These Heroes have helped with all the images on the Site by extraction or editing
These Heroes have contributed by constructing HTML, CSS or JS code for the Site
These Heroes have contributed by providing data on numerous guide elements such as: Awakening Quests, Hero Stats, Ability Details, etc.
These Heroes have contibuted by helping test the Site before release
Our Heroic Forge Master compiled an exhaustive list of data on just about everything relating to The Forge and Runestones
These Heroes have contributed by melting down precious equipment in the Furnace... FOR SCIENCE!!!!
Our resident Heroic Academy Expert provided the details on the Skills available in the Academy


Spoon fair trade, whipped mazagran black cup siphon. Beans, java spoon espresso aftertaste froth skinny mug redeye. Spoon americano, kopi-luwak et extra decaffeinated spoon, fair trade foam shop americano coffee. Organic, extra, mazagran chicory qui carajillo breve caffeine.
A huge thanks to Death to the Stock Photo for allowing us to use the beautiful photos that make this template really come to life. When using this template, make sure your photos are decent. Also make sure that the file size on your photos is kept to a minumum to keep load times to a minimum.
Grinder so chicory, cream roast brewed crema sit coffee filter. In seasonal foam brewed doppio mazagran wings variety. Breve caffeine blue mountain decaffeinated, irish strong in cup aged. Galão instant aroma qui white blue mountain grounds espresso.
 They are great for just about anything, the sky's the limit!
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nunc placerat diam quis nisl vestibulum dignissim. In hac habitasse platea dictumst. Interdum et malesuada fames ac ante ipsum primis in faucibus. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.
As, sit variety robust, strong blue mountain, body sweet half and half mocha organic beans. Cream body, shop that medium brewed galão organic skinny. Decaffeinated aromatic trifecta in bar crema eu brewed. Ut frappuccino cultivar est, irish, percolator wings organic extraction café au lait.
Pumpkin spice foam steamed est fair trade instant roast single origin. Breve, roast café au lait cappuccino filter, aftertaste trifecta half and half single shot as eu froth. Dripper, kopi-luwak, pumpkin spice aroma roast, qui acerbic spoon trifecta cup froth barista. Siphon, instant breve body frappuccino instant dark.
Coffee, seasonal arabica chicory frappuccino, irish seasonal, wings barista eu pumpkin spice trifecta. Latte trifecta lungo and carajillo at arabica robust. Cappuccino, cream, est, to go foam doppio coffee beans variety barista white macchiato. Id robusta, single origin pumpkin spice affogato cinnamon et barista redeye.
Phone: 04 0651 8892
Email<EMAIL_ADDRESS>Address:
14/47 Trafalgar Street
Annandale, NSW 2038
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Fugiat, vitae, distinctio, possimus repudiandae cupiditate ipsum excepturi dicta neque eaque voluptates tempora veniam esse earum sapiente optio deleniti consequuntur eos voluptatem.


--- layout: post title: 'Indiblogger #indiblr meet!' date: 2010-10-03 15:36:04.000000000 +02:00 categories: - blog tags: - indiblr status: publish type: post published: true meta: _edit_last: '1' _wp_old_slug: '' author: ---
Finally am here, at Fortune Park JP Celestial, No.5/43, Race Course Road, Bangalore! Don't even bother looking that up on google maps! Am here, and I still can't find it on the maps correctly! I reached here mostly by instinct and a lil bit of past recollections! Will definitely post in the accurate coordinates after I am done through with it. One thing for sure, the wifi here is good, let me do a speedtest.net soon!
Coming more as the session progresses...
PS : I am an hour and a half late due to the location confusion...
Pics coming soon...


?Ludwigia
peploides
(HBK.)
Raven
subsp.
peploides
Determined
by
Elsa
Zardini
1986
Missouri
Botanical
Garden
Ludwigia
sect.
Oligosperaium
Detennined
by
T*p*
Ramamoorthy
1980
Missouri
Botanical
Garden
SMITHSONIAN
INSTITUTION
From
the
UNITED
STATES
NATIONAL
HERBARIUM
PLANTS
OF
CUBA
Isle
of
Pines
Jussiaea
repens
var.
peploides
(H.B.K.)
Griseb.
(Det.
Alain)
Pond;
in
black
mud
and
floating.
Along
road
to
San
Francisco
de
las
Piedras.
Ní-
Ij.3727
KILLIP,
Collector
March
23,
1954
01447581
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Data Exploration¶
Note
A journey of a thousand miles begins with a single step – idiom, from Laozi
I wouldn’t say that understanding your dataset is the most difficult thing in data science, but it is really important and time-consuming. Data Exploration is about describing the data by means of statistical and visualization techniques. We explore data in order to understand the features and bring important features to our models.


I'm Ying, an analytics, product , and occasional photographer based in San Francisco.
In college, I discovered the power of making data-driven decisions.
In my free time, you can probably find me fawning over my Westie puppy, reading psychology books, or planning my next travel.


UTF-8 Miscellaneous Symbols
Range: Decimal 9728-9983. Hex 2600-26FF.
If you want any of these characters displayed in HTML, you can use the HTML entity found in the table below.
If the character does not have an HTML entity, you can use the decimal (dec) or hexadecimal (hex) reference.
Example

