capture([[0, 1, 1], [1, 9, 1], [1, 1, 9]]) == 9
How it is used: This concept shows how to model and examine various network configurations. The idea does not only apply to computer networks however, it can also be a model for the spread of disease or dissemination of rumors.
Precondition:
3 ≤ len(matrix) ≤ 10 matrix[0][0] == 0 all(len(row) == len(matrix[0]) for row in matrix) all(matrix[i][j] == matrix[j][i] for i in range(len(matrix)) for j in range(len(matrix))) all(0 < matrix[i][i] < 10 for i in range(1, len(matrix))) all(0 ≤ matrix[i][j] ≤ 1 for i in range(len(matrix)) for j in range(len(matrix)) if i != j)


You've discovered Purr City
Humans aren't usually allowed
They're too clumsy and destroy things
Humans cannot enter Purr City in their physical form
It wasn't built for big humans like you
We welcome curiosity though
We will let you come into our world
But not in human form
Click your paw on Purr City & let's go there


2.0.0
-v2.0.0 - December 31, 2019 (not released)- v2.0.0 - August 11, 2020
This is a major release with additional features functionality and bug fixes. More importantly this release moves Skyline fully to Python-3.8
IMPORTANT NOTICE
In order to upgrade to Python 3.8.x see Upgrading from py2 to py3
Skyline requires a modification to the runner.py of the python-daemon 2.2.x package to allow it to run on Python 3.
Summary of changes in v2.0.0
Lots of changes have been introduced to accommodate Python 3.x and 2.7, not all changes are documented here related to the py3 changes just the main ones and additional functionality. For a full list of the changes see https://github.com/earthgecko/skyline/compare/v1.3.0…v2.0.0 (v1.3.1 was a very minor change).
py3 (3262)
Update least_squares & grubbs algorithms by using sample standard deviation as per PR #124 (3256), thanks to @51wenyu
py3 Redis binary objects not strings (3266)
Always send anomaly_breakdown and exception metrics (3288)
Record the anomaly_end_timestamp (3306)
Gracefully handle db failure (3310)
Added multiple webapp api methods (3336, 3350)
Add CUSTOM_ALERT_OPTS to settings (3194) ported to Boundary
py3 - handle system parameter in Graphite cactiStyle (3294)
Handle urllib2 in py3 (3290)
flux.populateMetricQueue Full (3254)
flux - populate_metric_worker - handle None in datapoints (3312)
Allow Skyline to send metrics to another Carbon host (3250) in flux
py3 handle bytes and not str in pickles (3278)
horizon - listen - py3 handle msgpack bytes (3272)
Handle py2 xange and py3 range (3280)
py3 - handle pymemcache bytes not str (3304)
Handle csv.reader in py3 (3302)
Add label to features profile (2516) - Added missing echo label
Ionosphere spawn_learn_process hanging on docker (3296)
Vista - batch Graphite requests (3338)
Reduce vista logging (3258)
vista - fetcher add time_to_fetch metric (3260)
Deprecate string.replace for py3 (3270)
ionosphere_backend.py fallback to available ts_json (3286)
users DB table (3230)
Label and relate anomalies (2476)
Add label to features profile (2516)
fp creation json response (3348)
webapp - rebrow - jwt.encode generating bytes instead of a string in py3 (3268)
docker (3002) - functions added
Make Skyline docker handle OOMkiller (3318)
And many, many more …
Update notes
These update instruction apply to upgrading from v1.3.0 (or v1.3.1) to v2.0.0 only. However as with all Skyline updates it is possible to go through the update notes for each version and make your own update notes/process to take you from version x to version y.
Due to the nature of the upgrade ensure that you have v1.3.0 (or v1.3.1) running stable for a period on Python 2.7 before attempting to update to v2.0.0
There are changes to the DB in v2.0.0
There are changes to settings.py in v2.0.0, please ensure you diff your current and the new settings.py as there are appended additions but there are also additional settings that have been added to existing settings blocks. The additions and changes described below.
settings.py Changes
Modified settings:
settings.ALERTS
modified to include http_alerter typesettings.CUSTOM_ALERT_OPTS
modified to include ionosphere_link_pathsettings.DO_NOT_SKIP_LIST
modified to include skyline.exceptionssettings.BOUNDARY_METRICS
modified to include http_alerter typesettings.BOUNDARY_ALERTER_OPTS
modified to include http_alerter type examplesettings.VISTA_FETCH_GRAPHITE_METRICS
was changed to andsettings.VISTA_FETCH_METRICS
the example Prometheus metric using a custom URI was corrected to not pass a pre-population tuple
New settings:
CHECK_AIRGAPS
SKIP_AIRGAPS
IDENTIFY_UNORDERED_TIMESERIES
BATCH_PROCESSING
BATCH_PROCESSING_STALE_PERIOD
BATCH_PROCESSING_DEBUG
BATCH_PROCESSING_NAMESPACES
METRICS_INACTIVE_AFTER
How to update from v1.3.0 (and v1.3.1)
Deploy a new Python-3.8.3 virtualenv, see Running Skyline in a Python virtualenv
Ensure all the dependencies are at the correct versions in the new Python-3.8.3 virtualenv
PYTHON_MAJOR_VERSION="3.8"
PYTHON_VIRTUALENV_DIR="/opt/python_virtualenv"
PROJECT="skyline-py383"
cd "${PYTHON_VIRTUALENV_DIR}/projects/${PROJECT}"
source bin/activate
# This can take lots of minutes with the new pandas
bin/"pip${PYTHON_MAJOR_VERSION}" install $(cat "${CURRENT_SKYLINE_PATH}/requirements.txt" | grep "^numpy\|^scipy\|^patsy\|^pandas" | tr '\n' ' ')
# Remove the luminol egg as it will already be installed and pip will moan
cat "${CURRENT_SKYLINE_PATH}/requirements.txt" | grep -v "luminol" > /tmp/requirements.txt
# IF YOU ARE ON CENTOS 6 and running mainstream mysql-server-5.1 uncomment
# the following line and run it to fix to mysql-python-connector 8.0.6
#cat "${CURRENT_SKYLINE_PATH}/requirements.txt" | grep -v "luminol\|mysql-connector-python" > /tmp/requirements.txt
bin/"pip${PYTHON_MAJOR_VERSION}" install -r /tmp/requirements.txt
Download the new release tag or clone/update to get it to a temp location, ready to be deployed.
NEW_SKYLINE_VERSION="v2.0.0" # Your new Skyline version
OLD_SKYLINE_VERSION="v1.3.0" # Your old Skyline version
# OR v1.3.1 if you updated to that version
# OLD_SKYLINE_VERSION="v1.3.1" # Your old Skyline version
CURRENT_SKYLINE_PATH="/opt/skyline/github/skyline" # Your Skyline path
NEW_SKYLINE_PATH="${CURRENT_SKYLINE_PATH}.${NEW_SKYLINE_VERSION}" # Your new Skyline path
mkdir -p "${CURRENT_SKYLINE_PATH}.${NEW_SKYLINE_VERSION}"
cd "${CURRENT_SKYLINE_PATH}.${NEW_SKYLINE_VERSION}"
git clone https://github.com/earthgecko/skyline .
git checkout "$NEW_SKYLINE_VERSION"
# Fix python-daemon - which fails to run on Python 3 (numerous PRs are waiting
# to fix it https://pagure.io/python-daemon/pull-requests)
cp /opt/python_virtualenv/projects/skyline-py383/lib/python3.8/site-packages/daemon/runner.py /opt/python_virtualenv/projects/skyline-py383/lib/python3.8/site-packages/daemon/runner.py.bak
cat "$NEW_SKYLINE_PATH/utils/python-daemon/runner.2.2.4.py" > /opt/python_virtualenv/projects/skyline-py383/lib/python3.8/site-packages/daemon/runner.py
# minor change related to unbuffered bytes I/O
diff /opt/python_virtualenv/projects/skyline-py383/lib/python3.8/site-packages/daemon/runner.py.bak /opt/python_virtualenv/projects/skyline-py383/lib/python3.8/site-packages/daemon/runner.py
# settings.py
cp "$NEW_SKYLINE_PATH/skyline/settings.py" "$NEW_SKYLINE_PATH/skyline/settings.py.${NEW_SKYLINE_VERSION}.bak"
# DIFF your settings.py with the new settings.py
diff "${CURRENT_SKYLINE_PATH}/skyline/settings.py" "$NEW_SKYLINE_PATH/skyline/settings.py.${NEW_SKYLINE_VERSION}.bak"
# Create a new settings.py file in the new version based on your existing
# settings.py file
cat "${CURRENT_SKYLINE_PATH}/skyline/settings.py" > "$NEW_SKYLINE_PATH/skyline/settings.py"
# ADD the appropriate new settings to your settings file and modify any
# changed settings as appropriate for your set up.
vi "$NEW_SKYLINE_PATH/skyline/settings.py"
# Stop any/all service controls like monit, etc that are controlling
# Skyline services.
# Stop Skyline DB related services
SKYLINE_SERVICES="webapp
ionosphere
luminosity
panorama"
for i in $SKYLINE_SERVICES
do
# /etc/init.d/$i stop
# or
systemctl stop $i
done
# BACKUP THE DB AND APPLY THE NEW SQL
BACKUP_DIR="/tmp" # Where you want to backup the DB to
MYSQL_USER="<YOUR_MYSQL_USER>"
MYSQL_HOST="<IP_ADDRESS>" # Your MySQL IP
MYSQL_DB="skyline" # Your MySQL Skyline DB name
# Backup DB
mkdir -p $BACKUP_DIR
mysqldump -u$MYSQL_USER -p $MYSQL_DB > $BACKUP_DIR/pre.$NEW_SKYLINE_VERSION.$MYSQL_DB.sql
# Update DB
mysql -u$MYSQL_USER -p $MYSQL_DB < "${NEW_SKYLINE_PATH}/updates/sql/${NEW_SKYLINE_VERSION}.sql"
# Stop all other Skyline services
SKYLINE_SERVICES="horizon
analyzer
mirage
crucible
boundary
ionosphere
luminosity
panorama
webapp
vista
flux"
for i in $SKYLINE_SERVICES
do
# /etc/init.d/$i stop
# or
systemctl stop $i
done
Move your current Skyline directory to a backup directory and move the new Skyline v2.0.0 with your new settings.py from the temp location to your working Skyline directory, (change your paths as appropriate) e.g.
mv "$CURRENT_SKYLINE_PATH" "${CURRENT_SKYLINE_PATH}.${OLD_SKYLINE_VERSION}"
mv "$NEW_SKYLINE_PATH" "$CURRENT_SKYLINE_PATH"
Start the all Skyline services (change as appropriate for your set up) e.g.
# Start all other Skyline services
SKYLINE_SERVICES="panorama
horizon
flux
vista
luminosity
analyzer
mirage
crucible
boundary
ionosphere
webapp"
for i in $SKYLINE_SERVICES
do
# /etc/init.d/$i start
# or
systemctl start $i
done
# Restart any/all service controls like monit, etc that are controlling
# Skyline services.
Check the logs
# How are they running
tail -n 20 /var/log/skyline/*.log
# Any errors - each app
find /var/log/skyline -type f -name "*.log" | while read skyline_logfile
do
echo "#####
# Checking for errors in $skyline_logfile"
cat "$skyline_logfile" | grep -B2 -A10 -i "error ::\|traceback" | tail -n 60
echo ""
echo ""
done


