cf.DimensionCoordinate.create_bounds¶
-
DimensionCoordinate.
create_bounds
(bound=None, cellsize=None, flt=0.5, max=None, min=None)[source]¶ Create cell bounds.
Creates new cell bounds, irrespective of whether the cells already have cell bounds. The new bounds are not set on the dimension coordinate construct, but if that is desired they may always be added with the
set_bounds
method, for instance:>>> b = d.create_bounds() >>> d.set_bounds(b)
By default, Voronoi cells are created by defining cell bounds that are half way between adjacent coordinate values. For non-cyclic coordinates, extrapolation is used so that the first and last cells have their coordinates in their centres.
For instance, the Voronoi cells created for non-cyclic coordinates
0, 40, 90, 180
have bounds(-20, 20), (20, 65), (65, 135), (135, 225)
. If those coordinates were instead cyclic with a period of360
, then the bounds would be(-90, 20), (20, 65), (65, 135), (135, 270)
.See also
- Parameters
- bound: data_like scalar, optional
If set to a value larger (smaller) than the largest (smallest) coordinate value, then bounds are created which include this value and for which each coordinate is in the centre of its bounds. This can be useful for recreating the bounds of vertical coordinates from Earth system models.
- Parameter example:
For coordinates
1, 2, 10
, settingbound=0.5
will result in bounds of(0.5, 1.5), (1.5, 2.5), (2.5, 17.5)
.- Parameter example:
Beware that if bound is too large or small, then some returned bounds values will by physically incorrect. For coordinates
1, 2, 10
, the setting ofbound=-2
will result in bounds of(-2, 4), (4, 0), (0, 20)
.
- cellsize: optional
Define the size of each cell that is created. Created cells are allowed to overlap and do not have to be contigious. The cellsize parameter may be one of:
data_like scalar
Defines the cell size, either in the same units as the coordinates or in the units provided. Note that in this case, the position of each coordinate within the its cell is determined by the flt parameter.
-
A time duration defining the cell size. Only applicable to reference time coordinates. It is possible to “anchor” the cell bounds via the
cf.TimeDuration
instance parameters. For example, to specify cell size of one calendar month, starting and ending on the 15th day:cellsize=cf.M(day=15)
(seecf.M
for details). Note that the flt parameter is ignored in this case.
- flt: array_like scalar, optional
When creating cells with sizes specified by the cellsize parameter, define the fraction of each cell which is less than its coordinate value. By default flt is 0.5, so that each cell has its coordinate at its centre. Ignored if cellsize is not set.
- Parameter example:
For coordinates
1, 2, 10
settingflt=0.25
will result in bounds of(0.75, 1.75), (1.75, 2.75), (9.75, 10.75)
.- Parameter example:
For decreasing coordinates
2, 0, -12
, settingcellsize=6, flt=0.9
will result in bounds of(2.6, -3.4), (0.6, -5.4), (-11.4, -17.4)
.
- max: data_like scalar, optional
Limit the created bounds to be no more than this number.
- Parameter example:
To ensure that all latitude bounds are at most
90
:max=90
, ormax=cf.Data(90, 'degrees_north')
.
- min: data_like scalar, optional
Limit the created bounds to be no less than this number.
- Parameter example:
To ensure that all latitude bounds are at least
-90
:min=-90
, ormin=cf.Data(-90, 'degrees_north')
.
- Returns
Bounds
The new coordinate cell bounds.
Examples
Create bounds for Voronoi cells:
>>> d = cf.DimensionCoordinate(data=[0.0, 40, 90, 180]) >>> b = d.create_bounds() >>> print(b.array) [[-20. 20.] [ 20. 65.] [ 65. 135.] [135. 225.]] >>> d = d[::-1] >>> b = d.create_bounds() >>> print(b.array) [[225. 135.] [135. 65.] [ 65. 20.] [ 20. -20.]]
Cyclic coordinates have a co-located first and last bound:
>>> d.period(360) >>> d.cyclic(0) set() >>> b = d.create_bounds() >>> print(b.array) [[270. 135.] [135. 65.] [ 65. 20.] [ 20. -90.]]
