--- title: "Analysis of COVID-19 source of infection" author: "Wenqing Cao" date: '2020-09-27' output: html_document bibliography: "references.bib" tags: - R Markdown - plot - regression categories: R ---
We investigated the OpenToronto COVID-19 Cases in Toronto data. Performed data diagnostics, and results showed that the most common way cause people infected is close contact with a case in Toronto. These results are important because once we know the source of infection, we can then make move to protect people from infection.
## # A tibble: 1 x 10
## title id topics civic_issues excerpt dataset_category num_resources formats
## <chr> <chr> <chr> <chr> <chr> <chr> <int> <chr>
## 1 COVI… 64b5… <NA> <NA> <NA> <NA> 1 <NA>
## # … with 2 more variables: refresh_rate <chr>, last_refreshed <date>
## # A tibble: 17,872 x 1
## `Source of Infection`
## <chr>
## 1 Travel
## 2 Travel
## 3 Travel
## 4 Travel
## 5 Travel
## 6 Travel
## 7 Travel
## 8 Travel
## 9 Travel
## 10 Travel
## # … with 17,862 more rows
While COVID-19 pandemic is still affecting people in various categories globally, it brings people atteion unprecedentedly. By examining the source of infection, we can get a better understanding of how to prevent it from the root. In Toronto, most people, almost 90%, get infected by close contacting with COVID-19 carrier. Recent research indicates that Human-to-human transmission occurs primarily through direct contact or air droplets. The higher risk of transmission is within approximately 1 meter (about 3 feet) from the infected person; however, the maximum distance is still undetermined (Repici et al. (2020)). However, what is the most effective way to protect spreading where people live in the same house is still undetermined.
In our study, we analyzed the most popular topics: COVID-19 in Toronto. We looked at core data features in OpenToronto and applied Source of Infection from that. We discovered that the most common infection source is Close Contact with a case.Except N/A, the second higher infection is by community.
We examined Source of Infection in the OpenToronto and found out that close contact with a case is the dominant reason cause people infected. However, we did not look at and account for other factors that could potentially affect this finding, including age, specific location, and date. Source of Infection is dominated by close contact with a case, and there were a lot of missing values which could potentially affect our model. Future work should be targeted at examining the these other than source of infection and how they cause people get infected.
The COVID-19 Cases in Toronto was conducted to an ongoing COVID-19 outbreak, in the context of an evolving global pandemic. This data set contains demographic, geographic, and severity information for all confirmed and probable cases reported to and managed by Toronto Public Health since the first case was reported in January 2020. This includes cases that are sporadic (occurring in the community) and outbreak-associated. The data are extracted from the provincial communicable disease reporting system (iPHIS) and Toronto’s custom COVID-19 case management system (CORES) and combined for reporting. Each line summarizes information for an individual case.
The covid_data
dataset is a subset of the COVID-19 Cases in Toronto under the name “data” that provides a tool for teaching survey datasets and their analysis.
Variables in the covid_data
dataset and their description are listed in the table below:
| Covid_data survey variables | Description |
|---|---|
| Age Group | Age at time of illness. Age groups (in years): ≤19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90+, unknown (blank) |
| Source of Infection | The most likely way that sporadic cases acquired their COVID-19 infection. |
| Currently in ICU | Cases that are currently admitted to the intensive care unit (ICU) |
| _id | Unique row identifier for Open Data database |
| Assigned_ID | A unique ID assigned to cases by Toronto Public Health for the purposes of posting to Open Data, to allow for tracking of specific cases. |
| Outbreak Associated | Outbreak associated cases are associated with outbreaks of COVID-19 in Toronto healthcare institutions and healthcare settings (e.g. long-term care homes, retirement homes, hospitals, etc.) and other Toronto congregate settings (such as homeless shelters). |
| Neighbourhood Name | Toronto is divided into 140 geographically distinct neighborhoods that were established to help government and community agencies with local planning by providing socio-economic data for a meaningful geographic area. Please note that client postal code information is mapped to the most up-to-date census tract (CT) and neighbourhood information available from the city. As a result, neighbourhood information is not available for those with missing postal code or when postal code could not be mapped/linked to a CT. |
| FSA | Forward sortation area (i.e. first three characters of postal code) based on the case’s primary home address. FSA values are generated from client postal codes. One FSA can span multiple neighbourhoods. |
| Classification | The application of the provincial case definition to categorize the cases as confirmed or probable, according to standard criteria. |
Below is a graph showing the type and missing value pattern of each variable. Our main variables of interest: Source of infection.
Below are detailed statistics on the type of each variable in the dataset as well as its missing value pattern. The below also gives some summary statistics on the dataset itself such as the number of rows, the number of columns and column type frequency.
| Name | covid_data |
| Number of rows | 17872 |
| Number of columns | 18 |
| _______________________ | |
| Column type frequency: | |
| character | 16 |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Outbreak Associated | 0 | 1 | 8 | 19 | 0 | 2 | 0 |
| Age Group | 0 | 1 | 3 | 14 | 0 | 10 | 0 |
| Neighbourhood Name | 0 | 1 | 3 | 35 | 0 | 141 | 0 |
| FSA | 0 | 1 | 3 | 3 | 0 | 97 | 0 |
| Source of Infection | 0 | 1 | 6 | 25 | 0 | 8 | 0 |
| Classification | 0 | 1 | 8 | 9 | 0 | 2 | 0 |
| Episode Date | 0 | 1 | 10 | 10 | 0 | 217 | 0 |
| Reported Date | 0 | 1 | 10 | 10 | 0 | 212 | 0 |
| Client Gender | 0 | 1 | 4 | 11 | 0 | 5 | 0 |
| Outcome | 0 | 1 | 5 | 8 | 0 | 3 | 0 |
| Currently Hospitalized | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Currently in ICU | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Currently Intubated | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Ever Hospitalized | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Ever in ICU | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Ever Intubated | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| _id | 0 | 1 | 152582.50 | 5159.35 | 143647 | 148114.75 | 152582.5 | 157050.25 | 161518 | ▇▇▇▇▇ |
| Assigned_ID | 0 | 1 | 9026.35 | 5248.05 | 1 | 4482.75 | 8984.5 | 13529.25 | 18202 | ▇▇▇▇▇ |
## [1] "Close contact" "Community"
## [3] "Healthcare" "Institutional"
## [5] "N/A - Outbreak associated" "Pending"
## [7] "Travel" "Unknown/Missing"
##
## Close contact Community Healthcare
## 6419 2471 1142
## Institutional N/A - Outbreak associated Pending
## 344 5974 70
## Travel Unknown/Missing
## 830 622
##
## Close contact Community Healthcare
## 0.359 0.138 0.064
## Institutional N/A - Outbreak associated Pending
## 0.019 0.334 0.004
## Travel Unknown/Missing
## 0.046 0.035
35.9% of the sporadic cases acquired their COVID-19 infection in this dataset are by Close contact with a case, 33.4% of the sporadic cases acquired their COVID-19 infection in this dataset are N/A. 13.8% of the sporadic cases acquired their COVID-19 infection in this dataset are by Community. 6.4% of the sporadic cases acquired their COVID-19 infection in this dataset are by Healthcare. 4.6% of the sporadic cases acquired their COVID-19 infection in this dataset are by Travel.3.5% of the sporadic cases acquired their COVID-19 infection in this dataset are missing. 1.9% of the sporadic cases acquired their COVID-19 infection in this dataset are by Institutional. 0.4% of the sporadic cases acquired their COVID-19 infection in this dataset are pending.
In this graph we find that the frequency distribution of source of infection differs a lot for all cases. We see that the top higher infection is by close contact with a case. Except N/A, the second higher infection is by community. From this graph we can probably guess that source of infection is different amongs different cases.
The most common way cause people infected is close contact with a case in Toronto. Except N/A, the second higher infection is by community. This is evident from the graph.
To avoid close contact with a case, there are some ways:
Once a family member has symptoms of covid, the government should encourage household members to report the situation.If the report is positive, you can get a discount in hospital and give financial support.
There is currently an app running by the government which collaborate with apple and google which can anonymously share the covid_19 data with each other, and the government should force everyone to download. The non-parametric ANOVA tests shows statistically significant results. Specifically, there are significant differences among the source of infection of different cases.
To avoid the source of infection by community, there are some ways:
Strengthen community supervision，place more alcohol disinfectants.
Personnel visit registration in the front desk.
There were missing data for source of infection and they were excluded since it is hard to impute values for distribution. It only constitutes 3.5% of the dataset because we have such a large datsaet.
Nonetheless, because of our large sample size we are able to conclude that the most common way cause people infected is close contact with a case in Toronto.
Observations such for Outbreak-associated cases are N/A in which we miss plenty of information and lead to biased results. There could be other sources that affect infection. Our analysis did not adjust for the confounder variables and how they affect the source of infection. Examples include, age, specific location, and date. Our next steps include looking at how these variables affect infection count and how they can affect the relationship between infection count and sources of infection.
Code and data supporting this analysis is available at: “https://github.com/purenb666/STA304.git”.


