Background: I am a psychonaut. I have experimented with DMT (ayahausca), MET, DiPT, Shrooms, 4-HO-MET, 5-MeO-MiPT, 2C-D, 2C-E, 2C-I, 25I-NBOMe, MDMA, MDMC, 4-MMC, and MXE. I have used 4-HO-MET three times, MXE three times, and shrooms a dozen times. I feel comfortable with those three substances, and I thoroughly enjoy them.
Set: I have mentally prepared myself for this journey by decreasing my alcohol and marijuana consumption this week, reading Plato’s Republic, and listening to Terence McKenna. Earlier today I ran for 30 minutes, and went into the sauna for 10 minutes, to rid myself of toxins. I have eaten two small meals, a protein shake, and a multivitamin today. I waited 4 hours after my last meal before ingesting the substances. I prepared hot chocolate to drink with the shrooms.
Setting: Today is 11/11/11, a reason for ritual; the trees decided to rid themselves of leaves today; the time is right. I have converted my bedroom into a shamanic sanctuary. My mattress pad is on the floor, covered with soft pillows and blankets, while my bed just has a comforter and one pillow. I have unplugged most of the electronics in my room, except for my laptop and phone. I plan on laying on my bed, in total darkness, listening to Shpongle and Native American Icaros through my speakers. My phone will be left on “record” mode in case I want to record my thoughts. I journey alone.
Start: 9:09 – Ate 1 g of mushrooms, drinking hot chocolate to mask the taste.
9:15 – Consumed 25 mg of MXE in orange juice
9:35 – I begin to feel off baseline. I feel a little drunk, and I can tell that the MXE is beginning to take effect.
9:40 –I started the soundtrack I prepared for this night, I placed 50 mg MXE and 20 mg of 4-HO-MET under my tongue, turned off all of the lights, then lay down in my bed. the journey begins ☺
***The first part of this report was written during/before my trip, from here on out I base my time estimations on the soundtrack I created. For convenience, I would like to place the 0:00 mark at 9:40, when I started the soundtrack. I am writing this the next day***
0:10 – I swallowed some of my saliva, it was accompanied by a really bitter taste. I notice some mild hallucinations. I could feel the synergy of all the drugs interacting with one another, fighting for dominance. Right now I feel in control.
0:20 – The MXE seems to be dominating the trip. I feel calm, lightweight, and very euphoric. The hallucinations alternate from the blocky, fluid-like MXE visuals, to the grid-like tryptamine visuals. It comes in waves, and I still feel in control of this experience.
0:30 –I notice that the hallucinations have kicked up a notch. I see dark trails behind my hands as I move them around, and I look around the room. I realize that I’m tripping hard, and might not be in control of this for much longer, so I swallow all the powder that was left in my mouth, and I washed it down with water and orange juice, swishing the liquid around my mouth. I notice that I can’t feel the bottom of my mouth where the chemicals were, but I don’t care. I lay back down in my bed, and feel waves of energy coursing through my body. I’m have no nausea, feel calm, and very comfortable.
0:40 – Things get very weird. The hallucinations begin to become real, and time starts to loose meaning. My consciousness become liquidly, and starts to break apart like a fluid.
1:?? – At this point, ordinary reality was completely obliterated. I felt like I had broken a hole in the space-time continuum, and every human being on Earth was aware that this had occurred. I will attempt to describe what happened, as best I can, but I can only tell it as a story:
I had embarked on a shamanic journey. Many have travelled this path before. At first I was lost. So, I allowed the spirits to guide me, which was my only source of comfort, but there were many of them to choose from. Many enticed me, but I wouldn’t let myself get taken over by them, because I couldn’t tell which ones were good, and which ones were evil. This happened in cycles: every time that I left the comfort of a spirit, I came closer to death. It was a process. I realized that reality was a creation of my mind, and I had become my environment.
Once I became more aware of this new form of existence, I realized that what I thought were spirits, were actually shamans around the world.  I entered a connection with a shaman that was somewhere in South America. And through him, I began to learn how to use my new powers. We began to create energy fields, but I would struggle against the connection.  I didn’t know if he was good or bad. When I struggled against the connection, I was brought closer to death.
At some point, I thought that I was dying. I was on the cusp of being alive / dead. But something deep within me said “NO, YOU WILL NOT DIE. YOU MUST CHOOSE TO LIVE” But how, I asked? “YOU MUST BECOME.” I began to wave my hands around in a circular motion, which was responsible for forming the world around me. I started to take deep breaths, in, and out, in and out. That’s how I knew that I was alive. But whenever I reached what I thought was the peak, I would come back down to the cusp of death. I was stuck in this loop, and every time that I came back down, I was closer to death.
Through this process, I became more powerful. Terence McKenna was with me, and he told me, “You can do it!! You can do it!!! Don’t give up!! Remember what you have learned. Every single occurrence in your life has lead to this moment. This is REAL; this is ACTUALLY happening. Novelty is reaching the infinity point. This is the end of history.”
I felt as if I was in a birthing process. I could feel the presences of the DMT elves, and they encouraged me to do something. At first it was very difficult, and I thought it was impossible. But deep down inside I realized that I was the Messiah, I was the One, I was Neo. I would succeed where everybody before me had failed. This was the purpose of my life, this was why I existed. I began to move around my bed in strange motions, and I felt like I was hatching out of a cosmic egg. I started to become become God-like. And then…. I became God.
I realized that…. I …am responsible for all of existence. I became the One, which is the Universe, which is the Mind.. “You did it!!, You did it!!!  You have fulfilled the prophecy!! All of the elves were cheering. I could hear thousands of people cheering. This is what the machine elves had always wanted us to do, and I was the first human to do it. I felt like I had accomplished something of extreme importance.
At this point, I became the Universe. I was reality. I was existence. I was all that was. I had become. I had the ability to gather all that is, and I held infinite in the palm of my hand. I understood how the whole was contained in each part. And everything was music. I had become Shpongled. “You are me and I am you”. There was no more fear. I was in complete control of everything. I was God. And the spirits to came to ME, for guidance.
3:30 - Even though I had become a God, I realized that there was a reason we exist in an animal body, our living vesicle. Returning to my animal body from the higher realm was extremely therapeutic. All of the problems and worries in my life are insignificant. It was a true rebirth.  I still felt like I was responsible for all of existence, even though I had an animal body. I remember turning on the heater in my room, and feeling as if I was the heat. We are the Gods, which is the Universe, which is our Mind.
Commentary:
I must warn anybody that has read this far, that there are huge risks involved with what I did. I could easily see how somebody with a weaker mind could have gone permanently insane or died. My Ayahausca experience and knowledge of shamanism, combined with a strong will to live, was what prevented my trip from being bad. With that being said, this was the most profound / therapeutic experience of my entire life.
What lessons did I learn? Free will exists. We must choose to live. We are in control of our existence. We are the Gods, which is the Universe, which is our Mind.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


I love reddit cause it is just really funny. I honestly just use it for the memes. Whenever I wanna scroll some dank memes, I just go to reddit on my phone. I also occasionally use it for the pretty nature pictures that I can find on some subreddits, and also TopTalent subreddits that show people doing really cool things. Idiots in cars is also a favorite of mine.
Pinterest is where I go when I am bored and looking for a fun project. I use it mainly for art inspiration and for craft ideas. I think it's formatted sort of badly, so I don't spend a lot of time on here, but I do like to use it once in a while.


Up to 700x faster than most alternative solutions!
Simpa has a predominantly small commission that does not depend on the amount sent
Even a not very intelligent person can cope with this


Focusing on process forced me to nut out vital parts of the site before even touching a computer. I completely overhauled my site and feel like it now has more purpose than before. I'm naturally a disorganised person and don't go for a process approach usually (although it's something I've been told to do many times before), however I am aware that mistakes and oversights are much more common when no process is followed and have personally suffered many times because of this. So if I'm able to master this skill it is something that will increase my productivity and end results greatly.
I've always been open to meditation and have attempted it many times with various degrees of success. It has helped me through difficult times of my life and assisted with putting several things in perspective.
The main thing I took away from chapter 2 was that it's okay to "fail" at meditation because even failure trains your "meditation muscle" and you will eventually get better. I also discovered that there's no correct way to sit, and that whatever position makes you feel relaxed but alert, or like a mountain, or a stream......or a butterfly in the breeze, is the best for you. Breathe as though your life depends on it.
It's funny (at least to me) that in order to be free, to unleash our default state of happiness, we have to practice. That there's a technique to being natural and to knowing oneself is hilarious.


Reports a form element (
input
,
textarea
, or
select
) without an associated label. Suggests creating a new label. Based on WCAG 2.0:
H44
.


