|
| Local Properties | ||
| Managed Object Types | Data Object Types | All Properties | All Methods |
Properties
| Name | Type | Description |
|---|---|---|
| cbrcCacheEnabled* | xsd:boolean |
Flag to specify whether common CBRC digest cache is enabled for this
virtual machine.
The common CBRC cache is shared between the hot added disks in the VM.
If this flag is set to 'true' the VM will allocate a commont digest
cache on power on.
Since vSphere API 6.5 |
| disableAcceleration* | xsd:boolean |
Flag to turn off video acceleration for a virtual machine console window.
|
| diskUuidEnabled* | xsd:boolean |
Indicates whether disk UUIDs are being used by this virtual machine.
If this flag is set to false, disk UUIDs are not exposed to the guest.
Since products before ESX 3.1 do not support disk UUIDs, moving virtual machines from a platform that supports UUID to a platform that does not support UUIDs could result in unspecified guest behavior. For virtual machines where the ability to move to older platforms is important, this flag should be set to false. If the value is unset, the behavior 'false' will be used. Since VI API 2.5 |
| enableLogging* | xsd:boolean |
Flag to enable logging for a virtual machine.
|
| faultToleranceType* | xsd:string |
Indicates the type of fault tolerance type the virtual machine is
configured to use.
VirtualMachineFaultToleranceType represents the set of possible values. Since vSphere API 6.0 |
| htSharing* | xsd:string | Deprecated.
as of vSphere API 6.6.0.
Specifies how the VCPUs of a virtual machine are allowed to
share physical cores on a hyperthreaded system. Two VCPUs are
"sharing" a core if they are both running on logical CPUs of
the core at the same time. See VirtualMachineHtSharing |
| monitorType* | xsd:string |
vmx process type. See VirtualMachineFlagInfoMonitorType
for possible values for this property.
Since VI API 2.5 |
| recordReplayEnabled* | xsd:boolean | Deprecated.
as of vSphere API 6.0
Flag to specify whether record and replay operations are
allowed for this virtual machine.
If this flag is set to 'true', instruction virtualization
will use hardware virtualization (HV) support. I.e.,
virtualExecUsage will be set to 'hvOn'.
If this flag is set to 'false' for a virtual machine that
already has a recording, replay will be disallowed, though
the recording will be preserved.
If the value is unset, the behavior 'false' will be used.
Since vSphere API 4.0 |
| runWithDebugInfo* | xsd:boolean | Deprecated.
As of VI API 2.5, use monitorType.
Flag to specify whether or not to run in debug mode.
|
| snapshotDisabled* | xsd:boolean | Deprecated.
As of vSphere API 4.0. The flag is ignored by the server.
Flag to specify whether snapshots are disabled for this virtual
machine.
Since VI API 2.5 |
| snapshotLocked* | xsd:boolean |
Flag to specify whether the snapshot tree is locked for this virtual machine.
Since VI API 2.5 |
| snapshotPowerOffBehavior* | xsd:string |
Specifies the power-off behavior for a virtual machine that has
a snapshot. If the value is unset, the behavior 'powerOff' will
be used. See VirtualMachinePowerOffBehavior Since VI API 2.5 |
| useToe* | xsd:boolean |
Flag to specify whether or not to use TOE (TCP/IP Offloading).
|
| vbsEnabled* | xsd:boolean |
Flag to specify if Virtualization-based security
is enabled for this virtual machine.
If set to true when creating a new VM, the following VM
properties might be modified automatically:
- If vim.vm.FlagInfo.vvtdEnabled is not set to false ,
it is set to true . Else error is returned.
- If vim.vm.ConfigSpec.nestedHVEnabled is not set to false ,
it is set to true . Else error is returned.
- If vim.vm.BootOptions.efiSecureBootEnabled is not set to
false , it is set to true . Else error is
returned.
- If vim.vm.firmware is not set to bios , it is set
to efi . Else error is returned.
Since vSphere API 6.7 |
| virtualExecUsage* | xsd:string |
Indicates whether or not the system will try to use Hardware
Virtualization (HV) support for instruction virtualization,
if available.
By default, VMware software will determine whether or not to use hardware virtualization support based on various factors such as the guest operating system type and the physical hardware. Certain workloads can benefit from explicitly turning hardware virtualization support on or off. If the value is unset, the value will default to hvAuto. VirtualMachineFlagInfoVirtualExecUsage represents the set of possible values. New processors can enable two hardware acceleration technologies for virtualization, one for instruction virtualization and the other for MMU virtualization. Intel names its hardware-assisted instruction virtualization as VT, and its hardware-assisted MMU virtualization as EPT. AMD calls them as AMD-V and RVI, respectively. For details on these technologies, please refer to documents from the processor vendors. virtualExecUsage controls instruction virtualization; while virtualMmuUsage controls MMU virtualization. "On" allows hardware acceleration, while "off" only allows software solution. There are four meaningful combinations. (hvAuto, automatic) - The host chooses which feature to use. (hvOn, on) - Use both VT/AMD-V and EPT/RVI. (hvOn, off) - Use VT/AMD-V but do not use EPT/RVI. (hvOff, off) - Do not use any of these hardware acceleration technologies. Since vSphere API 4.0 |
| virtualMmuUsage* | xsd:string |
Indicates whether or not the system will try to use nested page
table hardware support, if available.
By default, VMware software will determine whether or not to use nested page table hardware support based on various factors such as the guest operating system type and the physical hardware. Certain workloads can benefit from explicitly turning nested page table hardware support on or off; this can be set using nptUsage flag. If the value is unset, the value will default to automatic. VirtualMachineFlagInfoVirtualMmuUsage represents the set of possible values. Since VI API 2.5 |
| vvtdEnabled* | xsd:boolean |
Flag to specify if Intel Virtualization Technology for Directed I/O
is enabled for this virtual machine.
When creating a new VM:
- If vim.vm.FlagInfo.vbsEnabled is set to true ,
and this flag is set to false error is returned.
- If this flag is unset and vim.vm.FlagInfo.vbsEnabled is set to
true , the value of this flag is set to true .
Since vSphere API 6.7 |
| Properties inherited from DynamicData | ||
| None |
| Top of page | Local Properties | ||
| Managed Object Types | Data Object Types | All Properties | All Methods |


Using conda-forge to manage your CI¶
Conda-forge, and specifically conda-smithy
contains a lot of
tools for building and deploying continuous integration (CI)
infrastructure on a variety of different platforms and architectures!
Wouldn’t it be nice if you could reuse all of this hard work, so
you don’t have to write or manage your own CI configurations?!
By adding a recipe/
directory to your repository, the conda-smithy
command ci-skeleton
let’s you hook into well-tested and robust
CI infrastructure. Using the conda-smithy rerender
command, you
can then keep your repository up-to-date with any needed changes!
Getting Started¶
The ci-skeleton
command helps get you started by preparing a repository
to have the proper structure such that the rerender
command will correctly
add the CI configurations. Let’s see an example!
Suppose you have a repository for a project called myproj
. In the
root level of the repository, you can run the following command:
~/repo $ conda smithy ci-skeleton myproj
This will produce output like the following:
Generating ~/repo/conda-forge.yml
Generating ~/repo/recipe/meta.yaml
Updating ~/repo/.gitignore
A CI skeleton has been generated! Please use the following steps
to complete the CI setup process:
1. Fill out recipe/meta.yaml with your install and test code
2. Commit all changes to the repo.
$ git add . && git commit -m "ran conda smithy skeleton"
3. Remember to register your repo with the CI providers.
4. Rerender this repo to generate the CI configurations files.
This can be done with:
$ conda smithy rerender -c auto
At any time in the future, you will be able to automatically update your
CI configuration by re-running the rerender command above. Happy testing!
As you can see, this generates and updates a few important files.
The first file it creates is the conda-forge.yml
file. This is
specifically constructed to tell conda smithy rerender
that we are
not running myproj
CI as a regular feedstock. The .gitignore
is
modified to not accidentally add unwanted conda-smithy temporary files
to your repository.
Also the steps that the ci-skeleton
spits out are very important
for wiring everything up properly. Luckily, they are easy to perform!
Let’s go through them one-by-one!
1. Fill out recipe/meta.yaml¶
The ci-skeleton
command emits an example meta.yaml
file for
building myproj
, hence the “skeleton” part of the name. If you
don’t want the skeleton to be produced in the recipe/
directory,
you can use the -r
option to supply an alternative.
The meta.yaml looks like:
{% set name = "myproj" %}
{% set version = environ.get('GIT_DESCRIBE_TAG', 'untagged')|string|replace('-','_') %}
package:
name: {{ name|lower }}
version: {{ version }}
source:
git_url: {{ environ.get('FEEDSTOCK_ROOT', '..') }}
build:
# Uncomment the following line if the package is pure Python and the recipe
# is exactly the same for all platforms. It is okay if the dependencies are
# not built for all platforms/versions, although selectors are still not allowed.
# See https://conda-forge.org/docs/maintainer/knowledge_base.html#noarch-python
# for more details.
# noarch: python
number: {{ environ.get('GIT_DESCRIBE_NUMBER', '0') }}
string: {{ [build_number, ('h' + PKG_HASH), environ.get('GIT_DESCRIBE_HASH', '')]|join('_') }}
# If the installation is complex, or different between Unix and Windows,
# use separate bld.bat and build.sh files instead of this key. By default,
# the package will be built for the Python versions supported by conda-forge
# and for all major OSs. Add the line "skip: True # [py<35]" (for example)
# to limit to Python 3.5 and newer, or "skip: True # [not win]" to limit
# to Windows.
script: "{{ PYTHON }} -m pip install . -vv"
requirements:
build:
# If your project compiles code (such as a C extension) then add the required
# compilers as separate entries here. Compilers are named 'c', 'cxx' and 'fortran'.
- {{ compiler('c') }}
host:
- python
- pip
run:
- python
test:
# Some packages might need a `test/commands` key to check CLI.
# List all the packages/modules that `run_test.py` imports.
imports:
- myproj
# Run your test commands here
commands:
- myproj --help
- pytest
# declare any test-only requirements here
requires:
- pytest
# copy over any needed test files here
source_files:
- tests/
# Uncomment and fill in myproj metadata
#about:
# home: https://github.com/conda-forge/conda-smithy
# license: BSD-3-Clause
# license_family: BSD
# license_file: LICENSE
# Uncomment the following if this will be on a forge
# Remove these lines if this is only be used for CI
#extra:
# recipe-maintainers:
# - BobaFett
# - LisaSimpson
This recipe is configured to correctly grab the source code and the version
information from git. It also stubs out adding any test files that you might
want to have conda-build
use when it runs the test suite.
Note
Because you are using conda-forge, conda-build, etc as your CI, it is important to run the full test suite here.
Note
Metadata such as licenses and maintainers are likely less important, because in the default case, packages created here will never be uploaded to a channel. Feel free to delete or ignore these fields.
2. Commit the changes¶
Once you have written your recipe, it is important to save the modifications! Just run the following commands:
~/repo $ git add . && git commit -m "ran conda smithy skeleton"
3. Register with the CI providers¶
This is important! It you haven’t done so already, you’ll need to go to the CI providers (Travis, Circle, Azure, etc.) and enable CI to for your repository. Each CI provider that you use will have documentation on how to get set up with them.
4. Rerender¶
Last, but certainly not least, we need to generate the CI configuration
scripts! This is based on the content of the recipe as well as the
provider selections made in the conda-forge.yml
file. (Please
refer to these docs
for a complete list of CI providers.)
In order to generate the CI configuration files, run:
~/repo $ conda smithy rerender -c auto
Pushing those changes up to the repo should now give be building and testing your package on CI!
Keeping Up-to-date¶
A major advantage of using ci-skeleton
is that once it has been
setup, it is very easy to keep your CI system up-to-date. If you
modify your recipe to enable new architectures, you want to
run on a different provider, or even if the CI system changes out from under you,
getting back up and running is as easy as rerendering.
You just need to repeat step 4, above:
~/repo $ conda smithy rerender -c auto
This will generate and replace the CI configuration files for the current time and state of the recipe. It is just that easy!


