[Update: Rolling out] Google Maps incognito mode comes to Android this month, iOS ‘soon’
by Ben SchoonAfter testing the feature with some users in beta, Google has today announced that incognito mode for Maps will start rolling out to Android and iOS users in the next few weeks.
Incognito mode first made its debut in Chrome and expanded to YouTube last year. As part of Google’s promise back at I/O, it will be making its way to other Google apps – including Search – soon starting with Maps.
Rolling out starting this month on Android, incognito mode for Google Maps allows users to search for locations and get navigation directions all without tieing their data to a Google account. That means Google won’t associate the data with any particular user or save it. It also means that the experience while using incognito mode won’t be personalized.
Update 11/1: True to its word, Google started rolling out Incognito mode for Maps on October 31st. The update won’t be available to everyone right away, but over the “next few days,” it should be widely available for Android users.
The feature can be turned on and off with a toggle in the account switcher. Google says that the feature will be coming to iOS, but there’s no timeline outside of “soon” attached.
When you turn on Incognito mode in Maps, your Maps activity on that device, like the places you search for, won’t be saved to your Google Account and won’t be used to personalize your Maps experience. You can easily turn on Incognito mode by selecting it from the menu that appears when you tap your profile photo, and you can turn it off at any time to return to a personalized experience…
More on Google Maps:
- Hey Google, reporting features for Maps on Android Auto are long overdue
- Google Maps tests using your chosen Assistant voice when navigating
- Google Maps for Android adds dedicated Street View layer
FTC: We use income earning auto affiliate links. More.


{% set title="404 - Can't find that" %} {% extends "base.html" %} {% block content %}
the page you were looking for was not found


Heartbleed Update
We'd like to provide an update to our users in light of the recent discovery of the
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We, like many others discovered we were vulnerable to the Heartbleed bug at the beginning of this week. We immediately took steps to patch our systems and are happy to inform you that
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Out of an abundance of caution, however, we are asking all users of Write.app to please change their passwords immediately. Although we have no reason to suspect any personal or account information was divulged, we still encourage everyone to regularly change their passwords anyway.
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Read more about our Heartbleed Status on the official blog and learn more about how secure our site really is, as verified by independent third parties, by following the links on
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Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Serves 4-6
Preheat oven to 230C/450F/Gas 8.
Rub the meat with softened butter and place it in a roasting tin.
Using a small, sharp, pointed knife make a series of small deep slits about 4cm (1½in) apart all over the leg of lamb, inserting a sliver of garlic and a small sprig of rosemary in each slit.
Pour a tablespoon or so of the olive oil over the meat and cook for about 30 minutes.
While the meat is cooking, place the potatoes and onions in a bowl, toss together and season.
After 30 minutes, take the meat from the oven and lift it out of the tin.
Layer the potatoes and onions into the fat in the tin and pour over the stock.
Return the roasting tin to the low shelf and place the lamb directly on the shelf immediately above - so that the juices will 'weep' onto the potatoes.
Roast for about another hour depending on how well you like your meat cooked.
Dress the salad leaves with the lemon and remaining olive oil, then season with salt and pepper.
Carve the lamb and serve with the potatoes and salad.


This is my continueing blog as I go through the sprints atEnspiral Dev Academy.
There will be new blogs added as the weeks go on.


Elon Musk left embarrassed after bulletproof Tesla cracks
Elon Musk was left embarrassed while demonstrating Tesla’s first “bulletproof” truck after a metal ball smashed the glass. SEE THE VIDEO HERE
The red-faced Tesla CEO tried to show off the durability of the Cybertruck’s “armour glass” windows by having chief designer Franz von Holzhausen throw a metal ball at them.
The New York Post reports that the glass cracked, drawing groans from the crowd gathered in Los Angeles for the Blade Runner-inspired vehicle’s unveiling.
“Oh my f**king God,” Musk muttered onstage. “Well, maybe that was a little too hard.”
The flap came minutes after Musk claimed the Cybertruck’s hard-edge body is “literally bulletproof” to a 9mm handgun.
“We threw wrenches, we threw everything — we even literally threw the kitchen sink at the glass and it didn’t break,” Mr Musk said. “For a little weird reason, it broke now. I don’t know why.”
Mr Musk took the mishap in stride, noting that the ball didn’t go through the window.
“Room for improvement,” he quipped.
A panel of the window glass survived another onstage test in which a metal ball was dropped on it from above.
The futuristic truck is made from the same “ultra-hard” stainless steel alloy that will be used in the SpaceX Starship rocket, Mr Musk said. While he didn’t shoot the truck onstage, Mr Musk did have Mr von Holzhausen smack it with a sledgehammer.
“When you say something’s built tough, that’s what we mean,” Mr Musk said in a subtle dig at Ford, a major player in the pick-up truck market.
The materials aren’t all that’s unconventional about the Cybertruck.
It has an angular design that Mr Musk said was influenced by the 1977 James Bond movie The Spy Who Loved Me, as well as Blade Runner.
The Tesla co-founder claimed on Twitter that it will be the “official truck of Mars.”
Tesla will offer three Cybertruck models starting at $US39,900 ($A57,000)with production expected to start in late 2021.
The $US69,900 ($A102,000) high-end model of the truck will come with a battery range of more than 800 kilometres and more than 6350kgs of towing capacity, according to Tesla.
This article originally appeared in the New York Post and is republished here with permission


001 /* 002 * Licensed to the Apache Software Foundation (ASF) under one 003 * or more contributor license agreements. See the NOTICE file 004 * distributed with this work for additional information 005 * regarding copyright ownership. The ASF licenses this file 006 * to you under the Apache License, Version 2.0 (the 007 * "License"); you may not use this file except in compliance 008 * with the License. You may obtain a copy of the License at 009 * 010 * http://www.apache.org/licenses/LICENSE-2.0 011 * 012 * Unless required by applicable law or agreed to in writing, 013 * software distributed under the License is distributed on an 014 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY 015 * KIND, either express or implied. See the License for the 016 * specific language governing permissions and limitations 017 * under the License. 018 */ 019 package org.apache.shiro.authz.annotation; 020 021 import java.lang.annotation.ElementType; 022 import java.lang.annotation.Retention; 023 import java.lang.annotation.RetentionPolicy; 024 import java.lang.annotation.Target; 025 026 /** 027 * Requires the currently executing {@link org.apache.shiro.subject.Subject Subject} to have all of the 028 * specified roles. If they do not have the role(s), the method will not be executed and 029 * an {@link org.apache.shiro.authz.AuthorizationException AuthorizationException} is thrown. 030 *
 031 * For example, 032 *
 033 * @RequiresRoles("aRoleName");
 034 * void someMethod(); 035 *
 036 * means <tt>someMethod()</tt> could only be executed by subjects who have been assigned the 037 * 'aRoleName' role. 038 * 039 *
*Usage Note*: Be careful using this annotation if your application has a dynamic 040 * security model where roles can be added and deleted at runtime. If your application allowed the 041 * annotated role to be deleted during runtime, the method would not be able to 042 * be executed by anyone (at least until a new role with the same name was created again). 043 * 044 *
If you require such dynamic functionality, only the 045 * {@link RequiresPermissions RequiresPermissions} annotation makes sense - Permission 046 * types will not change during runtime for an application since permissions directly correspond to how 047 * the application's functionality is programmed (that is, they reflect the application's functionality only, not 048 * who is executing the the functionality). 049 * 050 * @see org.apache.shiro.subject.Subject#hasRole(String) 051 * @since 0.1 052 */ 053 @Target({ElementType.TYPE, ElementType.METHOD}) 054 @Retention(RetentionPolicy.RUNTIME) 055 public @interface RequiresRoles { 056 057 /** 058 * A single String role name or multiple comma-delimitted role names required in order for the method 059 * invocation to be allowed. 060 */ 061 String[] value(); 062 063 /** 064 * The logical operation for the permission check in case multiple roles are specified. AND is the default 065 * @since 1.1.0 066 */ 067 Logical logical() default Logical.AND; 068 }


Select icon to swith to the 'selection' mode
This mode allows the user to select elements in the graph and perform the following graphical operations zoom in/out, pan, rotate.
Refer to link Selection Mouse Actions for more information regarding this capability.
Select icon to swith to the 'add' mode to manually create nodes and transactions.
Refer to link Add Mouse Actions for more information regarding this capability.
By default, directed transactions are generated by the application.
Select icon to indicate that the next new transaction is to be directed.
Select icon to indicate that the next new transaction is to be undirected.


All about Lensing: Strong, Weak, and Micro¶
Strong Lensing¶
Modeling¶
PyAutoLens - Automated Strong Gravitational Lens Modeling
From James Nightingale. Based on Adaptive Semi-linear Inversion of Strong Gravitational Lens Imaging and AutoLens: Automated Modeling of a Strong Lens’s Light, Mass and Source
lenstronomy - Software package for lens model reconstruction of imaging data
By Simon Birrer. lenstronomy is a multi-purpose package to model strong gravitational lenses.
Based on Lenstronomy: multi-purpose gravitational lens modelling software package
-
Visilens is a python module for modeling gravitational lensing systems observed by a radio/mm interferometer like ALMA or ATCA.
Weak Lensing¶
Some of the tools overlap with the cosmology tools section.
Galaxy-Galaxy Lensing Tools¶
xshear - Measure the tangential shear around a set of lenses
By Erin Sheldon. Measure the tangential shear around a set of lenses. This technique is also known as cross-correlation shear, hence the name xshear.
dsigma - Pure-python galaxy-galaxy lensing framework for the HSC survey
By Song Huang. dsigma is a pure-python galaxy-galaxy lensing pipeline designed for the Hyper-Suprime Cam survey. It should be flexible enough to work with other surveys after adapting the same data format.
-
SWOT is a code to compute two-point statistics for very large data sets, based on “divide and conquer” algorithms. Github repo is here
Is a great tool to do galaxy-galaxy lensing.


