Datasets:
Reset23
/
the-stack-v2-new-python

Dataset card Data Studio Files
xet
Community
1
blob_id
stringlengths
40
40
directory_id
stringlengths
40
40
path
stringlengths
2
616
content_id
stringlengths
40
40
detected_licenses
listlengths
0
69
license_type
stringclasses
2 values
repo_name
stringlengths
5
118
snapshot_id
stringlengths
40
40
revision_id
stringlengths
40
40
branch_name
stringlengths
4
63
visit_date
timestamp[us]
revision_date
timestamp[us]
committer_date
timestamp[us]
github_id
int64
2.91k
686M
⌀
star_events_count
int64
0
209k
fork_events_count
int64
0
110k
gha_license_id
stringclasses
23 values
gha_event_created_at
timestamp[us]
gha_created_at
timestamp[us]
gha_language
stringclasses
220 values
src_encoding
stringclasses
30 values
language
stringclasses
1 value
is_vendor
bool
2 classes
is_generated
bool
2 classes
length_bytes
int64
2
10.3M
extension
stringclasses
257 values
content
stringlengths
2
10.3M
authors
listlengths
1
1
author_id
stringlengths
0
212
7ef19c5af8cd92e5af1c9714f074511ef1854b6e
1ab7b3f2aa63de8488ce7c466a67d367771aa1f2
/Ricardo_OS/Python_backend/venv/lib/python3.8/site-packages/wcwidth/table_zero.py
ab5150f51734a90985f4d011aeb89f7d575dd887
[ "MIT" ]
permissive
icl-rocketry/Avionics
9d39aeb11aba11115826fd73357b415026a7adad
95b7a061eabd6f2b607fba79e007186030f02720
refs/heads/master
2022-07-30T07:54:10.642930
2022-07-10T12:19:10
2022-07-10T12:19:10
216,184,670
9
1
MIT
2022-06-27T10:17:06
2019-10-19T09:57:07
C++
UTF-8
Python
false
false
310,230
py
"""Zero_Width table, created by bin/update-tables.py.""" # Generated: 2020-06-23T16:03:21.187024 ZERO_WIDTH = { '4.1.0': ( # Source: DerivedGeneralCategory-4.1.0.txt # Date: 2005-02-26, 02:35:50 GMT [MD] # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00486,), # Combining Cyrillic Titlo..Combining Cyrillic Psili (0x00488, 0x00489,), # Combining Cyrillic Hundr..Combining Cyrillic Milli (0x00591, 0x005b9,), # Hebrew Accent Etnahta ..Hebrew Point Holam (0x005bb, 0x005bd,), # Hebrew Point Qubuts ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x00615,), # Arabic Sign Sallallahou ..Arabic Small High Tah (0x0064b, 0x0065e,), # Arabic Fathatan ..Arabic Fatha With Two Do (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006de, 0x006e4,), # Arabic Start Of Rub El H..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x00901, 0x00902,), # Devanagari Sign Candrabi..Devanagari Sign Anusvara (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00954,), # Devanagari Stress Sign U..Devanagari Acute Accent (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b43,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00d41, 0x00d43,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f90, 0x00f97,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01032,), # Myanmar Vowel Sign Ai ..Myanmar Vowel Sign Ai (0x01036, 0x01037,), # Myanmar Sign Anusvara ..Myanmar Sign Dot Below (0x01039, 0x01039,), # Myanmar Sign Virama ..Myanmar Sign Virama (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0135f, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01dc0, 0x01dc3,), # Combining Dotted Grave A..Combining Suspension Mar (0x020d0, 0x020eb,), # Combining Left Harpoon A..Combining Long Double So (0x0302a, 0x0302f,), # Ideographic Level Tone M..Hangul Double Dot Tone M (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe23,), # Combining Ligature Left ..Combining Double Tilde R (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '5.0.0': ( # Source: DerivedGeneralCategory-5.0.0.txt # Date: 2006-02-27, 23:41:27 GMT [MD] # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00486,), # Combining Cyrillic Titlo..Combining Cyrillic Psili (0x00488, 0x00489,), # Combining Cyrillic Hundr..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x00615,), # Arabic Sign Sallallahou ..Arabic Small High Tah (0x0064b, 0x0065e,), # Arabic Fathatan ..Arabic Fatha With Two Do (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006de, 0x006e4,), # Arabic Start Of Rub El H..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x00901, 0x00902,), # Devanagari Sign Candrabi..Devanagari Sign Anusvara (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00954,), # Devanagari Stress Sign U..Devanagari Acute Accent (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b43,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d41, 0x00d43,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f90, 0x00f97,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01032,), # Myanmar Vowel Sign Ai ..Myanmar Vowel Sign Ai (0x01036, 0x01037,), # Myanmar Sign Anusvara ..Myanmar Sign Dot Below (0x01039, 0x01039,), # Myanmar Sign Virama ..Myanmar Sign Virama (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0135f, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01dc0, 0x01dca,), # Combining Dotted Grave A..Combining Latin Small Le (0x01dfe, 0x01dff,), # Combining Left Arrowhead..Combining Right Arrowhea (0x020d0, 0x020ef,), # Combining Left Harpoon A..Combining Right Arrow Be (0x0302a, 0x0302f,), # Ideographic Level Tone M..Hangul Double Dot Tone M (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe23,), # Combining Ligature Left ..Combining Double Tilde R (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '5.1.0': ( # Source: DerivedGeneralCategory-5.1.0.txt # Date: 2008-03-20, 17:54:57 GMT [MD] # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065e,), # Arabic Fathatan ..Arabic Fatha With Two Do (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006de, 0x006e4,), # Arabic Start Of Rub El H..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x00901, 0x00902,), # Devanagari Sign Candrabi..Devanagari Sign Anusvara (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00954,), # Devanagari Stress Sign U..Devanagari Acute Accent (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f90, 0x00f97,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0135f, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01dc0, 0x01de6,), # Combining Dotted Grave A..Combining Latin Small Le (0x01dfe, 0x01dff,), # Combining Left Arrowhead..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302f,), # Ideographic Level Tone M..Hangul Double Dot Tone M (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a67c, 0x0a67d,), # Combining Cyrillic Kavyk..Combining Cyrillic Payer (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c4,), # Saurashtra Sign Virama ..Saurashtra Sign Virama (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe26,), # Combining Ligature Left ..Combining Conjoining Mac (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '5.2.0': ( # Source: DerivedGeneralCategory-5.2.0.txt # Date: 2009-08-22, 04:58:21 GMT [MD] # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065e,), # Arabic Fathatan ..Arabic Fatha With Two Do (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006de, 0x006e4,), # Arabic Start Of Rub El H..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00900, 0x00902,), # Devanagari Sign Inverted..Devanagari Sign Anusvara (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00955,), # Devanagari Stress Sign U..Devanagari Vowel Sign Ca (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f90, 0x00f97,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135f, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01dc0, 0x01de6,), # Combining Dotted Grave A..Combining Latin Small Le (0x01dfd, 0x01dff,), # Combining Almost Equal T..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302f,), # Ideographic Level Tone M..Hangul Double Dot Tone M (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a67c, 0x0a67d,), # Combining Cyrillic Kavyk..Combining Cyrillic Payer (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c4,), # Saurashtra Sign Virama ..Saurashtra Sign Virama (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bc,), # Javanese Vowel Sign Pepe..Javanese Vowel Sign Pepe (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe26,), # Combining Ligature Left ..Combining Conjoining Mac (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x11080, 0x11081,), # Kaithi Sign Candrabindu ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '6.0.0': ( # Source: DerivedGeneralCategory-6.0.0.txt # Date: 2010-08-19, 00:48:09 GMT [MD] # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065f,), # Arabic Fathatan ..Arabic Wavy Hamza Below (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006df, 0x006e4,), # Arabic Small High Rounde..