I have done my MS in Computer Science from San Francisco State University. After completing my Masters, I started working in a started in bay area. Name of the startup was SugarCRM. I worked there as a fullstack developer in LAMP stack. I was involved in building various features, one of the features that I am proud of building is 'User Dashlet' for Sugar6 that consists of dashlets of the most frequently used modules by the user. I implemented it using HTML5, CSS, jQuery in the front end and PHP and MySQL in the backend. For Sugar 7.0 I have written 'Contacts' widget in javascript and related RestfulAPI in the backend. At SugarCRM, I was also involved critical (high priority) and some very complex bug fixes (involving code changes in some of the very complex modules like workflow and studio). I would also monitor daily build on jenkins. Have also written php unit tests.
After working at SugarCRM for 3 years, I realized that I needed to take a break for my family since I had my second kid then. Hence in late 2013 I left my job at Sugar and we moved to east coast since we have family here so wanted to be close to the family.
After a break of one and half years in 2015, I again started working in a small startup named codex technologies. Codex technologies works on projects for contract. There I worked remotely for a hospital website. I worked in javascript and react. I worked there for a year.
After working for quite sometime at codex I gained immense interest in react hence to gain expertise in it I applied to and got accpeted by one of the best coding boot camps of New York City, named Fullstack Academy. There I did an immersive web development course in javascript, react, redux, node, express and sequelize for 5 months. In those five months I created several applications using these technologies. One of the projects that I am extremely proud of is Codehuddle. Codehuddle is listed in my portfolio.
After completing my course at Fullstack Academy in Feb 2017, I worked as a contractor for two months at a startup known as CuratedCare. This website gives access to well qualified and experienced baby sitters and teachers. Website allows to make bookings. Booking module was to be implemented in react-redux. That is what I did there in two months contractorship.
Now I have started looking for fulltime fullstack or frontend developer position in a company where the team environment is friendly and collaborative and open to new and innovative ideas.


