GEOG*3480
GIS and Spatial Analysis
Statistical Analysis of
Spatial Data
John Lindsay
Fall 2015
Readings
- Jensen and Jensen Chapter 8
Topics
- Over the next two lectures, we'll discuss:
- Descriptive Statistics
- Descriptive Spatial Statistics
- Spatial Autocorrelation
- Point Pattern Analysis
- Quadrat Analysis
- Nearest-Neighbour Analysis
- Directional Analysis
Descriptive Statistics
- Measures of central tendency
- Mode, median, and mean (\(\overset{-}x\))
- \(\overset{-}x = \frac {\underset{i=1}{\overset{N}{\Sigma}} x} {N}\)
Descriptive Statistics
- Measures of dispersion
- Variance (\(s^2\))
- Standard deviation (\(s\))
- \(s^2 = \frac {\underset{i=1}{\overset{N}{\Sigma}} (x_i - \overset{-}x)^2} {N - 1}\)
- \(s = \sqrt \frac {\underset{i=1}{\overset{N}{\Sigma}} (x_i - \overset{-}x)^2} {N - 1}\)
Descriptive Statistics
- Skewness
- Measure of the asymmetry of a distribution
- Kurtosis
- Measure of the peakedness of a distribution
(From: Jensen & Jensen 2013)
(From: Jensen & Jensen 2013)
Descriptive Spatial Statistics
- Mean Centre
- Measure of central tendency that can be used to determine the
centre of a distribution plotted in geographic coordinates.
- Standard Distance
- Measure of dispersion of geographically distributed data.
(From: Jensen & Jensen 2013)
Tobler’s first law
- The first law of geography: “everything is related to everything else,
but near things are more related than distant things.” (Tobler, 1970)
- This simple statement forms the basis for a great deal of
geographical analysis and is concept underlying the idea of
spatial autocorrelation.
- Synonymous with the concept of spatial
dependence in geostatistics
Spatial autocorrelation
- Correlation of a variable with itself through space.
- Correlation versus spatial autocorrelation
- Actually bad news and good news
- Bad for statistical reasons
- Good because, “if geography is worth studying at all, it must be
because phenomena do not vary randomly through space”
(O'Sullivan and Unwin, 2003, pg. 28)
- Essential for spatial modelling through Interpolation
Spatial autocorrelation
- Three possibilities:
- Clustered (positive autocorrelation):
nearby locations are likely to be similar to one another.
- Random (autocorrelation near zero):
no spatial effect is discernible, and observations
seem to vary randomly through space
- Dispersed (negative autocorrelation):
observations from nearby observations are
likely to be different from one another.
Spatial autocorrelation
Moran's \(I\)
- Moran's \(I\) measures the interdependence in spatial distributions.
- Used with interval/ratio level data
- Used to detect spatial trends
- -1 ≤ \(I\) ≤ 1
- \(I\) = -1 = dispersed
- \(I\) = 0 = random
- \(I\) = +1 = clustered
Moran's \(I\)
\(I = \frac {N}{\underset{i=1}{\overset{N} \Sigma} \underset{j=1}{\overset{N} \Sigma} w_{ij}} \frac {\underset{i=1}{\overset{N} \Sigma} \underset{j=1}{\overset{N} \Sigma} w_{ij} (x_i - \overset{-} x) (x_j - \overset{-} x)}{\underset{i=1}{\overset{N} \Sigma} {(x_i - \overset{-} x)^2}}\)
Where \(\overset{-} x\) is the mean of variable \(x\); \( x_i \) is the
value at \(i\); \(j\) is a neighbour of \(i\); \( w_{ij} \) is the weight between neighbours \(i\) and \(j\).
(From: Jensen & Jensen 2013)
(From: Jensen & Jensen 2013)
Point Pattern Analysis
- Mapped point data often exhibit distinct patterning.
- Patterns result from the spatial component of a control on the phenomenon.
- Understanding the pattern can help with understanding the controlling
forces on the phenomenon.
Point Pattern Analysis
- The patterns that we're interested in with Point Pattern
Analysis (PPA) result from the locations of individual points and not on their
attributes, for which spatial autocorrelation is more relevant.
- Quadrat Analysis and
Nearest-Neighbour Analysis the the two most common methods for PPA
Quadrat Analysis
- A quadrat is a user-defined geographic area,
usually a square or rectangle, used to measure the distribution of a spatial
phenomenon.
- Quadrat analysis can be used to test
whether the phenomenon is uniformly distributed.
- The Chi Square test is used with quadrats.
(From: Jensen & Jensen 2013)
(From: Jensen & Jensen 2013)
Quadrat Analysis
- The value of Chi Square is compared with a table of critical values to determine
whether the points are statistically significantly different from a uniformly distribution.
- You should be thinking about the MAUP about now!
- The size, shape, and number of quadrats will impact the results
of the quadrat analysis.
Nearest-neighbour Analysis
- NNA is used in GIS to determine whether point sets are random or non-random.
- If a point set is found to be non-random then we are left with the task
of determining what controls the distribution.
- For each point in the set, find the distance to the closest neighbour.
(From: Jensen & Jensen 2013)
(From: Jensen & Jensen 2013)
Nearest-neighbour Analysis
\(d_e = \frac 1 {2 \sqrt{N/A}} = \frac 1 {2 \sqrt{p}} \)
- where \(d_e\) is the expected density (assuming random distribution);
\(N\) is the number of points; \(A\) is the study area; \(p\) is the point density.
\(NNR = \frac {Dist_{Obs}} {Dist_{Ran}} = \frac {d_a} {d_e} \)
- where \(NNR\) is the nearest-neighbour ratio;
\(Dist_{Obs}\) is the mean NN distance; \(Dist_{Ran}\) is the expected distance for a random distribution.
(From: Jensen & Jensen 2013)
Nearest-neighbour Analysis
- Warning: Our estimates of the point density is dependent on our
definition of the study area.
- If we change the extent of the study area, we change the results.
| Not so clustered | Very clustered |
|
|
Nearest-neighbour Analysis
- NNA is also sensitive to the non-uniformity of underlying space.
- NNA assumes that points are free to locate anywhere.
- Consider the gap in stream channel heads below. It's the result of Lake Ontario.
Circular Data
- Geographers distinguish between directional
(0°-360°) and axial (a.k.a. oriented; 0°-180°) data.
- Wind is directional; a road is axial.
- Directional and axial data can be plotted using Rose
Diagrams, which are like circular histograms.
Circular Data
\(\overset{-}\theta = tan^{-1}(\frac{\overset{N}{\underset{i=1}{\Sigma}}{sin \theta_i}} {\overset{N}{\underset{i=1}{\Sigma}}{cos \theta_i}}) \)
- where \(\overset{-}\theta\) is the mean direction,
derived from the vector resultant.
Circular Data
\(\overset{-}R = \frac 1 N \sqrt{(\overset{N}{\underset{i=1}{\Sigma}}{cos \theta_i})^2 + ({\overset{N}{\underset{i=1}{\Sigma}}{sin \theta_i}})^2} \)
- where \(\overset{-}R\) is the standardized length of the vector resultant,
called the mean resultant length, and is
a measure of dispersion.
- 0 ≤ \(\overset{-}R\) ≤ 1, where values near 1 indicate small angular dispersion
and vice versa.
Circular Data
- Axial (oriented) data cannot easily be treated as vectors
because there is nothing to distinguish one end of the line from the other.
- An angle of 179° is very close to one of 1°.
- To solve this double all the angles, calculate the statistics with the
doubled data, and then halve the angles to get the mean direction, mean resultant
length, etc.
- 45° × 2 = 90°
- 225° × 2 = 450° = 450° - 360° = 90°


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Makes the associated field (in the form) the one that has focus when the page loads.
Prevents the default action happening unless something has been added to the associated field.


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UnityPoint Health no longer planning to merge with Sanford Health
MADISON, Wis. - UnityPoint Health announced Tuesday that it no longer has plans to merge with the South Dakota-based Sanford Health.
According to a news release, UnityPoint said it had been seriously considering the merger over the last few months but decided it will not be moving forward with the partnership.
"I'm tremendously proud of our organization and we will continue to work tirelessly to evaluate any avenue that improves the delivery of health care," said Kevin Vermeer, president and CEO of UnityPoint Health. "Sanford is an exceptional organization with a bright future ahead. UnityPoint Health moves forward with strong roots and a fierce commitment to improving the experience of the people we serve."
The release did not provide a particular reason for the network's new decision.


{% extends "layout_picker.html" %} {% set serviceName = "Apply for a resident's parking permit" %} {% set pageTitle = "New resident permits" %} {% block content %}
Your temporary resident’s permit will start immediately and expire in 3 months.
Shortly before it expires, re-apply for your {{"6 or" if council.sixmonth}} 12 month resident’s parking permit – by that time, GOV.UK Verify should be able to verify your new address.
Next


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Abstract: In this work we present a gradient-based variational model for video editing, addressing the problem of propagating gradient-domain information along the optical flow of the video. The resulting propagation is temporally consistent and blends seamlessly with its spatial surroundings. In addition, the presented model is able to cope with additive illumination changes and handles occlusions/dis-occlusions. The problem of propagation along the optical flow arises in different video editing applications. In this work we consider the application where a user edits a frame by modifying the texture of an object's surface and wishes to propagate this editing throughout the video.
Full paper: "A Variational Model for Gradient-Based Video Editing", IJCV, 2012.
The supplementary material document to the sumbitted paper can be downloaded from here
A MATLAB implementation with data to test it can be downloaded from here.
| One lid setting experiments | |||||
|---|---|---|---|---|---|
| Sequence name | Input sequence | Editing domain | Result | Download | |
| Coca-cola sequence | avi | png | |||
| Magazine sequence | avi | png | |||
| Cloth wave | avi | png | |||
| Can and Box sequence | avi | png |
| Two lid setting experiments | |||||
|---|---|---|---|---|---|
| Sequence name | Input sequence | Editing domain | Result | Download | |
| Can and Box sequence | avi | png | |||
| Screen sequence (The files to download include results with different optical flow algos) |
avi | png | |||
| Pen holder sequence | avi | png |
| Double occlusion | ||||||
|---|---|---|---|---|---|---|
| Sequence name | Input sequence | Editing domain | Result (not handled) | Result (corrected) | Download | |
| Hand (double occlusion) | avi (not handled) avi (corrected) | png (not handled) png (corrected) |
| A zoom-in case (we edit the last frame and propagate backwards) | |||||
|---|---|---|---|---|---|
| Sequence name | Input sequence | Editing domain | Result | Download | |
| Cliff (a zoom in sequence) | avi | png |
| Zoom and tilt | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Sequence name | Input sequence | Editing domain | Result (1-lid bilinear) | Result (1-lid bicubic) | Result (2-lid bilinear) | Download | |||||
| Cloth tilt | avi (1-lid bilinear) | png (1-lid bilinear) | avi (1-lid bicubic) | png (1-lid bicubic) | avi (2-lid bilinear) | png (2-lid bilinear) |