Methods
defaultPopupTextProvider(element) → {string|undefined}
The default popup text provider, which tries its best to provide a description
(in English) based on the tags of the element.
Parameters:
| Name | Type | Description | ||||||
|---|---|---|---|---|---|---|---|---|
element |
object | The element returned by the Overpass API.
Properties
|
- Source:
Returns:
- Type
- string | undefined
focusOn(map, query, center)
Centers the map on something.
This will perform a search using Nominatim, and center the map on the first result. If no result is found, the coordinates are unchanged.
If the result of this function isn't satisfying, try to make a more specific query e.g. "Antwerpen Belgium", or simply set the coordinates manually.
The nominatim query will be performed using the following options:
This function can be used even if the map has not been initialized yet. If that's the case, the parameter 'center' must be provided.
If you use this on the map returned by showMap, it has already be initialized so the center parameter is useless and can be omitted.
This will perform a search using Nominatim, and center the map on the first result. If no result is found, the coordinates are unchanged.
If the result of this function isn't satisfying, try to make a more specific query e.g. "Antwerpen Belgium", or simply set the coordinates manually.
The nominatim query will be performed using the following options:
- limit=1
- viewbox=current bounds (map.getBounds().toBBoxString())
- format=json
- q=query (the query string)
This function can be used even if the map has not been initialized yet. If that's the case, the parameter 'center' must be provided.
If you use this on the map returned by showMap, it has already be initialized so the center parameter is useless and can be omitted.
Parameters:
| Name | Type | Description |
|---|---|---|
map |
Leaflet.Map | the Leaflet map |
query |
string | The nominatim query. Example "Antwerpen", "Brugge", etc. |
center |
Leaflet.LatLng | undefined | The center of the search. Must be provided if the map has not been initialized yet. If the map has been initialized, the bounds of its view will be used instead. |
- Source:
getSimpleQuery(key, value) → {string}
Returns a simple overpass query string that returns all nodes, ways and relations
in the bbox that have a given key/value pair.
If key is undefined, all points will be returned. (Please be careful with the latter, if it's a large bbox, performance will be terrible, especially if you don't cap the number of features shown on screen)
If key is undefined, all points will be returned. (Please be careful with the latter, if it's a large bbox, performance will be terrible, especially if you don't cap the number of features shown on screen)
Parameters:
| Name | Type | Description |
|---|---|---|
key |
string | undefined | The key we're looking for |
value |
string | undefined | The value of the key we're looking for. |
- Source:
Returns:
the query string
- Type
- string
PopupTextProvider(element) → {string|undefined}
Parameters:
| Name | Type | Description | ||||||
|---|---|---|---|---|---|---|---|---|
element |
object | The element returned by the Overpass API.
Properties
|
- Source:
Returns:
- Type
- string | undefined
queryAndShowFeatures(map, query, conf)
Queries the Overpass API and adds the resulting features to the map.
This is asynchronous, the points will be added as soon as the API gives us a response.
Errors will be printed to the console if the query fails.
A warning will be printed to the console if some points are ignored due to the cap. (see Configuration.maxElements)
Errors will be printed to the console if the query fails.
A warning will be printed to the console if some points are ignored due to the cap. (see Configuration.maxElements)
Parameters:
| Name | Type | Description |
|---|---|---|
map |
Leaflet.Map | the map |
query |
string | the query. For generating simple queries, @see getSimpleQuery Note: occurences of {{bbox}} inside the query will be replaced with the bounds of the map. |
conf |
Configuration | undefined | the configuration object. Can be undefined to use the default configuration. |
- Source:
showMap(mapId, conf, focusQuery) → {Leaflet.Map}
Creates and displays a map in place of the div with id mapId.
If conf.canUseDivAttributes is set to true, this will call conf.useDivAttributes to update the configuration based on the attributes of the div (when present)
If conf.canUseDivAttributes is set to true, this will call conf.useDivAttributes to update the configuration based on the attributes of the div (when present)
Parameters:
| Name | Type | Description |
|---|---|---|
mapId |
string | The HTML id of the div that should become the map |
conf |
Configuration | the Configuration object. Can be undefined. If that's the case, a default-constructed object will be used. However, if you want to reuse the configuration object later, it's better to pass one. |
focusQuery |
string | undefined | If defined, calls focusOn to initialize the map using the focusQuery. Note that if this parameter is used, the focusOn attribute of the div will be ignored. |
- Source:
Returns:
The created Leaflet Map.
- Type
- Leaflet.Map


{% extends "layout.html" %} {% block page_title %} What kind of bank account does your business use? - Bank details - Anti-money laundering registration - GOV.UK {% endblock %} {% block content %} {% include "includes/phase_banner_research.html" %} What kind of bank account does your business use? This section is: Bank details What kind of bank account does your business use?


Happy New Year 🎊
PREMANSHU YADAV
Github
LinkedIn
Resume
Email
Copyright @ Premanshu Yadav. Code on Github.


Sprint 44 review - August 3, 2016
There is a ManageIQ Sprint review every 3 weeks, and everyone in the community is welcome to participate. To join our Sprint review meeting, please use this Bluejeans link.
Sprint 44 is a normal 3 week sprint ending August 1, 2016. There were 345 pull requests consisting of a typical and good mix of enhancements and bugs. We’re gaining some momentum with this being the second best sprint in terms of PRs merged. One of the highlights in the manageiq_docs repo is the upstreaming of all the CloudForms documentation. Everyone can now contribute to the ManageIQ docs upstream, and the downstream docs will be built off of that.
Several nice surprises on the providers side: Google added support for metrics collection, which is a huge milestone in making Google Cloud a first class citizen in ManageIQ; a team of Nuage developers with their first merged PR adding inventory support and modeling for Nuage cloud networking; and XLAB has been working with us to add support for VMware vCloud provider authentication and inventory collection (including vApps). Many thanks to the contributing partners!
Quality Engineering joins the sprint demo for the first time. In addition, there are lots more interesting developments in the sprint, you can dive into the details with us in the following video, which also includes a demo on C3 charts interactivity.
If you’d like to check out a particular section of the review meeting, here are the respective timestamps:
- Sprint Statistics (Oleg Barenboim) - 1:04
- Community Update (Carol Chen) - 4:55
- Providers (Greg Blomquist) - 6:58
- User Interface (Dan Clarizio) - 14:12
- Platform (Gregg Tanzillo) - 21:25
- REST API (Alberto Bellotti) - 28:04
- Automate (Greg McCullough) - 32:26
- Performance (Dennis Metzger) - 38:11
- Quality Engineering (Dave Johnson) - 39:30
- Discussion - 43:23
As mentioned in the sprint review, the “Last Week in ManageIQ” blog series is off to a good start. The two entries corresponding to Sprint 44, with additional looks at specific PRs merged during this period, are as follows:
Slide deck:
Changelog:
Added
- Automate
- Simulation: RBAC filtering applied to Object Attributes
- Service Provisioning: Exposed number_of_vms when building the provision request for a service
- Service Dialogs: Support for “visible” flag for dynamic fields
- Expose Compliance and ComplianceDetail models
- New associations on VmOrTemplate and Host models:
expose :compliances
expose :last_compliance
- New Service Models
- Compliance:
expose :compliance_details
- ComplianceDetail:
expose :compliance
,expose :miq_policy
- Compliance:
- Generic Object: Service models created for GenericObject and GenericObjectDefinition
- Platform
- PostgreSQL High Availability
- Added repmgr to support automatic failover
- Maintain list of active standby database servers
- Added pg-dsn_parser for converting DSN to a hash
- Tenancy: Added parent_id to CloudTenant as prerequisite for mapping OpenStack tenants to ManageIQ tenants
- Watermark reports updated to be based on max of daily max value instead of max of average value
- Nice values added to worker processes
- PostgreSQL High Availability
- Providers
- Google Compute Engine: Metrics
- Hawkular
- Operations: Add Deployment, Start/stop deployment
- Performance reports for datasources
- Collect more metrics for datasource
- Kubernetes: Cross-linking with OpenStack instances
- Microsoft Azure: Support floating IPs during provisioning
- Nuage: Inventory of Managed Cloud Subnets
- Red Hat Enterprise Virtualization: v4 API
- VMware vSphere: Storage Profiles modeling and inventory
- VMware vCloud: Initial PRs for modeling and inventory
- REST API
- Support for arbitrary resource paths
- Work started on ManageIQ API Client
- Support for Arbitration Profiles
- Support for Cloud Networks queries
- Support for Arbitration Settings
- Updated /api/users to support edits of user settings
- User Interface
- Both UIs updated to latest PatternFly and Angular PatternFly
- Self Service UI language selections separated from Operations UI
- Internationalization
- Virtual Columns
- Toolbars
- Removed string interpolation (for better localization)
- Changed to use gettext’s pluralization
- Ansible Tower Jobs moved to the Configuration tab (from Clouds/Stacks)
- Interactivity added to C3 charts on C&U screens
Changed
- Automate
- Simulation: Updated defaults
- Entry-point:
/System/Process/Request
(Previous value of “Automation”) - Execute Method: Enabled
- Entry-point:
- Infrastructure Provision: Updated memory values for VM provisioning dialogs to 1, 2, 4, 8, 12, 16, 32 GB
- Simulation: Updated defaults
- Performance: Reduced the time and memory required to schedule Capacity and Utilization data collection.
- Platform: Expression refactoring and cleanup with relative dates and times
- Providers: Hawkular
- Upgrade of Hawkular gem to 2.3.0
- Skip unreachable middleware providers when reporting
- Add re-checking authentication status functionality/button
- User Interface
- Converted to TreeBuilder - Snapshot, Policy, Policy RSOP, C&U Build Datastores and Clusters/Hosts, Automate Results
- CodeMirror version updated (used for text/yaml editors)
Removed
- Platform
- Removed rubyrep
- Removed hourly checking of log growth and rotation if > 1gb
- User Interface: Explorer Presenter RJS removal


When Balthisar Tidy opens a document, it tries to use the Tidy option
input-encoding
in Preferences. The typical
modern practice is to use UTF-8 encoding for all files, however there are many
legacy file encoding formats from different countries and different
operating systems. Balthisar Tidy supports every encoding offered by
Mac OS X.
There is no tried and true way to determine the encoding format for most formats. For the most part, Balthisar Tidy will assume that you, the user, know what you are doing.
In same cases, though, it’s obvious that the input-encoding
is wrong. In these few cases, and when the correct preference is
selected, Tidy will offer you the chance to change the encoding format,
as shown below.
Tidy’s criterion for success is whether opening the document yields any
text at all. As indicated in the warning, it’s still possible that the
input-encoding
is incorrect. You should examine the Source
HTML (not the Tidy HTML!) to verify that the correct
input-encoding
is selected. If some non-Western characters
still look incorrect, you can manually choose the correct
input-encoding
from the Tidy Options pane. If
you use this input-encoding
, you can also set it as the default in
Balthisar Tidy’s Preferences.