I will display ♠

I will display ♠

I will display ♠

Will display as:
I will display ♠
I will display ♠
I will display ♠
Try it Yourself »
Older browsers may not support all the HTML5 entities in the table below.
Chrome has good support. But (currently) only IE 11+ and Firefox 35+ support all the entities.
| Char | Dec | Hex | Entity | Name |
|---|---|---|---|---|
| ☀ | 9728 | 2600 | BLACK SUN WITH RAYS | |
| ☁ | 9729 | 2601 | CLOUD | |
| ☂ | 9730 | 2602 | UMBRELLA | |
| ☃ | 9731 | 2603 | SNOWMAN | |
| ☄ | 9732 | 2604 | COMET | |
| ★ | 9733 | 2605 | BLACK STAR | |
| ☆ | 9734 | 2606 | WHITE STAR | |
| ☇ | 9735 | 2607 | LIGHTNING | |
| ☈ | 9736 | 2608 | THUNDERSTORM | |
| ☉ | 9737 | 2609 | SUN | |
| ☊ | 9738 | 260A | ASCENDING NODE | |
| ☋ | 9739 | 260B | DESCENDING NODE | |
| ☌ | 9740 | 260C | CONJUNCTION | |
| ☍ | 9741 | 260D | OPPOSITION | |
| ☎ | 9742 | 260E | BLACK TELEPHONE | |
| ☏ | 9743 | 260F | WHITE TELEPHONE | |
| ☐ | 9744 | 2610 | BALLOT BOX | |
| ☑ | 9745 | 2611 | BALLOT BOX WITH CHECK | |
| ☒ | 9746 | 2612 | BALLOT BOX WITH X | |
| ☓ | 9747 | 2613 | SALTIRE | |
| ☔ | 9748 | 2614 | UMBRELLA WITH RAIN DROPS | |
| ☕ | 9749 | 2615 | HOT BEVERAGE | |
| ☖ | 9750 | 2616 | WHITE SHOGI PIECE | |
| ☗ | 9751 | 2617 | BLACK SHOGI PIECE | |
| ☘ | 9752 | 2618 | SHAMROCK | |
| ☙ | 9753 | 2619 | REVERSED ROTATED FLORAL HEART BULLET | |
| ☚ | 9754 | 261A | BLACK LEFT POINTING INDEX | |
| ☛ | 9755 | 261B | BLACK RIGHT POINTING INDEX | |
| ☜ | 9756 | 261C | WHITE LEFT POINTING INDEX | |
| ☝ | 9757 | 261D | WHITE UP POINTING INDEX | |
| ☞ | 9758 | 261E | WHITE RIGHT POINTING INDEX | |
| ☟ | 9759 | 261F | WHITE DOWN POINTING INDEX | |
| ☠ | 9760 | 2620 | SKULL AND CROSSBONES | |
| ☡ | 9761 | 2621 | CAUTION SIGN | |
| ☢ | 9762 | 2622 | RADIOACTIVE SIGN | |
| ☣ | 9763 | 2623 | BIOHAZARD SIGN | |
| ☤ | 9764 | 2624 | CADUCEUS | |
| ☥ | 9765 | 2625 | ANKH | |
| ☦ | 9766 | 2626 | ORTHODOX CROSS | |
| ☧ | 9767 | 2627 | CHI RHO | |
| ☨ | 9768 | 2628 | CROSS OF LORRAINE | |
| ☩ | 9769 | 2629 | CROSS OF JERUSALEM | |
| ☪ | 9770 | 262A | STAR AND CRESCENT | |
| ☫ | 9771 | 262B | FARSI SYMBOL | |
| ☬ | 9772 | 262C | KHANDA | |
| ☭ | 9773 | 262D | HAMMER AND SICKLE | |
| ☮ | 9774 | 262E | PEACE SYMBOL | |
| ☯ | 9775 | 262F | YIN YANG | |
| ☰ | 9776 | 2630 | TRIGRAM FOR HEAVEN | |
| ☱ | 9777 | 2631 | TRIGRAM FOR LAKE | |
| ☲ | 9778 | 2632 | TRIGRAM FOR FIRE | |
| ☳ | 9779 | 2633 | TRIGRAM FOR THUNDER | |
| ☴ | 9780 | 2634 | TRIGRAM FOR WIND | |
| ☵ | 9781 | 2635 | TRIGRAM FOR WATER | |
| ☶ | 9782 | 2636 | TRIGRAM FOR MOUNTAIN | |
| ☷ | 9783 | 2637 | TRIGRAM FOR EARTH | |
| ☸ | 9784 | 2638 | WHEEL OF DHARMA | |
| ☹ | 9785 | 2639 | WHITE FROWNING FACE | |
| ☺ | 9786 | 263A | WHITE SMILING FACE | |
| ☻ | 9787 | 263B | BLACK SMILING FACE | |
| ☼ | 9788 | 263C | WHITE SUN WITH RAYS | |
| ☽ | 9789 | 263D | FIRST QUARTER MOON | |
| ☾ | 9790 | 263E | LAST QUARTER MOON | |
| ☿ | 9791 | 263F | MERCURY | |
| ♀ | 9792 | 2640 | FEMALE SIGN | |
| ♁ | 9793 | 2641 | EARTH | |
| ♂ | 9794 | 2642 | MALE SIGN | |
| ♃ | 9795 | 2643 | JUPITER | |
| ♄ | 9796 | 2644 | SATURN | |
| ♅ | 9797 | 2645 | URANUS | |
| ♆ | 9798 | 2646 | NEPTUNE | |
| ♇ | 9799 | 2647 | PLUTO | |
| ♈ | 9800 | 2648 | ARIES | |
| ♉ | 9801 | 2649 | TAURUS | |
| ♊ | 9802 | 264A | GEMINI | |
| ♋ | 9803 | 264B | CANCER | |
| ♌ | 9804 | 264C | LEO | |
| ♍ | 9805 | 264D | VIRGO | |
| ♎ | 9806 | 264E | LIBRA | |
| ♏ | 9807 | 264F | SCORPIUS | |
| ♐ | 9808 | 2650 | SAGITTARIUS | |
| ♑ | 9809 | 2651 | CAPRICORN | |
| ♒ | 9810 | 2652 | AQUARIUS | |
| ♓ | 9811 | 2653 | PISCES | |
| ♔ | 9812 | 2654 | WHITE CHESS KING | |
| ♕ | 9813 | 2655 | WHITE CHESS QUEEN | |
| ♖ | 9814 | 2656 | WHITE CHESS ROOK | |
| ♗ | 9815 | 2657 | WHITE CHESS BISHOP | |
| ♘ | 9816 | 2658 | WHITE CHESS KNIGHT | |
| ♙ | 9817 | 2659 | WHITE CHESS PAWN | |
| ♚ | 9818 | 265A | BLACK CHESS KING | |
| ♛ | 9819 | 265B | BLACK CHESS QUEEN | |
| ♜ | 9820 | 265C | BLACK CHESS ROOK | |
| ♝ | 9821 | 265D | BLACK CHESS BISHOP | |
| ♞ | 9822 | 265E | BLACK CHESS KNIGHT | |
| ♟ | 9823 | 265F | BLACK CHESS PAWN | |
| ♠ | 9824 | 2660 | ♠ | BLACK SPADE SUIT |
| ♡ | 9825 | 2661 | WHITE HEART SUIT | |
| ♢ | 9826 | 2662 | WHITE DIAMOND SUIT | |
| ♣ | 9827 | 2663 | ♣ | BLACK CLUB SUIT |
| ♤ | 9828 | 2664 | WHITE SPADE SUIT | |
| ♥ | 9829 | 2665 | ♥ | BLACK HEART SUIT |
| ♦ | 9830 | 2666 | ♦ | BLACK DIAMOND SUIT |
| ♧ | 9831 | 2667 | WHITE CLUB SUIT | |
| ♨ | 9832 | 2668 | HOT SPRINGS | |
| ♩ | 9833 | 2669 | QUARTER NOTE | |
| ♪ | 9834 | 266A | EIGHTH NOTE | |
| ♫ | 9835 | 266B | BEAMED EIGHTH NOTES | |
| ♬ | 9836 | 266C | BEAMED SIXTEENTH NOTES | |
| ♭ | 9837 | 266D | MUSIC FLAT SIGN | |
| ♮ | 9838 | 266E | MUSIC NATURAL SIGN | |
| ♯ | 9839 | 266F | MUSIC SHARP SIGN | |
| ♰ | 9840 | 2670 | WEST SYRIAC CROSS | |
| ♱ | 9841 | 2671 | EAST SYRIAC CROSS | |
| ♲ | 9842 | 2672 | UNIVERSAL RECYCLING SYMBOL | |
| ♳ | 9843 | 2673 | RECYCLING SYMBOL FOR TYPE-1 PLASTICS | |
| ♴ | 9844 | 2674 | RECYCLING SYMBOL FOR TYPE-2 PLASTICS | |
| ♵ | 9845 | 2675 | RECYCLING SYMBOL FOR TYPE-3 PLASTICS | |
| ♶ | 9846 | 2676 | RECYCLING SYMBOL FOR TYPE-4 PLASTICS | |
| ♷ | 9847 | 2677 | RECYCLING SYMBOL FOR TYPE-5 PLASTICS | |
| ♸ | 9848 | 2678 | RECYCLING SYMBOL FOR TYPE-6 PLASTICS | |
| ♹ | 9849 | 2679 | RECYCLING SYMBOL FOR TYPE-7 PLASTICS | |
| ♺ | 9850 | 267A | RECYCLING SYMBOL FOR GENERIC MATERIALS | |
| ♻ | 9851 | 267B | BLACK UNIVERSAL RECYCLING SYMBOL | |
| ♼ | 9852 | 267C | RECYCLED PAPER SYMBOL | |
| ♽ | 9853 | 267D | PARTIALLY-RECYCLED PAPER SYMBOL | |
| ♾ | 9854 | 267E | PERMANENT PAPER SIGN | |
| ♿ | 9855 | 267F | WHEELCHAIR SYMBOL | |
| ⚀ | 9856 | 2680 | DIE FACE-1 | |
| ⚁ | 9857 | 2681 | DIE FACE-2 | |
| ⚂ | 9858 | 2682 | DIE FACE-3 | |
| ⚃ | 9859 | 2683 | DIE FACE-4 | |
| ⚄ | 9860 | 2684 | DIE FACE-5 | |
| ⚅ | 9861 | 2685 | DIE FACE-6 | |
| ⚆ | 9862 | 2686 | WHITE CIRCLE WITH DOT RIGHT | |
| ⚇ | 9863 | 2687 | WHITE CIRCLE WITH TWO DOTS | |
| ⚈ | 9864 | 2688 | BLACK CIRCLE WITH WHITE DOT RIGHT | |
| ⚉ | 9865 | 2689 | BLACK CIRCLE WITH TWO WHITE DOTS | |
| ⚊ | 9866 | 268A | MONOGRAM FOR YANG | |
| ⚋ | 9867 | 268B | MONOGRAM FOR YIN | |
| ⚌ | 9868 | 268C | DIGRAM FOR GREATER YANG | |
| ⚍ | 9869 | 268D | DIGRAM FOR LESSER YIN | |
| ⚎ | 9870 | 268E | DIGRAM FOR LESSER YANG | |
| ⚏ | 9871 | 268F | DIGRAM FOR GREATER YIN | |
| ⚐ | 9872 | 2690 | WHITE FLAG | |
| ⚑ | 9873 | 2691 | BLACK FLAG | |
| ⚒ | 9874 | 2692 | HAMMER AND PICK | |
| ⚓ | 9875 | 2693 | ANCHOR | |
| ⚔ | 9876 | 2694 | CROSSED SWORDS | |
| ⚕ | 9877 | 2695 | STAFF OF AESCULAPIUS | |
| ⚖ | 9878 | 2696 | SCALES | |
| ⚗ | 9879 | 2697 | ALEMBIC | |
| ⚘ | 9880 | 2698 | FLOWER | |
| ⚙ | 9881 | 2699 | GEAR | |
| ⚚ | 9882 | 269A | STAFF OF HERMES | |
| ⚛ | 9883 | 269B | ATOM SYMBOL | |
| ⚜ | 9884 | 269C | FLEUR-DE-LIS | |
| ⚝ | 9885 | 269D | OUTLINED WHITE STAR | |
| ⚞ | 9886 | 269E | THREE LINES CONVERGING RIGHT | |
| ⚟ | 9887 | 269F | THREE LINES CONVERGING LEFT | |
| ⚠ | 9888 | 26A0 | WARNING SIGN | |
| ⚡ | 9889 | 26A1 | HIGH VOLTAGE SIGN | |
| ⚢ | 9890 | 26A2 | DOUBLED FEMALE SIGN | |
| ⚣ | 9891 | 26A3 | DOUBLED MALE SIGN | |
| ⚤ | 9892 | 26A4 | INTERLOCKED FEMALE AND MALE SIGN | |
| ⚥ | 9893 | 26A5 | MALE AND FEMALE SIGN | |
| ⚦ | 9894 | 26A6 | MALE WITH STROKE SIGN | |
| ⚧ | 9895 | 26A7 | MALE WITH STROKE AND MALE AND FEMALE SIGN | |
| ⚨ | 9896 | 26A8 | VERTICAL MALE WITH STROKE SIGN | |
| ⚩ | 9897 | 26A9 | HORIZONTAL MALE WITH STROKE SIGN | |
| ⚪ | 9898 | 26AA | MEDIUM WHITE CIRCLE | |
| ⚫ | 9899 | 26AB | MEDIUM BLACK CIRCLE | |
| ⚬ | 9900 | 26AC | MEDIUM SMALL WHITE CIRCLE | |
| ⚭ | 9901 | 26AD | MARRIAGE SYMBOL | |
| ⚮ | 9902 | 26AE | DIVORCE SYMBOL | |
| ⚯ | 9903 | 26AF | UNMARRIED PARTNERSHIP SYMBOL | |
| ⚰ | 9904 | 26B0 | COFFIN | |
| ⚱ | 9905 | 26B1 | FUNERAL URN | |
| ⚲ | 9906 | 26B2 | NEUTER | |
| ⚳ | 9907 | 26B3 | CERES | |
| ⚴ | 9908 | 26B4 | PALLAS | |
| ⚵ | 9909 | 26B5 | JUNO | |
| ⚶ | 9910 | 26B6 | VESTA | |
| ⚷ | 9911 | 26B7 | CHIRON | |
| ⚸ | 9912 | 26B8 | BLACK MOON LILITH | |
| ⚹ | 9913 | 26B9 | SEXTILE | |
| ⚺ | 9914 | 26BA | SEMISEXTILE | |
| ⚻ | 9915 | 26BB | QUINCUNX | |
| ⚼ | 9916 | 26BC | SESQUIQUADRATE | |
| ⚽ | 9917 | 26BD | SOCCER BALL | |
| ⚾ | 9918 | 26BE | BASEBALL | |
| ⚿ | 9919 | 26BF | SQUARED KEY | |
| ⛀ | 9920 | 26C0 | WHITE DRAUGHTS MAN | |
| ⛁ | 9921 | 26C1 | WHITE DRAUGHTS KING | |
| ⛂ | 9922 | 26C2 | BLACK DRAUGHTS MAN | |
| ⛃ | 9923 | 26C3 | BLACK DRAUGHTS KING | |
| ⛄ | 9924 | 26C4 | SNOWMAN WITHOUT SNOW | |
| ⛅ | 9925 | 26C5 | SUN BEHIND CLOUD | |
| ⛆ | 9926 | 26C6 | RAIN | |
| ⛇ | 9927 | 26C7 | BLACK SNOWMAN | |
| ⛈ | 9928 | 26C8 | THUNDER CLOUD AND RAIN | |
| ⛉ | 9929 | 26C9 | TURNED WHITE SHOGI PIECE | |
| ⛊ | 9930 | 26CA | TURNED BLACK SHOGI PIECE | |
| ⛋ | 9931 | 26CB | WHITE DIAMOND IN SQUARE | |
| ⛌ | 9932 | 26CC | CROSSING LANES | |
| ⛍ | 9933 | 26CD | DISABLED CAR | |
| ⛎ | 9934 | 26CE | OPHIUCHUS | |
| ⛏ | 9935 | 26CF | PICK | |
| ⛐ | 9936 | 26D0 | CAR SLIDING | |
| ⛑ | 9937 | 26D1 | HELMET WITH WHITE CROSS | |
| ⛒ | 9938 | 26D2 | CIRCLED CROSSING LANES | |
| ⛓ | 9939 | 26D3 | CHAINS | |
| ⛔ | 9940 | 26D4 | NO ENTRY | |
| ⛕ | 9941 | 26D5 | ALTERNATE ONE-WAY LEFT WAY TRAFFIC | |
| ⛖ | 9942 | 26D6 | BLACK TWO-WAY LEFT WAY TRAFFIC | |
| ⛗ | 9943 | 26D7 | WHITE TWO-WAY LEFT WAY TRAFFIC | |
| ⛘ | 9944 | 26D8 | BLACK LEFT LANE MERGE | |
| ⛙ | 9945 | 26D9 | WHITE LEFT LANE MERGE | |
| ⛚ | 9946 | 26DA | DRIVE SLOW SIGN | |
| ⛛ | 9947 | 26DB | HEAVY WHITE DOWN-POINTING TRIANGLE | |
| ⛜ | 9948 | 26DC | LEFT CLOSED ENTRY | |
| ⛝ | 9949 | 26DD | SQUARED SALTIRE | |
| ⛞ | 9950 | 26DE | FALLING DIAGONAL IN WHITE CIRCLE IN BLACK SQUARE | |
| ⛟ | 9951 | 26DF | BLACK TRUCK | |
| ⛠ | 9952 | 26E0 | RESTRICTED LEFT ENTRY-1 | |
| ⛡ | 9953 | 26E1 | RESTRICTED LEFT ENTRY-2 | |
| ⛢ | 9954 | 26E2 | ASTRONOMICAL SYMBOL FOR URANUS | |
| ⛣ | 9955 | 26E3 | HEAVY CIRCLE WITH STROKE AND TWO DOTS ABOVE | |
| ⛤ | 9956 | 26E4 | PENTAGRAM | |
| ⛥ | 9957 | 26E5 | RIGHT-HANDED INTERLACED PENTAGRAM | |
| ⛦ | 9958 | 26E6 | LEFT-HANDED INTERLACED PENTAGRAM | |
| ⛧ | 9959 | 26E7 | INVERTED PENTAGRAM | |
| ⛨ | 9960 | 26E8 | BLACK CROSS ON SHIELD | |
| ⛩ | 9961 | 26E9 | SHINTO SHRINE | |
| ⛪ | 9962 | 26EA | CHURCH | |
| ⛫ | 9963 | 26EB | CASTLE | |
| ⛬ | 9964 | 26EC | HISTORIC SITE | |
| ⛭ | 9965 | 26ED | GEAR WITHOUT HUB | |
| ⛮ | 9966 | 26EE | GEAR WITH HANDLES | |
| ⛯ | 9967 | 26EF | MAP SYMBOL FOR LIGHTHOUSE | |
| ⛰ | 9968 | 26F0 | MOUNTAIN | |
| ⛱ | 9969 | 26F1 | UMBRELLA ON GROUND | |
| ⛲ | 9970 | 26F2 | FOUNTAIN | |
| ⛳ | 9971 | 26F3 | FLAG IN HOLE | |
| ⛴ | 9972 | 26F4 | FERRY | |
| ⛵ | 9973 | 26F5 | SAILBOAT | |
| ⛶ | 9974 | 26F6 | SQUARE FOUR CORNERS | |
| ⛷ | 9975 | 26F7 | SKIER | |
| ⛸ | 9976 | 26F8 | ICE SKATE | |
| ⛹ | 9977 | 26F9 | PERSON WITH BALL | |
| ⛺ | 9978 | 26FA | TENT | |
| ⛻ | 9979 | 26FB | JAPANESE BANK SYMBOL | |
| ⛼ | 9980 | 26FC | HEADSTONE GRAVEYARD SYMBOL | |
| ⛽ | 9981 | 26FD | FUEL PUMP | |
| ⛾ | 9982 | 26FE | CUP ON BLACK SQUARE | |
| ⛿ | 9983 | 26FF | WHITE FLAG WITH HORIZONTAL MIDDLE BLACK STRIPE |


