What is LCA in binary tree explain and how to find lca for two node in binary search tree
Answer:
LCA in binary tree:
- LCA stand for lowest common ancestor   let's take their are two node n1 & n2 in Binary Tree T then their lcs is defined as the most closest(lowest) node of which both are descendants
-
LCA of 10 and 14 is 12
LCA of 8 and 22 is 20
-
so we can see when we come down in the tree T the node which most lowest and descendants of both node . Here we can think every node is descendants of itself
-
Now lets' see how to solve this
- One thing is clear that we need to trasverse the tree and as it is bst it easy to do so during trasverse we use to find node n such that n1<n<n2 or n is equal to one of given node
- Trasverse the tree for each node
- check if node n is greater than both node then the lcs lies on left side
- if node n is greater than both nodes than lcs lies on right side
- otherwise that node is your lcs return that
NOTE :
- Tree node is it has data ,left ,right
- tree has both node n1 and n2 present
Pseudo code
- first check if node.data >n1 && node.data >n2 go to left
- if first condition is false the check if node.data <n1 && node.data <n2 go to right
- At last if both condition fail then return node
Complexity analysis:
Time complexity :O(h) h->height of tree .
Space complexity :O(h) recursive stack.


Pies. Tons and tons of pies. I'm not exactly a foodie, but having my fingers in a load of pies effectively describes me best. I try doing justice to every project I take up: This might just be over-optimism here, but I haven't dropped the ball (or pie) on any yet. Frankly, I've had a higher success rate of 'pulling out the plum' than most, so I guess my pies have a healthy dose of luck too.
Gee, I'm hungry now.
Web tech is something I'm generally inclined towards. That aside, I'm skilled at content strategy and social media, up-to-date with SEO technologies, experienced with typography and colour matching (which, IMHO, goes a long way in making websites look absolutely gorgeous), and very decent when it comes to graphic design and editing.
Like a big bundle of gifts under a pine tree. If you've gotten me on-board for a project of yours, Merry Christmas.
From the verbosity of this project, you KNOW I love to talk. Feel free to drop me a line: Even if we can't work together, I'll be sure to try and help you along your way. Stalk me on Facebook


Crate url [−] [src]
rust-url is an implementation of the URL Standard for the Rust programming language.
It builds with Cargo.
To use it in your project, add this to your Cargo.toml
file:
[dependencies.url]
git = "https://github.com/servo/rust-url"
Supporting encodings other than UTF-8 in query strings is an optional feature that requires rust-encoding and is off by default. You can enable it with Cargo’s features mechanism:
[dependencies.url]
git = "https://github.com/servo/rust-url"
features = ["query_encoding"]
… or by passing --cfg 'feature="query_encoding"'
to rustc.
URL parsing and data structures
First, URL parsing may fail for various reasons and therefore returns a Result
.
use url::{Url, ParseError}; assert!(Url::parse("http://[:::1]") == Err(ParseError::InvalidIpv6Address))
Let’s parse a valid URL and look at its components.
use url::{Url, SchemeData}; let issue_list_url = Url::parse( "https://github.com/rust-lang/rust/issues?labels=E-easy&state=open" ).unwrap(); assert!(issue_list_url.scheme == "https".to_string()); assert!(issue_list_url.domain() == Some("github.com")); assert!(issue_list_url.port() == None); assert!(issue_list_url.path() == Some(&["rust-lang".to_string(), "rust".to_string(), "issues".to_string()][..])); assert!(issue_list_url.query == Some("labels=E-easy&state=open".to_string())); assert!(issue_list_url.fragment == None); match issue_list_url.scheme_data { SchemeData::Relative(..) => {}, // Expected SchemeData::NonRelative(..) => panic!(), }
The scheme
, query
, and fragment
are directly fields of the Url
struct:
they apply to all URLs.
Every other components has accessors because they only apply to URLs said to be
“in a relative scheme”. https
is a relative scheme, but data
is not:
use url::{Url, SchemeData}; let data_url = Url::parse("data:text/plain,Hello#").unwrap(); assert!(data_url.scheme == "data".to_string()); assert!(data_url.scheme_data == SchemeData::NonRelative("text/plain,Hello".to_string())); assert!(data_url.non_relative_scheme_data() == Some("text/plain,Hello")); assert!(data_url.query == None); assert!(data_url.fragment == Some("".to_string()));
Base URL
Many contexts allow URL references that can be relative to a base URL:
<link rel="stylesheet" href="../main.css">
Since parsed URL are absolute, giving a base is required:
use url::{Url, ParseError}; assert!(Url::parse("../main.css") == Err(ParseError::RelativeUrlWithoutBase))
UrlParser
is a method-chaining API to provide various optional parameters
to URL parsing, including a base URL.
use url::{Url, UrlParser}; let this_document = Url::parse("http://servo.github.io/rust-url/url/index.html").unwrap(); let css_url = UrlParser::new().base_url(&this_document).parse("../main.css").unwrap(); assert!(css_url.serialize() == "http://servo.github.io/rust-url/main.css".to_string());
Modules
| form_urlencoded |
Parser and serializer for the |
| format |
Formatting utilities for URLs. |
| percent_encoding | |
| punycode |
Punycode (RFC 3492) implementation. |
| urlutils |
These methods are not meant for use in Rust code, only to help implement the JavaScript URLUtils API: http://url.spec.whatwg.org/#urlutils |
Structs
| Ipv6Address |
A 128 bit IPv6 address |
| RelativeSchemeData |
Components for URLs in a relative scheme such as HTTP. |
| Url |
The parsed representation of an absolute URL. |
| UrlParser |
A set of optional parameters for URL parsing. |
Enums
| Host |
The host name of an URL. |
| ParseError |
Errors that can occur during parsing. |
| SchemeData |
The components of the URL whose representation depends on where the scheme is relative. |
| SchemeType |
Determines the behavior of the URL parser for a given scheme. |
Functions
| parse_path |
Parse |
| whatwg_scheme_type_mapper |
Type Definitions
| ErrorHandler |
This is called on non-fatal parse errors. |
| ParseResult |


Python as a Language is Inescabably Coupled with its Implementation Part 2: The Tracer
I was recently discussing some dumb Python tricks at work with some colleagues and showed them this old Gist I wrote, which in modern times I would rewrite to look like this:
import functools
import inspect
import sys
@functools.lru_cache
def getlines(filename):
with open(filename, "r") as file_handle:
return tuple(file_handle)
@functools.lru_cache
def getline(filename, line_number):
return getlines(filename)[line_number - 1]
def tracefunction(frame, event, arg):
if event == "line":
info = inspect.getframeinfo(frame)
fname, lineno, fn = info.filename, info.lineno, info.function
print(
f"> Function: {fn} (in file {fname}:{lineno}) "
f"| {getline(fname, lineno)}"
)
return tracefunction
def registertracefunction():
sys.settrace(tracefunction)
registertracefunction()
def mainfunction():
for x in range(10):
print(x * 5)
if __name__ == "__main__":
mainfunction()
Run the code for a treat!
while not as elegantly self-bootstrapping as other languages like a Lisp, Python does have lots of idiomatic, strange ways of doing Python in Python. In this case, you can set a trace function (normally reserved for writing debuggers) and do something very meta and print the dang line as it executes.
Now, this is neat and clever but it’s also largely a CPython-specific set of internal details: how would one expect to implement this API in another “flavor” of Python like IronPython or Jython which have their own runtimes that look nothing like the CPython bytecode interpreter?
Note this is in the standard library. Those poor developers would have to either 1) write this API off and get derogatorily accused of stunting their Python or do insane things to make what is trivial in CPython with byzantine .Net or JVM internals* to achieve parity.
* How would one expect this to work when calling native .Net code? What kind of decoration would need to be annotated to the compiled Python VM bytecode to make the Python runtime in that system aware that it’s reentered Python? It fills me with fear.


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shipping & handling costs. This discount is not redeemable for cash, nor
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TEI Usage Demo
Application to visualize the number of litigations by month
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Subject
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Visit the official government website for more information Click Here
The scheme has been launched with an impetus on crop sector. The scheme has extended coverage under localized risks, post-harvest losses etc. and aims at adoption of technology for the purpose of yield estimation. Through increased farmer awareness and low farmer premium rates the scheme aims at increasing the crop insurance penetration in India.Providing financial support to farmers suffering crop loss/damage arising out of unforeseen events Stabilizing the income of farmers to ensure their continuance in farming Encouraging farmers to adopt innovative and modern agricultural practices Ensuring flow of credit to the agriculture sector; which will contribute to food security, crop diversification and enhancing growth and competitiveness of agriculture sector besides protecting farmers from production risks.