Chapter 1 Introduction to spatial data in R
Learning Objectives
- Create point, line, and polygon shapefiles as
sp
andsf
objects.- Read shapefiles into
sp
andsf
objects- Examine
sp
andsf
objects- Read GeoTiff single and multiband into a
raster
object.- Examine
raster
objects
1.1 Conceptualizing spatial vector objects in R
In vector GIS we deal with, points, lines, and polygons, like so:
Challenge
Discuss with your neighbor: What information do we need to store in order to define points, lines, polygons in geographic space?
There are currently two main approaches in R to handle geographic vector data:
1.1.1 The sp
package
The first general package to provide classes and methods for spatial data types that was developed for R is called sp
1. Development of the sp
package began in the early 2000s in an attempt to standardize how spatial data would be treated in R and to allow for better interoperability between different analysis packages that use spatial data. The package (first release on CRAN in 2005) provides classes and methods to create points, lines, polygons, and grids and to operate on them. About 350 of the spatial analysis packages use the spatial data types that are implemented in sp
i.e. they “depend” on the sp
package and many more are indirectly dependent.
The foundational structure for any spatial object in sp
is the Spatial
class. It has two “slots” (new-style S4 class objects in R have pre-defined components called slots):
a bounding box
a CRS class object to define the Coordinate Reference System
This basic structure is then extended, depending on the characteristics of the spatial object (point, line, polygon).
To manually build up a spatial object in sp
we could follow these steps:
I. Create geometric objects (topology)
Points (which may have 2 or 3 dimensions) are the most basic spatial data objects. They are generated out of either a single coordinate or a set of coordinates, like a two-column matrix or a dataframe with a column for latitude and one for longitude.
Lines are generated out of Line
objects. A Line
object is a spaghetti collection of 2D coordinates2 and is generated out of a two-column matrix or a dataframe with a column for latitude and one for longitude. A Lines
object is a list of one or more Line
objects, for example all the contours at a single elevation.
Polygons are generated out of Polygon
objects. A Polygon
object is a spaghetti collection of 2D coordinates with equal first and last coordinates and is generated out of a two-column matrix or a dataframe with a column for latitude and one for longitude. A Polygons
object is a list of one or more Polygon
objects, for example islands belonging to the same country.
II. Create spatial objects
Spatial*
object (*
stands for Points, Lines, or Polygons).
This step adds the bounding box (automatically) and the slot for the Coordinate Reference System or CRS (which needs to be filled with a value manually). SpatialPoints
can be directly generated out of the coordinates. SpatialLines
and SpatialPolygons
objects are generated using lists of Lines
or Polygons
objects respectively (more below).
III. Add attributes (Optional:)
Add a data frame with attribute data, which will turn your Spatial*
object into a Spatial*DataFrame
object. The points in a SpatialPoints
object may be associated with a row of attributes to create a SpatialPointsDataFrame
object. The coordinates and attributes may, but do not have to be keyed to each other using ID values.
SpatialLinesDataFrame
and SpatialPolygonsDataFrame
objects are defined using SpatialLines
and SpatialPolygons
objects and data frames. The ID fields are here required to match the data frame row names.
If, for example we wanted to build up an sp
Object that would contain highways we would do the following.
First we would create a Line
object that holds one highway. We use a matrix with two columns of arbitrary numbers, for x and y coordinates.
ln1 <- Line(matrix(runif(6), ncol=2))
str(ln1)
#> Formal class 'Line' [package "sp"] with 1 slot
#> ..@ coords: num [1:3, 1:2] 0.468 0.341 0.407 0.252 0.971 ...
Note the @ coords
slot which holds the coordinates.
Ok, let’s create another Line
object for another highway:
ln2 <- Line(matrix(runif(6), ncol=2))
Now we combine the two highways to a Lines
object. Note how we add a unique ID for each highway. This step allows to combine generate multiple line strings, so we could add more lines under the same ID.
lns1 <- Lines(list(ln1), ID = c("hwy1"))
lns2 <- Lines(list(ln2), ID = c("hwy2"))
str(lns1)
#> Formal class 'Lines' [package "sp"] with 2 slots
#> ..@ Lines:List of 1
#> .. ..$ :Formal class 'Line' [package "sp"] with 1 slot
#> .. .. .. ..@ coords: num [1:3, 1:2] 0.468 0.341 0.407 0.252 0.971 ...
#> ..@ ID : chr "hwy1"
The Line
objects are now in a list and we have an additional ID
slot, which uniquely identifies each Line
object.
Now we turn this into a geospatial object by creating a SpatialLines
object:
sp_lns <- SpatialLines(list(lns1, lns2))
str(sp_lns)
#> Formal class 'SpatialLines' [package "sp"] with 3 slots
#> ..@ lines :List of 2
#> .. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
#> .. .. .. ..@  ID : chr "hwy1"
#> .. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
#> .. .. .. ..@  ID : chr "hwy2"
#> ..@ bbox : num [1:2, 1:2] 0.341 0.196 0.926 0.971
#> .. ..- attr(*, "dimnames")=List of 2
#> .. .. ..$ : chr [1:2] "x" "y"
#> .. .. ..$ : chr [1:2] "min" "max"
#> ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
#> .. .. ..@ projargs: chr NA
Note how this adds the @ bbox
with the bounding box coordinates and @ proj4string
to hold the Coordinate Reference System - in our case NA
as we have not assigned any projection.
Finally we create some attributes to each highway and create a SpatialLinesDataframe
. The way we do this is that we create a regular data.frame
and join it to the spatial object via the unique ID.
dfr <- data.frame(id = c("hwy1", "hwy2"), # note how we use the same IDs from above!
cars_per_hour = c(78, 22))
sp_lns_dfr <- SpatialLinesDataFrame(sp_lns, dfr, match.ID = "id")
str(sp_lns_dfr)
#> Formal class 'SpatialLinesDataFrame' [package "sp"] with 4 slots
#> ..@ data :'data.frame': 2 obs. of 2 variables:
#> .. ..$ id : Factor w/ 2 levels "hwy1","hwy2": 1 2
#> .. ..$ cars_per_hour: num [1:2] 78 22
#> ..@ lines :List of 2
#> .. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
#> .. .. .. ..@  ID : chr "hwy1"
#> .. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
#> .. .. .. ..@  ID : chr "hwy2"
#> ..@ bbox : num [1:2, 1:2] 0.341 0.196 0.926 0.971
#> .. ..- attr(*, "dimnames")=List of 2
#> .. .. ..$ : chr [1:2] "x" "y"
#> .. .. ..$ : chr [1:2] "min" "max"
#> ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
#> .. .. ..@ projargs: chr NA
Note the additional @ data
slot here, where we find the attribute information.
There are a number of spatial methods are available for the object classes in sp
. Among the ones I use more frequently are:
| function | and what it does |
|---|---|
bbox() |
returns the bounding box coordinates |
proj4string() |
sets or retrieves projection attributes as object of the CRS class. |
CRS() |
creates an object of class of coordinate reference system arguments |
spplot() |
plots a separate map of all the attributes unless specified otherwise |
coordinates() |
set or retrieve the spatial coordinates. For spatial polygons it returns the centroids. |
over(a, b) |
used for example to retrieve the polygon or grid indices on a set of points |
spsample() |
sampling of spatial points within the spatial extent of objects |
1.1.2 The sf
package
The second package, first released on CRAN in late October 2016, is called sf
3. It implements a formal standard called “Simple Features” that specifies a storage and access model of spatial geometries (point, line, polygon). A feature geometry is called simple when it consists of points connected by straight line pieces, and does not intersect itself. This standard has been adopted widely, not only by spatial databases such as PostGIS, but also more recent standards such as GeoJSON.
If you work with PostGis or GeoJSON you may have come across the WKT (well-known text) format (Fig 1.1 and 1.2)
sf
implements this standard natively in R. Data are structured and conceptualized very differently from the sp
approach.
In sf
spatial objects are stored as a simple data frame with a special column that contains the information for the geometry coordinates. That special column is a list with the same length as the number of rows in the data frame. Each of the individual list elements then can be of any length needed to hold the coordinates that correspond to an individual feature.
To create a spatial sf
object manually the basic steps would be:
I. Create geometric objects (topology)
Geometric objects (simple features) can be created from a numeric vector, matrix or a list with the coordinates. They are called sfg
objects for Simple Feature Geometry.b Similarly to sp
there are functions that help create simple feature geometries, like st_point()
, st_linestring()
, st_polygon()
and more.
II. Combine all individual single feature objects for the special column.
The feature geometries are then combined into a Simple Feature Collection with st_sfc()
. which is nothing other than a simple feature geometry list-column. The sfc
object also holds the bounding box and the projection information.
- Add attributes.
Lastly, we add the attributes to the the simple feature collection with the st_sf()
function. This function extends the well known data frame in R with a column that holds the simple feature collection.
So if we created the same highway object from above as sf
object we would first generate LINESTRINGs as simple feature geometries out of a matrix with coordinates:
lnstr_sfg1 <- st_linestring(matrix(runif(6), ncol=2))
lnstr_sfg2 <- st_linestring(matrix(runif(6), ncol=2))
class(lnstr_sfg1)
#> [1] "XY" "LINESTRING" "sfg"
We would then combine this into a simple feature collection :
(lnstr_sfc <- st_sfc(lnstr_sfg1, lnstr_sfg2)) # just one feature here
#> Geometry set for 2 features
#> geometry type: LINESTRING
#> dimension: XY
#> bbox: xmin: 0.1160083 ymin: 0.07180527 xmax: 0.8961863 ymax: 0.8873388
#> epsg (SRID): NA
#> proj4string: NA
#> LINESTRING (0.266583 0.6571762, 0.2915551 0.887...
#> LINESTRING (0.8961863 0.5288989, 0.7907187 0.43...
And lastly use our data frame from above to generate the sf
object:
(lnstr_sf <- st_sf(dfr , lnstr_sfc))
#> Simple feature collection with 2 features and 2 fields
#> geometry type: LINESTRING
#> dimension: XY
#> bbox: xmin: 0.1160083 ymin: 0.07180527 xmax: 0.8961863 ymax: 0.8873388
#> epsg (SRID): NA
#> proj4string: NA
#> id cars_per_hour lnstr_sfc
#> 1 hwy1 78 LINESTRING (0.266583 0.6571...
#> 2 hwy2 22 LINESTRING (0.8961863 0.528...
There are many methods available in the sf
package, to find out use
methods(class="sf")
#> [1] [ [[<- $<-
#> [4] aggregate as.data.frame cbind
#> [7] coerce dbDataType dbWriteTable
#> [10] extent extract identify
#> [13] initialize mask merge
#> [16] plot print raster
#> [19] rasterize rbind show
#> [22] slotsFromS3 st_agr st_agr<-
#> [25] st_area st_as_sf st_bbox
#> [28] st_boundary st_buffer st_cast
#> [31] st_centroid st_collection_extract st_convex_hull
#> [34] st_coordinates st_crop st_crs
#> [37] st_crs<- st_difference st_geometry
#> [40] st_geometry<- st_intersection st_is
#> [43] st_line_merge st_nearest_points st_node
#> [46] st_point_on_surface st_polygonize st_precision
#> [49] st_segmentize st_set_precision st_simplify
#> [52] st_snap st_sym_difference st_transform
#> [55] st_triangulate st_union st_voronoi