Based on what you've told us:
You get free prescriptions and NHS sight tests
Because you're over 60, you get free:
-
NHS prescriptions
- NHS sight tests
You don't get help towards the cost of:
- NHS dental check ups and treatment
- New or replacement glasses or contact lenses
- NHS wigs and fabric supports
- Travel to receive NHS treatment
What you should do next
Prescriptions
To get free prescriptions tick box C, (third box down) on the back of the prescription form.


{% extends 'skeleton_base.html' %} {% load jfutags %} {% block title %}Upload{% endblock %} {% block content %}
To start the process, please enter your information below. This information is used for the copyright of the photos and also to identify the winners for each event.


Kanakadasa is a world saint, says Kateel
by Special CorrespondentTaking an indirect jibe at the former Chief Minister Siddaramaiah, Nalin Kumar Kateel, State BJP president and Dakshina Kannada MP, on Friday said those who have gained by taking the name of Kanakadasa have done little for the great reformer.
Speaking at the Kanaka Jayanti celebrations organised by the district administration here, Mr. Kateel said, “While those who have gained by taking the name of the Kanakadasa did nothing, it was Chief Minister B.S. Yediyurappa who took up the work of developing Kaginele (the birthplace of Kanakadasa),” he said.
Mr. Kateel said Kanakadasa who pleased god with his “Bhatki Siddanta” did not link god with any religion.
This great reformer from the Bhakti movement is a world saint and should not be restricted to the community he belonged to.
The MP commended the State government for organising Kanaka Jayanti that helps to profess ideals of the great reformer that needs to be imbibed.


| Home | Libraries | People | FAQ | More |
boost::range_run_storage::transform — Apply a unary transformation to a single InfiniteRangeRunStorage, or a binary transform to two InfiniteRangeRunStorage.
template<typename In, typename UnaryOp, typename Out> Out & transform(In & in, UnaryOp const & unary_op, Out & out); template<typename In, typename UnaryOp, typename Out> Out & transform(In const & in, UnaryOp const & unary_op, Out & out); template<typename Left, typename Right, typename BothOp, typename LeftOp, typename RightOp, typename Out> Out & transform(Left const & left, Right const & right, BothOp const & both_op, LeftOp const & left_op, RightOp const & right_op, Out & out); template<typename Left, typename Right, typename BinOp, typename Out> Out & transform(Left const & left, Right const & right, BinOp const & binary_op, Out & out);
The three-parameter form of transform, transform(in, unary_op, out), effectively copies the runs from in to out after applying unary_op to each element value.
The four-parameter form of transform, transform(left, right, binary_op, out), steps through the series left and right in lock-step. Where two runs overlap, the result of binary_op(left_value, right_value) is written to out. Where part of a left run does not overlap with a right, the result of binary_op(left_value, rrs::zero(right)) is written to out. Where part of a right run does not overlap with a left, the result of binary_op(rrs::zero(left), right_value) is written to out.
The six-parameter form of transform, transform(left, right, both_op, left_op, right_op, out), steps through the series left and right in lock-step. Where two runs overlap, the result of both_op(left_value, right_value) is written to out. Where part of a left run does not overlap with a right, the result of left_op(left_value) is written to out. Where part of a right run does not overlap with a left, the result of right_op(right_value) is written to out.
| Parameters: |
|
||||||
| Requires: |
In is a model of InfiniteRangeRunStorage. Left is a model of InfiniteRangeRunStorage. Right is a model of InfiniteRangeRunStorage. UnaryOp is a model of UnaryFunction. LeftOp is a model of UnaryFunction. RightOp is a model of UnaryFunction. BothOp is a model of BinaryFunction. BinaryOp is a model of BinaryFunction. Out is a model of OrderedInserter. |
| Copyright © 2006 Eric Niebler |


Efficiency trending Efficiency status MC to Anchor TPC->ITS match (no pileup cut,|DCA|<3cm???) TPC->ITS match A/C ratio TPC->ITS match (pileup cut,|DCA|<3cm???) TPC->ITS match (pileup cut,|DCA|<3cm???) TPC->TRD match (ITS pileup cut?)


Dear all,
We, the members of the Executive Board at Oberoi International School’s Model United Nations conference, welcome you to another much-awaited year of animated debate on the 9th and 10th of October at our campus.
OISMUN is a journey of self-discovery, a wormhole to the insightful perceptions of the youth - their hopes, beliefs and ideologies. It is a platform where we engage and encourage you to go beyond mere discussion, to undertake your responsibility in shaping tomorrow’s world.
We wish to guide you in your endeavors to bring peace and harmony to the world that is plagued by ubiquitous problems, each with their own convoluted complications. In order to achieve this, we will facilitate a conducive forum to foster spirited dialogue to come to harmonious solutions of the highest standards.
Our five committees will be addressing critical issues of today, that yearn for the outlooks of the youngest and the brightest to warrant a better future. Thought-provoking agendas range from Border Disputes in the Western Sahara to the Universal Legalization of Homosexuality - issues that are being discussed in the actual UN.
Our goal is to empower you to lead and build bridges, to learn and connect with unique individuals, and to network into a wider spectrum - all while having fun.
This could be your chance to shine. We leave the fate of the world in your hands.
OISMUN 2015.
It’s time to begin.
Ayan Saraf,
Secretary-General


{% extends 'MainApp/base.html' %} {% comment %} * REFERENCES * Title: Autocomplete for Addresses and Search Terms * Author: Google * Date Published: Last updated 11/20/2020 * Date Accessed: Nov 18th, 2020 * Code version: v3.43 * URL: https://developers.google.com/maps/documentation/javascript/places-autocomplete#address_forms * Software License: Apache 2.0 License (for code), Creative Commons Attribution 4.0 License (for article) * REFERENCES * Title: Get Started with Bootstrap * Author: Bootstrap * Date Published: N/A Continuous updates * Date Accessed: October 4, 2020 * Code version: v4.5.3 * URL: https://getbootstrap.com/ * Software License: Code licensed MIT, docs CC BY 3.0. * REFERENCES * Title: Get Started with Django * Author: Django * Date Published: N/A Continuous updates * Date Accessed: September 13, 2020 * Code version: v3.1.3 * URL: https://www.djangoproject.com/ * Software License: BSD-3 {% endcomment %} {% block content %} {% load static %}


{% extends "base.html" %} {% load staticfiles humanize %} {% block title %}{% trans "About" %} |{% endblock %} {% block nav_li_class_about %} class="current"{% endblock %}> {% block header%}
{% blocktrans with count=total_people countries=countries|length|apnumber %}Django People lists {{ count }} Django developers from around the world, in {{ countries }} different countries. The aim of the site is to help Django developers find like-minded souls near them, and hopefully kick-start some local meet-ups and user groups.{% endblocktrans %}
{% blocktrans %}The site was originally built and open-sourced by Simon Willison and Natalie Downe. The code is available on github. You can post feedback and bug reports on the repository's issues page.{% endblocktrans %}
{% blocktrans %}Country data and location lookups come from GeoNames, while the maps themselves are from OpenStreetMap and powered by Leaflet. Country flags and icons are by Mark James; US state flags come from Wikipedia. Profile pictures come from Gravatar.{% endblocktrans %}
{% blocktrans %}Deployment is gunicorn, hosted on Heroku.{% endblocktrans %}


Form test 6: Label must reference a form control
Group 1: Labels with no for value
Group 2: Labels with a for value that doesn't reference a real form control
Group 3: Labels with a for value that references a real form control
Group 4: Hidden labels
Group 5: Label References non-control
Group 6: Multiple Labeling


|
{{#if terminated}}
Terminated.
{{/if}}
Last seen at {{stationNameFromStanox lastSeen}} running {{variation_status}} varying from the timetable by {{timetable_variation}} minutes at {{actualTimestamp}} (planned {{plannedTimestamp}}) and going to {{stationNameFromStanox goingTo}} should be there in {{next_report_run_time}} minutes.
|
{{/if}}
{{/with}}


@@include('../partials/head/head.html')
@@include('../partials/layout/header.html')
@@include('../partials/components/latest-posts-section.html')
@@include('../partials/layout/footer.html') @@include('../partials/scripts/common.html')


--- title: Get 247Sports Team Commitments keywords: fastai sidebar: home_sidebar nb_path: "j_notebooks/Get.247-Team.ipynb" ---
years = cc.get_defYears()
headers= cc.get_header()
schoolsList = cc.get_schoolsList()
conf = 'acc'
#But you are going to have to go another two layers down
all_recruits = []
for y in years:
for school in schoolsList:
if (school['conference'][0] == conf):
url = cc.create_url247('team', school['247'], y)
#r = requests.get(url, headers=headers)
#with open("..//html//247//" + conf + "//teams//" + school['247'] + "_" + y + ".html", "w") as write_file:
# write_file.write(r.text)
print(school['247'] + ': ' + str(y))
time.sleep(4)


Jason Greenblatt joins Republican Jewish Coalition board
Former Trump Special Envoy to the Middle East joins RJC board of directors following resignation form White House position.
by Gary WilligUS President Donald Trump's former Special Envoy to the Middle East Jason Greenblatt has joined the Republican Jewish Coalition's board of directors.
RJC National Chairman Senator Norm Coleman said: "Jason Greenblatt served our country admirably as an adviser to President Donald Trump and as someone passionately concerned for the security and peace of Israel. We commend his outstanding contributions to the US-Israel alliance and to the Trump Administration's efforts to develop a plan for a stable, peaceful settlement of the Israeli-Palestinian conflict."
Greenblatt said: "It's an honor for me to participate in the Republican Jewish Coalition, a respected organization that has done great work for over three decades. Republican leaders in DC and around the country know the tremendous value of the RJC's work. The RJC is building a strong grassroots network of dedicated activists who bring Republican ideals and ideas to to life in the Jewish community. I look forward to contributing to that cause."