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# Error Message Conventions ## Length The error message reported by a detector should typically be short; think of typical compiler error messages you see from `kotlinc` or `javac`. This is particularly important when your lint check is running inside the IDE, because the error message will typically be shown as a tooltip as the user hovers over the underlined symbol. It's tempting to try to fully explain what's going on, but lint has separate facilities for that -- the issue explanation metadata. When lint generates text and html reports, it will include the explanation metadata. Similarly, in the IDE, users can pull up the full explanation with a tooltip. 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Solutions include finding newer versions or alternative libraries that don't have the same problem (for example, for \`httpclient\` use \`HttpUrlConnection\` or \`okhttp\` instead), or repackaging the library using something like \`jarjar\`. * [DuplicateStrings] Duplicate string value \`HELLO\`, used in \`hello_caps\` and \`hello\`. Use \`android:inputType\` or \`android:capitalize\` to treat these as the same and avoid string duplication. * [DuplicateUsesFeature] Duplicate declaration of uses-feature \`android.hardware.camera\` * [EllipsizeMaxLines] Combining \`ellipsize=start\` and \`lines=1\` can lead to crashes. Use \`singleLine=true\` instead. * [EmptySuperCall] No need to call \`super.someOtherMethod\`; the super method is defined to be empty * [EnforceUTF8] iso-latin-1: Not using UTF-8 as the file encoding. This can lead to subtle bugs with non-ascii characters * [EnqueueWork] WorkContinuation \`cont\` not enqueued: did you forget to call \`enqueue()\`?


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{% load static %} {% load composites %} {% load input_field %} {% load format_utils %}
Court File No.:
Court Registry: {% required responses.court_registry_for_filing %}
This is the affidavit
of
{% if responses.which_claimant == 'both' or responses.which_claimant == 'Claimant 1' %}
{% required responses|name_you %}
{% endif %}
{% if responses.which_claimant == 'both' %}and
{% endif %}
{% if responses.which_claimant == 'both' or responses.which_claimant == 'Claimant 2' %}
{% required responses|name_spouse %}
{% endif %}
in this case,
and was made on
In the Supreme Court of British Columbia
Claimant 1: {% include "partials/name_with_alias.html" with name=responses|name_you use_other_name=responses.any_other_name_you other_names=responses.other_name_you class_name='form-entry_claimant' %}
Claimant 2: {% include "partials/name_with_alias.html" with name=responses|name_spouse use_other_name=responses.any_other_name_spouse other_names=responses.other_name_spouse class_name='form-entry_claimant' %}
AFFIDAVIT - DESK ORDER DIVORCE
{% if responses.which_claimant == 'both' %}We{% else %}I{% endif %},
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{% else %}{% endif %},
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{% required responses.address_to_send_official_document_street_you trail=',' %}
{% required responses.address_to_send_official_document_city_you size=100 trail=',' %}
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{% endif %}
{% if responses.address_to_send_official_document_country_you == 'Other' %}
{% required responses.address_to_send_official_document_other_country_you size=100 trail=',' %}
{% else %}
{% required responses.address_to_send_official_document_country_you size=100 trail=',' %}
{% endif %}
{% required responses.address_to_send_official_document_postal_code_you size=0 trail=',' %}
{% required responses.occupation_you %},
{% endif %}
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{% endif %}
{% if responses.which_claimant == 'both' or responses.which_claimant == 'Claimant 2' %}
{% if responses|name_spouse %}
{% include "partials/name_with_alias.html" with name=responses|name_spouse use_other_name=responses.any_other_name_spouse other_names=responses.other_name_spouse %}
{% else %}{% endif %}, of
{% required responses.address_to_send_official_document_street_spouse trail=',' %}
{% required responses.address_to_send_official_document_city_spouse size=100 trail=',' %}
{% if responses.address_to_send_official_document_prov_spouse %}
{{ responses.address_to_send_official_document_prov_spouse }},
{% endif %}
{% if responses.address_to_send_official_document_country_spouse == 'Other' %}
{% required responses.address_to_send_official_document_other_country_spouse size=100 trail=',' %}
{% else %}
{% required responses.address_to_send_official_document_country_spouse size=100 trail=',' %}
{% endif %}
{% required responses.address_to_send_official_document_postal_code_spouse size=0 trail=',' %}
{% required responses.occupation_spouse %},
{% endif %}
SWEAR (OR AFFIRM) THAT:
There are children of the marriage as defined by {{ derived.child_support_acts }}, and
those children are
| Name | Age | Birthdate [dd/mmm/yyyy] |
|---|---|---|
| {{ child.child_name }} | {{ child.child_birth_date|age }} | {{ child.child_birth_date|date_formatter }} |
The following arrangements for parenting have been made:
{% if responses.what_parenting_arrangements %}Pursuant to section 5 of the Name Act, I am seeking to change my name from {{ responses|name_you }} to {{ responses.name_change_you_fullname }}. This application for a name change relates only to myself.
{% endif %} {% if responses.name_change_spouse == 'YES' %}Pursuant to section 5 of the Name Act, I am seeking to change my name from {{ responses|name_spouse }} to {{ responses.name_change_spouse_fullname }}. This application for a name change relates only to myself.
{% endif %}


We do not compromise on quality of our end products.
We make sure our products are delivered on time.
We give special attention to our R&D activities.
We are committed to satisfy our customers!