public interface RoomEventHandler extends RoomHandler
| Modifier and Type | Method and Description |
|---|---|
void |
onParticipantJoined(ParticipantRequest request,
java.lang.String roomName,
java.lang.String newUserName,
java.util.Set<UserParticipant> existingParticipants,
RoomException error)
Called as a result of
RoomManager.joinRoom(String, String, ParticipantRequest) . |
void |
onParticipantLeft(ParticipantRequest request,
java.lang.String roomName,
java.lang.String userName,
java.util.Set<java.lang.String> remainingParticipantIds,
RoomException error)
Called as a result of
RoomManager#leaveRoom(String, String, ParticipantRequest) or
RoomManager.evictParticipant(String) (admin action). |
void |
onPublishMedia(ParticipantRequest request,
java.lang.String publisherName,
java.lang.String sdpAnswer,
java.util.Set<java.lang.String> participantIds,
RoomException error)
Called as a result of
RoomManager#publishMedia(String, ParticipantRequest, MediaElement...)
. |
void |
onRecvIceCandidate(ParticipantRequest request,
RoomException error)
Called as a result of
RoomManager.onIceCandidate(String, String, int, String, ParticipantRequest)
. |
void |
onRoomClosed(java.lang.String roomName,
java.util.Set<java.lang.String> participantIds)
Called as a result of
RoomManager.closeRoom(String) or
RoomManager.evictParticipant(String) - server domain methods, not
as a consequence of a room API request. |
void |
onRoomCreated(ParticipantRequest request,
java.lang.String roomName)
Called as a result of
RoomManager.joinRoom(String, String, ParticipantRequest) when the
specified room doesn't exist and it's successfully created. |
void |
onSendMessage(ParticipantRequest request,
java.lang.String message,
java.lang.String userName,
java.lang.String roomName,
java.util.Set<java.lang.String> participantIds,
RoomException error)
Called as a result of
RoomManager.sendMessage(String, String, String, ParticipantRequest)
. |
void |
onSubscribe(ParticipantRequest request,
java.lang.String sdpAnswer,
RoomException error)
Called as a result of
RoomManager.subscribe(String, String, ParticipantRequest) . |
void |
onUnpublishMedia(ParticipantRequest request,
java.lang.String publisherName,
java.util.Set<java.lang.String> participantIds,
RoomException error)
Called as a result of
RoomManager.unpublishMedia(ParticipantRequest) . |
void |
onUnsubscribe(ParticipantRequest request,
RoomException error)
Called as a result of
RoomManager.unsubscribe(String, ParticipantRequest) . |
onIceCandidate, onMediaElementError, onPipelineError
void onRoomCreated(ParticipantRequest request, java.lang.String roomName)
RoomManager.joinRoom(String, String, ParticipantRequest)
when the
specified room doesn't exist and it's successfully created. Normally this
event won't have to be notified to the user(s).request
- instance of ParticipantRequest
POJO to identify
the user and the requestroomName
- the room's namevoid onParticipantJoined(ParticipantRequest request, java.lang.String roomName, java.lang.String newUserName, java.util.Set<UserParticipant> existingParticipants, RoomException error)
RoomManager.joinRoom(String, String, ParticipantRequest)
. The new
participant should be responded with all the available information: the
existing peers and, for any publishers, their stream names. The current
peers should receive a notification of the join event.request
- instance of ParticipantRequest
POJO to identify
the user and the requestroomName
- the room's namenewUserName
- the new userexistingParticipants
- instances of UserParticipant
POJO
that represent the already existing peerserror
- instance of RoomException
POJO, includes a code and
error message. If not null, then the join was unsuccessful and the
user should be responded accordingly.void onParticipantLeft(ParticipantRequest request, java.lang.String roomName, java.lang.String userName, java.util.Set<java.lang.String> remainingParticipantIds, RoomException error)
RoomManager#leaveRoom(String, String, ParticipantRequest)
or
RoomManager.evictParticipant(String)
(admin action). The user
should receive an acknowledgement if the operation completed
successfully, and the remaining peers should be notified of this event.request
- instance of ParticipantRequest
POJO to identify
the user and the requestroomName
- the room's nameuserName
- the departing user's nameremainingParticipantIds
- identifiers of the participants in the
roomerror
- instance of RoomException
POJO, includes a code and
error message. If not null, then the operation was unsuccessful
and the user should be responded accordingly.void onPublishMedia(ParticipantRequest request, java.lang.String publisherName, java.lang.String sdpAnswer, java.util.Set<java.lang.String> participantIds, RoomException error)
RoomManager#publishMedia(String, ParticipantRequest, MediaElement...)
. The user should receive the generated SPD answer from the local WebRTC
endpoint, and the other peers should be notified of this event.request
- instance of ParticipantRequest
POJO to identify
the user and the requestpublisherName
- the user namesdpAnswer
- String with generated SPD answer from the local WebRTC
endpointparticipantIds
- identifiers of ALL the participants in the room
(includes the publisher)error
- instance of RoomException
POJO, includes a code and
error message. If not null, then the operation was unsuccessful
and the user should be responded accordingly.void onUnpublishMedia(ParticipantRequest request, java.lang.String publisherName, java.util.Set<java.lang.String> participantIds, RoomException error)
RoomManager.unpublishMedia(ParticipantRequest)
. The user should
receive an acknowledgement if the operation completed successfully, and
all other peers in the room should be notified of this event.request
- instance of ParticipantRequest
POJO to identify
the user and the requestpublisherName
- the user nameparticipantIds
- identifiers of ALL the participants in the room
(includes the publisher)error
- instance of RoomException
POJO, includes a code and
error message. If not null, then the operation was unsuccessful
and the user should be responded accordingly.void onSubscribe(ParticipantRequest request, java.lang.String sdpAnswer, RoomException error)
RoomManager.subscribe(String, String, ParticipantRequest)
. The
user should be responded with generated SPD answer from the local WebRTC
endpoint.request
- instance of ParticipantRequest
POJO to identify
the user and the requestsdpAnswer
- String with generated SPD answer from the local WebRTC
endpointerror
- instance of RoomException
POJO, includes a code and
error message. If not null, then the operation was unsuccessful
and the user should be responded accordingly.void onUnsubscribe(ParticipantRequest request, RoomException error)
RoomManager.unsubscribe(String, ParticipantRequest)
. The user
should receive an acknowledgement if the operation completed successfully
(no error).request
- instance of ParticipantRequest
POJO to identify
the user and the requesterror
- instance of RoomException
POJO, includes a code and
error message. If not null, then the operation was unsuccessful
and the user should be responded accordingly.void onSendMessage(ParticipantRequest request, java.lang.String message, java.lang.String userName, java.lang.String roomName, java.util.Set<java.lang.String> participantIds, RoomException error)
RoomManager.sendMessage(String, String, String, ParticipantRequest)
. The user should receive an acknowledgement if the operation completed
successfully, and all the peers in the room should be notified with the
message contents and its origin.request
- instance of ParticipantRequest
POJO to identify
the user and the requestmessage
- String with the message bodyuserName
- name of the peer that sent itroomName
- the current room nameparticipantIds
- identifiers of ALL the participants in the room
(includes the sender)error
- instance of RoomException
POJO, includes a code and
error message. If not null, then the operation was unsuccessful
and the user should be responded accordingly.void onRecvIceCandidate(ParticipantRequest request, RoomException error)
RoomManager.onIceCandidate(String, String, int, String, ParticipantRequest)
. The user should receive an acknowledgement if the operation completed
successfully (no error).request
- instance of ParticipantRequest
POJO to identify
the user and the requesterror
- instance of RoomException
POJO, includes a code and
error message. If not null, then the operation was unsuccessful
and the user should be responded accordingly.void onRoomClosed(java.lang.String roomName, java.util.Set<java.lang.String> participantIds)
RoomManager.closeRoom(String)
or
RoomManager.evictParticipant(String)
- server domain methods, not
as a consequence of a room API request. All resources on the server,
associated with the room, have been released. The existing participants
in the room should be notified of this event so that the client-side
application acts accordingly.roomName
- the room that's just been closedparticipantIds
- identifiers of the participants in the room


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Guidance
Funding values for colleges, training organisations, local authorities and employers with a contract for 2014 to 2015.
The information sets out the total amount of funding that has been paid to colleges, training organisations and employers (providers) and compares this to what was delivered.
The information includes:
In 2014 to 2015 all providers and employers delivering adult skills provision were funded for their delivery up to the value of their allocation and for all good quality apprenticeship delivery. The SFA operated performance management arrangements to increase allocations during the year to meet the demand from employers for high-quality apprenticeships and traineeships.
The funding year contract values provide maximum contract value information for colleges, training organisations and employers that have a contract for 2014 to 2015. It excludes contract values where these are not finalised. These allocation/contract values are subject to the performance management arrangements set out in the Operational Performance Management Rules 2014 to 2015 .
We will publish this information annually, before the start of the contract year. We will update it throughout the year, following the performance management points and final claim submissions.
The updated 2014 to 2015 funding year contract values published:
For further information, please contact your Central Delivery Service adviser.
To support colleges and training organisations in discussion with local government authorities in future Adult Education Budget (AEB) commissioning discussions we have published the geographic delivery data for 2014 to 2015 . We have based the data on learners’ postcodes and it relates to non-apprenticeship Adult Skills Budget (ASB) delivery in the 2014 to 2015 funding year.


Checkbox is an extension to standard checkbox element with theming.
Checkbox can either be used in multiple selection with other checkboxes or as a single checkbox to provide a boolean value.
Multiple mode is enabled by default, ngModel property refers to an array to bind the selected values.
As ngModel is two-way binding enabled, prepopulating the model array with values is enough to display the related checkboxes as checked by default.
The label attribute provides a label text for the checkbox. This label is also clickable and toggles the checked state.
A single boolean value can be bound using the ngModel property as well by enabling the binary option.
Checkbox can be used in a model driven form as well. In this case, due to an issue in Angular bind the formControl instance instead of using formControlName.
| Name | Type | Default | Description |
|---|---|---|---|
| name | string | null | Name of the checkbox group. |
| value | any | null | Value of the checkbox. |
| label | string | null | Label of the checkbox. |
| disabled | boolean | false | When present, it specifies that the element should be disabled. |
| binary | boolean | false | Allows to select a boolean value instead of multiple values. |
| tabindex | number | null | Index of the element in tabbing order. |
| inputId | string | null | Identifier of the focus input to match a label defined for the component. |
| ariaLabelledBy | string | null | Establishes relationships between the component and label(s) where its value should be one or more element IDs. |
| ariaLabel | string | null | Used to define a string that labels the input element. |
| style | object | null | Inline style of the component. |
| styleClass | string | null | Style class of the component. |
| labelStyleClass | string | null | Style class of the label. |
| checkboxIcon | string | pi pi-check | Icon class of the checkbox icon. |
| readonly | boolean | false | When present, it specifies that the component cannot be edited. |
| required | boolean | false | When present, it specifies that checkbox must be checked before submitting the form. |
| trueValue | any | null | Value in checked state. |
| falseValue | any | null | Value in unchecked state. |
| Name | Parameters | Description |
|---|---|---|
| onChange | checked: Boolean value to represent new state of checkbox. originalEvent: Browser event | Callback to invoke on checkbox click. |
| Name | Parameters | Description |
|---|---|---|
| focus | - | Applies focus. |
Following is the list of structural style classes, for theming classes visit theming page.
| Name | Element |
|---|---|
| p-chkbox | Container element |
| p-chkbox-box | Container of icon. |
| p-chkbox-icon | Icon element. |
| p-chkbox-label | Label element. |
| p-label-active | Label element of a checked state. |
| p-label-focus | Label element of a focused state. |
| p-label-disabled | Label element of a disabled state. |
None.