#> [58] st_wrap_dateline st_write st_zm
#> see '?methods' for accessing help and source code
Here are some of the other highlights of sf
you might be interested in:
provides fast I/O, particularly relevant for large files
- spatial fuctions that rely on GEOS and GDAL and PROJ external libraries are directluy linked into the package, so no need to load additional external packages (like in
sp
)
sf
objects can be plotted directly withggplot
sf
directly reads from and writes to spatial databases such as PostGISsf
is compatible with thetidyvderse
approach, (but see some pitfalls here)
Note that sp
and sf
are not the only way spatial objects are conceptualized in R. Other spatial packages may use their own class definitions for spatial data (for example spatstat
).
There are packages specifically for the GeoJSON and for that reason are more lightweight, for example:
Usuallly you can find functions that convert objects to and from these formats.
Challenge
Similarly to the example above generate a Point object in R. Use both, the
sp
and thesf
“approach”.
- Create a matrix
pts
of random numbers with two columns and as many rows as you like. These are your points.- Create a dataframe
attrib_df
with the same number of rows as yourpts
matrix and a column that holds an attribute. You can make up any attribute.- Use the appropriate commands and
pts
to create
- a
SpatialPointsDataFrame
and- an
sf
object with a gemoetry column of classsfc_POINT
.
- Try to subset your spatial object using the attribute you have added and the way you are used to from regular data frames.
- How do you determine the bounding box of your spatial object?
1.2 Creating a spatial object from a lat/lon table
Often in your research might have a spreadsheet that contains latitude, longitude and perhaps some attribute values. You know how to read the spreadsheet into a data frame with read.table
or read.csv
. We can then very easily convert the table into a spatial object in R.
1.2.1 With sf
An sf
object can be created from a data frame in the following way. We take advantage of the st_as_sf()
function which converts any foreign object into an sf
object. Similarly to above, it requires an argument coords
, which in the case of point data needs to be a vector that specifies the data frame’s columns for the longitude and latitude (x,y) coordinates.
my_sf_object <- st_as_sf(myDataframe, coords)
st_as_sf()
creates a new object and leaves the original data frame untouched.
We use read.csv()
to read philly_homicides.csv
into a dataframe in R and name it philly_homicides_df
.
philly_homicides_df <- read.csv("data/philly_homicides.csv")
str(philly_homicides_df )
#> 'data.frame': 3883 obs. of 10 variables:
#> $ DC_DIST : int 22 1 1 1 1 1 1 1 1 1 ...
#> $ SECTOR : Factor w/ 30 levels "1","2","3","4",..: 1 6 6 6 9 10 6 14 14 5 ...
#> $ DISPATCH_DATE : Factor w/ 2228 levels "2006-01-01","2006-01-02",..: 2139 10 62 90 125 132 133 139 199 211 ...
#> $ DISPATCH_TIME : Factor w/ 1263 levels "00:00:00","00:01:00",..: 799 1 804 561<PHONE_NUMBER> 86 1063 1013 ...
#> $ LOCATION_BLOCK : Factor w/ 3305 levels " 100 E CHAMPLOST AVE",..:<PHONE_NUMBER> 806 828 588 579 853 883 625 ...
#> $ UCR_GENERAL : int 100 100 100 100 100 100 100 100 100 100 ...
#> $ OBJ_ID : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ TEXT_GENERAL_CODE: Factor w/ 4 levels "Homicide - Criminal",..: 1 1 1 1 1 2 1 1 1 1 ...
#> $ POINT_X : num -75.2 -75.2 -75.2 -75.2 -75.2 ...
#> $ POINT_Y : num 40 39.9 39.9 39.9 39.9 ...
We convert the philly_homicides_df
data frame into an sf
object with st_as_sf()
philly_homicides_sf <- st_as_sf(philly_homicides_df, coords = c("POINT_X", "POINT_Y"))
str(philly_homicides_sf)
#> Classes 'sf' and 'data.frame': 3883 obs. of 9 variables:
#> $ DC_DIST : int 22 1 1 1 1 1 1 1 1 1 ...
#> $ SECTOR : Factor w/ 30 levels "1","2","3","4",..: 1 6 6 6 9 10 6 14 14 5 ...
#> $ DISPATCH_DATE : Factor w/ 2228 levels "2006-01-01","2006-01-02",..: 2139 10 62 90 125 132 133 139 199 211 ...
#> $ DISPATCH_TIME : Factor w/ 1263 levels "00:00:00","00:01:00",..: 799 1 804 561<PHONE_NUMBER> 86 1063 1013 ...
#> $ LOCATION_BLOCK : Factor w/ 3305 levels " 100 E CHAMPLOST AVE",..:<PHONE_NUMBER> 806 828 588 579 853 883 625 ...
#> $ UCR_GENERAL : int 100 100 100 100 100 100 100 100 100 100 ...
#> $ OBJ_ID : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ TEXT_GENERAL_CODE: Factor w/ 4 levels "Homicide - Criminal",..: 1 1 1 1 1 2 1 1 1 1 ...
#> $ geometry :sfc_POINT of length 3883; first list element: 'XY' num -75.2 40
#> - attr(*, "sf_column")= chr "geometry"
#> - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA
#> ..- attr(*, "names")= chr "DC_DIST" "SECTOR" "DISPATCH_DATE" "DISPATCH_TIME" ...
Note the additional geometry list-column which now holds the simple feature collection with the coordinates of all the points.
To make it a complete geographical object we assign the WGS84 projection, which has the EPSG code 4326:
st_crs(philly_homicides_sf)
#> Coordinate Reference System: NA
st_crs(philly_homicides_sf) <- 4326 # we can use EPSG as numeric here
st_crs(philly_homicides_sf)
#> Coordinate Reference System:
#> EPSG: 4326
#> proj4string: "+proj=longlat +datum=WGS84 +no_defs"
We will save this object as a shapefile on our hard drive for later use. (Note that by default st_write
checks if the file already exists, and if so it will not overwrite it. If you need to force it to overwrite use the option delete_layer = TRUE
.)
st_write(philly_homicides_sf, "data/PhillyHomicides", driver = "ESRI Shapefile")
# to force the save:
st_write(philly_homicides_sf, "data/PhillyHomicides", driver = "ESRI Shapefile", delete_layer = TRUE)
1.2.2 With sp
A SpatialPointsDataFrame
object can be created directly from a table by specifying which columns contain the coordinates. This can be done in one step by using the coordinates()
function. As mentioned above this function can be used not only to retrieve spatial coordinates but also to set them, which is done in R fashion with:
coordinates(myDataframe) <- value
value
can have different forms – in this context needs to be a character vector which specifies the data frame’s columns for the longitude and latitude (x,y) coordinates.
If we use this on a data frame it automatically converts the data frame object into a SpatialPointsDataFrame
object.
Below, we convert the philly_homicides_df
data frame into a spatial object with using the coordinates
function and check with class(philly_homicides_df)
again to examine which object class the table belongs to now. Note that the coordinates()
function if used in this way replaces the original data frame.
coordinates(philly_homicides_df) <- c("POINT_X", "POINT_Y")
class(philly_homicides_df) # !!
#> [1] "SpatialPointsDataFrame"
#> attr(,"package")
#> [1] "sp"
Assigning the projection:
is.projected(philly_homicides_df) # see if a projection is defined
#> [1] NA
proj4string(philly_homicides_df) <- CRS("+init=epsg:4326") # this is WGS84
is.projected(philly_homicides_df) # voila! hm. wait a minute..
#> [1] FALSE
To save the sp
object out as a shapefile we need to load another library, called rgdal
(more on this below.)
# to save out using writeOGR from rgdal
library(rgdal)
# note that we need to save the philly_homicides_df, which we converted to sp object!
writeOGR(philly_homicides_df, "data/PhillyHomicides", "PhillyHomcides", driver = "ESRI Shapefile")
# to force save:
writeOGR(philly_homicides_df, "data/PhillyHomicides", "PhillyHomcides", driver = "ESRI Shapefile", overwrite_layer = TRUE)
1.3 Loading shape files into R
1.3.1 How to do this in sf
sf
relies on the powerful GDAL library, which is automatically linked in when loading sf
. We can use st_read()
, which simply takes the path of the directory with the shapefile as argument.
# read in
philly_sf <- st_read("data/Philly/")
#> Reading layer `PhillyTotalPopHHinc' from data source `/Users/cengel/Anthro/R_Class/R_Workshops/R-spatial/data/Philly' using driver `ESRI Shapefile'
#> Simple feature collection with 384 features and 17 fields
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: 1739497 ymin: 457343.7 xmax: 1764030 ymax: 490544.9
#> epsg (SRID): NA
#> proj4string: +proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs
# take a look at what we've got
str(philly_sf) # note again the geometry column
#> Classes 'sf' and 'data.frame': 384 obs. of 18 variables:
#> $ STATEFP10 : Factor w/ 1 level "42": 1 1 1 1 1 1 1 1 1 1 ...
#> $ COUNTYFP10: Factor w/ 1 level "101": 1 1 1 1 1 1 1 1 1 1 ...
#> $ TRACTCE10 : Factor w/ 384 levels "000100","000200",..: 347 350 353 329 326 345 46 82 173 15 ...
#> $ GEOID10 : Factor w/ 384 levels "42101000100",..: 347 350 353 329 326 345 46 82 173 15 ...
#> $ NAME10 : Factor w/ 384 levels "1","10.01","10.02",..: 281 284 287 262 259 279 299 354 86 3 ...
#> $ NAMELSAD10: Factor w/ 384 levels "Census Tract 1",..: 281 284 287 262 259 279 299 354 86 3 ...
#> $ MTFCC10 : Factor w/ 1 level "G5020": 1 1 1 1 1 1 1 1 1 1 ...
#> $ FUNCSTAT10: Factor w/ 1 level "S": 1 1 1 1 1 1 1 1 1 1 ...
#> $ ALAND10 : num 2322732 4501110 1004313 1271533 1016206 ...
#> $ AWATER10 : num 66075 8014 1426278 8021 0 ...
#> $ INTPTLAT10: Factor w/ 384 levels "+39.8798897",..: 369 380 68 333 325 368 16 93 188 63 ...
#> $ INTPTLON10: Factor w/ 384 levels "-074.9667387",..: 1 5 137 14 27 4 272 367 119 147 ...
#> $ GISJOIN : Factor w/ 384 levels "G4201010000100",..: 347 350 353 329 326 345 46 82 173 15 ...
#> $ Shape_area: num 2388806 4509124 2430591 1279556 1016207 ...
#> $ Shape_len : num 6851 10567 9257 4928 5920 ...
#> $ medHHinc : num 54569 NA<PHONE_NUMBER>7 69981 ...
#> $ totalPop : num 3695 703 1643 4390 3807 ...
#> $ geometry :sfc_MULTIPOLYGON of length 384; first list element: List of 1
#> ..$ :List of 1
#> .. ..$ : num [1:55, 1:2] 1763647 1763473 1763366 1763378 1763321 ...
#> ..- attr(*, "class")= chr "XY" "MULTIPOLYGON" "sfg"
#> - attr(*, "sf_column")= chr "geometry"
#> - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
#> ..- attr(*, "names")= chr "STATEFP10" "COUNTYFP10" "TRACTCE10" "GEOID10" ...
Two more words about the geometry column: You can name this column any way you wish. Secondly, you can remove this column and revert to a regular, non-spatial data frame at any dime wiht st_drop_geometry()
.
The default plot
of an sf
object is a multi-plot of the first attributes, with a warning if not all can be plotted:
plot(philly_sf)
#> Warning: plotting the first 10 out of 17 attributes; use max.plot = 17 to
#> plot all
In order to only plot the polygon boundaries we need to directly use the geometry column. We use the st_geometry()
function to extract it:
plot(st_geometry(philly_sf))
Let’s add a subset of polygons with only the census tracts where the median houshold income is more than $60,000. We can extract elements from an sf
object based on attributes using your prefered method of subsetting data frames.
# subset the familar way
philly_sf_rich <- philly_sf[philly_sf$medHHinc > 60000, ]
# or
philly_sf_rich <- subset(philly_sf, medHHinc > 60000)
plot(st_geometry(philly_sf_rich), add=T, col="red")
Piping works as well!
library(dplyr)
philly_sf %>%
filter(medHHinc > 60000) %>%
st_geometry() %>%
plot(col="red", add=T)
1.3.2 How to work with rgdal
and sp
In order to read spatial data into R and turn them into Spatial*
family objects we require the rgdal
package, which provides bindings to GDAL4.
We can read in and write out spatial data using:
readOGR() and writeOGR() (for vector)
readGDAL() and writeGDAL() (for raster/grids)
The parameters provided for each function vary depending on the exact spatial file type you are reading. We will take an ESRI shapefile as an example. A shapefile - as you know - consists of various files of the same name, but with different extensions. They should all be in one directory and that is what R expects.