I am Computer Programming student currently studying Computer Programming and Analysis program at Seneca College. I am looking for a challenging Co-op position as a programmer or a web developer.
GPA: 3.8
GPA: 3.21
Inventory Management System is an application to keep track of items in the inventory. We can add items, remove items and store the details of the items in the inventory.It can be used commercially to keep track in the warehouse or stores and helps the business.
I did all the development of the project myself. This application uses almost all the concept of the C programming such as function, pointers, file handling. I was able to understand C programming more doing this project.
Aid Management application can be used during the natural disasters to keep track of the aid supplied in the area of disaster. This application keep track of all the items to be supplied and and figures out if there is any deficiency of the aid items.
This application uses C programming. I did all the coding of the project myself as it was an individual project. I learnt about the whole application development using the C program it the professional level.
This website can be used to store the database of the employees in an organization. The user can create an account and access the list of persons of the employees, add or delete employee and update the employee record. This application uses HTML, JavaScript, Node.js, MongoDB, Express.js, HandleBars etc.
This project was important for me to understand the concepts of back end development. Even though it was a very simple project, it uses important concepts such as database handling, user logins, and and using cloud services for database storage.It helpeb me to see the big picture of backend development.
Apart from being a full time student, I enjoy most of my time being outdoors. In the winter, I like playing in the snow. During the warmer months here in Toronto, I enjoy cycling, travelling and hiking.
When forced indoors, I follow a number of sci-fi and non-fiction genre movies and television shows, I am an aspiring chef, and I spend a large amount of my free time exploring the latest technolgy advancements in the world and playing my newborn baby daughter.


zoo.tfpark package¶
Subpackages¶
Submodules¶
zoo.tfpark.estimator module¶
-
class
zoo.tfpark.estimator.
TFEstimator
(model_fn, optimizer=None, model_dir=None, config=None, params=None, warm_start_from=None)[source]¶ Bases:
object
-
evaluate
(input_fn, eval_methods, steps=None, checkpoint_path=None)[source]¶ Evaluates the model given evaluation data input_fn.
Parameters: input_fn – A function that constructs the input data for evaluation. The function should construct and return one of the following: * A TFDataset object, each elements of which is a tuple (features, labels). * A tf.data.Dataset object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below. * A tuple (features, labels): Where features is a tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs. :param eval_methods: a list of strings to specify the evaluation metrics to be used in this model :param steps: Number of steps for which to evaluate model. :param checkpoint_path: Path of a specific checkpoint to evaluate. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, evaluation is run with newly initialized Variables instead of ones restored from checkpoint.
Returns: A dict containing the evaluation metrics specified in model_fn keyed by name.
-
predict
(input_fn, checkpoint_path=None)[source]¶ Outputs predictions for given features.
Parameters: input_fn – A function that constructs the features. - A TFDataset object, each elements of which is a tuple (features, None).
- A tf.data.Dataset object: Outputs of Dataset object must have
same constraints as below. * features: A tf.Tensor or a dictionary of string feature name to Tensor. features are consumed by model_fn. They should satisfy the expectation of model_fn from inputs. * A tuple, in which case the first item is extracted as features.
Parameters: checkpoint_path – Path of a specific checkpoint to predict. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, prediction is run with newly initialized Variables instead of ones restored from checkpoint.
Return: Evaluated values of predictions tensors.
-
set_constant_gradient_clipping
(min, max)[source]¶ Configure constant clipping settings.
Parameters: - min_value – the minimum value to clip by
- max_value – the maxmimum value to clip by
-
set_gradient_clipping_by_l2_norm
(clip_norm)[source]¶ Configure L2 norm clipping settings.
Parameters: clip_norm – gradient L2-Norm threshold
-
train
(input_fn, steps=None)[source]¶ Trains a model given training data input_fn.
Parameters: input_fn – A function that constructs the input data for evaluation. The function should construct and return one of the following: * A TFDataset object, each elements of which is a tuple (features, labels). * A tf.data.Dataset object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below. * A tuple (features, labels): Where features is a tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs. :param steps: Number of steps for which to train the model.
Returns: self, for chaining.
-
zoo.tfpark.model module¶
-
class
zoo.tfpark.model.
KerasModel
(model)[source]¶ Bases:
object
-
evaluate
(x=None, y=None, batch_per_thread=None, distributed=False)[source]¶ Evaluate a model on a given dataset
Parameters: x – Input data. It could be: - a TFDataset object
- A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. :param y: Target data. Like the input data x, It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely). If x is a TFDataset, y should not be specified (since targets will be obtained from x). :param batch_per_thread: The default value is 1. When distributed is True,the total batch size is batch_per_thread * rdd.getNumPartitions. When distributed is False the total batch size is batch_per_thread * numOfCores. :param distributed: Boolean. Whether to do prediction in distributed mode or local mode. Default is True. In local mode, x must be a Numpy array.
-
fit
(x=None, y=None, batch_size=None, epochs=1, validation_split=0.0, validation_data=None, distributed=False, **kwargs)[source]¶ Train the model for a fixed num of epochs
Arguments: :param x: Input data. It could be: - a TFDataset object - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. :param y: Target data. Like the input data x, It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely). If x is a TFDataset, y should not be specified (since targets will be obtained from x). :param batch_size: Integer or None. Number of samples per gradient update. If x is a TFDataset, you do not need to specify batch_size. :param epochs: Integer. Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided. :param validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. :param validation_data: Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. validation_data will override validation_split. validation_data could be: - tuple (x_val, y_val) of Numpy arrays or tensors - TFDataset :param distributed: Boolean. Whether to do prediction in distributed mode or local mode. Default is True. In local mode, x must be a Numpy array.
-
static
load_model
(path)[source]¶ Load an existing keras model (with weights) from HDF5 file.
Parameters: path – String. The path to the pre-defined model. Returns: KerasModel.
-
metrics_names
¶
-
predict
(x, batch_per_thread=None, distributed=False)[source]¶ Use a model to do prediction.
Parameters: x – Input data. It could be: - a TFDataset object
- A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. :param batch_per_thread: The default value is 1. When distributed is True,the total batch size is batch_per_thread * rdd.getNumPartitions. When distributed is False the total batch size is batch_per_thread * numOfCores. :param distributed: Boolean. Whether to do prediction in distributed mode or local mode. Default is True. In local mode, x must be a Numpy array.
-
save_model
(path)[source]¶ Save the model to a single HDF5 file.
Parameters: path – String. The path to save the model.
-
set_train_summary
(summary)[source]¶ Set training summary for visualization.
Parameters: summary – bigdl.optim.optimizer.TrainSummary
-


Plot
Santiago, an Andalusian shepherd boy, has a dream about finding a treasure in the Pyramids of Egypt. A gypsy woman and an old man claiming to be a mysterious king advise him to pursue it. “To realize one’s destiny is a person’s only obligation,” the old man tells him. “And, when you want something, all the universe conspires in helping you to achieve it.”
With the courage of an adventurer, Santiago sells his sheep and travels to Tangier in Africa. After a thief steals his money, Santiago takes a job with a crystal merchant who unwittingly teaches Santiago important lessons for his longing journey ahead. After working at the crystal shop for a year, Santiago earns enough money to cover his losses and return home. But then something unexpected happens. On a desert caravan, Santiago meets an intriguing Englishman. The Englishman’s passion for knowledge and his relentless quest to uncover the secrets of alchemy inspire Santiago to pursue his own dream of finding the treasure. As the Englishman searches for the two–hundred–year–old alchemist who resides in the desert oasis, Santiago falls in love with a young woman, Fatima. Exposed to the greatest and eternal alchemy of all-love-Santiago thinks he has found the treasure. But the greatest test of all is yet to come. With the help of the alchemist, Santiago completes the last leg of his journey–dangerous and infused with discoveries of the most profound kind–to find that the treasure he was looking for was waiting for him in the place where he least expected.
This story, timeless and entertaining, exotic yet simple, breaks down the journey we all take to find the most meaningful treasures in our lives into steps that are at once natural and magical. It is about the faith, power, and courage we all have within us to pursue the intricate pat of a Personal Legend, a path charged by the mysterious magnet of destiny but obscured by distractions. Santiago shows how along the way we learn to trust our hearts, read the seemingly inconspicuous signs, and understand that as we look to fulfill a dream, it looks to find us just the same, if we let it.


In this repository we try to solve the problem of training a deep learning model to learn representations to figure out classify german traffic signs.
Most of the discussion done here can be obtained at a glance on the following:
It is important to to observe the following notebooks:
This notebook contains the explorations done to the provided German Traffic Dataset provided by Udacity, we also use the notebook to explore how to use the tf.data
API.
In this notebook we take our previously trained model in /models
(Link to download pretrained models here: model link ) and observe the validation score obtained in the validation dataset and make inference in both validation data and unexplored data.
In this notebook we explore in which cases the model makes a mistake and compute again the validation score manually.
You should a data
folder that contains:
test.p
train.p
valid.p
This repository consists of a Dockerfile
and docker-compose.yml
that contains all the dependencies and services for notebook visualization and training.
Do not forget to build the services for the first time:
(all the commands have root directory in the root of the repository)
$ docker-compose build
$ docker-compose up notebook
$ docker-compose up train
$ docker-compose up ipython-shell
All the code for training and testing is contained in a package called traffic_sign_classifier
The tree structure of the module consists of:
german_traffic_dataset.py: Which is the module that contains the data pipelines and data augmentation
german_traffic_densenet.py: Which is the module that contains the model function for the densenet architecure and their parameters.
german_traffic_main_densenet.py: Module that contains the main loop for training.
configs.yml Config file that contains the main hyperparameters for training. They can be overrided by command line arguments. But is preferable to use just modify the configs.yml
After training or you can download the models from here:
The structure should have a folder model and a data folder such as:
Before deciding the architecture we make a exploration of the dataset.
As we can see:
Our training dataset consists of 347999 elements for training, is a good dataset, but:
it is completly imbalanced so this suggest in adding data augmentation on our data pipelines. This is done in lines 17 to 31 in german_traffic_dataset.py
def train_preprocess(image, label):
if label in [11, 12, 13, 15, 17, 18, 22, 26, 30, 35]:
image = tf.image.random_flip_left_right(image)
if label in [1, 5, 12, 15, 17]:
image = tf.image.random_flip_up_down(image)
image = tf.image.random_brightness(image, max_delta=32.0 / 255.0)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
# Make sure the image is still in [0, 1]
image = tf.clip_by_value(image, 0.0, 1.0)
return image, label
We artifically make some random augmentations in certain classes.
The visualization is done in :
The preprocessing stages and the data augmentation are in the german_traffic_dataset.py the preprocessing consists of:
def input_fn(images, labels, params, training):
ds = tf.data.Dataset.from_tensor_slices((
images,
labels,
))
ds = ds.map(parse_function, num_parallel_calls=4)
if training:
ds = ds.map(train_preprocess, num_parallel_calls=4)
ds = ds.apply(tf.data.experimental.shuffle_and_repeat(
buffer_size = params.buffer_size,
count = params.epochs,
))
#ds ds.map(lambda x, y: ({"images": x}, y))
# ds = ds.map(lambda x, y: (tf.cast(x, tf.float32), tf.cast(y, tf.int32)))
ds = ds.batch(params.batch_size, drop_remainder=True)
ds = ds.prefetch(buffer_size=2)
return ds
Now that we have our dataset input_fn and trainig data as a tf.data.Dataset
we can iterate over our model architecture.
We decide for this problem implement the DenseNet architecture:
Using tensorboard we can see the graph representation of the DenseNet class definded in german_traffic_densenet.py:
We follow the paper closely by defining the blocks:
In a more high level we follow:
We use tf.AdamOptimizer(0.0001)
with a initial learning rate of 0.0001. We trained for 300 epochs. And resulting in the following loss graphs:
We observe that we achive a 96% training and a 95% accuracy on the train and eval datasets respectively.
Also our loss seems to indicate that we could have gone with a better model and more accuracy if we had trained for more steps:
As seen in our notebooks
Model Accuracy and Validation Exploration
Using the validation set:
On a more manual observation we observe that, in the validation set we make a mistake often with images of the first class:.
We made inference on new images that are located in the folder test_images
We make inference on validation images that were never seen either in evaluation or training, and web images.
More info in the Jupyter Notebook Model Accuracy
Validation images
We see that we make two mistakes, which I think is pretty good.
External Web Images
Now we take external images from the web and make inference.
So we see that in the case that we make one mistake, the network have a certain distribution to the correct class I think that we should let train the model for a few more epochs.