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You have 1 unimplemented size optimization which could reduce the size of your next release. Reducing the size of your app can lead to more installs.
You have 1 unimplemented size optimization which could reduce the size of your next release. Reducing the size of your app can lead to more installs.
Bevon OG
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Bevon OG
You have 1 unimplemented size optimization which could reduce the size of your next release. Reducing the size of your app can lead to more installs.
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Bevon OG
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Bevon OG
You have 1 unimplemented size optimization which could reduce the size of your next release. Reducing the size of your app can lead to more installs.
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plinkQC is a R/CRAN package for genotype quality control in genetic association studies. It makes PLINK basic statistics (e.g.missing genotyping rates per individual, allele frequencies per genetic marker) and relationship functions easily accessible from within R and allows for automatic evaluation of the results.
plinkQC generates a per-individual and per-marker quality control report. A step-by-step guide on how to run these analyses can be found here.
Individuals and markers that fail the quality control can subsequently be removed with plinkQC to generate a new, clean dataset.
plinkQC facilitates an ancestry check for study individuals based on comparison to reference datasets. The processing of the reference datasets is documented in detail here.
Removal of individuals based on relationship status via plinkQC is optimised to retain as many individuals as possible in the study.
The current github version of plinkQC is: 0.3.4 and can be installed via
library(devtools)
install_github("meyer-lab-cshl/plinkQC")
The current CRAN version of plinkQC is: 0.3.4 and can be installed via
install.packages("plinkQC")
A log of version changes can be found here.


Home
Example 1
Using a single image loader
-
+
Example 2
Loading all images but only showing one
-
+


--- layout: page status: publish published: true title: Public Safety author: display_name: Jessie Pechmann login: jpechmann email<EMAIL_ADDRESS>url: '' author_login: jpechmann author_email<EMAIL_ADDRESS>wordpress_id: 9893 wordpress_url: http://gis.utah.gov/?page_id=9893 date: '2012-06-19 11:06:38 -0600' date_gmt: '2012-06-19 17:06:38 -0600' categories: [] tags: - sgid - Data - utah - gis - mapping - location - points - dataset - download - shapefile - geodatabase - metadata - shp - gdb - digital - geographic - database - state - statewide - vector - public safety - safety - emergency - correctional - jail - fire stations - law enforcement - police - police stations - firemen - policemen - airport police - border patrols - community policing - constables - drug enforcement agents - indian reservation police - jails - juvenile detention hones - military police - prisons - secret service - transit police - united states bureau of alchol - tobacco - and firearms - federal bureau of investigation - united states marshals ---
Data Type: GIS Data Layer
Steward(s): AGRC
Abstract:
This data package has three datasets with facilities relating to public safety in Utah. Public safety facilities include correctional facilities, fire stations, and law enforcement facilities. These datasets come from different sources and therefore there might be some overlap.
Data Content:
The following data is available for download:


This section provides all information about the GlassCat container global configuration.
Current GlassCat server version is: {{version}}
Current GlassCat server locale is: {{locale}}


{% load i18n horizon %}
{% blocktrans %} Move networks from 'Available Networks' to 'Selected Networks' by clicking the button, or dragging and dropping. You can change the NIC order by dragging and dropping as well. {% endblocktrans%}


19 February 2003
Disease Outbreak Reported
As of 19 February results from two laboratories have confirmed the presence of an avian influenza virus in a child in Hong Kong SAR. Tests conducted in two samples from this single patient have identified the virus as the strain of influenza A(H5N1). A similar virus caused an outbreak in Hong Kong SAR in 1997, with 18 cases detected and six deaths.
In the current outbreak, a 9-year-old boy who travelled to Fujian Province (China) in January with his mother and his two sisters became ill on February 9 and was admitted to a Hong Kong hospital on February 12. He has recovered and is in a stable condition. Other members of his family presented with a similar illness. The child’s sister and father have died. The boy’s mother was ill but has recovered.
It is not yet known whether the other family members were also infected with influenza A(H5N1). A medical and epidemiological investigation is ongoing in Hong Kong to determine the cause of those illnesses. Results should be available in the next few days. Investigations are ongoing to determine the source of the infection.
The World Health Organization is collaborating closely with health authorities in Hong Kong SAR and China in investigating the outbreak. The WHO Global Influenza Surveillance network has been alerted.


Tell your non-tech friend a story about a time you got blocked on a simple problem, and how you solved it
What was the problem?
I had a small problem when I was completing 2.5 Paying off technical debt. I was trying create a pull request to merge my tidying branch to the master, and was following all steps correcty, but for some reason it was not working for me at all.
What problem solving techniques did you use?
I would go over all the steps, github, tried google, and went over the commend line, I went in circles for some time and decided to begin from the start again. The second time around it worked, I put it down to the fact that I must have done something small that wasn't allowing it to work (typos have gotten me a few times also).
How did you feel throughout the process?
At first it was fine, I have hit small road blocks before and managed, I did begin to get frustrated slightly as it continuously didn't work, however I didn't quite get to the point where I felt I needed external help. Thinking to myself, just start again, luckily this worked.
What did you learn?
I learnt that I need to slow down in my steps so I am not missing something small, and not to worry too much as there is most probably always a way to solve the problem. I am however, yet to figure out why the initial ull request did not work.
Problem solving techniques
Pseudocode - I find this technique very useful when others are trying to help. It breaks down the code for me, especially if it is quite complex or messy code.
Trying something - I have definitely tried many things before but always have in the back of my mind that I may completely screw something up which could have a big effect! This has not happened yet ;)
Rubber ducky method - I am yet to try this technique as I tend to gravitate more to others.
Reading error messages - This I have found very helpful in the command line in particular. It is quite tricky to understand the error message itself some times, however I tend to pair it with another technique, i.e google, to help out.
Console.logging - I found this great while I was working through the javascript challenges as a great way to test code. However, I am yet to fully understand how to utilise it with my own code.
Googling - Has definitely been the biggest technique I have used, which I guess is both good and bad. Good in the way it is quick and almost always provides an answer. Bad in terms of relying on it heavily, and possibly getting the wrong information.
Asking your peers for help - I am yet to stumble across a problem where I have yet to ask peers for help through slack. However, I am lucky enough to have my sister's help who seems to get all the questions I am asking.
Asking coaches for help - Again, I am yet to reach out to the coaches for help, but I am sure it will come soon - esecially with bootcamp approaching.
Improving your process with reflection - I enjoy the process of reflection, especially at the end of each sprint as it helps me identify my strengths and weaknesses so I am able to focus on them a bit more. Otherwise I would probably not pay much attention to why a problem arises, or trying to build skills in a particular area.


{% extends "base.html"%} {% block content %} {% load static %}
Welcome {{ user }}, your measurement history is listed below.
{{brafitting.bra_size}}
Suggested Bra:
{{suggestion}}
{% endfor %}You have no measurements saved, click here to find your best fit!
{% endif %}


I am Header 1 for this page!
Hello there, awesome person! My favourite websites on internet are :-
Harckerrank
Codechef
Coursera


DOLORES CLAIBORNE A film review by Jon A. Webb Copyright 1995 Jon A. Webb
Directed by Taylor Hackford Written by Tony Gilroy With: Kathy Bates (title role), Jennifer Jason Leigh (Dolores's daughter), David Straithairn (Dolores's husband), Christopher Plummer (investigating detective). 130 minutes
There are two kinds of Stephen King films: (1) adaptations of his novels and stories with supernatural elements, which range from real trash (NIGHT SHIFT, THE MANGLER, etc.) to well-meaning mediocre to good productions (THE DARK HALF, the television productions of IT and THE STAND, THE SHINING, etc.) and (2) the Castle Rock productions of his stories and novels without supernatural elements: STAND BY ME, MISERY, and now DOLORES CLAIBORNE.
The first category is sometimes entertaining, sometimes thrilling, sometimes embarrassing. The second category is consistently as good as mainstream American cinema gets.
Dolores Claiborne is a resident of a small island off the coast of Maine.  When I read the novel I found it weak. The central scene in the book with Dolores's head silhouetted against the sky while a solar eclipse reaches totality fell flat for me.
But in the movie this scene sings. We see Kathy Bates's rough face in the left and in the upper right a perfect dark circle and crescent of awesome beauty. Then we shift to the circle of the view from the well, then back again. I was really overwhelmed.
The story has been sharpened considerably from the novel. The novel is centered around Dolores herself and her hard life; the movie is centered around Dolores's relationship with her daughter Selena. Not only is the story better cinematically, it's stronger in any case.
But even a much weaker script would have been saved by this exceptional cast. You have Kathy Bates, who of course won an Academy Award for her work in MISERY; the highly talented and versatile Jennifer Jason Leigh; David Straithairn; and Christopher Plummer.
Bates, at least, and maybe Leigh as well, deserves another Oscar nomination for her performance. Their two faces fill the screen; the constrast between Bates's plump roughness and Leigh's gaunt youth is rich and full of meaning in itself. The absorbing puzzle they find themselves in, with Bates remembering everything but hiding it under her crustiness, and Leigh remembering nothing--barely remembering her mother, even--drives the movie and makes that transcendent moment as the eclipse reaches totality stunning. Leigh's spirited turn in the courtroom recalls other famous Hollywood defenses.
The cinemaphotography appropriately shows the modern scenes gray and drained of life, as they have by events, and earlier scenes full of garish color. The last scene keeps this drained effect, marking the way these two women cannot really recover their youth and innocence.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


/
Find how to use every component on Puppertino.
A new Puppertino version is on the horizon, check this branch for more details!
How to get up and running with Puppertino.
Fancy buttons of macOS and iOS? We have them.
Create elegant forms. Using our amazing components.
Trying to show a warning? Do it in a cool way.
Pop-up actions from iOS are now in Puppertino.
Complement your beautiful app with curated icons.
Font sizing based on human guidelines for your web/app.
Use the vibrant colors of Puppertino to make your project beautiful.
The magnificent segmented controls of iOS are now in Puppertino.
Add depth to your project with this curated collection of shadows.
The amazing tabs for iOS and macOS now featured in Puppertino
Want that sweet dark mode with your Puppertino app? It's here now.