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|
Files | |
| file | benedettelli-nxt2wifi.h |
Dani's WiFi Sensor. | |
Defines | |
| #define | WS_WEBLABEL 1 |
| #define | WS_WEBBUTTON 2 |
| #define | WS_WEBCHECKBOX 3 |
| #define | WS_WEBSLIDER 4 |
| #define | WS_WEBBARGRAPH 5 |
| #define | WF_SEC_OPEN 0 |
| #define | WF_SEC_WEP_40 1 |
| #define | WF_SEC_WEP_104 2 |
| #define | WF_SEC_WPA_KEY 3 |
| #define | WF_SEC_WPA_PASSPHRASE 4 |
| #define | WF_SEC_WPA2_KEY 5 |
| #define | WF_SEC_WPA2_PASSPHRASE 6 |
| #define | WF_SEC_WPA_AUTO_KEY 7 |
| #define | WF_SEC_WPA_AUTO_PASSPHRASE 8 |
| #define | AD_HOC 1 |
| #define | INFRASTRUCTURE 0 |
| #define | N2WchillOut() wait1Msec(50) |
| #define | N2WsetIPAddress(X) _N2WsetPar("IPAD", X) |
| #define | N2WsetMask(X) _N2WsetPar("MASK", X) |
| #define | N2WsetGateway(X) _N2WsetPar("GWAY", X) |
| #define | N2WsetDNS1(X) _N2WsetPar("DNS1", X) |
| #define | N2WsetDNS2(X) _N2WsetPar("DNS2", X) |
| #define | N2WsetSSID(X) _N2WsetPar("SSID", X) |
| #define | N2WsetNetbiosName(X) _N2WsetPar("NAME", X) |
Functions | |
| intrinsic int | StringFind (const char *sSrce, const char *pzFindString) asm(opcdStringOps |
| intrinsic int | variableRefCharPtr (sSrce) |
| intrinsic int | variableRefCharPtr (pzFindString)) |
| int | N2WgetNumericResponse (tMassiveArray &buf) |
| void | N2WgetStringResponse (const tMassiveArray &buf, string &response) |
| bool | N2WsetDebug (bool en) |
| bool | N2WConnect (bool custom) |
| bool | N2WDisconnect () |
| bool | N2WStopConnecting () |
| bool | N2WDelete () |
| bool | N2WSave () |
| bool | N2WLoad () |
| void | N2WReset () |
| bool | N2WSecurity (int mode, const ubyte *pKeypass, int keylen, int keyind) |
| bool | N2WSecurityWPA2Key (tBigByteArray &key, int len) |
| bool | N2WSecurityWPA2Passphrase (const string passphrase) |
| bool | N2WSecurityWPAKey (tBigByteArray &key, int len) |
| bool | N2WSecurityWPAPassphrase (const string &passphrase) |
| bool | N2WSecurityWEP104 (const string &passphrase) |
| bool | N2WSecurityOpen () |
| bool | N2WsetAdHoc (bool adhoc) |
| bool | _N2WsetPar (const string type, const string param) |
| bool | N2WsetDHCP (bool yes) |
| bool | N2WsetDefaultProfile (ubyte profile) |
| bool | N2WCustomExist () |
| bool | N2WsetHibernate (bool hibernate) |
| bool | N2WsetPowerSave (bool powersave) |
| int | N2WStatus () |
| bool | N2WConnected () |
| bool | N2WgetIP (string &IP) |
| void | N2WgetMAC (string &mac) |
| bool | N2WUDPOpenClient (int id, string ip, int port) |
| bool | N2WUDPOpenServer (int id, int port) |
| int | N2WUDPAvail (int id) |
| int | N2WUDPRead (int id, int datalen) |
| bool | N2WUDPWrite (int id, tHugeByteArray &data, int datalen) |
| bool | N2WUDPClose (int id) |
| bool | N2WUDPFlush (int id) |
| bool | N2WTCPOpenClient (int id, string host, int port) |
| bool | N2WTCPOpenClient (int id, char *host, int port) |
| bool | N2WTCPOpenServer (int id, int port) |
| bool | N2WTCPDetachClient (int id) |
| bool | N2WTCPClose (int id) |
| bool | N2WTCPFlush (int id) |
| int | N2WTCPAvail (int id) |
| int | N2WTCPRead (int id, int datalen) |
| int | N2WTCPWrite (int id, tHugeByteArray &data, int datalen) |
| void | N2WTCPClientIP (int id, string &ip) |
| void | N2WTCPClientMAC (int id, string &mac) |
| bool | N2WenableWS (bool enable) |
| bool | N2WreadWS (ubyte &type, ubyte &ID, ubyte &state, ubyte &value) |
| bool | N2WwriteWS (ubyte type, ubyte id, tHugeByteArray &wsmessage, ubyte wsmsglen) |
| bool | N2WclearFields () |
Variables | |
| string | N2WscratchString |
| intrinsic int | strOpFind |
| intrinsic int | functionReturn |
Dani's WiFi Sensor
| #define AD_HOC 1 |
Definition at line 63 of file benedettelli-nxt2wifi.h.
| #define INFRASTRUCTURE 0 |
Definition at line 64 of file benedettelli-nxt2wifi.h.
| #define N2WchillOut | ( | ) | wait1Msec(50) |
Wait 50ms between messages, this allows transmission to be done
Definition at line 66 of file benedettelli-nxt2wifi.h.
| #define N2WsetDNS1 | ( | X ) | _N2WsetPar("DNS1", X) |
Macro for setting the first DNS server IP address
Definition at line 70 of file benedettelli-nxt2wifi.h.
| #define N2WsetDNS2 | ( | X ) | _N2WsetPar("DNS2", X) |
Macro for setting the second DNS server IP address
Definition at line 71 of file benedettelli-nxt2wifi.h.
| #define N2WsetGateway | ( | X ) | _N2WsetPar("GWAY", X) |
Macro for setting the gateway IP address
Definition at line 69 of file benedettelli-nxt2wifi.h.
| #define N2WsetIPAddress | ( | X ) | _N2WsetPar("IPAD", X) |
Macro for setting the IP address
Definition at line 67 of file benedettelli-nxt2wifi.h.
| #define N2WsetMask | ( | X ) | _N2WsetPar("MASK", X) |
Macro for setting the netmask
Definition at line 68 of file benedettelli-nxt2wifi.h.
| #define N2WsetNetbiosName | ( | X ) | _N2WsetPar("NAME", X) |
Macro for setting the Netbios Name
Definition at line 73 of file benedettelli-nxt2wifi.h.
| #define N2WsetSSID | ( | X ) | _N2WsetPar("SSID", X) |
Macro for setting the SSID to connect to
Definition at line 72 of file benedettelli-nxt2wifi.h.
| #define WF_SEC_OPEN 0 |
Open Security (none)
Definition at line 53 of file benedettelli-nxt2wifi.h.
| #define WF_SEC_WEP_104 2 |
104 bit WEP
Definition at line 55 of file benedettelli-nxt2wifi.h.
| #define WF_SEC_WEP_40 1 |
40 bit WEP
Definition at line 54 of file benedettelli-nxt2wifi.h.
| #define WF_SEC_WPA2_KEY 5 |
WPA2 using key
Definition at line 58 of file benedettelli-nxt2wifi.h.
| #define WF_SEC_WPA2_PASSPHRASE 6 |
WPA2 using passphrase
Definition at line 59 of file benedettelli-nxt2wifi.h.
| #define WF_SEC_WPA_AUTO_KEY 7 |
Automatically determine WPA type and use key
Definition at line 60 of file benedettelli-nxt2wifi.h.
| #define WF_SEC_WPA_AUTO_PASSPHRASE 8 |
Automatically determine WPA type and use passphrase
Definition at line 61 of file benedettelli-nxt2wifi.h.
| #define WF_SEC_WPA_KEY 3 |
WPA using key
Definition at line 56 of file benedettelli-nxt2wifi.h.
| #define WF_SEC_WPA_PASSPHRASE 4 |
WPA using passphrase
Definition at line 57 of file benedettelli-nxt2wifi.h.
| #define WS_WEBBARGRAPH 5 |
Web page widget: bargraph
Definition at line 51 of file benedettelli-nxt2wifi.h.
| #define WS_WEBBUTTON 2 |
Web page widget: button
Definition at line 48 of file benedettelli-nxt2wifi.h.
| #define WS_WEBCHECKBOX 3 |
Web page widget: checkbox
Definition at line 49 of file benedettelli-nxt2wifi.h.
| #define WS_WEBLABEL 1 |
Web page widget: label
Definition at line 47 of file benedettelli-nxt2wifi.h.
| #define WS_WEBSLIDER 4 |
Web page widget: slider
Definition at line 50 of file benedettelli-nxt2wifi.h.
| bool _N2WsetPar | ( | const string | type, |
| const string | param | ||
| ) |
Set a specific parameter.
Note: this is an internal function and should not be used directly.
| type | the parameter type |
| param | the value to pass to the parameter |
Definition at line 351 of file benedettelli-nxt2wifi.h.
| bool N2WclearFields | ( | ) |
Clears all webpage fields. This cannot be used together with normal TCP/UDP operations.
Definition at line 1051 of file benedettelli-nxt2wifi.h.
| bool N2WConnect | ( | bool | custom ) |
Connect to the currently configured WiFi network
| custom | use the default or custom profile |
Definition at line 145 of file benedettelli-nxt2wifi.h.
| bool N2WConnected | ( | ) |
Are we connected to the WiFi network?
Definition at line 464 of file benedettelli-nxt2wifi.h.
| bool N2WCustomExist | ( | ) |
Check if a the custom profile exists.
Definition at line 403 of file benedettelli-nxt2wifi.h.
| bool N2WDelete | ( | ) |
Delete the currently configured custom profile
Definition at line 194 of file benedettelli-nxt2wifi.h.
| bool N2WDisconnect | ( | ) |
Disconnect from the current WiFi network
Definition at line 161 of file benedettelli-nxt2wifi.h.
| bool N2WenableWS | ( | bool | enable ) |
Enable the built-in webserver. This cannot be used together with normal TCP/UDP operations.
| enable | enables the web server if set to true, disables it if false |
Definition at line 944 of file benedettelli-nxt2wifi.h.
| bool N2WgetIP | ( | string & | IP ) |
Get the current IP address. If the address could not be determined <IP_ADDRESS> is returned.
| IP | the string to put the address into |
Definition at line 475 of file benedettelli-nxt2wifi.h.
| void N2WgetMAC | ( | string & | mac ) |
Get the NXT2WIFI's MAC address.
| mac | string to hold MAC address |
Definition at line 501 of file benedettelli-nxt2wifi.h.
| int N2WgetNumericResponse | ( | tMassiveArray & | buf ) |
Parse the buffer and return the number in the NXT2WIFI response
| buf | the buffer to pull the number from |
Definition at line 87 of file benedettelli-nxt2wifi.h.
| void N2WgetStringResponse | ( | const tMassiveArray & | buf, |
| string & | response | ||
| ) |
Parse the buffer and return the string in the NXT2WIFI response
| buf | the buffer to pull the string from |
| response | the string to hold the response from the sensor |
Definition at line 110 of file benedettelli-nxt2wifi.h.
| bool N2WLoad | ( | ) |
Load the currently configured custom profile
Definition at line 220 of file benedettelli-nxt2wifi.h.
| bool N2WreadWS | ( | ubyte & | type, |
| ubyte & | ID, | ||
| ubyte & | state, | ||
| ubyte & | value | ||
| ) |
Read data from the web server. This cannot be used together with normal TCP/UDP operations.
Type can be one of the following:
| type | the type of widget |
| ID | ID of the element that was pressed or submitted |
| state | additional data from the UI element |
| value | the value of the UI element |
Definition at line 968 of file benedettelli-nxt2wifi.h.
| void N2WReset | ( | ) |
Reset the NXT2WIFI sensor.
Definition at line 232 of file benedettelli-nxt2wifi.h.
| bool N2WSave | ( | ) |
Save the currently configured custom profile
Definition at line 208 of file benedettelli-nxt2wifi.h.
| bool N2WSecurity | ( | int | mode, |
| const ubyte * | pKeypass, | ||
| int | keylen, | ||
| int | keyind | ||
| ) |
Confgure the security settings for the custom profile
Note: this is an internal function and shouldn't be used directly
| mode | the security mode to use |
| pKeypass | the keypass to use |
| keylen | the length of the key |
| keyind | used for WEP, usually set to 0 |
Definition at line 247 of file benedettelli-nxt2wifi.h.
| bool N2WSecurityOpen | ( | ) |
Use no security at all. Just as effective as WEP but less annoying.
Definition at line 323 of file benedettelli-nxt2wifi.h.
| bool N2WSecurityWEP104 | ( | const string & | passphrase ) |
Set the WEP passphrase. Please don't use this, it's very insecure.
| passphrase | the WEP passphrase to use |
Definition at line 311 of file benedettelli-nxt2wifi.h.
| bool N2WSecurityWPA2Key | ( | tBigByteArray & | key, |
| int | len | ||
| ) |
Set the WPA2 key
| key | the WPA2 key to use |
| len | the length of the WPA2 key |
Definition at line 266 of file benedettelli-nxt2wifi.h.
| bool N2WSecurityWPA2Passphrase | ( | const string | passphrase ) |
Set the WPA2 passphrase
| passphrase | the WPA2 passphrase to use |
Definition at line 276 of file benedettelli-nxt2wifi.h.
| bool N2WSecurityWPAKey | ( | tBigByteArray & | key, |
| int | len | ||
| ) |
Set the WPA key
| key | the WPA key to use |
| len | the length of the WPA key |
Definition at line 289 of file benedettelli-nxt2wifi.h.
| bool N2WSecurityWPAPassphrase | ( | const string & | passphrase ) |
Set the WPA passphrase
| passphrase | the WPA passphrase to use |
Definition at line 299 of file benedettelli-nxt2wifi.h.
| bool N2WsetAdHoc | ( | bool | adhoc ) |
Use ad-hoc network or infrastructure
| adhoc | if true, use adhoc, otherwise use infrastructure mode |
Definition at line 334 of file benedettelli-nxt2wifi.h.
| bool N2WsetDebug | ( | bool | en ) |
Enable debugging
| en | whether or not to enable debugging |
Definition at line 128 of file benedettelli-nxt2wifi.h.
| bool N2WsetDefaultProfile | ( | ubyte | profile ) |
Set the default profileto connect to after initial startup
| profile | The profile to connect to, 0 = none, 1 = custom profile, 2 = default |
Definition at line 385 of file benedettelli-nxt2wifi.h.
| bool N2WsetDHCP | ( | bool | yes ) |
Configure to use DHCP.
| yes | if set to true, use DHCP |
Definition at line 368 of file benedettelli-nxt2wifi.h.
| bool N2WsetHibernate | ( | bool | hibernate ) |
Enter or exit hibernation mode
| hibernate | enter hibernation mode if true, exit if false |
Definition at line 419 of file benedettelli-nxt2wifi.h.
| bool N2WsetPowerSave | ( | bool | powersave ) |
Enable or disable power saving
| powersave | enable powersaving if true, disable if false |
Definition at line 436 of file benedettelli-nxt2wifi.h.
| int N2WStatus | ( | ) |
Get the current connection status.
Definition at line 451 of file benedettelli-nxt2wifi.h.
| bool N2WStopConnecting | ( | ) |
Stop reconnecting when disconnected
Definition at line 176 of file benedettelli-nxt2wifi.h.
| int N2WTCPAvail | ( | int | id ) |
Check if there are bytes available for reading on the specified connection.
| id | the connection ID to use, can be 1 to 4 |
Definition at line 808 of file benedettelli-nxt2wifi.h.
| void N2WTCPClientIP | ( | int | id, |
| string & | ip | ||
| ) |
Get the remote client's IP address
| id | the connection ID to use, can be 1 to 4 |
| ip | string to hold IP address |
Definition at line 891 of file benedettelli-nxt2wifi.h.
| void N2WTCPClientMAC | ( | int | id, |
| string & | mac | ||
| ) |
Get the remote client's MAC address. Only useful for local network client.
| id | the connection ID to use, can be 1 to 4 |
| mac | string to hold MAC address |
Definition at line 917 of file benedettelli-nxt2wifi.h.
| bool N2WTCPClose | ( | int | id ) |
Close the specified connection
| id | the connection ID to use, can be 1 to 4 or 0 for all |
Definition at line 777 of file benedettelli-nxt2wifi.h.
| bool N2WTCPDetachClient | ( | int | id ) |
Closes a connection to a remote client
| id | the connection ID to use, can be 1 to 4 |
Definition at line 758 of file benedettelli-nxt2wifi.h.
| bool N2WTCPFlush | ( | int | id ) |
Flush the buffers of the specified connection
| id | the connection ID to use, can be 1 to 4 or 0 for all |
Definition at line 792 of file benedettelli-nxt2wifi.h.
| bool N2WTCPOpenClient | ( | int | id, |
| string | host, | ||
| int | port | ||
| ) |
Open a TCP connection to a remote host on a port
| id | the connection ID to use, can be 1 to 4 |
| host | the IP address of name of the remote host |
| port | the port of the service on the remote host |
Definition at line 690 of file benedettelli-nxt2wifi.h.
| bool N2WTCPOpenClient | ( | int | id, |
| char * | host, | ||
| int | port | ||
| ) |
Open a TCP connection to a remote host on a port
| id | the connection ID to use, can be 1 to 4 |
| host | the IP address of name of the remote host |
| port | the port of the service on the remote host |
Definition at line 714 of file benedettelli-nxt2wifi.h.
| bool N2WTCPOpenServer | ( | int | id, |
| int | port | ||
| ) |
Open a listening UDP socket on the specified port
| id | the connection ID to use, can be 1 to 4 |
| port | the port on which to start listening |
Definition at line 739 of file benedettelli-nxt2wifi.h.
| int N2WTCPRead | ( | int | id, |
| int | datalen | ||
| ) |
Read the specified number of bytes from the connection ID. Bytes are read into the RS485rxbuffer variable.
| id | the connection ID to use, can be 1 to 4 |
| datalen | the number of bytes to read |
Definition at line 827 of file benedettelli-nxt2wifi.h.
| int N2WTCPWrite | ( | int | id, |
| tHugeByteArray & | data, | ||
| int | datalen | ||
| ) |
Write the specified number of bytes to the connection ID.
| id | the connection ID to use, can be 1 to 4 |
| data | the tHugeByteArray containing the data to be transmitted |
| datalen | the number of bytes to read |
Definition at line 865 of file benedettelli-nxt2wifi.h.
| int N2WUDPAvail | ( | int | id ) |
Check if there are bytes available for reading on the specified connection.
| id | the connection ID to use, can be 1 to 4 |
Definition at line 571 of file benedettelli-nxt2wifi.h.
| bool N2WUDPClose | ( | int | id ) |
Close the specified connection. Use 0 to close all connections
| id | the connection ID to use, can be 1 to 4 or 0 for all |
Definition at line 657 of file benedettelli-nxt2wifi.h.
| bool N2WUDPFlush | ( | int | id ) |
Flush the buffers of the specified connection
| id | the connection ID to use, can be 1 to 4 or 0 for all |
Definition at line 672 of file benedettelli-nxt2wifi.h.
| bool N2WUDPOpenClient | ( | int | id, |
| string | ip, | ||
| int | port | ||
| ) |
Open a UDP datastream to a remote host on a port
| id | the connection ID to use, can be 1 to 4 |
| ip | the IP address of the remote host |
| port | the port of the service on the remote host |
Definition at line 529 of file benedettelli-nxt2wifi.h.
| bool N2WUDPOpenServer | ( | int | id, |
| int | port | ||
| ) |
Open a listening UDP socket on the specified port
| id | the connection ID to use, can be 1 to 4 |
| port | the port on which to start listening |
Definition at line 552 of file benedettelli-nxt2wifi.h.
| int N2WUDPRead | ( | int | id, |
| int | datalen | ||
| ) |
Read the specified number of bytes from the connection ID. Bytes are read into the RS485rxbuffer variable.
| id | the connection ID to use, can be 1 to 4 |
| datalen | the number of bytes to read |
Definition at line 594 of file benedettelli-nxt2wifi.h.
| bool N2WUDPWrite | ( | int | id, |
| tHugeByteArray & | data, | ||
| int | datalen | ||
| ) |
Write the specified number of bytes to the connection ID.
| id | the connection ID to use, can be 1 to 4 |
| data | the tHugeByteArray containing the data to be transmitted |
| datalen | the number of bytes to read |
Definition at line 634 of file benedettelli-nxt2wifi.h.
| bool N2WwriteWS | ( | ubyte | type, |
| ubyte | id, | ||
| tHugeByteArray & | wsmessage, | ||
| ubyte | wsmsglen | ||
| ) |
Write data to the webserver.
Type can be one of the following:
| type | the type of webpage element |
| id | the field number in the web page |
| wsmessage | data to be transmitted |
| wsmsglen | length of the data |
Definition at line 1006 of file benedettelli-nxt2wifi.h.
| intrinsic int StringFind | ( | const char * | sSrce, |
| const char * | pzFindString | ||
| ) |
| intrinsic int variableRefCharPtr | ( | pzFindString | ) |
| intrinsic int variableRefCharPtr | ( | sSrce | ) |
| intrinsic int functionReturn |
Definition at line 79 of file benedettelli-nxt2wifi.h.
| string N2WscratchString |
string for tmp formatting, scratch data
Definition at line 77 of file benedettelli-nxt2wifi.h.
| intrinsic int strOpFind |
Definition at line 79 of file benedettelli-nxt2wifi.h.