NICStats user guide
Document History
|
Date |
Change |
|
May 11, 2009 |
First
publication |
Contents
-c <event count
tracking flags>
-y <event cycle count
tracking flags>
Appendix A: NICStats
sample output
Output of GetHardwareInfo
Action
Output for GetRSSParams
Action
NICStats is a simple command line based tool that provides information on network adaptors installed in a server. The tool queries OIDs and counters at the NDIS level and provides useful information on a Network Interface Card’s (NIC) capabilities and configuration details.
NICStats can be used to gather the following information
· NIC capabilities
· NIC driver information
· RSS capabilities
· RSS parameters (Indirection table, etc.)
· Power management information
· NIC specific performance counters and event cycles
This information applies for the
following operating systems:
Windows Server®
2008 R2
Windows 7
The application included in this package is named NICStats.exe.
To run NICStats, open up a command prompt with administrator privileges. Use commands listed in the NICStats examples section to verify that the tool works fine.
NICStats does not require any other special configurations.
1. Enumerate all physical adaptors in the
machine
nicstats -a Enum -t AllPhysical
2. Get hardware info for the adaptor with
ifIndex of 12
nicstats -t ifindex -i 12
-a GetHardwareInfo
3. Get RSS capabilities for the adaptor with
ifIndex of 12
nicstats -t ifIndex -i 12 -a GetRSSCaps
4. Get RSS parameters for the adaptor with
ifIndex of 12
nicstats -t ifIndex -i 12 -a GetRSSParams
5. Get power management info for the adaptor
with ifIndex of 12
nicstats –t ifIndex -i 12 -a GetPowerInfo
This section provides detailed information of all the command line arguments supported by NICStats.
Description
Performs action selected option
(from list below)-
1. Enum - Lists all adaptors installed in the system
2. GetHardwareInfo - Provides hardware information for the selected interface (Should be used with -i option)
3. GetRSSCaps - Returns RSS capabilities of the interface (RSS revision, number of queues,...)
4. GetRSSParams - Returns RSS parameters of the interface (Hash function, hash fields, indirection table)
5. CalcRssCpu - Returns the RSS target processor for an interface given the source and destination IP address and source and destination TCP port numbers
6. ResetPerfCounters - Resets all per processor performance coutners to zero
7. StartPerfCounters - Start per processor performance counters. (Use -c and -y options to specify coutners.)
8. StopPerfCounters - Stop per processor performance counters
9. GetPerfCounters - Displays per processor perfromance counters
10. GetPowerInfo - Returns power management capabilities of the selected interface
Default Value
Enum
Description
DWORD describing counters selected for tracking. Used with -a StartPerfCounters
Bitmap specifying which event to keep track of:
· DPC count 0x00000001
· ISR count 0x00000002
· Receive_Indications 0x00000004
· Return_NBL calls 0x00000008
· Received NBLs 0x00000010
· Returned NBLs 0x00000020
· Send calls 0x00000040
· Send_Complete calls 0x00000080
· Sent NBs 0x00000100
· Sent_Complete NBs 0x00000200
· HAL build_SG calls 0x00000400
· Indirection table changes 0x00000800
· Low resource recv indication calls 0x00001000
· Low resource recv indication NBLs 0x00002000
· Queue DPC on non-current processor 0x00004000
DO NOT specify 0x before the mask.
Description
Specifies destination related info.
IP=<IP destination address> is the IP address of the LOCAL host
TCP=<TCP destination port> is TCP port number of the LOCAL application
Description
Interface identifier.
The interface identifier value is interpreted based on the interface type specified in the –t option. If this option if the Action needs an interface, an interface is not specified, and there is only one physical adaptor in the system, it will be picked as the default interface.
Description
Stores the output in the specified file name.
Default Value
stderr
Description
Specifies source related info.
IP=<IP source address> is the IP address of the REMOTE host
TCP=<TCP source port> is the TCP port number of the REMOTE application
Description
Specifies type of interface to be considered
1. ifIndex - Interface index
2. ifName - NetLUID
3. ifAlias - Conenction name
4. ifDesc - Friendly name, DeviceName (using \Device\{GUID})
5. ALL - All adaptors
6. AllPhysical - All physical adaptors
7. EnumIndex - Use the index returned back by Enumeration
Default Value
AllPhysical for -a Enum
ifIndex for the rest
-x <get cost>
Description
Description
DWORD specifying which which event cycle counts should to be recorded.
Bitmap specifying which event cycles to keep track of:
· Total DPC cycles 0x00000001
· ISR cycles 0x00000002
· NDIS receive_indication cycles 0x00000004
· Stack receive_indication cycles 0x00000008
· Return_NBL NDIS cycles 0x00000010
· Return_NBL miniport cycles 0x00000020
· NDIS send cycles 0x00000040
· Miniport send cycles 0x00000080
· NDIS send_complete cycles 0x00000100
· Stack send_complete cycles 0x00000200
· HAL build_SG cycles 0x00000400
· Indirection table change cycles 0x00000800
This section lists output from several useful commands. Key inferences from the output are highlighted.
Adapter:
**********************************************
Driver NDIS version: 6.20
Interrupt type: Message interrupts
MSI supported
MSI Enabled
MSI-X Supported. MSI-X
table entries: 10
MSI-X messages: 42
MSI-X message affinity:
Message 0 Affinity:<PHONE_NUMBER>000001
Message 1
Affinity:<PHONE_NUMBER>000002
Message 2 Affinity:<PHONE_NUMBER>000004
Message 3 Affinity:<PHONE_NUMBER>000008
Message 4 Affinity:<PHONE_NUMBER>000010
Message 5 Affinity:<PHONE_NUMBER>000020
Message 6 Affinity:<PHONE_NUMBER>000040
Message 7 Affinity:<PHONE_NUMBER>000080
Message 8 Affinity:<PHONE_NUMBER>000001
Message 9 Affinity:<PHONE_NUMBER>000001
Message 10 Affinity:<PHONE_NUMBER>000001
Message 11
Affinity:
0000000000000002
Message 12
Affinity:
0000000000000004
Message 13
Affinity:
0000000000000008
Message 14
Affinity:
0000000000000010
Message 15
Affinity:
0000000000000020
Message 16
Affinity:
0000000000000040
Message 17
Affinity:
0000000000000080
Message 18
Affinity:<PHONE_NUMBER>000001
Message 19
Affinity:
0000000000000002
Message 20
Affinity:
0000000000000004
Message 21
Affinity:
0000000000000008
Message 22
Affinity:<PHONE_NUMBER>000010
Message 23
Affinity:
0000000000000020
Message 24
Affinity:
0000000000000040
Message 25
Affinity:
0000000000000080
Message 26
Affinity:<PHONE_NUMBER>000001
Message 27
Affinity:
0000000000000002
Message 28
Affinity:
0000000000000004
Message 29
Affinity:
0000000000000008
Message 30
Affinity:<PHONE_NUMBER>000010
Message 31
Affinity:
0000000000000020
Message 32
Affinity:
0000000000000040
Message 33
Affinity:
0000000000000080
Message 34
Affinity:
0000000000000001
Message 35
Affinity:
0000000000000002
Message 36
Affinity:
0000000000000004
Message 37
Affinity:
0000000000000008
Message 38
Affinity:
0000000000000010
Message 39
Affinity:
0000000000000020
Message 40
Affinity:
0000000000000040
Message 41
Affinity:
0000000000000080
MSI
capabilities reported in PCI configuration space:
MSI capabilities information record was
found
MultipleMessageCapable
0, Number of messages 1
MultipleMessageEnable:
0, Number of messages: 1
CapableOf64Bits: 1
PerVectorMaskCapable: 1
MSI-X
capabilities reported in PCI Configuration Space:
MSI-X capabilities information record
was found
Number of MSI-X table entries: 10
MSIXEnable:
1
PCI
information:
Device type: PCI Express Treated As PCI
Legacy PCI current speed and mode: NOT applicable
Max payload size: 512
Max read request size: 512
Current link speed: 2.5 Gbps
Current link Width: 4
Max link speed:
2.5 Gbps
Max link width: 4
PCI Express version:
PCI Version 1.1
Interrupt support information from PCI
bus driver:
Line Based interrupts Supported
MSI interrupts Supported
MSI-X interrupts Supported
Number of interrupt
messages: 10
Adapter:
**********************************************
RSS
capabilities:
RSS Capabilities Revision: 1
MSI Supports: Yes
Classification at ISR: Yes
Classification at DPC: NO
MSI-X Support: Yes
TCP/IPv4 Support: Yes
TCP/IPv6 Support:
Yes
IPv6 extension headers support: NO
Number of interrupt messages: 1
Number of RSS receive queues: 8
Adapter:
**********************************************
RSS
Parameters:
RSS Parameters Revision: 2
RSS current state: On
RSS Hash function: Toeplitz
RSS hash fields: , IPv6, TCP/IPv6
Hash secret key size: 40
Indirection Table Size: 256
Indirection Table: (Format=CPU Group, CPU Number)
0, 0 0, 1
0, 2 0, 3 0, 4
0, 5 0, 6 0, 7
0, 0 0, 1
0, 2 0, 3 0, 4
0, 5 0, 6 0, 7
0, 0 0, 1
0, 2 0, 3 0, 4
0, 5 0, 6 0, 7
0, 0 0, 1
0, 2 0, 3 0, 4
0, 5 0, 6 0, 7
0, 0 0, 1
0, 2 0, 3 0, 4
0, 5 0, 6 0, 7
0, 0 0, 1
0, 2 0, 3 0, 4
0, 5 0, 6 0, 7
0, 0 0, 1
0, 2 0, 3 0, 4
0, 5 0, 6 0, 7
0, 0 0, 1
0, 2 0, 3 0, 4
0, 5 0, 6 0, 7
Adapter:
Intel(R) Gigabit ET Dual Port Server Adapter #2
RSS
Processor Information:
RSS Processor Info Revision: 1
RSS Base Processor Group:0
Number:0
Max number of RSS processors: 8
Preferred NUMA Node: 0
Number of RSS processors: 8
Processor List: (Format=PreferenceIndex: Group,Number)
0:
0, 0 0: 0, 1
0: 0, 2 0: 0,
3 0:
0, 4 0: 0, 5
0: 0, 6 0: 0,
7
Adapter:
*********************************************
Driver NDIS version: 6.0
Stack lowest version: 6.0
CurrentDevicePowerState:
PowerDeviceD0
Device Bus Capabilities
SystemWake:
PowerSystemHibernate
DeviceWake:
PowerDeviceD3
Device Power Capabilities
SupportedWoLPacketPatterns:
WOL_BITMAP_PATTERN
WOL_MAGIC_PACKET
SupportedProtocolOffloads:
None
NumTotalWoLPatterns: 0
MinMagicPacketWakeUp: NdisDeviceStateD3
MinPatternWakeUp: NdisDeviceStateD3
MinLinkChangeWakeUp: NdisDeviceStateUnspecified
Current Power Capabilities
SupportedWoLPacketPatterns:
WOL_BITMAP_PATTERN
SupportedProtocolOffloads:
None
NumTotalWoLPatterns: 0
MinMagicPacketWakeUp: NdisDeviceStateD3
MinPatternWakeUp: NdisDeviceStateD3
MinLinkChangeWakeUp: NdisDeviceStateUnspecified
Active Power Capabilities
EnabledWoLPacketPatterns:
WOL_BITMAP_PATTERN
WOL_MAGIC_PACKET
EnabledProtocolOffloads:
None
WakeUpFlags:
None
Number of applied WoL
patterns: 4
Number of applied Protocol Offloads: 0
Disabled Power Capabilities: None
Device Power Keywords
WakeOnPattern: Unspecified
WakeOnMagicPacket: Unspecified
DeviceSleepOnDisconnect:
Unspecified
PMARPOffload: Unspecified
PMNSOffload: Unspecified
PMWiFiRekeyOffload: Unspecified


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encoder_outputs, _, encoder_sequence_length = self.encoder.encode(
inputs,
sequence_length=features["length"],
mode=mode)
with tf.variable_scope("generator"):
return dataset, process_fn, padded_shapes_fn
def _build(self, features, labels, params, mode):
length = self._get_features_length(features)
with tf.variable_scope("encoder"):
inputs = self.inputter.transform_data(
features,