When reading in a shapefile, readOGR()
requires the following two arguments:
datasource name (dsn) # the path to the folder that contains the files
# this is a path to the folder, not a filename!
layer name (layer) # the shapefile name WITHOUT extension
# this is not a path but just the name of the file!
Setting these arguments correctly can be cause of much headache for beginners, so let me spell it out:
Firstly, you obviously need to know the name of shapefile.
Secondly, you need to know the name and location of the folder that contains all the shapefile parts.
Lastly,
readOGR
only reads the file and dumps it on your screen. But similarly when reading csv tables you want to actually work with the file, so you need to assign it to an R object.
Now let’s do this.
We load the rgdal
package and read PhillyTotalPopHHinc
into an object called philly
by using the readOGR
function5. We can also examine the object and confirm what it is with class()
.
library(rgdal)
philly_sp <- readOGR("data/Philly/", "PhillyTotalPopHHinc")
#> OGR data source with driver: ESRI Shapefile
#> Source: "/Users/cengel/Anthro/R_Class/R_Workshops/R-spatial/data/Philly", layer: "PhillyTotalPopHHinc"
#> with 384 features
#> It has 17 fields
class(philly_sp)
#> [1] "SpatialPolygonsDataFrame"
#> attr(,"package")
#> [1] "sp"
Very similarly to the above we can create a simple plot of the polygons with the plot
command, which directly understands the SpatialPolygonsDatafame
object and then plot a subset of polygons with a median household income (medHHinc
) of over $60,000 on top of the plot of the entire city.
plot(philly_sp)
philly_sp_rich <- subset(philly_sp, medHHinc > 60000)
plot(philly_sp_rich, add=T, col="red")
1.4 Raster data in R
Raster files, as you might know, have a much more compact data structure than vectors. Because of their regular structure the coordinates do not need to be recorded for each pixel or cell in the rectangular extent. A raster is defined by:
- a CRS
- coordinates of its origin
- a distance or cell size in each direction
- a dimension or numbers of cells in each direction
- an array of cell values
Given this structure, coordinates for any cell can be computed and don’t need to be stored.
The raster
package6 is a major extension of spatial data classes to access large rasters and in particular to process very large files. It includes object classes for RasterLayer
, RasterStacks
, and RasterBricks
, functions for converting among these classes, and operators for computations on the raster data. Conversion from sp
type objects into raster
type objects is possible.
If we wanted to do create a raster object from scratch we would do the following:
# specify the RasterLayer with the following parameters:
# - minimum x coordinate (left border)
# - minimum y coordinate (bottom border)
# - maximum x coordinate (right border)
# - maximum y coordinate (top border)
# - resolution (cell size) in each dimension
r <- raster(xmn=-0.5, ymn=-0.5, xmx=4.5, ymx=4.5, resolution=c(1,1))
r
#> class : RasterLayer
#> dimensions : 5, 5, 25 (nrow, ncol, ncell)
#> resolution : 1, 1 (x, y)
#> extent : -0.5, 4.5, -0.5, 4.5 (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
Note that this raster object has a CRS defined! If the crs argument is missing when creating the Raster object, the x coordinates are within -360 and 360 and the y coordinates are within -90 and 90, the WGS84 projection is used by default!
Good to know.
To add some values to the cells we could the following.
class(r)
#> [1] "RasterLayer"
#> attr(,"package")
#> [1] "raster"
r <- setValues(r, runif(25))
class(r)
#> [1] "RasterLayer"
#> attr(,"package")
#> [1] "raster"
plot(r); points(coordinates(r), pch=3)
(See the rasterVis
package for more advanced plotting of Raster*
objects.)
RasterLayer objects can also be created from a matrix.
class(volcano)
#> [1] "matrix"
volcano.r <- raster(volcano)
class(volcano.r)
#> [1] "RasterLayer"
#> attr(,"package")
#> [1] "raster"
And to read in a raster file we can use the raster()
function. This raster is generated as part of the NEON Harvard Forest field site.
library(raster)
HARV <- raster("data/HARV_RGB_Ortho.tif")
Typing the name of the object will give us what’s in there:
HARV
#> class : RasterLayer
#> band : 1 (of 3 bands)
#> dimensions : 2317, 3073, 7120141 (nrow, ncol, ncell)
#> resolution : 0.25, 0.25 (x, y)
#> extent : 731998.5, 732766.8, 4712956, 4713536 (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
#> data source : /Users/cengel/Anthro/R_Class/R_Workshops/R-spatial/data/HARV_RGB_Ortho.tif
#> names : HARV_RGB_Ortho
#> values : 0, 255 (min, max)
We can plot it like this:
plot(HARV)
We can find out about the Coordinate Reference System with this:
crs(HARV)
#> CRS arguments:
#> +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84
#> +towgs84=0,0,0
See what you can do with such an object:
methods(class=class(HARV))
#> [1] ! != [ [[
#> [5] [<- %in% == $
#> [9] $<- addLayer adjacent aggregate
#> [13] all.equal area Arith as.array
#> [17] as.character as.data.frame as.factor as.integer
#> [21] as.list as.logical as.matrix as.raster
#> [25] as.vector asFactor atan2 bandnr
#> [29] barplot bbox boundaries boxplot
#> [33] brick buffer calc cellFromRowCol
#> [37] cellFromXY cellStats clamp click
#> [41] clump coerce colFromCell colFromX
#> [45] colSums Compare contour coordinates
#> [49] corLocal cover crop crosstab
#> [53] crs<- cut cv density
#> [57] dim dim<- direction disaggregate
#> [61] distance extend extent extract
#> [65] flip focal freq getValues
#> [69] getValuesBlock getValuesFocal gridDistance head
#> [73] hist image interpolate intersect
#> [77] is.factor is.finite is.infinite is.na
#> [81] is.nan isLonLat KML labels
#> [85] layerize length levels levels<-
#> [89] lines localFun log Logic
#> [93] mask match Math Math2
#> [97] maxValue mean merge minValue
#> [101] modal mosaic names names<-
#> [105] ncell ncol ncol<- nlayers
#> [109] nrow nrow<- origin origin<-
#> [113] overlay persp plot predict
#> [117] print proj4string proj4string<- quantile
#> [121] raster rasterize readAll readStart
#> [125] readStop reclassify res resample
#> [129] RGB rotate rowColFromCell rowFromCell
#> [133] rowFromY rowSums sampleRandom sampleRegular
#> [137] sampleStratified scale select setMinMax
#> [141] setValues shift show spplot
#> [145] stack stackSelect subs subset
#> [149] Summary summary t tail
#> [153] text trim unique update
#> [157] values values<- Which which.max
#> [161] which.min writeRaster writeStart writeStop
#> [165] writeValues xFromCell xFromCol xmax
#> [169] xmin xres xyFromCell yFromCell
#> [173] yFromRow ymax ymin yres
#> [177] zonal zoom
#> see '?methods' for accessing help and source code
We can explore the distribution of values contained within our raster using the hist() function which produces a histogram. Histograms are often useful in identifying outliers and bad data values in our raster data.
hist(HARV)
#> Warning in .hist1(x, maxpixels = maxpixels, main = main, plot = plot, ...):
#> 1% of the raster cells were used. 100000 values used.
Notice that a warning message is produced when R creates the histogram.
This warning is caused by the default maximum pixels value of 100,000 associated with the hist function. This maximum value is to ensure processing efficiency as our data become larger! We can force the hist
function to use all cell values.
ncell(HARV)
#> [1] 7120141
hist(HARV, maxpixels = ncell(HARV))
At times it may be useful to explore raster metadata before loading them into R. This can be done with:
GDALinfo("path-to-raster-here")
A raster dataset can contain one or more bands. We can view the number of bands in a raster using the nlayers()
function.
nlayers(HARV)
#> [1] 1
We can use the raster()
function to import one single band from a single OR from a multi-band raster. For multi-band raster, we can specify which band we want to read in.
HARV_Band2 <- raster("data/HARV_RGB_Ortho.tif", band = 2)
plot(HARV_Band2)
To bring in all bands of a multi-band raster, we use the stack()
function.
HARV_stack <- stack("data/HARV_RGB_Ortho.tif")
# how many layers?
nlayers(HARV_stack)
#> [1] 3
# view attributes of stack object
HARV_stack
#> class : RasterStack
#> dimensions : 2317, 3073, 7120141, 3 (nrow, ncol, ncell, nlayers)
#> resolution : 0.25, 0.25 (x, y)
#> extent : 731998.5, 732766.8, 4712956, 4713536 (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
#> names : HARV_RGB_Ortho.1, HARV_RGB_Ortho.2, HARV_RGB_Ortho.3
#> min values : 0, 0, 0
#> max values : 255, 255, 255
What happens when we plot?
plot(HARV_stack)
If we know that it is an RGB multiband raster we can plot them all in one
plotRGB(HARV_stack)
1.4.1 RasterStack vs RasterBrick
The R RasterStack
and RasterBrick
object types can both store multiple bands. However, how they store each band is different. The bands in a RasterStack
are stored as links to raster data that is located somewhere on our computer. A RasterBrick
contains all of the objects stored within the actual R object. Since in the RasterBrick
, all of the bands are stored within the actual object its object size is much larger than the RasterStack
object.
In most cases, we can work with a RasterBrick
in the same way we might work with a RasterStack
. However, a RasterBrick
is often more efficient and faster to process - which is important when working with larger files.
We can turn a RasterStack
into a RasterBrick
in R by using brick(StackName)
. Use the object.size()
function to compare stack and brick R objects.
object.size(HARV_stack)
#> 44248 bytes
HARV_brick <- brick(HARV_stack)
object.size(HARV_brick)
#> 170897168 bytes
Going back to the sp
package, a simple grid can be built like this:
# specify the grid topology with the following parameters:
# - the smallest coordinates for each dimension, here: 0,0
# - cell size in each dimension, here: 1,1
# - number of cells in each dimension, here: 5,5
gtopo <- GridTopology(c(0,0), c(1,1), c(5,5)) # create the grid
datafr <- data.frame(runif(25)) # make up some data
SpGdf <- SpatialGridDataFrame(gtopo, datafr) # create the grid data frame
summary(SpGdf)
#> Object of class SpatialGridDataFrame
#> Coordinates:
#> min max
#> [1,] -0.5 4.5
#> [2,] -0.5 4.5
#> Is projected: NA
#> proj4string : [NA]
#> Grid attributes:
#> cellcentre.offset cellsize cells.dim
#> 1 0 1 5
#> 2 0 1 5
#> Data attributes:
#> runif.25.
#> Min. :0.01797
#> 1st Qu.:0.30836
#> Median :0.55850
#> Mean :0.52374
#> 3rd Qu.:0.73536
#> Max. :0.96848
R Bivand (2011) Introduction to representing spatial objects in R↩
Coordinates should be of type double and will be promoted if not.↩
E. Pebesma & R. Bivand (2016)Spatial data in R: simple features and future perspectives↩
GDAL supports over 200 raster formats and vector formats. Use
ogrDrivers()
andgdalDrivers()
(without arguments) to find out which formats yourrgdal
install can handle.↩Unlike read.csv readOGR does not understand the
~
as valid element of a path. This (on Mac) will not work:philly_sp <- readOGR("~/Desktop/data/Philly/", "PhillyTotalPopHHinc")
↩Note that
sp
also allows to work with raster structures. TheGridTopology
class is the key element of raster representations. It contains: (a) the center coordinate pair of the south-west raster cell, (b) the two cell sizes in the metric of the coordinates, giving the step to successive centres, and (c) the numbers of cells for each dimension. There is also aSpatialPixels
object which stores grid topology and coordinates of the actual points.↩