Cellsize units must be equivalent to the coordinate units, or if the cell has no units then they are assumed to be the same as the coordinates:
>>> d = cf.DimensionCoordinate(data=cf.Data([0, 2, 4], 'km')) >>> b = d.create_bounds(cellsize=cf.Data(2000, 'm')) >>> print(b.array) [[-1 1] [ 1 3] [ 3 5]] >>> b = d.create_bounds(cellsize=2) >>> print(b.array) [[-1 1] [ 1 3] [ 3 5]]
Cells may be non-contiguous and may overlap:
>>> d = cf.DimensionCoordinate(data=[1.0, 2, 10]) >>> b = d.create_bounds(cellsize=1) >>> print(b.array) [[ 0.5 1.5] [ 1.5 2.5] [ 9.5 10.5]] >>> b = d.create_bounds(cellsize=5) >>> print(b.array) [[-1.5 3.5] [-0.5 4.5] [ 7.5 12.5]]
Reference date-time coordinates can use
cf.TimeDuration
cell sizes:>>> d = cf.DimensionCoordinate( ... data=cf.Data(['1984-12-01', '1984-12-02'], dt=True) ... ) >>> b = d.create_bounds(cellsize=cf.D()) >>> print(b.datetime_array) [[cftime.DatetimeGregorian(1984, 12, 1, 0, 0, 0, 0) cftime.DatetimeGregorian(1984, 12, 2, 0, 0, 0, 0)] [cftime.DatetimeGregorian(1984, 12, 2, 0, 0, 0, 0) cftime.DatetimeGregorian(1984, 12, 3, 0, 0, 0, 0)]] >>> b = d.create_bounds(cellsize=cf.D(hour=12)) >>> print(b.datetime_array) [[cftime.DatetimeGregorian(1984, 11, 30, 12, 0, 0, 0) cftime.DatetimeGregorian(1984, 12, 1, 12, 0, 0, 0)] [cftime.DatetimeGregorian(1984, 12, 1, 12, 0, 0, 0) cftime.DatetimeGregorian(1984, 12, 2, 12, 0, 0, 0)]]
Cell bounds defined by calendar months:
>>> d = cf.DimensionCoordinate( ... data=cf.Data(['1985-01-16: 12:00', '1985-02-15'], dt=True) ... ) >>> b = d.create_bounds(cellsize=cf.M()) >>> print(b.datetime_array) [[cftime.DatetimeGregorian(1985, 1, 1, 0, 0, 0, 0) cftime.DatetimeGregorian(1985, 2, 1, 0, 0, 0, 0)] [cftime.DatetimeGregorian(1985, 2, 1, 0, 0, 0, 0) cftime.DatetimeGregorian(1985, 3, 1, 0, 0, 0, 0)]] >>> b = d.create_bounds(cellsize=cf.M(day=20)) >>> print(b.datetime_array) [[cftime.DatetimeGregorian(1984, 12, 20, 0, 0, 0, 0 cftime.DatetimeGregorian(1985, 1, 20, 0, 0, 0, 0] [cftime.DatetimeGregorian(1985, 1, 20, 0, 0, 0, 0 cftime.DatetimeGregorian(1985, 2, 20, 0, 0, 0, 0]]
Cell bounds defined by calendar years:
>>> d = cf.DimensionCoordinate( ... data=cf.Data(['1984-06-01', '1985-06-01'], dt=True) ... ) >>> b = d.create_bounds(cellsize=cf.Y(month=12)) >>> print(b.datetime_array) [[cftime.DatetimeGregorian(1983, 12, 1, 0, 0, 0, 0) cftime.DatetimeGregorian(1984, 12, 1, 0, 0, 0, 0)] [cftime.DatetimeGregorian(1984, 12, 1, 0, 0, 0, 0) cftime.DatetimeGregorian(1985, 12, 1, 0, 0, 0, 0)]]


This invokes the General CRC Dialog to calculate any general CRC by entering parameters or selecting from a list of parameters of standard CRCs.


Temperature is a physical quantity. At a fundamental level, temperature is related to the kinetic energy of atoms and molecules. India is one of the most climatically diverse countries in the world; so the temperature varies largely based on the geographical location and time of the year. From desert in the north-west to the sea in the south uptill the himalayas, the range is fluctuous. India's lowest recorded temperature was −60.0 °C in Dras, Ladakh; and the highest temperature ever recorded in India occurred on 19 May 2016 in Phalodi, Jodhpur District, Rajasthan at 51.0 °C
The Climate of Mumbai is a tropical, wet and dry climate. Mumbai's climate can be best described as moderately hot with high level of humidity. Its coastal nature and tropical location ensure temperatures do not fluctuate much throughout the year. The highest temperature ever recorded in Mumbai's history is 42.2 °C , on 14 April 1952. While the lowest is 7.4 °C, on 22 January 1962, both of which were recorded at the airport. The mean average temperature is 27.2 °C. The mean maximum average temperatures is about 32 °C in summer and 30 °C in winter, while the average minimums are 25 °C in summer and 18 °C in winter.
Humidity is the concentration of water vapour present in the air. Water vapor, the gaseous state of water, is generally invisible to the human eye. Humidity depends on the temperature and pressure of the surroundings. Relative humidity is most widely used measurement unit. The relative humidity unit measures how much water the air can hold over a fairly wide temperature range, and it measures how much moisture is currently in the air. It’s represented by a percentage. 100-percent represents the air being at its full capacity of moisture, and 0-percent represents air that doesn’t have any moisture in it. Humidity plays an important role for surface life. For animal life dependent on perspiration (sweating) to regulate internal body temperature, high humidity impairs heat exchange efficiency by reducing the rate of moisture evaporation from skin surfaces.