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00859, 0x0085b,), # Mandaic Affrication Mark..Mandaic Gemination Mark (0x00900, 0x00902,), # Devanagari Sign Inverted..Devanagari Sign Anusvara (0x0093a, 0x0093a,), # Devanagari Vowel Sign Oe..Devanagari Vowel Sign Oe (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00957,), # Devanagari Stress Sign U..Devanagari Vowel Sign Uu (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f8d, 0x00f97,), # Tibetan Subjoined Sign L..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135d, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01be6, 0x01be6,), # Batak Sign Tompi ..Batak Sign Tompi (0x01be8, 0x01be9,), # Batak Vowel Sign Pakpak ..Batak Vowel Sign Ee (0x01bed, 0x01bed,), # Batak Vowel Sign Karo O ..Batak Vowel Sign Karo O (0x01bef, 0x01bf1,), # Batak Vowel Sign U For S..Batak Consonant Sign H (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01dc0, 0x01de6,), # Combining Dotted Grave A..Combining Latin Small Le (0x01dfc, 0x01dff,), # Combining Double Inverte..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02d7f, 0x02d7f,), # Tifinagh Consonant Joine..Tifinagh Consonant Joine (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302f,), # Ideographic Level Tone M..Hangul Double Dot Tone M (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a67c, 0x0a67d,), # Combining Cyrillic Kavyk..Combining Cyrillic Payer (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c4,), # Saurashtra Sign Virama ..Saurashtra Sign Virama (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bc,), # Javanese Vowel Sign Pepe..Javanese Vowel Sign Pepe (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe26,), # Combining Ligature Left ..Combining Conjoining Mac (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x11001, 0x11001,), # Brahmi Sign Anusvara ..Brahmi Sign Anusvara (0x11038, 0x11046,), # Brahmi Vowel Sign Aa ..Brahmi Virama (0x11080, 0x11081,), # Kaithi Sign Candrabindu ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '6.1.0': ( # Source: DerivedGeneralCategory-6.1.0.txt # Date: 2011-11-27, 05:10:22 GMT [MD] # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065f,), # Arabic Fathatan ..Arabic Wavy Hamza Below (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006df, 0x006e4,), # Arabic Small High Rounde..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00859, 0x0085b,), # Mandaic Affrication Mark..Mandaic Gemination Mark (0x008e4, 0x008fe,), # Arabic Curly Fatha ..Arabic Damma With Dot (0x00900, 0x00902,), # Devanagari Sign Inverted..Devanagari Sign Anusvara (0x0093a, 0x0093a,), # Devanagari Vowel Sign Oe..Devanagari Vowel Sign Oe (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00957,), # Devanagari Stress Sign U..Devanagari Vowel Sign Uu (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f8d, 0x00f97,), # Tibetan Subjoined Sign L..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135d, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b4, 0x017b5,), # Khmer Vowel Inherent Aq ..Khmer Vowel Inherent Aa (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01bab, 0x01bab,), # Sundanese Sign Virama ..Sundanese Sign Virama (0x01be6, 0x01be6,), # Batak Sign Tompi ..Batak Sign Tompi (0x01be8, 0x01be9,), # Batak Vowel Sign Pakpak ..Batak Vowel Sign Ee (0x01bed, 0x01bed,), # Batak Vowel Sign Karo O ..Batak Vowel Sign Karo O (0x01bef, 0x01bf1,), # Batak Vowel Sign U For S..Batak Consonant Sign H (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01cf4, 0x01cf4,), # Vedic Tone Candra Above ..Vedic Tone Candra Above (0x01dc0, 0x01de6,), # Combining Dotted Grave A..Combining Latin Small Le (0x01dfc, 0x01dff,), # Combining Double Inverte..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02d7f, 0x02d7f,), # Tifinagh Consonant Joine..Tifinagh Consonant Joine (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302d,), # Ideographic Level Tone M..Ideographic Entering Ton (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a674, 0x0a67d,), # Combining Cyrillic Lette..Combining Cyrillic Payer (0x0a69f, 0x0a69f,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c4,), # Saurashtra Sign Virama ..Saurashtra Sign Virama (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bc,), # Javanese Vowel Sign Pepe..Javanese Vowel Sign Pepe (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0aaec, 0x0aaed,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0aaf6, 0x0aaf6,), # Meetei Mayek Virama ..Meetei Mayek Virama (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe26,), # Combining Ligature Left ..Combining Conjoining Mac (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x11001, 0x11001,), # Brahmi Sign Anusvara ..Brahmi Sign Anusvara (0x11038, 0x11046,), # Brahmi Vowel Sign Aa ..Brahmi Virama (0x11080, 0x11081,), # Kaithi Sign Candrabindu ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x11100, 0x11102,), # Chakma Sign Candrabindu ..Chakma Sign Visarga (0x11127, 0x1112b,), # Chakma Vowel Sign A ..Chakma Vowel Sign Uu (0x1112d, 0x11134,), # Chakma Vowel Sign Ai ..Chakma Maayyaa (0x11180, 0x11181,), # Sharada Sign Candrabindu..Sharada Sign Anusvara (0x111b6, 0x111be,), # Sharada Vowel Sign U ..Sharada Vowel Sign O (0x116ab, 0x116ab,), # Takri Sign Anusvara ..Takri Sign Anusvara (0x116ad, 0x116ad,), # Takri Vowel Sign Aa ..Takri Vowel Sign Aa (0x116b0, 0x116b5,), # Takri Vowel Sign U ..Takri Vowel Sign Au (0x116b7, 0x116b7,), # Takri Sign Nukta ..Takri Sign Nukta (0x16f8f, 0x16f92,), # Miao Tone Right ..Miao Tone Below (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '6.2.0': ( # Source: DerivedGeneralCategory-6.2.0.txt # Date: 2012-05-20, 00:42:34 GMT [MD] # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065f,), # Arabic Fathatan ..Arabic Wavy Hamza Below (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006df, 0x006e4,), # Arabic Small High Rounde..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00859, 0x0085b,), # Mandaic Affrication Mark..Mandaic Gemination Mark (0x008e4, 0x008fe,), # Arabic Curly Fatha ..Arabic Damma With Dot (0x00900, 0x00902,), # Devanagari Sign Inverted..Devanagari Sign Anusvara (0x0093a, 0x0093a,), # Devanagari Vowel Sign Oe..Devanagari Vowel Sign Oe (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00957,), # Devanagari Stress Sign U..Devanagari Vowel Sign Uu (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f8d, 0x00f97,), # Tibetan Subjoined Sign L..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135d, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b4, 0x017b5,), # Khmer Vowel Inherent Aq ..Khmer Vowel Inherent Aa (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01bab, 0x01bab,), # Sundanese Sign Virama ..Sundanese Sign Virama (0x01be6, 0x01be6,), # Batak Sign Tompi ..Batak Sign Tompi (0x01be8, 0x01be9,), # Batak Vowel Sign Pakpak ..Batak Vowel Sign Ee (0x01bed, 0x01bed,), # Batak Vowel Sign Karo O ..Batak Vowel Sign Karo O (0x01bef, 0x01bf1,), # Batak Vowel Sign U For S..Batak Consonant Sign H (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01cf4, 0x01cf4,), # Vedic Tone Candra Above ..Vedic Tone Candra Above (0x01dc0, 0x01de6,), # Combining Dotted Grave A..Combining Latin Small Le (0x01dfc, 0x01dff,), # Combining Double Inverte..