Verify formatted log message:
This inspection reports formatting issues related to log messages that use the formatting feature supported by some log frameworks.
Powered by Log Support


The resource faults pane gives you access to each Resource Fault that has been created. Through it you can add, remove, disable, modify, or copy resource faults. Access these fault operations by right clicking a fault
Add a fault by right clicking anywhere in the Resource Faults Pane and selecting "Create a new Resource Faults."
Select which columns are visible in this pane by right clicking anywhere in the pane and selecting "Select Visible Columns"
Turn a Resource Fault on or off quickly by toggling the check in the On/Off Column.
To show the Resource Faults pane double-click the Resource Faults Node in the Project Pane.
On/Off – If the Resource Fault will execute on next access.
Resource Name – The resource the fault applies to.
Type – How access to the resource will be failed.
Right click a resource fault for the following options:
Copy – Copy the Fault Details to the clipboard. Sample data follows:
Resource: comctl32.dll
Fault: Invalid File Type
Test On: True
Modify Resource Fault … - This option takes you to the last page of the Resource fault wizard where you can edit the specifics of the resource fault.
Delete Resource Fault – Removes the fault completely
Create a new Resource Fault – Opens the Create a New Resource Fault Wizard.


Before Change
spec, batch_size=batch_size, max_length=10)
// Each element has its batch index in the 100s place.
actions = tf.stack([
tf.Variable(x * 100).count_up_to(15 + x * 100)
for x in range(batch_size)
])
add_op = replay_buffer.add_batch(actions)
items = replay_buffer.gather_all()
expected = [
After Change
]
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(add_data())
items = replay_buffer.gather_all()
items_ = self.evaluate(items)
self.assertAllClose(expected, items_)


setting up html5 forms
autofocus
autoComplete
placeholder
pattern
required
search - doesn't work for real yet in early 2015
email
url
phone -- this one didn't work in the class... he said we'd come back to it
numbers - gives you spinner
Time - the spinners showed up in Chrome but not in Firefox


These data represent confirmed cases of COVID-19 reported to the Georgia Department of Public Health as of 12:00pm 03/19/2020. A confirmed case is defined as a person who has tested positive for 2019 novel coronavirus. These data represent confirmed cases of COVID-19 reported to the Georgia Department of Public Health as of 12:00pm 03/19/2020. A confirmed case is defined as a person who has tested positive for 2019 novel coronavirus.


This guide shows you how to use the Evothings BLE API to connect to Bluetooth Low Energy devices from JavaScript.
Please note that this guide applies to Evothings Viewer 1.5 or later, and the Cordova BLE plugin version 2.0.0 or later (included with Evothings Viewer).
We take you through the steps to detect and connect to a BLE device. You can use this guide for basically any BLE device.
Native support for Bluetooth Low Energy on Android and iOS is provided by the Cordova BLE plugin. This plugin is included with Evothings Viewer, which makes it quick to get started with development of a BLE application in JavaScript. When the app is ready for testing and deployment, the Cordova build system is used to build a custom app with the application code and the BLE plugin. This app can then be published on the app stores.
The following are the steps needed to connect to a BLE device and start reading/writing data:
A characteristic is like a command or function, it is a place you read or write to fetch data or control the device (turning on or off a LED or a motor, for example). Services are collections of characteristics, they group related commands/functions.
What you need to know to program a BLE device are the UUIDs of the services and characteristics you wish to use. This information is typically found in the documentation from the device manufacturer. Use these UUIDs in the code when making calls to the BLE library functions. Below we show code examples of how to do this.
A BLE device exposes its communication interface through services and characteristics. A service can have one or more characteristics. Each characteristic can also have one or more descriptors (descriptors tend to be accessed less frequently by application code).
For example, a BLE-enabled thermometer typically has a temperature service that has a characteristic that allows you to read the temperature.
Each service and characteristic has a universally unique id (UUID). You use these ids when setting up the communication with the BLE device from JavaScript. The UUIDs are usually found in the device documentation provided by the manufacturer.
TI SimpleLink Multi-Standard CC2650 SensorTag
As an example, we will use the TI SensorTag CC2650 and the Luxometer Service. The CC2650 has several sensors, each sensor has a service, each service has characteristics for turning a sensor on/off, setting the update interval, and for reading data.
Other BLE devices will have difference services and characteristics, but the general principle is the same. Some devices have options to configure the device, others may not need any specific configuration. Some devices you primarily read data from (like a thermometer), others you primarily write data to (like remotely controlled machinery).
The Luxometer of the TI SensorTag CC2650 has the following UUIDs:
| Luxometer service UUID: | f000aa70-0451-4000-b000-000000000000 |
| Sensor on/off characteristic UUID: | f000aa71-0451-4000-b000-000000000000 |
| Sensor update interval characteristic: | f000aa73-0451-4000-b000-000000000000 |
| Sensor data characteristic: | f000aa71-0451-4000-b000-000000000000 |
These UUIDs are used in the example code shown below. Use the UUIDs for the services and characteristics of the device yo are working with in your actual application code.
In the code examples below, console.log() is used to print debug data. To output log statements in Evothings Workbench, you can map console.log to hyper.log, as follows:
// Redirect console.log to Evothings Workbench. if (window.hyper && window.hyper.log) { console.log = hyper.log }
Log data is output in the Console Window in Evothings Workbench. Click the button named "Console" in the Workbench to open this window.
First step when communicating with any BLE device is to establish a connection to it, and to do that you first need to find the device. This step is called scanning. When scanning for devices, you will get all BLE devices within range. This means you must somehow find out which device is the one you wish to connect to.
To determine the identity of a device using scanning, you can use several methods. Two useful ways are to use the name of the device and/or the advertised service UUIDs of the device.
Here is how to find the TI SensorTag CC2650 by its name:
// Start scanning. Two callback functions are specified. evothings.ble.startScan( onDeviceFound, onScanError) // This function is called when a device is detected, here // we check if we found the device we are looking for. function onDeviceFound(device) { if (device.advertisementData.kCBAdvDataLocalName == 'CC2650 SensorTag') { console.log('Found the TI SensorTag!') } } // Function called when a scan error occurs. function onScanError(error) { console.log('Scan error: ' + error) }
By comparison, here is how find the device by advertised service UUID:
evothings.ble.startScan( onDeviceFound, onScanError) // Here we check for a specific service UUID advertised by the device. function onDeviceFound(device) { // kCBAdvDataServiceUUIDs is an array of strings with service UUIDs. var advertisedServiceUUIDs = device.advertisementData.kCBAdvDataServiceUUIDs if (advertisedServiceUUIDs.indexOf('0000aa10-0000-1000-8000-00805f9b34fb') > -1) { console.log('Found the TI SensorTag!') } }
You can also specify the service UUID in the options parameter to startScan, this will instruct the native BLE system to report only devices that advertises the given service UUID:
// The callback function onDeviceFound
will be called only for devices
// that advertise the given service UUID.
evothings.ble.startScan(
onDeviceFound,
onScanError,
{ serviceUUIDs: ['0000aa10-0000-1000-8000-00805f9b34fb'] })
function onDeviceFound(device)
{
console.log('Found the TI SensorTag!')
}
An approach to selecting which device to connect to is to display a list of devices and their names in the application user interface, and let the user select a device. The list is dynamically constructed during scanning, and can be updated and sorted by distance or by RSSI value.
The advertised service UUIDs (kCBAdvDataServiceUUIDs) used above is part of the BLE advertisement data. This is a block of bytes that is broadcasted by a BLE device when it is in advertisement mode. Have a look at the advertisement data documentation for a list of available fields (note that these fields may be undefined or empty, depending on the BLE device).
It can happen that a device cannot be uniquely identified by its name and/or advertised service UUIDs. Depending on the device, there may be other data in the advertisement that can be used to identify it. Eddystone beacons, for example, broadcast ids and URLs as part of the advertisement data.Here is how to output a printable representation of the advertisement data structure:
function onDeviceFound(device) { console.log('Found device:' + JSON.stringify(device.advertisementData)) }
You can also print the entire device structure:
function onDeviceFound(device) { console.log('Found device:' + JSON.stringify(device)) }
When the device has been found, next step is to connect to it. Typically scanning is now stopped, and the connect function is called. Here is an example:
evothings.ble.startScan( onDeviceFound, onScanError) // This function is called when a device is detected, here // we check if we found the device we are looking for. function onDeviceFound(device) { if (device.advertisementData.kCBAdvDataLocalName == 'CC2650 SensorTag') { // Stop scanning. evothings.ble.stopScan() // Connect. evothings.ble.connectToDevice( device, onConnected, onDisconnected, onConnectError) } } // Called when device is connected. function onConnected(device) { console.log('Connected to device') } // Called if device disconnects. function onDisconnected(device) { console.log('Disconnected from device') } // Called when a connect error occurs. function onConnectError(error) { console.log('Connect error: ' + error) }By default, the function
evothings.ble.connectToDevice
reads all services, characteristics and descriptors from the device.
Optionally, you can specify the services to read (reading only the services you need can improve performance):
evothings.ble.connectToDevice( device, onConnected, onDisconnected, onConnectError) { serviceUUIDs: ['f000aa70-0451-4000-b000-000000000000'] })
Characteristics are like commands or functions, they can be read to obtain data, or written to control some aspect of a device.
For example, on the TI SensorTag, you turn a sensor on and off by writing to a characteristic. When the sensor is on, you can read data from another characteristic. Both characteristics are part of the same service.
Use functions evothings.ble.getService
and evothings.ble.getCharacteristic
to get the characteristic you want to read and write. To read and write descriptors, use function evothings.ble.getDescriptor
to get the descriptor to write/read.
Here is an example how to turn on and read the Luxometer of the TI SensorTag:
// UUID of Luxometer service. var LUXOMETER_SERVICE = 'f000aa70-0451-4000-b000-000000000000' // UUID of Luxometer config characteristic (write 1 to turn sensor ON, 0 to turn OFF). var LUXOMETER_CONFIG = 'f000aa72-0451-4000-b000-000000000000' // UUID of Luxometer data characteristic. var LUXOMETER_DATA = 'f000aa71-0451-4000-b000-000000000000' // Get Luxometer service and characteristics. var service = evothings.ble.getService(device, LUXOMETER_SERVICE) var configCharacteristic = evothings.ble.getCharacteristic(service, LUXOMETER_CONFIG) var dataCharacteristic = evothings.ble.getCharacteristic(service, LUXOMETER_DATA) // Turn Luxometer ON. evothings.ble.writeCharacteristic( device, configCharacteristic, new Uint8Array([1]), onLuxometerActivated, onLuxometerActivatedError) function onLuxometerActivated() { console.log('Luxometer is ON') // Enable notifications from the Luxometer. evothings.ble.readCharacteristic( device, dataCharacteristic, onLuxometerRead, onLuxometerReadError) } function onLuxometerActivatedError(error) { console.log('Luxometer activate error: ' + error) } function onLuxometerRead(data) { // Get raw sensor value (data buffer has little endian format). var raw = new DataView(data).getUint16(0, true) console.log('Raw Luxometer value: ' + raw) } function onLuxometerReadError(error) { console.log('Read Luxometer error: ' + error) }
The data read from a device is in raw a byte format. Documentation from the device manufacturer contains information about how to calculate the actual value. As an example, here is how you calculate the Luxometer value in lux units:
// Calculate the light level from raw sensor data. // Return light level in lux. function calculateLux(data) { // Get 16 bit value from data buffer in little endian format. var value = new DataView(data).getUint16(0, true) // Extraction of luxometer value, based on sfloatExp2ToDouble // from BLEUtility.m in Texas Instruments TI BLE SensorTag // iOS app source code. var mantissa = value & 0x0FFF var exponent = value >> 12 var magnitude = Math.pow(2, exponent) var output = (mantissa * magnitude) var lux = output / 100.0 // Return result. return lux }
It is common that an application needs to continuously read data from a BLE device. Examples include reading a sensor and send data to the cloud, or to use the accelerometer of a sensor device to control a game on the mobile phone.
Notifications are an efficient way to read data continuously from a BLE device. The process is similar to reading data, the main difference is that the callback function will be called repeatedly until the notification is disabled.
Here is a code example of how to enable and handle notifications:
function onLuxometerActivated() { console.log('Luxometer is ON') // Enable notifications from the Luxometer. evothings.ble.enableNotification( device, dataCharacteristic, onLuxometerNotification, onLuxometerNotificationError) } // Called repeatedly until disableNotification is called. function onLuxometerNotification(data) { var lux = calculateLux(data) console.log('Luxometer value: ' + lux) } function onLuxometerNotificationError(error) { console.log('Luxometer notification error: ' + error) }
Here is a complete code example. Note that file cordova.js
is included with the BLE plugin, this file is not part of the application code, it is bundled with the plugin and with Evothings Viewer.
Source code of file index.html
(drag this file into Evothings Workbench and click RUN to launch in Evothings Viewer):
<!DOCTYPE html>   <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, user-scalable=no, shrink-to-fit=no, initial-scale=1.0, minimum-scale=1.0, maximum-scale=1.0" /> <title>BLE Plugin API Demo</title>
BLE Plugin API Demo