jQuery - Calendar Heatmap
Update the above Calendar Heatmap
Random Dates
Empty
Append Dates
Toggle Weekdays
Default (standard)
Blues (blue)
Earth (earth)
Electric (electric)
Greens (green)
Picknick (picknick)
Reds (red)
Teals (teal)
Viridis (viridis)


Pokemon Sword and Shield rolls out lengthy overview trailer
Are you ready for battle?
by Chris MoyseNintendo has released a five-minute-plus trailer for the upcoming Pokémon Sword & Shield, offering fans a showcase of the regions, trainers, abilities and, of course, Pokémon themselves, all waiting to be explored when the games hit Nintendo Switch next week.
The preview offers a look at the huge Galar region - from its wide-open fields to its opulent cities - as well as an array of the Pokémon old and new just waiting to be checked off on your Pokédex, and the friends and rivals that you'll make along your journey. We also see gym battles with the new faction leaders, multiplayer Dynamax conflicts, character customisation option, and some of our fave Pokémon in their wild Gigantamax forms.
All this action awaits budding trainers come November 15, when Pokemon Sword and Shield launch for Nintendo Switch.


Quick guide
1. Choose a pair of assets you want to trade. Just start typing asset name, then pick the right one.
2. Take a look at orderbooks to get an understanding of the pair market.
3. Finally, fill the form and submit your order.
P.S. {{::$ctrl.nonVerifiedNote}}


Every organization in the system is trying to solve one of the society's issues; providing food, shelter, healthcare, clothing, education, entertainment and so on. However, their importance for society differ.
One of the issues in the current system is that the consumers usually interact with only the organizations about their daily needs and allocate the resources based on this interaction, instead of their importance.
For example, one might think that education is more important for society than most of the other industries. On the other side, you should become a consumer of that area of business in order to make transactions, in this case either be a student or a relative of a student. If not, it would be out of the routine spending and cannot be supported in a systematic way.
Priority Index aims to give us the opportunity to directly set each industries' priority by comparing them side by side, then distribute the resources to related organizations based on the importance of their business.
To make the reasoning behind this new approach clear; if the society could solve its fundamental issues, it would be getting much easier to focus on higher level ones. Solve hunger first, solve poverty first, so we will have more people that can contribute to entertainment or produce better cosmetic products.
In next example, you will see four main industries. Which ones are more important and should have more resources?
... And so this leads to the paradox that because the disease (malaria) is only in the poorer countries, it doesn't get much investment. For example, there's more money put into baldness drugs than are put into malaria. Now, baldness, it's a terrible thing. And rich men are afflicted. And so that's why that priority has been set.
Industry
link below and rate each item based on their importance.
Organization
in breadcrumb links to navigate back to initial view and see the difference in Income.
This example should be enough as it is to demonstrate the idea how we could control and allocate our resources. However, in order to understand the real impact of this index, we should be able to compare the results of this example with the actual one. We would like to add this comparison in the future.
Alternatively, we could directly list the issues as well, instead of industries; providing clean water, shelter, curing diseases etc. The system allows to create such variations as seen in 'Prologue', as long as we can create proper categorization for the organizations.


Getting Started
Pybricks Modules
hubs
ev3devices
nxtdevices
iodevices
parameters
tools
robotics
media
messaging
Engineering Extras
Robot Educator Programs
Core Set Programs
Expansion Set Programs


Flash on a tube!? Blasphemy!
This page contains a simple flash element (simple in that it's only text and
images). Flash elements are animatinos/games/guis/html objects that a
creative and VERY patient individual can embed in a website. Flash actually
runs out of a script (Actionscript) - that allows someone like me to create
something fancy.
This flash animation uses a few simple tweens and some masks (actually, a whole bunch of masks) to hide and reveal moving elements sort of in time to the music.
Song: Red Heat
Artist: Noisia
This flash animation uses a few simple tweens and some masks (actually, a whole bunch of masks) to hide and reveal moving elements sort of in time to the music.
Song: Red Heat
Artist: Noisia


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Gamepad
- Class in
org.discobots.lib.controllers
Gamepad()
- Constructor for class org.discobots.lib.controllers.
Gamepad
Creates a gamepad with name "Gamepad" in USB port 0
Gamepad(int)
- Constructor for class org.discobots.lib.controllers.
Gamepad
Creates a gamepad with name "Gamepad" in USB port
port
Gamepad(int, String)
- Constructor for class org.discobots.lib.controllers.
Gamepad
Creates a gamepad with name
name
in USB port
port
gameSpecificInit()
- Method in class org.discobots.lib.autonomous.
AutonChooser
get()
- Method in class org.discobots.lib.controllers.
AxisButton
get()
- Method in class org.discobots.lib.controllers.
DpadButton
get()
- Method in class org.discobots.lib.motor.
RampedMotor
getDPAD()
- Method in class org.discobots.lib.controllers.
Fightstick
getDPAD()
- Method in class org.discobots.lib.controllers.
Gamepad
getDPADfromPOV(double)
- Static method in class org.discobots.lib.controllers.
Gamepad
getDrivetrain()
- Method in interface org.discobots.lib.autonomous.pid.
EncoderPID
getDrivetrain()
- Method in interface org.discobots.lib.autonomous.pid.
GyroPID
getDrivetrain()
- Method in class org.discobots.lib.autonomous.pid.
PIDSourceGyro
getInstance()
- Static method in class org.discobots.lib.util.
Debugger
getInstance()
- Static method in class org.discobots.lib.util.
Logger
getInverted()
- Method in class org.discobots.lib.motor.
RampedMotor
getL2()
- Method in class org.discobots.lib.controllers.
Fightstick
getLeftEncoder()
- Method in interface org.discobots.lib.autonomous.pid.
EncoderPID
getLX()
- Method in class org.discobots.lib.controllers.
Fightstick
getLX()
- Method in class org.discobots.lib.controllers.
Gamepad
getLY()
- Method in class org.discobots.lib.controllers.
Fightstick
getLY()
- Method in class org.discobots.lib.controllers.
Gamepad
getName()
- Method in class org.discobots.lib.controllers.
Fightstick
getName()
- Method in class org.discobots.lib.controllers.
Gamepad
getOutput()
- Method in class org.discobots.lib.autonomous.pid.
PIDDummyOutput
getPIDSourceType()
- Method in class org.discobots.lib.autonomous.pid.
PIDSourceAverageEncoder
getPIDSourceType()
- Method in class org.discobots.lib.autonomous.pid.
PIDSourceGyro
getPIDSourceType()
- Method in class org.discobots.lib.autonomous.pid.
PIDSourceTurningEncoder
getPosition()
- Method in class org.discobots.lib.autonomous.
AutonChooser
getR2()
- Method in class org.discobots.lib.controllers.
Fightstick
getRampband()
- Method in class org.discobots.lib.motor.
RampedMotor
getRightEncoder()
- Method in interface org.discobots.lib.autonomous.pid.
EncoderPID
getRX()
- Method in class org.discobots.lib.controllers.
Gamepad
getRY()
- Method in class org.discobots.lib.controllers.
Gamepad
getScaleConstant()
- Method in class org.discobots.lib.motor.
RampedMotor
getTurningFactor()
- Method in class org.discobots.lib.autonomous.commands.
ArcadeEncoderTurn
getYaw()
- Method in interface org.discobots.lib.autonomous.pid.
GyroPID
getYaw()
- Method in class org.discobots.lib.autonomous.pid.
PIDSourceGyro
GyroPID
- Interface in
org.discobots.lib.autonomous.pid
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--- layout: post title: "Feedback on ASP.NET vNext Dependency Injection" description: "ASP.NET vNext includes a Dependency Injection API. This post offers feedback on the currently available code." date: 2014-05-26 20:26 UTC tags: [Dependency Injection] --- {% include JB/setup %}
{{ page.description }}
As part of Microsoft's new openness, the ASP.NET team have made the next version of ASP.NET available on GitHub. Obviously, it's not yet done, but I suppose that the reasons for this move is to get early feedback, as well as perhaps take contributions. This is an extremely positive move for the ASP.NET team, and I'm very grateful that they have done this, because it enables me to provide early feedback, and offer my help.
It looks like one of the proposed new features of the next version of ASP.NET is a library or API simply titled Dependency Injection. In this post, I will provide feedback to the team on that particular sub-project, in the form of an open blog post. The contents of this blog post is also cross-posted to the official ASP.NET vNext forum.
The details on the motivation for the Dependency Injection library are sparse, but I assume that the purpose is provide 'Dependency Injection support' to ASP.NET. If so, that motivation is laudable, because Dependency Injection (DI) is the proper way to write loosely coupled code when using Object-Oriented Design.
Some parts of the ASP.NET family already have DI support; personally, I'm most familiar with ASP.NET MVC and ASP.NET Web API. Other parts have proven rather hostile towards DI - most notably ASP.NET Web Forms. The problem with Web Forms is that Constructor Injection is impossible, because the Web Forms framework doesn't provide a hook for creating new Page objects.
As far as I can tell, the current ASP.NET Dependency Injection code defines an interface for creating objects:
public interface ITypeActivator { object CreateInstance( IServiceProvider services, Type instanceType, params object[] parameters); }
In addition to this central interface, there are other interfaces that enable you to configure a 'service collection', and then there are Adapters for
It's an excellent idea to add 'Dependency Injection support' to ASP.NET, for the few places where it's not already present. However, as I've previously explained, a Conforming Container isn't the right solution. The right solution is to put the necessary extensibility points into the framework:
"perfection is attained not when there is nothing more to add, but when there is nothing more to remove." - Antoine de Saint ExupéryThese are my general recommendations to the team, but if desired, I'd also like to offer my assistance with any particular issues the team might encounter.