The rain in India is predominantly attributed to the monsoon. Usually, the term monsoon is used to refer to the rainy phase of a seasonally changing pattern, although technically there is also a dry phase. The term is also sometimes used to describe locally heavy but short-term rains. The monsoon of South Asia is among several geographically distributed global monsoons. It affects the Indian subcontinent, where it is one of the oldest and most anticipated weather phenomena and an economically important pattern every year from June through September. Mawsynram receives the highest rainfall in India. It is reportedly the wettest place on Earth, with an average annual rainfall of 11,872 millimetres. Jaisalmer district located in the state of Rajasthan has the lowest annual rainfall in India. It has a rainfall measuring upto just a small number of 8.3 cms. Ruyli that is located in Jaisalmer district of Rajasthan is particularly known to receive the lowest amount of rainfall.
Disclaimer- The website host do not claim the authenticity of the data mentioned above.
We rely on multiple sources to find the data.
We are not responisible for any discrepancy
in this statistics.


Keep calm and curry on
Almost all applications need authentication and authorization in some form. Authentication a pain in the neck for both developers and end users, and personally I want as little to do with it as possible. OpenId Connect and AzureAd offers a great way of delegating the job to somebody else, gaining Single Sign-On (SSO) as an added bonus.
We will cover these common cases:
This is the first post of in a two-part series. In the first post we cover AzureAD and OpenID Connect authentication. In the second post we cover Windows Authentication (Kerberos Negotiate) in F# on .NET Core 3, using Docker running on Kubernetes.
The complete example code is available on GitHub.
In the following we will be using F# on .NET Core 3.0, together with ASP.NET Core and Giraffe. Giraffe offers a functional escape valve from the dreads of OOP builder patterns, dependency injection and other complexities of ASP.NET Core. Our strategy is to configure the necessary ASP.NET middleware, and then escape as quickly as possible into the functional domain.
AzureAd is a complex machinery, with plenty of toggles. In this example we will set up AzureAd to authenticate a server-side application with permission to act on the behalf of users in the specified organisation (tenants). Additionally we require users to be authenticated with the tenant in order to access any application API:s. This step is actually optional, but usually a good idea.
To register your application with AzureAd, follow these steps:
appsettings.json
file below.appsettings.json
.User.Read.All
for mail, etc.). By default it can only read very basic information about the user.{
"AzureAd": {
"Instance": "https://login.microsoftonline.com",
"CallbackPath": "/signin-oidc",
"BaseUrl": "https://localhost:8085",
"ClientId": "",
"TenantId": "",
"ClientSecret": "",
"Scopes": ".default",
"GraphResourceId": "https://graph.microsoft.com/",
"GraphScopes": ".default"
},
"AllowedHosts": "*"
}
Next, we need to configure the necessary ASP.NET Core middleware to enable OpenId Connect authentication:
The code for the AzureAd and Graph authentication middleware have been tranlated to F# from this C# implementation by Microsoft.
The complete ASP.NET Core WebHost and middleware configuration for AzureAd:
let configureCors (builder : CorsPolicyBuilder) =
builder.WithOrigins([| "https://login.microsoftonline.com" |])
.AllowAnyMethod()
.AllowAnyHeader() |> ignore
let cookiePolicyOptions (opt : CookiePolicyOptions) =
opt.CheckConsentNeeded <- fun _ -> true
opt.MinimumSameSitePolicy <- Http.SameSiteMode.None
let authenticationOptions (opt : AuthenticationOptions) =
opt.DefaultScheme <-
CookieAuthenticationDefaults.AuthenticationScheme
opt.DefaultChallengeScheme <-
OpenIdConnectDefaults.AuthenticationScheme
opt.DefaultAuthenticateScheme <-
CookieAuthenticationDefaults.AuthenticationScheme
let hstsOptions (opt : HstsOptions) =
opt.IncludeSubDomains <- true
opt.MaxAge <- TimeSpan.FromDays(365.0)
let configureApp (app : IApplicationBuilder) =
app.UseDefaultFiles()
.UseHttpsRedirection()
.UsePathBase(PathString "/")
.UseStaticFiles(staticFileOptions)
.UseCookiePolicy()
.UseCors(configureCors)
.UseAuthentication()
.UseSession()
.UseGiraffe WebApp.webApp
let configureServices (services : IServiceCollection) =
let sp = services.BuildServiceProvider()
let config = sp.GetService<IConfiguration>()
let jsonSerializer = Thoth.Json.Giraffe.ThothSerializer()
services.AddGiraffe() |> ignore
services.AddSingleton<IJsonSerializer>(jsonSerializer) |> ignore
services.Configure(cookiePolicyOptions) |> ignore
services.Configure(hstsOptions) |> ignore
services.AddCors() |> ignore
services.AddSingleton<IGraphAuthProvider, GraphAuthProvider>() |> ignore
services.AddResponseCaching() |> ignore
services.AddDistributedMemoryCache() |> ignore