Fast Python Serialization with Ray and Apache Arrow
This post elaborates on the integration between Ray and Apache Arrow. The main problem this addresses is data serialization.
From Wikipedia, serialization is
… the process of translating data structures or object state into a format that can be stored … or transmitted … and reconstructed later (possibly in a different computer environment).
Why is any translation necessary? Well, when you create a Python object, it may have pointers to other Python objects, and these objects are all allocated in different regions of memory, and all of this has to make sense when unpacked by another process on another machine.
Serialization and deserialization are bottlenecks in parallel and distributed computing, especially in machine learning applications with large objects and large quantities of data.
Design Goals
As Ray is optimized for machine learning and AI applications, we have focused a lot on serialization and data handling, with the following design goals:
- It should be very efficient with large numerical data (this includes NumPy arrays and Pandas DataFrames, as well as objects that recursively contain Numpy arrays and Pandas DataFrames).
- It should be about as fast as Pickle for general Python types.
- It should be compatible with shared memory, allowing multiple processes to use the same data without copying it.
- Deserialization should be extremely fast (when possible, it should not require reading the entire serialized object).
- It should be language independent (eventually we’d like to enable Python workers to use objects created by workers in Java or other languages and vice versa).
Our Approach and Alternatives
The go-to serialization approach in Python is the pickle module. Pickle is very general, especially if you use variants like cloudpickle. However, it does not satisfy requirements 1, 3, 4, or 5. Alternatives like json satisfy 5, but not 1-4.
Our Approach: To satisfy requirements 1-5, we chose to use the Apache Arrow format as our underlying data representation. In collaboration with the Apache Arrow team, we built libraries for mapping general Python objects to and from the Arrow format. Some properties of this approach:
- The data layout is language independent (requirement 5).
- Offsets into a serialized data blob can be computed in constant time without reading the full object (requirements 1 and 4).
- Arrow supports zero-copy reads, so objects can naturally be stored in shared memory and used by multiple processes (requirements 1 and 3).
- We can naturally fall back to pickle for anything we can’t handle well (requirement 2).
Alternatives to Arrow: We could have built on top of Protocol Buffers, but protocol buffers really isn’t designed for numerical data, and that approach wouldn’t satisfy 1, 3, or 4. Building on top of Flatbuffers actually could be made to work, but it would have required implementing a lot of the facilities that Arrow already has and we preferred a columnar data layout more optimized for big data.
Speedups
Here we show some performance improvements over Python’s pickle module. The
experiments were done using pickle.HIGHEST_PROTOCOL
. Code for generating these
plots is included at the end of the post.
With NumPy arrays: In machine learning and AI applications, data (e.g., images, neural network weights, text documents) are typically represented as data structures containing NumPy arrays. When using NumPy arrays, the speedups are impressive.
The fact that the Ray bars for deserialization are barely visible is not a mistake. This is a consequence of the support for zero-copy reads (the savings largely come from the lack of memory movement).
Note that the biggest wins are with deserialization. The speedups here are multiple orders of magnitude and get better as the NumPy arrays get larger (thanks to design goals 1, 3, and 4). Making deserialization fast is important for two reasons. First, an object may be serialized once and then deserialized many times (e.g., an object that is broadcast to all workers). Second, a common pattern is for many objects to be serialized in parallel and then aggregated and deserialized one at a time on a single worker making deserialization the bottleneck.
Without NumPy arrays: When using regular Python objects, for which we cannot take advantage of shared memory, the results are comparable to pickle.
These are just a few examples of interesting Python objects. The most important case is the case where NumPy arrays are nested within other objects. Note that our serialization library works with very general Python types including custom Python classes and deeply nested objects.
The API
The serialization library can be used directly through pyarrow as follows. More documentation is available here.
x = [(1, 2), 'hello', 3, 4, np.array([5.0, 6.0])]
serialized_x = pyarrow.serialize(x).to_buffer()
deserialized_x = pyarrow.deserialize(serialized_x)
It can be used directly through the Ray API as follows.
x = [(1, 2), 'hello', 3, 4, np.array([5.0, 6.0])]
x_id = ray.put(x)
deserialized_x = ray.get(x_id)
Data Representation
We use Apache Arrow as the underlying language-independent data layout. Objects are stored in two parts: a schema and a data blob. At a high level, the data blob is roughly a flattened concatenation of all of the data values recursively contained in the object, and the schema defines the types and nesting structure of the data blob.
Technical Details: Python sequences (e.g., dictionaries, lists, tuples, sets) are encoded as Arrow UnionArrays of other types (e.g., bools, ints, strings, bytes, floats, doubles, date64s, tensors (i.e., NumPy arrays), lists, tuples, dicts and sets). Nested sequences are encoded using Arrow ListArrays. All tensors are collected and appended to the end of the serialized object, and the UnionArray contains references to these tensors.
To give a concrete example, consider the following object.
[(1, 2), 'hello', 3, 4, np.array([5.0, 6.0])]
It would be represented in Arrow with the following structure.
UnionArray(type_ids=[tuple, string, int, int, ndarray],
tuples=ListArray(offsets=[0, 2],
UnionArray(type_ids=[int, int],
ints=[1, 2])),
strings=['hello'],
ints=[3, 4],
ndarrays=[<offset of numpy array>])
Arrow uses Flatbuffers to encode serialized schemas. Using only the schema, we can compute the offsets of each value in the data blob without scanning through the data blob (unlike Pickle, this is what enables fast deserialization). This means that we can avoid copying or otherwise converting large arrays and other values during deserialization. Tensors are appended at the end of the UnionArray and can be efficiently shared and accessed using shared memory.
Note that the actual object would be laid out in memory as shown below.
The Arrow serialized representation would be as follows.
Getting Involved
We welcome contributions, especially in the following areas.
- Use the C++ and Java implementations of Arrow to implement versions of this for C++ and Java.
- Implement support for more Python types and better test coverage.
Reproducing the Figures Above
For reference, the figures can be reproduced with the following code.
Benchmarking ray.put
and ray.get
instead of pyarrow.serialize
and
pyarrow.deserialize
gives similar figures. The plots were generated at this
commit.
import pickle
import pyarrow
import matplotlib.pyplot as plt
import numpy as np
import timeit
def benchmark_object(obj, number=10):
# Time serialization and deserialization for pickle.
pickle_serialize = timeit.timeit(
lambda: pickle.dumps(obj, protocol=pickle.HIGHEST_PROTOCOL),
number=number)
serialized_obj = pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)
pickle_deserialize = timeit.timeit(lambda: pickle.loads(serialized_obj),
number=number)
# Time serialization and deserialization for Ray.
ray_serialize = timeit.timeit(
lambda: pyarrow.serialize(obj).to_buffer(), number=number)
serialized_obj = pyarrow.serialize(obj).to_buffer()
ray_deserialize = timeit.timeit(
lambda: pyarrow.deserialize(serialized_obj), number=number)
return [[pickle_serialize, pickle_deserialize],
[ray_serialize, ray_deserialize]]
def plot(pickle_times, ray_times, title, i):
fig, ax = plt.subplots()
fig.set_size_inches(3.8, 2.7)
bar_width = 0.35
index = np.arange(2)
opacity = 0.6
plt.bar(index, pickle_times, bar_width,
alpha=opacity, color='r', label='Pickle')
plt.bar(index + bar_width, ray_times, bar_width,
alpha=opacity, color='c', label='Ray')
plt.title(title, fontweight='bold')
plt.ylabel('Time (seconds)', fontsize=10)
labels = ['serialization', 'deserialization']
plt.xticks(index + bar_width / 2, labels, fontsize=10)
plt.legend(fontsize=10, bbox_to_anchor=(1, 1))
plt.tight_layout()
plt.yticks(fontsize=10)
plt.savefig('plot-' + str(i) + '.png', format='png')
test_objects = [
[np.random.randn(50000) for i in range(100)],
{'weight-' + str(i): np.random.randn(50000) for i in range(100)},
{i: set(['string1' + str(i), 'string2' + str(i)]) for i in range(100000)},
[str(i) for i in range(200000)]
]
titles = [
'List of large numpy arrays',
'Dictionary of large numpy arrays',
'Large dictionary of small sets',
'Large list of strings'
]
for i in range(len(test_objects)):
plot(*benchmark_object(test_objects[i]), titles[i], i)