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02d7f, 0x02d7f,), # Tifinagh Consonant Joine..Tifinagh Consonant Joine (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302d,), # Ideographic Level Tone M..Ideographic Entering Ton (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a674, 0x0a67d,), # Combining Cyrillic Lette..Combining Cyrillic Payer (0x0a69f, 0x0a69f,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c4,), # Saurashtra Sign Virama ..Saurashtra Sign Virama (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bc,), # Javanese Vowel Sign Pepe..Javanese Vowel Sign Pepe (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0aaec, 0x0aaed,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0aaf6, 0x0aaf6,), # Meetei Mayek Virama ..Meetei Mayek Virama (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe26,), # Combining Ligature Left ..Combining Conjoining Mac (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x11001, 0x11001,), # Brahmi Sign Anusvara ..Brahmi Sign Anusvara (0x11038, 0x11046,), # Brahmi Vowel Sign Aa ..Brahmi Virama (0x11080, 0x11081,), # Kaithi Sign Candrabindu ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x11100, 0x11102,), # Chakma Sign Candrabindu ..Chakma Sign Visarga (0x11127, 0x1112b,), # Chakma Vowel Sign A ..Chakma Vowel Sign Uu (0x1112d, 0x11134,), # Chakma Vowel Sign Ai ..Chakma Maayyaa (0x11180, 0x11181,), # Sharada Sign Candrabindu..Sharada Sign Anusvara (0x111b6, 0x111be,), # Sharada Vowel Sign U ..Sharada Vowel Sign O (0x116ab, 0x116ab,), # Takri Sign Anusvara ..Takri Sign Anusvara (0x116ad, 0x116ad,), # Takri Vowel Sign Aa ..Takri Vowel Sign Aa (0x116b0, 0x116b5,), # Takri Vowel Sign U ..Takri Vowel Sign Au (0x116b7, 0x116b7,), # Takri Sign Nukta ..Takri Sign Nukta (0x16f8f, 0x16f92,), # Miao Tone Right ..Miao Tone Below (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '6.3.0': ( # Source: DerivedGeneralCategory-6.3.0.txt # Date: 2013-07-05, 14:08:45 GMT [MD] # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065f,), # Arabic Fathatan ..Arabic Wavy Hamza Below (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006df, 0x006e4,), # Arabic Small High Rounde..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00859, 0x0085b,), # Mandaic Affrication Mark..Mandaic Gemination Mark (0x008e4, 0x008fe,), # Arabic Curly Fatha ..Arabic Damma With Dot (0x00900, 0x00902,), # Devanagari Sign Inverted..Devanagari Sign Anusvara (0x0093a, 0x0093a,), # Devanagari Vowel Sign Oe..Devanagari Vowel Sign Oe (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00957,), # Devanagari Stress Sign U..Devanagari Vowel Sign Uu (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f8d, 0x00f97,), # Tibetan Subjoined Sign L..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135d, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b4, 0x017b5,), # Khmer Vowel Inherent Aq ..Khmer Vowel Inherent Aa (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a1b, 0x01a1b,), # Buginese Vowel Sign Ae ..Buginese Vowel Sign Ae (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01bab, 0x01bab,), # Sundanese Sign Virama ..Sundanese Sign Virama (0x01be6, 0x01be6,), # Batak Sign Tompi ..Batak Sign Tompi (0x01be8, 0x01be9,), # Batak Vowel Sign Pakpak ..Batak Vowel Sign Ee (0x01bed, 0x01bed,), # Batak Vowel Sign Karo O ..Batak Vowel Sign Karo O (0x01bef, 0x01bf1,), # Batak Vowel Sign U For S..Batak Consonant Sign H (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01cf4, 0x01cf4,), # Vedic Tone Candra Above ..Vedic Tone Candra Above (0x01dc0, 0x01de6,), # Combining Dotted Grave A..Combining Latin Small Le (0x01dfc, 0x01dff,), # Combining Double Inverte..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02d7f, 0x02d7f,), # Tifinagh Consonant Joine..Tifinagh Consonant Joine (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302d,), # Ideographic Level Tone M..Ideographic Entering Ton (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a674, 0x0a67d,), # Combining Cyrillic Lette..Combining Cyrillic Payer (0x0a69f, 0x0a69f,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c4,), # Saurashtra Sign Virama ..Saurashtra Sign Virama (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bc,), # Javanese Vowel Sign Pepe..Javanese Vowel Sign Pepe (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0aaec, 0x0aaed,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0aaf6, 0x0aaf6,), # Meetei Mayek Virama ..Meetei Mayek Virama (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe26,), # Combining Ligature Left ..Combining Conjoining Mac (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x11001, 0x11001,), # Brahmi Sign Anusvara ..Brahmi Sign Anusvara (0x11038, 0x11046,), # Brahmi Vowel Sign Aa ..Brahmi Virama (0x11080, 0x11081,), # Kaithi Sign Candrabindu ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x11100, 0x11102,), # Chakma Sign Candrabindu ..Chakma Sign Visarga (0x11127, 0x1112b,), # Chakma Vowel Sign A ..Chakma Vowel Sign Uu (0x1112d, 0x11134,), # Chakma Vowel Sign Ai ..Chakma Maayyaa (0x11180, 0x11181,), # Sharada Sign Candrabindu..Sharada Sign Anusvara (0x111b6, 0x111be,), # Sharada Vowel Sign U ..Sharada Vowel Sign O (0x116ab, 0x116ab,), # Takri Sign Anusvara ..Takri Sign Anusvara (0x116ad, 0x116ad,), # Takri Vowel Sign Aa ..Takri Vowel Sign Aa (0x116b0, 0x116b5,), # Takri Vowel Sign U ..Takri Vowel Sign Au (0x116b7, 0x116b7,), # Takri Sign Nukta ..Takri Sign Nukta (0x16f8f, 0x16f92,), # Miao Tone Right ..Miao Tone Below (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '7.0.0': ( # Source: DerivedGeneralCategory-7.0.0.txt # Date: 2014-02-07, 18:42:12 GMT [MD] # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065f,), # Arabic Fathatan ..Arabic Wavy Hamza Below (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006df, 0x006e4,), # Arabic Small High Rounde..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00859, 0x0085b,), # Mandaic Affrication Mark..Mandaic Gemination Mark (0x008e4, 0x00902,), # Arabic Curly Fatha ..Devanagari Sign Anusvara (0x0093a, 0x0093a,), # Devanagari Vowel Sign Oe..Devanagari Vowel Sign Oe (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00957,), # Devanagari Stress Sign U..Devanagari Vowel Sign Uu (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c00, 0x00c00,), # Telugu Sign Combining Ca..Telugu Sign Combining Ca (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00c81, 0x00c81,), # Kannada Sign Candrabindu..Kannada Sign Candrabindu (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d01, 0x00d01,), # Malayalam Sign Candrabin..Malayalam Sign Candrabin (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f8d, 0x00f97,), # Tibetan Subjoined Sign L..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135d, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b4, 0x017b5,), # Khmer Vowel Inherent Aq ..Khmer Vowel Inherent Aa (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a1b, 0x01a1b,), # Buginese Vowel Sign Ae ..Buginese Vowel Sign Ae (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01ab0, 0x01abe,), # Combining Doubled Circum..Combining Parentheses Ov (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01bab, 0x01bad,), # Sundanese Sign Virama ..Sundanese Consonant Sign (0x01be6, 0x01be6,), # Batak Sign Tompi ..Batak Sign Tompi (0x01be8, 0x01be9,), # Batak Vowel Sign Pakpak ..Batak Vowel Sign Ee (0x01bed, 0x01bed,), # Batak Vowel Sign Karo O ..Batak Vowel Sign Karo O (0x01bef, 0x01bf1,), # Batak Vowel Sign U For S..Batak Consonant Sign H (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01cf4, 0x01cf4,), # Vedic Tone Candra Above ..Vedic Tone Candra Above (0x01cf8, 0x01cf9,), # Vedic Tone Ring Above ..Vedic Tone Double Ring A (0x01dc0, 0x01df5,), # Combining Dotted Grave A..Combining Up Tack Above (0x01dfc, 0x01dff,), # Combining Double Inverte..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02d7f, 0x02d7f,), # Tifinagh Consonant Joine..Tifinagh Consonant Joine (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302d,), # Ideographic Level Tone M..Ideographic Entering Ton (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a674, 0x0a67d,), # Combining Cyrillic Lette..Combining Cyrillic Payer (0x0a69f, 0x0a69f,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c4,), # Saurashtra Sign Virama ..Saurashtra Sign Virama (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bc,), # Javanese Vowel Sign Pepe..Javanese Vowel Sign Pepe (0x0a9e5, 0x0a9e5,), # Myanmar Sign Shan Saw ..Myanmar Sign Shan Saw (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa7c, 0x0aa7c,), # Myanmar Sign Tai Laing T..Myanmar Sign Tai Laing T (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0aaec, 0x0aaed,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0aaf6, 0x0aaf6,), # Meetei Mayek Virama ..Meetei Mayek Virama (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe2d,), # Combining Ligature Left ..Combining Conjoining Mac (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x102e0, 0x102e0,), # Coptic Epact Thousands M..Coptic Epact Thousands M (0x10376, 0x1037a,), # Combining Old Permic Let..Combining Old Permic Let (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x10ae5, 0x10ae6,), # Manichaean