services.AddSession() |> ignore
services.AddHttpContextAccessor() |> ignore
services.AddAuthentication(authenticationOptions)
.AddAzureAd(fun options -> config.Bind("AzureAd", options))
.AddCookie() |> ignore
WebHost
.CreateDefaultBuilder()
.UseKestrel()
.ConfigureAppConfiguration(appSettings)
.Configure(Action<IApplicationBuilder> configureApp)
.ConfigureServices(configureServices)
.UseWebRoot(publicPath)
.UseUrls("https://<IP_ADDRESS>:" + port.ToString() + "/")
.Build()
.Run()
In this example we set up a simple API using Giraffe (see WebApp.fs) , which allows the user to log in, log out and retrieve some basic user information:
let authenticate : HttpHandler =
challenge OpenIdConnectDefaults.AuthenticationScheme
|> requiresAuthentication
let signIn (next : HttpFunc) (ctx : HttpContext) =
authenticate next ctx
let signOut (next : HttpFunc) (ctx : HttpContext) =
task {
do! ctx.SignOutAsync()
return! next ctx
}
let webApp (next : HttpFunc) (ctx : HttpContext) =
choose [
routex "(/?)" >=> indexHtml
route "/signin" >=> signIn >=> redirectTo false "/?signin"
route "/signout" >=> signOut >=> redirectTo false "/?signout"
route "/api/me" >=> getUser
] next ctx
The /signin
endpoint triggers the authentication process by challenging the client to authenticate using the OpenId Connect scheme, using the requiresAuthentication
and challenge
functions from Giraffe. If the user isn’t already authenticated, it redirects the user to the OpenId Connect endpoint defined in appsettings.json
. Here we are using https://login.microsoftonline.com, for normal AzureAd authentication (e.g. using 2FA, etc.). If authentication succeeds the user is redirected back to the main page, with an added query string allowing the endpoint to take any additional actions necessary after login.
The /signout
endpoint triggers the standard logout process, and clears credentials and identities from the request context.
When the user has logged in, the request context contains only some very basic identity information about the user, like full name and user principal name (UPN). The /api/me
endpoint calls getUser
, which makes a request to the Microsoft Graph API, requesting the user’s e-mail address:
let private getAzureUser (ctx : HttpContext) =
let name = ctx.User.Identity.Name
let emailDecoder =
Decode.field "value" (Decode.list (Decode.field "mail" Decode.string))
let upn =
try
ctx.User.FindFirst(ClaimTypes.Name).Value.ToLower()
with _ -> "unknown"
task {
let! token = graphAppToken ctx
let query =
graphUrlf "users?$filter=userPrincipalName eq '%s'&$select=mail" upn
let content =
Http.RequestString (
query, headers = [ HttpRequestHeaders.Authorization token ]
)
let email =
match Decode.fromString emailDecoder content with
| Ok e -> try e.Head.ToLower() with _ -> "unknown"
| Error _ -> "unknown"
printfn "azureUser: %A" (name, upn, email)
return (name, email)
}
let getUser (next : HttpFunc) (ctx : HttpContext) =
task {
let! name, email = getAzureUser ctx
return! json (name, email) next ctx
}
The key function in the above code is graphAppToken ctx
(defined in Graph.fs in the example code):
let graphAppToken (ctx : HttpContext) =
let authProvider = ctx.RequestServices.GetService<IGraphAuthProvider>()
task {
let! accessToken = authProvider.GetUserAccessTokenAsync ""
let authHeader = AuthenticationHeaderValue("Bearer", accessToken)
return authHeader.ToString ()
}
This function requests a bearer token for the Graph API:s, which is then added to the standard HTTP Authorization tokens for the REST request.
Microsoft Graph contains a ton of useful REST API:s, which are a breeze to use. Have fun! I have suffered so you don’t have to.


"ZELOPHEHAD "first-born, of the tribe of Manasseh, and of the family of Gilead; died in the wilderness. Having left no sons, his daughters, concerned lest their father’s name should be “done away from among his family,” made an appeal to Moses, who, by divine direction, appointed it as “a statute of judgment” in Israel that daughters should inherit their father’s portion when no sons were left (Numbers 27:1-11). But that the possession of Zelophehad might not pass away in the year of jubilee from the tribe to which he belonged, it was ordained by Moses that his daughters should not marry any one out of their father’s tribe; and this afterwards became a general law (Numbers 36).
"ZELOPHEHAD "first-born, of the tribe of Manasseh, and of the family of Gilead; died in the wilderness. Having left no sons, his daughters, concerned lest their father’s name should be “done away from among his family,” made an appeal to Moses, who, by divine direction, appointed it as “a statute of judgment” in Israel that daughters should inherit their father’s portion when no sons were left (Numbers 27:1-11). But that the possession of Zelophehad might not pass away in the year of jubilee from the tribe to which he belonged, it was ordained by Moses that his daughters should not marry any one out of their father’s tribe; and this afterwards became a general law (Numbers 36).