|
|
__float128 | Zx |
| |
|
__float128 | Zy |
| |
|
__float128 | Cx |
| |
|
__float128 | Cy |
| |
|
__float128 | Zx2 |
| |
|
__float128 | Zy2 |
| |
|
__complex128 | Z |
| |
|
__complex128 | C |
| |
|
__float128 | colorPoly |
| |
|
__float128 | Exps |
| |
|
__float128 | DistanceMax |
| |
|
int | n |
| |
Definition at line 57 of file colors.h.
◆ colorPoly
| __float128 Color128::colorPoly |
◆ Cx
◆ Cy
◆ DistanceMax
| __float128 Color128::DistanceMax |
◆ Exps
| __float128 Color128::Exps |
◆ Zx
◆ Zx2
◆ Zy
◆ Zy2
The documentation for this struct was generated from the following file:


  I would have never done it if it weren't for the fact that Atom Egoyan, director of The Sweet Hereafter and The Adjuster was behind the camera. That and, let's admit it, I'm human.
Of course, knowing that Egoyan was working on it should have told me that Exotica wasn't going to be crap. That it wasn't going to be a simple striptease film, and that it would be close to as profoundly lyrical as The Sweet Hereafter was.
Exotica is a story of the intersection of lives. On the one hand you have a pregnant strip club owner, Zoe (Arsinee Khanjian), on the other, a disturbed DJ, Eric (Elais Koteas). Also in the cast is a seductive stripper Christina (Mia Kirshner) and a father of a murdered child, Francis (Bruce Greenwood). A customs official (Calvin Green) and a man in charge of a pet-smuggling ring (Thomas, Don McKellar) are also in the mix. With the magic that Egoyan has always had, however, he moves all of this into a pattern of coherence.
Their lives intersect, mainly, through the events leading up to and followwing the breaking of the rules at a strip club: that a stripper is not allowwed to be touched. They also intersect with the fact Eric and Christina were the two that found the murdered daughter. You with me? Francis is an accountant sent by the IRS (or the Canadian equivalant) to confirm that Thomas is involved in smuggling, and Thomas becomes his helper in answering a question.
To clear up misconceptions, Exotica isn't a movie about sex. Most people will become so caught up in the sex that they won't notice the vivid portrait of a man so depressed by his daughter's death that he is willing to pay his niece Tracey (Sarah Polley) $20 an hour to babysit a dead child. I did. And, good Christ, I liked it.
The performances are nearly flawless. Bruce Greenwood flexes his acting muscles as he did in The Sweet Hereafter, portraying a character so ripped apart by the events in his life that he goes to obsessive lengths to keep some sembalance of hope. Mia Kirshner does her job so perfectly that we do not see a stripper when we look at her: we see a multifacted character who has a very special relationship with Francis. Eric, Elais Koteas' finest role, is a man who has let his love for Christina turn into an obsession that becomes an inflamed jealousy. Thomas is a man trying to salvage whatever is left of his morals while the world turns insane around him.
The conflicting obsessions are pitted against one another, forming an intriguing mindgame that goes into the deapths of each character as we sit back in awe, watching a truly mythical story unfold in front of us.
Like with The Sweet Hereafter, Atom Egoyan crafts a tale capable of academia, rife with symbolism and haunting images. He drifts us back and forth throughout time, shows us characters that are to be pitied and loved at the same time.
Although I dare not use this as an example in everyday conversation, Exotica is a film that is a striking example of a lack of a villian. The antagonist, ironically, is each protagonist. Each person's personal demons, their problems, conflict with each other. For those who have grown to hate the archetypal battle between good and evil, this is a very refreshing movie to watch.
Let there be several warnings about this film, however. First, those who are very conservative or easily offended back off: Exotica is not for you.  Third, for those who lack a literary mind you should avoid this film: it takes a lot of intelligence to decipher. Fourth, don't watch it after midnight: you won't get it when it's that late.
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


Upcoming
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This website aims at listing, as completely as possible, announcements for the conferences, workshops and seminars in Mathematics, pure and applied, being organized within the "boundaries" of the Hellenic Mathematical Community.
The goal is twofold:
- to create a portal where the members of the Hellenic and International Mathematical Community can be informed about the research meetings taking place in Greece and Cyprus.
- to encourage especially undergraduate and graduate students from the eight Hellenic Departments of Mathematics to participate in such events as an initial step towards the further development of University level Mathematics in these departments.
In this direction, it would be greatly appreciated if the organizers (or participants) of any future mathematics conferences or seminars taking place in Greece or Cyprus would send the relative announcements, as well as any comments, to the e-mail address<EMAIL_ADDRESS>in order to keep this website up to date and further develop it.


Protocol Improvements
NavCoin Protocol Improvement Proposals (NPIPs) can be suggested and published by anyone who wants to contribute to the NavCoin protocol. Check out the "Help Wanted" or "Ideas" categories if you're looking for a PIP to work on, or suggest your own!
View Github Issues


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Welcome to the world of cars!
25 interesting facts about cars!
10 things you should know!
Classic for memory >>>
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The Witcher 3: Wild Hunt is a story-driven, next-generation open world role-playing game, set in a visually stunning fantasy universe, full of meaningful choices and impactful consequences. In The Witcher, you are a professional monster hunter, tasked with finding a child of prophecy in a vast open world rich with merchant cities, dangerous mountain passes, and forgotten caverns to explore.
There are hundreds of characters within the Witcher world, all with unique personalities, abilities and stories to tell. Some of the main characters include Geralt of Rivia, Yennefer of Vengerberg, Triss Merigold and Ciri. There are also lots of villainous characters such as Imlerith and Eredin who are part of the Wild Hunt.
The realm is filled with many creatures both magical and dangerous. The majority of creatures are dangerous and will act aggressively towards Geralt when he approaches. Some of the mystical creatures include Vampires, Wraiths, Werewolves and Griffins. Geralt can use his silver sword to attack non-humans and his steel sword against humans.
Gwent is a card game played by many innkeepers and peasants around the land. The game is turn based and the highest score on the board wins each round. The game is divided into 4 factions: Northern Realms, Nilfgaardian Empire, Scoiatael and Monsters. Unique hero cards are most valuable as they cannot be countered in the game.


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Located in Southeastern Europe, Serbia is a beautiful country With lots of landmarks such as:
There are also some great cities for you to visit, such as the wonderful city of Belgrade:
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{% extends "base.html" %} {% load i18n %} {% block content %}
{% trans "Your account is now activated." %}
{% endblock %} {% comment %} **registration/activation_complete.html** Used after successful account activation. This template has no context variables of its own, and should simply inform the user that their account is now active. {% endcomment %}


My name is Laura Camila Pinzón, I´m 21 years old, I´m student of systems engineering at the University of Cundinamarca.
I was born in fusagasuga cundinamarca on September 9, 1997.
I studied my primary school at Camilo Torres, then I studied my high school in the municipality of Viota, there I lived with my dad, when I graduated, I went back to fusagasugá and started studying at the University of Cundinamarca since 2015.

During my life I have had good and bad moments but I have always seen God in everything, and thanks to Him I am here.
my favorite verse!
August 23 was my first English Class 2 In class they made an evaluation about a book, a story about mice; we also did a reading in Englis and review of the past simple. The teacher also left a job on the book, I have to make a video and some slides. class is over quickly and in the class next week there will be exam.
On August 30 we had the first part of English, it was held in the Virtual Classroom and I had a good grade, in the oral part it took me a little longer but I felt good. I was very nervous at the beginning but after all I felt good.
Text
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TABLE OF INTERACTIONS WITH
SIMEPREVIR
From the French ANSM drug interactions document of
September 2016, pp. 198-199
SIMEPREVIR RxNorm: 1482790 ATC: J05AE14 |
DEXAMETHASONE RxNorm: 3264 ATC: A01AC02 C05AA09 D07AB19 D07XB05 D10AA03 H02AB02 R01AD03 S01BA01 S01CB01 S02BA06 S03BA01 |
With the dexamethasone for systemic use, risk of decrease of the plasma concentrations of simeprevir due to increase of its hepatic metabolism by the dexamethasone |
Not recommended With the exception of dexamethasone administered in a single dose |
SIMEPREVIR RxNorm: 1482790 ATC: J05AE14 |
ENZYME INDUCERS CLASS CODE: N03-J05-001 |
Decrease of he plasma concentrations of simeprevir due to increase of its hepatic metabolism by the inducer |
Not recommended |
SIMEPREVIR RxNorm: 1482790 ATC: J05AE14 |
STRONG INHIBITORS OF CYP3A4 CLASS CODE: J02-J05 |
Risk of increase of the plasma concentrations of simeprevir due to decrease of its heptatic metabolism by the inhibitor |
Not recommended |
SIMEPREVIR RxNorm: 1482790 ATC: J05AE14 |
ST JOHNS WORT Hypericum perforatum RxNorm: 258326 ATC: none found |
Risk of decrease of the plasma concentrations of simeprevir due to increase of its hepatic metabolism by the St Johns wort |
Not recommended |


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JsTetris is a highly customizable tetris game written in javascript,
full sources available, it is free to modify.
Author: Cezary Tomczak
Site: www.gosu.pl/tetris/
License: BSD revised (free for any use)
Tin Can additions by Rustici Software : Tin Can Home