{% extends base_site_template %} {% block title %}{% if validlink %}Enter your new password{% else %}Token Invalid{% endif %}{% endblock %} {% block content %} {% if not validlink %}
The page you are trying to access does not acknowledge the token being passed through.
Either the token has expired or you missed transcribing exactly as the original
If you think this is an error, please contact us
{% else %}You can now enter your new password.
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Should you prefer not to proceed, your old password will be preserved.
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| Tests passed/Failed/Skipped: | 1/0/0 |
| Started on: | Thu Jul 18 23:52:18 EDT 2019 |
| Total time: | 46 seconds (46138 ms) |
| Included groups: | |
| Excluded groups: |
| PASSED TESTS | |||
| Test method | Exception | Time (seconds) | Instance |
| run_cukes Test class: runner_cukes.TestRunner Test method: Runs Cucumber Features Show output Show all outputs Feature File: Features/GoogleFooterVerification2.feature Feature: Google Footer list Checkup Given I go to Google Home page (passed : 3s) Scenario: Selecting different footer links and checking the corresponding page When I see google home page (passed : 0s) Then I verified the Google footer list text (passed : 4s) Feature File: Features/MercuryTours.feature Feature: Mercury Tours Registration Scenario: Verification of user's Registration Given User goes to http://newtours.demoaut.com (passed : 1s) When User click on the Register link on top (passed : 0s) And User fills up the registration details (passed : 4s) And User clicks on submit button (passed : 1s) Then User verifies the registration is complete (passed : 2s) Feature File: Features/toolsqatable.feature Feature: Data Table immplementation Given I go to ToolsQA page (passed : 5s) Scenario: Checking the default coloumn When I see ToolsQA webtable page (passed : 0s) Then I verified the ToolsQA table default (passed : 10s) |
46 | runner_cukes.TestRunner@21213b92 |


| 072:027 Khan | : |
Except to a Messenger (from mankind) whom He has chosen (He informs him of unseen as much as He likes), and then He makes a band of watching guards (angels) to march before him and behind him. |
| 072:027 Maulana | : |
Except a messenger whom He chooses. For surely He makes a guard to go before him and after him, |
| 072:027 Pickthal | : |
Save unto every messenger whom He hath chosen, and then He maketh a guard to go before him and a guard behind him |
| 072:027 Rashad | : |
Only to a messenger that He chooses, does He reveal from the past and the future, specific news. |
| 072:027 Sarwar | : |
He causes angelic guards to march before and after him. |
| 072:027 Shakir | : |
Except to him whom He chooses as a messenger; for surely He makes a guard to march before him and after him, |
| 072:027 Sherali | : |
Except to a Messenger of HIS whom HE chooses. And then HE causes an escort of guarding angels to go before him and behind him, |
| 072:027 Yusufali | : |
"Except a messenger whom He has chosen: and then He makes a band of watchers march before him and behind him, |
Concepts/Themes Covered: الإيمان > الغيب > الغيب النفسي > القضاء والقدر Faith> The Unseen World> The Psychological Secret> Fate and Divine Decree الإيمان > الملائكة > قيامهم بأمر ربهم > كتابة أعمال بني آدم Faith> The Angels> Their Obedience to Allah> Writing Mankind's Deeds
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


DUMB AND DUMBER A film review by James Berardinelli Copyright 1994 James Berardinelli
RATING (0 TO 10): 3.8
Date Released: 12/16/94 Running Length: 1:47 Rated: PG-13 (Language, mature themes)
Starring: Jim Carrey, Jeff Daniels, Lauren Holly, Karen Duffy, Mike Starr, Charles Rocket Director: Peter Farrelly Producers: Charles B. Wessler, Brad Krevoy, and Steve Stabler Screenplay: Peter Farrelly & Bennett Yellin & Bobby Farrelly Cinematography: Mark Irwin Music: Todd Rundgren Released by New Line Cinema
Sometimes, a movie just can't live up to its advertising campaign (or its name, for that matter). DUMB AND DUMBER is one such case.  That's rather clever (although not original). Unfortunately, the same kind of humorists weren't involved in writing the script. "Dumb" actually overestimates the film's intelligence.
At its best, DUMB AND DUMBER is like an Ernest movie with a scatological bent. Of course, there's as much a place in motion pictures for lowbrow humor as there is for philosophical morality tales, but that assumes the jokes actually work. While I won't claim to have gone through this entire film without laughing, there are some long periods between chuckles. It's frequently like that with flatulence humor--you find it amusing the first time you're exposed to it (at the age of maybe five or six), but it quickly gets tiresome.
The thin plot thread upon which DUMB AND DUMBER hangs (not that anyone is seeing this picture for its storyline) has limo driver Lloyd Christmas (Jim Carrey) retrieving a briefcase left behind in an airport by a beautiful client (Lauren Holly). Along with his best friend and roommate Harry Dunne (Jeff Daniels), Lloyd starts the long drive west from Providence to Aspen to find his dream woman and return the locked briefcase, which just happens to be filled with money. The cross-country trek isn't easy, however.  Fortunately for Lloyd and Harry, the bad guys aren't exactly Valedictorian material, either.
While I found Jim Carrey tolerable (and, at times, even likable) in THE MASK, here he's back to his ACE VENTURA worst. If you appreciate Carrey, his first movie, or anything featuring Jim Varney's cinematic alter-ego, DUMB AND DUMBER is likely your sort of experience. Everyone else will either be offended, bored, or both.
And has Jeff Daniels' career really come to this? With GETTYSBURG and SPEED, things had been looking up.
For a movie that, with some reworking, could have come close to an extended MONTY PYTHON-type skit (and there is a bit about a dead parakeet), DUMB AND DUMBER ends up aiming a whole lot lower. This is more BENNY HILL territory. Then again, that's probably being unkind to the late British gentleman, since he did that kind of humor long before this tiresome screenplay came along.
The above represents the opinions of the author, and not necessarily those of Bellcore or any organization within Bellcore.
- James Berardinelli<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{{proposal.mainCategory}} - {{proposal.subCategory}}
{{proposal.productDescription}}
SKU: {{proposal.productSku}}
Unit size: {{proposal.productUnitSize}}
Maximum price: {{(proposal.maxPrice || 0) | number: 2}} GBP
End date: {{proposal.endDate | amDateFormat:'DD-MM-YYYY HH:mm:ss'}}
This BuyCo is closed.
Did you miss out on a great deal? You can create a new BuyCo for the same product and conditions.
Create proposal for {{proposal.productName}}
Ultimate delivery date: {{proposal.ultimateDeliveryDate | amDateFormat:'DD-MM-YYYY HH:mm:ss'}}
Promote this BuyCo in your network to get more people on board!
Time left to get your orders in:
The start payments from the buyers for this BuyCo have been completed.
All buyer payments for this BuyCo have been completed.
Enough successful deliveries have been reported to pay the seller.
Not enough successful deliveries have been reported to pay the seller.
All seller payments for this BuyCo have been completed.
The end date of the proposal has passed. You can request the BuyCo service to close it.
Request to closeThe proposal is closed, but not all payments have been completed. You can request the BuyCo service to process the payments according to the smart contract rules.
Request to process payments{{successMessage}}
You can be the first to back this proposal!
{{backer.address}}
(See payments:
pledge
initial
closing):
{{backer.amount}} unit(s)
correct delivery reported
problem with delivery reported
This backer hasn't paid the pledge payment and does not take part in the BuyCo.
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This interface should be used sparingly to replace type attribution errors for coins that can't easily be corrected by curators and reingested into Nomisma via direct RDF imports or intermediary spreadsheets that contain type URIs. For example, the PAS and British Museum data were scripted from text-based type references into URIs and are more likely to contain errors that are complicated to correct in Nomisma.
Found type
URI is invalid or the URI is not an nmo:TypeSeriesItem


Ten Best Target Black Friday 2019 Deals
by Madeline KaufmanBlack Friday is right around the corner. And that means retailers both big and small are lining up to start delivering discounts on a variety of products.One of those retailers, Target, is already out with its Black Friday 2019 deals. And like some of its competitors, it’s even offering some of its offers ahead of Black Friday in a bid to catch shoppers early and get them thinking about spending money sooner rather than later.
But with a Black Friday ad that spans a slew of pages and all kinds of categories, finding the best deals on the products you want won’t necessarily be easy. So, to help you with that, we’ve compiled the following roundup of the top ten best deals from Target’s Black Friday 2019 sale. From tech to home products, the following items are winners you should consider for your holiday shopping.
Fujifilm Instax Mini 9 Camera
Believe it or not, instant cameras are back in fashion in the tech world. And the Fujifilm Instax Mini 9 Camera might be one of the more popular options. With a Black Friday deal at $50, a $20 savings off its regular retail price of $70, it’s a winner.
Beats Solo3 Wireless On-Ear Headphones
The Beats Solo3 Wireless On-Ear Headphones usually retail for $300, but you can get them now for just $130—a $170 savings. They offer great sound, a compelling design, and up to 40 hours of battery life. Not bad.
Jetson Strike Hoverboard
If your kids like the idea of riding around town on a hoverboard, look no further than the Jetson Strike Hoverboard on sale this Black Friday at Target. It features a $95 price tag, helping you save $55 off its regular retail price of $150.
KitchenAid Professional 5 Qt. Mixer
If you want some help in the kitchen, run over to Target this Black Friday to pick up the KitchenAid Professional 5 Qt. Mixer. The device usually retails for $450, but you can save $220 and get it for just $230. It has a 5-quart bowl, great design and 10 optimized speeds.
Dyson V7 Motorhead Origin Cord-Free Vacuum
The Dyson V7 Motorhead Origin Cord-Free Vacuum delivers up to 30 minutes of cord-free suction on a single charge. It has a lightweight design, hygienic dirt-ejecting and more. Best of all, it usually retails for $300, but you can get it for $200—a $100 savings.
23andMe Personal Ancestry + Health Personal Genetic Service
A fun gift for the holidays, the 23andMe Personal Ancestry + Health Personal Genetic Service is a great way to learn more about yourself. It offers information on your ancestry, as well as 85 DNA-based health predispositions, carrier status and more. It usually goes for $200, but you can get it for $100 off, or $100.
Ninja 4qt Air Fryer Black AF101
The Ninja 4qt Air Fryer Black AF101 is all about helping you fry your foods without using oil. It offers a 4-quart bowl, a temperature range up to 400 degrees and a sleek design. You can get it for $80, a $40 savings off its regular retail price of $120.
TCL 65-Inch Roku 4K UHD HDR Smart TV
TCL’s 65-Inch Roku 4K UHD HDR Smart TV offers a 65-inch screen and 4K Ultra HD to deliver downright gorgeous visuals. It also runs on Roku’s smart software, so you can stream Netflix, Hulu, and other services from the set. It usually retails for $750, but you can save $350 and get it for $400 on Black Friday.
Ninja Foodi TenderCrisp Pressure Cooker
Looking to do a bit more in the kitchen? Look no further than the Ninja Foodi TenderCrisp Pressure Cooker. The device comes with a 6.5-quart pot and allows you to quickly cook ingredients with help from a Crisping Lid. It has 14 levels of safety for both pressure cooking and air frying. It usually goes for $230, but you can get it now for $60 off, or $170.
LG 65-Inch Class 4K UHD Smart LED HDR TV
LG makes some of the best televisions on the market, so you may want to consider the company’s 65-inch option on sale for Black Friday at Target. The television offers 4K Ultra HD visuals, a great design and support for popular streaming services, like Netflix, Hulu and others. It regularly retails for $900, but you can get it for $480, a $420 savings.