--- title: "Automatic Time-Series Forecasting with Prophet" author: "Gokhan Ciflikli" date: '2017-10-22' description: Predicting the weather in London using Facebook's open-source forecaster slug: prophet tags: - R - plot - prophet categories: - R - Prediction - Visualization ---
Time-series analysis is a battle on multiple fronts by definition. One has to deal with (dynamic) trends, seasonality effects, and good old noise. A general formula can be given as
y = level + trend + seasonality + noise
However, the relationships between these factors can be realized in many, and sometimes quite complex, ways. It is easy to over-fit noise, and the window for accurate prediction can be quite small (think of funnel-shaped confidence intervals).
For the purposes of this post, there will be roughly two groups of people based on their initial inference: the ones who think of a divine emissary and those who roll with Laurence “Prophet” Barnes. The first subset is likely to get inspiration from the Abrahamic tradition whereas the latter folks prefer shooting aliens in the face playing Crysis. Note that we do not enforce mutual-exclusiveness on this website, except for when we do. The royal we is good and alive. And we definitely thought of Mr. Nanosuit.
They call him Prophet…Remember him? Hint: He remembers you.
The Core Data Science team at Facebook developed an automated time-series forecasting package called the prophet
. It is maintained in parallel in both R and Python. The needs of massive companies like Facebook can go beyond the standard A/B testing when they want to test many features (and have access to So.Much.Data.) at once. A lot of their product features can also be influenced by seasonal behaviors. It is promoted as an easier-to-use alternative to the forecast
package.
For those of us who do not have such data,1 a Google search is in order. Wanting to use London as a data example for a while, I happen to came across weather data. The European Climate Assessment & Data website offers free downloads of daily observations at meteorological stations throughout the region. I custom queried the mean temperature readings from Heathrow, UK that were recorded between 1960-01-01 and 2017-09-30. The resulting subset contains about 21k observations. We do not need the source id (only Heathrow) and measurement validity (no variation; all valid) columns.
londontmp <- read.csv("londontmp.txt")
dim(londontmp)
## [1] 21093 4
colnames(londontmp)
## [1] "SOUID" "DATE" "TG" "Q_TG"
londontmp <- londontmp[, 2:3] #subset date and measurement
class(londontmp$DATE)
## [1] "integer"
The dates are imported as integers. Use lubridate
package to correctly format the dates:
library(lubridate)
Sys.setenv(TZ = "Europe/London") #only for locals!
londontmp[, 1] <- ymd(londontmp[, 1], locale = Sys.getlocale("LC_TIME")) #locale can be skipped
Prophet expects ds
and y
as input. Furthermore, the temperature units are in 0.1C. We should correct both before moving on:
colnames(londontmp) <- c("ds", "y")
londontmp$y <- londontmp$y / 10
summary(londontmp$y)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -8.90 6.70 11.10 11.11 15.60 29.00
Now we have nearly 57 years worth of daily observations of mean temperature in London. It’s good practice to conduct a sanity-check—the summary statistics look OK, but sometimes you need to look:
h <- hist(londontmp$y, xlab = "Degrees Celcius",
main = "Heathrow Temperature Readings 1960-2017",
ylim = c(0, 3000))
xfit <- seq(min(londontmp$y), max(londontmp$y))
yfit <- dnorm(xfit, mean = mean(londontmp$y), sd = sd(londontmp$y))
yfit <- yfit * diff(h$mids[1:2]) * length(londontmp$y)
lines(xfit, yfit, col = "#5694f1", lwd = 2)
Looking good. You can also opt for the kernel density by plot(density(londontmp$y))
if you are so inclined.
The prophet
package will take care of daily/monthly/yearly trends easily. However, we might be interested in trends longer than a year as well. Let’s look at decade averages. As it is the case in R, there are multiple ways of accomplishing the same task (looking at you, Py).  Let’s look for visual evidence of long-term change.
library(sm)
library(RColorBrewer)
colfill <- brewer.pal(6, "BrBG") #diverging palette
sm.density.compare(x = londontmp$y,
group = londontmp$decade,
xlab = "Mean Temperature (Celcius)",
col = colfill, lty = c(rep(1, 6)),
lwd = c(rep(2, 6)), xlim = c(-10, 30))
title(main = "Distributions by Decade")
legend("topright", levels(londontmp$decade),
fill = colfill, bty = "n")
The decades are grouped into two chunks; earlier earth-colored and later green-hues. If we pay attention to \(t, t+1\) patterns, we can identify several trends. The most volatile change happens during the transition from 60s to 70s, a swing of about 10C in terms of the peaks (note that we are looking at densities). During the 80s, there is a similar reversal, but much smaller in magnitude. Actually, we spot somewhat stable mean temperatures (less sharper peaks) starting in 80s all the way to the present. We are definitely experiencing more higher-than-average days with every passing decade since then:
library(psych) #yes, yes sapply and dirty deeds...I like using packages
describeBy(londontmp[, 2], londontmp$decade)
##
## Descriptive statistics by group
## group: 60s
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3653 10.26 5.7 10.6 10.4 6.67 -8.9 26.2 35.1 -0.2 -0.76
## se
## X1 0.09
## --------------------------------------------------------
## group: 70s
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3652 10.66 5.6 10.5 10.64 6.67 -4.2 27.6 31.8 0.06 -0.71
## se
## X1 0.09
## --------------------------------------------------------
## group: 80s
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3653 10.72 5.77 10.8 10.75 6.67 -7.6 27 34.6 -0.04 -0.66
## se
## X1 0.1
## --------------------------------------------------------
## group: 90s
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3652 11.47 5.67 11.2 11.44 6.52 -5.4 27.4 32.8 0.04 -0.66
## se
## X1 0.09
## --------------------------------------------------------
## group: 00s
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3653 11.83 5.61 11.7 11.85 6.38 -2.2 29 31.2 0 -0.73
## se
## X1 0.09
## --------------------------------------------------------
## group: 10s
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 2830 11.92 5.69 12.05 12.02 6.45 -4.1 28.7 32.8 -0.11 -0.66
## se
## X1 0.11
Let’s the see the magic of the prophet. (Sub) Daily seasonality is set to FALSE by default, I just wanted to highlight as it is a new feature. Our data do not have time-stamps so we cannot take advantage of it. You can also allow for MCMC sampling, although we will just go ahead using the package out-of-the-box.
library(prophet)
## Warning: package 'Rcpp' was built under R version 3.4.2
set.seed(1895) #in case you pass mcmc.samples below
m <- prophet(londontmp, daily.seasonality = FALSE)
## Initial log joint probability = -429.609
## Optimization terminated normally:
## Convergence detected: relative gradient magnitude is below tolerance
Following the vignette, we need to create a holder for future dates first. If you include history, the new dataset will have all the existing rows plus the ones created for the forecast. Below, we only create the dataframe for next two years:
future <- make_future_dataframe(m, periods = 365 * 2,
include_history = FALSE)
head(future)
## ds
## 1 2017-10-01
## 2 2017-10-02
## 3 2017-10-03
## 4 2017-10-04
## 5 2017-10-05
## 6 2017-10-06
Unsurprisingly, we can obtain forecasts by calling predict
(this is good!):
forecast <- predict(m, future)
head(forecast)
## ds trend seasonal seasonal_lower seasonal_upper seasonalities
## 1 2017-10-01 11.97663 2.702197 2.702197 2.702197 2.702197
## 2 2017-10-02 11.97668 2.594132 2.594132 2.594132 2.594132
## 3 2017-10-03 11.97672 2.429601 2.429601 2.429601 2.429601
## 4 2017-10-04 11.97676 2.350035 2.350035 2.350035 2.350035
## 5 2017-10-05 11.97681 2.240572 2.240572 2.240572 2.240572
## 6 2017-10-06 11.97685 2.135356 2.135356 2.135356 2.135356
## seasonalities_lower seasonalities_upper weekly weekly_lower
## 1 2.702197 2.702197 -0.004120552 -0.004120552
## 2 2.594132 2.594132 0.005020584 0.005020584
## 3 2.429601 2.429601 -0.040232351 -0.040232351
## 4 2.350035 2.350035 0.001408744 0.001408744
## 5 2.240572 2.240572 0.014909518 0.014909518
## 6 2.135356 2.135356 0.034211620 0.034211620
## weekly_upper yearly yearly_lower yearly_upper yhat_lower yhat_upper
## 1 -0.004120552 2.706317 2.706317 2.706317 11.41635 18.51276
## 2 0.005020584 2.589111 2.589111 2.589111 10.95705 18.43919
## 3 -0.040232351 2.469834 2.469834 2.469834 10.76735 18.13249
## 4 0.001408744 2.348626 2.348626 2.348626 10.59020 17.89945
## 5 0.014909518 2.225663 2.225663 2.225663 10.60131 17.75196
## 6 0.034211620 2.101144 2.101144 2.101144 10.50841 17.91079
## trend_lower trend_upper yhat
## 1 11.97663 11.97663 14.67883
## 2 11.97668 11.97668 14.57081
## 3 11.97672 11.97672 14.40632
## 4 11.97676 11.97676 14.32680
## 5 11.97681 11.97681 14.21738
## 6 11.97685 11.97685 14.11220
We can go ahead and plot calling the model and the forecast. Depending on your hardware, this may or may not be instantaneous. In addition, we are plotting nearly 60 years of daily data, so it will be messy to look at in the beginning:
plot(m, forecast)
Ehm, yes. As it is the case with most time-series packages, you can get component breakdowns that allows you to identify trends in varying resolutions:
prophet_plot_components(m, forecast)
Couple of points. The monthly component looks reasonable, peaking around August. Second, the weekly breakdown gives some support to the notion that the universe teases you until Friday and the weather trend that led up to the weekend is negatively correlated with your enjoyment of the said weekend. On the flip side, Tuesdays are particularly singled-out. I’m open to all kinds of UK-idiosyncratic explanations.
Finally, there is also the option for cross-validation (nice). If you use caret
,2 you might be familiar with the forward rolling origin technique, which is a cross-validation method for time-series data. Basically, you designate a chunk to act as the training sample, and identify a horizon value that determines the next testing sample size. Depending on your setup, you can either have a fixed-window (that always moves forward with each fold), or always include the previous chunks (a constantly growing training sample).
Prophet
has a similar function. We can choose the initial window as the first fifty years, and try to forecast the next two years until we run out of data3:
df.cv <- cross_validation(m, initial = 365 * 50, horizon = 365 * 2, units = "days")
## Initial log joint probability = -374.442
## Optimization terminated normally:
## Convergence detected: relative gradient magnitude is below tolerance
## Initial log joint probability = -648.25
## Optimization terminated normally:
## Convergence detected: relative gradient magnitude is below tolerance
## Initial log joint probability = -417.741
## Optimization terminated normally:
## Convergence detected: relative gradient magnitude is below tolerance
## Initial log joint probability = -413.471
## Optimization terminated normally:
## Convergence detected: relative gradient magnitude is below tolerance
## Initial log joint probability = -544.74
## Optimization terminated normally:
## Convergence detected: relative gradient magnitude is below tolerance
## Initial log joint probability = -414.229
## Optimization terminated normally:
## Convergence detected: relative gradient magnitude is below tolerance
head(df.cv)
## ds y yhat yhat_lower yhat_upper cutoff
## 1 2010-10-03 15.0 14.21399 10.45260 17.78065 2010-10-02
## 2 2010-10-04 14.8 14.10426 10.36684 17.35462 2010-10-02
## 3 2010-10-05 14.7 13.92199 10.24851 17.63122 2010-10-02
## 4 2010-10-06 15.7 13.82956 10.22811 17.57382 2010-10-02
## 5 2010-10-07 13.3 13.70796 10.42333 17.67298 2010-10-02
## 6 2010-10-08 16.9 13.60735 10.17033 16.96441 2010-10-02
Let’s start with a crude mean comparison:
t.test(df.cv$y, df.cv$yhat)
##
## Welch Two Sample t-test
##
## data: df.cv$y and df.cv$yhat
## t = 3.2735, df = 8629.5, p-value = 0.001067
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1473859 0.5873921
## sample estimates:
## mean of x mean of y
## 11.91995 11.55257
OK, not bad. Actually kind of good? Tingling senses? Yes, me too. Let’s look at the actual and the predicted values a bit more in detail:
summary(df.cv$y) #actual values
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.10 7.80 11.90 11.92 16.30 28.70
summary(df.cv$yhat) #predicted values
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.769 6.516 11.236 11.553 16.301 19.287
Ah, that’s not good! The curse of predicting extreme values, a common time-series ailment. Sometimes though, a plot in base R is more than a thousand descriptives:
plot(df.cv$y, df.cv$yhat, xlim = c(-5, 30),
ylim = c(-5, 30), xlab = "Actual Values",
ylab = "Predicted Values")
abline(a = 0, b = 1)
You probably want to squeeze the dots sideways so that they can also be on the edges. On a more serious note, there are a couple of reasons why this happens. First of all, we are predicting the next two years worth of daily data. I know some people make fun of meteorologists, but they are doing an amazing job in terms of forecasting given the hand they are dealt (i.e. probabilistic chaos). If an R package could just automatically give us accurate forecasts up to 2019, there will be certain employment-related consequences.
Second, as alluded in the introduction, there are several factors that can mess up a time-series forecast. Outliers, missing values (although prophet
can deal with those), and the frequency of data are the main culprits here. Combining both points, we would probably obtain higher quality forecasts if we try predicting the upcoming week or month. Finally, we are taking this path on purpose, as you would normally build up and test for shorter predictions (and keep pushing the model until it breaks) rather than intentionally trying to break the model and then recover it.
a.better.future <- make_future_dataframe(m, periods = 30,
include_history = FALSE)
a.better.forecast <- predict(m, a.better.future)
head(a.better.forecast)
## ds trend seasonal seasonal_lower seasonal_upper seasonalities
## 1 2017-10-01 11.97663 2.702197 2.702197 2.702197 2.702197
## 2 2017-10-02 11.97668 2.594132 2.594132 2.594132 2.594132
## 3 2017-10-03 11.97672 2.429601 2.429601 2.429601 2.429601
## 4 2017-10-04 11.97676 2.350035 2.350035 2.350035 2.350035
## 5 2017-10-05 11.97681 2.240572 2.240572 2.240572 2.240572
## 6 2017-10-06 11.97685 2.135356 2.135356 2.135356 2.135356
## seasonalities_lower seasonalities_upper weekly weekly_lower
## 1 2.702197 2.702197 -0.004120552 -0.004120552
## 2 2.594132 2.594132 0.005020584 0.005020584
## 3 2.429601 2.429601 -0.040232351 -0.040232351
## 4 2.350035 2.350035 0.001408744 0.001408744
## 5 2.240572 2.240572 0.014909518 0.014909518
## 6 2.135356 2.135356 0.034211620 0.034211620
## weekly_upper yearly yearly_lower yearly_upper yhat_lower yhat_upper
## 1 -0.004120552 2.706317 2.706317 2.706317 11.01016 18.39044
## 2 0.005020584 2.589111 2.589111 2.589111 11.00830 18.20630
## 3 -0.040232351 2.469834 2.469834 2.469834 10.89339 18.20116
## 4 0.001408744 2.348626 2.348626 2.348626 10.56261 18.10585
## 5 0.014909518 2.225663 2.225663 2.225663 10.78805 17.92865
## 6 0.034211620 2.101144 2.101144 2.101144 10.39699 17.91505
## trend_lower trend_upper yhat
## 1 11.97663 11.97663 14.67883
## 2 11.97668 11.97668 14.57081
## 3 11.97672 11.97672 14.40632
## 4 11.97676 11.97676 14.32680
## 5 11.97681 11.97681 14.21738
## 6 11.97685 11.97685 14.11220
Let me know if those object names are earned or not!
On a closing note, let’s get a single day prediction that would likely fall outside the +4/+16C interval (in real life):
better.days <- make_future_dataframe(m, periods = 120,
include_history = FALSE)
best.day <- better.days[better.days$ds == "2018-01-05", ]
best.day <- as.data.frame(best.day) #prophet requires df
best.day$ds <- best.day$best.day #list name becomes column name above
the.forecast <- predict(m, best.day)
the.forecast$yhat
## [1] 5.588844
RemindMe! 75 days “check prediction”
P.S. Contrary to popular belief, I am a good namer of objects. I use dots for objects and dashes for functions (in R, obviously). That’s +2 stack overflow style points right there!
Couple of years ago, I referenced one of their published papers in a class I was teaching. This is one of those times when you are social scientist in academia and everyone else has better data. And they put their n in the title. All 61 million of it. It’s an experiment as well. Observational crisis here I come.↩
I am aware that the technique existed before the package. However, I got acquainted with it while working with time-varying covariates in caret
.↩
I should note that this is a somewhat ridiculous first test in terms of time coverage. Definitely belongs to the “break the model first, then lower expectations” camp of package testing.↩