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Special Projects
This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.
Provided via Ohio Bobcats Hockey
Bird Arena when it first opened in 1958.
01.19.18
Ohio University is home to some of the most iconic and well-renowned athletic venues in the country. Off the banks of the Hocking River and State Route 33 rest Peden Stadium — considered the “crown jewel of the Mid-American Conference” — and The Convo, which has seen many historic basketball games and wrestling matches.
It’s fair that these two facilities garner the attention of collegiate athletic fans, but there’s just one more venue on campus that has housed truly one of the most successful programs in varsity or club sports.
In the shadows of the recognizable varsity home venues lies Bird Arena.
Since the program’s inception back in 1957, Bird has been the home to four national championship teams and a multitude of other admirable success along the way that has paved a stand-tall reputation in the collegiate hockey sphere.
For a program rooted in traditions, it all started at Bird.
On a frigid late December night in 1958, led by Tom Caldwell’s seven-goal performance, Ohio won its first home game in history against Ohio Northern in a 22-0 blowout en route to a 6-2-1 overall record in its infancy stage as a program.
From that moment on, Bird became more than just a building with a sheet of ice on the ground — it became home to a winning, breathing program. With a program win percentage at .681, winning is the only thing expected; unlike some athletic programs, winning is achieved often.
The arena’s rafters are home to multiple banners, including four ACHA national championships, two Ohio Intercollegiate Hockey Association titles and numerous conference banners, all of which indicate success at some point in the last sixty years.
While banners represent seasons of success, many games have stood the test of time, much like the Ohio Northern thrashing. In the final game of the 1964-65 season, Ohio defeated Wisconsin 4-3.
With so much history contained within four walls, coach Sean Hogan is aware that Bird has written its place within collegiate hockey.
“When players from the ‘60s come back it’s the same ice surfaced they played on,” Hogan said. “Everything’s been the same, which is great; it makes it historic.”
No program, however, is without its shortcomings. Most recently, Ohio danced the dance but forgot to tie its shoes in the last number, losing to Central Oklahoma in the 2017 ACHA National Championship Game 3-0.
In its 60th season as a program, No. 5 Ohio stands at 18-7-1. For the last six decades, the Bobcats have always been in contention for a championship run, but more so over the last three seasons.
With the pressure and expectation of winning, Hogan hopes to see his players enjoy the moment that they’re in during this weekend’s anniversary celebration.
“When you’re going through the program, I don’t think you see the magnitude of everything,” Hogan said. “They get to see that these (alumni) did the same thing in the same place that they’re doing now.”
With many historic moments that have stood the test of time — and more on the horizon — Ohio is in a good place. Surrounded by a passionate team, dedicated fan base and caring alumni, Bird Arena and the Bobcats will live to push towards another 60 years.
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Developers
Tip CityZen requires a fairly recent version of both npm and Node.js. To install both go here:
Get NPM
For reference when these docs were written we were using version 12.18.1 of Node.js and version 6.14.5 of npm
Install using NPM
The CityZen-Server project page on npm: CityZen-Server
To install CityZen first create the directory cityzen-server.
md CityZen-Server
Then open a command prompt, navigate to the directory CityZen-Server and type npm init.
npm init
Then you have to answer a series of questions about the project. Next to install the server you want type on the command line:
npm i @codeforlansing/cityzen-server
Or alternately you can install using npx:
npx @codeforlansing/cityzen-server
The CityZen-Client project page on npm: CityZen-Client
To be continued
TIP If you need a local development web server CityZen recommends http-server
Install using GitHub
The CityZen-Server project page on GitHub: CityZen-Server
There are two different methods to install CityZen from GithHub
The first method is to clone the project. Note you need to have the GIT CLI installed to use this method.
To install CityZen-Server go to the directory you want to use and type:
git clone https://github.com/codeforlansing/cityzen-server.git
Then go to the directory you chose and type:
npm install
To launch the server type:
npm start
The CityZen-Client page on Github: CityZen-Client
Here are directions to install the CityZen-Client from GitHub.
git clone https://github.com/codeforlansing/cityzen-client-vue.git
Then go to the directory you chose and type:
npm install
To start the client once it is successfully installed type and you're in the client directory:
npm start
The second method for installing is to download the zip files for both server and client. To do that on the respective GitHub repository sites, hit the green clone or download button and choose download and then download zip
Install using Docker
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.


Characteristics
content addressed
distributed
reliable
simple
fast
Let's get started
porcelain commands
the shiny surface
meant for the end user
housekeeping included
usually used for command line
plumbing commands
get your hands dirty
meant for the craftsmen
where the real work is done
intended to be upward compatible
use it for scripts
Locations
working tree
index
repository
remote
working tree
trees and blobs on local filesystem
based on a branch or other commit
can be "clean" or "dirty"
index
staging area
objects stored, but not commited
manage what the next commit will do
repository
everything Git knows about your files
usually the ".git" subdir of the working tree
may be "bare", i.e. without a working tree
remote
other repository "somewhere" else
fetch / pull to get changes
push to spread changes
Back to work again
Core data types
objects
references
Object types
blobs
trees
commits
(annotated) tags
Reference types
heads (aka branches)
(simple) tags
HEAD, FETCH_HEAD etc.
Let's do another project
One more thing