Abbreviation ..Manichaean Abbreviation (0x11001, 0x11001,), # Brahmi Sign Anusvara ..Brahmi Sign Anusvara (0x11038, 0x11046,), # Brahmi Vowel Sign Aa ..Brahmi Virama (0x1107f, 0x11081,), # Brahmi Number Joiner ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x11100, 0x11102,), # Chakma Sign Candrabindu ..Chakma Sign Visarga (0x11127, 0x1112b,), # Chakma Vowel Sign A ..Chakma Vowel Sign Uu (0x1112d, 0x11134,), # Chakma Vowel Sign Ai ..Chakma Maayyaa (0x11173, 0x11173,), # Mahajani Sign Nukta ..Mahajani Sign Nukta (0x11180, 0x11181,), # Sharada Sign Candrabindu..Sharada Sign Anusvara (0x111b6, 0x111be,), # Sharada Vowel Sign U ..Sharada Vowel Sign O (0x1122f, 0x11231,), # Khojki Vowel Sign U ..Khojki Vowel Sign Ai (0x11234, 0x11234,), # Khojki Sign Anusvara ..Khojki Sign Anusvara (0x11236, 0x11237,), # Khojki Sign Nukta ..Khojki Sign Shadda (0x112df, 0x112df,), # Khudawadi Sign Anusvara ..Khudawadi Sign Anusvara (0x112e3, 0x112ea,), # Khudawadi Vowel Sign U ..Khudawadi Sign Virama (0x11301, 0x11301,), # Grantha Sign Candrabindu..Grantha Sign Candrabindu (0x1133c, 0x1133c,), # Grantha Sign Nukta ..Grantha Sign Nukta (0x11340, 0x11340,), # Grantha Vowel Sign Ii ..Grantha Vowel Sign Ii (0x11366, 0x1136c,), # Combining Grantha Digit ..Combining Grantha Digit (0x11370, 0x11374,), # Combining Grantha Letter..Combining Grantha Letter (0x114b3, 0x114b8,), # Tirhuta Vowel Sign U ..Tirhuta Vowel Sign Vocal (0x114ba, 0x114ba,), # Tirhuta Vowel Sign Short..Tirhuta Vowel Sign Short (0x114bf, 0x114c0,), # Tirhuta Sign Candrabindu..Tirhuta Sign Anusvara (0x114c2, 0x114c3,), # Tirhuta Sign Virama ..Tirhuta Sign Nukta (0x115b2, 0x115b5,), # Siddham Vowel Sign U ..Siddham Vowel Sign Vocal (0x115bc, 0x115bd,), # Siddham Sign Candrabindu..Siddham Sign Anusvara (0x115bf, 0x115c0,), # Siddham Sign Virama ..Siddham Sign Nukta (0x11633, 0x1163a,), # Modi Vowel Sign U ..Modi Vowel Sign Ai (0x1163d, 0x1163d,), # Modi Sign Anusvara ..Modi Sign Anusvara (0x1163f, 0x11640,), # Modi Sign Virama ..Modi Sign Ardhacandra (0x116ab, 0x116ab,), # Takri Sign Anusvara ..Takri Sign Anusvara (0x116ad, 0x116ad,), # Takri Vowel Sign Aa ..Takri Vowel Sign Aa (0x116b0, 0x116b5,), # Takri Vowel Sign U ..Takri Vowel Sign Au (0x116b7, 0x116b7,), # Takri Sign Nukta ..Takri Sign Nukta (0x16af0, 0x16af4,), # Bassa Vah Combining High..Bassa Vah Combining High (0x16b30, 0x16b36,), # Pahawh Hmong Mark Cim Tu..Pahawh Hmong Mark Cim Ta (0x16f8f, 0x16f92,), # Miao Tone Right ..Miao Tone Below (0x1bc9d, 0x1bc9e,), # Duployan Thick Letter Se..Duployan Double Mark (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0x1e8d0, 0x1e8d6,), # Mende Kikakui Combining ..Mende Kikakui Combining (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '8.0.0': ( # Source: DerivedGeneralCategory-8.0.0.txt # Date: 2015-02-13, 13:47:11 GMT [MD] # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065f,), # Arabic Fathatan ..Arabic Wavy Hamza Below (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006df, 0x006e4,), # Arabic Small High Rounde..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00859, 0x0085b,), # Mandaic Affrication Mark..Mandaic Gemination Mark (0x008e3, 0x00902,), # Arabic Turned Damma Belo..Devanagari Sign Anusvara (0x0093a, 0x0093a,), # Devanagari Vowel Sign Oe..Devanagari Vowel Sign Oe (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00957,), # Devanagari Stress Sign U..Devanagari Vowel Sign Uu (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c00, 0x00c00,), # Telugu Sign Combining Ca..Telugu Sign Combining Ca (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00c81, 0x00c81,), # Kannada Sign Candrabindu..Kannada Sign Candrabindu (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d01, 0x00d01,), # Malayalam Sign Candrabin..Malayalam Sign Candrabin (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f8d, 0x00f97,), # Tibetan Subjoined Sign L..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135d, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b4, 0x017b5,), # Khmer Vowel Inherent Aq ..Khmer Vowel Inherent Aa (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a1b, 0x01a1b,), # Buginese Vowel Sign Ae ..Buginese Vowel Sign Ae (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01ab0, 0x01abe,), # Combining Doubled Circum..Combining Parentheses Ov (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01bab, 0x01bad,), # Sundanese Sign Virama ..Sundanese Consonant Sign (0x01be6, 0x01be6,), # Batak Sign Tompi ..Batak Sign Tompi (0x01be8, 0x01be9,), # Batak Vowel Sign Pakpak ..Batak Vowel Sign Ee (0x01bed, 0x01bed,), # Batak Vowel Sign Karo O ..Batak Vowel Sign Karo O (0x01bef, 0x01bf1,), # Batak Vowel Sign U For S..Batak Consonant Sign H (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01cf4, 0x01cf4,), # Vedic Tone Candra Above ..Vedic Tone Candra Above (0x01cf8, 0x01cf9,), # Vedic Tone Ring Above ..Vedic Tone Double Ring A (0x01dc0, 0x01df5,), # Combining Dotted Grave A..Combining Up Tack Above (0x01dfc, 0x01dff,), # Combining Double Inverte..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02d7f, 0x02d7f,), # Tifinagh Consonant Joine..Tifinagh Consonant Joine (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302d,), # Ideographic Level Tone M..Ideographic Entering Ton (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a674, 0x0a67d,), # Combining Cyrillic Lette..Combining Cyrillic Payer (0x0a69e, 0x0a69f,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c4,), # Saurashtra Sign Virama ..Saurashtra Sign Virama (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bc,), # Javanese Vowel Sign Pepe..Javanese Vowel Sign Pepe (0x0a9e5, 0x0a9e5,), # Myanmar Sign Shan Saw ..Myanmar Sign Shan Saw (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa7c, 0x0aa7c,), # Myanmar Sign Tai Laing T..Myanmar Sign Tai Laing T (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0aaec, 0x0aaed,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0aaf6, 0x0aaf6,), # Meetei Mayek Virama ..Meetei Mayek Virama (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe2f,), # Combining Ligature Left ..Combining Cyrillic Titlo (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x102e0, 0x102e0,), # Coptic Epact Thousands M..Coptic Epact Thousands M (0x10376, 0x1037a,), # Combining Old Permic Let..Combining Old Permic Let (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x10ae5, 0x10ae6,), # Manichaean Abbreviation ..Manichaean Abbreviation (0x11001, 0x11001,), # Brahmi Sign Anusvara ..Brahmi Sign Anusvara (0x11038, 0x11046,), # Brahmi Vowel Sign Aa ..Brahmi Virama (0x1107f, 0x11081,), # Brahmi Number Joiner ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x11100, 0x11102,), # Chakma Sign Candrabindu ..Chakma Sign Visarga (0x11127, 0x1112b,), # Chakma Vowel Sign A ..Chakma Vowel Sign Uu (0x1112d, 0x11134,), # Chakma Vowel Sign Ai ..Chakma Maayyaa (0x11173, 0x11173,), # Mahajani Sign Nukta ..Mahajani Sign Nukta (0x11180, 0x11181,), # Sharada Sign Candrabindu..Sharada Sign Anusvara (0x111b6, 0x111be,), # Sharada Vowel Sign U ..Sharada Vowel Sign O (0x111ca, 0x111cc,), # Sharada Sign Nukta ..Sharada Extra Short Vowe (0x1122f, 0x11231,), # Khojki Vowel Sign U ..Khojki Vowel Sign Ai (0x11234, 0x11234,), # Khojki Sign Anusvara ..Khojki Sign Anusvara (0x11236, 0x11237,), # Khojki Sign Nukta ..Khojki Sign Shadda (0x112df, 0x112df,), # Khudawadi Sign Anusvara ..Khudawadi Sign Anusvara (0x112e3, 0x112ea,), # Khudawadi Vowel Sign U ..Khudawadi Sign Virama (0x11300, 0x11301,), # Grantha Sign Combining A..Grantha Sign Candrabindu (0x1133c, 0x1133c,), # Grantha Sign Nukta ..Grantha Sign Nukta (0x11340, 0x11340,), # Grantha Vowel Sign Ii ..Grantha Vowel Sign Ii (0x11366, 0x1136c,), # Combining Grantha Digit ..Combining Grantha Digit (0x11370, 0x11374,), # Combining Grantha Letter..Combining Grantha Letter (0x114b3, 0x114b8,), # Tirhuta Vowel Sign U ..Tirhuta Vowel Sign Vocal (0x114ba, 0x114ba,), # Tirhuta Vowel Sign Short..Tirhuta Vowel Sign Short (0x114bf, 0x114c0,), # Tirhuta Sign Candrabindu..Tirhuta Sign Anusvara (0x114c2, 0x114c3,), # Tirhuta Sign Virama ..Tirhuta Sign Nukta (0x115b2, 0x115b5,), # Siddham Vowel Sign U ..Siddham Vowel Sign Vocal (0x115bc, 0x115bd,), # Siddham Sign Candrabindu..Siddham Sign Anusvara (0x115bf, 0x115c0,), # Siddham Sign Virama ..Siddham Sign