---
layout: post
title: Quote
date: '2012-12-04T14:55:00.000-05:00'
author: ricleal
tags:
- Programming
- C++
- Geeky
comments: true
---
I usually don't like to share other people phrases, but this one is to good to keep just for me:
C makes it easy to shoot yourself in the foot.  Bjarne Stroustrup


| Created: | 8/19/2020 1:14:53 PM |
| Modified: | 8/19/2020 1:14:53 PM |
| Project: | |
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Public varchar(255) created_by_user_last_name |
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Public varchar(255) created_by_user_email |
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Public varchar(255) updated_by_user_last_name |
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| Owner | public |
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| isFinalSpecialization: | 0 |


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Overview: 3
Object-Oriented Programming
| GEOS SDK TechDocs
|
|
| 2 The System Architecture
| 4 Multitasking and Multithreading
Object-Oriented Programming (OOP) is a popular way of organizing programs, especially programs using graphical user interfaces. GEOS is an object-oriented system, programmed entirely in Object Assembly, a Geoworks innovation. This section describes the concepts behind OOP and how it is implemented in GEOS. If you are familiar with OOP concepts, you may skip this section; because terminology can differ from system to system, however, you will probably want to at least skim the rest of the section. (A full discussion of GEOS messaging and object manipulation can be found in the GEOS Programming chapter.)
Object-Oriented Programming is simply a way to organize code and data differently from traditional procedural programming. Anything done with procedural programming can be done in an object-oriented way and vice versa. However, OOP offers several significant advantages over procedural programming:
-
Modularity
Especially in large systems, modularity can lead to cleaner, more manageable, and more easily expandable programs. OOP provides tight modularity with each component (
object
) having a strict API. This modularity also makes OOP systems ideal for the multithreaded environment of GEOS.
-
Simplicity
People interact with things individually. Each object in our environment has a certain set of characteristics and certain behaviors. OOP systems use this point of view as a foundation for programs that are easy to understand.
-
Efficiency
The efficient design of objects in an OOP system can lead to less repetition of reusable code. Several objects working independently may use the same code, thus reducing the impact on memory usage.
-
Reduction of code
Because functionality (functions, routines, etc.) is inherited by object types from other object types (known as classes), it doesn't have to implemented again and again. Instead, one version of the code can exist in a particular class and be inherited by other classes, thus reducing coding and debugging time.
Objects, Messages, and Methods
Classes and Inheritance
| GEOS SDK TechDocs
|
|
| 2 The System Architecture
| 4 Multitasking and Multithreading


Comments
-
Greg Hurrell
Just to clarify, accepts both "absolute" URIs (with schemes) like http://example.com/ and "relative" URIs (relative to the site root) like "/blog/new-wiki".
-
Greg Hurrell
Currently we allow the creation of such links but they don't actually work when we try to use them. Sloppy validation.
-
Greg Hurrell
Need to extract some code our of the Article model which has to deal with pretty much exactly the same thing (wiki redirects). Stick it somewhere in
lib
where it can be mixed in to both models or otherwise reused. -
Greg Hurrell
Ok, this is now done. Will be included in the next deployment.
-
Greg Hurrell
Status changed:
- From: new
- To: closed
Add a comment
Comments are now closed for this issue.


{% extends "layout.html" %} {% block page %}
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Syntax:
logand &rest integers => result-integer
logandc1 integer-1 integer-2 => result-integer
logandc2 integer-1 integer-2 => result-integer
logeqv &rest integers => result-integer
logior &rest integers => result-integer
lognand integer-1 integer-2 => result-integer
lognor integer-1 integer-2 => result-integer
lognot integer => result-integer
logorc1 integer-1 integer-2 => result-integer
logorc2 integer-1 integer-2 => result-integer
logxor &rest integers => result-integer
Arguments and Values:
integers---integers.
integer---an integer.
integer-1---an integer.
integer-2---an integer.
result-integer---an integer.

The next figure lists the meaning of each of the functions. Where an `identity' is shown, it indicates the value yielded by the function when no arguments are supplied.
Function Identity Operation performed logandc1 --- and complement of integer-1 with integer-2 logandc2 --- and integer-1 with complement of integer-2 logand -1 and logeqv -1 equivalence (exclusive nor) logior 0 inclusive or lognand --- complement of integer-1 and integer-2 lognor --- complement of integer-1 or integer-2 lognot --- complement logorc1 --- or complement of integer-1 with integer-2 logorc2 --- or integer-1 with complement of integer-2 logxor 0 exclusive or
Figure 12-18. Bit-wise Logical Operations on Integers
Negative integers are treated as if they were in two's-complement notation.
Examples:
(logior 1 2 4 8) => 15 (logxor 1 3 7 15) => 10 (logeqv) => -1 (logand 16 31) => 16 (lognot 0) => -1 (lognot 1) => -2 (lognot -1) => 0 (lognot (1+ (lognot 1000))) => 999 ;;; In the following example, m is a mask. For each bit in ;;; the mask that is a 1, the corresponding bits in x and y are ;;; exchanged. For each bit in the mask that is a 0, the ;;; corresponding bits of x and y are left unchanged. (flet ((show (m x y) (format t "~%m = #o~6,'0O~%x = #o~6,'0O~%y = #o~6,'0O~%" m x y))) (let ((m #o007750) (x #o452576) (y #o317407)) (show m x y) (let ((z (logand (logxor x y) m))) (setq x (logxor z x)) (setq y (logxor z y)) (show m x y)))) >> m = #o007750 >> x = #o452576 >> y = #o317407 >> >> m = #o007750 >> x = #o457426 >> y = #o312557 => NIL
Side Effects: None.
Affected By: None.
Exceptional Situations:
Should signal type-error if any argument is not an integer.
See Also:
Notes:
(logbitp k -1) returns true for all values of k.
Because the following functions are not associative, they take exactly two arguments rather than any number of arguments.
(lognand n1 n2) == (lognot (logand n1 n2)) (lognor n1 n2) == (lognot (logior n1 n2)) (logandc1 n1 n2) == (logand (lognot n1) n2) (logandc2 n1 n2) == (logand n1 (lognot n2)) (logiorc1 n1 n2) == (logior (lognot n1) n2) (logiorc2 n1 n2) == (logior n1 (lognot n2)) (logbitp j (lognot x)) == (not (logbitp j x))


The affix plugin toggles between three classes: .affix
, .affix-top
, and .affix-bottom
. Each class represents a particular state. You must add CSS properties to handle the actual positions, with the exception of position:fixed
on the .affix
class.
Tips: The Affix plugin is often used together with the Scrollspy plugin.
Add data-spy="affix"
to the element you want to spy on, and the data-offset-top|bottom="number"
attribute to calculate the position of the scroll.

Enable manually with:
$('.nav').affix({offset: {top: 150}});
Options can be passed via data attributes or JavaScript. For data attributes, append the option name to data-
., as in data-offset=""
.
| Name | Type | Default | Description |
|---|---|---|---|
| offset | number | object | function | 10 | Specifies the number of pixels to offset from screen when calculating position of scroll. When using a single number, the offset is added to both top and bottom directions. If you only want to control the top or the bottom, use an object, like offset: {top: 25} For multiple offsets, use offset: {top: 25, bottom: 50} Tip: Use a function to dynamically provide an offset (can be useful for responsive designs) |
| target | selector | node | element | the window object | Specifies the target element of the affix |
The following table lists all available affix events.
| Event | Description |
|---|---|
affix.bs.affix | Occurs before fixed positioning is added to the element (e.g., when the .affix-top class is about to be replaced with the .affix class) |
affixed.bs.affix | Occurs after fixed positioning is added to the element (e.g., after the .affix-top class is replaced with the .affix class) |
affix-top.bs.affix | Occurs before the top element returns to its original (non-fixed) position (e.g., the .affix class is about to be repalced with .affix-top ) |
affixed-top.bs.affix | Occurs after the top element returns to its original (non-fixed) position (e.g., the .affix class has been repalced with .affix-top ) |
affix-bottom.bs.affix | Occurs before the bottom element returns to its original (no-fixed) position (e.g., the .affix class is about to be replaced with .affix-bottom ) |
affixed-bottom.bs.affix | Occurs after the bottom element returns to its original (non-fixed) position (e.g., the .affix class has been replaced with .affix-bottom ) |