Jerimiah Woods
-Aspiring Software Developer-
-Aspiring Software Developer-
4511 Puttor Drive
Eau Claire, WI 54701
(715) 572-4512
<EMAIL_ADDRESS>Eau Claire, WI 54701
(715) 572-4512
<EMAIL_ADDRESS>Peer Feedback
Feedback From Agile Team
Project: Property World
Our team performed four one-week sprints. The following is anonymous feedback given to our instructor when the project was over. (We were required to evaluate each team member's performance.) He then passed the feedback on to us. Here is what I recieved, verbatim:
Strengths
Areas to improve
Strengths
- This team member’s strengths are that they are very open to learning new things. He always wants to try new things to help further a project along, and to learn things to help his career. He is very good at working with others as well.
- Jerry is a great team player. He makes sure he gets his tasks done but also makes sure that everyone around him is succeeding as well. I still think Jerry is great with programming because he can look at the code and access what it is trying to accomplish rather than how it is trying to accomplish if that makes any sense.
- Jerry is very active in all aspects of this project, his questions and discussion about the stories and everything helped a lot in understanding how to handle a lot about this project. He was the member that was most helpful however he could and when he could.
- He’s a great team member to work with, very helpful and has a big influence towards decisions and to go about much of the stories and he’ll be a huge steal for whoever else he works for as he’ll be an amazing team member.
- Jerry did a good job of taking on tasks that would improve his knowledge. He also did a great job of trying to keep the team on track over the course of this project.
Areas to improve
- I think that he could improve by asking more questions of each of the team members. I think that he understands at some level how our team worked but some clarifying questions about what each member was doing would help a lot.
- Jerry took a much stronger position in the team after the midterm review. I still think he could stand his ground a little
- Jerry just needs to work on keeping in touch a bit more so we know more of how he’s doing outside of the meetings we have.
- Jerry could try to pair with more than just one team member to try and learn from them as well.


This is a github.io page for Change, a progressive web app for consulting the Book of Changes.
The license for this project is LICENSE.txt.
The software is derived from the PWA Starter Kit and depends heavily on Lit Html and Lit Element from Google's Polymer Project. (And, no, I didn't forget to use the MWC Material Web Components, but they really aren't ready at the moment.)
The content is derived from many versions of the Book of Changes on the web and may be subject to additional copyrights.


|
F´ Flight Software - C/C++ Documentation
NASA-v1.5.0
A framework for building embedded system applications to NASA flight quality standards.
|
The Svc::TlmChan
Component is used to store telemetry values written by other components. The values are stored in serialized form. The data is stored as a set of telemetry channels in a table. The data can be individually read back or periodically pushed to another component for transporting out of the system. Svc::TlmChan
is a implementation of the Svc::TlmStore
component in the Svc/Tlm
directory.
The requirements for Svc::TlmChan
are as follows:
| Requirement | Description | Verification Method |
|---|---|---|
| TLC-001 | The Svc::TlmChan component shall provide an interface to submit telmetry | Unit Test |
| TLC-002 | The Svc::TlmChan component shall provide an interface to read telmetry | Unit Test |
| TLC-003 | The Svc::TlmChan component shall provide an interface to run periodically to write telemetry | Unit Test |
| TLC-004 | The Svc::TlmChan component shall write changed telemetry channels when invoked by the run port | Unit Test |
The Svc::TlmChan
component has the following component diagram:
The Svc::TlmChan
component uses the following port types:
| Port Data Type | Name | Direction | Kind | Usage |
|---|---|---|---|---|
Svc::Sched | Run | Input | Asynchronous | Execute a cycle to write changed telemetry channels |
Fw::Tlm | TlmRecv | Input | Synchronous Input | Update a telemetry channel |
Fw::Tlm | TlmGet | Input | Synchronous Input | Read a telemetry channel |
Fw::Com | PktSend | Output | n/a | Write a set of packets with updated telemetry |
The Svc::TlmChan
component has an input port TlmRecv
that receives channel updates from other components in the system. These calls from the other components are made by the component implementation classes, but the generated code in the base classes takes the type specific channel value and serializes it, then makes the call to the output port. The Svc::TlmChan
component can then store the channel value as generic data. The channel values are stored in an internal double-buffered table, and a flag is set when a new value is written to the channel entry.
When a request is made for a non-existent channel, the call will return with an empty buffer in the Fw::TlmBuffer value argument. This is to cover the case where a channel is defined in the system, but has not been written yet. If the channel has not ever been defined, there is no way to programmatically determine that from the TlmGet port call.
The implementation uses a hashing function that is tuned in the configuration file TlmChanImplCfg.hpp
. See section 3.5 for description.
This diagram shows the scenario where telemetry packets are generated for an external user:
This diagram shows the scenario where telemetry packets are stored an retrieved as a database:
Svc::TlmChan
has no state machines.
In order to speed up lookups for storing and reading telemetry channels, a simple hash function is used to select a location in an array of hash table slots. A configuration value in TlmChanImplCfg.h
defines a set of hash buckets to store the telemetry values. The number of buckets has to be at least as large as the number of telemetry values defined in the system. The number of channels in the system can be determined by invoking make comp_report_gen
from the deployment directory. The number of has table slots TLMCHAN_NUM_TLM_HASH_SLOTS
and the hash value TLMCHAN_HASH_MOD_VALUE
in the configuration file can be varied to balance the amount of memory for slots versus the distribution of buckets to slots. See TlmChanImplCfg.h
for a procedure on how to tune the algorithm.
Dictionaries: HTML MD
| Document | Link |
|---|---|
| Design Checklist | Link |
| Code Checklist | Link |
| Unit Test Checklist | Link |
| File | Contents |
|---|---|
| Unit Test Output | Link |
| Coverage - TlmChanImpl.cpp | Link |
| Coverage - TlmChanImplGet.cpp | Link |
| Coverage - TlmChanImplRecv.cpp | Link |
| Coverage - TlmChanImplTask.cpp | Link |
| Coverage - TlmChanComponentAc.cpp | Link |
| Date | Description |
|---|---|
| 6/23/2015 | Design review edits |
| 7/22/2015 | Design review actions |
| 9/28/2015 | Unit Test Review additions |


| PostgreSQL 9.6.15 Documentation | |||
|---|---|---|---|
| Prev | Up | Chapter 50. System Catalogs | Next |
The view pg_config describes the compile-time configuration parameters of the currently installed version of PostgreSQL. It is intended, for example, to be used by software packages that want to interface to PostgreSQL to facilitate finding the required header files and libraries. It provides the same basic information as the pg_config PostgreSQL client application.
By default, the pg_config view can be read only by superusers.