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In this post, I hope to inform potential Salvia users of the power and seriousness of this substance. My experience was unlike that of any other drug, and it was something that I will never forget.
It began when I recieved a package of Salvia Divinorum leaves I had ordered from an internet herbalist. For economy's sake, I created my own extract (approximately 10x) using acetone as a solvent. I redeposited the extract of about 10 grams onto 1 gram of normal salvia leaves.
I was impatient to try the drug, however I managed to prepare in a few ways. Over the past week, I had been feeling unusually happy and healthy, so I decided I was at a good point to try a new substance. I am 18, on no medication whatsoever, and I have no medical conditions. I invited a friend over to be my sitter. We had both read many reports of Salvia experiences to gauge what he might expect of my behavior.
I used a glass pipe with a small bowl. It held about a quarter gram of salvia leaves. I this bowlfull in two hits. I felt very little except a slight lightheadedness that I usually get with any type of smoke inhalation.
I refilled the bowl with another 1/4 gram of the extract, and took my next hit. I realized that the glass pipe was a bad idea because lit ashes were being sucked into my throat. However i didnt seem to care. My Head felt as though it were bouncing from side to side, and I felt quite excited that I was being effected. The last thing I said before entering the trip was 'I'm almost there'
Now began one of the most peculiar experiences of my life. As I took the last hit of extract, my reality split into two. For a very small moment, my mind processed the smoking process, and in a second reality i saw a purple column suspended in space. Rings of fire (which I associated with the smoke) were passing through the column. I felt as though I had a goal. I also felt bombarded by something I couldnt quite define.
I felt a bending sensation during my last moments in physical reality. The room became divided by a three-dimensional grid; I was stuck in this grid.
For about 8 minutes as I later found I hallucinated. I may have taken too large of a dose because much of my trip I could not immediately recall. However I remember a wall with several holes, cartoonish heads popping out of them, etc. According to my sitter, I was talking during the entire trip, however my words were not english and made no sense. For some odd reason though, they rhymed.
As I came out of the trip I was frightened. I was filled things to say, but as I said them they made no sense to me. As far as I can gather from later pondering, I was speaking my emotions, which at the time had no words to associate with them. I returned to reality and moved onto the couch that my sitter was on. I felt incredibly vulnerable, but thankful that I was back and safe. I tried to tell him that I was ok, but i couldnt get out more than a few words at a time. Over the course of the next two minutes, my power of speech came back.
All in all, from smoking the first hit to the point where i regained coherent speech, 9-12 minutes passed. I had no idea what amount of time had passed as i reentered. I had read about intense time distortion, but I did not know if my experience was longer or shorter than the actual time. It was timeless. That, I believe, is why the trip was so confusing.
I can surely say that I learned a few things from Salvia. I learned new feelings of loss and vulnerability (mainly from briefly losing the power of speech). At the same time, I feel as though I had learned a bit of wisdom about my own mind, and the fact that perhaps we do have simultaneous dimensions in our subconscious. When people say that Salvia isn't for everyone, they are most definitely right. I actively sought the drug out, I researched it extensively, and I was out to find some of the knowledge that one of the world's only hallucinogens can provide.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Serving XML: Flat file to XML
This article describes how to tranform flatfiles to XML with the
ServingXML pipeline language.
This article discusses pipelines where the input is a flat file and the output
is XML.
Counting Positions in Bytes and Characters
Fixed Record Length Files
These include
-
Flat files with no record delimiters, and the length of each type of
record is fixed. Since line delimited is the default, you need to
explicitly specifiy that the file has no record delimiters (with the
lineDelimited=false
option). In addition, you must either
-
explicitly specify the length of each type of record (with the
recordLength
attribute), or
-
provide enough information about the positions and widths
of the fields in the record so that ServingXML can calculate the length.
-
Flat files with record delimiters, but the length of each type of record
(including delimiters) is still fixed. In this case you have the option
of either reading the file as a line delimited file (the default), or as a
fixed record length file (by explicitly specifying the record length,
including delimiters.)
Note that it is not necessary that all records in the file have the same
length, only that all records of the same type have the same length.
Record Segments and Repeating Groups


Simple is better, but many themes are over designed. So, Book is built.
Get started
- fork the repo.
- clone
git clone https://github.com/username/book
- run
jekyll serve -w
http://localhost:4000 should be ready.
Customize
_config.yml file
- title: site name
- description: site description
- url: site url
- avatar: absolute path of avatar which is a picture (140px * 140px) on the index page.
- favicon: absolute path of site favicon
- google_analytics: id for google analystics
- disqus_shortname: shortname of disque comment system
- about: content on the about page.
you can write more than one paragraph but the format should be like the original one.
the special word
?link?
will be replaced by one of the links which are listed under links.
warning
Please do not delete the first two lines of style.css file.
End.
If you like it, star it :).
If you have a problem, github issue is ready.


AFP five keeps hot run going
Former Letran star Boyet Bautista produced five triples and the same number of assists as defending champion Armed Forces of the Philippines (AFP) extended its winning run to six games with an 86-84 win over National Housing Authority (NHA) in the 8th UNTV Cup at the San Juan City gym.
The 5-foot-6 Bautista shot 5-of-6 from the 3-point area on the way to scoring 17 points as the AFP Cavaliers stayed on top of Group A with a perfect 6-0 record.
Three other AFP players scored in double figures with Jerry Lumongsod leading the way with 19 while Rolando Pascual and Romeo Almerol chipped in 15 and 11 points, respectively, to help boost AFP’s bid for an outright semis berth in the annual tournament for public servants.
Not to be left out was Department of Environment and Natural Resources which likewise stretched its unbeaten run to six games by downing Department of Agriculture, 94-85, in Group B action of the tournament organized by UNTV President and CEO Daniel Razon.
The DENR Warriors started slow and trailed the Food Masters in the first two quarters before outscoring their rivals, 26-17, in the third to seize momentum and prevail.
Meantime, Malacañang-Philippine Sports Commission drew big games from Erick dela Cuesta, Martin Antonio and Visnu Das Javier to turn back PhilHealth, 98-81, for a 5-1 mark.
Dela Cuesta paced the Kamao with 20 points but its Javier who shone brightly as he posted 15 points, six assists, three rebounds and a steal. Antonio contributed 16 points.
First Game
Malacanang-PSC ( 98) - Dela Cuesta 20, Antonio 16, Javier 15, Besa 11, Jimenez 11, Roque 8, Javier 6, Punzalan 4, Gadong 3, Garrido 2, Ignario 2.
Philhealth (91) - Aldave 34, Mazo 20, Emata 17, Hernandez 13, Armojallas 6, Pacheco 1.
Quarterscores: 24-26, 47-47, 75-68, 98-91.
Second Game
DENR (94) - Abanes 23, Ayson 18, Rivera 16, Bangal 13, Parreño 11, Mamac 8, Atablanco 2, Calungcagin 2, Baluyot 1.
DA (85) - Manigo 23, Fernandez 15, Mastelero 11, Casaysayan 11, Oreta 7, Silva 6, Dematera 6, Mayran 3, Hassan 1, Comerciase 1.
Quarterscores: 14-27, 39-41, 65-58, 94-85.
Third Game
AFP (86) - Lumongsod 19, Baustista 17, Pascual 15, Almerol 11, Cordero 7, Rosopa 6, Casulla 6, Tan 3, Fernandez 2.
NHA (84) - Vitug 33, Dizon 14, Mercado 10, Lustestica 6, Alastoy 6, Dizon 6, W. Tibay 3, Prado 3, Sumayang 3.
Quarterscores: 12-19, 37- 37, 64-58, 86-84.


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WITNESS LIST Judiciary Committee March 7, 2005-12:00A HB 1326 For: Brown Ph.D, Mel (Self and District Judges & County Court-At-Law Judges of Montgomery County) Carr, Brent (Self) Klager, James E. (Self and myself) On: del Llano, Ann (ACLU of Texas) Bramblett, District Judge Mary Anne (Self) Francis, Robert W. (Self) Harris, Asst. Attorney General David A. (Office of the Attorney General) Reynolds, Carl (TDCJ- General Counsel) Shelsy, Marshall (Harris County Criminal Courts At Law) HB 1394 For: Miller, David (City of Kennedale) HB 1418 For: Cobos, David M. (Justices of the Peace & Constables Assoc of Texas) Gonzalez, Robert Bobby (Self) Lane, Jim E. (Self and JPCA) Paiz, Luz M. (Self and JPCA) Smith, Russell (Self and Texas Justice of Peace & Constables Assn.) Tanguma, Virginia (Justice Peace Constable Association) Vasquez, Program Manager Michael (Texas Conference of Urban Counties) HB 1576 For: Cobos, David M. (Justices of the Peace & Constables Assc of TX) Smith, Russell (Self and Texas Justice of Peace & Constables Assn.) HB 1597 Registering, but not testifying: For: Vasquez, Program Manager Michael (Texas Conference of Urban Counties) On: Smith, Russell (Self and Texas Justice of Peace & Constables Asson.)


Calls from the browser into the applet:
The applet didn't load.
Calls from the applet into the browser:


Git hooks allow scripts to be invoked when certain important git repository actions occur. This configuration controls the execution of client-side hooks on Jenkins agents. It is recommended that git hooks be disabled on Jenkins agents.
Most git repositories do not use hooks in the repository and do not need repository hooks. In those rare cases where repository hooks are needed, it is highly recommended that they are disabled on the Jenkins controller and on Jenkins agents.
Client-side hooks are not copied when the repository is cloned. However, client-side hooks might be installed in a repository by build steps or by misconfiguration.
If hook scripts are allowed on agents, a client-side hook script installed in a repository on a Jenkins agent will execute when the matching git operation is performed.
For example, if hooks are allowed on agents and a git repository on an agent includes a post-checkout
hook, the hook script will run on the agent after any checkout in that repository.
If hooks are allowed on agents and a git repository on an agent includes a pre-auto-gc
hook, the hook script will run on the agent before any automatic git garbage collection task.
See "Customizing Git - Git Hooks" for more details about git repository hooks.