View output in the console log.

Download Evothings Studio and get started within minutes. It is fun and easy!
Ask questions and discuss IoT app development on Gitter: gitter.im/evothings/evothings


pymatgen.core.composition module¶
-
class
ChemicalPotential
(*args, **kwargs)[source]¶ Bases:
dict
,monty.json.MSONable
Class to represent set of chemical potentials. Can be: multiplied/divided by a Number multiplied by a Composition (returns an energy) added/subtracted with other ChemicalPotentials.
Parameters: **kwargs (*args,) – any valid dict init arguments
-
get_energy
(composition, strict=True)[source]¶ Calculates the energy of a composition.
Parameters: - composition (Composition) – input composition
- strict (bool) – Whether all potentials must be specified
-
-
class
Composition
(*args, **kwargs)[source]¶ Bases:
collections.abc.Hashable
,collections.abc.Mapping
,monty.json.MSONable
Represents a Composition, which is essentially a {element:amount} mapping type. Composition is written to be immutable and hashable, unlike a standard Python dict.
Note that the key can be either an Element or a Specie. Elements and Specie are treated differently. i.e., a Fe2+ is not the same as a Fe3+ Specie and would be put in separate keys. This differentiation is deliberate to support using Composition to determine the fraction of a particular Specie.
Works almost completely like a standard python dictionary, except that __getitem__ is overridden to return 0 when an element is not found. (somewhat like a defaultdict, except it is immutable).
Also adds more convenience methods relevant to compositions, e.g., get_fraction.
It should also be noted that many Composition related functionality takes in a standard string as a convenient input. For example, even though the internal representation of a Fe2O3 composition is {Element(“Fe”): 2, Element(“O”): 3}, you can obtain the amount of Fe simply by comp[“Fe”] instead of the more verbose comp[Element(“Fe”)].
>>> comp = Composition("LiFePO4") >>> comp.get_atomic_fraction(Element("Li")) 0.14285714285714285 >>> comp.num_atoms 7.0 >>> comp.reduced_formula 'LiFePO4' >>> comp.formula 'Li1 Fe1 P1 O4' >>> comp.get_wt_fraction(Element("Li")) 0.04399794666951898 >>> comp.num_atoms 7.0
Very flexible Composition construction, similar to the built-in Python dict(). Also extended to allow simple string init.
Parameters: - form supported by the Python built-in dict() function. (Any) –
- A dict of either {Element/Specie (1.) –
amount},
{string symbol:amount}, or {atomic number:amount} or any mixture of these. E.g., {Element(“Li”):2 ,Element(“O”):1}, {“Li”:2, “O”:1}, {3:2, 8:1} all result in a Li2O composition.
- Keyword arg initialization, similar to a dict, e.g., (2.) – Composition(Li = 2, O = 1)
- addition, the Composition constructor also allows a single (In) –
- as an input formula. E.g., Composition (string) –
- allow_negative – Whether to allow negative compositions. This argument must be popped from the **kwargs due to *args ambiguity.
-
add_charges_from_oxi_state_guesses
(oxi_states_override=None, target_charge=0, all_oxi_states=False, max_sites=None)[source]¶ Assign oxidation states basedon guessed oxidation states.
See oxi_state_guesses for an explanation of how oxidation states are guessed. This operation uses the set of oxidation states for each site that were determined to be most likley from the oxidation state guessing routine.
Parameters: - oxi_states_override (dict) – dict of str->list to override an element’s common oxidation states, e.g. {“V”: [2,3,4,5]}
- target_charge (int) – the desired total charge on the structure. Default is 0 signifying charge balance.
- all_oxi_states (bool) – if True, an element defaults to all oxidation states in pymatgen Element.icsd_oxidation_states. Otherwise, default is Element.common_oxidation_states. Note that the full oxidation state list is very inclusive and can produce nonsensical results.
- max_sites (int) – if possible, will reduce Compositions to at most this many sites to speed up oxidation state guesses. If the composition cannot be reduced to this many sites a ValueError will be raised. Set to -1 to just reduce fully. If set to a number less than -1, the formula will be fully reduced but a ValueError will be thrown if the number of atoms in the reduced formula is greater than abs(max_sites).
Returns: Composition, where the elements are assigned oxidation states based on the results form guessing oxidation states. If no oxidation state is possible, returns a Composition where all oxidation states are 0.
-
almost_equals
(other, rtol=0.1, atol=1e-08)[source]¶ Returns true if compositions are equal within a tolerance.
Parameters: - other (Composition) – Other composition to check
- rtol (float) – Relative tolerance
- atol (float) – Absolute tolerance
-
alphabetical_formula
¶ Returns a formula string, with elements sorted by alphabetically e.g., Fe4 Li4 O16 P4.
-
amount_tolerance
= 1e-08¶ Special formula handling for peroxides and certain elements. This is so that formula output does not write LiO instead of Li2O2 for example.
-
anonymized_formula
¶ An anonymized formula. Unique species are arranged in ordering of increasing amounts and assigned ascending alphabets. Useful for prototyping formulas. For example, all stoichiometric perovskites have anonymized_formula ABC3.
-
as_dict
()[source]¶ Returns: dict with species symbol and (unreduced) amount e.g., {“Fe”: 4.0, “O”:6.0} or {“Fe3+”: 4.0, “O2-“:6.0}
-
average_electroneg
¶
-
element_composition
¶ Returns the composition replacing any species by the corresponding element.
-
elements
¶ Returns view of elements in Composition.
-
formula
¶ Returns a formula string, with elements sorted by electronegativity, e.g., Li4 Fe4 P4 O16.
-
fractional_composition
¶ Returns the normalized composition which the number of species sum to 1.
Returns: Normalized composition which the number of species sum to 1.
-
classmethod
from_dict
(d)[source]¶ Creates a composition from a dict generated by as_dict(). Strictly not necessary given that the standard constructor already takes in such an input, but this method preserves the standard pymatgen API of having from_dict methods to reconstitute objects generated by as_dict(). Allows for easier introspection.
Parameters: d (dict) – {symbol: amount} dict.
-
get_atomic_fraction
(el)[source]¶ Calculate atomic fraction of an Element or Specie.
Parameters: el (Element/Specie) – Element or Specie to get fraction for. Returns: Atomic fraction for element el in Composition
-
get_el_amt_dict
()[source]¶ Returns: Dict with element symbol and (unreduced) amount e.g., {“Fe”: 4.0, “O”:6.0} or {“Fe3+”: 4.0, “O2-“:6.0}
-
get_integer_formula_and_factor
(max_denominator=10000, iupac_ordering=False)[source]¶ Calculates an integer formula and factor.
Parameters: - max_denominator (int) – all amounts in the el:amt dict are first converted to a Fraction with this maximum denominator
- iupac_ordering (bool, optional) – Whether to order the formula by the iupac “electronegativity” series, defined in Table VI of “Nomenclature of Inorganic Chemistry (IUPAC Recommendations 2005)”. This ordering effectively follows the groups and rows of the periodic table, except the Lanthanides, Actanides and hydrogen. Note that polyanions will still be determined based on the true electronegativity of the elements.
Returns: A pretty normalized formula and a multiplicative factor, i.e., Li0.5O0.25 returns (Li2O, 0.25). O0.25 returns (O2, 0.125)
-
get_reduced_composition_and_factor
()[source]¶ Calculates a reduced composition and factor.
Returns: A normalized composition and a multiplicative factor, i.e., Li4Fe4P4O16 returns (Composition(“LiFePO4”), 4).
-
get_reduced_formula_and_factor
(iupac_ordering=False)[source]¶ Calculates a reduced formula and factor.
Parameters: iupac_ordering (bool, optional) – Whether to order the formula by the iupac “electronegativity” series, defined in Table VI of “Nomenclature of Inorganic Chemistry (IUPAC Recommendations 2005)”. This ordering effectively follows the groups and rows of the periodic table, except the Lanthanides, Actanides and hydrogen. Note that polyanions will still be determined based on the true electronegativity of the elements. Returns: A pretty normalized formula and a multiplicative factor, i.e., Li4Fe4P4O16 returns (LiFePO4, 4).
-
get_wt_fraction
(el)[source]¶ Calculate weight fraction of an Element or Specie.
Parameters: el (Element/Specie) – Element or Specie to get fraction for. Returns: Weight fraction for element el in Composition
-
hill_formula
¶
-
is_element
¶ True if composition is for an element.
-
iupac_formula
¶ Returns a formula string, with elements sorted by the iupac electronegativity ordering defined in Table VI of “Nomenclature of Inorganic Chemistry (IUPAC Recommendations 2005)”. This ordering effectively follows the groups and rows of the periodic table, except the Lanthanides, Actanides and hydrogen. Polyanions are still determined based on the true electronegativity of the elements. e.g. CH2(SO4)2
-
num_atoms
¶ Total number of atoms in Composition. For negative amounts, sum of absolute values
-
oxi_prob
= None¶
-
oxi_state_guesses
(oxi_states_override=None, target_charge=0, all_oxi_states=False, max_sites=None)[source]¶ Checks if the composition is charge-balanced and returns back all charge-balanced oxidation state combinations. Composition must have integer values. Note that more num_atoms in the composition gives more degrees of freedom. e.g., if possible oxidation states of element X are [2,4] and Y are [-3], then XY is not charge balanced but X2Y2 is. Results are returned from most to least probable based on ICSD statistics. Use max_sites to improve performance if needed.
Parameters: - oxi_states_override (dict) – dict of str->list to override an element’s common oxidation states, e.g. {“V”: [2,3,4,5]}
- target_charge (int) – the desired total charge on the structure. Default is 0 signifying charge balance.
- all_oxi_states (bool) – if True, an element defaults to all oxidation states in pymatgen Element.icsd_oxidation_states. Otherwise, default is Element.common_oxidation_states. Note that the full oxidation state list is very inclusive and can produce nonsensical results.
- max_sites (int) – if possible, will reduce Compositions to at most this many sites to speed up oxidation state guesses. If the composition cannot be reduced to this many sites a ValueError will be raised. Set to -1 to just reduce fully. If set to a number less than -1, the formula will be fully reduced but a ValueError will be thrown if the number of atoms in the reduced formula is greater than abs(max_sites).
Returns: - A list of dicts - each dict reports an element symbol and average
oxidation state across all sites in that composition. If the composition is not charge balanced, an empty list is returned.
-
static
ranked_compositions_from_indeterminate_formula
(fuzzy_formula, lock_if_strict=True)[source]¶ Takes in a formula where capitilization might not be correctly entered, and suggests a ranked list of potential Composition matches. Author: Anubhav Jain
Parameters: - fuzzy_formula (str) – A formula string, such as “co2o3” or “MN”, that may or may not have multiple interpretations
- lock_if_strict (bool) – If true, a properly entered formula will only return the one correct interpretation. For example, “Co1” will only return “Co1” if true, but will return both “Co1” and “C1 O1” if false.
Returns: A ranked list of potential Composition matches
-
reduced_composition
¶ Returns the reduced composition,i.e. amounts normalized by greatest common denominator. e.g., Composition(“FePO4”) for Composition(“Fe4P4O16”).
-
reduced_formula
¶ Returns a pretty normalized formula, i.e., LiFePO4 instead of Li4Fe4P4O16.
-
special_formulas
= {'Cl': 'Cl2', 'CsO': 'Cs2O2', 'F': 'F2', 'H': 'H2', 'HO': 'H2O2', 'KO': 'K2O2', 'LiO': 'Li2O2', 'N': 'N2', 'NaO': 'Na2O2', 'O': 'O2', 'RbO': 'Rb2O2'}¶
-
to_data_dict
¶ A dict with many keys and values relating to Composition/Formula, including reduced_cell_composition, unit_cell_composition, reduced_cell_formula, elements and nelements.
Type: Returns
-
to_reduced_dict
¶ Dict with element symbol and reduced amount e.g., {“Fe”: 2.0, “O”:3.0}
Type: Returns
-
total_electrons
¶
-
weight
¶
-
reduce_formula
(sym_amt, iupac_ordering=False)[source]¶ Helper method to reduce a sym_amt dict to a reduced formula and factor.
Parameters: - sym_amt (dict) – {symbol: amount}.
- iupac_ordering (bool, optional) – Whether to order the formula by the iupac “electronegativity” series, defined in Table VI of “Nomenclature of Inorganic Chemistry (IUPAC Recommendations 2005)”. This ordering effectively follows the groups and rows of the periodic table, except the Lanthanides, Actanides and hydrogen. Note that polyanions will still be determined based on the true electronegativity of the elements.
Returns: (reduced_formula, factor).