Breast Cancer is the most common cancer among Filipina women.
Mammograms are the best way to find breast cancer early, when it is easier to treat and before it is big enough to feel or cause symptoms.
What Are the Symptoms?
There are different symptoms of breast cancer, and some people have no symptoms at all. Symptoms can include—
• Any change in the size or the shape of the breast.
• Pain in any area of the breast.
• Nipple discharge other than breast milk (including blood).
• A new lump in the breast or underarm.
What Are the Risk Factors?
Some main factors that affect your chance of getting breast cancer include—
Being a woman and getting older are the main risk factors for breast cancer. Studies have shown that your risk for breast cancer is due to a combination of factors. The main factors that influence your risk include being a woman and getting older. Most breast cancers are found in women who are 50 years old or older. Some women will get breast cancer even without any other risk factors that they know of. Having a risk factor does not mean you will get the disease, and not all risk factors have the same effect. Most women have some risk factors, but most women do not get breast cancer. If you have breast cancer risk factors, talk with your doctor about ways you can lower your risk and about screening for breast cancer.
• Getting older. The risk for breast cancer increases with age; most breast cancers are diagnosed after age 50.
• Genetic mutations. Inherited changes (mutations) to certain genes, such as BRCA1 and BRCA2. Women who have inherited these genetic changes are at higher risk of breast and ovarian cancer.
• Reproductive history. Early menstrual periods before age 12 and starting menopause after age 55 expose women to hormones longer, raising their risk of getting breast cancer.
• Having dense breasts. Dense breasts have more connective tissue than fatty tissue, which can sometimes make it hard to see tumors on a mammogram. Women with dense breasts are more likely to get breast cancer.
• Personal history of breast cancer or certain non-cancerous breast diseases.  Some non-cancerous breast diseases such as atypical hyperplasia or lobular carcinoma in situ are associated with a higher risk of getting breast cancer.
• Family history of breast or ovarian cancer. A woman’s risk for breast cancer is higher if she has a mother, sister, or daughter (first-degree relative) or multiple family members on either her mother’s or father’s side of the family who have had breast or ovarian cancer. Having a first-degree male relative with breast cancer also raises a woman’s risk.
• Previous treatment using radiation therapy. Women who had radiation therapy to the chest or breasts (like for treatment of Hodgkin’s lymphoma) before age 30 have a higher risk of getting breast cancer later in life.
• Women who took the drug diethylstilbestrol (DES), which was given to some pregnant women in the United States between 1940 and 1971 to prevent miscarriage, have a higher risk. Women whose mothers took DES while pregnant with them are also at risk.
• Not being physically active. Women who are not physically active have a higher risk of getting breast cancer.
• Being overweight or obese after menopause. Older women who are overweight or obese have a higher risk of getting breast cancer than those at a normal weight.
• Taking hormones. Some forms of hormone replacement therapy (those that include both estrogen and progesterone) taken during menopause can raise risk for breast cancer when taken for more than five years. Certain oral contraceptives (birth control pills) also have been found to raise breast cancer risk.
• Reproductive history. Having the first pregnancy after age 30, not breastfeeding, and never having a full-term pregnancy can raise breast cancer risk.
• Drinking alcohol. Studies show that a woman’s risk for breast cancer increases with the more alcohol she drinks.
Research suggests that other factors such as smoking, being exposed to chemicals that can cause cancer, and changes in other hormones due to night shift working also may increase breast cancer risk.


This document gives a few suggestions to analyse a dataset composed by one numeric and one categoric
variable.
The dataset used as an example provides the quantity of weapons exported by the top 50 largest exporters in 2017. This dataset has been downloaded on the Stockholm International Peace Research Institute website and more information about it are available here.
A clean version at .csv
format is available on github, and the table beside shows how it looks like.
# Libraries
library(tidyverse)
library(hrbrthemes)
library(kableExtra)
options(knitr.table.format = "html")
# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/7_OneCatOneNum.csv", header=TRUE, sep=",")
# show data
data %>% head(6) %>% kable() %>%
kable_styling(bootstrap_options = "striped", full_width = F)
| Country | Value |
|---|---|
| United States | 12394 |
| Russia | 6148 |
| Germany (FRG) | 1653 |
| France | 2162 |
| United Kingdom | 1214 |
| China | 1131 |
Even if the most common way to represent this kind of information is probably to use barplots, lollipop can be a very good alternative, especially if you have a high number of entities. It is basically the same thing, but the bar is replaced by a line and a dot at the end. It allows the figure to be less cluttered.
Moreover, it is a good practice to plot a horizontal version, to make the labels more easily readable. Last, sorting your entities gives much more insight to the plot.
data %>%
filter(!is.na(Value)) %>%
arrange(Value) %>%
mutate(Country=factor(Country, Country)) %>%
ggplot( aes(x=Country, y=Value) ) +
geom_segment( aes(x=Country ,xend=Country, y=0, yend=Value), color="grey") +
geom_point(size=3, color="#69b3a2") +
coord_flip() +
theme_ipsum() +
theme(
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
legend.position="none"
) +
xlab("")
Here is the barplot version that looks very similar to the lollipop. This version is all right in this case where there is a lot of difference from one bar to the other. It would look way to cluttered with no or few differences.
data %>%
filter(!is.na(Value)) %>%
arrange(Value) %>%
mutate(Country=factor(Country, Country)) %>%
ggplot( aes(x=Country, y=Value) ) +
geom_bar(stat="identity", fill="#69b3a2") +
coord_flip() +
theme_ipsum() +
theme(
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
legend.position="none"
) +
xlab("")
The circular barplot is an alternative to the barplots, where each bar is placed around a circle instead of along a line. The benefit is that your figure gets more eye-catching, and that it uses space more efficiently. However, differences between groups get harder to read. In my opinion, this kind of graphic is worth it if you have a high number of groups and if obvious differences exist between group.
# Order data
tmp <- data %>%
filter(!is.na(Value)) %>%
arrange(desc(Value)) %>%
mutate(Country=factor(Country, Country))
# Set a number of 'empty bar'
empty_bar=10
# Add lines to the initial tmpset
to_add = matrix(NA, empty_bar, ncol(tmp))
colnames(to_add) = colnames(tmp)
tmp=rbind(tmp, to_add)
tmp$id=seq(1, nrow(tmp))
# Get the name and the y position of each label
label_tmp=tmp
number_of_bar=nrow(label_tmp)
angle= 90 - 360 * (label_tmp$id-0.5) /number_of_bar # I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)
label_tmp$hjust<-ifelse( angle < -90, 1, 0)
label_tmp$angle<-ifelse(angle < -90, angle+180, angle)
label_tmp$Country <- gsub("United States", "US", label_tmp$Country)
label_tmp$Country <- paste(label_tmp$Country, " (", label_tmp$Value,")", sep="")
# Make the plot
ggplot(tmp, aes(x=as.factor(id), y=Value)) + # Note that id is a factor. If x is numeric, there is some space between the first bar
geom_bar(stat="identity", fill=alpha("#69b3a2", 0.8)) +
ylim(-7000,13000) +
theme_minimal() +
theme(
axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(rep(-1,4), "cm")
) +
coord_polar(start = 0) +
geom_text(data=label_tmp, aes(x=id, y=Value+200, label=Country ), color="black", fontface="bold",alpha=0.6, size=2.5, angle= label_tmp$angle, hjust=label_tmp$hjust, inherit.aes = FALSE ) +
geom_text( aes(x=24, y=8000, label="Who sells more weapons?"), color="black", inherit.aes = FALSE)
Please note that in this case the circular barplot do not look very good due to a lot of very small values that are hardly visible on the chart.
A treemap represents each entity as a rectangle, with an area that is proportional to the numeric variable of the dataset.
It is a good way to show a general overview of the data organization and is probably more eye-catching than the previous barplot. However, it is less precise in the sense that it is harder to make accurate comparisons between groups.
# Package
library(treemap)
# Plot
treemap(data,
# data
index="Country",
vSize="Value",
type="index",
# Main
title="",
palette="Dark2",
# Borders:
border.col=c("black"),
border.lwds=1,
# Labels
fontsize.labels=0.5,
fontcolor.labels="white",
fontface.labels=1,
bg.labels=c("transparent"),
align.labels=c("left", "top"),
overlap.labels=0.5,
inflate.labels=T # If true, labels are bigger when rectangle is bigger.
)
Circle packing is the circular version of the treemap. Each groupd is represented as a circle instead of a rectangle, and its area is proportional to the numeric value.
# Libraries
library(ggraph)
library(igraph)
library(tidyverse)
library(viridis)
# We need a data frame giving a hierarchical structure. Let's consider the flare dataset:
tmp <- data %>% filter(!is.na(Value))
edges <- data.frame(
from = "o",
to = tmp$Country
)
vertices = rbind(tmp, data.frame(Country="o", Value=1)) %>% mutate(name=Country)
mygraph <- graph_from_data_frame( edges, vertices=vertices )
ggraph(mygraph, layout = 'circlepack', weight="Value") +
geom_node_circle( aes(fill=name)) +
scale_fill_viridis(discrete=TRUE) +
geom_node_label(aes(label=name, size=Value)) +
theme_void() +
theme(
legend.position="none"
)
You can learn more about each type of graphic presented in this story in the dedicated sections. Click the icon below:
Any thoughts on this? Found any mistake? Have another way to show the data? Please drop me a word on Twitter or in the comment section below:
A work by Yan Holtz for data-to-viz.com


{% extends "extend/email.html" %} {% block body %}
Hello,
{{ reporter }} just made a report against {{ person }}
{{ reporter }}
{{ person }}
The report reads:
{{ report }}


PXELINUX-OPTIONS(1) Syslinux Project PXELINUX-OPTIONS(1) NAME pxelinux-options - utility to set PXELINUX hard-coded options. SYNOPSIS pxelinux-options DESCRIPTION pxelinux-options sets PXELINUX hard-coded options. SEE ALSO syslinux(1) HOMEPAGE More information about the Syslinux project can be found on the homepage at <http://syslinux.zytor.com/>. AUTHOR pxelinux-options was written by H. Peter Anvin<EMAIL_ADDRESS>This manual page was written by Daniel Baumann <daniel.baumann@progress- technologies.net>. 4.00 2010-06-22 PXELINUX-OPTIONS(1)


CEMETERY MAN (DELLAMORTE DELLAMORE) A film review by Bryant Frazer Copyright 1996 Bryant Frazer
Directed by Michele Soavi Written by Gianni Romoli (based on Tiziano Sclavi's DELLAMORTE DELLAMORE) Cinematography by Mauro Marchetti Music by Manuel de Sica Starring Rupert Everett, Anna Falchi, and Francois Hadji-Lazaro Italy (English language), 1994 U.S. Release: 1996
GRADE: A-
Francesco Dellamorte has a bit of a problem. He's the live-in watchman at Buffalora Cemetery in northern Italy, where the corpses are crawling back out of their graves after spending a mere week or so in the ground. As you can imagine, that's something of a nuisance, but if he reports it to the authorities, he's certain of one thing -- they'll shut down the cemetery to investigate, and Francesco will be out of a job. Since he can't have that happening, he keeps a loaded pistol with him, which he carries to the door whenever he answers a knock. It's usually just one of them coming back, and a single bullet blown solidly through the head -- where have you gone, George Romero? -- takes a zombie down easily enough. For a misanthrope like Francesco, it's a pretty good gig.
Surrounded by death, and with only the clumsy and deformed Gnaghi for company, Francesco's life is pretty stable until he falls for a mourning widow.  Naturally, the old man comes back. The woman dies in her spouse's ensuing fit of jealous violence, and Francesco is stricken with despair. Naturally, She comes back again. And again.
That's only the surface of the remarkable DELLAMORTE DELLAMORE. You might expect even a stylish horror director to milk these situations for all they're worth, but Michele Soavi knows that zombie hijinks have been done to death by such precocious directors as Sam Raimi and Peter Jackson. Accordingly, the movie never stops moving, twisting and turning its way to an oddly existential climax. The scenario, written for the screen by Gianni Romoli from a comic book scenario by Tiziano Sclavi, concentrates on the human characters rather than the zombies, and gives as much play to turmoil of the spirit as it does to the carnage that spills from the body. The world of Francesco Dellamorte runs parallel to George Romero's zombie apocalypse. Like Romero's trilogy, and quite unlike many of its imitators, DELLAMORTE DELLAMORE is a zombie movie with character.
I have to wonder what American audiences are expecting on the way into this picture, given that the normally staid October Films has created a mild cheeseball of an ad campaign to push the film into U.S. theaters. "Zombies, Guns and Sex, OH MY!" reads the tagline, stripped across poster art that may lead audiences to believe that CEMETERY MAN is actually a cheap horror flick from the 50s or 60s. I find it hard to believe that this campaign will actually attract a discriminating audience, but stranger things have happened, and we'll just have to see. Rest assured that CEMETERY MAN/DELLAMORTE DELLAMORE is a confident, creepy little horror film with a winning sense of humor, a sure feel for outrageous imagery, and a healthy mean streak.
As played by Rupert Everett (READY TO WEAR), Francesco is a misfit and a nihilist. (The main character in the Dylan Dog comic book series originated by Sclavi is based on Everett, and his casting here is something of a coup.) He's also a remarkable Everyman who commands our attention and our sympathy as he slouches toward the inevitable.  (You may have seen Lazaro as the meanest-looking cyclops in THE CITY OF LOST CHILDREN.) By the time he develops a decidedly unhealthy crush on the mayor's daughter (and the mayor's daughter's disembodied head), Lazaro has invited viewers to inhabit his character, and the results are unsettling. The relationship is consummated at the end of the film, but these two are shown early on to be classic codependents. Along those lines, the movie exhibits a well-developed sense of humor that Goes a long way toward eliciting the viewer's sympathy. The characters aren't very pleasant, but you start to identify with them in spite of yourself. By the time the movie is over, their predicament almost seems to take on mythic proportions.
Herein seems to lie the problem for many American critics, who have been less than impressed with what may be a vigorous political allegory. I guess I'm a little slow, but I didn't understand right away that the Italian citizens who are zombified -- a disquietingly fascist troupe of boy scouts, Buffalora's highest ranking incompetent bureaucrat -- may represent the dead archetypes of Italian society come back to haunt the living. Instead, I fell for the grisly comedy and the sharp cinematic style, which references such influential pictures as VERTIGO and ONCE UPON A TIME IN THE WEST (on which Soavi's progenitor Dario Argento received a story credit). And while that bastion of genre reporting, CINEFANTASTIQUE, had decidedly unkind words for the film's alleged misogyny, I read it instead as a look inside poor Dellamorte's head. Francesco, as noted above, is an equal opportunity curmudgeon, and if She is treated as the most maddening of all the characters, it's because she is the object of Francesco's most maddening obsessions. At the same time, I think these critics complain a little too much. Stephen Holden, writing in THE NEW YORK TIMES, criticized the film's decision to make Francesco impotent -- missing the important joke, which is that he's really not impotent at all, despite the rumor around town (you have to wonder if Holden left partway through).
The cemetery itself is a triumph of production design, an inhabited world with curious nooks and crannies (the Ossuary, Gnaghi's cellar in the watchman's house). It's also a representation of Francesco's state of mind, and the essence of the movie rests in the ways he discovers to break away from it. Gory and playful, darkly humorous and flippantly bleak, Soavi's film is a joyride through a sullen state of mind. After Francesco takes his revenge on the world outside, and sets himself to escaping from the life he's made, DELLAMORTE DELLAMORE finally offers up its own definition of madness.
+ + + -- DEEP FOCUS (Movie Reviews) http://www.panix.com/~bfrazer/flicker/
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
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Related links: index of all rec.arts.movies.reviews reviews