Death Stranding's 1.05 update has vague 'performance improvements'
Play it again, Sam
by Jordan DevoreAt this point, the only thing that can tear me away from playing more Death Stranding is a barrage of "install this update, ya dummy!" prompts. That's all the respite my PS4 is gonna get right now.
The latest Death Stranding update, which brings the game to version 1.05, is a succinct one – it includes "various performance improvements." It's hard to take that at face value, but it's the lone note.
I can't say I've had many (or really any?) notable performance issues, but then again, I'm playing on a PlayStation 4 Pro, and after 16 hours, I'm still chipping away at Chapter 3 even though it's become clear that Sam should move on to bigger and better things. Oh well. There's no rush. I can already tell it's going to take me the rest of the year to finish this game, and I happily accept that slow-burn fate.
I'm having a ball clearing out MULE camps, topping up Auto-Pavers, and making sure my compatriots have access to Generators, especially in nasty, far-off places. Speaking of: more people should use the "Request Upgrade" feature on the map screen. Help me help you. I'll even smash that like button.
Seeing the on-screen message "a path you laid down was used by someone else" pop up at regular intervals is the video game equivalent of those scalp-massager tools. I can't get enough.
As for future Death Stranding updates, I'd expect to see further optimizations and adjustments, at least in the short term. That's been the trend since launch, but I hope some surprises await us.


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“Date-Night Planner is highly poetic, sharp, and engaging app. It’s easy to fall in love with its ingenuity.
This app is an ode to a deep appreciation of
dating and to the dance of the masculine and feminine as old as time.”
Dazrin T
Welcome to the Login Page
True art of preparing for the date-night is to understand the subtle dynamics of sub-communication
between two people – the complex melodiousness of attraction, excitement, nervousness, envy, judgment,
and desire in a social milieu… and how you can learn to seize it all in a spirit of fun and magic for
everyone in your circle. You will be able to smile in your advanced years, knowing that you’ve
experienced life at its fullest, that you have loved and been loved, and that you aren't having any
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I don’t even remember how I came across this app but it was truly a magical gift. I must have attracted
Date-Night Planner into my life. As a woman, I have to say, this is the best app I have ever used in my
entire life.
Carmen H


This website gives me the opportunity to show off some of my coding skills.
Certified Full Stack Developer through Woz U. Earning a Bachelors degree in I.T through Liberty University
I am constantly trying to develop my skills and become a better Developer
While I am technically trained as a Full Stack Web Developer. I have a real passion for the front-end user interface side of development


B.Tech. III (CO) Semester – 5 |
L |
T |
P |
C |
CO308 : UNIX PROGRAMMING (CS-IV) |
3 |
0 |
2 |
4 |
(03 Hours) |
||||
Introduction to Unix system, review of the basic concepts of Files & Directories, Logging in, Input & Output, Programs & Processes, error handling, System calls and Library functions, Unix Standardization, Limits, primitive system data types. |
||||
(05 Hours) |
||||
Introduction to the Files in UNIX - File Descriptors, File Handle Representation, System calls for File Input & Output, I/O efficiency, File Sharing. |
||||
(09 Hours) |
||||
File types in UNIX, System calls and their use for non-regular types of files, Set-User-ID and Set-Group-ID, File Access Permissions, Concept of Sticky bit, File size, File Truncation, Symbolic Links. File Times, Directory Handling & Manipulations, Special Device files, Summary of File Access Permission Bits. |
||||
(05 Hours) |
||||
Introduction, Password File, Shadow Passwords, Group Files, Other Data files used by the system to store vital information, Login Accounting Files, System Identification, Time and Date Routines. |
||||
(05 Hours) |
||||
The Environment of a Unix Process, main function, Process termination functions, Environment Lists, Memory Layout of a C Program, Shared Libraries, Automatic, Register and Volatile Variables. |
||||
(10 Hours) |
||||
Introduction, Identifiers, Creating processes, File Sharing, Race conditions, Execution, Changing user IDs and Group Ids, Interpreter files, Set-User-ID, User Identification, Process Times |
||||
(05 Hours) |
||||
Introduction, Pipes, Coprocesses, FIFOs, Message Queues, Semaphores, Shared Memory, Client Server Properties. |
||||
(Total Contact Time: 42 Hours) |
||||
PRACTICALS |
||||
1). Implement cat command. |
||||
BOOKS RECOMMENDED |
||||
1). W Richard Stevens, Stephen A Rago; “Advanced Programming in the UNIX Environment”; 2/E, Addison Wesley Professional, 2005. |


Plot Linking¶
In the upper left of each time series plot there is a Link checkbox. These checkboxes allow the user to perform the same operation on multiple plots simultaneously. For example, if the user links all three plots, then uses zoom in the first plot, each of the other plots will be zoomed to the same scale. In addition, a vertical line indicating the cursor position, normally shown in each individual plot, is duplicated on any linked plots. Two linked plots are shown in Figure 33.
In order to link plots, the time scale (x-axis) must be identical in the plots to be linked. If the scales are not identical, an error message will be displayed, and the plots will not be linked.


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Which programming language suits you best? Let's find out!
Ruby is a very high-level, multi-purpose programming language first released in 1995.
It’s one of the most fun and creative programming languages to work with, because you will most often find several ways to solve a specific problem.
Learning Ruby will definitely reward you with a steep learning curve and seeing results fairly quickly. For instance, you can build a web application from scratch surprisingly fast using the Ruby on Rails framework.
Therefore, Ruby is the most useful programming language for launching web apps as quickly as possible among startups and small businesses.
Originally developed by Microsoft to run on their .NET framework, C# (pronounced C-sharp) is based on C and C++.
C# was originally designed to be easy to learn and use. Obviously that makes it a simple language to start with.
C#, a high-level language, meaning that it reads a bit like English. On top of that, it abstracts away a lot of those complex tasks needed to make sure the computer is able to deal with your code – contrary to C++, for instance.
However, it’s still not as high-level as Python, for example – and therefore not quite as easy for a beginner to learn.
Not to be confused with Java, JavaScript is the programming language of the Web. It breathes life into websites, making them more interactive, interesting to use, and user-friendly.
These days almost every web page you use daily uses JavaScript. Needless to say, it’s one of the most popular web development languages and among the most powerful programming languages out there – therefore being an excellent skill to possess in the job market.
Different frameworks and libraries – such as AngularJS and jQuery or React, respectively – make using JavaScript for front-end development even more efficient and interesting.
JavaScript is a client-side language itself, meaning that the code is executed on the user’s browser. However, with new technologies such as Node.js, it can also be used as a server-side language. This makes JavaScript even more powerful through the additional versatility, ultimately making it a full-stack language.