Nukta (0x115dc, 0x115dd,), # Siddham Vowel Sign Alter..Siddham Vowel Sign Alter (0x11633, 0x1163a,), # Modi Vowel Sign U ..Modi Vowel Sign Ai (0x1163d, 0x1163d,), # Modi Sign Anusvara ..Modi Sign Anusvara (0x1163f, 0x11640,), # Modi Sign Virama ..Modi Sign Ardhacandra (0x116ab, 0x116ab,), # Takri Sign Anusvara ..Takri Sign Anusvara (0x116ad, 0x116ad,), # Takri Vowel Sign Aa ..Takri Vowel Sign Aa (0x116b0, 0x116b5,), # Takri Vowel Sign U ..Takri Vowel Sign Au (0x116b7, 0x116b7,), # Takri Sign Nukta ..Takri Sign Nukta (0x1171d, 0x1171f,), # Ahom Consonant Sign Medi..Ahom Consonant Sign Medi (0x11722, 0x11725,), # Ahom Vowel Sign I ..Ahom Vowel Sign Uu (0x11727, 0x1172b,), # Ahom Vowel Sign Aw ..Ahom Sign Killer (0x16af0, 0x16af4,), # Bassa Vah Combining High..Bassa Vah Combining High (0x16b30, 0x16b36,), # Pahawh Hmong Mark Cim Tu..Pahawh Hmong Mark Cim Ta (0x16f8f, 0x16f92,), # Miao Tone Right ..Miao Tone Below (0x1bc9d, 0x1bc9e,), # Duployan Thick Letter Se..Duployan Double Mark (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0x1da00, 0x1da36,), # Signwriting Head Rim ..Signwriting Air Sucking (0x1da3b, 0x1da6c,), # Signwriting Mouth Closed..Signwriting Excitement (0x1da75, 0x1da75,), # Signwriting Upper Body T..Signwriting Upper Body T (0x1da84, 0x1da84,), # Signwriting Location Hea..Signwriting Location Hea (0x1da9b, 0x1da9f,), # Signwriting Fill Modifie..Signwriting Fill Modifie (0x1daa1, 0x1daaf,), # Signwriting Rotation Mod..Signwriting Rotation Mod (0x1e8d0, 0x1e8d6,), # Mende Kikakui Combining ..Mende Kikakui Combining (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '9.0.0': ( # Source: DerivedGeneralCategory-9.0.0.txt # Date: 2016-06-01, 10:34:26 GMT # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065f,), # Arabic Fathatan ..Arabic Wavy Hamza Below (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006df, 0x006e4,), # Arabic Small High Rounde..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00859, 0x0085b,), # Mandaic Affrication Mark..Mandaic Gemination Mark (0x008d4, 0x008e1,), # Arabic Small High Word A..Arabic Small High Sign S (0x008e3, 0x00902,), # Arabic Turned Damma Belo..Devanagari Sign Anusvara (0x0093a, 0x0093a,), # Devanagari Vowel Sign Oe..Devanagari Vowel Sign Oe (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00957,), # Devanagari Stress Sign U..Devanagari Vowel Sign Uu (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c00, 0x00c00,), # Telugu Sign Combining Ca..Telugu Sign Combining Ca (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00c81, 0x00c81,), # Kannada Sign Candrabindu..Kannada Sign Candrabindu (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d01, 0x00d01,), # Malayalam Sign Candrabin..Malayalam Sign Candrabin (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f8d, 0x00f97,), # Tibetan Subjoined Sign L..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135d, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b4, 0x017b5,), # Khmer Vowel Inherent Aq ..Khmer Vowel Inherent Aa (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x01885, 0x01886,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a1b, 0x01a1b,), # Buginese Vowel Sign Ae ..Buginese Vowel Sign Ae (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01ab0, 0x01abe,), # Combining Doubled Circum..Combining Parentheses Ov (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01bab, 0x01bad,), # Sundanese Sign Virama ..Sundanese Consonant Sign (0x01be6, 0x01be6,), # Batak Sign Tompi ..Batak Sign Tompi (0x01be8, 0x01be9,), # Batak Vowel Sign Pakpak ..Batak Vowel Sign Ee (0x01bed, 0x01bed,), # Batak Vowel Sign Karo O ..Batak Vowel Sign Karo O (0x01bef, 0x01bf1,), # Batak Vowel Sign U For S..Batak Consonant Sign H (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01cf4, 0x01cf4,), # Vedic Tone Candra Above ..Vedic Tone Candra Above (0x01cf8, 0x01cf9,), # Vedic Tone Ring Above ..Vedic Tone Double Ring A (0x01dc0, 0x01df5,), # Combining Dotted Grave A..Combining Up Tack Above (0x01dfb, 0x01dff,), # Combining Deletion Mark ..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02d7f, 0x02d7f,), # Tifinagh Consonant Joine..Tifinagh Consonant Joine (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302d,), # Ideographic Level Tone M..Ideographic Entering Ton (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a674, 0x0a67d,), # Combining Cyrillic Lette..Combining Cyrillic Payer (0x0a69e, 0x0a69f,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c5,), # Saurashtra Sign Virama ..Saurashtra Sign Candrabi (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bc,), # Javanese Vowel Sign Pepe..Javanese Vowel Sign Pepe (0x0a9e5, 0x0a9e5,), # Myanmar Sign Shan Saw ..Myanmar Sign Shan Saw (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa7c, 0x0aa7c,), # Myanmar Sign Tai Laing T..Myanmar Sign Tai Laing T (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0aaec, 0x0aaed,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0aaf6, 0x0aaf6,), # Meetei Mayek Virama ..Meetei Mayek Virama (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe2f,), # Combining Ligature Left ..Combining Cyrillic Titlo (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x102e0, 0x102e0,), # Coptic Epact Thousands M..Coptic Epact Thousands M (0x10376, 0x1037a,), # Combining Old Permic Let..Combining Old Permic Let (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x10ae5, 0x10ae6,), # Manichaean Abbreviation ..Manichaean Abbreviation (0x11001, 0x11001,), # Brahmi Sign Anusvara ..Brahmi Sign Anusvara (0x11038, 0x11046,), # Brahmi Vowel Sign Aa ..Brahmi Virama (0x1107f, 0x11081,), # Brahmi Number Joiner ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x11100, 0x11102,), # Chakma Sign Candrabindu ..Chakma Sign Visarga (0x11127, 0x1112b,), # Chakma Vowel Sign A ..Chakma Vowel Sign Uu (0x1112d, 0x11134,), # Chakma Vowel Sign Ai ..Chakma Maayyaa (0x11173, 0x11173,), # Mahajani Sign Nukta ..Mahajani Sign Nukta (0x11180, 0x11181,), # Sharada Sign Candrabindu..Sharada Sign Anusvara (0x111b6, 0x111be,), # Sharada Vowel Sign U ..Sharada Vowel Sign O (0x111ca, 0x111cc,), # Sharada Sign Nukta ..Sharada Extra Short Vowe (0x1122f, 0x11231,), # Khojki Vowel Sign U ..Khojki Vowel Sign Ai (0x11234, 0x11234,), # Khojki Sign Anusvara ..Khojki Sign Anusvara (0x11236, 0x11237,), # Khojki Sign Nukta ..Khojki Sign Shadda (0x1123e, 0x1123e,), # Khojki Sign Sukun ..Khojki Sign Sukun (0x112df, 0x112df,), # Khudawadi Sign Anusvara ..Khudawadi Sign Anusvara (0x112e3, 0x112ea,), # Khudawadi Vowel Sign U ..Khudawadi Sign Virama (0x11300, 0x11301,), # Grantha Sign Combining A..Grantha Sign Candrabindu (0x1133c, 0x1133c,), # Grantha Sign Nukta ..Grantha Sign Nukta (0x11340, 0x11340,), # Grantha Vowel Sign Ii ..Grantha Vowel Sign Ii (0x11366, 0x1136c,), # Combining Grantha Digit ..Combining Grantha Digit (0x11370, 0x11374,), # Combining Grantha Letter..Combining Grantha Letter (0x11438, 0x1143f,), # Newa Vowel Sign U ..Newa Vowel Sign Ai (0x11442, 0x11444,), # Newa Sign Virama ..Newa Sign Anusvara (0x11446, 0x11446,), # Newa Sign Nukta ..Newa Sign Nukta (0x114b3, 0x114b8,), # Tirhuta Vowel Sign U ..Tirhuta Vowel Sign Vocal (0x114ba, 0x114ba,), # Tirhuta Vowel Sign Short..Tirhuta Vowel Sign Short (0x114bf, 0x114c0,), # Tirhuta Sign Candrabindu..Tirhuta Sign Anusvara (0x114c2, 0x114c3,), # Tirhuta Sign Virama ..Tirhuta Sign Nukta (0x115b2, 0x115b5,), # Siddham Vowel Sign U ..Siddham Vowel Sign Vocal (0x115bc, 0x115bd,), # Siddham Sign Candrabindu..Siddham Sign Anusvara (0x115bf, 0x115c0,), # Siddham Sign Virama ..Siddham Sign Nukta (0x115dc, 0x115dd,), # Siddham Vowel Sign Alter..Siddham Vowel Sign Alter (0x11633, 0x1163a,), # Modi Vowel Sign U ..Modi Vowel Sign Ai (0x1163d, 0x1163d,), # Modi Sign Anusvara ..Modi Sign Anusvara (0x1163f, 0x11640,), # Modi Sign Virama ..Modi Sign Ardhacandra (0x116ab, 0x116ab,), # Takri Sign Anusvara ..Takri Sign Anusvara (0x116ad, 0x116ad,), # Takri Vowel Sign Aa ..Takri Vowel Sign Aa (0x116b0, 0x116b5,), # Takri Vowel Sign U ..Takri Vowel Sign Au (0x116b7, 0x116b7,), # Takri Sign Nukta ..Takri Sign Nukta (0x1171d, 0x1171f,), # Ahom Consonant Sign Medi..Ahom Consonant Sign Medi (0x11722, 0x11725,), # Ahom Vowel Sign I ..Ahom Vowel Sign Uu (0x11727, 0x1172b,), # Ahom Vowel Sign Aw ..Ahom Sign Killer (0x11c30, 