Executables¶
REGoth features several executables, whose usage is documented here.
General¶
All executables have the following command line options available:
-h
,--help
: Print the help message--version
: Print the version of REGoth-v
: Increase the verbosity level
Core engine¶
The core engine’s command line options are the following:
-g
,--game-assets
, or the first positional argument: Path to a valid Gothic or Gothic II installation--video-x-res
: X resolution--video-y-res
: Y resolution--video-fullscreen
: Whether to run the game in fullscreen
REGothWorldViewer¶
This custom engine allows viewing arbitrary worlds. The world to load can be configured using
the -w
or --world
command line arguments.
Available worlds for Gothic are:
“world”
“oldmine”
“freemine”
“orcgraveyard”
“orctempel”
Available worlds for Gothic II are:
“newworld” (Island of Khorinis)
“oldworld” (Valley of Mines)
“addonworld” (Jharkendar - Only available with the addon Gothic II: Night of the Raven)
“dragonisland” (Island of Irdorath)


House Prices: Advanced Regression Techniques is a kaggle competition to predict the house prices, which aims to practice feature engineering, RFs, and gradient boosting. After exploring and referring others’ methods, I decide to do it by myself to improve my python skill in data science and data analysis ability.
Competition Description
Ask a home buyer to describe their dream house, and they probably won’t begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition’s dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
The data contains three parts, one is data_description.txt
, which gives brief summary of the data information, another two, train.csv
and test.csv
, are the datasets used to train and test. More details of the data fields’ description are available here: [link].
Data Analysis and Feature Extraction
Preliminary Analysis
Data Preview
First of all, we need to load the data and have a glance of it.
import pandas as pd
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
train_labels = train.pop('SalePrice') # separate labels from train dataset
data = pd.concat([train, test], keys=['train', 'test'])
print(data.columns) # check column decorations
print('rows:', data.shape[0], ', columns:', data.shape[1]) # count rows of total dataset
print('rows in train dataset:', train.shape[0])
print('rows in test dataset:', test.shape[0])
the output is shown below:
Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',
'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',
'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',
'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',
'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',
'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',
'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',
'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',
'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',
'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',
'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',
'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',
'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',
'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',
'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',
'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',
'SaleCondition'],
dtype='object')
rows: 2919, columns: 80
rows in train dataset: 1460
rows in test dataset: 1459
Here we have 2919 lines records in total, 1460 lines for training and 1459 lines for testing. Each line has 79 properties (Id
is eliminated). Then we take a look at the situation of data missing:
nans = pd.concat([train.isnull().sum(), train.isnull().sum() / train.shape[0], test.isnull().sum(), test.isnull().sum() / test.shape[0]], axis=1, keys=['Train', 'Percentage', 'Test', 'Percentage'])
print(nans[nans.sum(axis=1) > 0])
we find that Alley
, FireplaceQu
, PoolQC
, Fence
and MiscFeature
suffer almost or more than 50% data missing. Normally, if the property has more 15% information loss, we should delete it.
Train Percentage Test Percentage
MSZoning 0 0.000000 4 0.002742
LotFrontage 259 0.177397 227 0.155586
Alley 1369 0.937671 1352 0.926662
Utilities 0 0.000000 2 0.001371
Exterior1st 0 0.000000 1 0.000685
Exterior2nd 0 0.000000 1 0.000685
MasVnrType 8 0.005479 16 0.010966
MasVnrArea 8 0.005479 15 0.010281
BsmtQual 37 0.025342 44 0.030158
BsmtCond 37 0.025342 45 0.030843
BsmtExposure 38 0.026027 44 0.030158
BsmtFinType1 37 0.025342 42 0.028787
BsmtFinSF1 0 0.000000 1 0.000685
BsmtFinType2 38 0.026027 42 0.028787
BsmtFinSF2 0 0.000000 1 0.000685
BsmtUnfSF 0 0.000000 1 0.000685
TotalBsmtSF 0 0.000000 1 0.000685
Electrical 1 0.000685 0 0.000000
BsmtFullBath 0 0.000000 2 0.001371
BsmtHalfBath 0 0.000000 2 0.001371
KitchenQual 0 0.000000 1 0.000685
Functional 0 0.000000 2 0.001371
FireplaceQu 690 0.472603 730 0.500343
GarageType 81 0.055479 76 0.052090
GarageYrBlt 81 0.055479 78 0.053461
GarageFinish 81 0.055479 78 0.053461
GarageCars 0 0.000000 1 0.000685
GarageArea 0 0.000000 1 0.000685
GarageQual 81 0.055479 78 0.053461
GarageCond 81 0.055479 78 0.053461
PoolQC 1453 0.995205 1456 0.997944
Fence 1179 0.807534 1169 0.801234
MiscFeature 1406 0.963014 1408 0.965045
SaleType 0 0.000000 1 0.000685
Labels (“SalePrice”)
After that, let’s extract some information from the SalePrice
, which is also the most important thing, since our task is to predict it. From the information shown below, we can derive the data summary of SalePrice
, which provides mean
, standard deviation
, minimum
, maximum
and so forth.
print(train_labels.describe())
count 1460.000000
mean<PHONE_NUMBER>90
std 79442.502883
min 34900.000000
25%<PHONE_NUMBER>00
50%<PHONE_NUMBER>00
75%<PHONE_NUMBER>00
max<PHONE_NUMBER>00
Name: SalePrice, dtype: float64
Then we plot its distribution histogram and normal probalility graph.
from scipy import stats
import seaborn as sns
sns.plt.figure()
sns.plt.subplot(1, 2, 1)
sns.plt.title("Sale Prices Dist")
sns.distplot(train_labels, fit=stats.norm)
sns.plt.subplot(1, 2, 2)
stats.probplot(train_labels, plot=sns.plt)
sns.plt.show()
print("Skewness: %f" % train_labels.skew())
print("Kurtosis: %f" % train_labels.kurt())
# Skewness and Kurtosis
Skewness: 1.882876
Kurtosis: 6.536282
The graph shows that the distribution of SalePrice
deviates from the normal distribution, it has peak value and it is positive biased, but it does not follow the diagonal line (right side), the diagonal line represents the normal distribution in normal probability graph, and a good data distribution should follow this line closely.
In order to solve such problems, we can explore to use the logarithmic transformation to see if it is able to handle these problems.
sns.plt.figure()
sns.plt.subplot(1, 2, 1)
sns.plt.title("Sale Prices Dist")
sns.distplot(np.log(train_labels), fit=stats.norm)
sns.plt.subplot(1, 2, 2)
stats.probplot(np.log(train_labels), plot=sns.plt)
sns.plt.show()
print("Skewness: %f" % np.log(train_labels).skew())
print("Kurtosis: %f" % np.log(train_labels).kurt())
# Skewness and Kurtosis
Skewness: 0.121335
Kurtosis: 0.809532
See the graph below, it is obvious that the data distribution follows diagonal line better than the previous one, although some outliers exist. thus, it seems that the log transformation is a good choice to transform the data.
Systematic Analysis
Data Clean, Integration and Correlation Analysis
After having some preliminary understanding of the data, next step, we need to explore the relationship between those properties and labels, as well as the internal relationship among those properties and so forth. In the preliminary analysis, we did the analysis on the overall dataset (training dataset + testing dataset). However, in this part, we should only consider the training part (To make the analysis more fair).
There are (generally) two types of properties, one is numeric and another is categorical. First of all, we need to drop some unused properties according to the data missing information. As mentioned before, Alley
, FireplaceQu
, PoolQC
, Fence
and MiscFeature
suffer almost or more than 50% data loss:
- Alley: Type of alley access
- FireplaceQu: Fireplace quality
- PoolQC: Pool quality
- Fence: Fence quality
- MiscFeature: Miscellaneous feature not covered in other categories
Under normal conditions, those features are not within the scope of our consideration when we buy a house, so these features are not important and should be eliminated from the dataset.
train.drop(['Id', 'MiscFeature', 'Fence', 'PoolQC', 'FireplaceQu', 'Alley'], axis=1, inplace=True)
Here we draw the correlation coefficient matrix to explore the coorelation among different properties:
# draw correlation coefficient matrix
corrmat = train.corr()
f, ax = sns.plt.subplots(figsize=(12, 9))
sns.heatmap(corrmat, vmax=.8, square=True)
sns.plt.yticks(rotation=0)
sns.plt.xticks(rotation=90)
sns.plt.show()
From the graph below, we can clearly see that there are two group of features show high correlation (the two big red blocks), one is the correlation coefficient between TotalBsmtSF
and 1stFlrSF
, another is Garage...
feature group.
The two examples indicate that these features have strong correlation with each other, actually, the degree of correlation reaches the situation of multicollinearity, thus, we can treat that those features contains almost the same information. If you see the SalePrice
, you will find that the SalePrice
has strong correlations not only GrLivArea
, TotalBsmtSF
and OverallQual
, but also some other features, here we draw the correlation coefficient matrix of SalePrice
to show the top 10 features:
cols = corrmat.nlargest(10, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(train[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values,
xticklabels=cols.values)
sns.plt.yticks(rotation=0)
sns.plt.xticks(rotation=90)
sns.plt.show()
The graph below gives top 10 features which have strong correlation with each other. We can derive here that
OverallQual
,GrLivArea
andTotalBsmtSF
have strong correlation withSalePrice
.GarageCars
andGarageArea
not only have strong correlation withSalePrice
, but also have strong correlation with each other. It is easy to imagine that the number of cars store in garage is strong depends on the area of garage, which means that one of them is enough to represent the relationship betweenSalePrice
andGarage...
. Here, we chooseGarageCars
, since it has slightly higher score (Same for otherGarage...
features).- The relationship between
TotalBsmtSF
and1stFlrSF
is almost same asGarageCars
andGarageArea
, we can also choose one of them, but here we will use another strategy, and will be discussed later. - According to the data description and the graph here, we find that
GrLivArea
andTotRmsAbvGrd
are the similar features too. Their correlation is 0.83, and theGrLivArea
represents the “Above grade (ground) living area square feet”, whileTotRmsAbvGrd
indicates “Total rooms above grade (does not include bathrooms)”. - Another thing is that the
FullBath
andYearBuilt
do not have siginificantly strong correlation withSalePrice
.
Here we also show the scatter graph between SalePrice
and the most important features:
sns.set()
cols = ['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']
sns.pairplot(train[cols], size=2.5)
sns.plt.show()
This scatters graph gives much more affluent information of the relationships among those features.
As mentioned just now, we will drop the feature GarageArea
. For Bsmt...
features group, if you have studied the data in Excel carefully, you will find that TotalBsmtSF
equals to the sum of BsmtFinSF1
, BsmtFinSF2
and BsmtUnfSF
, which means that by keeping the TotalBsmtSF
, BsmtFinSF1
, BsmtFinSF2
and BsmtUnfSF
are able to be eliminated. Since we also need to handle TotalBsmtSF
and 1stFlrSF
, here we visually show their relationship with SalePrice
:
f, (ax1, ax2, ax3) = sns.plt.subplots(1, 3)
data_total = pd.concat([train['SalePrice'], train['TotalBsmtSF']], axis=1)
data_total.plot.scatter(x='TotalBsmtSF', y='SalePrice', ylim=(0, 800000), ax=ax1)
data1 = pd.concat([train['SalePrice'], train['1stFlrSF']], axis=1)
data1.plot.scatter(x='1stFlrSF', y='SalePrice', ylim=(0, 800000), ax=ax2)
data2 = pd.concat([train['SalePrice'], train['2ndFlrSF']], axis=1)
data2.plot.scatter(x='2ndFlrSF', y='SalePrice', ylim=(0, 800000), ax=ax3)
sns.plt.show()
we can derive the fighre below, the first graph is TotalBsmtSF
-SalePrice
, the second is 1stFlrSF
-SalePrice
and the last one is 2ndFlrSF
-SalePrice
. Generally, all of these three features have closely relationship with SalePrice
, and follow the exponential distribution. Only some specific situations that the TotalBsmtSF
has no influence on SalePrice
(those zero values), while 2ndFlrSF
does also (although there are much more zero values in 2ndFlrSF
, the trend is similar).
The description of TotalBsmtSF
, 1stFlrSF
and 2ndFlrSF
:
- TotalBsmtSF: Total square feet of basement area.
- 1stFlrSF: First Floor square feet.
- 2ndFlrSF: Second floor square feet.