Image
Analytics Zoo provides a series of Image APIs for end-to-end image processing pipeline, including image loading, pre-processing, inference/training and some utilities on different formats.
Load Image
Analytics Zoo provides APIs to read image to different formats:
Load to Data Frame
Scala:
package com.intel.analytics.zoo.pipeline.nnframes
object NNImageReader {
def readImages(path: String, sc: SparkContext, minPartitions: Int = 1, resizeH: Int = -1, resizeW: Int = -1): DataFrame
}
Read the directory of images from the local or remote source, return DataFrame with a single column "image" of images.
- path: Directory to the input data files, the path can be comma separated paths as the list of inputs. Wildcards path are supported similarly to sc.binaryFiles(path).
- sc: SparkContext to be used.
- minPartitions: Number of the DataFrame partitions, if omitted uses defaultParallelism instead
- resizeH: height after resize, by default is -1 which will not resize the image
- resizeW: width after resize, by default is -1 which will not resize the image
Python:
class zoo.pipeline.nnframes.NNImageReader
static readImages(path, sc=None, minPartitions=1, resizeH=-1, resizeW=-1, bigdl_type="float")
ImageSet
ImageSet
is a collection of ImageFeature
. It can be a DistributedImageSet
for distributed image RDD or
LocalImageSet
for local image array.
You can read image data as ImageSet
from local/distributed image path, or you can directly construct a ImageSet from RDD[ImageFeature] or Array[ImageFeature].
Scala APIs:
object com.intel.analytics.zoo.feature.image.ImageSet
def array(data: Array[ImageFeature]): LocalImageSet
Create LocalImageSet from array of ImeageFeature
- data: array of ImageFeature
def rdd(data: RDD[ImageFeature]): DistributedImageSet
Create DistributedImageSet from rdd of ImageFeature
- data: array of ImageFeature
def read(path: String, sc: SparkContext = null, minPartitions: Int = 1, resizeH: Int = -1, resizeW: Int = -1): ImageSet
Read images as Image Set. If sc is defined, read image as DistributedImageSet from local file system or HDFS. If sc is null, Read image as LocalImageSet from local file system
- path: path to read images. If sc is defined, path can be local or HDFS. Wildcard character are supported. If sc is null, path is local directory/image file/image file with wildcard character
- sc: SparkContext
- minPartitions: A suggestion value of the minimal splitting number for input data.
- resizeH: height after resize, by default is -1 which will not resize the image
- resizeW: width after resize, by default is -1 which will not resize the image
Example:
// create LocalImageSet from an image folder
val localImageSet = ImageSet.read("/tmp/image/")
// create DistributedImageSet from an image folder
val distributedImageSet2 = ImageSet.read("/tmp/image/", sc, 2)
Python APIs:
class zoo.feature.image.ImageSet
read(path, sc=None, min_partitions=1, resize_height=-1, resize_width=-1, bigdl_type="float")
Read images as Image Set. If sc is defined, read image as DistributedImageSet from local file system or HDFS. If sc is null, Read image as LocalImageSet from local file system
- path: path to read images. If sc is defined, path can be local or HDFS. Wildcard character are supported. If sc is null, path is local directory/image file/image file with wildcard character
- sc: SparkContext
- min_partitions: A suggestion value of the minimal splitting number for input data.
- resize_height height after resize, by default is -1 which will not resize the image
- resize_width width after resize, by default is -1 which will not resize the image
Python example:
# create LocalImageSet from an image folder
local_image_set2 = ImageSet.read("/tmp/image/")
# create DistributedImageSet from an image folder
distributed_image_set = ImageSet.read("/tmp/image/", sc, 2)
Image Transformer
Analytics Zoo provides many pre-defined image processing transformers built on top of OpenCV. After create these transformers, call transform
with ImageSet to get transformed ImageSet. Or pass the transformer to NNEstimator/NNClassifier to preprocess before training.
Scala APIs:
package com.intel.analytics.zoo.feature.image
object ImageBrightness
def apply(deltaLow: Double, deltaHigh: Double): ImageBrightness
Adjust the image brightness.
- deltaLow: low bound of brightness parameter
- deltaHigh: high bound of brightness parameter
Example:
val transformer = ImageBrightness(0.0, 32.0)
val transformed = imageSet.transform(transformer)
Python APIs:
class zoo.feature.image.imagePreprocessing.ImageBrightness
def __init__(delta_low, delta_high, bigdl_type="float")
Adjust the image brightness.
- delta_low: low bound of brightness parameter
- delta_high: high bound of brightness parameter
Example:
transformer = ImageBrightness(0.0, 32.0)
transformed = imageSet.transform(transformer)
Scala APIs:
package com.intel.analytics.zoo.feature.image
object ImageBytesToMat
def apply(byteKey: String = ImageFeature.bytes,
imageCodec: Int = Imgcodecs.CV_LOAD_IMAGE_UNCHANGED): ImageBytesToMat
Transform byte array(original image file in byte) to OpenCVMat
- byteKey: key that maps byte array. Default value is ImageFeature.bytes
- imageCodec: specifying the color type of a loaded image, same as in OpenCV.imread. 1. CV_LOAD_IMAGE_ANYDEPTH - If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit. 2. CV_LOAD_IMAGE_COLOR - If set, always convert image to the color one 3. CV_LOAD_IMAGE_GRAYSCALE - If set, always convert image to the grayscale one 4. >0 Return a 3-channel color image. Note The alpha channel is stripped from the output image. Use negative value if you need the alpha channel. 5. =0 Return a grayscale image. 6. <0 Return the loaded image as is (with alpha channel). Default value is Imgcodecs.CV_LOAD_IMAGE_UNCHANGED.
Example:
val imageSet = ImageSet.read(path, sc)
imageSet -> ImageBytesToMat()
3D Image Support
Create ImageSet for 3D Images
For 3D images, you can still use ImageSet as the collection of ImageFeature3D. You can create ImageSet for 3D images in the similar way as for 2D images. Since we do not provide 3D image reader in analytics zoo, before create ImageSet, we suppose you already read 3D images to tensor(scala) or numpy array(python).
Scala example:
val image = ImageFeature3D(tensor)
// create local imageset for 3D images
val arr = Array[ImageFeature](image)
val localImageSet = ImageSet.array(arr)
// create distributed imageset for 3D images
val rdd = sc.parallelize(Seq[ImageFeature](image))
val imageSet = ImageSet.rdd(rdd)
Python example:
# get image numpy array
img_np =
# create local imageset for 3D images
local_imageset = LocalImageSet(image_list=[img_np])
# create distributed imageset for 3D images
rdd = sc.parallelize([img_np])
dist_imageSet = DistributedImageSet(image_rdd=rdd)
3D Image Transformers
Analytics zoo also provides several image transformers for 3D Images.
The usage is similar as 2D image transformers. After create these transformers, call transform
with ImageSet to get transformed ImageSet.
Currently we support three kinds of 3D image transformers: Crop, Rotation and Affine Transformation.
Crop transformers
Crop3D
Scala:
import com.intel.analytics.zoo.feature.image3d.Crop3D
// create Crop3D transformer
val cropper = Crop3D(start, patchSize)
val outputImageSet = imageset.transform(cropper)
Crop a patch from a 3D image from 'start' of patch size. The patch size should be less than the image size. * start: start point array(depth, height, width) for cropping * patchSize: patch size array(depth, height, width)
Python:
from zoo.feature.image3d.transformation import Crop3D
crop = Crop3D(start, patch_size)
transformed_image = crop(image_set)
- start: start point list[]depth, height, width] for cropping
- patch_size: patch size list[]depth, height, width]
RandomCrop3D
Scala:
import com.intel.analytics.zoo.feature.image3d.RandomCrop3D
// create Crop3D transformer
val cropper = RandomCrop3D(cropDepth, cropHeight, cropWidth)
val outputImageSet = imageset.transform(cropper)
Crop a random patch from an 3D image with specified patch size. The patch size should be less than the image size. * cropDepth: depth after crop * cropHeight: height after crop * cropWidth: width after crop
Python:
from zoo.feature.image3d.transformation import RandomCrop3D
crop = RandomCrop3D(crop_depth, crop_height, crop_width)
transformed_image = crop(image_set)
- crop_depth: depth after crop
- crop_height: height after crop
- crop_width: width after crop
CenterCrop3D
Scala:
import com.intel.analytics.zoo.feature.image3d.CenterCrop3D
// create Crop3D transformer
val cropper = CenterCrop3D(cropDepth, cropHeight, cropWidth)
val outputImageSet = imageset.transform(cropper)
Crop a cropDepth
x cropWidth
x cropHeight
patch from center of image. The patch size should be less than the image size.
* cropDepth: depth after crop
* cropHeight: height after crop
* cropWidth: width after crop
Python:
from zoo.feature.image3d.transformation import CenterCrop3D
crop = CenterCrop3D(crop_depth, crop_height, crop_width)
transformed_image = crop(image_set)
- crop_depth: depth after crop
- crop_height: height after crop
- crop_width: width after crop
Rotation
Scala:
import com.intel.analytics.zoo.feature.image3d.Rotate3D
// create Crop3D transformer
val rotAngles = Array[Double](yaw, pitch, roll)
val rot = Rotate3D(rotAngles)
val outputImageSet = imageset.transform(rot)
Rotate a 3D image with specified angles. * rotationAngles: the angles for rotation. Which are the yaw(a counterclockwise rotation angle about the z-axis), pitch(a counterclockwise rotation angle about the y-axis), and roll(a counterclockwise rotation angle about the x-axis).
Python:
from zoo.feature.image3d.transformation import Rotate3D
rot = Rotate3D(rotation_angles)
transformed_image = rot(image_set)
Affine Transformation
Scala:
import com.intel.analytics.zoo.feature.image3d.AffineTransform3D
import com.intel.analytics.bigdl.tensor.Tensor
// create Crop3D transformer
val matArray = Array[Double](1, 0, 0, 0, 1.5, 1.2, 0, 1.3, 1.4)
val matTensor = Tensor[Double](matArray, Array[Int](3, 3))
val trans = Tensor[Double](3)
trans(1) = 0
trans(2) = 1.8
trans(3) = 1.1
val aff = AffineTransform3D(mat=matTensor, translation = trans, clampMode = "clamp", padVal = 0)
val outputImageSet = imageset.transform(aff)
Affine transformer implements affine transformation on a given tensor. To avoid defects in resampling, the mapping is from destination to source. dst(z,y,x) = src(f(z),f(y),f(x)) where f: dst -> src
- mat: [Tensor[Double], dim: DxHxW] defines affine transformation from dst to src.
- translation: [Tensor[Double], dim: 3, default: (0,0,0)] defines translation in each axis.
- clampMode: [String, (default: "clamp",'padding')] defines how to handle interpolation off the input image.
- padVal: [Double, default: 0] defines padding value when clampMode="padding". Setting this value when clampMode="clamp" will cause an error.
Python:
from zoo.feature.image3d.transformation import AffineTransform3D
affine = AffineTransform3D(affine_mat, translation, clamp_mode, pad_val)
transformed_image = affine(image_set)
- affine_mat: numpy array in 3x3 shape.Define affine transformation from dst to src.
- translation: numpy array in 3 dimension.Default value is np.zero(3). Define translation in each axis.
- clamp_mode: str, default value is "clamp". Define how to handle interpolation off the input image.
- pad_val: float, default is 0.0. Define padding value when clampMode="padding". Setting this value when clampMode="clamp" will cause an error.