Unlimited Games How should Red play 21?
pips Red=160 White=140
(CorrectAnswer= 0/0, ErrorScore= 0)
Normally in this type of the position making the 5-pt with 7/5, 6/5 would be very strong but here there is very significant duplication of fours with 13/11, 6/5. So much so that making the 5-pt is very nearly a blunder.
13/10 is way too passive so 13/11, 6/5 wins by a distance.
13/10 is way too passive so 13/11, 6/5 wins by a distance.
1. Cubeful 2-ply 13/11 6/5 Eq.: -0.050 0.514 0.141 0.007 - 0.486 0.204 0.022 2. Cubeful 2-ply 7/5 6/5 Eq.: -0.121 (-0.071) 0.488 0.140 0.007 - 0.512 0.199 0.018 3. Cubeful 2-ply 13/10 Eq.: -0.192 (-0.142) 0.469 0.127 0.005 - 0.531 0.202 0.017 4. Cubeful 0-ply 6/3 Eq.: -0.253 (-0.202) 0.450 0.111 0.005 - 0.550 0.215 0.019 5. Cubeful 0-ply 24/23 13/11 Eq.: -0.260 (-0.210) 0.452 0.121 0.007 - 0.548 0.247 0.015


I am writing this report after reading one by
Dave, who described the same problems that I am having. Continuing long after last having taken acid there is this feeling of weirdness or 'the confusion'. Anyone else who can affirm this happening should submit a report as it is a very interesting side effect.
Firstly, the curious thing about Dave's experience and my own are our ages. He writes that he is 15. I am 14. Maybe this has something to do with the haziness that's been affecting us and maybe not.
Now for some background. Over the course of the past month and a half I have taken a total of 12 hits of acid. This has been an incredibly beneficial experience, bringing out amazing amounts of insight and a very definite motivation to become the most successful person I can be. Acid has played an important role in the course of my life.
Now on to the side effect Dave and I have experienced. Ever since dosing, I have felt like there is a haziness to my perception of the world. Things seem different and confusing. Nothing makes any sense, even if I have found an answer and I know it is correct. When everything is explained perfectly, rationally I know that it makes sense, but I still don't understand. It is really hard to put this into words. Imagine the way that it feels when you are trying to figure out if there is a god (bad example, I know). No matter what you believe, even if you are sure you're right, it seems like there must be a deeper meaning to what you've discovered.
I'll try to give a more rational example. Quite frequently I will think to myself, 'How did I get here? Why am I lucky enough to have realized that I can shape my life at such a young age?', to which the answer is 'You simply are. Whether it's random chance, or if you have a purpose, this is how it's happening.'
Alright, sure. The problem is that this answer only leaves me wondering 'Why?' even more. Ever since I've taken acid (all 12 hits were done in the space of 3 weeks), NOTHING makes any sense. Everything seems to have a deeper meaning that won't show itself. Rational answers leave me absolutely baffled.
I feel disoriented. I have no way to place myself and no perspective. Proportions seem bizarre and I feel simultaneously insignificant and completely empowered (let me remind you this is under the influence of no drugs and I haven't dosed in several weeks). Objects won't just 'be'. When looking at a vending machine earlier, it was completely unbelievable to me that it existed. Why does it exist? How does it exist? Well, someone invented it of course. Why did someone invent it? To make money. How is it possible that someone invented it? And so on and so on.
OK, finally words are coming to me to summarize it. Everything, no matter what it is, seems to have a deeper meaning. No matter how far I analyze it, I still can't figure out why anything is the way it is. I feel infinitely curious. Answers are never satisfying anymore. (if I've been rambling these past few paragraphs maybe you'll understand how my thoughts keep tumbling over each other)
At this point, I'm not certain what recommendation to make about LSD. On the one hand, it has led me to completely reforming the way that I am and my life will be a much happier one because of it. On the other hand, this permanent lack of understanding is driving me absolutely insane. The possibility of it being an underlying schizophrenia brought out by the acid (my family has a history of psychosis) absolutely scares the hell out of me.
What makes me satisfied with my decision to take it is this: If I had not done acid I would still be stuck in another type of haze -- not feeling in control of my life. Like so many other kids my age I was drifting aimlessly down the path of normalcy. Now I have taken first place in one of the largest debate tournaments in my state, have pulled up my grades, and am looking forward to my sophomore year. I will definetely continue to do acid, even if the consequence is this always-unsettled feeling.
Hope this was helpful... It's important to document underage use of LSD, as there is currently very little on the subject. I encourage any other kids my age to submit their storys. Thank you to the Erowid team for making this site possible. Now I know I'm not alone in this problem.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Hello guys, I’m here with you again! So we have made it to the 8th post of the Machine Learning tutorial series. The topic of today’s post is about Decision Tree, an algorithm that is widely used in classification problems (and sometimes in regression problems, too). Decision Tree is well-known not only for its great performance on classification, but also for its easy-to-understand algorithm.
As we have already seen up to now, in Linear Regression and Logistic Regression tutorials, understanding how a learning algorithm works is somehow irritated. We got to go through boring theories, boring mathematical explanations and so on. Although I tried my best to make the explanation as simple as I can, but you know, functions is still functions, matrices are still matrices, there is no other way to get rid of those terms. But you won’t have to go through that pain today. Decision Tree can totally be explained using human-understandable natural language. So keep reading, okay?
And before we get started, it’s great to know that we have made it to the 8th post of the Machine Learning tutorial series. If you take a look back, I’m quite sure you will be surprised by how much you have progressed. We have learned two learning algorithms: Linear Regression and Logistic Regression respectively. We have worked on some simple dataset and visualized how your Model performed. At the moment, I’m quite sure that you are now familiar with Machine Learning. So in this post, we will use a more complicated set of data and see how our learning algorithms handle it.
So, let’s talk about Decision Tree.  In your daily life, you make many decisions exactly the same way Decision Tree does, subconsciously (of course). For example, your friend Joe invited you to his party. You may asked him back: “Any girls tonight?”. He said yes. You asked him again: “Will Miley join too?”. “Of course, homie!”, he replied. And you accepted his invitation. The example I have just showed you is one of many situations that you may face everyday, in which you have to make your own decision on something. For that example, I can express it using the graph below:
Simply enough, right? You may be wondering: Is this real that such simple algorithm can solve complicated classification problem? The answer is: Yes! To make it more clear to you, let’s consider a bigger one:
| Weather | Temperature | Humidity | Injure | Mood | RUN |
|---|---|---|---|---|---|
| clear | <10 | <70 | slightly | happy | NO |
| shower | 20~30 | >80 | fit | stressed | YES |
| storm | 10~20 | >80 | fit | happy | NO |
| shower | 10~20 | >80 | slightly | stressed | YES |
| clear | >30 | 70~80 | fit | lazy | YES |
| storm | 20~30 | >80 | fit | stressed | NO |
| clear | >30 | 70~80 | severe | happy | NO |
| clear | 10~20 | <70 | severe | stressed | NO |
| shower | 10~20 | 70~80 | slightly | happy | NO |
| shower | >30 | >80 | fit | happy | YES |
| storm | 20~30 | 70~80 | slightly | happy | NO |
| clear | 10~20 | <70 | slightly | happy | ? |
Let’s see what we have here. Here’s the dataset I created for demonstration. I actually created based on my running experience, but some examples may sound weird to you, please ignore them for now, lol.
Let’s say we want to predict whether I am gonna go for a run, based on some factors such as Weather, Temperature or even my Mood! And as you has learned so far, Weathers, Temperature, Humidity, Injure, Mood are just the features, and the YES or NO in the RUN column is the targets (or labels).
Before going further into an appropriate explanation on how the algorithm works, I will first show you how we can solve the problem using the approach above.
First, let’s randomly pick one feature from the features above. We will use that feature as the first condition, exactly the same way you asked “Any girls tonight?” above. To make it easy, let’s pick the first one, Weather. As you can see, Weather can be either clear or shower or even storm. We will then split our data into three groups according to the Weather feature. In terms of Decision Tree, each time we use a feature to split the data, we create one node, and each group is called one subset. Let’s first see how the clear Weather subset looks like:
| Weather | Temperature | Humidity | Injure | Mood | RUN |
|---|---|---|---|---|---|
| clear | <10 | <70 | slightly | happy | NO |
| clear | >30 | 70~80 | fit | lazy | YES |
| clear | >30 | 70~80 | severe | happy | NO |
| clear | 10~20 | <70 | severe | stressed | NO |
Let’s take a look at the RUN column. Obviously, just knowing the Weather is clear is not enough to decide whether to make a run or not. Just like the example above, you didn’t make your decision after just one question, right? So we will have to look for another feature, hoping it will help us decide. Let’s pick the Temperature feature. Now we can omit the Weather column (because it contains only clear), and just like what we did, we see that the Temperature feature can be either <10 or 10~20 or > 30, so we will now have three more subsets, let’s say, subsets of the subset above (where Weather is clear):
| Temperature | Humidity | Injure | Mood | RUN |
|---|---|---|---|---|
| <10 | <70 | slightly | happy | NO |
| Temperature | Humidity | Injure | Mood | RUN |
|---|---|---|---|---|
| 10~20 | <70 | severe | stressed | NO |
| Temperature | Humidity | Injure | Mood | RUN |
|---|---|---|---|---|
| >30 | 70~80 | fit | lazy | YES |
| >30 | 70~80 | severe | happy | NO |