[NOTE: you can download an .ipynb
version of this file here]
Lab 4 - Acquiring data from the web in Python
In this lab, we will interact with a few APIs to get a feel for how they work and how you can make the most of them when trying to access data on the web. To follow this session, you will need to be able to access the following:
- The internet
- A Python installation such as the “Geographic Data Science Stack 2019” in the University of Liverpool computers, or the
gds_env
Docker container in your own machine (see here for instructions) - A Mapbox API token, which you can access through your Mapbox account
%matplotlib inline
from IPython.display import IFrame
import contextily as cx
import geopandas
import json
import matplotlib.pyplot as plt
import mercantile
import pandas
import requests
UK Police API
First pass
IFrame("https://data.police.uk/docs/", 300, 150)

Let’s look for crime events by location. For example, we can take December of 2019 on a given location (53.40146
lat and -2.96459
lon) and build the query:
url = ('https://data.police.uk/api/outcomes-at-location?'\
'date=2019-12&lat=53.40146&lng=-2.96459')
url
'https://data.police.uk/api/outcomes-at-location?date=2019-12&lat=53.40146&lng=-2.96459'
You can check on your browser manually if this works:
IFrame(url, 300, 150)

Now we’ll access it programmatically using requests
:
%time r = requests.get(url)
CPU times: user 20.5 ms, sys: 4.59 ms, total: 25.1 ms
Wall time: 1.82 s
# First 100 characters only
r.content[:100]
b'[{"category":{"code":"awaiting-court-result","name":"Awaiting court outcome"},"date":"2019-12","pers'
This is encoded in bytes, we need to decode it into a string and convert it into a data structure that we can work with.
crimes = json.loads(r.content)
type(crimes)
list
len(crimes)
1337
crimes[0]
{'category': {'code': 'awaiting-court-result',
'name': 'Awaiting court outcome'},
'date': '2019-12',
'person_id': None,
'crime': {'category': 'violent-crime',
'location_type': 'Force',
'location': {'latitude': '53.409256',
'street': {'id': 911941, 'name': 'On or near Norton Street'},
'longitude': '-2.974566'},
'context': '',
'persistent_id': '2fee8abfce463f0fd79844b4d4c630c0aacfdd5b127a9f77192eeeafde9433a7',
'id': 79311446,
'location_subtype': 'ROAD',
'month': '2019-11'}}
Now this looks more Pythonic! Let’s convert it into a pandas.DataFrame
:
crimes_db = pandas.DataFrame(crimes)
crimes_db.head()
| category | date | person_id | crime | |
|---|---|---|---|---|
| 0 | {'code': 'awaiting-court-result', 'name': 'Awa... | 2019-12 | None | {'category': 'violent-crime', 'location_type':... |
| 1 | {'code': 'no-further-action', 'name': 'Investi... | 2019-12 | None | {'category': 'burglary', 'location_type': 'For... |
| 2 | {'code': 'no-further-action', 'name': 'Investi... | 2019-12 | None | {'category': 'other-theft', 'location_type': '... |
| 3 | {'code': 'unable-to-prosecute', 'name': 'Unabl... | 2019-12 | None | {'category': 'violent-crime', 'location_type':... |
| 4 | {'code': 'drugs-possession-warning', 'name': '... | 2019-12 | None | {'category': 'drugs', 'location_type': 'Force'... |
Now, for some applications this might be fine and we’d be good to go. For mapping however, this does not give us a working location. The reason is that the output from the API is more complex than a flat dictionary.
crimes[0]
{'category': {'code': 'awaiting-court-result',
'name': 'Awaiting court outcome'},
'date': '2019-12',
'person_id': None,
'crime': {'category': 'violent-crime',
'location_type': 'Force',
'location': {'latitude': '53.409256',
'street': {'id': 911941, 'name': 'On or near Norton Street'},
'longitude': '-2.974566'},
'context': '',
'persistent_id': '2fee8abfce463f0fd79844b4d4c630c0aacfdd5b127a9f77192eeeafde9433a7',
'id': 79311446,
'location_subtype': 'ROAD',
'month': '2019-11'}}
To pull out the lon/lat coordinates, we need to drill deeper into the location
entry, which is within the crime
bit:
crimes[0]["crime"]["location"]
{'latitude': '53.409256',
'street': {'id': 911941, 'name': 'On or near Norton Street'},
'longitude': '-2.974566'}
Parsing a single crime event
For convenience, let’s pull out a single crime:
cr = crimes[0]
cr
{'category': {'code': 'awaiting-court-result',
'name': 'Awaiting court outcome'},
'date': '2019-12',
'person_id': None,
'crime': {'category': 'violent-crime',
'location_type': 'Force',
'location': {'latitude': '53.409256',
'street': {'id': 911941, 'name': 'On or near Norton Street'},
'longitude': '-2.974566'},
'context': '',
'persistent_id': '2fee8abfce463f0fd79844b4d4c630c0aacfdd5b127a9f77192eeeafde9433a7',
'id': 79311446,
'location_subtype': 'ROAD',
'month': '2019-11'}}
cr.keys()
dict_keys(['category', 'date', 'person_id', 'crime'])
Let’s say, for every crime event, we want to keep the following attributes:
- Category code
- Crime ID, category, and location (lon/lat, as a geometry)
- Date
- Location type and subtype
Manually, we can extract those bits into a Series
object:
cr_parsed = pandas.Series({
'category_code': cr['category']['code'],\
'crime_category': cr['crime']['category'], \
'cime_id': cr['crime']['id'],\
'cime_category': cr['crime']['category'], \
'longitude': float(cr['crime']['location']['longitude']), \
'latitude': float(cr['crime']['location']['latitude']), \
'date': cr['crime']['month'], \
'crime_location_type': cr['crime']['location_type'], \
'crime_location_subtype': cr['crime']['location_subtype']
})
cr_parsed
category_code awaiting-court-result
crime_category violent-crime
cime_id 79311446
cime_category violent-crime
longitude -2.97457
latitude 53.4093
date 2019-11
crime_location_type Force
crime_location_subtype ROAD
dtype: object
One way to move into a more automated version is to turn the above into a small function:
def parse_crime_event(cr):
cr_parsed = pandas.Series({
'category_code': cr['category']['code'],\
'crime_category': cr['crime']['category'], \
'cime_id': cr['crime']['id'],\
'cime_category': cr['crime']['category'], \
'longitude': float(cr['crime']['location']['longitude']), \
'latitude': float(cr['crime']['location']['latitude']), \
'date': cr['crime']['month'], \
'crime_location_type': cr['crime']['location_type'], \
'crime_location_subtype': cr['crime']['location_subtype']
})
return cr_parsed
Which means we can use it on any crime event with the same structure:
parse_crime_event(cr)
category_code awaiting-court-result
crime_category violent-crime
cime_id 79311446
cime_category violent-crime
longitude -2.97457
latitude 53.4093
date 2019-11
crime_location_type Force
crime_location_subtype ROAD
dtype: object
parse_crime_event(crimes[10])
category_code no-further-action
crime_category criminal-damage-arson
cime_id 79308783
cime_category criminal-damage-arson
longitude -2.97504
latitude 53.39
date 2019-11
crime_location_type Force
crime_location_subtype ROAD
dtype: object
To note:
- It works because argument is
cr
and thecr
object is used throughout - A function (method) can be applied in any context where the object passed as
cr
(it doesn’t need to be named that way) meets the expectations of the method (ie. has all the attributes it expects)
Extending to all crimes in the batch
Armed with the function above, we can apply it to all the crimes in our initial batch. For this, we will use a for
loop:
# Start an empty list to store parsed crimes dynamically
parsed = []
# Loop over each crime event in the list of crimes
for cr in crimes:
# Parse a single crime
pc = parse_crime_event(cr)
# Store the parsed crime into the list created
parsed.append(pc)
# Conver the list into a DataFrame
parsed = pandas.DataFrame(parsed)
parsed.head()
| category_code | crime_category | cime_id | cime_category | longitude | latitude | date | crime_location_type | crime_location_subtype | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | awaiting-court-result | violent-crime | 79311446 | violent-crime | -2.974566 | 53.409256 | 2019-11 | Force | ROAD |
| 1 | no-further-action | burglary | 80011089 | burglary | -2.975439 | 53.408602 | 2019-12 | Force | ROAD |
| 2 | no-further-action | other-theft | 80015947 | other-theft | -2.978599 | 53.409277 | 2019-12 | Force | ROAD |
| 3 | unable-to-prosecute | violent-crime | 79309151 | violent-crime | -2.984887 | 53.408614 | 2019-11 | Force | ROAD |
| 4 | drugs-possession-warning | drugs | 80007412 | drugs | -2.964395 | 53.390561 | 2019-12 | Force | ROAD |
Now, to complete the job and be able to work with these data in a (web) mapping environment, we need to turn lon/lat coordinates into geometries. For that, we will rely on geopandas
(see here for more detail).
points = geopandas.points_from_xy(parsed["longitude"],
parsed["latitude"]
)
geo_db = geopandas.GeoDataFrame(parsed,
geometry=points,
crs="EPSG:4326"
)
geo_db.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x7f0649effcf8>
Exercise Explore the API for “Stop and search” by area data from the Police API and try to obtain data through it. Specifically try:
- Request data using the “Specific point” endpoint to pull down a batch of data around the Roxby Building.
- For the API ninjas out there (ie. optional), try to extract data using the “Custom area” endpoint. For example, create a polygon using
geojson.net
and query data available within the polygon.
Basemaps API
This section will cover the access of basemaps served as tilesets through the standard XYZ protocol. For this, we will use the contextily
library, first as end-users, and then we will peak a bit into its guts to get a better understanding of its inner workings.
The XYZ protocol exposes maps as images for portions of the Earth we will call tiles. The XYZ name stands from the “coordinates” used to locate a given tile. This of the entire planet split up into squares, each of them available with a unique combination of X and Y numbers. Now add a third one (Z) for the zoom level: lower values use less tiles to cover the world, while higher resolution levels (higher Z) will cover progressively smaller areas, but with more detail.
Most XYZ APIs expose tiles directly over HTTP, which means we can access them from the browser. A handy tool in this context is the mercantile
library, which handles conversions from lon/lat coordinates into tiles. For example, to get the tile XYZ for the coordinates we used above, a zoom level 12, we can run:
mercantile.tile(-2.96459, 53.40146, 11)
Tile(x=1007, y=663, z=11)
We can access now the Stamen Terrain tile for that tile:
tile_url = ("http://tile.stamen.com/terrain/"\
"11/1007/663.jpg")
tile_url
'http://tile.stamen.com/terrain/11/1007/663.jpg'
IFrame(tile_url, 300, 300)