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Let’s demo RivGraph on the Brahmaputra River!¶
This demo shows some of the core functionality and convenient plotting and exporting features provided by RivGraph for a braided river network. The basic steps of RivGraph-ing a braided river include:
Instantiate river class
Skeletonize the binary mask
Compute the network (links and nodes)
Prune the network
Compute morphologic metrics (lengths, widths)
Compute a mesh (6.1 - Adjust mesh parameters)
Assign flow directions for each link
A note on topologic metrics
Along the way, we’ll export some geotiffs and GeoJSONs (or shapefiles if you prefer) for inspection in QGIS. RivGraph requires a binary mask of the channel network, preferably georeferenced (i.e., a GeoTiff) in a projected coordinate reference system.
1. Instantiate river class¶
from rivgraph.classes import river
import matplotlib.pyplot as plt
import os
# Define the path to the georeferenced binary image.
mask_path = './data/Brahmaputra_Braided_River/Brahmaputra_mask.tif'
# Results will be saved with this name
name = 'Brahma'
# Where to store RivGraph-generated geotiff and geovector files.
results_folder = './data/Brahmaputra_Braided_River/Results'
# Set the exit sides of the river relative to the image. In this case, the
# Brahmaputra is "entering" the image from the North and "exiting" the
# image from the South.
es = 'NS' # The first character is the upstream side
# Boot up the river class! We set verbose=True to see progress of processing.
brahma = river(name, mask_path, results_folder, exit_sides=es, verbose=True)
# The mask has been re-binarized and stored as an attribute of brahma:
plt.imshow(brahma.Imask)
<matplotlib.image.AxesImage at 0x1eb4f279e50>
2. Skeletonize the binary mask¶
# Simply use the skeletonize() method.
brahma.skeletonize()
# The skeletonized image is stored as an attribute to the brahm class. Let's take a look.
plt.imshow(brahma.Iskel)
<matplotlib.image.AxesImage at 0x1eb4f7e90a0>
The skeleton is hard to see; perhaps we’d like to look at it closer? One option is to save it as a geotiff and pull it up in a GIS (like QGIS).
# We use the write_geotiff() method with the 'skeleton' option.
brahma.to_geotiff('skeleton')
Geotiff written to dataBrahmaputra_Braided_RiverResultsBrahma_skel.tif.
The georeferenced Brahmaputra skeleton has been written to disk, so we can pull it up in QGIS along with the georeferenced mask:
3. Compute the network (links and nodes)¶
# Simply use the compute_network() method.
brahma.compute_network()
Resolving links and nodes...done.
# Now we can see that the "links" and "nodes" dictionaries ahve been added
# as attributes to the brahma class:
links = brahma.links
nodes = brahma.nodes
print('links: {}'.format(links.keys()))
print('nodes: {}'.format(nodes.keys()))
links: dict_keys(['idx', 'conn', 'id', 'n_networks'])
nodes: dict_keys(['idx', 'conn', 'id'])
The links dictionary currently contains four keys: - idx: a list of all the pixel indices that make up the link (indices created with input mask shape and np.ravel_multi_index) - conn : a two-element list containing the node ids of the link’s endpoints - id: each link has a unique id; the ordering is irrelevant - n_networks: the number of disconnected networks (==1 if the input mask contains a single connected blob)
The nodes dictionary currently contains three keys: - idx: the index of the node’s position within the original image (i.e. np.ravel_multi_index()) - conn: an N-element list containing the N link ids of the links connected to this node. - id: each node has a unique id; the ordering is irrelevant
We can visualze the network in a couple of ways. First, we can plot with matplotlib:
brahma.plot('network')
Nodes and links are labeled with their ids. We can zoom in if plotting in an interactive matplotlib window, or we can export the network links and nodes as geovector files and pull ’em into QGIS:
brahma.to_geovectors('network', ftype='json')
# Let's see where the network geovector files were written:
print(brahma.paths['links'])
print(brahma.paths['nodes'])
dataBrahmaputra_Braided_RiverResultsBrahma_links.json dataBrahmaputra_Braided_RiverResultsBrahma_nodes.json
And dragging these into QGIS: You can query different links and nodes using the Identify tool. Note that their properties (‘conn’ and ‘id’) are appended.
If you pan around a bit, you’ll notice that there are many “dangling” links, or links connected only at one end. These links play no role in the connectivity of the network, and we therefore may want to remove them. We don’t want to remove the inlet and outlet links, however. RivGraph will prune these dangling links but preserve the inlet and outlet links by exploiting the exit_sides we supplied earlier. Let’s prune the network.
4. Prune the network¶
brahma.prune_network()
# We see that 'inlets' and 'outlets' have been added to the nodes dictionary:
print(brahma.nodes.keys())
# We can get the node ids of the inlets and outlets
print('inlets:', brahma.nodes['inlets'])
print('outlets:', brahma.nodes['outlets'])
dict_keys(['idx', 'conn', 'id', 'inlets', 'outlets'])
inlets: [247]
outlets: [1919]
5. Compute morphologic metrics (lengths, widths)¶
Now that the network is resolved and pruned, we can compute some link metrics.
brahma.compute_link_width_and_length()
# Let's look at histograms of link widths and lengths:
trash = plt.hist(brahma.links['len_adj'], bins=50)
plt.ylabel('count')
plt.xlabel('link length (m)')
plt.title('Histogram of link lengths')
Computing distance transform...done.
Computing link widths and lengths...done.
Text(0.5, 1.0, 'Histogram of link lengths')
In the above figure, we see that almost all the links are shorter than 5 km. This histogram will be different for each braided river, and can depend on the resolution of your input binary mask. Resolving smaller channels generally, though not always, produces smaller average link lengths as longer links are broken to connect to the smaller ones.
Note: the lengths are reported in meters bcause that is the unit of the provided mask’s CRS (coordinate reference system). You can check this unit:
print(brahma.unit)
meter
6. Compute a mesh¶
In preparation for setting flow directions of each link, we will compute an “along-valley” mesh. This mesh is created based on the overall morphology of the braided river as opposed to individual channels within the network. The objective of the mesh is to create an along-channel grid that contains transects that are roughly perpendicular to the river “centerline” and approximately evenly-spaced.
While this mesh is necessary for setting flow directions, it is also useful for measuring along-river characteristics (like width, erosion rates, braiding index, etc.). The generation of this mesh requires a few parameters that ideally would be user-defined each time. However, for simplicity, RivGraph will make estimates of these parameters based primarily off the average link width. These parameters are described after we generate a default mesh.
# Note that we provide no arguments to the compute_mesh() function.
brahma.compute_mesh()
Computing centerline...done.
Computing link widths and lengths...done.
Generating mesh...done.
Before we play with the mesh-generation parameters, let’s take a look at
what we’ve generated with the compute_mesh()
function.
First, we see that a centerline was computed. We can access this centerline:
print(brahma.centerline)
(array([764625., 764595., 764565., ..., 785475., 785505., 785535.]), array([2753535., 2753505., 2753475., ..., 2633625., 2633655., 2633655.]))
We get a numpy array of two arrays of columns, rows of the centerline. This isn’t very interpretable as-is, but we can export the centerline as a geovector file:
brahma.to_geovectors('centerline', ftype='json')
The centerline is exported as a shapefile or GeoJSON, depending on the filetype you specify. (GeoJSON is default.) Let’s take a look at this centerline in QGIS:
A little jagged, but it’s fine for our purposes. The centerline is computed by filling in all the islands of the network, then using a distance transform to find the “centermost” pixels.
Now let’s take a look at the mesh we generated. We first need to export it:
brahma.to_geovectors('mesh', ftype='json')
The mesh consists of two files: meshlines
, or transects, and
meshpolys
. If we want to see where these files were generated, we
can check the paths dictionary:
print(brahma.paths['meshlines'])
print(brahma.paths['meshpolys'])
dataBrahmaputra_Braided_RiverResultsBrahma_meshlines.json dataBrahmaputra_Braided_RiverResultsBrahma_meshpolys.json
Let’s see what the mesh looks like by dragging these GeoJSONs into QGIS:
The mesh does a pretty good job of meeting our two criteria: approximately perpendicular to the centerline, and evenly-spaced along the centerline. It’s not perfect, but we can play with the parameters to adjust it.
6.1 Adjust mesh parameters¶
We may want to alter certain features of the mesh, like the spacing of
transects or the overall width of the mesh. When we call
compute_mesh()
, there are three optional keyword arguments we can
provide to control mesh properties. These are:
grid_spacing
: The along-centerline distance between each transect.
The default value is the average link width, found by
np.mean(brahm.links['wid_adj'])
.
smoothing
: The degree of smoothing to perform on the centerline
before creating its offsets. This parameter is expressed in terms of the
fraction of the total centerline length, so the default
smoothing=0.1
will use a moving-average with a window size of 10% of
the length of the centerline.
buf_halfwidth
: The distance from the centerline to each edgeline of
the buffer. The default is 10% wider than the maximum width of the mask,
which ensures that the mask is fully covered by the mesh.
We can check what values of each of these were used above:
# grid_spacing
print('grid_spacing: {}'.format(brahma.avg_chan_width))
# buf_halfwidth
print('buf_halfwidth: {}'.format(brahma.max_valley_width_pixels * brahma.pixlen * 1.1))
# smoothing by default was 0.1
print('smoothing: {}'.format(0.1))
grid_spacing:<PHONE_NUMBER>141467
buf_halfwidth: 15631.83213830036
smoothing: 0.1
You may have noticed that the mesh transects near the beginning of the reach (top of image) aren’t quite a perpendicular to the centerline as we might like. Let’s try smoothing the centerline a bit more and reducing the buffer width to make these more perpendicular. The grid spacing will not affect the overall mesh strucutre, so we’ll leave it the same for comparison purposes.
brahma.compute_mesh(buf_halfwidth=10000, smoothing=0.25)
Generating mesh...done.
Here’s a side-by-side comparison with the default mesh:
There are two primary differences between our custom mesh and the
default one. First, the custom mesh is narrower as a direct result of us
reducing buf_halfwidth
from 15631 to 10000. Secondly, the custom
mesh transects are significantly more parallel to the centerline than
the default mesh. This resulted from increasing smoothing
from 0.1
to 0.25. These two parameters, plus grid_spacing
, can be used to
design a mesh to suit your needs.
7. Assign flow directions to each link¶
Now we want to determine the long-term, steady-state flow direction in each link. The algorithms used here are described in this paper.
brahma.assign_flow_directions()
Setting link directionality...Using dataBrahmaputra_Braided_RiverResultsBrahma_fixlinks.csv to manually set flow directions. Attempting to fix 2 cycles. All cycles were resolved. done.
A statement appears that tells us that a .csv file was created for us to
set flow directions manually. This file is only created if it does not
already exist. So if we re-run assign_flow_directions()
, we should
not see this message again.
brahma.assign_flow_directions()
Setting link directionality...Using dataBrahmaputra_Braided_RiverResultsBrahma_fixlinks.csv to manually set flow directions. Attempting to fix 2 cycles. All cycles were resolved. done.
Now we see a message stating that RivGraph will use the .csv file to set flow directions manually. However, we haven’t populated the file yet so no there is no information for RivGraph to use. We’ll revisit this at the end of this section.
We also see a message stating that RivGraph is
Attempting to fix 2 cycles.
A cycle is a set of links within a graph
that feeds into itself. RivGraph’s directionality algorithms enforce the
condition that no cycles should be present in the resulting graph. If
any cycles are found after setting all link directions, RivGraph
attempts to fix them. This does not mean that all the links’ directions
are correct, but rather that the resulting graph contains no cycles. In
this case, RivGraph was able to resolve both cycles.
Let’s inspect the resulting link directions. We again have two options: we can plot the links via matplotlib within Python:
brahma.plot('directions')
Cyan represents the upstream portion of a link, and magenta the downstream. As before, this is difficult to see without zoooming. So we can export a geotiff that contains directionality information for inspection in QGIS:
brahma.to_geotiff('directions')
Geotiff written to dataBrahmaputra_Braided_RiverResultsBrahma_link_directions.tif.
Dragging into QGIS (and adding a colorbar legend), we see
I’ve circled two short links in yellow, and noted that their flow direction was set as going right-to-left. Ideally, this junction would be comprised of a single node replacing these links, but RivGraph does not have the capability yet to simplify the network (a feature request has been made for this). From cursory inspection, we could make the argument that flow should instead go left-to-right for these two links. Let’s force flow the opposite direction as an example of how to manually set links.
First, we need to identify the link ID’s of each of these links, as well as the desired upstream nodes. Using the Identify tool in QGIS with the links and nodes GeoJSON layers turned on, this is easy:
We see that the red-highlighted link’s ID is 2280
, and it’s
(upstream, downstream) nodes are 1631, 1650
. We want to reverse this
order so that the upstream node ID is 1650
. Open the
Brahma_fixlinks
csv and enter this information. I have also done
this for the link to the right of this one.
Simply save the .csv and re-run the assign_flow_directions()
method:
brahma.assign_flow_directions()
Setting link directionality...Using dataBrahmaputra_Braided_RiverResultsBrahma_fixlinks.csv to manually set flow directions. Attempting to fix 2 cycles. All cycles were resolved. done.
After exporting the directions
geotiff again, we can plot it against
the original:
We see that RivGraph used the information in our .csv to manually set the flow directions for both these links in the opposite direction of the original solution. It is important to note that RivGraph guarantees that whatever link flow directions are specified in the .csv will be preserved throughout the direction-setting process. RivGraph uses an iterative approach to set link directionalities, where knowing the direction of a nearby link can be used to set links of unknown directions. This means that if you incorrectly set a link’s direction manually, you could “infect” the nearby links with wrong directions! Indeed, we see from the above comparison that the upper-right link’s direction has also been reversed, even though we didn’t specify it. Whether or not this is correct is determined by the user.
8. A note on topologic metrics¶
If you’ve looked through the delta example, you’ll see the final section covers computing topolgic metrics. In order to compute these metrics, some additional finagling of the network is required. We have not yet implemented the required pre-processing for braided rivers. However, many of the functions in the delta metrics script can be used on braided rivers, provided you first pre-process your braided river network properly.