KDEINIT4(8) KDE User's Manual KDEINIT4(8) NAME kdeinit4 - KDE process launcher. SYNOPSIS kdeinit4 [--help] [[--no-fork] | [--no-kded] | [--suicide]] [[+programs] | [programs]] DESCRIPTION kdeinit4 is a process launcher somewhat similar to the famous init used for booting UNIX. It executes KDE programs and kdeinit loadable modules (KLMs) starting them more efficiently. Using kdeinit4 to launch KDE applications makes starting a typical KDE application a couple times faster and reduces memory consumption by a substantial amount. kdeinit4 is linked against all libraries a standard KDE application needs. With this technique, starting an application becomes much faster because now only the application itself needs to be linked whereas otherwise both the application as well as all the libaries it uses need to be linked. DISADVANTAGES The process name of applications started via kdeinit4 is "kdeinit4". This problem can be corrected to a degree by changing the application name as shown by ps. However, applications like killall will only see kdeinit4 as the process name. To workaround this, use kdekillall (from kdesdk/scripts) for applications started via kdeinit4. OPTIONS --help Show help about options --no-fork Do not fork, i.e. do not exit until all the executed programs ends --no-kded Do not start kded --suicide Terminate when no KDE applications are left running +programs runs the programs handling requests programs runs the programs without handling requests USAGE A standard way to run this program is by simply specifying the following command at the prompt kdeinit4 program or kdeinit4 +program FILES /tmp/kde-$USER/kdeinit4_$INSTANCE ... ENVIRONMENT VARIABLES $HOME Specifies the home directory of the current user $KDE_HOME_READONLY Specifies if the home directory of the current user is read only $KDE_IS_PRELINKED If set, tells kdeinit4 that the KDE programs are pre-linked. (Prelinking is a process that allows you to speed up the process of dynamic linking.) $KDE_DISPLAY If set, tells kdeinit4 that it is running on a KDE desktop. SEE ALSO kded, kdekillall BUGS There are probably tons of bugs. Use bugs.kde.org[1] to report them. AUTHORS Waldo Bastian<EMAIL_ADDRESS>Author. Mario Weilguni<EMAIL_ADDRESS>Author. Lubos Lunak<EMAIL_ADDRESS>Author. NOTES 1. bugs.kde.org http://bugs.kde.org 0.01.01 2008-10-03 KDEINIT4(8)