| |
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New Engineer of Record at {{ mine.mine_name }} requires Ministry
acknowledgement.
|
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As of {{ start_date }}, a new Engineer of Record, {{ party.first_name }}
{{ party.last_name }}, has been assigned to {{ tsf_name }} at
{{ mine.mine_name }} and requires Ministry acknowledgement to allow for
the mine's compliance.
Mine number: {{ mine.mine_no }}
Submitted: {{ submitted_at }}
|
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public abstract class ResponseStateManager
extends java.lang.Object
ResponseStateManager is the helper class to
StateManager
that knows the specific rendering technology being used to generate
the response. It is a singleton abstract class, vended by the RenderKit
. This class knows the mechanics of
saving state, whether it be in hidden fields, session, or some combination of the two.
| Modifier and Type | Field and Description |
|---|---|
static java.lang.String |
CLIENT_WINDOW_PARAM
The name of the hidden field that refers to the encoded ClientWindow. |
static java.lang.String |
CLIENT_WINDOW_URL_PARAM
The name of the URL query parameter for transmitting the client window id. |
static java.lang.String |
NON_POSTBACK_VIEW_TOKEN_PARAM
The value of this constant is taken to be the name of a request parameter whose value is inspected to verify the
safety of an incoming non-postback request with respect to the currently configured |
static java.lang.String |
RENDER_KIT_ID_PARAM
The name of the request parameter used by the default implementation of
ViewHandler.calculateRenderKitId(jakarta.faces.context.FacesContext) to derive a RenderKit ID. |
static java.lang.String |
VIEW_STATE_PARAM
Implementations must use this constant field value as
the name of the client parameter in which to save the state between requests.
|
| Constructor and Description |
|---|
ResponseStateManager() |
| Modifier and Type | Method and Description |
|---|---|
java.lang.Object |
getComponentStateToRestore(FacesContext context)
Deprecated.
This method has been replaced by
getState(jakarta.faces.context.FacesContext, java.lang.String) . The default implementation returns null . |
java.lang.String |
getCryptographicallyStrongTokenFromSession(FacesContext context)
Compliant implementations must return a cryptographically strong token for use to protect views in this application. |
java.lang.Object |
getState(FacesContext context,
java.lang.String viewId)
The implementation must inspect the current request and return an Object
representing the tree structure and component state passed in to a previous invocation of
writeState(jakarta.faces.context.FacesContext,java.lang.Object) . |
java.lang.Object |
getTreeStructureToRestore(FacesContext context,
java.lang.String viewId)
Deprecated.
This method has been replaced by
getState(jakarta.faces.context.FacesContext, java.lang.String) . The default implementation returns null . |
java.lang.String |
getViewState(FacesContext context,
java.lang.Object state)
Return the specified state as a
String without any markup related to the rendering technology supported
by this ResponseStateManager. |
boolean |
isPostback(FacesContext context)
Return true if the current request is a postback.
|
boolean |
isStateless(FacesContext context,
java.lang.String viewId)
If the preceding call to |
void |
writeState(FacesContext context,
java.lang.Object state)
Take the argument
state and write it into the output using the
current ResponseWriter , which must be correctly positioned already. |
void |
writeState(FacesContext context,
StateManager.SerializedView state)
Deprecated.
This method has been replaced by
writeState(jakarta.faces.context.FacesContext,java.lang.Object) . The default implementation creates a two
element Object array with the first element being the return from calling
StateManager.SerializedView.getStructure() , and the second being the return from StateManager.SerializedView.getState() . It then
passes this Object array to writeState(jakarta.faces.context.FacesContext, java.lang.Object) . |
public static final java.lang.String RENDER_KIT_ID_PARAM
The name of the request parameter used by the default implementation of
ViewHandler.calculateRenderKitId(jakarta.faces.context.FacesContext)
to derive a RenderKit ID.
public static final java.lang.String VIEW_STATE_PARAM
Implementations must use this constant field value as
the name of the client parameter in which to save the state between requests. The
id
attribute must be a concatenation of the return from
UIComponent.getContainerClientId(jakarta.faces.context.FacesContext)
, the return from
UINamingContainer.getSeparatorChar(jakarta.faces.context.FacesContext)
, this constant field value, the separator char,
and a number that is guaranteed to be unique with respect to all the other instances of this kind of client parameter
in the view.
It is strongly recommend that implementations guard against cross site scripting attacks by at least making the value of this parameter difficult to predict.
public static final java.lang.String CLIENT_WINDOW_PARAM
The name of the hidden field that refers to the encoded ClientWindow. This field is only used if
ClientWindow.CLIENT_WINDOW_MODE_PARAM_NAME
is not "none". The id
attribute must be a concatenation of the return from UIComponent.getContainerClientId(jakarta.faces.context.FacesContext)
,
the return from UINamingContainer.getSeparatorChar(jakarta.faces.context.FacesContext)
, this constant field value, the
separator char, and a number that is guaranteed to be unique with respect to all the other instances of this kind of
client parameter in the view. The value of this parameter is the return from
ClientWindow.getId()
.
public static final java.lang.String CLIENT_WINDOW_URL_PARAM
The name of the URL query parameter for transmitting the client window id. This parameter is only used if
ClientWindow.CLIENT_WINDOW_MODE_PARAM_NAME
is not "none". The name of the parameter
is given by the constant value of this field. The value of this parameter is the return from
ClientWindow.getId()
.
public static final java.lang.String NON_POSTBACK_VIEW_TOKEN_PARAM
The value of this constant is taken to be the name of a request parameter whose value is inspected to verify the
safety of an incoming non-postback request with respect to the currently configured Set
of protected
views for this application.
public void writeState(FacesContext context, java.lang.Object state) throws java.io.IOException
Take the argument state
and write it into the output using the
current ResponseWriter
, which must be correctly positioned already.
Call FacesContext.getViewRoot()
. If StateHolder.isTransient()
returns
true
, take implementation specific action so that the following call to isStateless(jakarta.faces.context.FacesContext, java.lang.String)
returns
true
and return. Otherwise, proceed as follows.
If the state is to be written out to hidden fields, the implementation must take care to make all necessary character replacements to make the Strings suitable for inclusion as an HTTP request paramater.
If the state saving method for this application is
StateManager.STATE_SAVING_METHOD_CLIENT
, the implementation
must encrypt the state to be saved to the client
in a tamper evident manner.
If the state saving method for this application is
StateManager.STATE_SAVING_METHOD_SERVER
, and the current request is an
Ajax
request PartialViewContext.isAjaxRequest()
returns true
),
use the current view state identifier if it is available (do not generate a new identifier).
Write out the render kit identifier associated with this ResponseStateManager
implementation with the
name as the value of the String
constant ResponseStateManager.RENDER_KIT_ID_PARAM
. The
render kit identifier must not be written if:
Application.getDefaultRenderKitId()
orjakarta.faces.render.RenderKitFactory.HTML_BASIC_RENDER_KIT
and Application.getDefaultRenderKitId()
returns null
.
For backwards compatability with existing ResponseStateManager
implementations, the default
implementation of this method checks if the argument is an instance of SerializedView
. If so, it calls
through to
writeState(jakarta.faces.context.FacesContext,jakarta.faces.application.StateManager.SerializedView)
. If
not, it expects the state to be a two element Object array. It creates an instance of SerializedView
and
stores the state as the treeStructure, and passes it to
writeState(jakarta.faces.context.FacesContext,jakarta.faces.application.StateManager.SerializedView)
.
The ClientWindow
must be written using these steps. Call
ExternalContext.getClientWindow()
. If the result is null
, take no further
action regarding the ClientWindow
. If the result is non-null
, write a hidden field whose
name is CLIENT_WINDOW_PARAM
and whose id is
<VIEW_ROOT_CONTAINER_CLIENT_ID><SEP>jakarta.faces.ClientWindow<SEP><UNIQUE_PER_VIEW_NUMBER>
where <SEP> is the currently configured UINamingContainer.getSeparatorChar()
.
<VIEW_ROOT_CONTAINER_CLIENT_ID> is the return from UIViewRoot.getContainerClientId()
on the view
from whence this state originated. <UNIQUE_PER_VIEW_NUMBER> is a number that must be unique within this view,
but must not be included in the view state. The value of the field is implementation dependent but must uniquely
identify this window within the user's session.
context
- The FacesContext
instance for the current requeststate
- The serialized state information previously savedjava.io.IOException
- if the state argument is not an array of length 2.@Deprecated public void writeState(FacesContext context, StateManager.SerializedView state) throws java.io.IOException
writeState(jakarta.faces.context.FacesContext,java.lang.Object)
. The default implementation creates a two
element Object
array with the first element being the return from calling
StateManager.SerializedView.getStructure()
, and the second being the return from StateManager.SerializedView.getState()
. It then
passes this Object
array to writeState(jakarta.faces.context.FacesContext, java.lang.Object)
.
Take the argument state
and write it into the output using the current ResponseWriter
, which
must be correctly positioned already.
If the StateManager.SerializedView
is to be written out to hidden fields, the
implementation must take care to make all necessary character replacements to make the Strings suitable for inclusion
as an HTTP request paramater.
If the state saving method for this application is
StateManager.STATE_SAVING_METHOD_CLIENT
, the implementation may encrypt the state
to be saved to the client. We recommend that the state be unreadable by the client, and also be tamper evident. The
reference implementation follows these recommendations.
context
- The FacesContext
instance for the current requeststate
- The serialized state information previously savedjava.io.IOException
- if the state cannot be written for any reasonpublic boolean isStateless(FacesContext context, java.lang.String viewId)
If the preceding call to writeState(jakarta.faces.context.FacesContext, java.lang.Object)
was stateless,
return true
. If the preceding call to writeState()
was stateful, return false
. Otherwise
throw IllegalStateException
.
To preserve backward compatibility with custom implementations that may have extended from an earlier version of this
class, an implementation is provided that returns false
. A compliant implementation must override this
method to take the specified action.
context
- The FacesContext
instance for the current requestviewId
- View identifier of the view to be restoredjava.lang.NullPointerException
- if the argument context
is null
.java.lang.IllegalStateException
- if this method is invoked and the statefulness of the preceding call to
writeState(jakarta.faces.context.FacesContext, java.lang.Object)
cannot be determined.public java.lang.Object getState(FacesContext context, java.lang.String viewId)
The implementation must inspect the current request and return an Object
representing the tree structure and component state passed in to a previous invocation of
writeState(jakarta.faces.context.FacesContext,java.lang.Object)
.
If the state saving method for this application is
StateManager.STATE_SAVING_METHOD_CLIENT
, writeState()
will have
encrypted the state in a tamper evident manner. If the state fails to decrypt, or decrypts but indicates evidence of
tampering, a ProtectedViewException
must be thrown.
For backwards compatability with existing ResponseStateManager
implementations, the default
implementation of this method calls getTreeStructureToRestore(jakarta.faces.context.FacesContext, java.lang.String)
and getComponentStateToRestore(jakarta.faces.context.FacesContext)
and
creates and returns a two element Object
array with element zero containing the structure
property and element one containing the state
property of the SerializedView
.
context
- The FacesContext
instance for the current requestviewId
- View identifier of the view to be restoredwriteState
. If this is an initial
request, this method returns null
.@Deprecated public java.lang.Object getTreeStructureToRestore(FacesContext context, java.lang.String viewId)
getState(jakarta.faces.context.FacesContext, java.lang.String)
. The default implementation returns null
.
The implementation must inspect the current request and return the tree structure Object passed to it on a previous
invocation of writeState()
.
context
- The FacesContext
instance for the current requestviewId
- View identifier of the view to be restored@Deprecated public java.lang.Object getComponentStateToRestore(FacesContext context)
getState(jakarta.faces.context.FacesContext, java.lang.String)
. The default implementation returns null
.
The implementation must inspect the current request and return the component state Object passed to it on a previous
invocation of writeState()
.
context
- The FacesContext
instance for the current requestpublic boolean isPostback(FacesContext context)
Return true if the current request is a postback. This method is leveraged from the Restore View Phase to
determine if ViewHandler.restoreView(jakarta.faces.context.FacesContext, java.lang.String)
or
ViewHandler.createView(jakarta.faces.context.FacesContext, java.lang.String)
should be called. The default implementation must return
true
if this ResponseStateManager
instance wrote out state on a previous request to which
this request is a postback, false
otherwise.
The implementation of this method for the Standard HTML RenderKit must consult the
ExternalContext
's requestParameterMap
and return true
if and
only if there is a key equal to the value of the symbolic constant VIEW_STATE_PARAM
.
For backwards compatibility with implementations of ResponseStateManager
prior to Jakarta Server Faces
1.2, a default implementation is provided that consults the ExternalContext
's
requestParameterMap
and return true
if its size is greater than 0.
context
- the FacesContext
for the current request.public java.lang.String getViewState(FacesContext context, java.lang.Object state)
Return the specified state as a String
without any markup related to the rendering technology supported
by this ResponseStateManager.
context
- the FacesContext
for the current requeststate
- the state from which the String version will be generated frompublic java.lang.String getCryptographicallyStrongTokenFromSession(FacesContext context)
Compliant implementations must return a cryptographically strong token for use to protect views in this application.
For backwards compatability with earlier revisions, a default implementation is provided that simply returns
null
.
context
- the FacesContext
for the current request