Let’s take a look at three new subsets above. The first two subsets are already clear, because the RUN column in each subset contains only one value. In terms of Decision Tree, we call the result in those subsets Leaf Nodes, and when they are now pure, which means that the output contains only one value. Meanwhile, the third subset still requires some further work. But it’s quite easy now. Let’s go ahead and use the Injure feature to create two new subsets:
| Humidity | Injure | Mood | RUN |
|---|---|---|---|
| 70~80 | fit | lazy | YES |
| Humidity | Injure | Mood | RUN |
|---|---|---|---|
| 70~80 | severe | happy | NO |
Up to now, all the nodes in the clear Weather subset are clear, since there’s no any node in which the RUN column contains more than one value. We can now move on to the rest two subsets: the shower Weather subset and the storm Weather subset. By doing exactly the same way, we will get a result in the end, where all the subsets are clear. I have created a another graph for a better visualization:
Using the graph above, we can now predict the value of the RUN column for our last row above:
So, it’s likely that I’m gonna stay at home with my PlayStation 4 that night, lol.
So that’s it. Just easy to understand like I said earlier, right? Of course, we have some kind of mathematical explanation for how Decision Tree actually does. For example, choosing which Feature to split in the beginning is not done randomly, but depends on some considerations. And each time we need to create new subsets from the parent subset, the process is repeated again. Why do we have to make things such complicated, you may ask. Technically say, Decision Tree is a greedy algorithm, which means that it’s likely to fall into local-minimum rather than the desired global-minimum, which means we may get an ugly result if we run out of luck. I will give you a simple explanation for Decision Tree algorithm below for ones who concern. You can skip it to jump directly to the Python Implementation because the explanation is just optional.
So you decided to give Decision Tree’s algorithm a look. I really appreciate that. Knowing what is behind the scenes in somehow unrejectable, right? I won’t waste any other minute of your time. Let’s go straight into the algorithm. Actually, since Decision Tree was introduced quite long ago, the original algorithm has been revised and improved so many times, which the successor became more complex and robust than its predecessor. Among those, ID3 may probably be the best well-known algorithm. I think once you understand ID3, you can understand all its successors without problems.
So, how does ID3 algorithm work? To make it as simple as possible, I list out all the steps below:
, where \(K\) is the number of classes, \(p(y_j)\) is the proportion of number of elements of \(y_j\) class to the number of entire elements in output of \(S\):
\(H(S)\) tell us how uncertain our dataset is. It ranges from \(0\) to \(1\), which \(0\) is the case when the output contains only one class (pure), whereas \(1\) is the most uncertain case.
And in case you may ask, yes, that’s exactly the same as the entropy cost function that I showed you in Logistic Regression tutorial (except the \(\frac{1}{m}\) term). As you already knew, the smaller the entropy function, the better classification result we can achive.
Information Gain is computed seperately on each feature of the current dataset \(S\), whose value indicates how much the uncertainty in S was reduced after splitting \(S\) using feature \(A\). We can see that it looks like some kind of derivative, where we take the difference of the Entropy before and after splitting:
, where \(A\) is the feature used for splitting, \(n\) is the possible number of values of \(A\), \(p(t)\) is the proportion of number of elements whose values is \(t\) to the number of all elements of feature \(A\).
That’s it. But just skimming through the algorithm may not make any sense about how the algorithm works, right? So, let’s use the dataset above as an example.
First, our current \(S\) would be the entire original table, right? We will compute its Entropy \(H(S)\). Look at the RUN column (which is our output), it has \(4\) YES and \(7\) NO over \(11\) examples, so its Entropy will be as follow:
Next, we will compute Information Gain on each feature. Let’s first look at the Weather feature. It has three possible values: clear, shower and storm. clear Weather has \(4\) examples, so we have: \(p(clear)=\frac{4}{11}\). In those \(4\) examples, we have \(1\) YES and \(3\) NO, so the Entropy of clear Weather will be:
We now can do similarly to the rest two values to obtain values like below:
Note that in the case of storm Weather, its output contains only NO value, so its Entropy will be \(0\), you don’t need to compute it by hand (and even if you do, you’ll soon realize that it is impossible because of the term \(\log_2(0)\)!).
And I would like to talk a little bit about the case when \(H(t)=1\), which I mentioned it as the most uncertain case. We can only obtain that value when the proportion of each class is equal to others’. In the case of our current dataset, if the number of YES is equal to the number of NO on the considered subset, then it’s easy to see that there is a big chance that it can’t be fully classified (that’s why we call it the most uncertain case).
So now we can compute the Information Gain on the Weather feature as follow:
Continue repeat this process with other features, you will likely end up with results like this:
From the results above, IG on Weather has the highest value, so use Weather as a splitting condition will have the highest chance to reduce the uncertainty of dataset \(S\), and may lead to a good classification in the end.
So that’s all I have to tell you about Decision Tree’s ID3 algorithm. Hopely this explanation somehow can help you have a deeper understanding about what was actually done behind the scenes. You may want to read more about its successors such as C4.5 or C5.0 algorithms. And you will find them not so hard to understand at all!
Now, let’s jump to the implementation! Can’t wait no more, can you?
As I mentioned in the previous tutorials, the scikit-learn library comes bundled with everything you need to implement Machine Learning algorithms with ease. Furthermore, the library provides us many methods to generate data for learning purpose. And today I will use one of them to create a more complicated dataset, just to see how well Decision Tree can handle that.
First, let’s import necessary modules as usual:
If you went through my last tutorial, you may now know a proper way to evaluate the performance of a Machine Learning Model using the cross_val_score method. And if you didn’t, you can always give it a look here: Cross Validation.
Next, let’s using make_classification, a method provided by scikit-learn library to generate data for classification problems.
Let’s plot the data we created to see what it looks like:
In my case, the data looks like below. Note that your graph may be way different, since the make_classification method generates data in some unpredictable way.
As you might notice, I also included Logistic Regression. That is because I want to compare the performance of the two classification algorithms that we have known so far. From now on, you will learn some more algorithms, and it’s always a good practice to compare between them, to see what algorithm work best for a particular problem.
Let’s train our Model using one algorithm at a time, and print out the mean accuracy of each algorithm respectively. We will, of course, use the cross_val_score method for evaluation:
So let’s run our code a few times and see how the two algorithms perform. Note that you have to run the make_classification too, or you will likely get the same results!
Here’s my results:
As you see in the result above, Decision Tree’s performance is not any better (or even worse) than Logistic Regression’s. Let’s go ahead and try customizing the parameters of make_classification method (like increasing number of features, number of classes or number of samples, etc), you will likely realize that the two algorithms don’t have any significant difference. That is because the data generated by make_classification is linear, which means that Logistic Regression shouldn’t find any difficulty in fitting the data. Also note that we initialized both with default hyper-parameters (don’t mess with our \(\theta\) parameters, I will have a post on tuning hyper-parameter soon!), which means that our algorithms can perform even better!
So, let’s try another dataset. This time I will create a non-linear dataset, and see if these two algorithms can handle as well as they did above. Just like make_classification method for generating data for classification problems, scikit-learn library provides us some more methods to generate some particular dataset. One of those is make_circles method, which helps generate data with circular distribution. So let’s use it to create a new dataset, and see how it looks like:
The code for drawing the graph is exactly same as above so I omit it for now. I highly recommend you to put everything in a python file, so you just need to modify a few lines, without having to re-run the code for drawing the graph!
In my case, I have a graph like below:
As you could see, our data is now no longer linearly distributed. I can’t wait to see how the two algorithms handle it. After I run the code three times, I got the result below:
Now what? The accuracy of Logistic Regression seems horrible, right? That was somehow predictable, because we have already talked in the previous tutorial that a straight line may not work well with non-linear dataset. But look at the results of Decision Tree, they are satisfying, I think, since we haven’t tuned any hyper-parameters of the algorithm. So now you have your first tool to deal with non-linear dataset! Decision Tree is simply incredible. The algorithm is easy to understand, which you can explain to your friends just like telling them a story, and yet the performance is impressive.
So today, we have just talked about Decision Tree and used it to solve a few classification problems. We have seen an outstanding performance on different kinds of data. It doesn’t matter whether our dataset is linear or non-linear, the algorithm can do the jobs just fine. That is definitely a great tool that you have just added to your Machine Learning toolbox!
In the next post, I will tell you about another powerful classification algorithm, the one which nearly turned Neural Network into history (Neural Network was introduced quite very long ago, though). If you want to know what it is, stay updated for the next post! See you!