Exploration 8.1.3.
As you might imagine, linear transformations are foundational to how computers display and manipulate images. However, we must engage in some mathematical trickery to translate images across the screen. Read the discussion immediately preceding Activity 2.6.4. Then work through the problems presented there.in-context


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- loading…


Generic store providing access to the current state of a node.
- Source:
Members
statusError
Add a status code to an error object.
- Source:
Methods
getDevice(id, cb)
Get the details of a specific device.
Parameters:
| Name | Type | Description |
|---|---|---|
id |
String | Identity of the device being queried. String as UUID. |
cb |
NodeStore~deviceCallback | Callback with the device result. |
- Source:
getDevices(skipopt, limitopt, cb)
Get all the devices available at this node.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
skip |
number |
<optional> |
Number of devices to skip before starting the listing. |
limit |
number |
<optional> |
Limit the number of values returned. |
cb |
NodeStore~devicesCallback | Callback providing the list of devlices. |
- Source:
getSelf(cb)
Retrieve details of the node as the result of the callback.
Parameters:
| Name | Type | Description |
|---|---|---|
cb |
NodeStore~nodeCallback | Callback with the node result. |
- Source:
putDevice(device, cb)
Add or update a device on this node. The
following conditions are checked:
- If the device already exists, the version number must be more up-to-date. If null/undefined, a new version number will be generated.
- The node_id property is set to the identifier of this node. If it is null/undefined, it will be set.
- All referenced senders must exist in this store.
- All referenced receivers must exist in this store.
- The device is valid.
Parameters:
| Name | Type | Description |
|---|---|---|
device |
Device | [description] |
cb |
NodeStore~deviceCallback | [description] |
- Source:
putSelf(node, cb)
Update the defails of the node represented by this store. The
following conditions are checked:
- The version number is more up-to-date than the stored self.
- All service references are to known devices.
- The identifier of the passed node is the same as the one that is stored.
- The node is valid.
Parameters:
| Name | Type | Description |
|---|---|---|
node |
Node | Node value to replace the current one. |
cb |
NodeStore~nodeCallback | Callback with the updated node or details of update issues. |
- Source:
Type Definitions
deviceCallback(err, result)
Callback with the single requested device.
Parameters:
| Name | Type | Description |
|---|---|---|
err |
Error | Error retrieving the device's details. |
result |
Device | Single requested device. |
- Source:
devicesCallback(err, result, totalopt, pageOfopt, pagesopt, sizeopt)
Callback to contain a list of devices.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
err |
Error | Error retrieving the list of devices. | |
result |
Array.<Device> | List of devices. | |
total |
number |
<optional> |
Total number of devices. |
pageOf |
number |
<optional> |
Current page number, starting from 1. |
pages |
number |
<optional> |
Total number of pages. |
size |
number |
<optional> |
Number of items on this page. Up to the provided limit. |
- Source:
nodeCallback(err, result)
Callback containing a single node result.
Parameters:
| Name | Type | Description |
|---|---|---|
err |
Error | Error retrieving the node's details. |
result |
Node | Node details. |
- Source:


Module: ActiveSupport::Dependencies::Loadable
| Relationships & Source Files | |
| Defined in: | activesupport/lib/active_support/dependencies.rb |
Overview
::Object includes this module.
Class Method Summary
Instance Method Summary
- #load_dependency(file)
-
#require_dependency(file_name, message = "No such file to load -- %s.rb")
Interprets a file using mechanism and marks its defined constants as autoloaded.
- #require_or_load(file_name)
-
#unloadable(const_desc)
Mark the given constant as unloadable.
- #load(file, wrap = false) private
- #require(file) private
Class Method Details
.exclude_from(base)
[ GitHub ]225 226 227 228 229 230
# File 'activesupport/lib/active_support/dependencies.rb', line 225
def self.exclude_from(base) base.class_eval do define_method(:load, Kernel.instance_method(:load)) private :load end end
Instance Method Details
#load(file, wrap = false) (private)
[ GitHub ]284 285 286 287 288
# File 'activesupport/lib/active_support/dependencies.rb', line 284
def load(file, wrap = false) result = false load_dependency(file) { result = super } result end
#load_dependency(file)
[ GitHub ]254 255 256 257 258 259 260 261 262 263
# File 'activesupport/lib/active_support/dependencies.rb', line 254
def load_dependency(file) if Dependencies.load? && Dependencies.constant_watch_stack.watching? Dependencies.new_constants_in(Object) { yield } else yield end rescue Exception => exception # errors from loading file exception.blame_file! file if exception.respond_to? :blame_file! raise end
#require(file) (private)
[ GitHub ]290 291 292 293 294
# File 'activesupport/lib/active_support/dependencies.rb', line 290
def require(file) result = false load_dependency(file) { result = super } result end
#require_dependency(file_name, message = "No such file to load -- %s.rb")
Interprets a file using ActiveSupport::Dependencies.mechanism and marks its defined constants as
autoloaded. file_name
can be either a string or respond to to_path
.
Use this method in code that absolutely needs a certain constant to be defined at that point. A typical use case is to make constant name resolution deterministic for constants with the same relative name in different namespaces whose evaluation would depend on load order otherwise.
245 246 247 248 249 250 251 252
# File 'activesupport/lib/active_support/dependencies.rb', line 245
def require_dependency(file_name, = "No such file to load -- %s.rb") file_name = file_name.to_path if file_name.respond_to?(:to_path) unless file_name.is_a?(String) raise ArgumentError, "the file name must either be a String or implement #to_path -- you passed #{file_name.inspect}" end Dependencies.depend_on(file_name, ) end
#require_or_load(file_name)
[ GitHub ]232 233 234
# File 'activesupport/lib/active_support/dependencies.rb', line 232
def require_or_load(file_name) Dependencies.require_or_load(file_name) end
#unloadable(const_desc)
Mark the given constant as unloadable. Unloadable constants are removed each time dependencies are cleared.
Note that marking a constant for unloading need only be done once. Setup or init scripts may list each unloadable constant that may need unloading; each constant will be removed for every subsequent clear, as opposed to for the first clear.
The provided constant descriptor may be a (non-anonymous) module or class, or a qualified constant name as a string or symbol.
Returns true
if the constant was not previously marked for unloading,
false
otherwise.
278 279 280
# File 'activesupport/lib/active_support/dependencies.rb', line 278
def unloadable(const_desc) Dependencies.mark_for_unload const_desc end


The Browser extension implements user agent detection. The following values are exposed as properties of the module:
| Property name | Type | Details |
|---|---|---|
name | String | The browser name, for example, "Firefox" or "Internet Explorer" |
version | String | The unparsed version number of the browser software |
major | Number | The major version number |
minor | Number | The minor version number |
engineVersion | String | The unparsed version number of the browser engine |
engineMajor | Number | The major engine version number |
engineMinor | Number | The minor engine version number |
mobile | Boolean | An educated guess as to whether this is a mobile platform |
User-agent detection is not highly favoured these days. Possibly this is because it's been done
badly in the past: comparing version numbers as strings and finding that "10" is less than "9" or
only allowing for one major version digit in a regular expression and then blowing up with a null pointer
exception when the regex fails to match. This is compounded by a history of obfuscation and downright
dishonesty in the user agent string that makes it ridiculously hard to mine for correct and useful
information One might imagine that there's an industry-wide conspiracy to ensure that
no property of the navigator
object returns a usable value (examine
navigator.appName
; it is almost always "Netscape").
Since corporate IT departments are very resistant to change, many professional developers
are forced to support browsers that are now very legacy indeed. Many home users are perfectly happy
with Windows XP which means that we'll have to support Internet Explorer 8 for some time to come.
These legacy browsers throw up issues that no amount of feature detection can overcome. For example,
imagine that you have some dynamic, pre-formatted content that you need to display so you set it
as the innerHTML
of a <pre> element. If the text is separated by "\n", it displays fine
on IE8 and every other browser, but on IE7 the lines do not break. So you replace all "\n" with "\r\n".
Now it displays fine on IE7 and every other browser, but on IE8 the text has extra line breaks.
You could maybe set it, examine the offset height (assuming you know what the "correct" offset
height might be) and make a correction. Or, simpler and more reliable, you can determine that your code is
running on a known problem platform and compensate.
The fixes for the Browser extension are defined in the _browser namespace of the fix options.
The identify
fix does something useful with the user agent information: it adds three
classes to the document element identifying the engine, engine major version and engine minor version.
For example, on Firefox 24 (engine version "Gecko 24.0"), these classes are added to the <html>
element:
gecko gecko-24 gecko-24-0
You can use these classes to help overcome CSS issues thrown up by different engines (you know: the same line of CSS positions an element slightly differently on every browser and only one is right).
This fix has to be explicitly enabled:
UsefulJS.fix({ _browser : { identify : true } });