THE BEST WII U
GAMES OF 2016
Nintendo's Wii U has a reputation as a bit of a video game industry punching bag. It's much less powerful than the competition, its tablet-like GamePad controller hasn't been utilized well in many games, and most games don't come out on the console.
All of those things, as well as the fact that plenty of people don't realize it's a whole new console, have made it Nintendo's least successful console. But the games that are exclusive to Wii U tend to be really, really good. To that end, we put together a list with the best games to play on the Wii U right now:


Some Drawable may also be {@link plot3d.rendering.Wireframeable3d} to offer wireframe display control on 3d objects for which it make sense: Polygons, Spheres, etc. A Point has obviously no existing wireframe mode.
Pure Drawable are implicitely considered as Primitive shapes and provide a function for drawing themself. A Composite3d may encapsulate several Primitives and delegate rendering or transformation to its Primitives.
When designing a Composite3d, the programer must be aware that no real SceneGraph is currently available, and that the glscale, glrotate and gltransform calls must be set statically into the draw function of the Composite3d.


Sensex hits new intra-day high with Infosys shares rising 2.4%
The Sensex rose 0.6 per cent, or 222 points, to close at 40,470, with Infosys shares rising 2.4 per cent, making one of the highest contribution to the gauge's gain
by BloombergThe Sensex advanced to a record in a volatile session on Wednesday, after Infosys said the company is winning deals even as it faces a “distraction” from whistle-blowers.
The Sensex rose 0.6 per cent, or 222 points, to close at 40,470, with Infosys shares rising 2.4 per cent, making one of the highest contribution to the gauge’s gain.
The Nifty rose 0.4 per cent, or 44 points, to end at 11,961 after touching an intra-day high of 12,003. Sentiment also got a boost from better-than-expected earnings for the September quarter.
Of the 32 Nifty firms to have reported quarterly earnings, 21 have either beaten or matched the average analyst estimate, while one didn’t have enough projections.


You go next door because the lights are on upstairs. Maybe {redacted} and her Xbox are up there. I hope she is not the type of person who tries to make it look like all she does is read. Never one to use the internet by having a lot of no-tech stuff downstairs. Then upstairs, it's like a Best Buy on steroids. You ring the doorbell, and the door opens.  It says 'Welcome to the Library" in big fancy letters.


“dnc.org.nz” (Active)
| Query Date Time | 5 January 2018 7:15 am |
| Domain Name | dnc.org.nz |
| Query Status | Active |
| Domain Date Registered | 23 April 2002 12:00 am |
| Domain Date Billed Until | 23 January 2018 12:00 am |
| Domain Date Last Modified | 23 December 2017 11:32 pm |
| Domain Delegate Requested | yes |
| Domain Signed | no |
| Registrar Name | Domain Name Commissioner |
| Registrar Address | PO Box 11881 |
| Registrar City | Wellington |
| Registrar Province | North-ish |
| Registrar Postal Code | 1234 |
| Registrar Country | NZ (NEW ZEALAND) |
| Registrar Phone | +64 4 472 1600 |
| Registrar Fax | +64 4 495 2115 |
| Registrar Email |<EMAIL_ADDRESS>|
| Registrant Contact Name | Domain Name Commission Ltd |
| Registrant Contact Address | PO Box 11882 |
| Registrant Contact City | Auckland |
| Registrant Contact Postal Code | 6142 |
| Registrant Contact Country | NZ (NEW ZEALAND) |
| Registrant Contact Phone | +64 4 472 1700 |
| Registrant Contact Email |<EMAIL_ADDRESS>|
| Admin Contact Name | Domain Name Commission Admin |
| Admin Contact Address | PO Box 11883 |
| Admin Contact City | Christchurch |
| Admin Contact Postal Code | 6143 |
| Admin Contact Country | NZ (NEW ZEEELAND) |
| Admin Contact Phone | +64 4 472 1800 |
| Admin Contact Email |<EMAIL_ADDRESS>|
| Technical Contact Name | Domain Name Commission Tech |
| Technical Contact Address | PO Box 11884 |
| Technical Contact City | Hutt |
| Technical Contact Postal Code | 6144 |
| Technical Contact Country | NZ (NEW ZULAND) |
| Technical Contact Phone | +64 4 472 1900 |
| Technical Contact Email |<EMAIL_ADDRESS>|
| NS Name | ns1.dns.net.nz |
| NS Name | ns2.dns.net.nz |
| NS Name | ns3.dns.net.nz |
| NS Name | ns4.dns.net.nz |
Terms of Use:
By submitting a WHOIS query you are entering into an agreement with Domain
Name Commission Ltd on the following terms and conditions, and subject to
all relevant .nz Policies and procedures as found at https://dnc.org.nz/.
It is prohibited to:
- Send high volume WHOIS queries with the effect of downloading part of or
all of the .nz Register or collecting register data or records;
- Access the .nz Register in bulk through the WHOIS service (ie. where a
user is able to access WHOIS data other than by sending individual queries
to the database);
- Use WHOIS data to allow, enable, or otherwise support mass unsolicited
commercial advertising, or mass solicitations to registrants or to
undertake market research via direct mail, electronic mail, SMS, telephone
or any other medium;
- Use WHOIS data in contravention of any applicable data and privacy laws,
including the Unsolicited Electronic Messages Act 2007;
- Store or compile WHOIS data to build up a secondary register of
information;
- Publish historical or non-current versions of WHOIS data; and
- Publish any WHOIS data in bulk.
Copyright Domain Name Commission Limited (a company wholly-owned by Internet
New Zealand Incorporated) which may enforce its rights against any person or
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The WHOIS service is provided by NZRS Limited.