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Arkansas youth treatment center allegations investigated
FAYETTEVILLE, Ark. (AP) - An Arkansas youth mental health treatment center is being investigated by the state Department of Human Services amid allegations of abuse, neglect and sexual contact among residents.
The Arkansas Democrat-Gazette reports the allegations also include excessive use of physical and chemical restraints at Piney Ridge Treatment Center in Fayetteville, which is licensed for 102 patients ages 7 to 17.
Piney Ridge CEO Adrienne Catalina told the newspaper that residents are monitored constantly, abuse allegations are investigated and reported to proper authorities and guardians, and that physical and chemical restraints are used according to state and federal regulations.
DHS spokeswoman Amy Webb said the center has work to do to improve and is being monitored.
The state’s actions could range from a plan to make corrections to revoking the facility’s license.
Information from: Arkansas Democrat-Gazette, http://www.arkansasonline.com
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200 CIGARETTES A film review by Matt Williams
RATING: * 1/2 out of * * * *
>From the title of 200 Cigarettes, one might assume that it is either a propaganda film from the tobacco lobby, or a comical entry for the Guinness Book of World Records. However, in actuality, the film turns out to be a threadbare ensemble comedy, struggling to be an 80s nostalgia film, and failing. In fact, the 200 of the title could be a reference to the number of minute subplots in the film, each of which holds about as much substance as a wisp of cigarette smoke.
The film is set on New Year's Eve, 1981. Monica (Martha Plimpton) is hosting a party, and the film cuts back and forth between many of her invited guests as they meander their way across New York City, slowly making their way to her apartment.
There's not really a main plotline, but the subplot with the most prominence is that of Lucy (Courtney Love) and Kevin (Paul Rudd). Kevin has just painfully broken up with his girlfriend, Ellie (Janeane Garofalo), and Lucy wants to cheer him up by taking him to the party. However, as the night progresses, it is obvious that a romance is budding between the two. But, can Paul recover from his break-up in time to notice Lucy? Or is this match not meant to be?
The search for love is common throughout most of 200 Cigarettes' myriad subplots. For example, a clumsy, but earnest girl (Katie Holmes), can't help falling in love with a love-phobic lout (Jay Mohr). Or, two lost Long Island girls (Christina Ricci and Gaby Hoffman) discover love in the most unlikeliest of places. The emcee for the evening is a disco-friendly cab driver (Dave Chappelle) who manages to meet just about everyone that night.
The main problem with 200 Cigarettes is a common one to the ensemble comedy. Very few of the film's subplots actually work, and the film spends far too little time with them. Instead, we are constantly reunited with barely recognizable characters we care little about in situations both tedious and contrived.
The actors in 200 Cigarettes aren't able to help matters much. Christina Ricci is buried beneath her screech of a New Yawk accent. Ben Affleck wanders aimlessly through his scenes as a highly desirable bartender. Jay Mohr's character inexplicably takes a 180 degree turn halfway through the film. Katie Holmes is lost amid some uninspired bits of slapstick. Paul Rudd and Courtney Love are nearly the only appealing ones in the film, but there is far too little of them to be spread around.
The film is set in 1981, but that seems to be more of a marketing ploy than an actual setting. There's nary a nostalgic bone to be found in this body. Sure, barely a moment goes by without an early 80s song floating by... but, if you're wise, you'll buy the soundtrack, and skip the movie.
200 Cigarettes is not an absolute disaster. It's more like a crowded party in which there are only one or two interesting people. Awkward, overcrowded and mostly boring...it's not a party to remember.
Copyright 1999 Matt Williams
- Matt Williams<EMAIL_ADDRESS>Reviewer for Cinematter: http://www.cinematter.com Home of nearly 700 reviews, and information on over 750 upcoming releases
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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[aromatic bitters not available] References
[web page file tree] External links
- newton's laws project gifted
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- married and looking in pittsburgh
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--- layout: post title: "Proof of Concept Rendering Page" ---
Hello world says Richardson (1987).
This should be author parenthetical (Sear 1993).
This should be with page number Gagliardo and Packer (2006, 123).
This should be a cf. [c.f. Carettoni p. 555]
This should be authorless says foo, (1993)
But frankly, if we're doing stuff like this, we can just say Russell (2011).
Gagliardo, Maria C., and James E. Packer. 2006. "A New Look at Pompey's Theater: History, Documentation, and Recent Excavation." American Journal of Archaeology 110 (1). Archaeological Institute of America: 93--122. http://www.jstor.org/stable/40026361.
Richardson, L. 1987. "A Note on the Architecture of the Theatrum Pompei in Rome." American Journal of Archaeology 91 (1). Archaeological Institute of America: 123--26. http://www.jstor.org/stable/505461.
Russell, Amy. 2011. "The Definition of Public Space in Republican Rome." University of California: eScholarship. http://www.escholarship.org/uc/item/8zr398bq.
Sear, Frank B. 1993. "The Scaenae Frons of the Theater of Pompey." American Journal of Archaeology 97 (4). Archaeological Institute of America: 687--701. http://www.jstor.org/stable/506718.