In this case, since all of them are the area information of the house and their relationships with SalePrice
are similar, so our strategy is creating a new feature named as TotalSF
to add those three features, then drop them.
train.drop(['BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF'], axis=1, inplace=True)
train['TotalBsmtSF'] = train['TotalBsmtSF'].fillna(0)
train['1stFlrSF'] = train['1stFlrSF'].fillna(0)
train['2ndFlrSF'] = train['2ndFlrSF'].fillna(0)
train['TotalSF'] = train['TotalBsmtSF'] + train['1stFlrSF'] + train['2ndFlrSF']
train.drop(['TotalBsmtSF', '1stFlrSF', '2ndFlrSF'], axis=1, inplace=True)
train.drop(['GarageArea'], axis=1, inplace=True) # as analysis before
and the relationship between TotalSF
and SalePrice
is show below:
Intuitively, the merged feature has a better exponential relationship with SalePrice
, compare to the previous three fatures. The two outliers appear in the graph can be considered as the abnormal values and delete them.
In the discuss above, we only anslysis and clear some of the most important numeric features, other numeric also needs to be analyzed as well as the categorical features. Becasue of the space limit, here we do not talk much on this process, and below gives some features that I choose to get rid of, since I think they do not have much correlation to SalePrice
or some of these features have strong correlation with the features we already decide to retain (which means they are similar, so keep one of them is enough).
train.drop(['Utilities', 'RoofMatl', 'MasVnrArea', 'MasVnrType', 'Heating', 'LowQualFinSF',
'BsmtFullBath', 'BsmtHalfBath', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',
'Functional', 'GarageYrBlt', 'GarageCond', 'GarageType', 'GarageFinish', 'GarageQual', 'WoodDeckSF',
'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea',
'MiscVal'], axis=1, inplace=True)
After this process, the following features are remains:
print(train.columns)
Index(['MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',
'LotShape', 'LandContour', 'LotConfig', 'LandSlope', 'Neighborhood',
'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'OverallQual',
'OverallCond', 'YearBuilt', 'YearRemodAdd', 'RoofStyle', 'Exterior1st',
'Exterior2nd', 'ExterQual', 'ExterCond', 'Foundation', 'HeatingQC',
'CentralAir', 'Electrical', 'GrLivArea', 'FullBath', 'HalfBath',
'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual', 'TotRmsAbvGrd',
'Fireplaces', 'GarageCars', 'PavedDrive', 'MoSold', 'YrSold',
'SaleType', 'SaleCondition', 'SalePrice', 'TotalSF'],
dtype='object')
There is another important process is to fill the NA
values within those features, normally, for categorical features, we can fill the NA
with most frequent values or No(such feature), while for the numeric features, we can fill NA
with mean value or zero. And we do not talk too much about this here. Next step, we will analysis the relationship between each feature and SalePrice
.
Univariate Analysis
Actually, there are four features which act as siginificantly important roles in this issue. Those four features are OverallQual
, YearBuilt
, TotalBsmtSF
(after data integration process, it becomes TotalSF
) and GrLivArea
.
OverallQual and SalePrice
Here we explore the relationship between OverallQual
and SalePrice
:
overall_qual = pd.concat([train['SalePrice'], train['OverallQual']], axis=1)
f, ax = sns.plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x='OverallQual', y="SalePrice", data=overall_qual)
fig.axis(ymin=0, ymax=800000)
sns.plt.show()
It is obvious that the distribution trends of OverallQual
and SalePrice
are same. Generally, the SalePrice
increase while OverallQual
increase.
YearBuilt and SalePrice
Here is the relationship between YearBuilt
and SalePrice
:
year_built = pd.concat([train['SalePrice'], train['YearBuilt']], axis=1)
f, ax = sns.plt.subplots(figsize=(16, 8))
fig = sns.boxplot(x='YearBuilt', y="SalePrice", data=year_built)
fig.axis(ymin=0, ymax=800000)
sns.plt.xticks(rotation=90)
sns.plt.show()
Although they do not show a strong following trends, this graph also indicates that, generally, the house price is higher while the built year is later.
TotalSF and SalePrice
Here is the relationship between TotalSF
and SalePrice
:
As discussed before, this two features have strong correlation, and their trends are same. Since TotalSF
is one of the most important feature, so let’s go deep. Here we show the distribution histogram and normal probalility graph of TotalSF
to explore its normality:
sns.plt.figure()
sns.plt.subplot(1, 2, 1)
sns.plt.title("TotalSF Dist")
sns.distplot(train['TotalSF'], fit=stats.norm)
sns.plt.subplot(1, 2, 2)
stats.probplot(train['TotalSF'], plot=sns.plt)
sns.plt.show()
print("Skewness: %f" % train['TotalSF'].skew())
print("Kurtosis: %f" % train['TotalSF'].kurt())
# Skewness and Kurtosis
Skewness: 1.776700
Kurtosis: 12.621968
Compare with the distribution histogram and normal probalility graph of SalePrice
we draw before, you will find that their situation are exactly similar， TotalSF
shows positive biased, deviates from the normal distribution, has peak value.
Same as SalePrice
, we process logarithmic transformation on TotalSF
and draw the graph again:
The graph above shows that after log transformation, the normality of TotalSF
becomes better. And below is the homoscedasticity of variance graph of TotalSF
and SalePrice
:
sf = np.log(train['TotalSF'])
sp = np.log(train['SalePrice'])
sns.plt.scatter(sf[sf > 0], sp[sf > 0])
sns.plt.show()
This graph shows that SalePrice
performs the same level of change within variable range of TotalSF
. In this case, we may take the logarithmic transformation for TotalSF
to fit SalePrice
well.
GrLivArea and SalePrice
Here we do the same thing as TotalSF
for GrLivArea
. First, we draw the relationship between GrLivArea
and SalePrice
:
Then plot the distribution histogram and normal probalility graph of GrLivArea
to show its normality.
# Skewness and Kurtosis
Skewness: 1.366560
Kurtosis: 4.895121
And do the logarithmic transformation on GrLivArea
and show the graph again:
Samely, the normality of GrLivArea
becomes better after log transformation. Next, we explore the homoscedasticity of variance graph of GrLivArea
and SalePrice
:
This graph shows that SalePrice
performs the same level of change within variable range of GrLivArea
. In this case, we may take the logarithmic transformation for GrLivArea
to fit SalePrice
well.
Others Univariables analysis
Below shows the relationships between SalePrice
and LotFrontage
, LotArea
, LotShape
, LandContour
, LandShape
and LotConfig
. From graph, we can derive that the LogFrontage
shows slightly similar trends as SalePrice
, it tends to increase while SalePrice
increase. However, others do not have such feature, while each value in each feature does not give a clear trends.
And the following six features also do not have a clear relationship on SalePrice
:
However, although YearRemodAdd
feature does not give a clear trends, compare to YearBuilt
feature, it still indicates that the house price is higher while the RemodAdd
year is later.
There are still many features do not show here, but each of them are worth to analyze. For analysis, we stop here and make a short summary.
- For
Bsmt...
feature group, drop all of them, exceptTotalBsmtSF
, then mergeTotalBsmtSF
with1stFlrSF
and2ndFlrSF
to create a new feature named asTotalSF
. - For
Garage...
feature group, drop all of them, exceptGarageCars
. - Drop the features whose missing percentage is large than 20%.
- Drop the features that we treat them as the trivial features for the house prices predictation.
- Fill
NA
with most frequent value or zero or mean.
and so forth.
Conclusion
In the above, I describe a procedure to analysis the data and the machine learning model for training and predicting the data will be introduced in next article. Actually, with the data process procedures introduced above and the machine learning model (I choose a model with combination of gradient boosting regressor and elastic net), I achieve the score 0.126. It is not a very good result, but, anyway, this is just a practice after learning the basic knowledge of data science and mechine learning. Below is the data clean, integration and transformation codes:
import pandas as pd
import numpy as np
from sklearn import ensemble, linear_model
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.utils import shuffle
from method2.functions import train_test
import warnings
warnings.filterwarnings('ignore')
# load data
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
train_labels = train.pop('SalePrice')
data = pd.concat([train, test], keys=['train', 'test'])
# drop the data with high missing percentage
data.drop(['Id', 'MiscFeature', 'Fence', 'PoolQC', 'FireplaceQu', 'Alley'], axis=1, inplace=True)
# drop the Bsmt feature group, Garage feature group as well as some trivial features
data.drop(['Utilities', 'RoofMatl', 'MasVnrArea', 'MasVnrType', 'Heating', 'LowQualFinSF', 'BsmtFullBath',
'BsmtHalfBath', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinSF2',
'BsmtUnfSF', 'BsmtFinType2', 'Functional', 'WoodDeckSF', 'OpenPorchSF',
'GarageYrBlt', 'GarageCond', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageArea',
'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal'], axis=1, inplace=True)
# merge TotalBsmtSF, 1stFlrSF, 2ndFlrSF to TotalSF
data['TotalBsmtSF'] = data['TotalBsmtSF'].fillna(0)
data['1stFlrSF'] = data['1stFlrSF'].fillna(0)
data['2ndFlrSF'] = data['2ndFlrSF'].fillna(0)
data['TotalSF'] = data['TotalBsmtSF'] + data['1stFlrSF'] + data['2ndFlrSF']
data.drop(['TotalBsmtSF', '1stFlrSF', '2ndFlrSF'], axis=1, inplace=True)
# MSSubClass as categorical
data['MSSubClass'] = data['MSSubClass'].astype(str)
# MSZoning: filling NA with most popular values
data['MSZoning'] = data['MSZoning'].fillna(data['MSZoning'].mode()[0])
# LotFrontage: fill NA with mean value
data['LotFrontage'] = data['LotFrontage'].fillna(data['LotFrontage'].mean())
# OverallCond as categorical
data['OverallCond'] = data['OverallCond'].astype(str)
# Electrical: fill NA with most popular values
data['Electrical'] = data['Electrical'].fillna(data['Electrical'].mode()[0])
# KitchenAbvGr as categorical
data['KitchenAbvGr'] = data['KitchenAbvGr'].astype(str)
# KitchenQual: fill NA with most popular values
data['KitchenQual'] = data['KitchenQual'].fillna(data['KitchenQual'].mode()[0])
# GarageCars: fill NA with 0
data['GarageCars'] = data['GarageCars'].fillna(0.0)
# SaleType: fill NA with most popular values
data['SaleType'] = data['SaleType'].fillna(data['SaleType'].mode()[0])
# Year and Month as categorical
data['YrSold'] = data['YrSold'].astype(str)
data['MoSold'] = data['MoSold'].astype(str)
# Exterior1st and Exterior2nd: fill NA with most popular values
data['Exterior1st'] = data['Exterior1st'].fillna(data['Exterior2nd'].mode()[0])
data['Exterior2nd'] = data['Exterior2nd'].fillna(data['Exterior2nd'].mode()[0])
# Standardizing LotFrontage and LotArea
numeric_data = data.loc[:, ['LotFrontage', 'LotArea', 'GrLivArea', 'TotalSF']]
numeric_data_standardized = (numeric_data - numeric_data.mean())/numeric_data.std()
# Log transformation of labels, GrLivArea and TotalSF
train_labels = np.log(train_labels)
# data['GrLivArea'] = np.log(data['GrLivArea'])
# data['TotalSF'] = np.log(data['TotalSF'])
# Getting Dummies from Condition1 and Condition2
conditions = set([x for x in data['Condition1']] + [x for x in data['Condition2']])
dummies = pd.DataFrame(data=np.zeros((len(data.index), len(conditions))), index=data.index, columns=conditions)
for i, cond in enumerate(zip(data['Condition1'], data['Condition2'])):
dummies.ix[i, cond] = 1
data = pd.concat([data, dummies.add_prefix('Condition_')], axis=1)
data.drop(['Condition1', 'Condition2'], axis=1, inplace=True)
# Getting Dummies from Exterior1st and Exterior2nd
exteriors = set([x for x in data['Exterior1st']] + [x for x in data['Exterior2nd']])
dummies = pd.DataFrame(data=np.zeros((len(data.index), len(exteriors))), index=data.index, columns=exteriors)
for i, ext in enumerate(zip(data['Exterior1st'], data['Exterior2nd'])):
dummies.ix[i, ext] = 1
data = pd.concat([data, dummies.add_prefix('Exterior_')], axis=1)
data.drop(['Exterior1st', 'Exterior2nd'], axis=1, inplace=True)
# Getting Dummies from all other categorical vars
for col in data.dtypes[data.dtypes == 'object'].index:
for_dummy = data.pop(col)
data = pd.concat([data, pd.get_dummies(for_dummy, prefix=col)], axis=1)
# copy data
data_standardized = data.copy()
# Replacing numeric feature by standardized values
data_standardized.update(numeric_data_standardized)
# Splitting dataset to train and test
train_data = data.loc['train'].select_dtypes(include=[np.number]).values
test_data = data.loc['test'].select_dtypes(include=[np.number]).values
# Splitting standardized features
train_data_st = data_standardized.loc['train'].select_dtypes(include=[np.number]).values
test_data_st = data_standardized.loc['test'].select_dtypes(include=[np.number]).values
I also explored others methods and make a midification to improve the result to 0.116. I will describe it in the future.