简要介绍用户界面
控制台UI分为两个窗格：一个编辑器窗格（左）和一个响应窗格（右）。使用编辑器类型的请求提交到Elasticsearch。结果将显示在右侧的响应窗格.
Console understands requests in a compact format, similar to cURL:
While typing a request, Console will make suggestions which you can than accept by hitting Enter/Tab. These suggestions are made based on the request structure as well as your indices and types.
A few quick tips, while I have your attention


The edit value modal allows you to change the value of a record field/element.
The edit value modal can be opened by:
You can click the OK button or press Ctrl + Enter to apply the value to the field you edited and close the modal.
Some value types have validation. If a value is invalid, the input will have a red border, the OK button will be disabled, and pressing Ctrl + Enter will not apply the value.
You can discard your changes to the value by clicking the Cancel button or by closing the modal.
String values and single line text values, such as Editor IDs and FULL Names.
Text values are multi-line string values, such as book descriptions. Some text values support HTML and CSS, and use a CodeMirror editor with syntax highlighting.
Number values are integer or floating point values. Number values have basic validation.
Reference values are form IDs corresponding to other records in the plugin or its master files. When editing reference values you are first required to provide a record signature to reference which is be limited to valid references for the field. You can then enter any substring contained in the editor ID, hexadecimal form ID, or FULL name of the record you want to reference to search for it. Search results will appear in a dropdown. You must select a search result from the dropdown or provide a full hexadecimal form ID to apply a reference value properly.
Color values are element structures which have a red, green, and blue value. The color editor allows you to specify a color value via an HSL gradient map or by entering an RGB/Hex color value into the text input. E.g. rgb(0, 128, 255)
or #0080FF
. Hex strings get converted to RGB strings once entered.
Enumeration values are integer fields with a small set of valid values which have unique meanings. E.g. The Armor Type
field on armor records supports the values 0, 1, and 2, which indicate Light Armor, Heavy Armor, and Clothing, respectively. Enumeration values can be selected from a dropdown.
Flag values are fields where each bit indicates whether or not a particular flag is enabled. Flags are displayed as a checkbox list.
Byte array values are binary fields. They can be edited from a minature hex editor which allows you to overwrite individual bytes without changing the fixed length of the byte array.


R/precision.R
precision_risk_difference.Rd
These functions calculate the precision of an estimate given a certain sample size. For ratio measures, like the risk ratio, rate ratio, and odds ratio, this is the ratio of the upper to lower limit of the confidence interval. For difference measures, like the risk difference or rate difference, this is the absolute width of the confidence interval.
precision_risk_difference(n_exposed, exposed, unexposed, group_ratio, ci = 0.95) precision_risk_ratio(n_exposed, exposed, unexposed, group_ratio, ci = 0.95) precision_rate_difference(n_exposed, exposed, unexposed, group_ratio, ci = 0.95) precision_rate_ratio(n_exposed, exposed, unexposed, group_ratio, ci = 0.95) precision_odds_ratio(n_cases, exposed_cases, exposed_controls, group_ratio, ci = 0.95)
| n_exposed, n_cases | In cohort studies, the number of exposed participants. In case-control studies, the number of cases. |
|---|---|
| exposed | The risk or rate among the exposed cohort. |
| unexposed | The risk or rate among the unexposed cohort. |
| group_ratio | In cohort studies, the ratio of the unexposed to the exposed. In case-control studies, the ratio of the controls to the cases. |
| ci | The confidence interval as a probability or percent. Default is .95. |
| exposed_cases | The proportion of exposed cases. |
| exposed_controls | The proportion of exposed controls. |
a tibble with precision, effect measure, and sample size
Rothman, K.J. and Greenland, S. 2018. Planning Study Size Based on Precision Rather Than Power. 29(5):599-603.
# From Rothman and Greenland 2018 precision_odds_ratio( n_cases = 500, exposed_cases = .6, exposed_controls = .4, group_ratio = 2 )#> [90m# A tibble: 1 x 9[39m #> precision odds_ratio n_cases n_controls n_total exposed_cases exposed_controls #> [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m #> [90m1[39m 1.55 2.25 500 [4m1[24m000 [4m1[24m500 0.6 0.4 #> [90m# ... with 2 more variables: group_ratio [3m[90m<dbl>[90m[23m, ci [3m[90m<dbl>[90m[23m[39m


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Austin, TX
Austin rapper Abhi the Nomad's stage name is inspired by a childhood spent all over the globe with his father, an Indian
diplomat. He seems to have inherited his father's knack for negotiation and applied it to
his own craft; his full-length debut, 2018's Marbled, strikes a pitch-perfect balance between
rap, r&b, indie rock, and more. Abhi's latest single, "FLUSH", signals an even more left-field
turn for the rapper, so we can’t wait to see what he brings to his Fest set!


Complete to below form details and hit the 'Submit' button - simple!
infoInstructions For Pasting Image Links
To paste a URL for an image and commit this to the database, simply find the image you want via google or directly from a webpage and then: Right click on the image and select the option 'copy image addess':


Enter key bindings strings below, one per line, for testing, then press "Update". Some things to note. Handlers that don't use the Control, Alt, or Meta keys will not work when an input, textarea, or other editable element is focused. This is by-design to avoid clobbering native text manipulation commands. The bindings in the colored boxes will only work if the corresponding inputs have focus. This allows for context- sensitive bindings.


PETER'S FRIENDS A film review by Michael Stoneheel Copyright 1993 Michael Stoneheel
After Kenneth Branagh first film several critics compared him to Lawrence Olivier. After his second film he was compared to Orson Welles. What next?
Personally, I liked his first two films. I thought he was an excellent actor but I didn't find any greatness in his directing. Knowing his intention to imitate other films, I expected a British version of THE BIG CHILL.
 It is a sentimental dramatic comedy (what a disturbing term) written by Rita Rudner and Branagh concerning a reunion of friends after ten years.
The film begins with several cinematographic experiments. Branagh used home-video photographing style to show the main characters' last meeting at 1982 (he was probably influenced by Woody Allen). This method can irritate the audience, create an authentic atmosphere, and give an impression of another objective character (namely the camera)--none has been achieved!
Afterwards, a sequence of events from the 80's is presented with some documentary effect. The arrival of all the friends to Peter's home could be considered as the only brilliant scene of the film. It is fast, funny, sarcastic and accurate. From this point the film circulates between a tedious melodrama and comedy. Several jokes in the film made me laugh (and it is very difficult during the last years) but some of the gags were used too much (for example, the laughter of Tony Slattery). The sentimental scenes are boring and it would have been better off without some of them.
The only thing left is to expect a good performance from such fine actors, well, forget it! Hugh Laurie and Imelda Staunton are annoying, Rita Rudner gives the best performance but when we understand it she is gone, Stephen Fry and Emma Thompson are reasonably good and Branagh is excellent in the start but soon he becomes too self-conscious with his acting. The main problem is that the actors do not relate to each other. What could have been a perfect ensemble turned to be a complete chaos.
This film is another proof that Branagh hasn't understood the cinema media yet. The script is suitable for television and not for cinema, the casting is not balanced and the cinematography has no effect. The only comfort is that due to Branagh's talent the film is mediocre but not bad!
.
The review above was posted to the
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Course Objectives: To get a basic understing of geographic principles allowing you to use GIS software to manipulate, transform and style data to make maps that displays data with integretiy and helps answer questions.
Schedule
Day 1: Intro Geography + Data and Intro to QGIS
Day 2: Using OpenStreetMap data in QGIS and Cartography
Day 3: Final Project!
Expectations
Listen!...if you aren't listening then why are you hear?
Participate...you will learn more that way
Ask questions when you are stuck
Be nice! We have a lot of different background and skills at this training
What are Maps?
Why Maps?
They look cool!
They are informative
They are useful tools
But hasn't the entire world been explored and mapped?
No!!! Our planet is dynamic and ever changing.
Humans and natural forces continue to shape & change the earth, and better technology let us map it more accurately.
Geography Basics
Maps are representations of the real world. This includes things that can be seen as well as representations of things that cannot.
The world is shaped as a(n): _______.
The world is shaped as a(n): ellipsoid.
Hard to put a 3D object (like an ellipsoid) on a 2D plane.
You have to rip, strech, or perform another distortions
Coordinates are the x,y values given to a particular location on a map. These are called longitude and latitude.
Geocoding is when you take text data like "The Gambia" and associate points with it. This is what google maps or OpenStreetMaps does when you search for a location
Reverse Geocoding Is when you take a longitude and a latitude and return a text value like "The Gambia"
Everything is related to everything else but near things are more related than distant things.
-Waldo Tobler
GIS Basics
Geographic phenomena on our map can be discrete or continuous.
Continuous types changes gradually, not abruptly ex: temperature, elevation, salinity.
Discrete types are divided into mutually exclusive parts, with all locations in one part having the same value.
Different types of data can be used to represent our geographic phenomenon.
But you have to make sure that the right data is used to represent the right type of phenomenon!!!
Nominal Data provides an identifier to discriminate between values.
Ordinal Data can be put in some natural sequence.
Interval Data allows for simple forms of computation like + and -.
Ratio Data allows for most, if not all, arithmetic computations.
GIS File Types
Map data is stored as a Vector or a Raster.
A raster is a set of regularly spaced cells with associated values.
The value of the cell is assumed to be valid for all the area within it.
Vectors, unlike rasters, attempt to explicitly georeference the geographic phenomena.
They can be a single point (x, y)or(x, y, z).
They can be a line. Which requires two or more nodes and a line drawn between them.
Another type of vectors are polygons. They are a collection of nodes and arcs.
Each section of vector data can have thematic data associated with it.
Now we have some basic GIS principles down...lets get mapping!!! Over to you Christine!