0x11c36,), # Bhaiksuki Vowel Sign I ..Bhaiksuki Vowel Sign Voc (0x11c38, 0x11c3d,), # Bhaiksuki Vowel Sign E ..Bhaiksuki Sign Anusvara (0x11c3f, 0x11c3f,), # Bhaiksuki Sign Virama ..Bhaiksuki Sign Virama (0x11c92, 0x11ca7,), # Marchen Subjoined Letter..Marchen Subjoined Letter (0x11caa, 0x11cb0,), # Marchen Subjoined Letter..Marchen Vowel Sign Aa (0x11cb2, 0x11cb3,), # Marchen Vowel Sign U ..Marchen Vowel Sign E (0x11cb5, 0x11cb6,), # Marchen Sign Anusvara ..Marchen Sign Candrabindu (0x16af0, 0x16af4,), # Bassa Vah Combining High..Bassa Vah Combining High (0x16b30, 0x16b36,), # Pahawh Hmong Mark Cim Tu..Pahawh Hmong Mark Cim Ta (0x16f8f, 0x16f92,), # Miao Tone Right ..Miao Tone Below (0x1bc9d, 0x1bc9e,), # Duployan Thick Letter Se..Duployan Double Mark (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0x1da00, 0x1da36,), # Signwriting Head Rim ..Signwriting Air Sucking (0x1da3b, 0x1da6c,), # Signwriting Mouth Closed..Signwriting Excitement (0x1da75, 0x1da75,), # Signwriting Upper Body T..Signwriting Upper Body T (0x1da84, 0x1da84,), # Signwriting Location Hea..Signwriting Location Hea (0x1da9b, 0x1da9f,), # Signwriting Fill Modifie..Signwriting Fill Modifie (0x1daa1, 0x1daaf,), # Signwriting Rotation Mod..Signwriting Rotation Mod (0x1e000, 0x1e006,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e008, 0x1e018,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e01b, 0x1e021,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e023, 0x1e024,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e026, 0x1e02a,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e8d0, 0x1e8d6,), # Mende Kikakui Combining ..Mende Kikakui Combining (0x1e944, 0x1e94a,), # Adlam Alif Lengthener ..Adlam Nukta (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '10.0.0': ( # Source: DerivedGeneralCategory-10.0.0.txt # Date: 2017-03-08, 08:41:49 GMT # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065f,), # Arabic Fathatan ..Arabic Wavy Hamza Below (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006df, 0x006e4,), # Arabic Small High Rounde..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00859, 0x0085b,), # Mandaic Affrication Mark..Mandaic Gemination Mark (0x008d4, 0x008e1,), # Arabic Small High Word A..Arabic Small High Sign S (0x008e3, 0x00902,), # Arabic Turned Damma Belo..Devanagari Sign Anusvara (0x0093a, 0x0093a,), # Devanagari Vowel Sign Oe..Devanagari Vowel Sign Oe (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00957,), # Devanagari Stress Sign U..Devanagari Vowel Sign Uu (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00afa, 0x00aff,), # Gujarati Sign Sukun ..Gujarati Sign Two-circle (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c00, 0x00c00,), # Telugu Sign Combining Ca..Telugu Sign Combining Ca (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00c81, 0x00c81,), # Kannada Sign Candrabindu..Kannada Sign Candrabindu (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d00, 0x00d01,), # Malayalam Sign Combining..Malayalam Sign Candrabin (0x00d3b, 0x00d3c,), # Malayalam Sign Vertical ..Malayalam Sign Circular (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f8d, 0x00f97,), # Tibetan Subjoined Sign L..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135d, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b4, 0x017b5,), # Khmer Vowel Inherent Aq ..Khmer Vowel Inherent Aa (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x01885, 0x01886,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a1b, 0x01a1b,), # Buginese Vowel Sign Ae ..Buginese Vowel Sign Ae (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01ab0, 0x01abe,), # Combining Doubled Circum..Combining Parentheses Ov (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01bab, 0x01bad,), # Sundanese Sign Virama ..Sundanese Consonant Sign (0x01be6, 0x01be6,), # Batak Sign Tompi ..Batak Sign Tompi (0x01be8, 0x01be9,), # Batak Vowel Sign Pakpak ..Batak Vowel Sign Ee (0x01bed, 0x01bed,), # Batak Vowel Sign Karo O ..Batak Vowel Sign Karo O (0x01bef, 0x01bf1,), # Batak Vowel Sign U For S..Batak Consonant Sign H (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01cf4, 0x01cf4,), # Vedic Tone Candra Above ..Vedic Tone Candra Above (0x01cf8, 0x01cf9,), # Vedic Tone Ring Above ..Vedic Tone Double Ring A (0x01dc0, 0x01df9,), # Combining Dotted Grave A..Combining Wide Inverted (0x01dfb, 0x01dff,), # Combining Deletion Mark ..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02d7f, 0x02d7f,), # Tifinagh Consonant Joine..Tifinagh Consonant Joine (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302d,), # Ideographic Level Tone M..Ideographic Entering Ton (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a674, 0x0a67d,), # Combining Cyrillic Lette..Combining Cyrillic Payer (0x0a69e, 0x0a69f,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c5,), # Saurashtra Sign Virama ..Saurashtra Sign Candrabi (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bc,), # Javanese Vowel Sign Pepe..Javanese Vowel Sign Pepe (0x0a9e5, 0x0a9e5,), # Myanmar Sign Shan Saw ..Myanmar Sign Shan Saw (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa7c, 0x0aa7c,), # Myanmar Sign Tai Laing T..Myanmar Sign Tai Laing T (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0aaec, 0x0aaed,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0aaf6, 0x0aaf6,), # Meetei Mayek Virama ..Meetei Mayek Virama (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe2f,), # Combining Ligature Left ..Combining Cyrillic Titlo (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x102e0, 0x102e0,), # Coptic Epact Thousands M..Coptic Epact Thousands M (0x10376, 0x1037a,), # Combining Old Permic Let..Combining Old Permic Let (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x10ae5, 0x10ae6,), # Manichaean Abbreviation ..Manichaean Abbreviation (0x11001, 0x11001,), # Brahmi Sign Anusvara ..Brahmi Sign Anusvara (0x11038, 0x11046,), # Brahmi Vowel Sign Aa ..Brahmi Virama (0x1107f, 0x11081,), # Brahmi Number Joiner ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x11100, 0x11102,), # Chakma Sign Candrabindu ..Chakma Sign Visarga (0x11127, 0x1112b,), # Chakma Vowel Sign A ..Chakma Vowel Sign Uu (0x1112d, 0x11134,), # Chakma Vowel Sign Ai ..Chakma Maayyaa (0x11173, 0x11173,), # Mahajani Sign Nukta ..Mahajani Sign Nukta (0x11180, 0x11181,), # Sharada Sign Candrabindu..Sharada Sign Anusvara (0x111b6, 0x111be,), # Sharada Vowel Sign U ..Sharada Vowel Sign O (0x111ca, 0x111cc,), # Sharada Sign Nukta ..Sharada Extra Short Vowe (0x1122f, 0x11231,), # Khojki Vowel Sign U ..Khojki Vowel Sign Ai (0x11234, 0x11234,), # Khojki Sign Anusvara ..Khojki Sign Anusvara (0x11236, 0x11237,), # Khojki Sign Nukta ..Khojki Sign Shadda (0x1123e, 0x1123e,), # Khojki Sign Sukun ..Khojki Sign Sukun (0x112df, 0x112df,), # Khudawadi Sign Anusvara ..Khudawadi Sign Anusvara (0x112e3, 0x112ea,), # Khudawadi Vowel Sign U ..Khudawadi Sign Virama (0x11300, 0x11301,), # Grantha Sign Combining A..Grantha Sign Candrabindu (0x1133c, 0x1133c,), # Grantha Sign Nukta ..Grantha Sign Nukta (0x11340, 0x11340,), # Grantha Vowel Sign Ii ..Grantha Vowel Sign Ii (0x11366, 0x1136c,), # Combining Grantha Digit ..Combining Grantha Digit (0x11370, 0x11374,), # Combining Grantha Letter..Combining Grantha Letter (0x11438, 0x1143f,), # Newa Vowel Sign U ..Newa Vowel Sign Ai (0x11442, 0x11444,), # Newa Sign Virama ..Newa Sign Anusvara (0x11446, 0x11446,), # Newa Sign Nukta ..Newa Sign Nukta (0x114b3, 0x114b8,), # Tirhuta Vowel Sign U ..Tirhuta Vowel Sign Vocal (0x114ba, 0x114ba,), # Tirhuta Vowel Sign Short..Tirhuta Vowel Sign Short (0x114bf, 0x114c0,), # Tirhuta Sign Candrabindu..Tirhuta Sign Anusvara (0x114c2, 0x114c3,), # Tirhuta Sign Virama ..Tirhuta Sign Nukta (0x115b2, 0x115b5,), # Siddham Vowel Sign U ..Siddham Vowel Sign Vocal (0x115bc, 0x115bd,), # Siddham Sign Candrabindu..Siddham Sign Anusvara (0x115bf, 0x115c0,), # Siddham Sign Virama ..Siddham Sign Nukta (0x115dc, 0x115dd,), # Siddham Vowel Sign Alter..Siddham Vowel Sign Alter (0x11633, 0x1163a,), # Modi Vowel Sign U ..Modi Vowel Sign Ai (0x1163d, 0x1163d,), # Modi Sign Anusvara ..Modi Sign Anusvara (0x1163f, 0x11640,), # Modi Sign Virama ..Modi Sign Ardhacandra (0x116ab, 0x116ab,), # Takri Sign Anusvara ..Takri Sign Anusvara (0x116ad, 0x116ad,), # Takri Vowel Sign Aa ..Takri