Temelini, Mark. 1993. "The Function of Pompey's Building Complex in the Campus Martius." ProQuest Dissertations Publishing. http://search.proquest.com/docview/89289599/.


Thomson's Derivation of the Larmor Formula
###Background
The Larmor formula is a neat little expression that is used to calculate the total power radiated by an accelerating point charge in the non-relativistic regime. I have seen this formula derived in a couple of different ways: first using Maxwell’s equations coupled with advanced/retarded potentials (Griffiths Chapter 11) and second using the Lienard-Wiechert potential (Jackson Chapter 9). Yes, to be fair, these are almost the same method; we just like to use the term “Lienard-Wiechert potential” in graduate school in order to impress people.
Yesterday, I had the pleasure of meeting up for coffee with one of my students. He showed me a simple geometric arguement for deriving the Larmor equation that was first introduced by J.J. Thomson. A description of the Thomson derivation can be found in many online sources and in Purcell’s book.
When I looked at some of the resources online, I found that a lot of instructors posted slightly abridged diagrams with their derivations. This can make it difficult to see where each variable comes from. I am going to go through the derivation in detail, refering to Purcell’s original diagram in Appendix B of his book. And Chaitanya, if you are reading this, thanks for the idea for the post!
###Purcell’s Diagram
Consider a point charge q at rest. The electric field lines from the point charge have a simple radial configuration shown in the figure below.
Now suppose that the point charge initially moves in the \(+\hat{x}\)-direction with a speed of \(v_o\). When it passes the origin of our coordinate system, it decelerates to a halt over a short amount of time \(\tau\). We then measure the electric field lines at some time \(T\) in the future where \(T \gg \tau\).
We can visualize the shape of the electric field lines using the diagram below:
Let’s go through this picture in a bit more detail. The speed of light in a vacuum is the universal “speed limit”. No information can travel faster than the speed of light. Thus, the electric field lines cannot instantly change in all regions of space - instead, the information about the acceleration of the particle propagates through these field lines with speed c.
The expression \(R = cT\) is the distance that a pulse of light would travel from the origin in a time \(T\). The expression \(R = c(T-\tau)\) is the distance that a pulse of light would travel when the particle moves from the origin to point A and then emits light. Notice that the difference between these two pulses creates a tiny shell in space. This shell divides space into two regions.
In Region I the electric field lines have not yet recieved the information about the deceleration, so they will look as if the particle is still traveling with speed \(v_o\) and nothing has changed. However, the field lines in Region II have received the information that the particle has decelerated, so they will match up to the final position of the particle (point A). The position of point A comes from kinematics. Solving for the acceleration yields:
\[\begin{align} \vec{v}_f &= \vec{v}_i + \vec{a}t\\[0.1cm] 0 &= v_o \hat{x} + \vec{a}\tau\\[0.1cm] \vec{a} &= -\frac{v_o}{\tau} \hat{x} \end{align}\]And then using that to find the final position:
\[\begin{align} \vec{x}_f &= \vec{x}_i + \vec{v}_i t + \frac{1}{2}\vec{a}t^2\\[0.1cm] &= 0 + v_o \tau \hat{x} - \frac{1}{2}\frac{v_o}{\tau}\tau^2 \hat{x}\\[0.1cm] &= v_o t \hat{x} - \frac{1}{2}v_o \tau \hat{x}\\[0.1cm] &= \frac{1}{2}v_o \tau \hat{x}\\[0.1cm] \end{align}\]The angle between AB and the x-axis is the same as the angle between CD and the x-axis - let’s call it \(\theta\). Since the particle would have traveled to a final position \(x = v_o T\) had it not decelerated, the perpendicular distance between AB and CD is \(v_oT sin\theta\).
The electric field lines need to be continuous, so their final shape is given by the series of segmets ABCD. This “kink” in the field lines is the electromagnetic wave that propagates through space.