THtmlViewer, TFrameViewer, and TFrameBrowser components
property OnObjectClick: TObjectClickEvent;
The OnObjectClick event occurs when the mouse is left clicked on an Object. Currently, all Form controls support this event. See the TObjectClickEvent type for information on the parameters.
The following handler will display the status of radio buttons and checkboxes whenever they are changed. Display only occurs for those controls having the OnClick="display" attribute:
procedure TForm1.ObjectClick(Sender, Obj: TObject; const OnClick: String);
var
S: string;
begin
if OnClick = 'display' then
begin
if Obj is TFormControlObj then
with TFormControlObj(Obj) do
begin
if TheControl is TCheckBox then
with TCheckBox(TheControl) do
begin
S := Value + ' is ';
if Checked then S := S + 'checked'
else S := S + 'unchecked';
MessageDlg(S, mtCustom, [mbOK], 0);
end
else if TheControl is TRadioButton then
with TRadioButton(TheControl) do
begin
S := Value + ' is checked';
MessageDlg(S, mtCustom, [mbOK], 0);
end;
end;
end;
end;
See also:


Lat & long:
DateTime:
Weather: Sunny Mostly Sunny Partly Sunny Intermittent Clouds Hazy Sunshine Mostly Cloudy Cloudy Dreary (Overcast) Fog Showers Mostly Cloudy w/ Showers Partly Sunny w/ Showers T-Storms Mostly Cloudy w/ T-Storms Partly Sunny w/ T-Storms Mostly Cloudy w/ Flurries Rain Flurries Partly Sunny w/ Flurries Snow Mostly Cloudy w/ Snow Ice Sleet Freezing Rain Rain and Snow Hot Cold Windy Clear Mostly Clear Partly Cloudy Intermittent Clouds Hazy Moonlight Mostly Cloudy Partly Cloudy w/ Showers Mostly Cloudy w/ Showers Partly Cloudy w/ T-Storms Mostly Cloudy w/ T-Storms Mostly Cloudy w/ Flurries Mostly Cloudy w/ Snow
Results:
TBNY App Test:Click here Zip Code:- Borough:- Weather:- Day:- Time:- PlaylistCode:- PlaylistURL:-


--- title: "Photographer Travels To The Edge Of The World To Capture The Breathtaking Beauty Of Patagonia" featimg: "http://static.boredpanda.com/blog/wp-content/uploads/2016/01/infrared-photography-patagonia-andy-lee-fb.png" description: "At the point where the Earth ends, on the edge of the world, is where this magic and mysterious land begins, full of unimaginable beauty." categories: article_url: "http://www.boredpanda.com/edge-of-the-world-patagonia-chile-mysteries/" social_media_msg: "Photographer travels to the edge of the world to capture the breathtaking beauty of Patagonia" ---
At the point where the Earth “ends,” on the edge of the world, is where this magic and mysterious land begins, full of unimaginable beauty.
Patagonia has been inhabited at least since the year 10,500 B.C., but was discovered more recently by colonists. Nonetheless, this part of the planet has always been a source of amazement, of unexplainable beauty, and indomitable nature.
This is why famous British photographer Andy Lee settled on Patagonia for his latest project: Patagonia Dreaming I.
Visiting the most inhospitable landscapes of Chilean Patagonia, Lee captured surreal images using infrared photography, his trademark technique.
More info: andylee.co | 500px | Behance | Facebook


Salmon Cookie Sales
Home
Sales
new Store
Store name
Max Cust Per Hour
Min Cust Per Hour
Avg Cookie Per Customer


Physics (-350)
by Aristotle
Physics is one of Aristotle's major works. A collection of lessons, the book covers theoretical, methodological, and philosophical issues of the ideas of motion and change in nature.
This collection ranges from 20th century geology texts to ancient descriptions of physics.
Physics is one of Aristotle's major works. A collection of lessons, the book covers theoretical, methodological, and philosophical issues of the ideas of motion and change in nature.
On Vaccination Against Smallpox is Edward Jenner's publication of his research into creating the smallpox vaccine. When in 1788 a smallpox outbreak hit Gloucestershire, Jenner noticed that people who worked with cattle and had caught the mild disease cowpox, which was not fatal, then never came down with smallpox. He found ways to test his observation, and in 1796 his experiments showed that giving a small dose of cowpox to a person gave that person immunity to smallpox. Despite much initial disbelief by his peers, the smallpox vaccine became one of the most significant scientific breakthroughs.
How Sammy Went to Coral-Land is a children’s short story about a salmon named Sammy who has grand adventures when he swims to Coral-Land alone.
This is a collection of general interest publications from the U.S. Geological Survey on rocks, fossils, and other geology topics.
The speaker questions science and what she takes away from others by her “dull realities.”


Error!
"{{ error }}"
Most likely:
You are encrypting with the wrong key.
You can't decrypt the info with the key being used.


Hello and welcome to the game! Let the pattern play COMPLETLY, then attempt to repeat back the pattern. You are given THREE chances!  Good Luck!
Brought To You By: Ashani Jewell


Hello, this is KAIST coin karaoke!
Construction of an air conditioner has been finished on last friday. Thanks for your waiting and have a good time with coin karaoke!


Enter your personal details and start journey with us
To keep connected with us please login with your personal info OR Sign Up


Miscellaneous Syntax¶ ↑
Ending an Expression¶ ↑
Ruby uses a newline as the end of an expression. When ending a line with an operator, open parentheses, comma, etc. the expression will continue.
You can end an expression with a ;
(semicolon). Semicolons
are most frequently used with ruby -e
.
Indentation¶ ↑
Ruby does not require any indentation. Typically, ruby programs are indented two spaces.
If you run ruby with warnings enabled and have an indentation mis-match, you will receive a warning.
alias
¶ ↑
The alias
keyword is most frequently used to alias methods.
When aliasing a method, you can use either its name or a symbol:
alias new_name old_name alias :new_name :old_name
For methods, Module#alias_method can
often be used instead of alias
.
You can also use alias
to alias global variables:
$old = 0 alias $new $old p $new # prints 0
You may use alias
in any scope.
undef
¶ ↑
The undef
keyword prevents the current class from responding
to calls to the named methods.
undef my_method
You may use symbols instead of method names:
undef :my_method
You may undef multiple methods:
undef method1, method2
You may use undef
in any scope. See also Module#undef_method
defined?
¶ ↑
defined?
is a keyword that returns a string describing its
argument:
p defined?(UNDEFINED_CONSTANT) # prints nil p defined?(RUBY_VERSION) # prints "constant" p defined?(1 + 1) # prints "method"
You don't need to use parenthesis with defined?
, but they
are recommended due to the low
precedence of defined?
.
For example, if you wish to check if an instance variable exists and that the instance variable is zero:
defined? @instance_variable && @instance_variable.zero?
This returns "expression"
, which is not what you
want if the instance variable is not defined.
@instance_variable = 1 defined?(@instance_variable) && @instance_variable.zero?
Adding parentheses when checking if the instance variable is defined is a
better check. This correctly returns nil
when the instance
variable is not defined and false
when the instance variable
is not zero.
Using the specific reflection methods such as instance_variable_defined?
for instance variables or const_defined? for constants is less error prone
than using defined?
.
BEGIN
and END
¶ ↑
BEGIN
defines a block that is run before any other code in the
current file. It is typically used in one-liners with ruby -e
.
Similarly END
defines a block that is run after any other
code.
BEGIN
must appear at top-level and END
will issue
a warning when you use it inside a method.
Here is an example:
BEGIN { count = 0 }
You must use {
and }
you may not use
do
and end
.
Here is an example one-liner that adds numbers from standard input or any files in the argument list:
ruby -ne 'BEGIN { count = 0 }; END { puts count }; count += gets.to_i'


Data Design Project
User Persona
- Name: Tammy Day
- Age: 20
- Gender: Female
- Profession: Retail worker
- Technology: Cell phone (Google Pixel), Dell Laptop with Windows 10
- Attitudes:
- Wants to buy Magic: the Gathering cards at a fair price
- Prefers to buy cards from local stores instead of websites
- Does not want to buy alternate versions of cards
- Buys newer cards
- Sells or trades cards when card prices increases
- Needs:
- The ability to find most recent card prices
- To compare card prices over time
- To know card contents (name, art, text)
User Story:
As a user, Tammy would like to search for card prices.
Use Case:
Title:Adding Magic card to watch list
Description:Tammy wants to monitor the price of a card.
Name and Role:Tammy is logged into website.
Precondition:Tammy is logged into website.
Postcondition:Card is added to watch list.
Interaction Flow:
- Tammy enters mtgstocks.com into her browser.
- Server returns main page.
- Tammy enters card name into search bar.
- Server returns card's web page with a picture of card and information.
- Tammy examines web page to determine if it is the proper card and clicks "Watch" button.
- Server adds card to watch list.
Frequency of Use: Four times a week.