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The filter generator is used to create angular filters.
To create a directive execute the following.
After the generator finishes one file will be created.
name
: file name [REQUIRED]param
: adds additional parameters to the filter function--noTest
: don't generate test automatically.To create a filter ex. baz filter run
The filter will be created in the filters folder.
Besides the filter the generator automatically adds a test to the test folder.
Note
If you don't want to create the test with the filter just add --noTest
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Water Body Records for Belton (Bell County)
Updated April 5, 2019
Weight Records | Catch & Release Records | Return to record search | Submit your record!
This page ranks weight records caught by rod & reel, fly rod and bow fishing. All-Tackle rankings are displayed on a separate page.
All-Ages
Rod & Reel
| Species |
Weight |
Length |
Date |
Angler |
Bait or Lure |
| Bass, Hybrid Striped |
13.56 |
28.00 |
Apr 13, 1994 |
Fred Shelton |
|
| Bass, Largemouth |
13.97 |
28.75 |
Apr 1, 2017 |
Kurt Luker |
Jig and Rage Craw |
| Bass, Smallmouth |
6.43 |
22.50 |
Feb 24, 1999 |
Ronald Gardner |
|
| Bass, Spotted |
3.75 |
19.75 |
Feb 19, 2010 |
Byron Albrecht |
|
| Bass, White |
2.64 |
17.50 |
Feb 25, 2017 |
Cody Petty |
plastic worm |
| Bluegill |
0.62 |
7.50 |
Apr 28, 2001 |
Bruce Smith |
Fly Rod, yellow WalMart popper |
| Buffalo, Smallmouth |
36.50 |
38.00 |
Jun 23, 1989 |
Roger Green |
|
| Catfish, Blue |
64.00 |
47.25 |
Nov 27, 2010 |
Kevin Gillis |
shad |
| Catfish, Channel |
8.01 |
25.13 |
Jul 28, 2018 |
Everett Mach |
bread |
| Catfish, Flathead |
62.10 |
45.00 |
Feb 20, 2016 |
Michael G. Allen |
cut shad |
| Crappie, White |
2.34 |
16.50 |
Feb 1, 1993 |
Lowell Stinnett |
|
| Drum, Freshwater |
7.42 |
32.00 |
Jun 29, 2008 |
Daniel Wilson |
spinner bait |
| Pacu, Red-bellied |
0.79 |
9.29 |
Sep 20, 1992 |
Joshua True |
|
| Sunfish, Redear |
0.70 |
9.88 |
Jul 7, 2018 |
Gage Cushman |
worm |
Fly Fishing
| Species |
Weight |
Length |
Date |
Angler |
Bait or Lure |
| Bass, Hybrid Striped |
2.50 |
18.50 |
May 5, 2007 |
Robert Maindelle |
Shad imitator |
| Bass, Smallmouth |
2.00 |
16.38 |
May 3, 2007 |
Robert Maindelle |
Shad imitator |
| Bass, Spotted |
1.00 |
13.13 |
Sep 8, 2007 |
Robert Maindelle |
|
| Bass, White |
1.13 |
13.50 |
Sep 3, 2007 |
Robert Maindelle |
shad imitator |
| Bluegill |
0.62 |
7.50 |
Apr 28, 2001 |
Bruce Smith |
yellow WalMart popper |
| Catfish, Channel |
1.26 |
16.50 |
Jul 4, 2006 |
Robert Maindelle |
molly bug |
Bow Fishing
| Species |
Weight |
Length |
Date |
Angler |
| Buffalo, Smallmouth |
50.40 |
42.50 |
Mar 26, 2017 |
Chris Ellenburg |
| Carp, Common |
36.25 |
37.00 |
Jul 2, 2017 |
Bethany Ellenburg |
| Carp, Grass |
38.71 |
40.00 |
Nov 8, 2007 |
Richard Fleury |
| Catfish, Channel |
2.95 |
19.00 |
Jan 11, 2008 |
Richard Fleury |
| Drum, Freshwater |
11.75 |
29.00 |
Jul 2, 2017 |
Bethany Ellenburg |
| Gar, Longnose |
31.38 |
60.75 |
Apr 22, 2018 |
Tracy Ross |
| Gar, Spotted |
5.48 |
42.00 |
Oct 20, 2013 |
Richard Fleury |
| Redhorse, Gray |
2.48 |
17.00 |
Sep 20, 2008 |
Richard Fleury |
Note: Bow fishing for blue, channel and flathead catfish was legal only from September 1, 2006 to August 31, 2008. Applications for bow fishing records can no longer be accepted for these species.
Junior Angler
Rod & Reel
| Species |
Weight |
Length |
Date |
Angler |
Bait or Lure |
| Bass, Hybrid Striped |
9.53 |
29.00 |
May 6, 2004 |
Matthew Bennett |
jigging spoon |
| Bass, Largemouth |
8.51 |
24.50 |
Jul 27, 2008 |
Miles Meckley |
shad |
| Bass, Smallmouth |
4.25 |
20.13 |
May 3, 2017 |
Jacob Manning |
live shad |
| Bass, White |
2.00 |
15.75 |
Dec 12, 2015 |
Nathaniel Giertz |
slab |
| Bluegill |
0.45 |
8.00 |
Jul 7, 2018 |
Asher Price |
crankbait |
| Catfish, Blue |
5.00 |
23.38 |
Jul 7, 2011 |
Madison Chappell |
pet spoon |
| Catfish, Channel |
8.01 |
25.13 |
Jul 28, 2018 |
Everett Mach |
bread |
| Catfish, Flathead |
39.00 |
43.00 |
Mar 24, 2017 |
Jace Martin |
Cut shad |
| Crappie, White |
1.65 |
14.50 |
Mar 30, 2008 |
Joshua Stegal |
tube jig |
| Drum, Freshwater |
7.42 |
32.00 |
Jun 29, 2008 |
Daniel Wilson |
spinner bait |
| Sunfish, Green |
0.13 |
4.25 |
Aug 18, 2009 |
Margaret Jacks |
Fly Rod, nymph |
| Sunfish, Longear |
0.13 |
5.25 |
May 22, 2008 |
Emily Boone |
worm |
| Sunfish, Redbreast |
0.25 |
6.13 |
Aug 19, 2009 |
Lane Bolin |
Fly Rod, nymph |
| Sunfish, Redear |
0.70 |
9.88 |
Jul 7, 2018 |
Gage Cushman |
worm |
Fly Fishing
| Species |
Weight |
Length |
Date |
Angler |
Bait or Lure |
| Bluegill |
0.13 |
5.13 |
Aug 19, 2009 |
Lane Bolin |
nymph |
| Sunfish, Green |
0.13 |
4.25 |
Aug 18, 2009 |
Margaret Jacks |
nymph |
| Sunfish, Redbreast |
0.25 |
6.13 |
Aug 19, 2009 |
Lane Bolin |
nymph |
All Ages
Rod & Reel
| Species |
Length |
Date |
Angler |
Bait or Lure |
| Bass, Hybrid Striped |
25.75 |
Apr 25, 2017 |
Hampton Haines |
live shad |
| Bass, Smallmouth |
20.75 |
Oct 20, 2018 |
Jason Delfraisse |
jerkbait |
| Bass, Spotted |
19.75 |
Feb 19, 2010 |
Byron Albrecht |
|
| Bass, White |
16.75 |
Oct 29, 2016 |
Brittany Santoro |
binsky lure |
| Catfish, Blue |
47.25 |
Nov 27, 2010 |
Kevin Gillis |
shad |
| Catfish, Flathead |
46.50 |
Apr 16, 2010 |
Terry Sanders |
shad |
Junior Angler
Rod & Reel
| Species |
Length |
Date |
Angler |
Bait or Lure |
| Bass, Hybrid Striped |
25.25 |
Jun 17, 2016 |
Gregory Hughes III |
live shad |
| Bass, Smallmouth |
20.13 |
May 3, 2017 |
Jacob Manning |
live shad |
| Bass, White |
15.75 |
Dec 12, 2015 |
Nathaniel Giertz |
slab |


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Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed. Often, programmers fall in love with Python because of the increased productivity it provides. Since there is no compilation step, the edit-test-debug cycle is incredibly fast. Debugging Python programs is easy: a bug or bad input will never cause a segmentation fault. Instead, when the interpreter discovers an error, it raises an exception. When the program doesn't catch the exception, the interpreter prints a stack trace. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and so on. The debugger is written in Python itself, testifying to Python's introspective power. On the other hand, often the quickest way to debug a program is to add a few print statements to the source: the fast edit-test-debug cycle makes this simple approach very effective.
JavaScript is a scripting or programming language that allows you to implement complex features on web pages — every time a web page does more than just sit there and display static information for you to look at — displaying timely content updates, interactive maps, animated 2D/3D graphics, scrolling video jukeboxes, etc. — you can bet that JavaScript is probably involved. It is the third layer of the layer cake of standard web technologies, two of which (HTML and CSS) we have covered in much more detail in other parts of the Learning Area.
Ruby is an interpreted, high-level, general-purpose programming language. It was designed and developed in the mid-1990s by Yukihiro "Matz" Matsumoto in Japan. ... It supports multiple programming paradigms, including procedural, object-oriented, and functional programming.


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{% extends "base.html" %} {% load i18n %} {% block content %} {% if user.is_authenticated %}
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MVCView
Extends:
HTMLElement → MVCView
this class is experimental. This class is an experimental featue, and may not be valid in the future
This class is a custom HTMLElement that provides some syntactic sugar for setting up Views and Models
Constructor Summary
| Public Constructor | ||
| public |
Simply call the super() constructor |
Member Summary
| Public Members | ||
| public get |
Return the model for the view. |
|
| public set |
Set the model the view will observe and modify. |
| Private Members | ||
| private |
The Model instance encapsulated by this element |
|
| private |
|
|
| private |
The View instance encapsulated by this element |
Method Summary
| Public Methods | ||
| public |
Adds a form around the content slot |
Public Constructors
Public Members
public get model: object source
Return the model for the view. May be null if model has not been supplied yet.


public class CompareMethods
extends java.lang.Object
| Constructor and Description |
|---|
CompareMethods() |
| Modifier and Type | Method and Description |
|---|---|
static double |
calcDiffAsDouble(java.lang.Object olderValue,
java.lang.Object youngerValue)
Calculate the difference between two values as double.
|
static double |
calcDiffPercentageAsDouble(java.lang.Object olderValue,
java.lang.Object youngerValue)
Calculate the difference in percentage between two values
as double.
|
private static Column |
compare2Columns(Column olderColumn,
Column youngerColumn,
java.lang.reflect.Method compareMethod)
Compare two columns by the given Method.
|
static CSVData |
compare2CSVFiles(CSVData olderCSVData,
CSVData youngerCSVData)
This method will initialize the columns which are used as identifiers in the comparison.
|
static java.lang.String |
compareAsString(java.lang.Object olderValue,
java.lang.Object youngerValue)
Compare two Strings.
|
private static CSVData |
doComparisionByManualDefinition(CSVData olderCSVData,
CSVData youngerCSVData,
Column identifierColumnOlder,
Column identifierColumnYounger)
This method is used when "-column.compareDef" arguments are provided.
|
private static CSVData |
doComparisionWithHierarchy(CSVData olderCSVData,
CSVData youngerCSVData,
Column identifierColumnOlder,
Column identifierColumnYounger)
!!!!!!!!!!!!!! UNDER CONSTRUCTION !!!!!!!!!!!!!!!!!!!!
|
private static CSVData |
doComparisionWithTopLevel(CSVData olderCSVData,
CSVData youngerCSVData,
Column identifierColumnOlder,
Column identifierColumnYounger)
This method is used when the argument "-column.toplevel" is provided.
|
private static CSVData |
doDefaultComparision(CSVData olderCSVData,
CSVData youngerCSVData,
Column identifierColumnOlder,
Column identifierColumnYounger)
This method is used when no "-column.compareDef" arguments are provided.
|
private static java.lang.reflect.Method |
getMethod(java.lang.String argument)
Get the Method to use for the Comparison by the argument.
|
private static Column |
initializeResultColumn(Column mergedIdentifierColumns,
Column unmergedIdentifierColumn,
Column valueColumn)
Initialize a Result Column, this will put the Values at the
correct position, so the values fit to the merged identifier
column.
|
public static CSVData compare2CSVFiles(CSVData olderCSVData, CSVData youngerCSVData)
olderCSVData
- the object with the CSVData which should be considered as older in the
comparisonyoungerCSVData
- the object with the CSVData which should be considered as younger in
the comparisonprivate static CSVData doComparisionWithHierarchy(CSVData olderCSVData, CSVData youngerCSVData, Column identifierColumnOlder, Column identifierColumnYounger)
This method is used when the arguments "-hierarchy" and "-hierarchyColumn" is provided. It will split the provided CSVData in parts, with the topLevel as a "delimiter". This method considers also hierarchical relations between the rows in the data, and creates subsets of CSVData in the parts. The parts are compared if in both files a topLevel with the same identifier is found. The result of all part comparisons is merged in one result file.
olderCSVData
- the object with the CSVData which should be considered as older in the
comparisonyoungerCSVData
- the object with the CSVData which should be considered as younger in
the comparisonidentifierColumnOlder
- the column of the older CSVData which is used to identify
the recordidentifierColumnYounger
- the column of the older CSVData which is used to identify
the recordprivate static CSVData doComparisionWithTopLevel(CSVData olderCSVData, CSVData youngerCSVData, Column identifierColumnOlder, Column identifierColumnYounger)
olderCSVData
- the object with the CSVData which should be considered as older in the
comparisonyoungerCSVData
- the object with the CSVData which should be considered as younger in
the comparisonidentifierColumnOlder
- the column of the older CSVData which is used to identify
the recordidentifierColumnYounger
- the column of the older CSVData which is used to identify
the recordprivate static CSVData doDefaultComparision(CSVData olderCSVData, CSVData youngerCSVData, Column identifierColumnOlder, Column identifierColumnYounger)
olderCSVData
- the object with the CSVData which should be considered as older in the
comparisonyoungerCSVData
- the object with the CSVData which should be considered as younger in
the comparisonidentifierColumnOlder
- the column of the older CSVData which is used to identify
the recordidentifierColumnYounger
- the column of the older CSVData which is used to identify
the recordprivate static CSVData doComparisionByManualDefinition(CSVData olderCSVData, CSVData youngerCSVData, Column identifierColumnOlder, Column identifierColumnYounger)
olderCSVData
- the object with the CSVData which should be considered as older in the
comparisonyoungerCSVData
- the object with the CSVData which should be considered as younger in
the comparisonidentifierColumnOlder
- the column of the older CSVData which is used to identify
the recordidentifierColumnYounger
- the column of the older CSVData which is used to identify
the recordpublic static double calcDiffAsDouble(java.lang.Object olderValue, java.lang.Object youngerValue) throws java.lang.Exception
java.lang.Exception
public static double calcDiffPercentageAsDouble(java.lang.Object olderValue, java.lang.Object youngerValue) throws java.lang.Exception
olderValue
- the older valueyoungerValue
- the younger valuejava.lang.Exception
public static java.lang.String compareAsString(java.lang.Object olderValue, java.lang.Object youngerValue) throws java.lang.Exception
olderValue
- the older valueyoungerValue
- the younger valuejava.lang.Exception
private static Column initializeResultColumn(Column mergedIdentifierColumns, Column unmergedIdentifierColumn, Column valueColumn)
mergedIdentifierColumns
- the column containing
the identifiers of both CSVDataunmergedIdentifierColumn
- the identifier Column of
one of the CSVDatavalueColumn
- the column containing the values from
the same CSVData as the unmergedIdentifierColumn.private static Column compare2Columns(Column olderColumn, Column youngerColumn, java.lang.reflect.Method compareMethod)
olderColumn
- the column of the older CSVDatayoungerColumn
- the column of the younger CSVDataprivate static java.lang.reflect.Method getMethod(java.lang.String argument)
argument
- the command line argument which
represents a method in Arguments.methodMap.