Vowel Sign Aa (0x116b0, 0x116b5,), # Takri Vowel Sign U ..Takri Vowel Sign Au (0x116b7, 0x116b7,), # Takri Sign Nukta ..Takri Sign Nukta (0x1171d, 0x1171f,), # Ahom Consonant Sign Medi..Ahom Consonant Sign Medi (0x11722, 0x11725,), # Ahom Vowel Sign I ..Ahom Vowel Sign Uu (0x11727, 0x1172b,), # Ahom Vowel Sign Aw ..Ahom Sign Killer (0x11a01, 0x11a06,), # Zanabazar Square Vowel S..Zanabazar Square Vowel S (0x11a09, 0x11a0a,), # Zanabazar Square Vowel S..Zanabazar Square Vowel L (0x11a33, 0x11a38,), # Zanabazar Square Final C..Zanabazar Square Sign An (0x11a3b, 0x11a3e,), # Zanabazar Square Cluster..Zanabazar Square Cluster (0x11a47, 0x11a47,), # Zanabazar Square Subjoin..Zanabazar Square Subjoin (0x11a51, 0x11a56,), # Soyombo Vowel Sign I ..Soyombo Vowel Sign Oe (0x11a59, 0x11a5b,), # Soyombo Vowel Sign Vocal..Soyombo Vowel Length Mar (0x11a8a, 0x11a96,), # Soyombo Final Consonant ..Soyombo Sign Anusvara (0x11a98, 0x11a99,), # Soyombo Gemination Mark ..Soyombo Subjoiner (0x11c30, 0x11c36,), # Bhaiksuki Vowel Sign I ..Bhaiksuki Vowel Sign Voc (0x11c38, 0x11c3d,), # Bhaiksuki Vowel Sign E ..Bhaiksuki Sign Anusvara (0x11c3f, 0x11c3f,), # Bhaiksuki Sign Virama ..Bhaiksuki Sign Virama (0x11c92, 0x11ca7,), # Marchen Subjoined Letter..Marchen Subjoined Letter (0x11caa, 0x11cb0,), # Marchen Subjoined Letter..Marchen Vowel Sign Aa (0x11cb2, 0x11cb3,), # Marchen Vowel Sign U ..Marchen Vowel Sign E (0x11cb5, 0x11cb6,), # Marchen Sign Anusvara ..Marchen Sign Candrabindu (0x11d31, 0x11d36,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3a, 0x11d3a,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3c, 0x11d3d,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3f, 0x11d45,), # Masaram Gondi Vowel Sign..Masaram Gondi Virama (0x11d47, 0x11d47,), # Masaram Gondi Ra-kara ..Masaram Gondi Ra-kara (0x16af0, 0x16af4,), # Bassa Vah Combining High..Bassa Vah Combining High (0x16b30, 0x16b36,), # Pahawh Hmong Mark Cim Tu..Pahawh Hmong Mark Cim Ta (0x16f8f, 0x16f92,), # Miao Tone Right ..Miao Tone Below (0x1bc9d, 0x1bc9e,), # Duployan Thick Letter Se..Duployan Double Mark (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0x1da00, 0x1da36,), # Signwriting Head Rim ..Signwriting Air Sucking (0x1da3b, 0x1da6c,), # Signwriting Mouth Closed..Signwriting Excitement (0x1da75, 0x1da75,), # Signwriting Upper Body T..Signwriting Upper Body T (0x1da84, 0x1da84,), # Signwriting Location Hea..Signwriting Location Hea (0x1da9b, 0x1da9f,), # Signwriting Fill Modifie..Signwriting Fill Modifie (0x1daa1, 0x1daaf,), # Signwriting Rotation Mod..Signwriting Rotation Mod (0x1e000, 0x1e006,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e008, 0x1e018,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e01b, 0x1e021,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e023, 0x1e024,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e026, 0x1e02a,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e8d0, 0x1e8d6,), # Mende Kikakui Combining ..Mende Kikakui Combining (0x1e944, 0x1e94a,), # Adlam Alif Lengthener ..Adlam Nukta (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '11.0.0': ( # Source: DerivedGeneralCategory-11.0.0.txt # Date: 2018-02-21, 05:34:04 GMT # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065f,), # Arabic Fathatan ..Arabic Wavy Hamza Below (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006df, 0x006e4,), # Arabic Small High Rounde..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x007fd, 0x007fd,), # Nko Dantayalan ..Nko Dantayalan (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00859, 0x0085b,), # Mandaic Affrication Mark..Mandaic Gemination Mark (0x008d3, 0x008e1,), # Arabic Small Low Waw ..Arabic Small High Sign S (0x008e3, 0x00902,), # Arabic Turned Damma Belo..Devanagari Sign Anusvara (0x0093a, 0x0093a,), # Devanagari Vowel Sign Oe..Devanagari Vowel Sign Oe (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00957,), # Devanagari Stress Sign U..Devanagari Vowel Sign Uu (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x009fe, 0x009fe,), # Bengali Sandhi Mark ..Bengali Sandhi Mark (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00afa, 0x00aff,), # Gujarati Sign Sukun ..Gujarati Sign Two-circle (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c00, 0x00c00,), # Telugu Sign Combining Ca..Telugu Sign Combining Ca (0x00c04, 0x00c04,), # Telugu Sign Combining An..Telugu Sign Combining An (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00c81, 0x00c81,), # Kannada Sign Candrabindu..Kannada Sign Candrabindu (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d00, 0x00d01,), # Malayalam Sign Combining..Malayalam Sign Candrabin (0x00d3b, 0x00d3c,), # Malayalam Sign Vertical ..Malayalam Sign Circular (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00eb9,), # Lao Vowel Sign I ..Lao Vowel Sign Uu (0x00ebb, 0x00ebc,), # Lao Vowel Sign Mai Kon ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f8d, 0x00f97,), # Tibetan Subjoined Sign L..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135d, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b4, 0x017b5,), # Khmer Vowel Inherent Aq ..Khmer Vowel Inherent Aa (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x01885, 0x01886,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a1b, 0x01a1b,), # Buginese Vowel Sign Ae ..Buginese Vowel Sign Ae (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01ab0, 0x01abe,), # Combining Doubled Circum..Combining Parentheses Ov (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01bab, 0x01bad,), # Sundanese Sign Virama ..Sundanese Consonant Sign (0x01be6, 0x01be6,), # Batak Sign Tompi ..Batak Sign Tompi (0x01be8, 0x01be9,), # Batak Vowel Sign Pakpak ..Batak Vowel Sign Ee (0x01bed, 0x01bed,), # Batak Vowel Sign Karo O ..Batak Vowel Sign Karo O (0x01bef, 0x01bf1,), # Batak Vowel Sign U For S..Batak Consonant Sign H (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01cf4, 0x01cf4,), # Vedic Tone Candra Above ..Vedic Tone Candra Above (0x01cf8, 0x01cf9,), # Vedic Tone Ring Above ..Vedic Tone Double Ring A (0x01dc0, 0x01df9,), # Combining Dotted Grave A..Combining Wide Inverted (0x01dfb, 0x01dff,), # Combining Deletion Mark ..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02d7f, 0x02d7f,), # Tifinagh Consonant Joine..Tifinagh Consonant Joine (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302d,), # Ideographic Level Tone M..Ideographic Entering Ton (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a674, 0x0a67d,), # Combining Cyrillic Lette..Combining Cyrillic Payer (0x0a69e, 0x0a69f,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c5,), # Saurashtra Sign Virama ..Saurashtra Sign Candrabi (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a8ff, 0x0a8ff,), # Devanagari Vowel Sign Ay..Devanagari Vowel Sign Ay (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bc,), # Javanese Vowel Sign Pepe..Javanese Vowel Sign Pepe (0x0a9e5, 0x0a9e5,), # Myanmar Sign Shan Saw ..Myanmar Sign Shan Saw (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa7c, 0x0aa7c,), # Myanmar Sign Tai Laing T..Myanmar Sign Tai Laing T (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0aaec, 0x0aaed,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0aaf6, 0x0aaf6,), # Meetei Mayek Virama ..Meetei Mayek Virama (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe2f,), # Combining Ligature Left ..Combining Cyrillic Titlo (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x102e0, 0x102e0,), # Coptic Epact Thousands M..Coptic Epact Thousands M (0x10376, 0x1037a,), # Combining Old Permic Let..Combining Old Permic Let (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x10ae5, 0x10ae6,), # Manichaean Abbreviation ..Manichaean Abbreviation (0x10d24, 0x10d27,), # Hanifi Rohingya Sign Har..Hanifi Rohingya Sign Tas (0x10f46, 0x10f50,), # Sogdian Combining Dot Be..Sogdian Combining Stroke (0x11001, 0x11001,), # Brahmi Sign Anusvara ..Brahmi Sign Anusvara (0x11038, 0x11046,), # Brahmi Vowel Sign Aa ..Brahmi Virama (0x1107f, 0x11081,), # Brahmi Number Joiner ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x11100, 0x11102,), # Chakma Sign Candrabindu ..Chakma Sign Visarga (0x11127, 0x1112b,), # Chakma Vowel Sign A ..Chakma Vowel Sign Uu (0x1112d, 0x11134,), # Chakma Vowel Sign Ai ..Chakma Maayyaa (0x11173, 0x11173,), # Mahajani