Maruti October 2019 Model Wise Sales Analysis – Swift, Alto, Eeco To XL6
by Team GaadiWaadiWith the exception of the Celerio and Vitara Brezza, all Maruti Suzuki Cars posted a healthy sales growth on MoM basis in October 2019
Thanks to the Diwali season, most carmakers have finally started seeing signs of improvement in their sales performance. In October 2019, most Maruti Suzuki cars posted a healthy sales growth on MoM basis, with the Ertiga clocking an improvement of a massive 418.8 per cent on the YoY basis. Even other models, except for the Celerio and Vitara Brezza, showed decent increase in demand last month.
Maruti Alto 800 and K10 Oct 2019 Sales:
The Maruti Alto twins clocked a monthly sale of 17,903 units last month, while it could manage pushing only 15,079 units in September 2019. This marked a MoM sales growth of 18.7 per cent, while YoY sales showed a decline of 19.3 per cent.
Maruti Baleno Oct 2019 Sales:
Maruti Baleno, the hot-selling B2-segment hatchback, sold 16,237 copies, thereby clocking a sales growth of 42.2 per cent, while YoY sales showed a decline of 13 per cent. In September 2019, the Baleno had sold 11,420 units.
Maruti Celerio Oct 2019 Sales:
The Celerio marked a monthly sale of 3,669 units to post a MoM sales decline of 11.4 per cent and 60.4 per cent on YoY basis.
Maruti Ciaz Oct 2019 Sales:
The hot-selling C2-segment sedan sold 2,371 units last month, up from 1,715 units sold in September 2019. This corresponds to a 38.3 per cent improvement in sales on MoM basis, while YoY sales growth remained negative at -39.1 per cent.
| Model | October 2019 Sales | September 2019 Sales |
| Maruti Suzuki Dzire | 19,569 | 15,662 |
| Maruti Suzuki Swift | 19,401 | 12,934 |
| Maruti Suzuki Alto | 17,903 | 15,079 |
| Maruti Suzuki Baleno | 16,237 | 11,420 |
| Maruti Suzuki Wagon-R | 14,359 | 11,757 |
| Maruti Suzuki S-Presso | 10,634 | 5,006 |
| Maruti Suzuki Vitara Brezza | 10,227 | 10,362 |
| Maruti Suzuki Eeco | 10,011 | 9,949 |
| Maruti Suzuki Ertiga | 7,197 | 6,284 |
| Maruti Suzuki XL6 | 4,328 | 3,840 |
| Maruti Suzuki Celerio | 3,669 | 4,140 |
| Maruti Suzuki Ciaz | 2,371 | 1,715 |
| Maruti Suzuki Ignis | 1,859 | 6,284 |
| Maruti Suzuki S-cross | 1,356 | 1,040 |
DataSource : AutoPunditz
Maruti Dzire Oct 2019 Sales:
The entry-level sedan from Maruti Suzuki and the country’s one of the highest-selling cars sold a whopping 19,569 copies in October 2019. This led to a 24.9 per cent improvement from 15,662 units sold in September 2019. It even marked an YoY sales growth of 12.4 per cent.
Maruti Eeco Oct 2019 Sales:
The only people’s mover in the carmaker’s portfolio sold 10,011 units in October 2019, up by a marginal 0.6 per cent from 9,949 units sold in September 2019. YoY sales growth, however, posted a massive increment of 49.1 per cent.
Maruti Ertiga Oct 2019 Sales:
The top-selling MPV marked a whopping 418.9 per cent YoY growth with a sale of 7,197 units in October 2019. MoM increase, however, was 14.5 per cent when compared to 6,284 units sold in September 2019.
Maruti Ignis Oct 2019 Sales:
The B1-segment cross-hatch from Maruti Suzuki sold 1,859 units last month, which corresponds to an impressive 46.8 per cent growth from 1,266 units sold in September 2019. However, YoY data shows a de-growth of 17.5 per cent.
Maruti S-Cross Oct 2019 Sales:
With a total sale of 1,356 units in October 2019, the S-Cross registered a 30.4 per cent growth on MoM basis from 1,040 units sold in September 2019. It also corresponded to an YoY de-growth of a pretty massive 59.1 per cent.
Maruti S-Presso Oct 2019 Sales:
The latest affordable car in the company’s lineup sold 10,634 units in October 2019. This has led to a MoM sales growth of 112.4 per cent from 5,006 units sold in September 2019, the car’s first month of market presence.
Maruti Swift Oct 2019 Sales:
The top-selling B1-segment offering from the nation’s most popular carmaker registered a monthly sale of 19,401 units in October 2019, which has led to a MoM sales growth of 50.0 per cent and an YoY increment of 12.7 percent. The funky hatchback had sold 12,944 units in September 2019.
Maruti Wagon R Oct 2019 Sales:
The high-selling tallboy sold 14,359 units on October 2019, a 22.1 per cent improvement from 11,757 units sold in September 2019. It also marked a 34.8 per cent growth in sale on YoY basis.
Maruti Vitara Brezza Oct 2019 Sales:
The top-selling sub-4-metre SUV sold 10,227 units in October 2019, a 1.3 per cent dip from 10,362 units sold in September 2019. YoY sales data shows a pretty huge 35.4 per cent drop in the popularity of this compact SUV.
Maruti XL6 Oct 2019 Sales:
The Ertiga-based cross-MPV has been selling pretty well ever since its introduction in August 2019. Last month, it sold 4,328 units, which has led to a MoM sales increment of 12.7 per cent from 3,840 units sold in September 2019.


the and for nonamer incidence sequences The human with were avian was variants sequence positions that position index are conserved influenza total & variant diversity viral major protein highly al., virus al. viruses IAV vaccine both each from one than between which all switching data more {background proteome observed Health motifs amino distinct entropy minor aligned motif not this number (index T-cell different immune margin only unique can {border-style World given low proteins reported also less px;} two #ffffff;} average change dynamics have variants, viruses, (HN) This diverse position. acid had other DRB_, against conservation epitope epitopes most position, sequences, they viruses. where multiple thus, Influenza across containing least positions. three variant, HLA but generally increased nonatypes prediction predominant selection such these Organization, additional analysis because distribution fitness host hosts incidences individual may once overlapping positions, selected sequence. shared similar study there used variability ∼%. (Table classified common completely denotes exhibited further highest majority maximum nonamers pandemic pattern preceding present proteome-wide represents respectively. sequences. shown targets variants. ∼%, %), ()collab: Click Database December Fig. HA, However, LINK Motif Notably, Research acids analyzed any based class conserved, design eight file. here its mixed-variable nearly new nine patterns population predicted proteomes px; strains target universal use {font-family (Fig. All Entropy Figure HLA-DRB*:, Organization Total alignment alignment. among antigenic corresponding current due font-size important incidence, increase index. mutations prevalent proteomes. provided provides represented studied subsequent their through while ./peerj./supp-Table Additionally, HLA-DRB*: Heiny Index Khan ONEe./journal.pone. Positions S). Thus, alignments analyses been bioinformatics change. comprised context diversity, double; effective entropy, equal focal fraction greater high higher hosts. identified index, into level many minor, partial peak peptide potential presence quantitative range relation relevance relevant second significant single small value variant. (Hu (World B-cell Dynamics Each HIV- HN, Highly Immune Infectious Perdana RNA There These University University, analysed available bold border-color border-width cell changes characteristic compared concern contrast, criteria definition demonstrated described design. dominant four frequency genome has hosts, incidence. including major, matched measure motifs. mutation mutation, occasionally peptides performed plot position; positive proteins, proteome, ranging remaining result sequence, solid; some subtypes therapeutics those values virus, work {font-style {font-weight (A) (Figs. (PB) (PB-F), (antigenic (nonatypes) (proteome-wide (–), (–, (∼% (∼%) Abd Although Avian For HLA-DR Kim NA, NP, Only PA-X, Park Sequence Shannon’s Sloan-Lancaster States Such Unique absolute aid alleles amongst appeared approach approved authored binding considered contained continued courier; defined designed development did difference distinctive draft. drafts effects em; epitopes, every example, experiments, final full-length half immunity implications indicate indicated indicates large length linear lower mutational nonatype our overcome paper, percentage possible proteins. pt;} relatively repeated resulting reviewed role several showed subtype suggests supertype-restricted switch system thought threat thus tools using vaccines virus. when within


Two of the sides of this right-angled triangle are given as: {{q.lengths.filter(x => x!=='?').join(' and ')}}
Find the length of the third side
{{feedBackMessage}}
Pythagoras' theorem states that in a right-angled triangle:
a2 + b2 = c2
Where a and b are the shorter sides of the triangle and c is the longest side (the hypotenuse)
If this isn't enough help for you, then study:
(These pages are not written on this site yet).


Oskar Speck
Oskar Speck was a German adventurer who paddled his kayak, Sunnschien, from Germany to Australia in the 1930s. His journey took him over 7 years. Arriving on Thursday Island in September 1939 at the outbreak the Second World War, he was arrested as an enemy alien and interned for the duration of the war. After the war he settled in Australia, finally coming to rest at Killcare on the New South Wales central coast.


WHEN A MAN LOVES A WOMAN A Film Review by James Berardinelli Copyright 1994 James Berardinelli
Rating (0 to 10): 7.5
Date Released: 5/13/94 (wide) Running Length: 2:06 Rated: R (Language, mature themes)
Starring: Meg Ryan, Andy Garcia, Tina Marjorino, Mae Whitman, Ellen Burstyn Director: Luis Mandoki Producers: Jon Avnet and Jordan Kerner Screenplay: Ronald Bass and Al Franken Music: Zbigniew Preisner Released by Touchstone Pictures
The previews for WHEN A MAN LOVES A WOMAN do this film an injustice. Heavy on poorly-edited melodramatic sequences, they give little inkling of the level of emotional honesty attained. Luis Mandoki's film succeeds not because it tackles alcoholism, but because it faces up to the trauma that eats away at the lives of the non- alcoholics in the family.
One failing of the script is that it assumes an unlikely level of ignorance from its audience. Alcoholism is such a pervasive social problem that it's hard to accept that anyone likely to see WHEN A MAN LOVES A WOMAN wouldn't have a better understanding of the disease than the movie gives them credit for. After all, everything from high school health classes to Oprah have, at one point or another, addressed the issue. Unlike AIDS, alcoholism is not a new disease that the public needs to be educated about.
WHEN A MAN LOVES A WOMAN centers on a seemingly-happily married couple. Michael Green (Andy Garcia) and his wife Alice (Meg Ryan) have, at first glance, the perfect relationship. But take a peek beneath the veneer, and there are problems. Alice is a habitual drinker, and her periods of sobriety are getting fewer and fewer. Meanwhile, Michael's duties as an airline pilot take him away from home for weeks at a time, keeping him ignorant of the extent of his wife's problem.
Completing the family unit are Jess (Tina Marjorino), Alice's daughter by another man whom Michael has adopted as his own, and Casey (Mae Whitman), the four-year old child of the Greens. The presence of these children, and their importance to the development of the story, is what elevates WHEN A MAN LOVES A WOMAN. As potent as some of the scenes between Michael and Alice are, those featuring Jess or Casey invariably have greater impact. It helps that both young actresses are believable.
Meg Ryan and Andy Garcia were probably given the lead roles more because of box office appeal than an ability to bring superior depth to their characters. Surprisingly, while neither turns in an exceptional performance, they are both solid, and each has a few scenes in which they shine.
The ending is too facile, and WHEN A MAN LOVES A WOMAN may take longer than necessary to arrive at its resolution. There are moments throughout when the script is apt to strike a raw nerve with some, as is often the case when a "real" issue is probed with any degree of sincerity. Whatever else it may do, this film does not play it safe, and the risks it takes keep the audience engaged by the drama.
WHEN A MAN LOVES A WOMAN is about pain. This is not an original topic for a movie - especially one about alcoholism - but the script does a good enough job establishing the dynamics of the Green family that we never doubt that the story deserves to be told. The film's poignancy is its strength, even as occasional didactic tendencies are its weakness. In balance, the former by far outweighs the latter, making this a worthwhile picture.
- James Berardinelli<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Connect to an FTP server.
#include <ftplib.h> int FtpConnect(const char *host, netbuf **nControl);
FtpConnect() establishes a connection to the FTP server on the specified machine and returns a handle which can be used to initiate data transfers. The host name should be specified as <host> or <host>:<port>. <host> may be either a host name or ip address. <port> may be either a service name or a port number.
If the connection to the remote server if successful, FtpConnect() returns 1. Otherwise, 0 is returned.