|
BrawlCrate v0.40a
Wii File Editor
|
Public Member Functions | |
| delegate void | VD (PortraitViewerTextureData sender) |
| void | TextureFrom (ResourceNode node, int charNum, int costumeNum) |
| PortraitViewerTextureData (int width, int height, SinglePortraitViewer parent) | |
| Constructor for the main texture in the PortraitViewer - getting the TEX0 using the PortraitViewer's TEX0For function More... | |
| PortraitViewerTextureData (int width, int height, Func< int, int, string > GetTEX0Func) | |
| For additional textures in the file - getting these TEX0s using lambda functions which return the path (not the node itself) More... | |
| void | Replace (string filename, bool useTextureConverter) |
Static Public Member Functions | |
| static string | ApplyExtension (string path, string filter, int filterIndex) |
| Method lifted directly from BrawlBox. More... | |
Public Attributes | |
| VD | OnUpdate |
Properties | |
| Func< ResourceNode, int, int, ResourceNode > | GetTEX0Func [get] |
| A PortraitViewer always has one texture, but it can have more than one. Each texture will get its own PortraitViewerTextureData instance, and this function will return the texture node given the container, character ID, and costume ID. More... | |
| Size | Size [get] |
| TEX0Node | Texture [get, set] |
| Panel | TexturePanel [get] |
|
inline |
Constructor for the main texture in the PortraitViewer - getting the TEX0 using the PortraitViewer's TEX0For function
|
inline |
For additional textures in the file - getting these TEX0s using lambda functions which return the path (not the node itself)
|
inlinestatic |
Method lifted directly from BrawlBox.
|
inline |
|
inline |
| delegate void BrawlCrate.BrawlManagers.CostumeManager.Portrait_Viewers.PortraitViewerTextureData.VD | ( | PortraitViewerTextureData | sender | ) |
| VD BrawlCrate.BrawlManagers.CostumeManager.Portrait_Viewers.PortraitViewerTextureData.OnUpdate |
|
get |
A PortraitViewer always has one texture, but it can have more than one. Each texture will get its own PortraitViewerTextureData instance, and this function will return the texture node given the container, character ID, and costume ID.
|
get |
|
getset |
|
get |


HTML Forms
email:
Yes
No
Check One
Check Two
Check Three
Attached file:
If you click the "Submit" button, the form-data will be sent to a page called "/action_page.php".


Arena Check-In Testing
-----BEGIN PRIVATE KEY----- REDACTED FOR SECURITY REASONS -----END PRIVATE KEY-----
classes can be managed at
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/*global Arenite:true*/
/*global Arenite:true*/
Collection of utility functions to handle objects. This is an integral part for the usage of the Namespace pattern since this provides the ability to, for example, retrieve the functions by namespace.
Arenite.Object = function () {
var _navigateToBeforeLast = function (object, path) {
if (!object) {
return;
}
var split = path.split(".");
var last = split.splice(split.length - 1, 1);
var result = object;
split.forEach(function (pathEl) {
if (!result) {
return;
}
if (!result[pathEl]) {
result[pathEl] = {};
}
result = result[pathEl];
});
return {object: result, path: last};
};
var _getInObject = function (object, path) {
if (!object) {
return;
}
var split = path.split(".");
var result = object;
split.forEach(function (pathEl) {
if (!result) {
return;
}
result = result[pathEl];
});
return result;
};
var _setInObject = function (object, path, element) {
var beforeLast = _navigateToBeforeLast(object, path);
if (beforeLast && beforeLast.object) {
beforeLast.object[beforeLast.path] = element;
return beforeLast.object[beforeLast.path];
}
};
var _deleteInObject = function (object, path) {
var beforeLast = _navigateToBeforeLast(object, path);
if (beforeLast && beforeLast.object) {
delete beforeLast.object[beforeLast.path];
}
};
var _extend = function (source, target) {
for (var f in target) {
if (target.hasOwnProperty(f)) {
if (source[f] && typeof source[f] === 'object') {
if (source[f].constructor === Array && target[f] && target[f].constructor === Array) {
source[f] = _uniq(source[f].concat(target[f]));
} else {
_extend(source[f], target[f]);
}
} else {
source[f] = target[f];
}
}
}
return source;
};
var _keys = function (obj) {
var keys = [], key;
for (key in obj) {
if (obj.hasOwnProperty(key)) {
keys.push(key);
}
}
return keys;
};
var _contains = function (obj, key) {
if (obj.length) {
var result = false;
obj.forEach(function (_key) {
result = result || _key === key;
});
return result;
} else {
for (var _key in obj) {
if (obj.hasOwnProperty(_key)) {
if (_key === key) {
return true;
}
}
}
}
return false;
};
var _uniq = function (arr) {
var result = [];
arr.forEach(function (el) {
if (result.indexOf(el) < 0) {
result.push(el);
}
});
return result;
};
var _merge = function (arr1, arr2, keepDups) {
var result = [];
arr1.forEach(function (el) {
result.push(el);
});
arr2.forEach(function (el) {
result.push(el);
});
return keepDups ? result : _uniq(result);
};
var _extract = function (obj, prop) {
var result = [];
for (var _key in obj) {
if (obj.hasOwnProperty(_key)) {
result.push(obj[_key][prop]);
}
}
return result;
};
var _obj = function (arr, key) {
var obj = {};
arr.forEach(function (elem) {
obj[_getInObject(elem, key)] = elem;
});
return obj;
};
var _forEach = function (obj, func) {
var key;
for (key in obj) {
if (obj.hasOwnProperty(key)) {
func(obj[key], key);
}
}
};
var _values = function (obj) {
var key;
var arr = [];
for (key in obj) {
if (obj.hasOwnProperty(key)) {
arr.push(obj[key]);
}
}
return arr;
};
var _array = function (obj) {
var arr = [];
_forEach(obj, function (el) {
arr.push(el);
});
return arr;
};
var _filter = function (obj, keys) {
var filtered = {};
keys.forEach(function (key) {
if (obj.hasOwnProperty(key)) {
filtered[key] = obj[key];
}
});
return filtered;
};
return {
object: {
###object.get Retrieves a property from an object. The property is expressed as a string, denoting a path.
get(object, path)
where object is the target object and path is the path of the value to be fetched.
get: _getInObject,
###object.set Sets a property in an object. The property is expressed as a string, denoting a path.
set(object, path, value)
where object is the target object,
path is the path of the value and value the value to be set at the given path.
set: _setInObject,
###object.delete Removes a property from an object. The property is expressed as a string, denoting a path.
get(object, path)
where object is the target object and path is the path of the value to be deleted.
delete: _deleteInObject,
###object.extend Extend merges to objects. The second object will “override” properties also existing in the first.
extend(object, other)
where object is the object to be merged and extended by other.
extend: _extend,
###object.keys Returns all the properties available to an object in the form of an array.
keys(object)
where object is the object from which the properties will be extracted.
keys: _keys,
###object.forEach Iterates through the object the equivalent to the way forEach works for arrays.
forEach(object, func(elem, key))
where object is the object to iterate. func(elem, key) is the function called for each element and receives the element and its key.
forEach: _forEach,
###object.values Returns all the values available to an object in the form of an array.
keys(object)
where object is the object from which the elements will be extracted.
values: _values,
###object.contains Determines if a element is present in an array or a key exists in an object:
contains(object, key)
where object is the object to test for the presence of key and key is the property/element to be tested.
contains: _contains,
###object.array Transforms the object to an array using the values for each key:
array(object)
where object is the object to be transformed into the array.
array: _array,
###object.filter Returns a filtered version of the object:
filter(object, keys)
where object is the object to be filtered and keys an array of keys to maintain.
filter: _filter
},
array: {
###array.contains Determines if a element is present in an array or a key exists in an object:
contains(object, key)
where object is the object to test for the presence of key and key is the property/element to be tested.
contains: _contains,
###array.uniq Filters an array returning a new one with the unique values.
contains(array)
where array is the array to be stripped o duplicate values
uniq: _uniq,
###array.merge Merges two arrays returning a new one with the unique values.
merge(arr1, arr2)
where arr1 and arr2 are the arrays to be merged
merge: _merge,
###array.extract Extract an array composed of a specified property of the subobjects of a given object
extract(object, property)
where object is the object whose members will be analysed property the property to be extracted from those members
extract: _extract,
###array.obj Extract an object indexed by a given key
obj(array, property)
where array is the array whose members will be analysed property the property of each element to be turned into the key of that element in the resulting object
obj: _obj
}
};
};