Four Point
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+
-
Start Timer
Stop Timer
Restart Timer
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public class TwoPartyTradeFlow
This asset trading flow implements a "delivery vs payment" type swap. It has two parties (B and S for buyer and seller) and the following steps:
S sends the class StateAndRef
pointing to what they want to sell to B, along with info about the price they require
B to pay. For example this has probably been agreed on an exchange.
B sends to S a class SignedTransaction
that includes the state as input, B's cash as input, the state with the new
owner key as output, and any change cash as output. It contains a single signature from B but isn't valid because
it lacks a signature from S authorising movement of the asset.
S signs it and hands the now finalised SignedWireTransaction back to B.
Assuming no malicious termination, they both end the flow being in posession of a valid, signed transaction that represents an atomic asset swap.
Note that it's the seller who initiates contact with the buyer, not vice-versa as you might imagine.
To initiate the flow, use either the runBuyer or runSeller methods, depending on which side of the trade your node is taking. These methods return a future which will complete once the trade is over and a fully signed transaction is available: you can either block your thread waiting for the flow to complete by using ListenableFuture.get or more usefully, register a callback that will be invoked when the time comes.
To see an example of how to use this class, look at the unit tests.
| Modifier and Type | Class and Description |
|---|---|
static class |
TwoPartyTradeFlow.Buyer |
static class |
TwoPartyTradeFlow.Seller |
static class |
TwoPartyTradeFlow.SellerTradeInfo |
static class |
TwoPartyTradeFlow.SignaturesFromSeller |
| Modifier and Type | Field and Description |
|---|---|
static TwoPartyTradeFlow |
INSTANCE
This asset trading flow implements a "delivery vs payment" type swap. It has two parties (B and S for buyer
and seller) and the following steps:
|
public static TwoPartyTradeFlow INSTANCE
This asset trading flow implements a "delivery vs payment" type swap. It has two parties (B and S for buyer and seller) and the following steps:
S sends the class StateAndRef
pointing to what they want to sell to B, along with info about the price they require
B to pay. For example this has probably been agreed on an exchange.
B sends to S a class SignedTransaction
that includes the state as input, B's cash as input, the state with the new
owner key as output, and any change cash as output. It contains a single signature from B but isn't valid because
it lacks a signature from S authorising movement of the asset.
S signs it and hands the now finalised SignedWireTransaction back to B.
Assuming no malicious termination, they both end the flow being in posession of a valid, signed transaction that represents an atomic asset swap.
Note that it's the seller who initiates contact with the buyer, not vice-versa as you might imagine.
To initiate the flow, use either the runBuyer or runSeller methods, depending on which side of the trade your node is taking. These methods return a future which will complete once the trade is over and a fully signed transaction is available: you can either block your thread waiting for the flow to complete by using ListenableFuture.get or more usefully, register a callback that will be invoked when the time comes.
To see an example of how to use this class, look at the unit tests.


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@Controller
@RequestMapping(value="unitTest/")
public class UnitTestController
extends java.lang.Object
Handles mappings of $PASTAUrl$/unitTest/...
Only teaching staff can access this url.
| Modifier and Type | Field and Description |
|---|---|
private java.util.Map<java.lang.String,java.lang.String> |
codeStyle |
protected org.apache.commons.logging.Log |
logger |
private SubmissionManager |
manager |
private UnitTestManager |
unitTestManager |
private UserManager |
userManager |
| Constructor and Description |
|---|
UnitTestController()
Initializes the codeStyle tag mapping of file endings to
javascript tag requirements for syntax highlighting.
|
| Modifier and Type | Method and Description |
|---|---|
java.lang.String |
deleteUnitTest(java.lang.String testName,
org.springframework.ui.Model model)
$PASTAUrl$/unitTest/delete/{testName}/
|
void |
downloadUnitTest(java.lang.String testName,
org.springframework.ui.Model model,
javax.servlet.http.HttpServletResponse response)
$PASTAUrl$/unitTest/{testName}/download/
|
PASTAUser |
getOrCreateUser()
Get or create the currently logged in user.
|
PASTAUser |
getOrCreateUser(java.lang.String username)
Get or create the user given a username
|
PASTAUser |
getUser()
Get the currently logged in user.
|
PASTAUser |
getUser(java.lang.String username)
Get the user given a username
|
java.lang.String |
home(NewUnitTest form,
org.springframework.validation.BindingResult result,
org.springframework.ui.Model model)
$PASTAUrl$/unitTest/ - POST
|
NewUnitTest |
returnNewUnitTestModel() |
Submission |
returnSubmissionModel() |
void |
setMyService(SubmissionManager myService) |
void |
setMyService(UnitTestManager myService) |
void |
setMyService(UserManager myService) |
java.lang.String |
testedUnitTest(java.lang.String testName,
org.springframework.ui.Model model)
$PASTAUrl$/unitTest/tested/{testName}/
|
java.lang.String |
updateTestCode(java.lang.String testName,
NewUnitTest form,
Submission subForm,
org.springframework.validation.BindingResult result,
org.springframework.ui.Model model)
$PASTAUrl$/unitTest/{testName}/ - POST
|
java.lang.String |
viewUnitTest(org.springframework.ui.Model model)
$PASTAUrl$/unitTest/
|
java.lang.String |
viewUnitTest(java.lang.String testName,
org.springframework.ui.Model model)
$PASTAUrl$/unitTest/{testName}/
|
protected final org.apache.commons.logging.Log logger
private SubmissionManager manager
private UserManager userManager
private UnitTestManager unitTestManager
private java.util.Map<java.lang.String,java.lang.String> codeStyle
public UnitTestController()
public void setMyService(SubmissionManager myService)
public void setMyService(UserManager myService)
public void setMyService(UnitTestManager myService)
@ModelAttribute(value="newUnitTestModel") public NewUnitTest returnNewUnitTestModel()
@ModelAttribute(value="submission") public Submission returnSubmissionModel()
public PASTAUser getOrCreateUser()
public PASTAUser getOrCreateUser(java.lang.String username)
username
- the username of the userpublic PASTAUser getUser()
public PASTAUser getUser(java.lang.String username)
username
- the username of the user@RequestMapping(value="{testName}/") public java.lang.String viewUnitTest(@PathVariable(value="testName") java.lang.String testName, org.springframework.ui.Model model)
View the details of a unit test. If the user has not authenticated: redirect to login. If the user is not a tutor: redirect to home. ATTRIBUTES:
| unikey | The PASTAUser for the logged in user |
| unitTest | The UnitTest for this test |
| latestResult | The UnitTestResult for the execution of the test run of the unit testing code. |
| node | The root FileTreeNode for the root of the unit test code. |
testName
- the short name (no whitespace) of the test.model
- the model being used@RequestMapping(value="{testName}/download/") public void downloadUnitTest(@PathVariable(value="testName") java.lang.String testName, org.springframework.ui.Model model, javax.servlet.http.HttpServletResponse response)
Download the unit test code on the machine. If the user has not authenticated or is not a tutor: do nothing Otherwise create a zip file with the name: $testName$.zip
testName
- the short name (no whitespace) of the unit testmodel
- the model being usedresponse
- the response being used to serve the zip@RequestMapping(value="{testName}/", method=POST) public java.lang.String updateTestCode(@PathVariable(value="testName") java.lang.String testName, @ModelAttribute(value="newUnitTestModel") NewUnitTest form, @ModelAttribute(value="submission") Submission subForm, org.springframework.validation.BindingResult result, org.springframework.ui.Model model)
Upload some code to test the unit test on the production machine or to update the unit tests on the system. If the user has not authenticated: redirect to login. If the user is not a tutor: redirect to home. If the user is an instructor:
UnitTestManager.updateUnitTestCode(NewUnitTest)
UnitTestManager.testUnitTest(Submission, String)
testName
- the short name (no whitespace) of the testform
- used for updating the unit test codesubForm
- used for testing the unit test coderesult
- binding results used for feedback.model
- the model being used.@RequestMapping(value="") public java.lang.String viewUnitTest(org.springframework.ui.Model model)
List all unit tests on the system. If the user has not authenticated: redirect to login. If the user is not a tutor: redirect to home. ATTRIBUTES:
| unikey | The PASTAUser for the currently logged in user. |
| allUnitTests | A collection of all UnitTest of all unit tests on the system. |
model
- the model being used.@RequestMapping(value="", method=POST) public java.lang.String home(@ModelAttribute(value="newUnitTestModel") NewUnitTest form, org.springframework.validation.BindingResult result, org.springframework.ui.Model model)
Add a new unit test to the system. If the user has not authenticated: redirect to login. If the user is not a tutor: redirect to home. If the user is an instructor:
UnitTestManager.addUnitTest(NewUnitTest)
form
- the new unit test formresult
- the binding result used for feedbackmodel
- the model being used@RequestMapping(value="delete/{testName}/") public java.lang.String deleteUnitTest(@PathVariable(value="testName") java.lang.String testName, org.springframework.ui.Model model)
Delete a unit test from the system.
If the user has not authenticated: redirect to login.
If the user is not a tutor: redirect to home.
If the user is an instructor: delete the unit test using
UnitTestManager.removeUnitTest(String)
then redirect to $PASTAUrl$/unitTest/
testName
- the short name (no whitespace) of the unit test.model
- the model being used.@RequestMapping(value="tested/{testName}/") public java.lang.String testedUnitTest(@PathVariable(value="testName") java.lang.String testName, org.springframework.ui.Model model)
Mark a unit test as tested. If the user has not authenticated: redirect to login. If the user is not a tutor: redirect to home. If the user is an instructor: mark the unit test as tested.
testName
- the short name (no whitespace) of the testmodel
- the model being used.