We can find the radial and angular components of the “kink” in the field. The radial component of the electric field simply comes from Coulomb’s law:
\[\begin{equation} E_r = \frac{kq}{R^2} \approx \left ( \frac{1}{cT} \right ) \frac{kq}{R} \end{equation}\]I expanded the expression using \(R = cT\) just to make future math a bit easier. Remember, \(T \gg \tau\), so \((T - \tau) \approx T\). The angular component of the electric field can be found using the ratio of the two electric field components. These components are proportional to the lengths that we calculated in the diagram above:
\[\begin{align} \frac{E_\theta}{E_r} &= \frac{v_o T sin\theta}{c\tau}\\[0.1cm] &= \frac{\frac{v_o}{\tau} T sin\theta}{c}\\[0.1cm] &= \frac{|a| T sin\theta}{c} \end{align}\]Then:
\[\begin{align} E_\theta &= \left [ \frac{|a| Tsin\theta}{c} \right ] E_r\\[0.1cm] &= \left [ \frac{|a| Tsin\theta}{c} \right ] \left [ \frac{1}{cT} \frac{kq}{R} \right ]\\[0.1cm] &= \frac{kq|a| sin\theta}{c^2 R}\\[0.1cm] \end{align}\]###The Larmor Formula
Now that we have \(E_\theta\), the derivation of the Larmor formula is straightforward. First, we can use the angular electric field to find the Poynting vector around the point charge:
\[\begin{align} |\vec{S}| &= \frac{1}{\mu_o} \left | \vec{E} \times \vec{B} \right |\\[0.1cm] &= \frac{1}{\mu_o} \left | |\vec{E}| |\vec{B}| sin(90^\circ) \right | \\[0.1cm] &= \frac{1}{\mu_o} \left ( EB \right )\\[0.1cm] &= \frac{1}{\mu_o} \left ( \frac{E^2}{c} \right )\\[0.1cm] &= \epsilon_o c E^2\\[0.1cm] &= \epsilon_o c \left [ \frac{kq|a| sin\theta}{c^2 R} \right ]^2\\[0.1cm] &= \frac{\epsilon_o k^2 q^2 a^2 sin^2\theta}{c^3 R^2}\\[0.1cm] \end{align}\]We can integrate this expression over all angles in order to get the total power dissipated:
\[\begin{align} P &= \int |\vec{S}| R^2 d\Omega\\[0.1cm] &= \int_0^{2\pi} \int_0^\pi |\vec{S}| R^2 sin\theta d\theta d\phi\\[0.1cm] &= \int_0^{2\pi} \int_0^\pi \frac{\epsilon_o k^2 q^2 a^2 sin^2\theta}{c^3 R^2} R^2 sin\theta d\theta d\phi\\[0.1cm] &= \frac{\epsilon_o k^2 q^2 a^2}{c^3} \int_0^{2\pi} \int_0^\pi sin^3\theta d\theta d\phi\\[0.1cm] &= \frac{2\pi \epsilon_o k^2 q^2 a^2}{c^3} \int_0^\pi sin^3\theta d\theta\\[0.1cm] &= \frac{8\pi \epsilon_o k^2 q^2 a^2}{3c^3}\\[0.1cm] \end{align}\]Expanding \(k = (4\pi\epsilon_o)^{-1}\) yields:
\[\begin{equation} P = \frac{8\pi \epsilon_o q^2 a^2}{48 \pi^2 \epsilon_o^2 c^3} \end{equation}\] \[\begin{equation} P = \frac{q^2 a^2}{6 \pi \epsilon_o c^3} \end{equation}\]Thus, we have an expression for the power dissipated by a particle as it accelerates.


About BlobCDN
BlobCDN is a simple CDN for generating SVG shapes on the fly.
How to use
Our SVG shapes generation service is powered by a simple URL-based
API. All accessible trough
/blob.svg?:Parameters
.
Parameters
seed=:Number|String
A given seed will always produce the same blob. Default to random.
extraPoints=:Number
Actual number of points will be `3 + extraPoints`. Default to 4.
randomness=:Number
Increases the amount of variation in point position. Default to 6.
size=:Number
Size of the bounding box in pixel. Default to 256.
fill=:HexColor
Color for shape. Default to random.
stroke=:HexColor
Color for stroke. Default to none.
strokeWidth=:Number
Width for stroke line. Default to 0.


A Minimum Viable Product
I was asked to talk to the summer class of plug and play startup camp about Minimum Viable Products. I thought it could benefit others by posting my discussion with the amazing entrepreneurs I met.
Definition
Simply stated, a minimum viable product is a new product allowing you to collect the maximum amount of validated learning about customers with the least effort. It is one of the core concepts of the lean startup model, a group of principles developed by Eric Ries.
Least Effort, according to Ries, is one cycle through the Build, Measure, and Learn loop. This means the product you are building is a single test and isn't an actual product the way you and I think about the word product.
If you have read my other posts, you know I love to learn, so MVP and lean methodology fit right in with my life learnings.
Purpose
The purpose of the minimum viable product is to validate some assumptions you are making about the value hypothesis of your product, the market demand for your product, or the growth hypothesis of your product. The MVP, which I will call it from now on, is the first step in determining whether people want what you are building. Creating an MVP gauges demand, helping preserve capital and find a product people want.
Once the MVP is completed, the rest of the lean methodologies take over to help you iterate and revise your product. You cannot and should not build an MVP if you aren't committing to a quick and iterative process. Remember, the MVP is something you build to get you through one cycle of the build, measure, learn loop as fast as possible.
Minimum
Start thinking small. Now think smaller. Now think smallest. Eric Ries states a minimum viable product is much more minimum than you think. If you are a developer, take these words to mind. You probably won't be coding for at least few weeks.
The concept here is, Sell it before you build it. You are looking for early adopters who are interested in paying for what you are building before you have written one line of code. Bill Gates did this when he brokered the deal for DOS to IBM. He didn't actually have DOS, but he sold it before he built it, or rather purchased it from someone else.