Around the Bay 30 km
Hamilton CAN
Website: bayrace.com
Race Winners
(111 runnings)
1:47:03.6 1:50:45.3 1:48:58.9 1:50:27 1:48:28.1 25 Mar
2007
1:32:49.8
1:46:51
26 Mar
2006
1:37:58.2
1:55:10.5
20 Mar
2005
1:38:48.0
1:58:07.6
28 Mar
2004
1:33:29
1:46:05
30 Mar
2003
1:35:02
1:52:50
24 Mar
2002
1:33:14
1:46:19
25 Mar
2001
1:36:40
1:54:21
26 Mar
2000
1:32:54
1:52:38
28 Mar
1999
1:36:31
1:52:38
29 Mar
1998
1:34:44
1:53:53
23 Mar
1997
1:35:35
1:56:22
31 Mar
1996
1:35:41*
1:56: *
02 Apr
1995
1:36:09
1:49:17
27 Mar
1994
1:32:55
1:50:54
28 Mar
1993
1:38:22
1:47:03
29 Mar
1992
1:40:26
1:54:42
24 Mar
1991
1:33:00
1:48:52
25 Mar
1990
1:35:34.5
1:54:16
26 Mar
1989
1:34:12
1:58:53
27 Mar
1988
1:39:22
1:58:38
29 Mar
1987
1:34:02
1:51:37
30 Mar 1986
1:39:53
1:55:03
31 Mar 1985
1:34:22
1:53:23
25 Mar 1984
1:33:53
1:46:29
27 Mar 1983
1:34:15
1:53:45
28 Mar 1982
1:34:51
2:02:02
29 Mar 1981
1:34:14
1:56:14
30 Mar 1980
1:35:18
2:03:38
25 Mar 1979
1:35:45
2:02:34
26 Mar 1978
1:39:48
27 Mar 1977
1:36:54
---
28 Mar 1976
1:42:04
2:35:
30 Mar 1975
1:41:16
2:41:
31 Mar 1974
1:39:40
25 Mar 1973
1:37:53
26 Mar 1972
1:40:18
03 Apr 1971
1:41:47
31 May 1970
2:32:21
25 Oct 1969
1:38:20
1968
1:36:02
1967
1:33:28
29 Oct 1966
1:37:29
30 Oct 1965
1:38:39
24 Oct 1964
1:42:48
19 Oct 1963
1:42:29
1962
---
07 Oct 1961
1:53:30
08 Oct 1960
1:52:40
10 Oct 1959
1:44:19
25 Oct 1958
1:42:07.5
1957
1:46:44
1956
1:52:16
1955
1:52:53
1954
1:54:29
1953
1:57:36
1952
1:48:25
1951
1:54:41
1950
1:54:30
01 Oct 1949
1:51:55
1948
1:52:25
1947
1:56:37
26 Oct 1946
1:54:29
27 Oct 1945
1:51:48
1944
1:52:40
1943
1:49:50
31 Oct 1942
1:47:40
01 Nov 1941
1:52:20
09 Nov 1940
1:49:30
1939
1:47:30
29 Oct 1938
1:46:14
30 Oct 1937
1:47:14
31 Oct 1936
1:45:20
1925-1935
---
31 May 1924
2:00:15
12 Nov 1923
1:52:24
07 Nov 1922
1:50:50
08 Nov 1921
1:50:04
1920
1:58:46
1917-1919
---
1916
1:51:58
1915
1:51:10
1914
1:52:17
27 Oct 1913
1:48:38
28 Oct 1912
1:46:15
30 Oct 1911
1:50:04
31 Oct 1910
1:50:52
25 Oct 1909
1:51:25
05 Nov 1908
1:51:16
31 Oct 1907
1:49:07
18 Oct 1906
1:49:25
26 Oct 1905
1:50:18
17 Nov 1904
1:48:53
15 Oct 1903
1:51:57
16 Oct 1902
1:52:31
28 Nov 1901
1:54:31
18 Oct 1900
1:51:52
19 Oct 1899
1:53:30
24 Nov 1898
1:54:05
25 Nov 1897
2:01:30
26 Nov 1896
2:12:25
02 Sep 1895
2:10:25
25 Dec 1894
2:14:
Notes: The distance was 30.73 km from 1894 thru 1924; from 1936 thru 1945 is was 30.18 km; from 1946 thru 1969
it was advertised as 30.73 km but was probably close to 30 km; in 1970 it was a marathon; from 1971 thru 1981 it was 30.18 km; from 1982 to date it has been
30 km. Women's results for 1975 thru 1977 are unofficial. The 1966 winner
was declared by officials after a deliberate tie with David-John Ellis (ON/CAN).
The course used for the 2015 race was modified from the previous certified
course due to road work and the modified course was not certified, however, the
course appears to have been at least 30 km.
31 Mar
2019
1:32:57.7
Daniel Kemoi (KEN)
1:45:57.2
Mengistu Emebet (ETH)
25 Mar
2018
1:35:10.7
Haron Kiptoo Sirma (KEN)
1:49:38.6
Dayna Pidhoresky-4
26 Mar
2017
1:34:51.9
Panuel Mkungo (KEN)
1:47:27.0
Dayna Pidhoresky-3
03 Apr
2016
1:35:16.2
Paul Kimaiyo Kimugul-3
1:47:37.7
Risper Gesabwa Biyaki (KEN)
29 Mar
2015
1:33:48.0
Paul Kimaiyo Kimugul-2
1:50:47.0
Dayna Pidhoresky-2
30 Mar
2014
1:35:34.6
Paul Kimaiyo Kimugul (KEN)
1:47:13.3
Krista Duchene-2
24 Mar
2013
1:35:44.1
Terence Attema (ON/CAN)
1:44:39.4
Lanni Marchant (CAN)
25 Mar
2012
1:33:20.4
Reid Coolsaet (CAN)
Krista Duchene (ON/CAN)
27 Mar
2011
1:37:15.0
Derek Nakluski (CAN)
Dayna Pidhoresky (CAN)
28 Mar
2010
1:32:21.9
Alene
Reta Emere-2
Lucy Njeri
Muhami-3
29 Mar
2009
1:35:29.0
Thomas Omwenga (KEN)
Lucy Njeri
Muhami-2
30 Mar
2008
1:33:06.0
Alene Reta
Emere (ETH)
Lucy Njeri Muhami (KEN)
Simon NjorogeKariuki (KEN)
Magdeline Mukunzi
Syombua (KEN)
Noureddine Betim (ALG)
Kathryn MacNamara-2
Joseph Ndiritu-4
Kathryn MacNamara (CAN)
Mustafa benNacer (MAR)
Lyudmila Korchagina-3
Joseph Nsengiyumya (RWA)
Lyudmila Korchagina-2
Joseph Ndiritu-3
Lyudmila Korchagina (RUS)
Joseph Ndiritu-2
Elizabeth Ruel (CAN)
Joseph Ndiritu (KEN)
Veronique vanderSmissen (CAN)
Paul aufdemBerge (USA)
May Allison-2
Sean Wade (NZL)
Danuta Bartoszek (CAN)
Paul Mbugua (KEN)
Katie Dosser (CAN)
Peter Maher-5
Cindy New (CAN)
Michael McGowan (CAN)
Kimberley Webb (CAN)
Sam Nyangincha (KEN)
May Allison (CAN)
David O'Keefe (USA)
Lizanne Bussieres (CAN)
Clive Hamilton (CAN)
Veronique Marot-2
Peter Maher-4
Veronique Marot (ENG)
Peter Maher-3
Laura Konantz-2
Peter Maher-2
Dorothy Goertzen-2
Paul Waldie
Laura Konantz (CAN)
Peter Maher (CAN)
Susan Stone-2
Bob Slipp
Jennifer Ditchfield (CAN)
Roger Martindill-2
Susan Stone (CAN)
Roger Martindill (CAN)
Anne Marie Malone (CAN)
Mike Dyon-3
Dorothy Goertzen (CAN)
David Edge (CAN)
Christine Keaney
Joe Sax (CAN)
Kathy Tanner
Mike Dyon-2
Vickie Scanlon
Mike Dyon (CAN)
Louise Adamson
Dean Foster (CAN)
.
Dave Northey (CAN)
Mary Copeland
Rich Hughson (CAN)
Tersilla Komac-2
Andy Boychuk-3
Tersilla Komac
Jerome Drayton-2
Jerome Drayton (CAN)
Peter Pimm (CAN)
Peter Lever (ON/CAN)
Andy Boychuk-2 (nc)
Patrick McMahon (IRL)
Tim Johnston (GBR)
A Fergus Murray (SCO)
Andy Boychuk (CAN)
David-John Ellis-2
Gordon Dickson-5
David-John Ellis (ON/CAN)
not held
Orville Atkins (ON/CAN)
Gar Williams (USA)
Gordon Dickson-4
Gordon Dickson-3
Gordon Dickson-2
Gordon Dickson
Barry Lush
Arnie Briggs
George Norman
H Browning Ross (USA)
Johnny Lafferty
Walt Fedorick-2
Paul Collins (CAN)
Scotty Rankine-7
Bill Steiner
Walt Fedorick
Johnny A Kelley (USA)
Scotty Rankine-6
Ab Morton
Scotty Rankine-5
Gerry Cote (CAN)
Scotty Rankine-4
Scotty Rankine-3
Scotty Rankine-2
Walt Hornby
Scotty Rankine (CAN)
not held
Art Scholes (CAN)
Frank Zuna-2
Frank Zuna
Albert Smoke
Jim Dellow
not held
Jim Corkery-2
Jim Corkery
Glen Jamieson
Jim Duffy-2
Jim Duffy
George Richards
Jimmy George-2
Jimmy George
Hans Holmer (NS/CAN)
Tom Coley (ON/CAN)
Tom Longboat (ON/CAN)
Mike Spring
Sammy Mellor-2
Billy Sherring-2
Sammy Mellor Jr
Billy LeBarre
Jack Caffery-2
Billy Sherring
Jack Caffery
Charlie Bates
Alex Donald
Frank Wood
William Marshall
* The 1996 race was marred by a draw bridge being lifted that cut off all but the first 50 runners. The results given are unofficial.
(nc) => national championship
Most Wins (1894-date)
Men
7
Scotty Rankine (CAN)
1936,1938-40,1942,1944,1948
Women
4
Dayna Pidhoresky (CAN)
2011, 2015, 2017-18
Prize Money (ADR qualified runners)
Year Men Women Total 2017 $6,165 $6,540 $12,705 2016 5,955 6,955 12,910 2015 7,175 7,250 14,425 2014 7,910 7,595 15,505 2013 8,065 9,040 17,105 2012 7,760 8,260 16,020 2011 8,535 8,715 17,125 2010 8,535 8,050 16,585 2009 7,140 6,290 13,430 2008 8,085 7,840 15,925 2007 8,295 7,445 15,740 2006 3,715 3,460 7,175 2005 2,480 2,730 5,210 2004 3,225 2,850 6,075 .... 2001 3,240 2,915 6,155 2000 4,035 3,825 7,860 1999 2,840 3,510 6,350 1998 2,335 2,480 4,815 1997 1,865 2,740 4,605 1996 n/a n/a n/a 1995 1,940 2,475 4,415 1994 4,740 2,775 7,515 .... 1991 1,110 740 1,850 Sums $115,020 $114,480 $229,500
Race Time Bias (sec/km)
Year Men Women 2015 -1.9 n/c .... 2008 -3.9 n/c 2007 -0.9 +0.4 2006 +0.9 n/c .... 2000 -1.1 +1.5 1999 -1.2 n/c .... 1997 -2.3 n/c .... 1994 +0.0 +2.4 Avgs -1.4 +0.9
Competitive Level (1990-date)
Year Men Women 2017 96 137 2016<PHONE_NUMBER> 150 n/c .... 2013 n/c 140 .... 2011 n/c 79 2010 178 31 2009 313 n/c 2008 327 95 2007<PHONE_NUMBER> 109 n/c .... 2002 n/c 76 2001 111 n/c 2000 210 75 1999 58 n/c .... 1994 347 n/c
Quality Performances through 2019
Men <1:33 5 <1:34 15 <1:35 29 <1:36 60 Women <1:46 2 <1:48 13 <1:50 23 <1:52 36
Last Updated on 04 Apr 2019 mp
Contributors: Malcolm Heyworth, Alex Wilson (1), Jack Leydig (26), Gary Pickett, Maurice Wilson (12), George Ondimu (3)