Hello {{name}}
Welcome to Myhood. This is a platform where you can interact with your neighbors, get notified on what is happening in your neighborhood. You can also register a business and access details of both police services and health facilities in your hood.


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Warrant of control payment confirmed
What happens next
When the warrant has been issued, the court will phone Mary Richards and send a letter asking for payment.
If Mary Richards doesn't pay within 7 days, a bailiff will visit the address you gave within 15 days of the issue of the warrant.
The bailiff usually visits up to 3 times. If they can't get your money back they'll return the warrant to the court.
If the defendant pays, the court will send you the money.
{% include "includes/survey.html" %} {% include "includes/contact.html" %}


{% include 'includes/error_summary.html' %}{# include if this page has validation requirements #}
Do you have any of these documents?
Select {{ 3 - docsLength }} {%if docsLength == 2%}document{%else%}documents{%endif%} from this list:
Select {{ 3 - docsLength }} {%if docsLength == 2%}document{%else%}documents{%endif%} from this list:


--- layout: page title: Lean Prover Zulip Chat Archive permalink: archive/113488general/69965weirdstructure.html ---
What's a structure that is add_comm_monoid + semigroup + mul_zero_class + distrib
?
or, is there a better grouping?
is that a noncommutative nonunital ring?
That would be a rng
but it's not in mathlib yet
so in the meantime is there a better grouping using what is currently available?
{% endraw %}


Context:
{context}
Please select the more appropriate response given the above context. If they are equally good or bad, select "Both are appropriate" or "Both are NOT appropriate".


Nausea,vomiting,fever,abdominal cramping,bloating,gas are the symptoms.
1.Children can be given ashtachoornam with a tablespoon of sugar or steel ghee.
2. Stir grinded four leaves of bitter gourd and drink.
3. If you drink 30 ml of crushed wild coriander juice on an empty stomach the abdominal pain will calm down.
4. Drink the 30ml juice of wild bitter gaurd.
5. Roast coriander leaves, pokanamthukki and karukappullu and mix in 50 ml of rice husk and Eat.
6. Crush a thousand neem leaves, heat it and drink 30 ml.
7. Squeeze Krishna Thulasi leaf and berry to juice,Add essential oil in it and serve each spoon daily.
8. Add two to three drops of lemongrass oil to a glass of water and drink
9. Drink water mixed with powdered wild cumin
10. Roast 10 g of sugar and mix it with water and drink it.
11. Grind half of a kadukka and mix it with water and drink it.
Sudden pain that begins on the right side of the lower abdomen,Sudden pain that begins around your navel and often shifts to your lower right abdomen,Pain that worsens if you cough, walk or make other jarring movements,Nausea and vomiting,Loss of appetite are the symptoms
1.Children can be given ashtachoornam with a tablespoon of sugar or steel ghee.
2. Stir grinded four leaves of bitter gourd and drink.
3. if you drink 30 ml of crushed wild coriander juice on an empty stomach the abdominal pain Will calm down.
4. Drink the 30ml juice of wild bitter gaurd.
5. Roast coriander leaves, pokanamthukki and karukappullu and mix in 50 ml of rice husk and Eat.
6. Crush a thousand neem leaves, heat it and drink 30 ml.
7. Squeeze Krishna Thulasi leaf and berry to juice,Add essential oil in it and serve each spoon daily.
8. Add two to three drops of lemongrass oil to a glass of water and drink
9.drink water mixed with powdered wild cumin
10. Roast 10 g of sugar and mix it with water and drink it.
11.  Roll out ripe mango and brush your teeth.
3. Chew a little bit of double sweetener.
4. Chewing coriander leaves in the mouth will change the bad breath.
5. Mix the alum stone with water and apply on the cheeks.
6. Roast coriander and chew occasionally.
7. Put the malabartamarind in the oven and brush your teeth with a little salt and pepper.
9. sweet potato rubbed lightly with honey and served in the morning without interruption for a few days , The smell will change.
10. Mix 50 grams of spinach, spinach and mango leaves mix with water and drink.
11. Mix triphala powder in whey and apply on cheeks,Serve.
12. Dry the wick of the betel leaf, add honey and put it in your mouth.
13. Brushing the teeth with burnt ash on the skin of the mouth also changes the bad breath Gets good color and strength.
14. Chew cardamom in the mouth after eating.
15. Brush with Dried lemons with salt and two drops of essential oil.
16. Rub the cheeks with mint tincture.
17. Keep the cloves in your mouth.
18. Rinse mouth with boiled water added with Vayamp, salt, sandalwood, fennel and ramacham .
The main symptoms of this disease are unbearable abdominal pain, severe vomiting, diarrhea and urinary incontinence. If left untreated, dehydration can lead to loss of a good percentage of fluid in the body due to vomiting and diarrhea are the symptoms.
1. Take one viladi pill three times a day after meals with seosoned buttermilk.
2. Eat the rice baked in the water that used to make kashaya with Hog plum bark, mango bark and jaggery bark.
3. Grind sesame seed and serve it with goatmilk.
4. Drink tender coconut water with honey and thrikadupowder.
5. Take 1-3 drops each of grass oil and eucalyptus oil in hot water.
6. Take Eashwaramulla root and Koovala root in equal proportions and make kashaya serve 50 ml each in four hours.
Passing fewer than three stools a week,Having lumpy or hard stools,Straining to have bowel movements,Feeling as though there's a blockage in your rectum that prevents bowel movements,Feeling as though you can't completely empty the stool from your rectum are the symptoms
1. Soak dates in water at night and squeeze in the morning and serve.
2. Eat with drumstick leaves.
3. Eat unripe pineapple juice.
4. Drink lemon juice to hot milk on an empty stomach as soon as you wake up in the morning.
5. Add one glass of fresh water regularly at bedtime and in the morning.
6. Serve with turmeric powder and jaggery before going to bed at night.
7. Eat 15 grams of mullankodi root.
8. Mix one ounce per day after mixing jaggery in coconut water covering for a week.
9. Add a little castor oil to hot milk and eat it.
Frequent loose, watery stools,Abdominal cramps,Abdominal pain,Fever,Bleeding,Lightheadedness or dizziness from dehydration are the symptoms.
1. Grind amakuram and serve with honey.
2. Roast the curry leaves and add it to the whey for three or four days Eat.
3. Add honey to the curd and serve
4. Add chukku,muthanga and adivideyam to the kashayam and serve with meals.
5. Add powdered adivideyam to muthanga kashayam and serve
6. Clean padikkaram and eat in ginger water.
7. Add sesame seeds and sesame leafs and make it a kashaya and add jaggery,serve this daily.
8. Serve the fry of Finely grinded curry leaves in chicken eggs.
9. Drink black tea with blackcumin and ayamodaka
10.serve ginger juice with a tincture of salt
Abdominal cramping,nausea,constipation,vomiting,acid reflux,weight loss,bloody stools are the symptoms.
1. Drink boiled water with papaya, turmeric, chuck and jaggery.
2. 1g of red turmeric root per 100ml of whey roared and stirred Serve morning and evening.
3. Use the whey regularly.
4. Serve 2 g of mashed muthanga tuber dipped in honey three times a day.
5. Eat pappaya regularly.
6. Serve mashed muthanga tuber with goat milk 30ml twice a day.
7. Serve smashed tendor guava and turmeric powder daily in gooseberry size Eat.
The main symptoms are fatigue, dizziness, sore throat, heartburn, abdominal pain and nausea are the symptoms
1. Drink infusion of coriander, garlic and fenugreek
2. Chew 2 peppercorns dipped in turmeric oil.
3. Add sugarcane juice and tippali powder to curd and eat it.
4. two cloves of garlic and cumin fried in ghee for food Eat regularly before.
5. drink the hotwater with lemon juice and honey
6. Crush ginger and make tea and drink it.
7. Take five grams each of chuck pepper, cardamom and garlic Drink the infusion.
8.Drink water with crushed grampoo
9. Roast cumin in sour milk and drink it.
10.Add powedered Dry mint, cumin and fennel and add either jaggery or add a little honey or take regularly before meals.
11. crushed garlic in milk and boiled and eat after dinner.
Swelling or bulge in the groin or scrotum,Increased pain at the site of the bulge,Pain while lifting,Increase in the bulge size over time,A dull aching sensation,A sense of feeling full or signs of bowel obstruction are the symptoms
1. Grind the boiled drumsick fiber, mustard in raw rice water and add castor oil, boil it and apply on skin.
2. Grind amariroot and eat it with oil.
3. Drink the kashaya made with cow urine and turmeric barkS.
4. Add oil to the juice of cotton leaves and eat it.
When the muscle spasms, the vocal cords snap shut, producing the hiccup sound. Hiccups are often rhythmic. They are usually just a temporary minor annoyance, but prolonged hiccups may signal a major medical problem are the symptoms
1.Squeeze pomegranate, mint and turmeric and pour 2 drops in each nostril.
2. Add goat's milk with dry ginger water and sugar.
3. Have a little honey frequently.
4. Take tincture of cherula root, devataram and dry ginger.
5. Sniffing breast milk
6. inhale smoke of burned mango leaf.
7. grind sandalwood and cumin and eat a half teaspoon of it in butter.
8. Add himalayan salt powder in hot water and drink.
9. Squeeze the stalks and serve with the juice.
10. Grind long pepper in lemon juice.
11.
12. Roast dry ginger and eat it with hon
Early fullness during a meal,Uncomfortable fullness after a meal,Discomfort in the upper abdomen, Burning in the upper abdomen,Bloating in the upper abdomen,Nausea.It is a symptom of many diseases, not just diseases.Feeling of heaviness in the abdomen, bloating, pain, loss of appetite, loss of appetite and bloating on the tongue. The main causes are nausea, vomiting, constipation and diarrhea are the symptoms
1. If kappa, pumpkin and wheat are not digested, fennel water should be drunk
2. Drink water cooked with ajowan.
3. Grind ginger,long pepper and pepper evenly and serve with jaggery.
4. Squeese ginger and add salt and lemon drops
5. Chew ginger with rocksalt
6. Chew grind koduthu root and salt
7. Serve grinded long pepper with sugarcane juice mixed with honey
8. Dried pumpkin seeds can be powdered and mixed with 5 g of powdered sugar, milk and honey and eaten regularly.
9. Serve 10gms of grinded ginger leaves
Vomiting or vomiting blood — which may appear red or black,Dark blood in stools, or stools that are black or tarry,Trouble breathing, Feeling faint,Nausea or vomiting,Unexplained weight loss,Appetite changes are the symptoms.
1. Banana flower is chopped and mixed with half a quantity of drumstick leaves to make a thoran and used three times a week to cure intestinal ulcers.
2. Crush Sweetwood Add make kashaya and have each teaspoon with milk.
3. In a glass of kumbalanga juice grind 15 punarnava leaves and 15 spinach leafs and drink it daily in the morning and evening
4. Mix sweet wood, sesame seeds and jaggery in milk and serve.
5. Grind almonds and drink a glass of milk.
6. Eat thoran made with drumstick leaves and banana flower for a week.
7. Continuous consumption of pure honey cures intestinal ulcers.