A regular expression is a special sequence of characters that helps you match or find other strings or sets of strings. Regular expressions are widely used in UNIX world.
RE is an expression that retrieves required information from the given data.
Used on huge data
It is used in data science
Used to retrieve data speedily
Module name is re
import re
Pattern Description
^ Matches beginning of line.
$ Matches end of line.
. Matches any single character except newline. Using m option allows it to match newline as well.
[...] Matches any single character in brackets.
[^...] Matches any single character not in brackets.
re* Matches 0 or more occurrences of preceding expression.
re+ Matches 1 or more occurrence of preceding expression.
re? Matches 0 or 1 occurrence of preceding expression.
re{ n} Matches exactly n number of occurrences of preceding expression.
re{ n,} Matches n or more occurrences of preceding expression.
re{ n, m} Matches at least n and at most m occurrences of preceding expression.
a| b Matches either a or b.
(re) Groups regular expressions and remembers matched text.
\w Matches word characters.
\W Matches nonword characters.
\s Matches whitespace. Equivalent to [\t\n\r\f].
\S Matches nonwhitespace.
\d Matches digits. Equivalent to [0-9].
\D Matches nondigits.
\A Matches beginning of string.
\Z Matches end of string. If a newline exists, it matches just before newline.
\z Matches end of string.
\n, \t, etc. Matches newlines, carriage returns, tabs, etc.
\1...\9 Matches nth grouped subexpression.
Search("finding word", string_name):
Returns first occurance.group(). If data not found return none.
Example.
# Write a RE to search for string starting with m and having 3 character
import re
str="man sun mop run mat wat"
rest=re.search('m\w\w',str)
#print(rest)
if rest:
print(rest.group())
else:
print("not matching with 1st charecter")
o/p:
man
findall():
Returns all the matching strings .
Example
#write a RE to findall for string starting with m and having 3 charecter
import re
str="man sun mop run mat wat"
rest=re.findall('m\w\w',str)
#print(rest)
for x in rest:
print(x)
o/p:
man
mop
mat
match():
Searches in the beginning. group() if not found return none.
Example
#write a RE to match for string starting with m and having 3 charecter
import re
str="man sun mop run mat wat"
rest=re.match('m\w\w',str)
#print(rest)
if rest: # also we can write if rest!='\0':
print(rest.group())
else:
print("not matching with 1st charecter")
o/p:
man
Example
# Write A RE to retrieve role number
import re
str='10 A,202 B,30 C,40 D'
res=re.findall('\d\d',str) # only take 2 digit
print(res)
res=re.findall('\d+',str) # here take all type of digit
print(res)
res=re.findall('\D+',str) #\D take alphabet only
for i in res:
print(i)
o/p:
['10', '20', '30', '40']
['10', '202', '30', '40']
A,
B,
C,
D
split():
Cut the string at the particular place indicated by RE.
Example
# split a string at numaric chars
import re
str="gopi 1200 vini 01251 subba 125510 roy 2541"
res=re.split('\d+',str)
#print(res)
for i in res:
print(i)
o/p:
gopi
vini
subba
roy
Example
# W A RE to splete a string where a comma and colon founded
import re
str="gopi, 1200 :vini 01251,: subba 125510: roy 2541"
rexp='[ , :]'
res=re.split(rexp,str)
for i in res:
print(i)
o/p:
gopi
1200
vini
01251
subba
125510
roy
2541
sub():
Substitute a new string in the place of existing string.
Syntax:
Re.sub("old_String","new_string",str)
Example
# W A RE to replace existing string with another one
import re
str="Java is very good programming language besause Java is scripting language"
res=re.sub("Java","Python",str)
print(res)
o/p:
Python is very good programming language besause Python is scripting language
Example
#retrieve the Bob from str
import re
str="V 20 1-2-1994,B 21 22-13-1895,K 24 13-8-1935"
# for DOB display
lst=re.findall("\d{1,2}-\d{1,2}-\d{4}",str)
print("DOB\t",lst)
o/p:
DOB ['1-2-1994', '22-13-1895', '13-8-1935']


The Akan people of Ghana name their children after the day of the week they were born.
"day names" have further meanings concerning the soul and character of the person.
What is your akanName??


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import models.amendModels.AmendPensionsTakenBetweenModel @import common.Links @this( layout: Layout, form: uk.gov.hmrc.govukfrontend.views.html.components.FormWithCSRF, submitButton: playHelpers.templates.submitButton, formInputMoney: playHelpers.templates.formInputMoney, helpTextAccordion: playHelpers.templates.helpTextAccordion, errorSummary: playHelpers.templates.errorSummary, formHiddenYesNoRadio: playHelpers.templates.formHiddenYesNoRadio ) @(amendPensionsTakenBetweenForm: Form[AmendPensionsTakenBetweenModel])(implicit request: Request[_], messages: Messages) @helpTextAccordionContent = {
@Messages("pla.ip14PensionsTakenBetween.para.2")
@Messages("pla.pensionsTakenBetween.para.3.nonInput") @Messages("pla.pensionsTakenBetween.helpLinkText") (@Messages("pla.base.newTab")).
} @hiddenYesNoContent = { @formInputMoney( field = amendPensionsTakenBetweenForm("amendedPensionsTakenBetweenAmt"), question = Messages("pla.pensionsTakenBetween.questionTwo"), labelClass = Some("govuk-label--m"), hideLabel = false, pageHeading = false )@Messages("pla.pensionsTakenBetween.para.1")
@helpTextAccordion( id = "ip14-amend-pensions-taken-between-help", summaryText = Messages("pla.pensionsTakenBetween.help"), hiddenContent = helpTextAccordionContent ) } @title = @{ if(amendPensionsTakenBetweenForm.errors.nonEmpty){ messages("site.title.error", messages("pla.ip14PensionsTakenBetween.title")) } else { messages("pla.ip14PensionsTakenBetween.title") } } @layout( pageTitle = title ){ @errorSummary(amendPensionsTakenBetweenForm.errors, Some("amendPensionsTakenBetween"))


This property controls the size of the mouse sphere. The result of these changes are only noticeable after interacting with the waveform.
Radius:30
Fluid Viscosity
12
This property determines how resistant to velocity changes is the fluid. A higher viscosity will result in a slower moving particle system - like honey. A lower viscosity, though, will result in a faster moving system - like water.
Viscosity:12
Elastic Intensity
15
This property controls how strong is the force pulling the particles back to the waveform. A higher value can accumulate enough speed to overshoot the waveform and create a sligshot effect.
Elasticity:15
Gradient Colors
Motion Blur
Enabled
This option slightly blurs and leaves a trail behind moving object. Helps to display a more realistic and believable image, especially for devices running at a low framerate.
Motion Blur:Enabled
Experiment
Atomize was conceptualized to simulate elastic systems under the effect of viscuous fluids.
This means we want you to have fun with some particles.
Hardware Acceleration not enabled or GPU not available.


--- layout: post date: 2011-01-14 time: "10:00:00" authors: ["Greg Wilson"] title: The Hacker Within tags: ["University of Wisconsin - Madison"] ---
I got back to Toronto late last night from visiting The Hacker Within, a grassroots student organization at the University of Wisconsin — Madison that helps science and engineering students figure out how to use computing in their research. I think it's a great model for schools elsewhere to adopt, and despite truly awful "service" from Delta Airlines [1], it was worth going down to chat with Katy, Nico, Paul, and everyone else, and to see a large auditorium filled with people (80? more?) learning how version control, the shell, Doxygen, and other tools can make them more productive researchers. And as a bonus, I can now take "sleep in a frat house" off my list of 1000 things to do before I die... :-)
[1] Check out @gvwilson + #delta on Twitter for the blow-by-blow.