--- layout: post title: "In the News: KUOW, The Seattle Times, and the Coronavirus" date: 2020-03-17 21:25:00 +0000 tags: image: https://s3.amazonaws.com/audreywatters/2017-11-03-news.jpg imagecredits: https://www.flickr.com/photos/62693815@N03/6277209256 ---
Not surprisingly, I'm doing a lot of media about how schools are making the shift online (or not) during this pandemic. Last week, I was on KUOW's The Record. And I talked to The Seattle Times' Katherine Long as well.


In this project, we're going to build a meteor game, where a ship flies around and shoots down meteors for points. Use the left and right arrow keys to steer, the up arrow key to move, and the space button to shoot.
1) Create a ship and backdrop for your game. Have your character move when you press the up arrow key, and have it turn when you press the left and right arrow keys.
2) Have your ship fire a laser when the space key is pressed. Have the lasers disappear when they touch a wall.
3) Create a large meteor and make it so that it appears on a point on the map when the game starts and move continuously in random direction when it spawns. Have the meteor bounce off of walls and edges of the game.
4) When a large meteor is hit by a laser, have it “turn” into two smaller meteors and have the laser disappear. Make these two asteroids continuously move and bounce off of walls, and have the laser disappear when it touches one.
5) When a meteor (large or small) is destroyed, have a score counter increase by one.
6) Make it so that when a certain score is reached, the level of the game increases.
7) Make the large meteors appear more frequently as the levels increase.
8) Add a life counter that decreases when the ship is hit by a meteor (feel free to add a visual cue that the ship has taken damage). Have the game end when you run out of lives.


The resume played an important role in my last year at the university, because it forces me to think about what I can do and want. The best way to improve it is to receive feedback from others. In addition, there are some key points that I want to talk about. Some advice on cover letter writing is given in the last section.
Personal Information
I think only four pieces of personal information are essential:
- E-mail address.
- City and country of residence.
- Phone number. HRs in some countries prefer to calls.
- Personal website and other links.
In particular, avoid the disclose of private information, like the detailed address.
Specific but Condensed
Skills and tools used in finished projects and internships should be very specific. For example:
Build digital twin for power grids in western Denmark. (client: Danish Energy Agency) @ Power flow calculation and optimal investment decision.
versus
Build digital twin for power grids in western Denmark with Python, PostgreSQL, and Google Cloud. (client: Danish Energy Agency) @ Power flow calculation, optimal investment
In order to spare some space, five techniques in my mind in a sequence of their difficulties can be helpful:
- Fill every line.
- Condense descriptions for whatever happened long time ago.
- Adjust margins.
- Adjust sizes of fonts.
- Tailor different versions for different types of positions, so only the most relevant terms are highlighted.
Here is an example for the first method:
Technical University of Denmark
Sept 18 - Dec 20
versus
Technical University of Denmark … Sept 18 - Dec 20
Separation by Bullets
For example:
A Python package to model power grids as multilayer networks. All the electric devices at one voltage level compose one layer. Moreover, it provides an interface for power system analysis, sensitivity analysis, etc. @ Unbalanced distribution networks can be modelled using impedance, and a bus impedance matrix can represent the whole power grid, then it is used to calculated power flow for given snapshot(s).
Conjunctions and long sentences can be avoided to a larger extent:
A Python package to model power grids as multilayer networks. @ All the electric devices at one voltage level compose one layer. @ An interface for power system analysis, sensitivity analysis, etc. @ Bus impedance matrix for unbalanced distribution networks.
Cover Letter
In my mind, resume should be focused on what you have done, and put everything else in the cover letter. Moreover, tomaska2018ten is a good starting point for how to write cover letters (for an academic position).
The tool I used to write and render cover letter is
pandoc-letter
. Here is the
example from its repository:
There are at least two advantages:
- It provides a standard layout for letters.
- It can be easily modified and compiled.
- The version control can be easily applied. For similar positions, the same branch can be focused on.
There are some pitfalls of writing that I would avoid:
- Too many sentences starting with “I”.
- Repeating contents in the resume: only the most related experiences should be highlighted again.
- No connections to the position: relate personal experiences to the position from time to time (at least mention the position several times).
- Hyperlinks not shown as endnotes: in case the reader print the letter.
- Ambiguous “what I want”: always state “what I want” in the very beginning.
- The disclosure of detailed personal address: it is not necessary, because the reader will reply through emails.
References
- Tomaska, L., & Nosek, J. (2018). Ten simple rules for writing a cover letter to accompany a job application for an academic position. To read it online, https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006132
- On April 9, 2020, some minor updates.
- On April 20, 2020, add some pitfalls of letter writing.


| Class Summary | |
|---|---|
| Album | Represents an photoalbum (a set of images). |
| BeanFactory | Used to initiate and retrieve Objects for Dependency Injection. |
| Cache | Caches pages and loads them from cache again. |
| ClassLoader | ClassLoader loads classes necessary for PIWI. |
| ClassLoaderCache | SimpleXML based cache implementation for the Classloader. |
| CompareValidator | Checks if the value of a field matches the value of another field. |
| ConfigurationManager | Manages the settings configured in 'config.xml'. |
| ConnectorFactory | Used to initiate and retrieve Connectors. |
| DataGridGenerator | Creates a table containing whose data comes from a database. |
| ExcelSerializer | Serializes the given XML to Excel (XSL). |
| ExceptionPageGenerator | Generates an errorpage. |
| File | Represents a file. |
| Folder | Represents a folder. |
| FormFactory | Used to retrieve Forms. |
| FormProcessor | Processes forms specified as PiwiForms. |
| FormProcessorWrapper | Wrappes the FormProcessor to make it accessible as a bean. |
| GeneratorFactory | Used to initiate and retrieve Generators. |
| HTMLSerializer | Serializes the given XML to HTML. |
| ImageNavigationGenerator | Creates a image based navigation. |
| JsonSerializer | Serializes the given XML to Json (json). |
| MSSQLConnector | Establishes connections to MSSQL databases. |
| MediaCenter | Used to handle Albums. |
| MySQLConnector | Establishes connections to MySQL databases. |
| PDFSerializer | Serializes the given XML to PDF. |
| Page | Renders the requested page and creates the navigation. |
| Pager | Helper class to page contents of a database query. |
| Pipeline | The Pipeline class selects a matching inputprovider implementation based on the file ending of the requested content. |
| PiwiXMLSerializer | Serializes the given XML to PiwiXML. |
| RangeValidator | Checks if the value of a field is in a specific range. |
| RegularExpressionValidator | Checks if he value of a field matches the specified regular expression. |
| Request | Provides information about the current request that is performed by a user. |
| RequiredFieldValidator | Checks if the value of a field is not empty. |
| ResourcesManager | Manages the localized labels. |
| SQLite2Connector | Establishes connections to SQLite 2 databases. |
| SQLite3Connector | Establishes connections to SQLite 3 databases. |
| SimpleGalleryGenerator | Generates a picture gallery. |
| SimpleTextNavigationGenerator | Creates a simple navigation. |
| Site | The Site class is the php representation of the site.xml / |
| SiteElement | Represents a page item. |
| SiteGenerator | Generates a complete page. |
| SiteMapGenerator | Generates a sitemap of the website. |
| SiteMapHelper | Provides helper functions to generate SiteMaps. |
| SiteMapPathNavigationGenerator | Creates a SiteMapPath which consists of the SiteElements leading to the requested page. |
| StreamManager | Manages the settings configured in 'streams.xml'. |
| StreamSerializer | Serializes the given XML to Excel (XSL). |
| StreamingPage | Renders the requested page and creates the navigation based on 'site.xml'. |
| TextSerializer | Serializes the given XML to Json (json). |
| UserSessionManager | Manages all Session features concerning a user. |
| Validator | Abstract class that validators have to inherit from. |
| WordSerializer | Serializes the given XML to Word (DOC). |
| XMLPage | Renders the requested page and creates the navigation based on 'site.xml'. |
| XSLTStylesheetStream | A Stream used to embed custom XSLT-Transformations into PIWI-XML. |
| XmlRoleProvider | XML RoleProvider. |
| Interface Summary | |
|---|---|
| Configuration | Interface that configurations have to implement. |
| Connector | Interface that database connectors have to implement. |
| Generator | Interface that Generators have to implement. |
| NavigationGenerator | Interface that navigation generators have to implement. |
| Preprocessor | Interface that Preprocessors have to implement. |
| RoleProvider | Interface that role providers have to implement. |
| SectionGenerator | Interface that SectionGenerators have to implement. |
| Serializer | Interface that serializers have to implement. |
| StepProcessor | Interface that Step Processors have to implement. |
| ezcBaseExportable | Interface for class of which instances can be exported using var_export(). |
| Exception Summary | |
|---|---|
| DatabaseException | Exception that is thrown, when access to a database fails. |
| PiwiException | Exception that is thrown, when something goes wrong during processing the requested page. |
| Function Summary | |
|---|---|
| __autoload | Implements the __autoload mechanism for PHP - which can only be done once per request. |
| exception_handler |


GSoC Week 03 - The One With Function Range (Part II)
Hey !
This week I worked on implementing a method for finding the range of a function in a given domain. Following from last weeek’s research on the same, I tried to develop these utility functions.
Implementation
Here, I have defined the two functions along with some of their implementation details:
continuous_in(f, x, interval)
The function returns the sub-domains as an Union
of Interval
in case a discontinuity exists in the interval
. If the function is continuous in the entire domain, the interval
itself is returned.
For this we need to consider 2 primary conditions:
Domain constraints for real functions I have also added some code for domain constraints in
sqrt
andlog
functions. Using thesolve_univariate_inequality
method (as the name suggests, it solves univariate inequalities), we calculate these constraints.
Givenf(x) = sqrt(g(x))
, we determine the range of values ofx
for which the functiong(x) >= 0
.
Similarly, forf(x) = log(g(x))
, the interval ofx
in whichg(x) > 0
is the constrained interval.Singularities For determining the discontinuities, I tried to solve the reciprocal of the given function using
solveset
:solveset(1/f, x, interval)
. Thesingularities
function can also be used here but its implementation is restricted to rational functions only. There are possibilities of improving this function to create a universal function which returns all the possible singularities of the function in a given domain.
function_range(f, x, domain)
Like the name suggests, this method returns the range of a univariate function in a given domain. This function is primarily designed for the purpose of solve_decomposition
.
This function calls the above implemented continuous_in
method for finding the actual domain of f
. Following this, we iterate over each Interval
object returned by continuous_in
.
By using the boundaries of the interval and first derivate test, we determine the crtical points in the interval and their corresponding critical values.
For determining the values of the function at the singularities, we determine its limit at that point.
For this, I use the limit
function of SymPy.
After calculating the local extremas, I calculate the global minima and maxima using the inf
(infimum) and sup
(supremum) of the FiniteSet
of all critical values. The range, which is the Interval
of these extremasm, is returned.
$ git log
PR#11141: Method for solving equations using Decomposition and Rewriting
Opened this week
PR#11224: Methods for finding the range of a function in a given domain
Final Thoughts
That was all for this week.
My task for the upcoming days would be to update my solve_decomposition
method to accomodate these methods.
I aim to get all these PR merged before the midterm evaluation.