Just as the original "Psycho" sired the slasher genre, John Carpenter's "Halloween" solidified the genre that became more shock for your buck than genuine scares. Amazing that so few directors followed Carpenter's model - atmosphere to spare and a chilling sense of doom that none of the sequels or "Friday the 13th" films ever came close to capture. "Halloween" has its flaws, but it is a superb scare show - a triumphant exercise in style that dictates its substance.
"Halloween" begins with a young kid walking around the outside of his house, looking for his sister.  All this time, Carpenter shows us what this kid does from his point-of-view - a very subjective stance that comes from Hitchcock and, of course, the notorious "Peeping Tom," which showed the killer filming his subjects as he killed them. The kid is Michael Myers, who is admitted to a mental institution. Nearly a decade later, Michael is still in a institution, and his psychiatrist, Dr. Sam Loomis (Donald Pleasance), is checking on him obsessively, aware that he is pure evil. During a rainy night, Michael escapes and drives away and now Loomis is in hot pursuit of someone he calls "inhumanly patient."
The story then shifts to October 31st, the celebrated day of Halloween, at the calm, homely town of Haddonfield, Illinois. We see a teenage woman named Laurie Strode (Jamie Lee Curtis) walking to school with a young kid who believes in bogeymen. They make plans for the evening since Laurie will be babysitting him (the title of the film was originally "The Babysitter Murders."). Then we meet some of Laurie's friends, who are actively dating and dreaming of sexual escapades, yet Laurie is the lonely, straight girl of the trio. There is the brunette-haired Annie (Nancy Kyes), whose father (Leigh Brackett) is a local cop, and Lynda (P.J. Soles), the ditzy blonde who ends every sentence with the word "totally." They all make plans for the evening since Annie and Laurie are babysitting, and Lynda wants to bring her boyfriend over at one house. And in this town, terror awaits - Michael Myers is paying a visit to wreak havoc.
"Halloween" is almost monochromatic in its look - the scenes during the day are overcast and the nights are truly dark where silhouettes and shadows exist - perfect time for Michael to attack (in one scene, Michael kills a dog and offscreen, we hear it whimper). Carpenter uses the hand-held camera effectively to build the tension by choosing to follow the subjective nature of its characters. Examples of such moments are when Annie hears noises outside her house as she prepares to do laundry, the car that comes to a screeching halt when Annie shouts, "Speed kills," the discovery of the dead bodies by Laurie, and so on. Subjectivity is especially well-handled when Laurie walks across the street to the house where Lynda and Annie are supposedly, and this scene, accompanied by Carpenter's famously eerie electronic score, is a hark back to Vera Miles' similar walk to the Bates House in "Psycho."
What is particularly arresting about "Halloween" is that the characters are not killed immediately - Carpenter lets us observe these teenagers and who they are. The long takes of Laurie, Annie and Lynda walking home from school show us the daily activities and thoughts of teenage girls, oblivious to the unseen terror about to take place. Laurie is not as oblivious, and conveniently sees Michael Myers hiding in bushes, driving by, or standing by his car while she looks from behind windows. Lots of subjective shots throughout, and the suspense builds with a real pulse eschewing any of the gore or blood that would have diminished its overall power. These tree-lined avenues of Middle America hide something inexplicable and without emotion, and all Laurie can do is run and cry when she finds that this killer is after her (in a way, Laurie's escape from an unstoppable killer led the way to Linda Hamilton's escape from the unstoppable Schwarzeneger in "The Terminator") There's a terrifying moment where Laurie begs for help from a neighbor, and the neighbor observes and closes her shades, thinking it is a Halloween prank.. "Oh, my God! Help," shouts the helpless Laurie.
There is a stretch of Catholicism in "Halloween" emanating from its semi-Production Code morals (though the Production Code was eliminated back in the late 50's). The idea is that any teenager girl or boy who has sex or lustful thoughts is killed by Michael Myers. Laurie is the only survivor because she is still a virgin, though she may have lustful thoughts about a guy in her class. "So. You do think about things like that, don't you Laurie," says Annie at one point. The only thing Laurie does that may make her a member of this triad is that she smokes marihuana, yet has more interest in chemistry and studying than any of her friends do. Since this premarital sex-is-a-sin complex began in "Halloween," it became a mode for all rip-offs to follow. You have sex, and you will die, as indicated in the post-modern "Scream."
Back in 1978, "Halloween" was made for a low-budget and went on to gross millions of dollars making it the biggest independent film ever (initial reviewers panned it until someone from Variety spoke highly of it). It is a superb motion picture, reveling in its atmospheric, nocturnal pull guaranteed to give you some major shivers. Precisely, what makes this horror film so central to the genre is acknowledging that it is what you don't see that can kill you in the dark.
For more reviews, check out JERRY AT THE MOVIES at http://buffs.moviething.com/buffs/faust/
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Dec picks out bad tempered I'm A Celeb camp-mate after noticing telling sign
The presenter thinks former footballer Ian Wright may turn out to be this year's hot-head after day one Down Under as the stars meet for the first time as they prepare for the Jungle
by Irishmirror.ieDeclan Donnelly has already made a big prediction about this year's series of I'm A Celebrity .
The 44-year-old isn't talking possible winners just yet - but he has picked out who could be the most bad tempered camp-mate.
The stars said hello for the first time on Sunday night as things finally got under way.
Dec usually says what he sees - and Ian Wright is in his eye-line early on.
The former Arsenal striker was the first to hit our screens as he pulled up in a red Ferrari - but will he be seeing red as the days go on?
"I think there could be one or two spiky characters," the host predicted.
"He might blow, he might just go - if he gets hungry..... He's also got quite a short attention span."
I’m A Celeb viewers were distracted by Declan during the launch night.
He appeared on the show with painful looking marks on his arm - and it left those at home guessing.
While the marks looked like they could either be bruises or insect bites, viewers had a range of wild ideas about what they could be.
“What’s up with Dec’s arm? That’s a nasty bruise,” one concerned fan commented on Twitter .
“What’s up with Dec’s right arm? Big bruise!!” another said.
Meanwhile, his co-host Ant McPartlin has had a poke at controversial Katie Hopkins. He admitted she wasn't his favourite of the bunch of previous I'm A Celebrity campmates.
Katie, 44, shot to fame on The Apprentice in 2007 and starred in that year's series of I'm A Celebrity... Get Me Out Of Here!
But Ant, 43, told The Sun : "Katie Hopkins was in I'm A Celebrity but you wouldn't really remember her doing it, as she didn't really shine."


PDFPageFormat
public enum PDFPageFormat
Source for page sizes: https://www.papersizes.org All sizes are calculated using 72 points/inch
-
Page formats mostly used in the USA
Declaration
Swift
case usHalfLetter
-
Declaration
Swift
case usLetter
-
Declaration
Swift
case usLegal
-
Declaration
Swift
case usJuniorLegal
-
Declaration
Swift
case usLedger
-
Page formats according to the American National Standards Institute
Declaration
Swift
case ansiA
-
Declaration
Swift
case ansiB
-
Declaration
Swift
case ansiC
-
Declaration
Swift
case ansiD
-
Declaration
Swift
case ansiE
-
A-Series of paper standard DIN 476 For more detail: https://en.wikipedia.org/wiki/Paper_size#A_series
Declaration
Swift
case a0
-
Declaration
Swift
case a1
-
Declaration
Swift
case a2
-
Declaration
Swift
case a3
-
Declaration
Swift
case a4
-
Declaration
Swift
case a5
-
Declaration
Swift
case a6
-
Declaration
Swift
case a7
-
Declaration
Swift
case a8
-
Declaration
Swift
case a9
-
Declaration
Swift
case a10
-
B-Series is the geometric mean of the A-series For more detail: https://en.wikipedia.org/wiki/Paper_size#B_series
Declaration
Swift
case b0
-
Declaration
Swift
case b1
-
Declaration
Swift
case b2
-
Declaration
Swift
case b3
-
Declaration
Swift
case b4
-
Declaration
Swift
case b5
-
Declaration
Swift
case b6
-
Declaration
Swift
case b7
-
Declaration
Swift
case b8
-
Declaration
Swift
case b9
-
Declaration
Swift
case b10
-
C-Series is ususally used for envelopes. Definition is written in ISO 269 For more detail: https://en.wikipedia.org/wiki/Paper_size#C_series
Declaration
Swift
case c0
-
Declaration
Swift
case c1
-
Declaration
Swift
case c2
-
Declaration
Swift
case c3
-
Declaration
Swift
case c4
-
Declaration
Swift
case c5
-
Declaration
Swift
case c6
-
Declaration
Swift
case c7
-
Declaration
Swift
case c8
-
Declaration
Swift
case c9
-
Declaration
Swift
case c10
-
Size defined in constants
Declaration
Swift
public var size: CGSize { get }
-
Swaps height and width to create a landscape format
Declaration
Swift
public var landscapeSize: CGSize { get }
-
Shorthand method to create a default
PDFPageLayout
based on the this formatssize
.Declaration
Swift
var layout: PDFPageLayout { get }
-
Returns the defined US paper size if this format is a US format. If it is not a US format, it will check other constants for correct size
Declaration
Swift
var usSize: CGSize { get }
-
Returns the defined ANSI paper size if this format is a ANSI format. If it is not a ANSI format, it will check other constants for correct size
Declaration
Swift
var ansiSize: CGSize { get }
-
Returns the defined A-Series paper size if this format is a A-Series format. If it is not a A-Series format, it will check other constants for correct size
Declaration
Swift
var aSize: CGSize { get }
-
Returns the defined B-Series paper size if this format is a B-Series format. If it is not a B-Series format, it will check other constants for correct size
Declaration
Swift
var bSize: CGSize { get }
-
Returns the defined C-Series paper size if this format is a C-Series format. If it is not a C-Series format, it will check other constants for correct size
Declaration
Swift
var cSize: CGSize { get }