Verily Life Sciences
Background
It is estimated that approximately 22 million Americans suffer from obstructive sleep apnea(OSA), with almost 80 percent of the cases of moderate and severe sleep apnea undiagnosed. This past summer, I interned as a user researcher at Verily Life Sciences on designing a care pathway to identify, diagnose, treat and manage individuals with sleep apnea.
Overview of My Work
Over the course of the internship, I achieved the following:
- Conducted two usability sessions with users to recommend design decisions to the product team
- Conducted a series of rapid in-house tests to get user feedback on the efficacy of high-level product design directions
- Conducted literature review on other end-to-end healthcare services, which in turn influenced several product decisions
- Managed a clinical study with 25 participants while coordinating with the engineering, clinical operations and user success teams
My Design Research Process
Usability Sessions
I created a discussion guide which had the following structure:
-
Introductory questions. These were meant to establish a rapport with the user and set a good starting point to ask more detailed questions about their experience with sleep apnea, e.g. How are you? How has your day been?
-
General questions about the user’s experience with managing OSA in the past, e.g. Tell me about OSA, when were you diagnosed with it, how has your experience been with dealing with it?
-
Defined tasks that the participant has to perform with the prototype. Before every task, the user was presented with a scenario that described the task and how they were expected to interact with the prototype. Each task had a set of goals for the user to achieve, which the interiewer would track.
-
Things to Observe During the Task These were a series of observations which the interviewer was tracking when the users were performing the tasks. The observations would help the design team to determine whether their assumptions on how users would interact with the different features in the prototype were accurate.
-
Post Task Questions A series of open-ended questions which allowed the interviewer to get detailed feedback from the users on each task that they performed with the prototype.
-
Debrief After the user had performed all the tasks, the interviewer asked them a series of general questions about the session and the prototype to get their general feedback.
Analysis Plan
After reviewing the raw notes from the usability sessions, I analysed the findings as follows:
The Outcome
The user research that I conducted over the course of my internship informed several key changes that were made to the product.These changes were well received by the users during our user studies, and they will hopefully help in engaging the product users.


About BU Libraries 2 Go
Boston University Libraries now offers a request and contactless pickup service for books and other circulating materials for research and study.
Stacks at all Libraries locations are closed to patrons until further notice and materials can only be accessed digitally or via BU Libraries 2 Go.
How It Works
Select items are now available for request via BU Libraries 2 Go. Current BU faculty, students, and staff may request items.
Request through BU Libraries Search
- Find your book(s) using the general search here
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Sign in
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When items are available, the
request button will appear
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Complete optional fields if necessary and
click request to submit
- You’ll see immediate confirmation that your request was submitted
- You'll receive a notification to your BU email confirming your request with pickup details
- When the time comes to pick up/return your books, see the additional instructions on the BU Libraries 2 Go page.
- In order to visit the Libraries, use the BU Libraries 2 Go service, or retrieve ILL items, you must first be cleared to come to campus in accordance with University COVID-19 protocols.
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For questions about your BU Libraries 2 Go request, please contact us at<EMAIL_ADDRESS>For all other assistance with services and research please contact us via chat or email at Ask a Librarian


Next: in-package, Previous: unexport, Up: Packages Dictionary
unintern
symbol &optional package ⇒ generalized-boolean
symbol—a symbol.
package—a package designator.
The default is the current package.
generalized-boolean—a generalized boolean.
unintern removes symbol from package. If symbol is present in package, it is removed from package and also from package’s shadowing symbols list if it is present there. If package is the home package for symbol, symbol is made to have no home package. Symbol may continue to be accessible in package by inheritance.
Use of unintern can result in a symbol that has no recorded home package, but that in fact is accessible in some package. Common Lisp does not check for this pathological case, and such symbols are always printed preceded by #:.
unintern returns true if it removes symbol, and nil otherwise.
(in-package "COMMON-LISP-USER") ⇒ #<PACKAGE "COMMON-LISP-USER"> (setq temps-unpack (intern "UNPACK" (make-package 'temp))) ⇒ TEMP::UNPACK (unintern temps-unpack 'temp) ⇒ T (find-symbol "UNPACK" 'temp) ⇒ NIL, NIL temps-unpack ⇒ #:UNPACK
unintern changes the state of the package system in such a way that the consistency rules do not hold across the change.
Current state of the package system.
Giving a shadowing symbol to unintern can uncover a name conflict that had previously been resolved by the shadowing. If package A uses packages B and C, A contains a shadowing symbol x, and B and C each contain external symbols named x, then removing the shadowing symbol x from A will reveal a name conflict between b:x and c:x if those two symbols are distinct. In this case unintern will signal an error.
Next: in-package, Previous: unexport, Up: Packages Dictionary


I have enjoyed development and design since the minute I started. Through an intensive software curriculum at Code Fellows in Portland, Oregon, I have perfected my front-end development and design skills. Soon after, I began working as a Web Developer at a Digital Agency in Temecula, Ca. This position inspired me to continue my education at Flatiron School Online and learn Full Stack Development.
Before pursuing Software Development, I worked in the engineering field as a Senior Field Service Technician at AFM Engineering for 4 years, building automation machinery and traveling the country performing complex repairs. After many years serving AFM Engineering I decided to follow my passion and pursue a career in software development. My experience in the engineering field has taught me valuable skills such as writing software for Programmable Logic Controllers, and troubleshooting millisecond critical automation systems which has prepared me to excel in the field of software development.
As a software developer, I had the opportunity to participate in many different projects that ranged from GPS Tracking Software, UI Development, Wordpress Development to Websites built from scratch, Front-end Development, Web Design, Web Applications, and more. My ultimate goal is to find a company where I will thrive and continue building incredible software that I will be proud to have created.


Similar to localRot() except it rotates many objects around a center point.
Rotates an object in sequence around the x, y, then z axis.
Similar to localRevRot() except it rotates many objects around a center point.
Rotates an object in reverse sequence around the z, y, then x axis.
Similar to localConcurrentAxisRot() except it rotates many objects around a center point.
As opposed to localRot(), localConcurrentAxisRot() can rotate the x,y,z axes at the same time.
Similar to localMove() except it moves many objects at a time.
Moves an object.
Similar to localResize() except it resizes many objects relative to a center point.
Resizes an object.
Similar to localUcRot() except it rotates many objects around a center point.
Rotates an object in sequence around the x, y, then z axis using unit circle values.
Similar to localRevUcRot() except it rotates many objects around a center point.
Rotates an object in reverse sequence around the z, y, then x axis using unit circle values.
Copy all of the transformation data from one place to another.
Transformation data affected:
* rotation
* location
* size
* unit circle coordinates
* bounds radius
Copy rotation data.
Transformation data affected:
* rotation
Copy position data.
Transformation data affected:
* location
Copy size data.
Transformation data affected:
* size
Copy unit circle coordinate data.
Transformation data affected:
* unit circle coordinates
Copy point data.
Copy vertex colors.
Copy texture coordinates.