Gene selection based on variance using the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions. Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) <doi:10.1155/2018/6591634>.
| Version: | 0.0.9 |
| Depends: | R (≥ 3.4.0), Biobase, lawstat |
| Imports: | MASS, graphics, stats |
| Suggests: | ALL |
| Published: | 2018-07-27 |
| Author: | Xuan Li [aut, ctb], Yuejiao Fu [aut, ctb], Xiaogang Wang [aut, ctb], Dawn L. DeMeo [aut, ctb], Kelan Tantisira [aut, ctb], Scott T. Weiss [aut, ctb], Weiliang Qiu [aut, cre] |
| Maintainer: | Weiliang Qiu <weiliang.qiu at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | MMDvariance results |
| Reference manual: | MMDvariance.pdf |
| Package source: | MMDvariance_0.0.9.tar.gz |
| Windows binaries: | r-devel: MMDvariance_0.0.9.zip, r-release: MMDvariance_0.0.9.zip, r-oldrel: MMDvariance_0.0.9.zip |
| OS X binaries: | r-release: MMDvariance_0.0.9.tgz, r-oldrel: MMDvariance_0.0.9.tgz |
Please use the canonical form https://CRAN.R-project.org/package=MMDvariance to link to this page.


Phase 0, Week 4
Tech Problems: Moore's Law
January 18, 2015
Have you ever noticed that whenever you buy a new computer, laptop, or phone, it's pretty much obsolete a month after it comes out? In 1965 (yeah, computers are that old), Gordon Moore of Intel wrote about this strange trend in a research paper. He noticed that the number of transistors in an integrated circuit doubles roughly every two years. The number of transistors is directly proportional to the computing power of the device. This trend is known as "Moore's Law," and has upheld since it was first published in 1965. This kind of exponential growth is great until you think about the fact that the size of a chip is limited. If we limit the size of the chip, we have to halve the size of the transistors every two years. Eventually, we will reach a lower limit where we simply cannot make a smaller transistor and will effectively cap out processing power at that point. Scary huh?
What can we do about this? There are a few proposed solutions. For now, we can keep our current manufacturing processes, but they will eventually fail once we reach the atomic limit on the size of our style of transistor. To avoid this, new types of transistors, or even computing will need to be developed. Once transistors reach a quantum scale (about 5nm or so), quantum effects like quantum tunneling (electrons moving through a barrier) become relevant and could cause errors if an electron tunnels through a transistor gate. The smallest theoretical transistor would be on which either contains an electron (on) or does not (off). Other interesting solutions include using small biological molecules as logical transistors.


HEAD
DANIELDEEPAK
:HOME
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DanielGitHub Site
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>>>>>>> FETCH_HEAD


Mr Byrne died on Monday at his home in Dublin and is survived by wife Kathleen, who is a harpist and author, and their daughters Crona and Suzy and their families. The funeral mass will take place in St Mary’s Pro-Cathedral in Dublin at 11:45 on Friday with Fr Leonard Maloney SJ acting as principal celebrant.
Archbishop Diarmuid Martin will also be participating in the ceremony and leading the ‘Prayers of Final Commendation’. Administrator at St Mary’s Pro Cathedral Fr Kieran McDermott will receive the remains of Mr Byrne into the cathedral.
Music will be led by the Palestrina Choir with conductor Ciaran Kelly and organist David Grealy.
The public have been advised that access to the Pro-Cathedral will be extremely limited with seating reserved for family and friends.
Those attending the funeral mass are asked to be in place by 11.15am and to be guided by gardaí outside the cathedral and stewards inside the building.
Mr Byrne’s family have also asked that anyone wishing to donate money instead of flowers to his memory should do so to the Irish Cancer Society and St Francis Hospice.
RTÉ’s coverage of the funeral will take place on television and online at 11.30am on Friday.
RTÉ One and RTÉ News Now will host a special programme featuring coverage of the funeral mass presented by Bryan Dobson, while John Bowman and Mary Kennedy will provide commentary of the ceremony.
Television coverage will be streamed live on the RTÉ Player and on RTÉ.ie, and will be available to audiences worldwide.
Books of condolence to honour the late broadcaster saw a steady stream of visitors since they opened on Tuesday.
Politicians, broadcasters and the public have been visiting the Mansion House in Dublin. Lord mayor of Dublin Paul McAuliffe opened the book there to allow the people of the capital to express their sympathies to Mr Byrne’s family.
In Cork, people queued at Cork’s City Hall to sign books of condolence.


- Name: jenkins
- Requirement: Ubuntu 12.04 or later
- Installation notes: Yes
- Subdomain: jenkins
- Container link: Docker’s official container
What is Jenkins?
“The leading open source automation server, Jenkins provides hundreds of plugins to support building, deploying and automating any project.”
Installation Notes
- Point your browser to jenkins.yourdomain.com
- Get the password by running
cat /usr/local/provisioner-data/jenkins/secrets/initialAdminPassword
in your server.
- Follow the instructions to configure your Jenkins installation and create your administration account
Technical Details
The data for Jenkins resides in /usr/local/provisioner-data/jenkins


GBP/USD Forecast: bullish above 1.5630
-
More options
Summary
USDJPY bull trend extension bias into October Another nudge to a new recovery high to eye karly October, already a nudge this morning above a key upside targets at the psychological/option resistance.
Manny Pacquiao will review the previous day close levels and calls given in his blog and present expected trading ranges in 4 majors and 2 commodity pairs.
The USD index is the major player in this market place. A solid forecast here (USD) can lead to substantial gains in the USD majors.
Legal disclaimer and risk disclosure
Trendsetter does not warrant or guarantee the accuracy, timeliness or completeness to its service or information contained therein. Trendsetter does not give, whatsoever, warranties, expressed or implied, to the results to be obtained by using its services or information it provided. Users are trading on their own risk and Trendsetter shall not be responsnd its t limited to damages caused by negligence whether such damages were foreseen or unforeseen.


Your browser does not support canvas. Please try again with a different browser.
HIGH SCORE:
SCORE:
LIFE:
GAME OVER
SCORE:
High Score!!
Restart
LOADING...


This tests that a usually overflow: hidden viewport should be scrollable when scaled. Otherwise, you can't get to content you would have been able to get to had you not been zoomed it.


Visualizing February 2015 Weather Data with Turf.js
February was a particularly cold month across most of the United States. For us here in Oregon, we’ve been enjoying warm weather… but most further east have had record lows and heavy snow. I’ve been interested in finding new ways to visualize this data. I’ll be using NOAA’s quality controlled logical climatological data to load weather station data across the US into a Postgres database. From there I hope to generate TIN’s using turf.js.
The initial hurdle is getting the stations and readings into our database. Our file from NOAA has the following pipe delineated headers:
WBAN|WMO|CallSign|ClimateDivisionCode|ClimateDivisionStateCode|ClimateDivisionStationCode|Name|State|Location|Latitude|Longitude|GroundHeight|StationHeight|Barometer|TimeZone
I don’t need all of these, just the WBAN (unique id of station), State, Location, Latitude and Longitude. Let’s create the initial table:
Enable postgis on database:
CREATE EXTENSION postgis;
Create table for stations:
CREATE TABLE weather_stations (
WBAN integer,
State char(2),
Location varchar(150),
Latitude decimal(9,6) NOT NULL,
Longitude decimal(9,6) NOT NULL
);
I deleted the erroneous columns from the original file for easy import. From psql command line, I ran the following command to bring the stations into postgres:
\copy weather_stations FROM 'csv location' DELIMITER '|' CSV HEADER;
Add an auto incremented primary key:
ALTER TABLE weather_stations ADD COLUMN id SERIAL PRIMARY KEY;
SELECT AddGeometryColumn ('public', 'weather_stations', 'geom', 4326, 'POINT', 2);
Lastly, let’s create actual points from our Latitude and Longitude fields:
UPDATE weather_stations SET
geom = ST_SetSRID(ST_MakePoint(Longitude, Latitude),4326)
If we look at our data in QGIS, we see this now!
Now that the stations are in our database, we need to import our weather station readings. The process will be similar to what we accomplished getting the stations in. The readings are comma delineated and contain information or daily recordings. The initial format looks like:
WBAN,YearMonthDay,Tmax,TmaxFlag,Tmin,TminFlag,Tavg,TavgFlag,Depart,DepartFlag,DewPoint,DewPointFlag,WetBulb,WetBulbFlag,Heat,HeatFlag,Cool,CoolFlag,Sunrise,SunriseFlag,Sunset,SunsetFlag,CodeSum,CodeSumFlag,Depth,DepthFlag,Water1,Water1Flag,SnowFall,SnowFallFlag,PrecipTotal,PrecipTotalFlag,StnPressure,StnPressureFlag,SeaLevel,SeaLevelFlag,ResultSpeed,ResultSpeedFlag,ResultDir,ResultDirFlag,AvgSpeed,AvgSpeedFlag,Max5Speed,Max5SpeedFlag,Max5Dir,Max5DirFlag,Max2Speed,Max2SpeedFlag,Max2Dir,Max2DirFlag
Obviously, we are only interested in the WBAN (station id), YearMonthDay, Tmax, Tmin and Tavg. These will give us the ability to map low, high and average daily temperatures. I went ahead and removed the other columns from the initial dataset. Let’s create the table in postgres. I used varchar for the temperatures because of some string M values which equal missing… we can filter these out later.
CREATE TABLE readings_feb (
WBAN integer,
YearMonthDay integer NOT NULL,
Tmax varchar(5),
Tmin varchar(5),
Tavg varchar(5)
);
Import csv file:
\copy readings_feb FROM 'D:\TannerGeo\WeatherTinTurf\data_raw\QCLCD201502\201502daily.csv' DELIMITER ',' CSV HEADER;
Finally, add a primary key:
ALTER TABLE readings_feb ADD COLUMN id SERIAL PRIMARY KEY;
To visualize the data in QGIS, I created a special view with just the low temp. Notice we’ve casted the tmin to int:
CREATE VIEW feb_weather AS
SELECT a.id, a.wban, a.yearmonthday, a.tmin::int, b.state, b.location, b.geom
FROM readings_feb a
LEFT JOIN weather_stations b
ON a.wban = b.wban
By adding a filter for just February 1st in QGIS and symbolizing by temperature, the cold weather trends become apparent. The mid-west and northeast have bone chilling lows while the west coast and southern US had relatively high temperatures.
To incorporate the data into our mapping application, it’s preferable that we work with a data format suited to our needs. I’ll be using geojson, mostly because this format will work best with turf.js. We could build a query to generate this straight from postgres, but I’d prefer to just use pgsql2shp and then GDAL’s ogr2ogr to convert to geojson.
Create shapefile using pgsql command line tools:
pgsql2shp -f weather_02012015 -h localhost -p 5432 -u username -P password geodata "SELECT * FROM feb_weather WHERE yearmonthday = 20150201";
Convert to GeoJSON using GDAL:
ogr2ogr -f GeoJSON ../geojson/02012015.geojson 02012015.shp
Our output file is 287KB could probably be smaller since we don’t necessarily need all of the fields to make the TIN map.
The only dependencies I know of that we need to create the map are turf.js and leaflet. I chose to use bower to install these dependencies:
bower install turf
bower install leaflet
For bringing in the geojson, I’m just going to assign the object to a variable and place it in a seperate .js file. For future reference though, calvinmetcalf has created a great helper for leaflet called the leaflet-ajax library for working with geojson files.
We now have a .js file called 02012015.js with the geojson assigned to the variable feb01.
var feb01 = {
type": "FeatureCollection",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
...
I want to first make sure I can create the TIN’s. To test, I use turf.js to build and add them to the map with no fill. By passing in TMIN, turf generates Z values that we can then use to generate our fill colors.
var tin = turf.tin(feb01, 'TMIN');
var geojson = L.geoJson(tin, {
style : {
weight : 1
}
});
geojson.addTo(map);
Our result is a visually interesting graphic:
For our fill colors, I’m going to use a library I built called classybrew. It uses the natural breaks (Jenks) method of statistical classification and applies colors based on Cynthia Brewers color palettes. I added a new diverging color palette for red-yellow-blue. This should give us a good thematic perspective of color.
To stylize the map, we first need to initialize and setup or statistical breaks and color palette based on our minimum temperature values.
// create classification for colors
var brew = new classyBrew();
var values = []; // tmin values
for( var i = 0; i < feb01.features.length; i++) {
values.push(feb01.features[i].properties['TMIN']);
}
brew.setSeries(values);
brew.setNumClasses(6);
brew.classify();
brew.setColorCode("BuYlRd");
Now that we have the classybrew object setup, we can request a color code based on a provided value with getColorInRange(value)
. We’ll use this function to return a color based on the average of minimum temperature from each station that makes up our triangles. This can be done during instantiation of our geojson layer. First, however we need to create our TIN from our points using turf.tin(geojson, z value)
.
var tin = turf.tin(feb01, 'TMIN');
var geojson = L.geoJson(tin, {
style : function (f) {
return {
weight : 1,
fillColor : (function () {
return brew.getColorInRange((f.properties.a + f.properties.b + f.properties.c) / 3.0, true);
}()),
fillOpacity : 0.85
}
}
});
geojson.addTo(map);
Our result is rather captivating:
If we look at a map from the NOAA National Climatic Data Center, we can see our map aligns with their model:
I think this is all I was looking to do for now. The output is visually interesting and shows how powerful turf.js is with very little code. I’d like to take this further and make a more in depth application with the data and turf.js. Look for more to come in the future, and thanks for following along!