Dash News presenter Isabel Barrios visits Mazal Gourmet in Caracas, Venezuela and pays for a banana cake with Dash Digital Cash! Visit https://discoverdash.c...
when do we meet the kmd girls?
richardt07-Aug-19 04:16 PM
Have a bag of KMD and didn't sell before rebrand. Eff me again
Caglio07-Aug-19 04:16 PM
Computer genie will dress himself into an unicorn suit, put a dildo on his forehead, and spread the word of KMD
3
Tek07-Aug-19 04:17 PM
ah yes, way better marketing than cute girls
richardt07-Aug-19 04:17 PM
It seems no matter what I do, I'm wrong
Caglio07-Aug-19 04:17 PM
Richardt, you should learn this
If something good is going to happen, sell before
However rebrand was fail, so yeah...
richardt07-Aug-19 04:18 PM
But selling before for Dash rebrand was a bad choice
Dash really blew up after rebrand
Tek07-Aug-19 04:19 PM
Just wait until the masses start using crypto.
they wont be using bitcoin
I'm betting dash and bch
will come out as the winrars of usage and eventually take over from corebutts(edited)
richardt07-Aug-19 04:21 PM
The problem with KMD rebrand was that they didn't have any projects/companies lined up to use and/or invest in the tech
Markus07-Aug-19 04:21 PM
what do u ppl even know about it?
they clearly said that they have fortune 500 companies but cant announce anything or talk about it
Tek07-Aug-19 04:22 PM
all I know is it went from cool unique sacred geonometry logo to generic powerbutton logo
richardt07-Aug-19 04:22 PM
I figured if they did, the rebrand release would've been a good time to announce it
Markus07-Aug-19 04:22 PM
nobody give a shit about logo
means nothing
Tek07-Aug-19 04:22 PM
and that's where you are wrong
but you wouldn't understand cause you are a pleb investor
Markus07-Aug-19 04:22 PM
how is tron's logo any better?
its not
Tek07-Aug-19 04:23 PM
lemme check the tron logo first
tron logo has some geonometry in there
Markus07-Aug-19 04:23 PM
bullshit
Tek07-Aug-19 04:23 PM
not sure if its sacred
Markus07-Aug-19 04:24 PM
either way nobody force anyone to invest or hold kmd, future will tell who were the real believers
and had the balls to hold in situations like this
will actually be rly interesting to see end of bull cycle where every project ppl cheer for gonna be
pretty sure top 50 will look very different
richardt07-Aug-19 04:26 PM
Top 50 coins will likely have a best marketing
not just best tech
Markus07-Aug-19 04:26 PM
ppl still dont get it
why would kmd market anything in current market situation? what will it help?
absolutely nothing
its all about right moment and this is not it
richardt07-Aug-19 04:27 PM
It's never the right moment, it seems
Markus07-Aug-19 04:28 PM
why would it be right moment when alts get fucked? come on
richardt07-Aug-19 04:28 PM
So you're right, maybe just not market anything ever
Buddz199307-Aug-19 04:28 PM
I have got the solution, look at my logo now
Tek07-Aug-19 04:28 PM
i do irc mode
nice
nice logo
woud totally buy bc of logo
Buddz199307-Aug-19 04:29 PM
yea
Markus07-Aug-19 04:29 PM
dont like, colors dont represent kmd at all
Buddz199307-Aug-19 04:29 PM
this is the rebrand logo 5.0
MarkusToday at 5:29 PM
dont like, colors dont represent kmd at all
its all about perspective..(edited)
Oszy07-Aug-19 04:32 PM
dont like, colors dont represent kmd at all should be red
2
Buddz199307-Aug-19 04:33 PM
You Sir won.
richardt07-Aug-19 04:36 PM
Antara release didn't even get an acknowledgement from AWS. So much for being an advanced tech partner
Buddz199307-Aug-19 04:37 PM
^
|(edited)
True
Neko07-Aug-19 04:37 PM
i think its going down because people still think its a zcash fork - which is also going down a lot
Buddz199307-Aug-19 04:37 PM
But my logo looks cool.
Oszy07-Aug-19 04:38 PM
AWS holds half the internet can't expect them to acknowledge personally everything they host(edited)
Buddz199307-Aug-19 04:38 PM
Also good argument.
richardt07-Aug-19 04:39 PM
I thought it is quite challenging to become an advance tech partner.
Markus07-Aug-19 04:39 PM
kmd is the only advanced tech partner of any crypto project
richardt07-Aug-19 04:40 PM
That's what I thought. So Antara release should've been pretty significant.
But perhaps crypto is still quite insignificant in the eyes of AWS
Markus07-Aug-19 04:41 PM
i have a feeling kmd does not want to hype too much and that there is alot of info what they never mentioned to the public
if u have the best tech ur definitely not stupid
its a tactical move
Buddz199307-Aug-19 04:42 PM
When announcements come it will be like "release the kraken" !
You can now buy groceries with Bitcoin Cash in the Tus Supermarket chain. Watch as Roger Ver tries it firsthand. Learn more about Elipay and Eligma - the com...
Markus07-Aug-19 04:43 PM
Dash having girls talking and promoting is just silly, kinda shows how desperate they are for cheap marketing
richardt07-Aug-19 04:44 PM
And KMD has a bunch of nerds saying we have the best tech?

Tek07-Aug-19 04:45 PM
that I must repent, for not that which goes in the mouth defiles a person but what goes out of it
polycryptoblog07-Aug-19 04:45 PM
I’m pretty Public
Markus07-Aug-19 04:45 PM
as a investor i dont see reason to show urself, its different if ur representing the project
so with that said what i say is my own opinions and have nothing to do with kmd itself(edited)
but i have learned that alot of ppl here have alot to learn
1
PHBA206107-Aug-19 04:47 PM
nothing wrong with staying anonymous
Tek07-Aug-19 04:48 PM
you should look in the mirror, markus
Markus07-Aug-19 04:48 PM
and i dont insult ppl on the internet who i dont know


Nov 17, 2011 Beyonce has finally revealed the dress she wore. to her 2008 wedding to husband Jay Z The couple's ceremony was arguably. the most closely
Jul 21, 2013. Jay-Z and Beyoncé have attended a rally seeking justice for Trayvon See the Big Bang Theory star in her. all-white wedding dress here
Apr 5, 2013 Beyonce and Jay-Z celebrated their 5th wedding anniversary (4-4-2013) in Havana, Beyonce was dressed in a safari print knee-length dress Jul 21, 2011. BEYONCE got the chance to wear her dream wedding dress in her new for the song without her real-life. husband Jay-Z was a little strange Jan 8, 2012 Beyoncé and Jay-Z. Welcome A Baby Girl! Was Here music video, which came out late last. year and showed the singer in her wedding dress.
Dec 10, 2012 Jay-Z and Beyonce attend the 2012 Sports Illustrated. Sportsman of the Year award The Bachelorette: Ashley Hebert's. wedding dress Apr 6, 2012 When Jay-Z and Beyonce tied the knot four years ago, the two kept. Have you ever dreamed of designing your very own wedding dress?
May 5, 2011 Beyoncé took a romantic trip to Paris with Jay-Z on their 3rd wedding 'Bey saw a couture wedding dress she. wants while they were there, but Aug 28, 2013. he talked his friendship with Beyonce and Jay Z, collaborations with Drake, We' re talking about Madonna in a wedding dress humping the Jan 17, 2013 Remember the extravagant dress Beyoncé wore in her 2011 Best Beyonce and Jay Z Top Vanity Fair's New Establishment List 10/10/2013 Jun 18, 2013. On Monday, the singer and hubby Jay-Z celebrated. the 10th Anniversary Party of the
figure in a white curve-hugging Roberto. Cavalli Spring 2013 cut-out dress Kelly Clarkson Is Married -- See Her Wedding Dress!
May 3, 2011 Jay-Z & Beyoncé Get Booed at the Met Gala! by Cristina Gibson. Check Out This Wedding Dress Sketch for Kim Kardashian · Kate Middleton
Beyoncé's Wedding Dress: Finally. Revealed! Nov 17, 2011 @ 12:55 pm. Beyoncé and Jay-Z kept their wedding photos under tight wrapsuntil now! In the Sep 4, 2013 When she secretly married Jay Z in Manhattan in 2008, and didn't even. release photos of her wedding dress until years later, when she Jan 16, 2013. The wedding gown Beyonce wore in her video for "Best Thing I Never Had is. Beyonce wedding dress Jay Z Admits Daughter Blue Ivy Is Jun 19, 2013 Video: Beyonce bares skin. for night out with Jay Z The dress was long, but sheer, and its daring. neckline showed plenty of cleavage Jul 8, 2011. Beyoncé's sparkly wedding dress in "Best Thing I Never Had" When Beyoncé married Jay-Z in 2008 it was a small, quiet affairwe never even


Manhunt after intruder enters girl's bedroom and sexually assaults her at 4.30am
Police are looking for a man aged between 30 and 50 after a young girl was subjected to 'an incredibly traumatic incident' in the dead of night
by Joseph Wilkes, https://www.facebook.com/dailymirror/A young girl suffered "an incredibly traumatic incident" when an intruder entered her bedroom in the small hours and sexually assaulted her.
Greater Manchester Police said they are "doing all that we can" to bring the attacker to justice and the traumatised girl is being given "specialist support'.
Police say a 6ft-tall white man aged between 30 and 50-years-old entered the girl's home and carried out the attack before fleeing after being disturbed by other people at the address.
Police were called at 4.30am but the man had left the scene by the time they arrived.
The attack took place on Albion Street, Radcliffe, on the edge of Manchester.
Afterwards, the man may have been seen on Outward Road heading towards Radcliffe.
The man was wearing light coloured trousers, a dark jacket and glasses.
Detective Superintendent Howard Millington said: “This was an incredibly traumatic incident for the young girl involved and we’re doing all we can to provide her with specialist support.
"Her welfare is one of our top priorities as we try to establish the full circumstances surrounding the incident.
“I appreciate that what happened may cause concern among residents in the local community but I want to make it clear that we are doing all that we can to trace the person responsible and bring them to justice.
"I would like to reassure people that this was an isolated incident and we will have increased patrols in the area whilst enquiries continue.
“I urge anyone who has any information, no matter how small, to please contact us”.
Contact police on 0161 856 8172 quoting incident number 344 15/11/19.
Or provide information anonymously by calling the independent charity Crimestoppers on 0800 555 111.