1. Drink half ounce of juice made with tamarind flower 2 times a day
2. Drink milk with grinded sesame seeds.
3. Clean karuka grass and drink the juice made with it in the next day.
4. Mix coconut water with jaggery water and close it,serve it after a week.
5. Eat winter melon with jaggery.
6. In a 30 gm pepper powder and 45 gm fennel powder add some honey,serve it 4gm each 2 times a day.
7. In Roasted onion add cumin, rock sugar and cow ghee and serve.
8. Apply mustard oil will reduce the piles.
9. Eat the mix of boiled jackfruit and pig ghee.
10. IN coconut milk and add 50gm raw rice ,100 gm chopped onion and close it for a week and eat.
A sore throat, which may be severe,Red swollen tonsils,Trouble swallowing,A white or yellow coating on the tonsils,Swollen glands in the neck,Fever,Bad breath are the symptoms.
1. Add ponkaram powder and honey and apply inside and out.
2. Add tamarind and garlic and add a little salt to the boiled water Add it to the throat and place it on the throat.
3. Small teak root rubbed on the neck with warm water
4.
5. Grind the chavarkaram and apply it with honey.
6. Combine honey and rocksalt and rub in the mouth and throat.
7. Add ghee to apricot kernels and serve for seven days.
8. Thifalamthodu, chinappavu and double sweet are equally powdered Add sugar and serve.
9. Grind the garlic without touching the water and apply on the scalp.
10. Fasting for a day can easily reduce the disease. tender coconut water or lemon juice can be used.
11. Add turmeric leaves, garlic leaves and pepper and serve 3 times a day for 4 days.
12. Eat Adakkamanian powder frequently.
Abdominal pain,Diarrhea,Fever,Lightheadedness,Vertigo,Rapid pulse,Excessive sweating,Dry mouth are the symptoms.
1. Gently rub the patient's back and spray water on the face.
2. It is good to drink boiled water or porridge water from time to time to cure water loss due to vomiting.
3. Grind kanmadam and apply on the palate. Vomiting of blood will subside.
4.Squeeze the root of adalodaka mix with milk and serve with sugar.
5. Grind mango nuts,rocksalt and malr and serve.
6. Mix cardamom powder with honey and serve three times a day.
7. 50 g infusion of Parpatakapull and kashaya serve with a small amount of honey.
8. Grind cardamom in ctendor coconut water and serve.
9. Drink porridge water with salt and sugar.
10. Squeeze a clove of garlic and mix it with honey.
11. Marry the sweet potato. 200, mix flower powder with honey and eat.
12. Take cumin, wild cumin and fenugreek and boil it in water and eat it.
13. Eat two or three cardamom powders.
Abdominal pain,diarrhea, nausea, or vomiting,gas/bloating,fatigue,unexplained weight loss,abdominal pain or tenderness are the symptoms.
1. 25 gms of ginger juice mixed with onion juice is added to the sediment and mixed with honey Serve at bedtime.
2. Roll the grinded ariveppila and serve regularly in the morning.
3. Roast grinded Ishwaramulla leaves and put it on the stomach in the morning. The worm will go with the nest.
4. Mix powdered kureshani and powdered jaggery and soak in water for a day Serve the next morning. The worms in the scab will subside.
5. Serve sugar mixed with coconut milk will reduce the problem of worms
6. Serve a teaspoon powdered mathalathod mixed with half glass hot water


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Roadmap
-
Second alpha version for second minor release.
Highlights
Solvers:
- Enhanced One-Line
API:
- solver restarts
- parameter constraints
- parameter penalties
- methods for monitors, map functions, etc.
Tools:
- math tools for statistics
- discrete measures
- constraint function generator
- penalty function generator
- symbolic math support
- convergence plotter
- hypercube plotter
- scenario plotter
Issues
A list of resolved issues is here.
-
Third alpha version for second minor release.
Highlights
Solvers:
- Least Squares
- Parallel Buckshot
- Parallel Lattice
- Asynchronous Iterations
API:
- concentration-of-measures uncertainty quantification
- optimal uncertainty quantification
- parameter sensitivity ( movie)
Tools:
- model factories
- contour plotter
- database lookup
Issues
A list of resolved issues is here.
-
First alpha version for third minor release.
Highlights
Solvers:
- Quasi-Newton BFGS
- Nonlinear Conjugate Gradient
- Newton-CG
- Simulated Annealing
- Branch and Bound
- Particle Swarm
- Parallel Conditional
- Parallel Probabilistic
API:
- derivative, gradient, and hessian capture
- parameter correlation
- simultaneous fitting using parameter constraints
Tools:
- domain decomposition
- performance testing suite
Issues
A list of resolved issues is here.
-
Second alpha version for third minor release.
Highlights
Solvers:
- Convex
API:
- uncertainty quantification with existing data
- improved monitoring and control of optimization jobs
- monitoring of parallel and distributed optimization jobs
- replace signal handler with event handler
- launching of solver 'circuit' (parallel, series, etc.)
Tools:
- failure testing suite
Issues
A list of resolved issues is here.
-
In-development effort.
Solvers:
- Metropolis
- Support Vector Machines
API:
- programmatic interface to control handler commands
- control of parallel and distributed optimization jobs
- improved control of job schedulers
- design of experiments
Tools:
- configurable n-variable monitors
Issues
A list of resolved issues is here.
Note: See
TracRoadmap for help on using
the roadmap.


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Z80 Documentation
The Z80 has been thoroughly documented in books and websites,
but there are a few things that I frequently need and don't
find easily, so I'm putting them here.
Flags and Conditions
Some Z80 instructions can set flags to indicate something about
the result of the operation. For example, when subtracting a value
from the A register, the Z flag will be set if
the resulting value is zero. The flags are stored in the F
register. It is not normally accessed, but can be read by using
the push af instruction.
Load instructions and some 16-bit math instructions don't set
the flag. Check the docs for the instruction.
- S (Sign): Copy of the most
significant bit of the result. If the value is intended
to be interpreted as a signed value, then the S
flag indicates a negative value. (But don't confuse
with the N register below.)
- Z (Zero): Set if the entire result is zero.
- H (Half carry): Set if the right nibble overflows,
in other words, if there's a carry from bit 3 to bit 4. This
is only used by the DDA instruction that adjusts for
BCD arithmetic.
- P/V (Parity or oVerflow): Logical instructions
(AND, shifts, etc.) set
this to 1 if the result has an even number of bits set.
Arithmetic instructions (ADD, NEG, etc.)
set it if the 2's complement result does not fit in the register.
Overflow is different from carry in that the carry flag assumes
the operands are unsigned, whereas overflow assumes they're signed.
Put differently, overflow is set when both signed operands
are positive and signed sum is negative or both signed
operands are negative and the signed sum is positive.
- N (Negative): Set if the last instruction was
a subtraction. This is only used by the DAA instruction
that adjusts for BCD arithmetic. You probably don't need this,
don't confuse it with S.
- C (Carry): Set if the result does not fit in
the register. This assumes unsigned operands, and indicates
that the 8th bit carried into the 9th bit.
Yes, the flag that indicates a negative result should have been labeled
N and that one that indicates a subtraction should have been
labeled S, but it's the reverse of that.
To use the flags, the most direct way is to jump only when
a flag is set or reset. To jump if the result is zero:
jp z,foo
Of the six flags above, four can be used this way:
- z: Jump if the Z flag is set, i.e.,
the result is zero. Also useful to test the results of
the BIT instructions. Subroutines that return
a boolean often use the Z flag as well, perhaps
to indicate success or failure.
- nz: Jump if the Z flag is reset, i.e.,
the result is non-zero.
- c: Jump if the C flag is set, i.e.,
if there was a carry out of bit 8. The straightforward use is to see
if an addition overflowed, but many instruction use carries
as inputs or outputs, such as the shifts and rotates.
- nc: Jump if the C flag is reset, i.e.,
there was no carry.
- pe: Jump if the P flag is set, i.e.,
there was even parity out of logical instructions. Also used
to test the overflow flag.
- po: Jump if the P flag is reset, i.e.,
there was odd parity out of logical instructions. Also used
to test the overflow flag.
- m: Jump if the S flag is set, i.e.,
the result is negative (assuming it's a signed value).
Mnemonic is "minus".
- p: Jump if the S flag is reset, i.e.,
the result is non-negative (assuming it's a signed value).
Mnemonic is "positive", though it also includes zero.
It can be tricky to think through how to use these. For example,
after cp 5, how do you jump if A is less than 5?
The value 5 is subtracted from A, so A < 5 is
the same as A − 5 < 0, which means we can use the
m test condition.
Instructions JP, CALL, and
RET all optionally take these conditions. The
JR instruction only supports the z,
nz, c, and nc conditions.
There are other ways to use these flags. The ADC (add-with-carry)
instruction will add the value of the carry flag to the result,
for example. Several variants of the shift and rotate instructions
will use the carry flag as the input bit.
Shifts and Rotates
(this page does a good job of this, with diagrams: http://www.chebucto.ns.ca/~af380/z-80-g.htm)
Quick reference of the various shift and rotate instructions.
The number 7 means existing bit 7, C means carry flag, and 0
and 1 mean the bit values 0 and 1.
SRA 7->REG->C Shift Right Arithmetic
SRL 0->REG->C Shift Right Logical
RR C->REG->C Rotate Right
RRC 0->REG->C Rotate Right Circular
SLA C<-REG<-0 Shift Left Arithmetic
SLL C<-REG<-1 Shift Left Logical (undocumented)
RL C<-REG<-C Rotate Left
RLC C<-REG<-C Rotate Left Circular