Now, to make a full basemap of an arbitrary extent at a given zoom level, we would need to identify all the tiles required, collect them, and compose a map that stitches them into a continuous mosaic. Luckily for us, the contextily
library does exactly that for us. There are two different ways of using it.
First, the easy way. The most straightforward way to make a basemap with contextily
is to provide a GeoDataFrame
, plot it, and the add a basemap in a given CRS. For example, let’s used the geo_db
table from the Police API:
ax = geo_db.plot(markersize=0.5, color='red')
cx.add_basemap(ax, crs=geo_db.crs)
Exercise The contextily.providers
module provides access to several tile providers:
cx.providers.keys()
dict_keys(['OpenStreetMap', 'OpenSeaMap', 'OpenPtMap', 'OpenTopoMap', 'OpenRailwayMap', 'OpenFireMap', 'SafeCast', 'Thunderforest', 'OpenMapSurfer', 'Hydda', 'MapBox', 'Stamen', 'Esri', 'OpenWeatherMap', 'HERE', 'FreeMapSK', 'MtbMap', 'CartoDB', 'HikeBike', 'BasemapAT', 'nlmaps', 'NASAGIBS', 'NLS', 'JusticeMap', 'Wikimedia', 'GeoportailFrance', 'OneMapSG'])
To swap the default for a particular provider, you need to use the url
attribute. For example, to use CARTO instead of Stamen:
ax = geo_db.plot(markersize=0.5, color='red')
cx.add_basemap(ax,
crs=geo_db.crs,
url=cx.providers.CartoDB.Voyager,
zoom=14
)
Explore the different providers and create similar maps as above using them.
Second, the flexible way. Sometimes, we don’t necessarily have a layer to plot under, or we want to have a bit more control over what we actually pull down. For these cases, we’ll swap add_basemap
for bounds2img
, which allows us to pass an arbitrary bounding box and retrieve a stitched up tile.
For example, we will replicate the basemap above:
geo_db.total_bounds
array([-2.98822 , 53.387346, -2.941042, 53.415206])
w, s, e, n = geo_db.total_bounds
With the bounding box, we can pull down the image (and its extent):
img, ext = cx.bounds2img(w, s, e, n, ll=True)
plt.imshow(img, extent=ext)
<matplotlib.image.AxesImage at 0x7f0649aada20>
A final trick that contextily
has under its hand is the ability to write a basemap into a .tiff
file so we can access it in a standard GIS, such as QGIS. The interface is the same as in bounds2img
, but in this case, we will be calling bounds2raster
:
img, ext = cx.bounds2raster(w,
s,
e,
n,
"my_basemap.tif",
ll=True
)
Fire up QGIS and inspect the file we’ve just created (my_basemap.tif
).
Directions API
To finish this lab, we will explore an API that allows us to tap into the output of computations that take place in the cloud, rather than a direct database. In particular, we will play witht he Mapbox Directions API. For accesing Mapbox services, you will need your token handy. The way we read it in here assumes you have a file called MAPBOX_TOKEN
in the same folder as this notebook:
TOKEN = open("MAPBOX_TOKEN").read()
The Directions API allows us to retrieve routing directions for different travel modes from two points. The general structure of the call is as follows:
dir_base = ("https://api.mapbox.com/directions/v5/mapbox/"\
"XXXmodeXXX/"\
"XXXorig_lonXXX,XXXorig_latXXX;"\
"XXXdest_lonXXX,XXXdest_latXXX"\
"?geometries=geojson"\
"&access_token=YOUR_MAPBOX_ACCESS_TOKEN")
dir_base
'https://api.mapbox.com/directions/v5/mapbox/XXXmodeXXX/XXXorig_lonXXX,XXXorig_latXXX;XXXdest_lonXXX,XXXdest_latXXX?geometries=geojson&access_token=YOUR_MAPBOX_ACCESS_TOKEN'
Now to build an actual query, we need to replace each parameter by a value. There are several ways of doing this, one possible one is:
query = dir_base.replace("XXXmodeXXX", "walking")\
.replace("XXXorig_lonXXX", "-2.8857242")\
.replace("XXXorig_latXXX", "53.3803881")\
.replace("XXXdest_lonXXX", "-2.9146053")\
.replace("XXXdest_latXXX", "53.3882099")
query
'https://api.mapbox.com/directions/v5/mapbox/walking/-2.8857242,53.3803881;-2.9146053,53.3882099?geometries=geojson&access_token=YOUR_MAPBOX_ACCESS_TOKEN'
For the real query, we will also include our own token stored in TOKEN
. Other than that, we’re all set! We can ask for the data in similar ways as above:
r = requests.get(query.replace("YOUR_MAPBOX_ACCESS_TOKEN", TOKEN))
We can examine the response:
# Blurb too long, reading only first 100 characters
r.content
b'{"routes":[{"weight_name":"routability","legs":[{"summary":"A562, A5058","steps":[],"distance":2279.8,"duration":1609,"weight":1609}],"geometry":{"coordinates":[[-2.8856,53.380119],[-2.886148,53.379974],[-2.88919,53.381962],[-2.893808,53.384666],[-2.897625,53.385567],[-2.899884,53.386322],[-2.909303,53.388119],[-2.909477,53.388096],[-2.909854,53.38805],[-2.910888,53.387917],[-2.912312,53.388474],[-2.912442,53.388382],[-2.913922,53.388592],[-2.914619,53.388218]],"type":"LineString"},"distance":2279.8,"duration":1609,"weight":1609}],"waypoints":[{"distance":30.857,"name":"Beaconsfield Road","location":[-2.8856,53.380119]},{"distance":1.225,"name":"Carsdale Road","location":[-2.914619,53.388218]}],"code":"Ok","uuid":"cel998PNFts5OWRdRCOgD_nhJDlPqDXHwnUGXMAByHFFuQa73jqjYQ=="}'
But, to make more sense, we can pass it through the json
library:
waypoints = json.loads(r.content)
waypoints
{'routes': [{'weight_name': 'routability',
'legs': [{'summary': 'A562, A5058',
'steps': [],
'distance': 2279.8,
'duration': 1609,
'weight': 1609}],
'geometry': {'coordinates': [[-2.8856, 53.380119],
[-2.886148, 53.379974],
[-2.88919, 53.381962],
[-2.893808, 53.384666],
[-2.897625, 53.385567],
[-2.899884, 53.386322],
[-2.909303, 53.388119],
[-2.909477, 53.388096],
[-2.909854, 53.38805],
[-2.910888, 53.387917],
[-2.912312, 53.388474],
[-2.912442, 53.388382],
[-2.913922, 53.388592],
[-2.914619, 53.388218]],
'type': 'LineString'},
'distance': 2279.8,
'duration': 1609,
'weight': 1609}],
'waypoints': [{'distance': 30.857,
'name': 'Beaconsfield Road',
'location': [-2.8856, 53.380119]},
{'distance': 1.225,
'name': 'Carsdale Road',
'location': [-2.914619, 53.388218]}],
'code': 'Ok',
'uuid': 'cel998PNFts5OWRdRCOgD_nhJDlPqDXHwnUGXMAByHFFuQa73jqjYQ=='}
Phew!  There is a lot of information there. For now, we will parse only the direction line that connects directions. For that, we need to do a bit of fiddling again. The basics are relatively straightforward, but “the devil is always in the details”!
route = waypoints['routes'][0]
route['properties'] = {"distance": waypoints["routes"][0]["distance"],
"duration": waypoints["routes"][0]["duration"]
}
routes = geopandas.GeoDataFrame.from_features([route], crs="EPSG:4326")
routes
| geometry | distance | duration | |
|---|---|---|---|
| 0 | LINESTRING (-2.8856 53.380119, -2.886148 53.37... | 2279.8 | 1609 |
With this, we can make a map just as above:
ax = routes.plot(color="purple", linewidth=3, figsize=(14, 14))
cx.add_basemap(ax, crs=routes.crs, url=cx.providers.Stamen.TonerLite)
Exercise Explore the documentation for the isochrone API and try to obtain results. For example, retrieve the area that can be reached within 15 minutes of the Roxby Building.
Exercise (II) Explore the documentation for the geocoding API and try to use it to automatically embed coordinates between addresses.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Literally it does not mean anything. It is a sequence of words without a sense of Latin derivation that make up a text also known as filler text, fictitious, blind or placeholder
The Lorem Ipsum text is used to fill spaces designated to host texts that have not yet been published. They use programmers, graphic designers, typographers to get a real impression of the digital / advertising / editorial product they are working on.
Lorem ipsum dolor sit amet, consectetur adipisci elit, sed eiusmod tempor incidunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur. Quis aute iure reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint obcaecat cupiditat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
Its origins date back to 45 BC. In fact, his words were randomly extracted from the De finibus bonorum et malorum , a classic of Latin literature written by Cicero over 2000 years ago.
The original Latin text
Dal De finibus bonorum et malorum sections 1.10.32 and 1.10.33 - Marco Tullio Cicerone
Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam eaque ipsa, quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt, explicabo.
No, this isn’t possible. We want all of our magazines to be available for all customers. So you have to order the magazine.
You can create a new account at the end of the order process or on the following page: Create new account. You can view all of your orders and subscriptions in your customer account. You can also change your addresses and your password.
No, you don’t have to create an account. But there are a few advantages if you create an account.
The products in our shop can only be ordered online at present and are sent out by Swiss Post.
No, we don’t have a physical store location at the moment. We accept only orders through our online shop and we’re shipping all orders with the Swiss Post Service. Please visit our shipping section for more details.
From time to time you will find us at design fairs and popup markets in Switzerland. Subscribe to our newsletter and you’ll receive the latest news.
Many of the magazines that we sell are from far-away countries. Prices depend largely on factors such as the shipping cost, customs duties, and general price changes imposed by our suppliers and the individual publishers. We therefore have to pass any changes in costs on to our customers. For subscription orders, however, the prices are not changed until the subscription is renewed. The prices at the time of ordering always apply.
Yes, we sell many different magazines from various publishers that were financed from capital raised on Kickstarter.com, Indiegogo.com, Startnext.de and other crowdfunding platforms. We’re always pleased to add new magazines that have been funded in this way, and we’ve even helped support some of them ourselves.
Our current General Terms & Conditions (GTC), including all important information about prices, payment methods, returns and cancellation periods, can be found on our website at loremnotipsum.com.
Please find logos, photos, press releases and more in Press and Media.
We are always looking for new and interesting Independent Magazines. Please drop us a short line if you know a magazine, which you want to be added to our shop. Please note that we cannot add all suggested magazines to our shop.
Please visit this page to view all of the countries, we are selling magazines from.
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Our retail prices are based on the trade price that we pay per magazine. We also have to take into account various other factors such as customs duties and shipping costs. These are especially high for magazines ordered from the USA or Australia, for example, and have to be considered in the final price. We also put a lot of time and effort into presenting each magazine in an optimum way in our shop and on social media. All of these factors therefore have to be taken into account when calculating the individual prices.


God bless Daniel McKinnon. After three proposal attempts, all foiled by Tori, Dan proposed after a stressful day spent moving into their first apartment together. Dan frequently likes to relive the moment by proposing on random mountain tops. Good news, Tori has said yes every time.