Things started when Apple started using touch bar
on MacBook. Admittedly, touch bar is good, but as a vimer, I found it really inconvenient to switch between normal
mode and insert
mode. One day, I could not bear it, so I decided to make some changes.
Vim has a nick name “Text editor of the Gods”, so there are a lot of things you can do about vim. You can customize your vim to make it easier to use, also more powerful.
Mapping
So I decided to use map
in Vimscript. Mapping is a very useful feature of Vimscript, and the magic of mapping is that mapping keys lets you tell Vim:
When I press these pattern, I want vim to do this perticular stuff rather than whatever vim would normally do.
You can treat is as a shortcut. And you can map keys in normal
mode, insert
mode and other modes.
So, for example, look at this Vimscript below:
:map - dd
When you put this line in your .vimrc
file and run source .vimrc
to make the .vimrc
work. You are telling vim that the next time, in normal
mode, when I press “-”, the vim will do exactly what you do with pressing two characters dd
. So the vim will delete that line. And I find map
command also works in visual
mode.
And, there are also some similar command. We can use nmap
, vmap
, imap
to tell vim to only use the mapping in normal
,visual
, or insert
mode respectively.
Side Effects of map
There are two side effects of using map
command.
- The danger of recursing
- May have conflict to vim-plugin mapping keys
For example,
nmap dd o<esc>jdd
you may think this command would open a new line, and esc to normal mode and move down one line and delete this line. However, because o<esc>jdd include “dd”, so it will do it recursively and can never end.
Nonrecursive Mapping
Vim offers another commands that would ignore recursive mapping. Just add nore in the front of map
. So now we would like to use noremap
rather than map
.
There are nnoremap
, inoremap
, vnoremap
and used in normal, insert and visual mode respectively. There are similar to map
sets.
Leaders
Another trick is leaders in vim.
There are some keys that we don’t use very often in our daily vim usage. Such as -
, +
, ,
. Those are really safe to be used in mappings.
So how about using them as a Prefix key.
let mapleader=','
Then we can use ‘leaders’ in vimscripts.
For example,
nnoremap <leader>x d$
and :source %
in vim to make it works. The next time when you press ,x
in normal mode, vim will delete to the end of current line from the cursor position.
Get rid of “<esc>”
Now come back to the problem I have, the touch bar. I feel really uncomfortable to use “<esc>” to switch between normal
mode and insert
mode. So I add the following lines to my .vimrc
file.
let mapleader=','
" save file
inoremap <leader>w <Esc>:w<cr>
noremap <leader>w :w<cr>
" map <Esc> to jj
inoremap jj <Esc>
The line starts with "
means comments.
I first use ,
as my leader. Then I map the save command to ,w
so next time whenever in insert
mode or normal
mode and when I press ,w
it would save the file and back to normal
mode. Because I can use ZZ
to save and quit, I do not need to map them. At last, I map “<esc>” to jj
. Because there is no continues two j characters in english words and j is exactly where my index finger is placed on the keyboard. There we are^_^


15.8 Optional Numeric Modules
Many other modules are
built on top of Numeric or cooperate with it. You
can download some of them from the same URL as
Numeric (http://sourceforge.net/projects/numpy). Some
of these extra modules may already be included in the package you
have downloaded. Documentation for the modules is also part of the
documentation for Numeric. A rich library of
scientific tools that work well with Numeric is
SciPy, available at http://www.scipy.org. I highly recommend it
if you are using Python for scientific or engineering computing.
Here are some key optional Numeric modules:
- MLab
-
MLab supplies many Python functions written
on top of Numeric.
MLab's functions are similar in
name and operation to functions supplied by the product
Matlab.
- FFT
-
FFT supplies Python-callable Fast Fourier
Transforms (FFTs) of data held in Numeric arrays.
FFT can wrap either the well-known
FFTPACK Fortran-coded library or the compatible
C-coded fftpack library.
- LinearAlgebra
-
LinearAlgebra supplies Python-callable functions,
operating on data held in Numeric arrays, that
wrap either the well-known LAPACK Fortran-coded
library or the compatible C-coded lapack_lite
library. LinearAlgebra lets you invert matrices,
solve linear systems, compute eigenvalues and eigenvectors, perform
singular value decomposition, and least-squares-solve overdetermined
linear systems.
- RandomArray
-
RandomArray supplies fast, high-quality
pseudo-random number generators, using various random distributions,
that work with Numeric arrays.
- MA
-
MA supports masked arrays (i.e., arrays that can
have missing or invalid values). MA supplies a
large subset of Numeric's
functionality, albeit sometimes at reduced speed. The extra
functionality of MA is the ability to associate to
each array an optional mask, an auxiliary array of
False and True, where
True indicates array elements that are missing,
unknown, or invalid. Computations propagate masks, and you can turn
masked arrays into plain Numeric ones by using a
fill-in value for invalid elements. MA is widely
applicable because experimental data quite often has missing or
inapplicable elements. Furthermore, when you need to extend or
specialize some aspect of
Numeric's behavior for your
application's purposes, it often turns out to be
simplest and most effective to start with
MA's sources rather than with
Numeric's. The latter are often
quite hard to understand and modify, due to the extreme degree of
optimization applied to them over the years.
|


Alex Vindman to Devin Nunes: ‘It's Lieutenant Colonel Vindman Please’
National Security Council official Alexander Vindman cut off the top Republican member of the House Intelligence Committee during the third public impeachment inquiry hearing and asked him to call him by his military rank.
by Kristina WongThe exchange occurred when Committee Ranking Member Devin Nunes (R-CA) began to ask Vindman a question.
“Mr. Vindman, you testified in your deposition that you did not know the whistleblower,” he began.
Vindman interrupted, saying, “Ranking member, it’s Lt. Col. Vindman please.”
The exchange prompted mocking from veterans and members of the military on social media:
Bernard Kerik, former New York City police commissioner and also an Army veteran, said the exchange showed Vindman’s arrogance:
Rep. Chris Stewart (R-UT), himself an Army veteran, questioned Vindman about his request later during the hearing.
“Very quickly — I’m curious when Ranking Member Nunes referred to you as Mr. Vindman, you quickly corrected him and wanted to be called Lt. Col. Vindman — do you always insist on civilians calling you by your military rank?” Stewart asked.
Vindman responded, “Mr. Stewart, Rep. Stewart, I’m in uniform wearing my military rank. I just thought it would be appropriate to stick with that.”
However, when Rep. Sean Maloney (D-NY) said “Mr. Vindman,” Vindman did not request that he call him “Lt. Col. Vindman.”
Stewart also noted that Vindman was wearing his military dress uniform despite not normally wearing one to the White House.
“Lt. Col. Vindman, I see you’re wearing your dress uniform, knowing that’s not the uniform of the day — you normally wear a suit to the White House,” he said.
“I think it’s a great reminder of your military service,” Stewart added.
Vindman also testified for his closed-door deposition in his uniform last month. An Army spokesman told Breitbart News at the time that Vindman would follow the dress code of the National Security Council when testifying.
Follow Breitbart News’s @Kristina_Wong.


1. Only XSLS file allowed to upload less than 15 MB
2. Sheet must have only number in one single column


Kelsey Lu enlists Lafawndah, Omar S and Skrillex for Blood Transfusion remix EP
by Henry Bruce-JonesPhoto by: Tyler Mitchell
Six artists rework tracks from Blood.
Kelsey Lu has enlisted the talents of Lafawndah, Dixon, Lil M, Omar S, NAR x ADR and Skrillex for Blood Transfusion, a collection of remixes of tracks from her album Blood, which was released earlier this year. Listen to all six tracks now.
Blood Transfusion is out now.
Read next: 20 under-the-radar club tracks you need to hear – October 2019
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