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Al-sheikh St, Al-Noflieen, Tripoli-Libya
<EMAIL_ADDRESS>Tel +218 21 340 7995
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Hello! I'm Isaiah, I have always had a knack for creating things with the intention of making things in life simplified.
I enjoy photography, graphic design, and above all, coding! Though I am in training as a Full-Stack Engineer, I for sure have
a special spot in my heart for front-end development. I have a passion for people and helping people become the best
versions of themselves that they can be. I am an extrovert with introverted tendencies and I am always learning!


Sir M. Visvesvaraya
Father of Modern Mysore State
Awarded with independent India’s greatest honor, The Bharat Ratna in 1955 (Image Source: www.southreport.com)
Test
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--- layout: default title: Home navigation-order: 1 --- {% include partials/hero-banner.html class="c-hero--overlay" imgsrc="https://cdn.colorlib.com/shapely/wp-content/uploads/sites/12/2016/03/photo-1443527216320-7e744084f5a7-1.jpg" content="kitchensink/hero-banner-example.html" %}


Percentages of single and divorced people in Rome Districts
Years 2010-2012. Source:
Rome Municipality


Get the download file working, or at least the links
Get some javascript up in here
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revel


Choose the options below then submit, and you will get your own unique password instantly generated by our script. All random passwords generated by this software are available on your local computer only. No passwords are transferred over the Internet. No personal data is stored by or captured through the site.
You will always find the latest source code version of this script in our GitHub repository. You can check out the source code :https://github.com/xinomilo/passwordgenerator
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Formed in 2004, Panic! At the Disco came together when high-school friends Spencer Smith (drums) and Ryan Ross (guitar) began covering blink-182 tunes together. After tiring of playing another group's material, they recruited two additional classmates, guitar/vocalist Brendon Urie and bassist Brent Wilson, and the newly formed quartet decided to model its name after a line in Name Taken's "Panic."
In the group's early experimental demos, the band created a sound that was different from the many death-metal groups that were performing in Las Vegas at the time. The band signed a recording contract without having performed a live show.
Urie met Brent Wilson while taking guitar classes offered at their high school and Wilson asked Urie to try out as lead guitarist for Panic! at the Disco, as they were looking for a replacement at the time.
In 2009, Weekes was hired to provide bass, synthesizer and backup vocals for Panic! at the Disco on a temporary, touring basis but was inducted into the band full time in mid-2010 as the band toured in China. He confirmed this in mid-2012 via Twitter.
Drummer for the pop rock band Panic! at the Disco who started off as the band's drum tech. He was previously in a band called Valencia, but filled in for Panic! on tour after Spencer Smith departed.


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NYCAASC 2018: Emergence Workshops
NYCAASC 2018 > Workshops: Track 1
Discovering Our Voices: Storytelling, Literature, and Writing
C 95, 11:45 - 1:10
Often times, it is difficult to tiptoe the line between exploring our cultural roots and finding our own individuality as Asian Americans through writing. We exist in an in-between of stereotypes and expectations -- an amalgamation of cultural influences from our heritage and upbringing. Mainstream Asian American literature (such as The Joy Luck Club and The Woman Warrior) is often criticized for their authenticity or lack thereof -- does this affect our own writing and how so? This workshop will explore the process of finding our unique selves through writing while still recalling and drawing upon our cultural roots.
Ed Lin
Ed Lin is a native New Yorker of Taiwanese and Chinese descent. He is the first author to win three Asian American Literary Awards. His seventh book, 99 Ways to Die, will be published by Soho Crime in October, and his first YA book, David Tung Can't Have a Girlfriend Until He Gets Into an Ivy League College, will be published by Kaya Press next year. Lin is also a financial journalist and is currently managing editor of Barrons.com, a Dow Jones Media Group property. He lives in Brooklyn with his wife, actress Cindy Cheung, and son.
Yasmin Majeed
Yasmin Adele Majeed is the assistant editor for the Asian American Writers' Workshop, where she works on the literary magazine, The Margins, and with fellowships for emerging writers. www.yasmaj.com.
Lisa Ko
Lisa Ko is the author of THE LEAVERS, a novel which won the 2016 PEN/Bellwether Prize for Socially Engaged Fiction and was a finalist for the 2017 National Book Award for Fiction, the 2018 PEN/Hemingway Award, and the 2017 Barnes and Noble Discover Great New Writers Award. Her writing has appeared in Best American Short Stories 2016, The New York Times, BuzzFeed, O. Magazine, and elsewhere. She has been awarded fellowships from the New York Foundation for the Arts, the Lower Manhattan Cultural Council, and the MacDowell Colony, among others. Born in Queens and raised in New Jersey, she lives in Brooklyn. Visit her at lisa-ko.com or on Twitter @iamlisako.
Sonya Chung
Sonya Chung is the author of the novels The Loved Ones (Relegation Books, 2016) and Long for This World (Scribner, 2010). She is a staff writer for the The Millions and founding editor of Bloom, and is a recipient of a Pushcart Prize nomination, the Charles Johnson Fiction Award, the Bronx Council on the Arts Writers' Residency, a MacDowell Colony Fellowship, and a Key West Literary Seminars residency. Sonya's stories, reviews, & essays have appeared in The Threepenny Review, Tin House, The Huffington Post, Buzzfeed, The Late American Novel: Writers on the Future of Books, Short: An International Anthology, and This is The Place: Women Writing About Home, among others. Sonya has taught fiction writing at Columbia University, NYU, Gotham Writers' Workshop, and College of Mount St. Vincent. Currently she lives in New York City and teaches at Skidmore College.