Creation, Props & Self
The documentation assumes relative familiarity with ReactJS.
ReasonReact doesn't use/need classes. The component creation API gives you a plain record, whose fields (like render
) you can override.
The component template is created through ReasonReact.statelessComponent("TheComponentName")
. The string being passed is for debugging purposes (the equivalent of ReactJS' displayName
).
As an example, here's the file Greeting.re
:
let component = ReasonReact.statelessComponent("Greeting");
In ReactJS, you'd create a component class and call it through JSX which transforms into React.createElement(myClass, {prop1: 'hello'})
under the hood. In ReasonReact, instead of passing the whole "class" (aka component template) into a hypothetical ReasonReact.createElement
function, you'd instead declare a make
function:
/* still in Greeting.re */
let component = ReasonReact.statelessComponent("Greeting");
let make = (~name, _children) => {
...component, /* spread the template's other defaults into here */
render: _self => <div> {ReasonReact.stringToElement(name)} </div>
};
The make
function is what's called by ReasonReact's JSX, described later. For now, the JSX-less way of calling & rendering a component is:
/* call the `make` function in the module `Greeting` */
let el = ReasonReact.element(Greeting.make(~name="John", [||]));
sandboxRender(el);
/* equivalent to <Greeting name="John" /> */
make
asks you to return the component record created above. You'd override a few fields, such as the familiar render
, initialState
, didMount
, etc., all described later.
Note: do not inline let component
into the make
function body like the following!
let make = _children => {
...(ReasonReact.statelessComponent("Greeting")),
render: self => <div />
}
Since make
is called at every JSX invocation, you'd be accidentally creating a fresh new component every time.
Props
Props are just the labeled arguments of the make
function, seen above. They can also be optional and/or have defaults, e.g. let make = (~name, ~age=?, ~className="box", _children) => ...
.
The last prop must be children
. If you don't use it, simply ignore it by naming it _
or _children
. Names starting with underscore don't trigger compiler warnings if they're unused.
The prop name cannot be ref
nor key
. Those are reserved, just like in ReactJS.
Following that example, you'd call that component in another file through <Foo name="Reason" />
. className
, if omitted, defaults to "box". age
defaults to None
. If you'd like to explicitly pass age
, simply do so: <Foo name="Reason" age=20 />
.
Neat Trick with Props Forwarding
Sometimes in ReactJS, you're being given a prop from the owner that you'd like to forward directly to the child:
<Foo name="Reason" age={this.props.age} />
This is a source of bugs, because this.props.age
might be accidentally changed to a nullable number while Foo
doesn't expect it to be so, or vice-versa; it might be nullable before, and now it's not and Foo
is left with a useless null check somewhere in the render.
In Reason, if you want to explicitly pass an optional ageFromProps
(whose type is option int
, aka None | Some int
), the following wouldn't work:
let ageFromProps = Some(10);
let _ = <Foo name="Reason" age=ageFromProps />
Because age
expects a normal int
when you do call Foo
with it, not an option int
! Naively, you'd be forced to solve this like so:
let ageFromProps = Some(10);
let el = switch (ageFromProps) {
| None => <Foo name="Reason" />
| Some(nonNullableAge) => <Foo name="Reason" age=nonNullableAge />
};
sandboxRender(el);
Cumbersome. Fortunately, here's a better way to explicitly pass an optional value:
let ageFromProps = Some(10);
sandboxRender(<Foo name="Reason" age=?ageFromProps />)
It says "I understand that age
is optional and that when I use the label I should pass an int. But I'd like to forward an option
value explicitly". This isn't a JSX trick we've made up; it's just a language feature! See the section on "Explicitly Passed Optional" in the Reason docs.
self
You might have seen the render: (self) => ...
part in make
. The concept of JavaScript this
doesn't exist in ReasonReact (but can exist in Reason, since it has an optional object system); the this
equivalent is called self
. It's a record that contains state
, retainedProps
, handle
and reduce
, which we pass around to the lifecycle events, render
and a few others, when they need the bag of information. These concepts will be explained later on.


TI-86 Link Protocol Guide v1.0 - System Backup Transfers
A System Backup is one large transmission containing all user-modifiable data in the calculator. The TI-86 Graph Link software does not support backups, so there is no standard backup file format. As of November 2000, only FastLink supports the TI-86's backup capability. The proprietary file format it uses can be found in the file formats section of this documentation.
The protocol for all system backup transfers is shown below. For information on packet formats, click here.
A computer can be either the sending unit or the receiving unit.
| Step | Direction | Packet | ||
|---|---|---|---|---|
|
1 |
|
|
|
VAR with backup-style header and type ID = 0x1D. |
|
2 |
|
ACK | ||
|
3 |
|
CTS or SKIP/EXIT. If exit, do step 4 and quit. | ||
|
4 |
|
ACK | ||
|
5 |
|
DATA - section 1 (system data) | ||
|
6 |
|
ACK | ||
|
7 |
|
DATA - section 2 (user data <=64K) | ||
|
8 |
|
ACK | ||
|
9 |
|
DATA - section 3 (user data >64K - see note below) | ||
|
10 |
|
ACK (see note below) | ||
|
11 |
|
DATA - section 4 (variable table) | ||
|
12 |
|
ACK |
Example (PC sending backup to TI): backup in '4 parts'
| PC: | 06 06 09 00 8C 36 1D FF FF 15 01 41 01 4C 00 | Variable header: Backup |
| TI: | 86 56 09 00 | Acknowledgement of header |
| 86 09 09 00 | Clear to send backup | |
| PC: | 06 56 00 00 | Acknowledgement of CTS |
| 06 15 8C 36 <368Ch bytes of data plus checksum> | Backup data section 1 (system data) | |
| TI: | 86 56 00 00 | Acknowledgement of data |
| PC: | 06 15 FF FF <FFFFh bytes of data plus checksum> | Backup data section 2 (user data <=64K) |
| TI: | 86 56 00 00 | Acknowledgement of data |
| PC: | 06 15 15 01 <115h bytes of data plus checksum> | Backup data section 3 (user data >64K) |
| TI: | 86 56 00 00 | Acknowledgement of data |
| PC: | 06 15 41 01 <141h bytes of data plus checksum> | Backup data section 4 (variable table) |
| TI: | 86 56 00 00 | Acknowledgement of data |
Example (PC sending backup to TI): backup in '3 parts'
| PC: | 06 06 09 00 8C 36 1D 01 20 00 00 3F 00 3E 01 | Variable header: Backup |
| TI: | 86 56 09 00 | Acknowledgement of header |
| 86 09 09 00 | Clear to send backup | |
| PC: | 06 56 00 00 | Acknowledgement of CTS |
| 06 15 8C 36 <368Ch bytes of data plus checksum> | Backup data section 1 (system data) | |
| TI: | 86 56 00 00 | Acknowledgement of data |
| PC: | 06 15 20 00 <2000h bytes of data plus checksum> | Backup data section 2 (user data <64K) |
| TI: | 86 56 00 00 | Acknowledgement of data |
| PC: | 06 15 3F 00 <3F00h bytes of data plus checksum> | Backup data section 4 (variable table) |
| TI: | 86 56 00 00 | Acknowledgement of data |


Seems like you'll be travelling for 2 hours. Here are some movie suggestions!
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam viverra euismod odio, gravida pellentesque urna varius vitae.


(not real)
Blogs, Newsletters, Media, Other Frameworks
Feedback, Follow Up, Social, Funnel
Put yourself out there
store stats, advertising analytics, keyword analytics, daily emails, reviews, intelligence
keyword analysis tool
great articles about launching and marketing apps


Malaika Arora heads to the gym in style
by FilmfareMalaika Arora is known for her smoking look, fit body and killer dance moves. The actress has been ageing like fine wine and the credit sure goes to her dedication to fitness and working out regularly. Malaika seems to be at the gym without fail every day. She even takes to her social media to update her fans with videos from her time at the gym. And much like the infamous airport looks, Malaika has pioneered “gym look” and we sure look forward to her OOTD.
Earlier today she was spotted stepping out of her car and heading to her gym is a super comfy outfit. The actress looked stunning as she was papped flaunting her washboard abs and toned legs. Her hair was left open and she sported a no-makeup look. She opted for a pair of coloured sunglasses and finished her gym look to perfection.
Check out the pictures below.


Nice, France
I'm an extroverted, intuitive thinker who values science, nature, music, and love above all things. I grew up in Vancouver, Canada, and graduated from a degree in Engineering Physics at the University of British Columbia in 2019. In 2021 I obtained a Master's degree in Mathematics at the École Polytechnique Fédérale de Lausanne. Now I'm completing my Ph.D. at the Université Côte d'Azur in Nice, France. I'm very grateful to have had the opportunity to run a marathon, publish several academic articles, and travel around the world working with extraordinary people.
 For me, it is without question the art form that is most interesting and inspiring. I have compiled a list of my favourite albums sorted by year of release. You can learn a lot about me - and maybe discover some new music - by checking out my playlist linked to below.
Killer AlbumsSince May, 2021, I have been in Nice, France working on a Ph.D. in mathematics in the Laboratoire J.A. Dieudonné. My research relates statistics and computational geometry. Specifically, I use tools from stochastic geometry to explore extreme spatio-temporal dependence in high-resolution gridded data.
Google ScholarIn the Summer of 2018, I was working with drones in the MIST Lab at Polytechnique Montréal. The lab uses a novel programming language called Buzz to implement decentralized algorithms for swarm robotics. I integrated Buzz (which is compiled with a C compiler) in Python to interface with the drones via a remote control. My internship had an experimental component in which I trained the drones to collaboratively carry fabrics, and presented my results the following year at the 2019 International Conference on Robotics and Automation (ICRA).
PublicationIn the summer of 2017, I did experimental physics research at the University of Würzburg. We used a Scanning Tunnelling Microscope to study the spins of atoms on the surface of metal substrates, and it was my responsibility to operate the microscope and to decipher how the spins were oriented on the surface of the samples. I suggested the idea that MnO chains have a chiral magnetic structure, which is the main finding in our team's publication in Nature Communications.
PublicationGrin Technologies was the perfect place to have my first engineering co-op. Grin manufactures and sells electric bicycle conversion kits which are made in-house. Not only did I gain a sweet electric bicycle from Grin, but I learned an uncountable number of skills as a software engineer, electrical engineer, electronics manufacturer, metal shop worker, and bike technician.
I started a project that took real-time serial data and updated a user interface, all independently, all in Python. Grin accompanied the software I was working on with a simple electrical device, and I had a hand in designing its circuitry. This device, the Battery Grinspector, is now manufactured and sold by Grin. I learned how solder, crimp wires, populate PCBs and test the products we were producing. I also became very familiar with the tools in the metal shop. I was constantly fabricating parts on the milling machine, band saw, drill press, and CNC machine. I spent a week or two constructing a wooden deck that spanned over 50 feet, and I gained technical experience in the maintenance and assembly of bicycles. I laced and trued many wheels for many customers and indeed for my own electric bike!
Grin websiteFive of my classmates and I were granted $10,000 to found a business that would transform exercise tracking. The business was never created. Our goal was to create a product that could be installed at gyms to track gym users' exercises, and feed that information to their smartphones. We created a proof-of-concept that used deep learning and computer vision to classify 10 different exercises. We were the only project in Engineering Physics to win the Industry Award, granted by UBC Applied Science. Here is an excerpt from our final reccomendation report:
"Once connected to a computer with the corresponding software, the user can begin recording with the Xbox Kinect. The user must then input their unique user id into the software. The user can then stand about two meters away from the camera, and begin any one of the 10 allowable exercises with any of the 3 allowable weights at any time. Shortly after the user stops exercising, the system will send to an online database the exercise they did, along with information about the weight and number of repetitions. The user can walk in and out of frame as they please, and can begin any other exercise thereafter."
My three teammates and I won the 2016 Engineering Physics robot competiton. Our challenge was to make a miniature autonomous taxi robot that could locate and pick up stuffed dolls that emmited an IR signal, and drop them off at some specified location. Our robot was built from raw materials cut on a water jet cutter and a laser cutter. All of the electrical circuits were soldered onto PCBs and installed in our robot. My main contributions were the signal processing circuits, power transmission circuits, and contributions to the codebase.
During my Bachelor's degree, I designed a video game and made it playable on a Super Nintendo Entertainment System (SNES). The game is written in Python. The game was "finished" well before I modified it for the SNES, but to do so, I had to get around the SNES controller timing protocol. Every 20 ms, the SNES sends the controller a portion of a 83 kHz square wave, and on a different line, the controller sends back a high pulse during the first period of the square wave if "B" is pressed, the second period if "Y" is pressed, and so on for each button on the controller. My Raspberry Pi could not read the pulses fast enough to correctly distinguish which buttons were being pushed, so I wrote a program in VHDL and put it on an FPGA to convert this timing protocol into 7 binary inputs to the Pi (one for each button). The game is also playable on a Windows PC, and a multiplayer version is playable over a LAN network.
Download (Windows, 10 MB)I thoroughly enjoyed working with my brother to create a card game called DELECTABLE. In the game, each player builds a restaurant by collecting chefs, servers, food products, and equipment. Our game uses a custom deck of 108 unique cards that we designed and had printed.
RulebookIn 2021, I completed a Master's degree in mathematics at EPFL in mathematics. I specialized in statistics, specifically extreme value theory. My master's thesis title is Inference for space-time max-stable processes with the Markov property in time.
Dr. Zinovy Reichstein with me and my classmates
Dr. Gordon Slade with me and my classmates
I graduated from an Engineering Physics degree at UBC, while representing my graduate class in the Engineering Physics Undergraduate Society. I graduated with a minor in honours mathematics and a specialization in electrical engineering. Eng Phys is the most intensive, and competitive engineering program offered at UBC, partly due to the number of credits we had to take, but also the amount of math and physics we take is comparable to a major in just that field. The first large project course in Engineering Physics is the Engineering Physics robot competition described above. For the second project course, we founded Increment Fitness. I graduated with an average of 87%, and more importantly, I really enjoyed my experience at UBC. Check out the link on the left for more information about Engineering Physics. The link on the right exemplifies the level of depth in physics that Engineering Physics encourages.
Eng Phys website Optical Pumping of RubidiumContact<EMAIL_ADDRESS>Last Modified: 05.08.2023