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Reading List
Rationale
I believe that every blogger should have a publicly available reading list. Books offer a way for people to systematically gain knowledge on specific topics. If said blogger is a known expert in a field, it is likely that they have consumed a lot of literature related to the field. Publicizing the books they have read would thus help people understand what they have to do to become experts themselves.
The books I have listed here are primarily non-fiction (I stopped reading fiction a while back). They relate to a variety of topics, and I believe that each occupies a place of high esteem among other writings of the same topic. Enjoy! :)
Books
Zero to One: Notes on Startups or Building the Future by Peter Thiel
- This is a comprehensive primer for anyone interested in entering or understanding the startup space. Thiel, while controversial in recent times, provides a concrete overview of modern ventures and how they drive us towards building a better future.
- He uses the dot-com bubble both as a marker for the dawn of the Information Age companies and a framework for what to do and not to do while deliberating on whether or not to start a venture.
- Next, he dives into a few important concepts when it comes to ideation, competitor analysis, and financing a startup. Some of these concepts include the last mover advantage, monopoly-driven innovation, and the power law of venture capital.
- The last part of the book explores Thiel’s understanding of what the future will be for two types of ventures: AI and Environmental (“Green”) startups. Overall, I found this to be extremely useful in terms of providing a general framework for the drivers of innovation and any common pitfalls that people face while in the process.
How to Win Friends and Influence People by Dale Carnegie
Mindfulness: An 8 Week Plan for Finding Peace in a Frantic World by Mark Williams and Danny Penman
The Theory of Poker by David Schlansky
- Too often I found myself clueless in terms of what my move should be in a poker game. It was then I decided that I needed to gain an understanding of the basic mathematics, betting patterns, and psychology of poker. I highly recommend this to anyone who’d like to do the same.
- Schlansky begins by covering the fundamentals of the math behind the game including expected values, odds, and equity. He then takes a ‘semi-digression’ to cover the fundamental theorem of poker, which is a simple rule that dictates the profit or loss that one makes while playing poker. He concludes the book with common playing strategies such as semi-bluffs, slowplaying, and check-raising. As a no-limit holdem player, I found his constant switching of games from holdem to Omaha to seven-card stud when he provided examples to be quite annoying. But, in terms of general principles, Schlansky does a fantastic job.


Here is a snippet from Phil Baker's excellent The Devil Is A Gentleman : The Life And Times Of Dennis Wheatley (2009):
Star Of Ill Omen is an extraordinary performance, with its characteristically fraught and tender Wheatley love story (quite unlike James Bond and his girls) embedded in a mind-bogglingly improbable Cold War potboiler. Realistic details are combined with a larger naivety about space that wouldn't be out of place in Dan Dare, and there are moments whose sheer weirdness compares with the work of proto-surrealist Raymond Roussel: when, for example, the insects [a breed of hyper-intelligent insects who buzz around like bees and give telepathic orders] show them black and white films of great moments in human history, and
“the bee-beetles who controlled the machine again pressed the lever; again the machine whirred and the words came, ‘Music while you work’, followed by the rumba.”
Wheatley never attempted science fiction again.


EVITA
A film review by Walter Frith
Copyright 1997 Walter Frith
Andrew Lloyd Weber and Tim Rice's internationally acclaimed musical 'Evita' is now a big screen motion picture featuring a sweeping pace despite its running time of approximately two hours and fifteen minutes. Some may come out feeling as if they've watched an extended music video and that feeling is justified but the integrity and originality of the play is kept intact by director Alan Parker. Parker is certainly no stranger to orchestrating and piecing together films with a musical background. 'Fame' (1980) and 'The Committments' (1991) are examples of Parker's talent and his eye for dramatization is nothing to shy away from, either, as his craft in that department was established with 'Midnight Express' (1978) and 'Mississippi Burning' (1988). Madonna who at first was a controversial choice to play the title role stars as Eva Duarte, a poor Spanish woman who rose to national and international attention when she married Argentinian dictator Juan Peron and campaigned to help the poor while politicizing her own gains and that of her husbands. Despite being a target for many years by critics, Madonna is electrifying in the picture and will probably receive an Oscar nomination for her work. Juan Peron is played in a straight forward manner by Jonathan Pryce and Antonio Banderas is Che, a man who follows the everyday antics of Eva. Fans of the musical will be pleased to see it revived as this is an almost zero speaking picture and 99.9999% song. It's vivid re-creation of that period is matched extensively by panoramic camera work. 'Evita' will probably be enjoyed better by people who have seen the play but for those new to the material, I don't think you'll be disappointed.
OUT OF 5> * * * 1/2
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Thinking of a design that could use 4 different processes was very difficult.
For the fusion 360 design, I struggled to be able to make the box appear to be wood, and the strap to show as leather. I even tried to create two separate designs and assemble them, but I had difficulty trying to take a design from a separate file and assemble it to the other object. I finally figured out that I just had to set the strap as a new body.


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Stefon Diggs Rocks ‘Woman Yelling At Cat’ Cleats For Game vs Broncos (PICS)
by Darrelle LincolnStefon Diggs is killing the cleat game.
From wearing cleats honoring Spongebob, to cleats paying homage to the return of the Popeyes Chicken Sandwich, there is absolutely nothing Diggs won’t wear.
For this week, the Minnesota Vikings WR decided to tap into the viral meme of the woman yelling at a cat and put it on his cleats.
The Vikings are coming off a huge win over the Dallas Cowboys and look to keep the good times rolling as they take on the Denver Broncos at home.