Demeter
A land-use and land-cover disaggregation and change detection model
Demeter is an open source Python package that was built to disaggregate projections of future land allocations generated by an integrated human-Earth systems model. Projected land allocation from these models are traditionally transferred to Earth System Models (ESMs) in a variety of gridded formats and spatial resolutions as inputs for simulating biophysical and biogeochemical fluxes. Existing tools for performing this translation generally require a number of manual steps which introduces error and are inefficient. Demeter makes this process seamless and repeatable by providing gridded land use and land cover change (LULCC) products downscaled directly from the Global Change Analysis Model (GCAM) in a variety of formats and resolutions commonly used by ESMs.
Getting Started with Demeter
Set up Demeter using the following steps:
Install Demeter from GitHub:
pip install git+http://github.com/JGCRI/demeter.git#egg=demeter
Download the example data using the following in a Python prompt:
import demeter # the directory that you want to download and extract the example data to data_dir = "<my data download location>" # download and unzip the package data to your local machine demeter.get_package_data(data_dir)
#. Setup your configuration file (.ini). Examples are located in the “example” directory that you just downloaded. Be sure to change the following variables to represent the local path. See the details for custom runs in the Setup section of this file. #.
To run Demeter:
import demeter # the path and file name that my example configuration (.ini) file was downloaded to config_file = '<path to my example config file>/demeter_config.ini' # run all time steps demeter.run_model(config_file=config_file, write_outputs=True)
Setup
Demeter requires the setup of several input files to begin a run. Examples of all input files can be found in the ‘examples’ directory and the expected file structure is outlined in the following:
Example directory
Inputs directory
Allocation directory
Constraint weighting file
GCAM landclass allocation file
Kernel density weighting file
Spatial landclass allocation file
Transition priority file
Treatment order file
Observed spatial data directory
Observed spatial data file
Constraint data directory
Constraint files
Projected GCAM land allocation directory
GCAM land allocation file
The following describes the requirements and format of each input.
Observed spatial data:
This file represents the area in square degrees of each land class existing within a grid cell. The grid cell size is defined by the user. This file must be presented as a comma-separated values (CSV) file having a header in the first row and must contain the field names and fields described in Table 1.
Field |
Description |
|---|---|
fid |
Unique integer ID for each grid cell latitude, longitude |
landclass |
Each land class field name (e.g., shrub, grass, etc.). Field names must not include commas. |
region_id |
The integer ID of the GCAM region that the grid cell is contained in. Exact field name spelling required. |
metric_id |
The integer ID of the GCAM basin that the grid cell is contained in. Exact field name spelling required. |
latitude |
The geographic latitude value of the grid cell centroid as a signed float. Exact field name spelling required. |
longitude |
The geographic longitude value of the grid cell centroid as a signed float. Exact field name spelling required. |
Table 1. Observed spatial data required fields and their descriptions.
Projected land allocation data:
This file is the formatted GCAM run output for land allocation projections. Since the format of this file can vary based on GCAM user preference, the file must be formatted to meet Demeter input requirements as described in Table 2. The file must be a CSV file having the header in the first row.
Field |
Description |
|---|---|
region |
The text name of the GCAM region. Exact field name spelling required. |
landclass |
Each land class field name (e.g., shrub, grass, etc.). Field names must not include commas. |
year |
Each year of the GCAM run as an integer (e.g., 2005, 2010, etc.) |
metric_id |
The integer ID of the GCAM basin. Exact field name spelling required. |
Table 2. Projected land allocation required fields from GCAM.
Allocation files:
Projected land class allocation:
This file defines how the land-use and land-cover classes in the GCAM projected land allocation data will be binned into final classes. See the projected land class allocation file in the example inputs for reference.
For example:
category |
demeter_class_1 |
demeter_class_2 |
demeter_class_3 |
|---|---|---|---|
gcam_class_1 |
0 |
0 |
1 |
gcam_class_2 |
0 |
0 |
1 |
gcam_class_3 |
0 |
1 |
0 |
gcam_class_4 |
0 |
1 |
0 |
gcam_class_5 |
1 |
0 |
0 |
Observational spatial data class allocation:
This file defines how the land-use and land-cover classes in the OSD will be binned into final land classes for output, which can be defined by the user and serve to place projected land allocation data from GCAM on a common scale with the on-the-ground representation of land-use and land-cover represented in the OSD. See the Observed spatial data class allocation file in the example inputs for reference.
For example:
category |
demeter_class_1 |
demeter_class_2 |
demeter_class_3 |
|---|---|---|---|
observed_class_1 |
1 |
0 |
0 |
observed_class_2 |
1 |
0 |
0 |
observed_class_3 |
0 |
1 |
0 |
observed_class_4 |
0 |
1 |
0 |
observed_class_5 |
0 |
0 |
1 |
Constraint weighting:
Constraints such as soil quality may be desirable to the user and can be prepared by assigning a weighted value from 0.0 to 1.0 for each grid cell in the OSD. Spatial maps of constraints should be provided by the user for application during the downscaling process. Users should note that constraining a grid cell to 0.0 may impede the ability to be able to achieve a projected land allocation from GCAM since land area is being excluded that GCAM expects. Each constraint file must have two fields: fid and weight. The fid field should correspond to the fid field in the OSD input and the weight field should be the weight of the constraint per the cell corresponding to the OSD input. Each file should be a CSV with no header.
For example:
category |
demeter_class_1 |
demeter_class_2 |
demeter_class_3 |
|---|---|---|---|
nutrient_availability |
0.0 |
0.4 |
0.0 |
soil_quality |
0.0 |
0.2 |
0.0 |
Kernel density weighting:
Weight the degree to which land classes subjected to a kernel density filter will be utilized during expansion to each class. Value from 0.0 to 1.0. See the kernel density weighting file in the example inputs for reference.
For example:
category |
demeter_class_1 |
demeter_class_2 |
demeter_class_3 |
|---|---|---|---|
kernel_density |
1.0 |
0.4 |
1.0 |
Transition Priority:
This ordering defines the preferential order of final land allocation (e.g., crops expanding into grasslands rather than forests). See the priority allocation file in the example inputs for reference. See the priority allocation file in the example inputs for reference.
For example:
category |
demeter_class_1 |
demeter_class_2 |
demeter_class_3 |
|---|---|---|---|
demeter_class_1 |
0 |
1 |
2 |
demeter_class_2 |
1 |
0 |
2 |
demeter_class_3 |
1 |
2 |
0 |
Treatment order:
Defines the order in which final land classes are downscaled. This will influence the results (e.g., if crops are downscaled first and overtake grassland, grassland will not be available for shrubs to overtake when processing shrub land). See the treatment order file in the example inputs for reference.
For example:
category |
order |
|---|---|
demeter_class_1 |
3 |
demeter_class_2 |
2 |
demeter_class_3 |
1 |
Constraints (not required):
A weight for each constraint, with a value ranging from -1.0 to 1.0, can be applied to each land class. If no constraints are desired, a user should simply provide a header-only file. For example, for a given land class, the weight for the soil quality constraint with a value of -1 indicates that one land class is fully constrained inversely (e.g., grasslands are opportunistic and grow readily in areas with a low soil quality); a weight of 0 indicates that soil quality exerts no constraint to the land class (e.g., forest, etc.); a weight of 1 for soil quality indicates that high soil quality will highly influence where the type will be spatially allocated (e.g. cropland). These constraints are developed in separate files as described in the following Constraints section. See the constraint weighting file in the example inputs for reference.
For example:
target_fid |
value |
|---|---|
1 |
0.5 |
2 |
0.7 |
3 |
0.9 |
4 |
0.0 |
5 |
0.1 |
Configuration file:
Demeter’s configuration file allows the user to customize each run and define where file inputs are and outputs will be. The configuration options and hierarchical level are described in Table 3.
Level |
Parameter |
Description |
|---|---|---|
STRUCTURE |
run_dir |
The full path of the root directory where the inputs and outputs directory are stored |
STRUCTURE |
input_dir |
The name of the input directory |
STRUCTURE |
output_dir |
The name of the output directory |
INPUTS |
allocation_dir |
The name of the directory that holds the allocation files |
INPUTS |
observed_dir |
The name of the directory that holds the observed spatial data file |
INPUTS |
constraints_dir |
The name of the directory that holds the constraints files |
INPUTS |
projected_dir |
The name of the directory that holds the GCAM projected land allocation file |
INPUTS - ALLOCATION |
spatial_allocation_file |
The file name with extension of the observed spatial data class allocation |
INPUTS - ALLOCATION |
gcam_allocation_file |
The file name with extension of the projected land class allocation |
INPUTS - ALLOCATION |
kernel_allocation_file |
The file name with extension of the kernel density weighting |
INPUTS - ALLOCATION |
transition_order_file |
The file name with extension of the priority allocation |
INPUTS - ALLOCATION |
treatment_order_file |
The file name with extension of the treatment order |
INPUTS - ALLOCATION |
constraints_file |
The file name with extension of the constraint weighting |
INPUTS - OBSERVED |
observed_lu_data |
The file name with extension of the observational spatial data |
INPUTS - PROJECTED |
projected_lu_data |
The file name with extension of the projected land allocation data from GCAM |
OUTPUTS |
diagnostics_output_dir |
The name of the directory that diagnostics outputs will be kept |
OUTPUTS |
log_output_dir |
The name of the directory that the log file outputs will be kept |
PARAMS |
scenario |
Scenario name |
PARAMS |
run_desc |
The description of the current run |
PARAMS |
observed_id_field |
Observed spatial data unique field name (e.g. target_fid) |
PARAMS |
start_year |
First time step to process (e.g., 2005) |
PARAMS |
end_year |
Last time step to process (e.g., 2100) |
PARAMS |
use_constraints |
1 to use constraints, 0 to ignore constraints |
PARAMS |
spatial_resolution |
Spatial resolution of the observed spatial data in decimal degrees (e.g. 0.25) |
PARAMS |
errortol |
Allowable error tolerance in square kilometres for non-accomplished change |
PARAMS |
timestep |
Time step interval (e.g., 5 years) for the output data. This time step is the increment that Demeter will process when starting with the base year. |
PARAMS |
proj_factor |
Factor to multiply the projected land allocation by |
PARAMS |
diagnostic |
0 to not output diagnostics, 1 to output |
PARAMS |
intensification_ratio |
Ideal fraction of land change that will occur during intensification. The remainder will be through expansion. Value from 0.0 to 1.0. |
PARAMS |
stochastic_expansion |
0 to not conduct stochastic expansion of grid cells, 1 to conduct |
PARAMS |
selection_threshold |
Threshold above which grid cells are selected to receive expansion for a target functional type from the kernel density filter. Value from 0.0 to 1.0; where 0 lets all land cells receive expansion and 1 only lets only the grid cells with the maximum likelihood expand. |
PARAMS |
kernel_distance |
Radius in grid cells used to build the kernel density convolution filter used during expansion |
PARAMS |
target_years_output |
Years to save data for; default is ‘all’; otherwise a semicolon delimited string (e.g., 2005; 2020) |
PARAMS |
save_tabular |
Save tabular spatial land cover as a CSV; define tabular units in tabular_units param |
PARAMS |
tabular_units |
Units to output the spatial land cover data in; either ‘sqkm’ or ‘fraction’ |
PARAMS |
save_transitions |
0 to not write CSV files for each land transitions per land type, 1 to write |
PARAMS |
save_netcdf_yr |
0 to not write a NetCDF file for each year of the fraction of land cover of each land class by grid cell; 1 to write |
Table 3. Configuration file hierarchy, parameters, and descriptions.