"Thank you very much for your hostility." Beautiful People
By Ross Anthony
A Serb and Croat scuffle on a London bus. Britain's gritty melting pot is in need of a pilot light; writer/director and Bosnian expatriate Jazmin Dizdar strikes that match from behind a camera.
A young punker follows in the footsteps of hardened junkies, while his caricature Archie & Edith parents remain out of touch. A physician recently separated from his wife attempts to hold onto his kids and his sanity, while a young couple contemplate aborting a child conceived in rape. The black sheep of British elite falls for an illegal refuge. An ambitious reporter flies to Bosnia determined to make sure Brits "give a shat about what's going on out there."
Chunky story lines in this gravy begin to mix as the filmmakers increase the heat and stir. The overworked doctor stops at the reporter's wife's home. Her aristocratic artistry is an excellent contrast to his crushing responsibilities. The Serb and Croat end up in adjacent hospital beds continuing their spats like two kindergartners while the nurse on duty appropriately scolds their inappropriate behavior. The most intriguing blend, an intoxicated punkster crawls onto a convenient crate at the airport for a snooze. Which just happens to land him smack in the middle of a Bosnian war zone. Dazed and confused, he awakens pulling a disposable razor from the supply pack upon which he'd just parachuted to the ground. His is the most endearing experience. Thick in the heartwarming department, but not an easy ride by any means. A fine story line with a flavor that anchors the film.
Plot lines are patchworked nicely through the climax as in "Magnolia," but run a pinch long and just a little hokey afterward. Save for a theatric political assistant, all performances work well. A piano score brightly spices the mainstay creating a filling entrée. And in addition to its other perspective-gaining merits, the film offers Americans an empathetic glimpse of the country we revolted against all those years ago.
Beautiful People. Copyright © 1998.  Written and Directed by Jazmin Dizdar. Produced by Ben Woolford at Tall/stories & Beautiful Stories. A Trimark presentation.
Grade..........................B+
-- Copyright © 2000. Ross Anthony, currently based in Los Angeles, has scripted and shot documentaries, music videos, and shorts in 35 countries across North America, Europe, Africa and Asia. For more reviews visit: http://RossAnthony.com
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
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Related links: index of all rec.arts.movies.reviews reviews


| DOSE: |
11 mg |
oral |
2C-I |
(powder / crystals) |
I had heard of this chemical not too long ago and was looking forward to buying some of it...it was white with a sweet smell(or was that the bag that i was smelling?!),i took it the next day without taking any of my prescription drugs(Prozac,Adderal), the day of the trip, and the day before.
When i measured the dose(visually) it looked like i had taken about 11 miligrams or so....though when i took it my mind-set was quite nervous but overall a mostly positive experience,anyways as soon as i put the powdered substance in my mouth i felt some definte effects ,the stuff tasted kind of like rubber(not that i've tasted rubber before) and also very bitter and hard to flush taste....the effects came on quite quick since i had little to eat....here's what happened starting from beginning(11:40 PM) to end(?),
T+5 seconds: I felt different, light-headed, and relaxed, not much going on.
T+20 minutes(12:00 AM) minutes: the effects have maximized somewhat, I had gotten more relaxed, giggly, and I had some Anti-anxiety properties which were very effective.
T+40 minutes(12:20 AM): At this time I was watching 'The Fifth Element' which was pretty insane because it felt like I was in the movie and got vivid gravity shifts.....i was laughing for about 10 minutes....at this time visuals felt much more vivid. I felt like I could feel something from a large distance away. I felt like I could fell what it/he/she was sad about or just felt emotionally and physically.....at times i felt like i was in pieces and felt real relaxed.
T+60 minutes (12:40 AM - 2:20 AM): I had qwit watching The Fifth Element because it was getting extremely stupid, at this time I could pick up on so many glitches/details, I went down stairs and wached Shaolin Soccer which was very moodlifting, funny, vivid,and it felt like i was in the movie....I wasn't getting much more effects this time,I had reached the plateau and was getting very mild visuals (though i wasn't focusing on any visuals though), the movie was REAL GOOD I really liked it alot and had recognized events that I had not recognized when I watched it when I was sober...when the movie was over it seemed I had went down in effects somewhat and was real relaxed and under control of anxiety,also was real tired.
T+3:40 hours (3:20 AM): at this time the effects have went down alot, basically just had mild relaxation and effect of something different was going on.
T+5:40 hours (5:20 AM): at this time I tried to go to sleep in my bed. when i was trying to go to sleep the visuals before one goes to sleep gets more vivid and and has a strong gravity high to it, not as tired as before at this time and still had relaxation effects on me.
T+14:20 hours (3:00 PM): at this time i have after effects that are mild and mostly feel cool about everything
Overall this experience was very enjoyable and pleasant and was almost just what i was looking for...I am going to do some tonight and increase the dose about 7 miligrams.
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.


Atlanta Marathon
Atlanta GA/USA
Web Site: www.atlantatrackclub.org
Race Winners
(50 runnings)
3:04:13 3:14:57 22 Nov
2007
2:40:17
3:24:12
23 Nov
2006
2:33:58
3:20:09
24 Nov
2005
2:37:43
3:12:52
25 Nov
2004
2:35:36
3:10:58
27 Nov
2003
2:38:42
2:54:29
28 Nov
2002
2:36:00
2:59:44
22 Nov
2001
2:33:27
2:55:57
23 Nov
2000
2:34:00
2:54:01
25 Nov
1999
2:36:40
2:56:20
26 Nov
1998
2:33:52
3:06:50
27 Nov
1997
2:32:57
3:15:22
28 Nov
1996
2:29:08
2:54:14
23 Nov
1995
2:31:43
2:56:29
24 Nov
1994
2:31:23
2:58:56
25 Nov
1993
2:32:41
3:09:03
26 Nov
1992
2:30:54
3:01:30
28 Nov
1991
2:28:52a
2:58:01a
22 Nov
1990
2:35:55a
2:59:25a
23 Nov
1989
2:30:26a
3:19:48a
24 Nov
1988
2:27:19a
2:57:06a
26 Nov
1987
2:32:15a
3:01:29a
27 Nov
1986
2:25:55a
3:08:29a
28 Nov
1985
2:30:13a
3:13:30a
22 Nov
1984
2:23:49a
2:58:41a
24 Nov
1983
2:29:10
3:16:21
25 Nov
1982
2:21:57
2:52:29
26 Nov
1981
2:21:36
2:52:05
20 Dec
1980
2:27:48
2:53:15
29 Dec
1979
2:14:49.9
3:04:05
30 Dec
1978
2:25:28
3:19:59
Jan
1978
2:27:57
3:17:39
18 Dec 1976
2:21:27
2:51:30
27 Dec 1975
2:17:49
2:57:11
28 Dec 1974
2:20:18
3:06:40
29 Dec 1973
2:20:58
30 Dec 1972
2:32:29
4:15:25
18 Dec 1971
2:16:18.2
26 Dec 1970
2:22:00
27 Dec 1969
2:34:59
21 Dec 1968
2:34:08.6
23 Dec 1967
2:26:02.6
18 Dec 1966
2:39:05
19 Dec 1965
2:37:55
19 Dec 1964
2:48:27
21 Dec 1963
2:57:20.7
Notes: An informal race was held in March 1963 with four finishers, won by Billy Daniel (GA/USA) and Dale Smith
(GA/USA) in 3:32:23. This race is not considered part of the series. The Atlanta Marathon was held on a double
loop course from 1963 thru 1980 when it was moved to a downtown venue. From 1984 thru 1991, the race was held from
Lithonia into Atlanta (pt/pt). The 1992 thru 1996 races were held on a loop course starting and finishing near
the Atlanta Fulton County Stadium. The 1997 and subsequent races have been held on a course closely following the
1996 Olympic Marathon course. The race was also known as the Peach Bowl Marathon thru 1975.
27 Oct
2013
2:39:04
J Penny (TN/USA)
2:54:52
Malia Mullen (CO/USA)
28 Oct
2012
2:23:25
Reuben Mwei Chebon (KEN)
3:09:09
Christine Rockey (SC/USA)
30 Oct
2011
2:33:45
Justin Gillette (MI/USA)
3:01:40
Melissa Gillette (MI/USA)
25 Nov
2010
---
only half marathon held
---
only half marathon held
26 Nov
2009
2:34:24
Nathan Tew (GA/USA)
Eileen Torres (GA/USA)
27 Nov
2008
2:39:52
Justin Leach (GA/USA)
Jacquelyn King (GA/USA)
Jeff Abbott (GA/USA)
Sandra Edith Torres (ARG)
Maurits vanderVeen (NED)
Bethany Hall (TN/USA)
Urs Jenzer (SUI)
Kimberly Lieb (GA/USA)
Dennis Bauer (GA/USA)
Ragan Petrie (GA/USA)
Scott Lowe (GA/USA)
Wendy Hall (GA/USA)
Mark Coughlin-2
Phyliss Tuggle (GA/USA)
John Bartlett (GA/USA)
Laura Drake-2
Paul Deaton-3
Laura Drake (FL/USA)
Mark Coughlin (GA/USA)
Lynn Holda-4
Paul Deaton-2
Kellie Smith (USA)
Paul Deaton (GA/USA)
Karen Miles (FL/USA)
Gabriel Helmlinger (USA)
Lynn Holda-3
Larry Baker (MI/USA)
Lynn Holda-2
Arega Abraha (ETH)
Lynn Holda (GA/USA)
Kevin Moats-7
Kami Semick (GA/USA)
Kevin Moats-6
Sherri Hintz (GA/USA)
Benji Durden (CO/USA)
Grace Wilson (GA/USA)
Robert Hollister (GA) & Paul Okerberg (GA/USA)
Sherri Odom (GA/USA)
Kevin Moats-5
Nicole Maxfield (GA/USA)
Kevin Moats-4
Barbara Adams-3
Wes Wessely-2
Barbara Adams-2
Kevin Moats-3
Kay Williamson (GA/USA)
Kevin Moats-2
Barbara Adams (GA/USA)
Kevin Moats (GA/USA)
Colleen Joiner-4
Todd Smoat (GA/USA)
Jane McCurdy (GA/USA)
Joel Majors (GA/USA)
Colleen Joiner-3
Richard Buerkle (GA/USA)
Colleen Joiner-2
Wes Wessely (GA/USA)
Colleen Joiner (GA/USA)
Mike Clark (AR/USA)
Iris Black (OH/USA)
Dan Henderson (GA/USA)
Julia Emmons (GA/USA)
Phil Bonfiglio (TN/USA)
Sally Applegate (OH/USA)
Lee Fidler (GA/USA)
Lisa Lorrain (GA/USA)
George Christopher (PA/USA)
Gayle Barron-3
Bruce Kidd (CAN)
Gayle Barron-2
Jeff Galloway-2
.
Don Kennedy (GA/USA)
Gayle Barron (GA/USA)
Neil Cusack (IRL)
Jack Bacheler (FL/USA)
Marshall Adams (NC/USA)
Ron Gaff (MA/USA)
Bruce LaBudde-3
Bruce LaBudde-2
Tom Graham (FL/USA)
Bruce LaBudde (GA/USA)
Jeff Galloway (FL/USA)
a => pt/pt course
Most Wins (1963-date)
Men
7
Kevin Moats (USA)
1984-86,1987-89,1992-93
Women
4
Colleen Joiner (USA)
1980-82,1984
Lynn Holda (GA/USA)
1994-96,1999
Prize Money (ADR qualified runners)
Year Men Women Total .... none none none 2012 $3,750 $3,350 $7,100 2011 2,300 2,500 4,800 Sums $6,050 $5,850 $11,900 Race Time Bias (sec/km) Insufficient data to calculate the race time bias.
Competitive Level
(1990-date)
Insufficient data to calculate the competitive level.
Quality Performances
Men <2:15 1 <2:16 1 <2:17 2 <2:18 3 <2:19 3 <2:20 3 Women No women have run under 2:50.
Finishers
Year Men Wmn M+F 2013 615<PHONE_NUMBER> 658<PHONE_NUMBER><PHONE_NUMBER> 2009 610<PHONE_NUMBER> 552<PHONE_NUMBER> 365<PHONE_NUMBER> 453<PHONE_NUMBER> 465<PHONE_NUMBER> 520<PHONE_NUMBER> 518<PHONE_NUMBER> 482<PHONE_NUMBER> 517<PHONE_NUMBER> 624<PHONE_NUMBER> 645<PHONE_NUMBER> 627<PHONE_NUMBER> 627<PHONE_NUMBER> 750<PHONE_NUMBER> 735<PHONE_NUMBER> 740<PHONE_NUMBER> 571<PHONE_NUMBER> 664<PHONE_NUMBER> 497<PHONE_NUMBER> 509<PHONE_NUMBER> 436 74 510 1988 453 69 522 1987 389 61 450 1986 360 72 432 1985 360 62 422 1984 491 74 565 1983 765 70 835 1982<PHONE_NUMBER> 1981 352 43 395 1980 119 11 130 1979 160 8 168 1978b 124 5 129 1978a 152 6 158 1976 130 4 134 1975 119 2 121 1974 121 1 122 1973 86 86 1972 101 1 102 1971<PHONE_NUMBER> 50 50 1969 39 39 1968 31 31 1967 31 31 1966 9 9 1965 10 10 1964 5 5 1963 8 8 Sums 19345 4737 24082 M/F 4.08
Last Updated on
28 Oct 2013
Contributors: Martin Rix, Marty Post, Jack Leydig (20), Franco Civai (2)


This application (https://splunkbase.splunk.com/app/4617/) provides compliance and triage dashboards for MITRE ATT&CK Framework that are fully integrated with Splunk Enterprise Security (https://splunkbase.splunk.com/app/263/) and Splunk ES Content Update (https://splunkbase.splunk.com/app/3449/) with drill-down capabilities.
Note: Although the app will work without ES Content Update, it is highly recommended since it comes with many correlation rules that have mitre_attack annotations already.
This application comes with predefined saved searches. Lookup Gen searches are scheduled to run daily after midnight.
mitre_all_rule_technique_lookup.csv
in order to generate a new lookup to properly display MITRE ATT&CK Compliance martix.