Framework Overview
Solvers
SudokuSpice provides two different solvers: the
ConstraintBased.PuzzleSolver
or the original
RuleBased.PuzzleSolver
.
Generally speaking, the rule-based solver is the fastest of the two when solving standard Sudoku puzzles. However, the constraint-based solver can be faster in some cases, especially when implementing custom rules or constraints.
Important concepts
SudokuSpice uses four main interfaces:
The
IPuzzle
Puzzles store the underlying Sudoku data. You likely only need the standard implementations,
Puzzle
for constraint-based tools, andPuzzleWithPossibleValues
for rule-based tools. However, the interface is provided in case you need to do something a little different, like work with a puzzle with jagged regions instead of the normal square box regions.The
IRule
Rules define - you guessed it - the rules for a puzzle. For example, standard Sudokus use the
RowUniquenessRule
, theColumnUniquenessRule
, and theBoxUniquenessRule
. For convenience and efficiency, these come prepackaged in theStandardRules
class. Rules do not directly modify theIPuzzle
or its possible values themselves. They should use anIReadOnlyPuzzle
and just enough internal state to efficiently provide the possible values of any given square according to only that rule.Rules are enforced by an
IRuleKeeper
. TheDynamicRuleKeeper
provides a general implementation that works with any number of rules. Custom implementations can provide even more efficiency, but are generally messier and more complex than simply creating custom rules.StandardRuleKeeper
is an example of this. Check out the benchmarks for performance comparisons. The rule keeper actually updates the possible values based on all the rules while ensuring that no rules are broken by any given update.The
IHeuristic
Heuristics are logical tricks that can be used to reduce the number of possible values for any given square. These are only used in solving when the framework would otherwise have to guess (i.e. all unset squares have at least two possible values), so they can be relatively expensive and still improve solving times.
Heuristics depend on an
IReadOnlyPuzzleWithMutablePossibleValues
. They directly modify the possible values during an update. They can alo optionally depend on one or more rules, as is demonstrated by theUniqueInRowHeuristic
. Heuristics can either be perfect heuristics, i.e. they reduce squares to only one possible value (like theUniqueIn*
heuristics), or they can be loose heuristics, i.e. they eliminate possible values from squares, but don't necessarily reduce them down to a single possible value.Due to heuristics' complexity and flexibility, no generic class is provided to enforce multiple heuristics. To enforce multiple heuristics, define a custom heuristic that implements your desired heuristics. This pattern is demonstrated by the
StandardHeuristic
.The
IConstraint
Constraints represent rules that the puzzle needs to satisfy. For example, the
RowUniquenessConstraint
enforces the constraint that "each row must contain all possible values."Constraints are implemented using a form of an exact-cover matrix. The exact-cover matrix combines two concepts into a single matrix. Each row represents a possible value for a single square, for example "Row: 1, Column: 0, Value: 2". We'll represent this in the short-form notation:
R1C0V2
. Each column represents a single objective that must be satisfied, for example, "Row 1 contains a 2." We'll represent columns in the short-form notation: "R1V2". These rows and columns can be combined into a single matrix containing 1s and 0s, where a 1 is placed in each column (i.e. objective) that a given row (i.e. possible square value) satisfies. For a standard Sudoku puzzle, this looks something like the following:R0V1 R0V2 ... R1V1 R1V2 ... C0V1 C0V2 ... B0V1 V0V2 ... B8V8 B8V9 R0C0V1 1 0 ... 0 0 ... 1 0 ... 1 0 ... 0 0 R0C0V2 0 1 ... 0 0 ... 0 1 ... 0 1 ... 0 0 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... R0C1V1 1 0 ... 0 0 ... 0 0 ... 1 0 ... 0 0 R0C1V2 0 1 ... 0 0 ... 0 0 ... 0 1 ... 0 0 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...  R8C8V8 0 0 ... 0 0 ... 0 0 ... 0 0 ... 1 0 R8C8V9 0 0 ... 0 0 ... 0 0 ... 0 0 ... 0 1 SudokuSpice's implementation of this matrix can be thought of as a sparse 2D-doubly linked list. Row headers (i.e. the
RxCxVx
cells in the first column) are represented byPossibility
s. Column headers (i.e. the cells in the first row) are represented byObjective
s. Rows and columns are connected by links, which represent the 1s in the matrix. Each link is connected up and down to the other '1s' that satisfy that objective, and connected left and right to the other '1s' that are present for that possibility.In addition, SudokuSpice's implementation extends the matrix to a larger graph to enable more complicated constraints. These use
OptionalObjective
s to groupPossibility
s and/or otherOptionalObjective
s. More details can be found in theExactCoverGraph
andOptionalObjective
docs. TheMagicSquaresConstraint
demonstrates how to use these optional objectives to implement a complicated constraint.The constraint-based solver uses constraints instead of rules. It does not provide a separate heuristics concept because the objectives inherently provide the
UniqueIn*
heuristics. Adding additional layers of heuristics would add complexity with minimal, if any, performance improvement.
For more information on extending SudokuSpice, see:
Non-unique (i.e. duplicate) values
What if you need to solve puzzles that allow multiple instances of the same value in a given region? For example, let's say that your puzzle contains the first 9 digits of Pi in each region:
3.14159265 -> 1, 1, 2, 3, 4, 5, 5, 6, 9
This is currently only supported by the rule-based solver. When constructing a Puzzle
, you can
also include the possible values for any given region:
var puzzle = new Puzzle(data, new int[] {1, 1, 2, 3, 4, 5, 5, 6, 9 });
This will be used to calculate the IPuzzle.CountPerUniqueValue
dictionary, and rules can use that
to properly enforce the correct number of values. The MaxCountPer* rules are provided for the
simple case that each box, column, or row needs to contain each value up to the max count as
specified in the CountPerUniqueValue
dictionary.
Namespaces
- SudokuSpice: Contains base classes for solving and generating puzzles.
- SudokuSpice.RuleBased: Contains classes for rule-based puzzle-solving and -generating.
- SudokuSpice.RuleBased.Heuristics: Contains standard heuristics and interfaces for creating custom heuristics.
- SudokuSpice.RuleBased.Rules: Contains rules and interfaces for creating custom rules.
- SudokuSpice.ConstraintBased: Contains classes for constraint-based puzzle-solving and -generating.
- SudokuSpice.ConstraintBased.Constraints: Contains standard constraints and the
IConstraint
interface.


Shaarli export of all bookmarks on Mon, 17 Jul 23 14:31:25 +0200
- The Legacy of Firefox OS. In the two years or so since Mozilla… | by Ben Francis | Medium
- In the two years or so since Mozilla announced the end of Firefox OS as a Mozilla-run project, the B2G source code has found its way into a surprising number of commercial products.
- Template Filters — Eleventy


| Package | Description |
|---|---|
| de.slothsoft.charts | |
| de.slothsoft.charts.common |
| Modifier and Type | Method and Description |
|---|---|
Title |
Chart.getTitle()
Returns the title this chart has.
|
| Modifier and Type | Method and Description |
|---|---|
Title |
Title.alignment(TextAlignment newAlignment)
Sets the alignment of the title.
|
Title |
Title.color(int newColor)
Sets the color as ARGB int, e.g.
|
Title |
Title.font(Font newFont)
Sets the font used to paint the title.
|
Title |
Title.position(Title.Position newPosition)
Sets the position of the title.
|
Title |
Title.size(double newSize)
Sets the height of the title, if one is drawn.
|
Title |
Title.text(String newText)
Sets the text that is the title or
null to show there is no title. |
Copyright © 2020. All rights reserved.


Laura Ayushi
https://lauraayushi.github.io/Pingpong/
Java
Ping Pong will count up to your number from 1 with the following exceptions:


| Interface | Description |
|---|---|
| RFC8628Constants2 |
Created by Jeff Gaynor
on 2/9/21 at 11:22 AM |
| RFC8693Constants2 |
Constants for RFC 8693: The token exchange endpoint.
|
| Class | Description |
|---|---|
| AbstractAccessTokenServlet2 |
Created by Jeff Gaynor
on 9/25/13 at 1:37 PM |
| ActiveTokenServlet |
Created by Jeff Gaynor
on 9/28/21 at 5:04 PM |
| BearerTokenServlet |
For endpoints that use bearer tokens.
|
| ClientServlet |
The client management servlet.
|
| ClientUtils |
Created by Jeff Gaynor
on 2/9/21 at 3:09 PM |
| GroupHandler |
This takes a list of attributes from an LDAP handler and converts it into a regularized
group structure.
|
| HeaderUtils |
Utilities for dealing with getting tokens that may be either sent as parameters
or in the authorization header .
|
| LDAPSSLSocketFactory |
A factory that creats SSL sockets as required by LDAP.
|
| MultiAuthServlet |
This class has the machinery for processing the various types of
Authorization for a servlet.
|
| NCSAGroupHandler |
Created by Jeff Gaynor
on 3/1/18 at 4:48 PM |
| OA2AdminRegistrationServlet |
Created by Jeff Gaynor
on 4/9/19 at 1:39 PM |
| OA2AuthorizedServlet |
Created by Jeff Gaynor
on 10/3/13 at 2:01 PM |
| OA2AuthorizedServletUtil |
This is set of calls to replace the old Authorized Servlet.
|
| OA2AutoRegistrationServlet |
Created by Jeff Gaynor
on 9/28/11 at 1:20 PM |
| OA2CertServlet |
Created by Jeff Gaynor
on 2/20/14 at 12:50 PM |
| OA2ClientUtils |
A budding set of utilities for working with clients.
|
| OA2DiscoveryServlet |
Created by Jeff Gaynor
on 12/12/16 at 1:18 PM |
| OA2ExceptionHandler |
Created by Jeff Gaynor
on 2/12/15 at 3:16 PM |
| OA2RegistrationServlet |
Created by Jeff Gaynor
on 3/20/14 at 4:48 PM |
| OA2ServletUtils |
Utilities for various servlets.
|
| OA2TokenUtils |
Created by Jeff Gaynor
on 5/19/21 at 8:37 AM |
| OA2UtilServlet |
A servlet to allow for certain utilities, such as checking if claims contain a given value.
|
| RFC7009 |
Token Revocation endpoint.
|
| RFC7662 |
The token introspection servlet
This implements RFC7662
|
| RFC8628ServletConfig |
Created by Jeff Gaynor
on 7/14/21 at 8:59 AM |
| RFC8628State |
Created by Jeff Gaynor
on 2/10/21 at 4:23 PM |
| TokenExchangeRecordRetentionPolicy |
Created by Jeff Gaynor
on 3/7/22 at 6:09 AM |
| TokenManagerServlet | |
| TokenManagerServlet.State | |
| UserInfoServlet |
Created by Jeff Gaynor
on 10/4/13 at 11:09 AM |
Copyright © 2022. All rights reserved.


Salazar lodges ban appeal with top sports court
Lausanne - Disgraced running coach Alberto Salazar has lodged an appeal against a four-year ban for doping offences, the Court of Arbitration for Sport announced on Monday.
Jeffrey Brown, a Texas endocrinologist who treated many of Salazar's athletes at Nike's Oregon Project training hub in Portland, has also appealed against his suspension.
CAS said it has "registered the appeals" against decisions rendered by the American Arbitration Association, North American Court of Arbitration for Sport Panel "in which they were found to have committed anti-doping rule violations and sanctioned with a four-year period of ineligibility."
CAS said two arbitration procedures had been opened and both men had requested more time to prepare their case.
"At this stage, it appears that the hearings in these two matters are unlikely to take place before March 2020," said CAS.
Salazar's doping offences include trafficking testosterone, tampering with the doping control process, and administering illicit infusions of the fat-burning substance, L-carnitine.
He denies any wrongdoing.
Last month Nike, which backed Salazar after his suspension by the US Anti-Doping Agency announced it was shutting down the Oregon Project.
Mark Parker, who at the time was Nike chief executive but has since stepped aside, said when Salazar's ban was announced that the company would still support the coach, best known for coaching Britain's four-time Olympic champion Mo Farah, in his appeal.