[ all classes ]
[ opennlp.tools.postag ]
Coverage Summary for Class: POSTaggerCrossValidator (opennlp.tools.postag)
| Class | Class, % | Method, % | Line, % |
|---|---|---|---|
| POSTaggerCrossValidator | 0% (0/ 1) | 0% (0/ 6) | 0% (0/ 48) |
1 /*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * http://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17
18 package opennlp.tools.postag;
19
20 import java.io.File;
21 import java.io.IOException;
22 import java.util.Map;
23
24 import opennlp.tools.dictionary.Dictionary;
25 import opennlp.tools.util.ObjectStream;
26 import opennlp.tools.util.TrainingParameters;
27 import opennlp.tools.util.eval.CrossValidationPartitioner;
28 import opennlp.tools.util.eval.Mean;
29
30 public class POSTaggerCrossValidator {
31
32 private final String languageCode;
33
34 private final TrainingParameters params;
35
36 private byte[] featureGeneratorBytes;
37 private Map<String, Object> resources;
38
39 private Mean wordAccuracy = new Mean();
40 private POSTaggerEvaluationMonitor[] listeners;
41
42 /* this will be used to load the factory after the ngram dictionary was created */
43 private String factoryClassName;
44 /* user can also send a ready to use factory */
45 private POSTaggerFactory factory;
46
47 private Integer tagdicCutoff = null;
48 private File tagDictionaryFile;
49
50 /**
51 * Creates a {@link POSTaggerCrossValidator} that builds a ngram dictionary
52 * dynamically. It instantiates a sub-class of {@link POSTaggerFactory} using
53 * the tag and the ngram dictionaries.
54 */
55 public POSTaggerCrossValidator(String languageCode,
56 TrainingParameters trainParam, File tagDictionary,
57 byte[] featureGeneratorBytes, Map<String, Object> resources,
58 Integer tagdicCutoff, String factoryClass,
59 POSTaggerEvaluationMonitor... listeners) {
60 this.languageCode = languageCode;
61 this.params = trainParam;
62 this.featureGeneratorBytes = featureGeneratorBytes;
63 this.resources = resources;
64 this.listeners = listeners;
65 this.factoryClassName = factoryClass;
66 this.tagdicCutoff = tagdicCutoff;
67 this.tagDictionaryFile = tagDictionary;
68 }
69
70
71 /**
72 * Creates a {@link POSTaggerCrossValidator} using the given
73 * {@link POSTaggerFactory}.
74 */
75 public POSTaggerCrossValidator(String languageCode,
76 TrainingParameters trainParam, POSTaggerFactory factory,
77 POSTaggerEvaluationMonitor... listeners) {
78 this.languageCode = languageCode;
79 this.params = trainParam;
80 this.listeners = listeners;
81 this.factory = factory;
82 this.tagdicCutoff = null;
83 }
84
85 /**
86 * Starts the evaluation.
87 *
88 * @param samples
89 * the data to train and test
90 * @param nFolds
91 * number of folds
92 *
93 * @throws IOException
94 */
95 public void evaluate(ObjectStream<POSSample> samples, int nFolds) throws IOException {
96
97 CrossValidationPartitioner<POSSample> partitioner = new CrossValidationPartitioner<>(
98 samples, nFolds);
99
100 while (partitioner.hasNext()) {
101
102 CrossValidationPartitioner.TrainingSampleStream<POSSample> trainingSampleStream = partitioner
103 .next();
104
105
106 if (this.tagDictionaryFile != null
107 && this.factory.getTagDictionary() == null) {
108 this.factory.setTagDictionary(this.factory
109 .createTagDictionary(tagDictionaryFile));
110 }
111
112 TagDictionary dict = null;
113 if (this.tagdicCutoff != null) {
114 dict = this.factory.getTagDictionary();
115 if (dict == null) {
116 dict = this.factory.createEmptyTagDictionary();
117 }
118 if (dict instanceof MutableTagDictionary) {
119 POSTaggerME.populatePOSDictionary(trainingSampleStream, (MutableTagDictionary)dict,
120 this.tagdicCutoff);
121 } else {
122 throw new IllegalArgumentException(
123 "Can't extend a TagDictionary that does not implement MutableTagDictionary.");
124 }
125 trainingSampleStream.reset();
126 }
127
128 if (this.factory == null) {
129 this.factory = POSTaggerFactory.create(this.factoryClassName, null, null);
130 }
131
132 factory.init(featureGeneratorBytes, resources, dict);
133
134 POSModel model = POSTaggerME.train(languageCode, trainingSampleStream,
135 params, this.factory);
136
137 POSEvaluator evaluator = new POSEvaluator(new POSTaggerME(model), listeners);
138
139 evaluator.evaluate(trainingSampleStream.getTestSampleStream());
140
141 wordAccuracy.add(evaluator.getWordAccuracy(), evaluator.getWordCount());
142
143 if (this.tagdicCutoff != null) {
144 this.factory.setTagDictionary(null);
145 }
146 }
147 }
148
149 /**
150 * Retrieves the accuracy for all iterations.
151 *
152 * @return the word accuracy
153 */
154 public double getWordAccuracy() {
155 return wordAccuracy.mean();
156 }
157
158 /**
159 * Retrieves the number of words which where validated
160 * over all iterations. The result is the amount of folds
161 * multiplied by the total number of words.
162 *
163 * @return the word count
164 */
165 public long getWordCount() {
166 return wordAccuracy.count();
167 }
168
169 private static POSTaggerFactory create(Dictionary ngram, TagDictionary pos) {
170 return new POSTaggerFactory(ngram, pos);
171 }
172 }


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{% extends "saas/base_dashboard.html" %} {% block localheadermeta %}
{{param.description}} {% if (param.type == 'object' or param.type == 'array') and param|schema_properties(definitions) %} {% endif %}
Once you have registered an account on djaodjin.com and created a site, the following Application Programmable Interface (API) will be available on your site will to provide profiles, billing and service access control - i.e. the administrative features required to run Software, as-a-Service, on the Internet.
Most API end points require authentication. DjaoApp supports three methods of authentication:
All API requests must be made over HTTPS. Calls made over plain HTTP will fail.
Browse the API endpoints by ...
This section contains APIs related to authenticating users such as registration, login, updating a user password, etc.
This section contains APIs to manage the checkout, billing and accounting workflows, including shopping carts, coupons, charges and balance statements.
The metrics APIs crunch the numbers and return various insight into the performance for the business. They are used to implement a balances dashboard for example.
This section contains APIs to manage the identity, such as name or email address, of users and organizations registered to the site.
This section contains APIs to manage roles and access rules.
This section contains APIs to manage subscriptions.
This section contains APIs to upload new templates for all pages, from login to checkout, or reset to the default theme.
This section contains APIs related to unauthenticated users.
This section contains APIs related to authenticated users.
This section contains APIs related to subscribers.
This section contains APIs related to providers.
This section contains APIs related to the broker.
This section contains APIs related to users.
A user represents a individual (or bot) with login credentials. Technically a user, after authentication, holds a session token that is passed to the server on each HTTP request. Users call API end points, and hold permissions to do so, through roles on organization and personal profiles.
Users are required to sign the terms of use of a site.
This section contains APIs related to billing profiles.
A billing, sometimes called an organization profile, has no login credentials. It typically represents a legal entity that can be charged for service. Users can operate on billing profiles (ex: subscribe the organization to a plan) though roles they hold on an billing profile.
Depending on its relationship within a workflow, a billing profile is referred as either a subscriber, a provider or the broker.
This section contains APIs related to roles.
A role is a relationship between a user and a billing or personal profile - shorten to profile later on - that grant the user certain permissions to act on the profile. Permissions are not directly tied to the Role object, but rather to an abstract representation of the role called a RoleDescription.
It is not uncommon in casual conversation to use role to mean RoleDescription when talking about permissions as the end result is about the same. None-the-less, the workflows involved to grant a role to a user, such as opt-ins and double opt-ins can only be set on the RoleDescription, affecting all role objects using that RoleDescription.
This section contains APIs related to subscription plans.
Billing profiles subscribe to one or many provider plans, hence becoming subscribers of that provider.
This section contains APIs related to subscriptions.
The subscription model defines the relationship between a subscriber and a provider through a plan.
This section contains APIs related to cart items.
In order to pay for a subscription, a billing profile will add plans to a cart, then checkout that cart, entering a payment method in the process.
This section contains APIs related to charges.
Payments for subscriptions results in charges on the payment processor.
This section contains APIs related to ad-hoc discounts.
This section contains APIs related to transactions.
Transactions are recorded in an append-only double-entry book keeping ledger.
This section contains APIs related to site theme and operations.
{{api_end_point.func|upper}} {{api_end_point.path}}
{{resp_code}}{% if resp_code == "204" %} No Content{% endif %}
curl{% if 'user' in api_end_point.tags %} -H 'Authorization: Bearer {{api_jwt_user|safe}}'{% endif %}{% if 'subscriber' in api_end_point.tags %} -H 'Authorization: Bearer {{api_jwt_subscriber|safe}}'{% endif %}{% if 'provider' in api_end_point.tags %} -H 'Authorization: Bearer {{api_jwt_provider|safe}}'{% endif %}{% if 'broker' in api_end_point.tags %} -H 'Authorization: Bearer {{api_jwt_broker|safe}}'{% endif %}{% if example.func in ('POST', 'PUT', 'PATCH', 'DELETE') %} -X {{example.func}}{% endif %}{% if example.requestBody %} -H 'Content-Type: application/json; charset=UTF-8' -d '{{example.requestBody}}'{% endif %}{# {{example.func}} #} {{api_base_url}}{% if example.path %}{{example.path.replace('&', '\&')}}{% else %}XXX{% endif %}
{% if example.resp %}
responds
{{example.resp}}{% endif %} {% endfor %}


The BHMGC maintains an active directory/listing of its members. This list contains names, phone numbers, and email addresses. It is the policy of our club that the directory is strictly limited for internal use related to BHMGC events and functions. It is not to be used externally, copied, or sold for non-BHMGC activities.
Violation of this policy will result in disciplinary action determined by the BHMGC Board, up to and including expulsion.
{{ m.name }} {{ m.email }}


So, I was supposed to fly back on Sunday but after 4 planes had "mechanical problems" our flight was canceled. I spent 9 hours in the airport yesterday and another 7 (so far) today, so I've had a lot of time to code. As a result, I'm done networking the main menu screen and PV has a login server that is up-and-running. It can hold characters (create and delete), manage accounts (create, retrieve, change password), process logins, give out current/max player information and handle disconnections. I'm finalizing that process right now and will be cleaning it up in the next week or so...after I do my homework and find some food. Hooray for having no groceries on the first day back after a trip!
Just out of curiosity, has anyone found new textures/models for the project? Also, where is silvert? I've got the OGG library set up on my computer and I'm ready to add his stuff to the game fairly soon.
Karl


Reset to deafults successfull. WE ARE READY FOR PRESENTATION.
We are worked together every day at MIC Armenia, pending new scenarios, to be completed.


Your mystery solving missions lead you to modern day Greece. You are invited to investigate the sudden death of an aspiring mathematician.
When you reach his household, you are immediately overwhelmed by the sight of scratch papers everywhere on the floor and the musky smell of a room that
feels like it has not seen daylight for ages. Disturbed, you think to yourself,
“So these are mathematicians, such an eccentric breed…” But nonetheless, you still decide to search around for clues about this person’s death.
After hours of rummaging around,
you finally gather all the clues you have for further inspection.
Apparently, before this aspiring mathematician’s death,
he was in the middle of solving a
Sandwich Sudoku puzzle.
There are scratch papers on notes about theorems. It seemed like he was planning to submit a groundbreaking paper as well.
- I would like to use omicron to denote my new constant named after me.
- The epsilon definition is crucial in understanding limits and the fundamentals of calculus.
- Oops! I forgot to include delta in the definition in Note 2.
- I will use mu nought to denote the vacuum permeability in a classical vacuum.
- Delta indicates small change and if I make delta improvements then over time I believe that I will surpass Archimedes.
- Derivatives also depend on deltas.
- Alpha denotes the significance level when proving null and alternative hypotheses.
- Lambda constants are so overrated!
- I just realised mu is named as a measure in measure theory.
Lastly, he has a diary named Diagon Alleys
where he kept his thoughts. His most recent thoughts were obscured by his drool. You are barely able to make out the sentence.

Can you figure out what is in the blank spaces?