EFA enquiries
Contact form https://form.education...
For all enquiries for the Education Funding Agency
Guidance
Advice for institutions on the financial support young people may be able to access to help them participate in education and training.
This is information for institutions. It shows the financial support young people may be able to get to help them take part in education and training.
It covers a number of schemes, often described as student support schemes:
The 16 to 19 Bursary Fund is to support 16 to 19 year olds who are the most financially disadvantaged and those who most need help with the costs of staying in education.
Students aged over 19 can apply for the discretionary bursary if they have an Education, Health and Care Plan ( EHCP ) or are continuing on a course they started aged between 16 and 18 (a ‘19+ continuer’).
There are 2 types of 16 to 19 bursaries:
Students in one or more of the groups below can apply for a vulnerable bursary of up to £1,200.
Institutions do not receive an allocation of funds for vulnerable bursaries. They draw down the funds by submitting a vulnerable bursary funding claim form via the Student Bursary Support Service (SBSS) portal .
The eligible groups are students who are:
Institutions must verify that young people meet the criteria for vulnerable student bursaries.
Funding claims for vulnerable student payments in the 2017 to 2018 academic year are available online from August 2017 via the SBSS portal .
Discretionary bursaries are awards made by institutions to individual students. They are designed to overcome the individual barriers to participation a student faces.
 But, they should target students who cannot stay in education without financial help for things such as:
Institutions should assess the help each student needs on an individual basis and not make blanket or flat rate payments to all students.
Education Funding Agency ( EFA ) has published information to help institutions which includes:
To be eligible for a free meal, students must have enrolled in further education provision funded via EFA .
Free meals are targeted at disadvantaged students. For the purposes of free meals eligibility, the definition of disadvantage is students being in receipt of, or having parents who are in receipt of, one or more of the qualifying benefits.
A student is only eligible to receive a free meal when they, or a parent/guardian on their behalf, have made a successful application to their institution.
To be eligible for a free meal in the 2017 to 2018 academic year, students must be between 16 and 18 on 31 August 2017.
Students aged over 19 are eligible to apply if they have an EHCP or are continuing on a course they started aged between 16 and 18 (a ‘19+ continuer’).
Institutions must provide a free meal to eligible students for each day that they attend their study programme, where this is appropriate.
EFA expect institutions to provide a meal free of charge to eligible students or to fund the free meal via an electronic credit or voucher the student can then redeem. This might be on or off-site where institutions have made arrangements with nearby food outlets.
C2L helps young parents under the age of 20 to continue in, and return to education after the birth of a child. It does this by providing funding for childcare whilst the young parent is studying. The scheme can provide up to a maximum total amount of support of £160 per child per week or up to a maximum of £175 per child per week in London.
The young parent’s study programme must have some public funding.
C2L will only fund childcare provision registered with Ofsted.
Young parents, their education institution and their childcare provider must all meet the eligibility criteria to receive C2L.
Young parents must complete and submit a new C2L application for each academic year. Childcare providers and education institutions also need to provide information for each young parent who applies. Students submit applications online via the SBSS portal . Applications for the 2017 to 2018 academic year will be available in May 2017.
There are 2 schemes currently offering support with accommodation costs for students:
The Residential Bursary Fund ( RBF ) provides financial help for students attending an institution delivering specialist provision.
The Residential Support Scheme ( RSS ) provides financial help for students who need to live away from home to study because the substantial Level 2 or Level 3 qualification within their study programme is not available within daily travelling distance.
To be eligible to apply for either scheme in the 2017 to 2018 academic year, students must be aged between 16 and 18 on 31 August 2017. Students aged over 19 are eligible to apply if they have an EHCP or are continuing on a course they started aged between 16 and 18 (a ‘19+ continuer’).
RBF provides financial help towards the costs of accommodation. It is only for students attending one of the institutions delivering specialist provision. This provision requires the student to be resident to study because the qualification is not available locally and/or because it requires students to be available at unsociable hours on a regular basis. RBF enables young people to get a qualification in a specialist subject they would not be able to get at a non-specialist institution.
RBF provides a contribution towards residential costs for those students the institution identifies face the most significant financial barriers to participation. It is not intended to provide support to all students on residentially delivered provision at an institution; or necessarily to cover the full costs for each student.
RBF is intended to help students with the costs of accommodation whilst they are in further education and so EFA expect the majority of RBF funding to be used for accommodation costs. RBF can also help with travel costs between the student’s term time lodgings and the institution where appropriate. RBF cannot be used to pay for travel costs for non-residential students (those living at home).
Institutions decide which students get RBF and how much support they receive. Institutions must target funds towards those facing the most significant financial barriers to participation.
Students should contact their institution for more advice and to apply.
In October institutions need to complete a short management information return for EFA . This is to report the number of students who received RBF and the amount of funding spent.

The intention of RSS is to help support students with the costs of living away from home to study when the substantial Level 2 or Level 3 qualification that is part of their study programme is not available within reasonable daily travelling distance.
To receive RSS students must meet all the eligibility criteria.
The substantial qualification they are studying must:
Any other elements that make up the study programme are not relevant when assessing RSS eligibility.
RSS can provide annual support of up to £3,458 (up to £4,079 in the London area) towards accommodation costs. If the student is assessed as eligible, the amount they receive is based on their actual accommodation costs up to the maximum award thresholds.
Young people on apprenticeship programmes, or any education or training where they get a wage, are not eligible to apply for support.
Students should contact their institution for more advice and to apply.
Institutions must verify the availability of each student’s substantial Level 2 or Level 3 qualification with EFA . Institutions must not make any RSS awards until EFA has confirmed whether the student is eligible.
DaDA funding offers income assessed support for tuition fees and living costs at a number of high quality private dance and drama institutions in England. The intention of DaDA funding is to provide a contribution to the costs of participating for talented individuals who want to become professional actors and dancers.
To be eligible to receive DaDA funding in the 2017 to 2018 academic year, students must be aged between 16 and 23 years old at the start of the academic year (for a dance course) and between 18 and 23 years old at the start of the academic year (for an acting course).
Only students enrolled on the Trinity College London (TCL) Level 5 and 6 Professional Diplomas in Dance, Acting and Musical Theatre at one of the institutions that are part of the DaDA scheme are eligible to apply for funding.
Institutions can only give DaDA funding to students who meet the residency conditions.
DaDA living costs support is only available to students who are ordinarily resident in England, Scotland or Wales, in other words they are a permanent resident in the UK. Students from Northern Ireland and EU countries are not eligible to receive living cost support.
Institutions use nationally set household income bands to assess each student’s eligibility and the amount of support.
Institutions who would like more information about any of the student support schemes should contact:
Contact form https://form.education...
For all enquiries for the Education Funding Agency
Published: 4 February 2014
Updated: 7 March 2017


If you can dream it, you can code it!
TEAM
We are an enthusiastic group of women, full of energy to accept the constant changes that characterize the IT sector. We are determined to take charge of our professional lives. Thanks to the knowledge and values that Adalab provides us with, we are ready to face this new challenge and we are In the mood for Code!
Strengths
We have chosen this change, we are willing to fight for it with lots of motivation and hope. We are creating a strong team in which cooperation and communication prevail and mutual support is key for us to keep moving forward.
Weakness
We are junior developers in process, assimilating lots of information in a short period of time. Despite the long hours of practice and personal commitment, we are aware of this just being the beginning and that we still have many hours to invest in our training and therefore, much to learn.
ABOUT US
-
-
-
Paula Mara Moreno
I have a History and a Literature Theory and Comparative Literature Degree, and my superpower is never get tired of challenges. I’ve been communication and marketing responsible for Music Business and many alternative cultural projects; after being in top of dozens of stages and living on tour with rock and roll bands... there’s another exciting thing to do in my life, coding!
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Natalia Pelegero
Hi! I’m Natalia. I have always been very passionate about languages so, when I was 20 I moved to the city of Valencia where I completed a degree in English Studies and I also studied Italian and Portuguese. Since then I’ve lived in different places, which has allowed me to get to know other cultures, to learn Greek and a bit of Dutch and to put my language skills into practice. I met the Adalab programme at a time when I was looking for a career change and, here I am, eager to learn and ready to study a new language. A programming one this time!


| Blinded Variable | Description | Nbr Lvls in Train | Examples | |
| Household_ID | Unique identifier for each household | 4.31 million | 1,2,3,4,… | |
| Vehicle | Unique identifier for each vehicle within a household. | 7.75 million (Household x Veh_ID combinations) | 1,2,3,… | |
| Calendar_Year | Calendar year during which vehicle was insured (not blinded) | 3 | 2005, 2006, 2007 | |
| Model_Year | Model year of vehicle (not blinded) | 29 | 1981, 1982, … , 2009 | |
| Blind_Make | Vehicle make (example: ACME, coded A) | 74 | A,…,Z,AA,…,AZ,BA,…,BZ,… | |
| Blind_Model | Vehicle model (example: ACME Roadster, coded A.1) | 1,302 | A.1, A.2,…,A.15,…,B.1,… | |
| Blind_Submodel | Vehicle submodel (example: ACME Roadster LS, coded A.1.1). For the base model--e.g. no suffix after the model name--submodel is coded as 0. | 2,739 | A.1.1,…,B.2.0,…,D.5.2,… | |
| Cat1 | Categorical vehicle variable | 11 | A,B,C,…,? | |
| Cat2 | Categorical vehicle variable | 4 | A,B,C,…,? | |
| Cat3 | Categorical vehicle variable | 7 | A,B,C,…,? | |
| Cat4 | Categorical vehicle variable | 4 | A,B,C,…,? | |
| Cat5 | Categorical vehicle variable | 4 | A,B,C,…,? | |
| Cat6 | Categorical vehicle variable | 7 | A,B,C,…,? | |
| Cat7 | Categorical vehicle variable | 5 | A,B,C,…,? | |
| Cat8 | Categorical vehicle variable | 4 | A,B,C,…,? | |
| Cat9 | Categorical vehicle variable | 2 | A,B,C,…,? | |
| Cat10 | Categorical vehicle variable | 4 | A,B,C,…,? | |
| Cat11 | Categorical vehicle variable | 7 | A,B,C,…,? | |
| Cat12 | Categorical vehicle variable | 7 | A,B,C,…,? | |
| OrdCat | Ordered categorical vehicle variable | 8 | 1,2,3,…,? | |
| Var1 | Continuous vehicle variable, mean 0 stdev 1 | |||
| Var2 | Continuous vehicle variable, mean 0 stdev 1 | |||
| Var3 | Continuous vehicle variable, mean 0 stdev 1 | |||
| Var4 | Continuous vehicle variable, mean 0 stdev 1 | |||
| Var5 | Continuous vehicle variable, mean 0 stdev 1 | |||
| Var6 | Continuous vehicle variable, mean 0 stdev 1 | |||
| Var7 | Continuous vehicle variable, mean 0 stdev 1 | |||
| Var8 | Continuous vehicle variable, mean 0 stdev 1 | |||
| NVCat | Categorical non-vehicle variable | 15 | A,B,C,… | |
| NVVar1 | Continuous non-vehicle variable, mean 0 stdev 1 | |||
| NVVar2 | Continuous non-vehicle variable, mean 0 stdev 1 | |||
| NVVar3 | Continuous non-vehicle variable, mean 0 stdev 1 | |||
| NVVar4 | Continuous non-vehicle variable, mean 0 stdev 1 | |||
| Claim_Amount | Non-negative real number, 99% zeros on the training set |


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Welcome to the MM4SPA Project
This is the official project page of the MM4SPA joint research project. Check out the project description, project partners (DSHS, L3S, KINEXON) and latest news. We will continue to update this page with more material, announcements and publications as the project unfolds.