You can do the same thing by developing some landing pages, sending some traffic to it, and measure the demand. Sending traffic can be done using ads, your twitter followers, facebook friends, or your blog community. If you don't have any of these, you should drop everything and start building a community you can leverage. Every entrepreneur seeks to have a huge network to learn from.
Viable
When you actually start to build product, the viable term is the most important to keep in mind. Your product offering needs to actual be helpful to your market. The level of viability can change from market to market. It is important however to not let yourself fall into the trap now before you. It is easy to state your market needs more viability than the next. Keep asking yourself the question, "Is there a small enough feature set for people to start getting something from it?"
Can't decide what to leave in and what to cut? The feature set probably doesn't even need to be built. It depends on what you are testing. RYou should test the assumption of the feature by building another MVP (fastest cycle through build, measure, repeat) and measuring whether people want that feature.
Product
It is important to remember you are validating the need for a product. It is totally okay if your first version, isn't even an actual product. As a developer, when I think about a problem, the first thing I want to do is start developing. The lean methodologies teach you to get away from this thinking and validate your business hypotheses.
Personally, I think the first few versions (if not the first 50) of your MVP should be nothing more than a landing page. Joel of Buffer, has a great story about how he tested the need for buffer by setting up a landing page. His first two iterations of the product was validating the need and validating if people are willing to pay for it.
This falls into the 4 steps Eric outlines in the book.
- Do consumers recognize that they have the problem you are trying to solve?
- If there was a solution, would they buy it?
- Would they buy it from us?
- Can we build a solution for that problem?
Joel's first two MVPs, validated steps 1 and 2. It is important to note, these steps need to be done in this order. Most companies jump right into step 4.
Nick Swinmurn, of Zappos, also built an MVP to answer a fundamental question to his business. Nick asked local shoe stores if he could take photos of their stock. In return he promised that he would post the pictures online and come back and buy them at full retail price should a customer buy them. He was testing if consumers were prepared to purchase shoes online.
His experiment proved customers were prepared to buy shoes online. All of the Zappos back end was missing, but he was able to prove out and learn something about his market.
First Step
Keep in mind your MVP is the first step in a constant, iterative, learning process. It helps to validate a consuming market which is willing to pay for their consumption. MVPs lead to a lot of failures. Prepare yourself to get answers you don't want to hear. Commit to testing out many hypothesis, no matter where it leads you. Continue to iterate when you have validated an assumption and commit to keep testing for features customers are willing to pay for.


Non-uniform Doom fire effect
Made with canvas, requestFrameAnimation and two-dimensional array
-
Min Fire
Debug
Max Fire
+


Freshly caught salmon fillet served with a delightful medley of sauteed garden vegetables. Served on a bed of seasoned brown rice.
Price:$ 15.00
Portobello Mushroom
A fulfilling portobello mushroom stuffed with vegetables of the day. Held together with parmesian cheese all atop a bed of brown rice
Price:$ 10.00
Chicken Saltimbocca
Chicken wrapped with proscuitto seasoned with pepper and topped with a few sage leafs. Served with freshly baked bread, and a side of seasonal vegetables.


Cambodian princess who rescued traditional ballet dies at age 76
by ReutersCambodia's Princess Norodom Bopha Devi, a former minister of culture who helped revive traditional Apsara dance after the brutal 1970s Khmer Rouge regime nearly wiped it out, has died at age 76, the royal palace said on Monday.
The princess died of natural causes in a hospital in neighbouring Thailand, Cambodia's Royal Palace said in a Facebook post.
The daughter of the late king Norodom Sihanouk and half sister of current King Norodom Sihamoni, Bopha Devi at young age became a dancer of Cambodian ballet, a form of stylized dance created in the royal courts for entertainment and ceremonies.
After the fall of the Khmer Rouge and a peace process that ended a decade of civil war, she served was the minister of culture and fine arts from 1998 to 2004.
Bopha Devi's cousin Prince Sisowath Thomico said her legacy was to rebuild the royal ballet troupe when she returned to Cambodia in early 1990s.
"Cambodia will remember that thanks to the Royal Highness Norodom Bopha Devi, the Cambodian Royal Ballet has been enlisted as a part of the world heritage," he said.


Create an in-memory data store, a simple generic list of colours
This app features:
- an in-memory data store
- the store is a 'per-request' store - it gets created when the controller is instantiated, and it is discarded after the
request has been serviced
- the store is a simple generic list of strings (of colours)
- as a bonus feature, ajax.html will call the web service, and use the results to modify the web page


import deepchem
from deepchem.molnet.load_function.bace_features import bace_user_specified_features
logger = logging.getLogger(__name__)
DEFAULT_DIR = deepchem.utils.data_utils.get_data_dir()
BACE_URL = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv"
BACE_URL = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv"
BACE_REGRESSION_TASKS = ["pIC50"]
BACE_CLASSIFICATION_TASKS = ["Class"]
class _BaceLoader(_MolnetLoader):