Showcase "HTML Request"
The Action Happens Here
Spanish Dictionary:
Single word:
Single word (Extended list):


It is safe to modify the on-disk cache while Polipo is running as long as no file is ever modified in place. More precisely, the only safe operations are to unlink (remove, delete) files in the disk cache, or to atomically add new files to the cache (by performing an exclusive open, or by using one of the ‘link’ or ‘rename’ system calls). It is not safe to truncate a file in place.


We do these things not because they are easy, but because we thought they were going to be easy @Maciej Cegłowski
We do these things not because they are easy, but because we thought they were going to be easy


{% macro countrySelect(countryName, countryId) %}
Complete your registration by selecting your funky suit.


@{cssStyle}
@{header}
Object Type
Name: @{objectType.name}
Known fields: @if{objectType.fields != null && !objectType.fields.empty}
@foreach{objectType.fields as field}
@{field.fieldType}
@{field.name}
@end{}
@else{}
No known fields.
@end{}
Rules that use this component:
@foreach{rules as rule}
@{rule.ruleName}
@end{}


How to settle the bill
A list of transactions will be shown here, as soon as two or more travellers with expeneses have been added.
{{ transaction.to }} gets {{ transaction.amount | number:'1.2-2' }} {{ backend.document.currency }} from {{ transaction.from }}
Share this trip
Anybody with a link to this document can it.
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HierDq.Rd
Calculating hierarchical diversity based on Hill numbers.When q=0, it is richness, when q=1, it is diversity of Shannon (exponential of shannon entropy), when q=2, it is diversity of Simpson.
HierDq(x, q, nreg, r, ncode)
| x | Genotype file, current version accepts genepop format. |
|---|---|
| q | The order of Hill numbers |
| nreg | number of regions |
| r | The population arrangement or the hierarchy in the region |
| ncode | The genotype coding type |
The function calculates diversity at ecosystem level, regional level, and local population level based on HierDpart package (Qin, X.2019).
Diversity at ecosystem level, regional level, and local population level.
Qin, X. (2019). HierDpart: partitioning hierarchical diversity and differentiation across metrics and scales, from genes to ecosystems. R package version 0.5. 0 https://cran. r-project. org/package= HierDpart.
<EMAIL_ADDRESS>f <- system.file('extdata',package='HierDpart') infile <- file.path(f, "Island.gen") file = diveRsity::readGenepop(infile, gp=2, bootstrap = FALSE) HierDisland=HierDq(infile,q=2,ncode=3,nreg = 4,r=c(7,4,2,3))


Jon Gruden celebrates win with Raiders fans in Oakland's final primetime game (Video)
by TSD StaffThe Raiders hosted their final game at Oakland-Alameda County Coliseum on Thursday night, in an AFC West divisional showdown against the Chargers, and fans were fired up from whistle to whistle.
Raiders supporters showed up to the Black Hole hours before the game even began, with the tailgating scene as electric as the game itself. It was, after all, the final time they’ll be shown on national TV, with the Raiders set to move to Las Vegas next season.
It was an entertaining contest from start to finish, with the Raiders jumping out to a 10-0 lead, but the Chargers slowly chipping away, and eventually getting ahead with roughly four minutes remaining. Oakland, however, put together a masterful scoring drive late, and the team ended up emerging victorious, 26-24.
Head coach Jon Gruden was so happy about it that he embraced fans in the stands afterward and celebrated along with them.
A memorable night in Oakland for sure.


--- title: 'Discrete Choice Modelling' author: admin date: '2020-11-03' subtitle: 'Econométrie des variables qualitatives' summary: '' authors: [IJ] lastmod: '2020-11-03' featured: no image: caption: '' focal_point: '' preview_only: no projects: [] link-citations: yes biblio-style: apalike bibliography: [data/biblio_SABD.bib] \nocite: | @Greene2008,@Greene2009, @McFaddenTrain2000, @Train2009, @Agresti2013, ---
This course introduces the statistical and econometric tools specific to the processing of categorical variables. It aims to present methods that can be used in business, for example odd-ratio type indicators and tests, classical logistic regression. It brings M2 students to approach the tools used in research within the framework of discrete choice model by providing multinomial logit type models and its extensions: nested logit model, latent class logit, mixed logit.
The objective of the course is to present and apply econometric methods for modeling qualitative variables and discrete choices. The former are suitable for modeling qualitative outcomes, ordered or not (eg: logit and probit models). Discrete choice models are used for the disaggregated study of individual consumption behaviors and the study of demand.
These methods make it possible, for example, to estimate and predict market shares between different products, willingness to pay for a product or for a specific attribute. This course will come back to the concepts of consumer surplus and to the solutions to estimate them.
Particular attention is given to the interpretation of the results and the method of construction of the model. In particular, the realization of a tutored project on a database resulting from my research leads the students to manage their analysis of an urban mobility issue by structuring it on the relevant literature and to build a model in a critical and argued manner.
This course is an applied course using R and RStudio software. One explicit desire and expectation of the course is the production of reproducible and transparent work using RStudio. The different analytical techniques are presented and applied through exercises and through a semester project that is the subject of a defense to prepare for writing a report.


The only thing focusable on this page should be svg a[xlink|href]
.
But in Blink and WebKit as soon as you add a focus
event listener to any SVG element, it becomes focusable.
Gecko and Trident are missing SVGElement.prototype.focus
(which is why this test will fail there).


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As the subject states, "Hi" from New Zealand.
I've been kicking the tyres on this project for a fair few weeks now and finally taken the plunge and ordered some gear.
A little bit about me: my name is Glen and I work in the IT field in Wellington, New Zealand. All this power monitoring stuff is new to me.
We have just had solar installed and some of the things I hope to address with this project is to fill in some of the blanks that getting a solar system provides. The biggest one being, where is my power coming from, and where is it going to?
That's it from me, I hope to contribute in some small way in the future.
Re: Hi from New Zealand
Hey Glen - there are quite a few Kiwis floating around on here. Me being one of them, from down in shakey ChCh. Welcome to OEM - I am sure you will find plenty of support and help on here - I certainly have.
Re: Hi from New Zealand
Thanks - I'll have plenty of questions for you I'm sure.
Im currently tinkering with the sketch you posted looking and am looking at the calibration figures at the moment - I'm trying to get my head around it :)
In fact, your setup is the inspiration for mine. I've gone the Arduino Ethernet (no shield) route with the emonTX shield. I think I first saw a post from you on geekzone which lead me here :)
Are you using the emonGLCD and if so, did you use Ethernet on that also?
Cheers
Re: Hi from New Zealand
Nope, no emonGLCD here, I display everything in openHAB (home automation software).
I have just uploaded my sketches to Github if you want to have a look (although it sounds like you already have them);
https://github.com/sumnerboy12/hardwicke/tree/master/emontx_pvrouter
There are others on here who can much better explain the calibration stuff...
Re: Hi from New Zealand
Thanks, I "think" I have the calibration stuff sorted now.
Re: Hi from New Zealand
Another kiwi here, from Chch also. I am in the final stages of building a new house (move in next week hopefully) and plan on monitoring energy usage there. I have purchased and built up an EmonTx Shield V2 which is currently on a EtherTen Arduino clone. Like sumnerboy, the plan is to communicate via ethernet rather than wireless, the Emon unit will be mounted in the data patch cabinet so is only a few inches from ethernet. When the house was being built I installed some conduit (~30cm) between the fuse board and the patch cabinet so I can run the CT cables between the two. All going well I would like to monitor each breaker individually, although that does mean cramming about 15 CTs in there.
Re: Hi from New Zealand
Welcome. Looking forward to hearing your progress.
My 2nd package from OEM is yet to get here but shouldn't be far off being able to install the monitoring stuff now :)
I hope you've got generous space to fit those 15 CT's in :)
Re: Hi from New Zealand
Hey PB - if you need any help setting up your EmonTX Shield and/or openHAB then drop me a line. I also run the www.smartthingsnz.co.nz online store for home automation products if you are interested in any Z-Wave gear.
Have you got any solar H2O or PV installed?
Good luck with the new build!
Re: Hi from New Zealand
What did you use for a case Sunmerboy?
I'm looking at designing and 3D printing one for the Ardunio Ethernet + emonTX Shield for lack of anything readily available!
Re: Hi from New Zealand
Don't have a case - the Arduino + EmonTx shield is screwed to the wall beside my disribution board, which is inside a hallway cupboard - so out of the way. A case would be great tho, so keep us posted how yours goes - would be interested to see how it looks!
Re: Hi from New Zealand
Will do.
Re: Hi from New Zealand
We have solar H20 installed, and prewired for PV. We were going to install PV until the Meridian buyback rate got chopped. Will see how much power we actually use and then make a call about adding PV later.
I could be keen on a case too, my current plan is to bodge something out of a jaycar box of some sort.