Sign Nukta ..Mahajani Sign Nukta (0x11180, 0x11181,), # Sharada Sign Candrabindu..Sharada Sign Anusvara (0x111b6, 0x111be,), # Sharada Vowel Sign U ..Sharada Vowel Sign O (0x111c9, 0x111cc,), # Sharada Sandhi Mark ..Sharada Extra Short Vowe (0x1122f, 0x11231,), # Khojki Vowel Sign U ..Khojki Vowel Sign Ai (0x11234, 0x11234,), # Khojki Sign Anusvara ..Khojki Sign Anusvara (0x11236, 0x11237,), # Khojki Sign Nukta ..Khojki Sign Shadda (0x1123e, 0x1123e,), # Khojki Sign Sukun ..Khojki Sign Sukun (0x112df, 0x112df,), # Khudawadi Sign Anusvara ..Khudawadi Sign Anusvara (0x112e3, 0x112ea,), # Khudawadi Vowel Sign U ..Khudawadi Sign Virama (0x11300, 0x11301,), # Grantha Sign Combining A..Grantha Sign Candrabindu (0x1133b, 0x1133c,), # Combining Bindu Below ..Grantha Sign Nukta (0x11340, 0x11340,), # Grantha Vowel Sign Ii ..Grantha Vowel Sign Ii (0x11366, 0x1136c,), # Combining Grantha Digit ..Combining Grantha Digit (0x11370, 0x11374,), # Combining Grantha Letter..Combining Grantha Letter (0x11438, 0x1143f,), # Newa Vowel Sign U ..Newa Vowel Sign Ai (0x11442, 0x11444,), # Newa Sign Virama ..Newa Sign Anusvara (0x11446, 0x11446,), # Newa Sign Nukta ..Newa Sign Nukta (0x1145e, 0x1145e,), # Newa Sandhi Mark ..Newa Sandhi Mark (0x114b3, 0x114b8,), # Tirhuta Vowel Sign U ..Tirhuta Vowel Sign Vocal (0x114ba, 0x114ba,), # Tirhuta Vowel Sign Short..Tirhuta Vowel Sign Short (0x114bf, 0x114c0,), # Tirhuta Sign Candrabindu..Tirhuta Sign Anusvara (0x114c2, 0x114c3,), # Tirhuta Sign Virama ..Tirhuta Sign Nukta (0x115b2, 0x115b5,), # Siddham Vowel Sign U ..Siddham Vowel Sign Vocal (0x115bc, 0x115bd,), # Siddham Sign Candrabindu..Siddham Sign Anusvara (0x115bf, 0x115c0,), # Siddham Sign Virama ..Siddham Sign Nukta (0x115dc, 0x115dd,), # Siddham Vowel Sign Alter..Siddham Vowel Sign Alter (0x11633, 0x1163a,), # Modi Vowel Sign U ..Modi Vowel Sign Ai (0x1163d, 0x1163d,), # Modi Sign Anusvara ..Modi Sign Anusvara (0x1163f, 0x11640,), # Modi Sign Virama ..Modi Sign Ardhacandra (0x116ab, 0x116ab,), # Takri Sign Anusvara ..Takri Sign Anusvara (0x116ad, 0x116ad,), # Takri Vowel Sign Aa ..Takri Vowel Sign Aa (0x116b0, 0x116b5,), # Takri Vowel Sign U ..Takri Vowel Sign Au (0x116b7, 0x116b7,), # Takri Sign Nukta ..Takri Sign Nukta (0x1171d, 0x1171f,), # Ahom Consonant Sign Medi..Ahom Consonant Sign Medi (0x11722, 0x11725,), # Ahom Vowel Sign I ..Ahom Vowel Sign Uu (0x11727, 0x1172b,), # Ahom Vowel Sign Aw ..Ahom Sign Killer (0x1182f, 0x11837,), # Dogra Vowel Sign U ..Dogra Sign Anusvara (0x11839, 0x1183a,), # Dogra Sign Virama ..Dogra Sign Nukta (0x11a01, 0x11a0a,), # Zanabazar Square Vowel S..Zanabazar Square Vowel L (0x11a33, 0x11a38,), # Zanabazar Square Final C..Zanabazar Square Sign An (0x11a3b, 0x11a3e,), # Zanabazar Square Cluster..Zanabazar Square Cluster (0x11a47, 0x11a47,), # Zanabazar Square Subjoin..Zanabazar Square Subjoin (0x11a51, 0x11a56,), # Soyombo Vowel Sign I ..Soyombo Vowel Sign Oe (0x11a59, 0x11a5b,), # Soyombo Vowel Sign Vocal..Soyombo Vowel Length Mar (0x11a8a, 0x11a96,), # Soyombo Final Consonant ..Soyombo Sign Anusvara (0x11a98, 0x11a99,), # Soyombo Gemination Mark ..Soyombo Subjoiner (0x11c30, 0x11c36,), # Bhaiksuki Vowel Sign I ..Bhaiksuki Vowel Sign Voc (0x11c38, 0x11c3d,), # Bhaiksuki Vowel Sign E ..Bhaiksuki Sign Anusvara (0x11c3f, 0x11c3f,), # Bhaiksuki Sign Virama ..Bhaiksuki Sign Virama (0x11c92, 0x11ca7,), # Marchen Subjoined Letter..Marchen Subjoined Letter (0x11caa, 0x11cb0,), # Marchen Subjoined Letter..Marchen Vowel Sign Aa (0x11cb2, 0x11cb3,), # Marchen Vowel Sign U ..Marchen Vowel Sign E (0x11cb5, 0x11cb6,), # Marchen Sign Anusvara ..Marchen Sign Candrabindu (0x11d31, 0x11d36,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3a, 0x11d3a,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3c, 0x11d3d,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3f, 0x11d45,), # Masaram Gondi Vowel Sign..Masaram Gondi Virama (0x11d47, 0x11d47,), # Masaram Gondi Ra-kara ..Masaram Gondi Ra-kara (0x11d90, 0x11d91,), # Gunjala Gondi Vowel Sign..Gunjala Gondi Vowel Sign (0x11d95, 0x11d95,), # Gunjala Gondi Sign Anusv..Gunjala Gondi Sign Anusv (0x11d97, 0x11d97,), # Gunjala Gondi Virama ..Gunjala Gondi Virama (0x11ef3, 0x11ef4,), # Makasar Vowel Sign I ..Makasar Vowel Sign U (0x16af0, 0x16af4,), # Bassa Vah Combining High..Bassa Vah Combining High (0x16b30, 0x16b36,), # Pahawh Hmong Mark Cim Tu..Pahawh Hmong Mark Cim Ta (0x16f8f, 0x16f92,), # Miao Tone Right ..Miao Tone Below (0x1bc9d, 0x1bc9e,), # Duployan Thick Letter Se..Duployan Double Mark (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0x1da00, 0x1da36,), # Signwriting Head Rim ..Signwriting Air Sucking (0x1da3b, 0x1da6c,), # Signwriting Mouth Closed..Signwriting Excitement (0x1da75, 0x1da75,), # Signwriting Upper Body T..Signwriting Upper Body T (0x1da84, 0x1da84,), # Signwriting Location Hea..Signwriting Location Hea (0x1da9b, 0x1da9f,), # Signwriting Fill Modifie..Signwriting Fill Modifie (0x1daa1, 0x1daaf,), # Signwriting Rotation Mod..Signwriting Rotation Mod (0x1e000, 0x1e006,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e008, 0x1e018,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e01b, 0x1e021,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e023, 0x1e024,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e026, 0x1e02a,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e8d0, 0x1e8d6,), # Mende Kikakui Combining ..Mende Kikakui Combining (0x1e944, 0x1e94a,), # Adlam Alif Lengthener ..Adlam Nukta (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '12.0.0': ( # Source: DerivedGeneralCategory-12.0.0.txt # Date: 2019-01-22, 08:18:28 GMT # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065f,), # Arabic Fathatan ..Arabic Wavy Hamza Below (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006df, 0x006e4,), # Arabic Small High Rounde..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x007fd, 0x007fd,), # Nko Dantayalan ..Nko Dantayalan (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00859, 0x0085b,), # Mandaic Affrication Mark..Mandaic Gemination Mark (0x008d3, 0x008e1,), # Arabic Small Low Waw ..Arabic Small High Sign S (0x008e3, 0x00902,), # Arabic Turned Damma Belo..Devanagari Sign Anusvara (0x0093a, 0x0093a,), # Devanagari Vowel Sign Oe..Devanagari Vowel Sign Oe (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00957,), # Devanagari Stress Sign U..Devanagari Vowel Sign Uu (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x009fe, 0x009fe,), # Bengali Sandhi Mark ..Bengali Sandhi Mark (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00afa, 0x00aff,), # Gujarati Sign Sukun ..Gujarati Sign Two-circle (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c00, 0x00c00,), # Telugu Sign Combining Ca..Telugu Sign Combining Ca (0x00c04, 0x00c04,), # Telugu Sign Combining An..Telugu Sign Combining An (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00c81, 0x00c81,), # Kannada Sign Candrabindu..Kannada Sign Candrabindu (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d00, 0x00d01,), # Malayalam Sign Combining..Malayalam Sign Candrabin (0x00d3b, 0x00d3c,), # Malayalam Sign Vertical ..Malayalam Sign Circular (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00ebc,), # Lao Vowel Sign I ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f8d, 0x00f97,), # Tibetan Subjoined Sign L..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135d, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b4, 0x017b5,), # Khmer Vowel Inherent Aq ..Khmer Vowel Inherent Aa (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x01885, 0x01886,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a1b, 0x01a1b,), # Buginese Vowel Sign Ae ..Buginese Vowel Sign Ae (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01ab0, 0x01abe,), # Combining Doubled Circum..Combining Parentheses Ov (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01bab, 0x01bad,), # Sundanese Sign Virama ..Sundanese Consonant Sign (0x01be6, 0x01be6,), # Batak Sign Tompi ..Batak Sign Tompi (0x01be8, 0x01be9,), # Batak Vowel Sign Pakpak ..Batak Vowel Sign Ee (0x01bed, 0x01bed,), # Batak Vowel Sign Karo O ..Batak Vowel Sign Karo O (0x01bef, 0x01bf1,), # Batak Vowel Sign U For S..Batak Consonant Sign H (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01cf4, 0x01cf4,), # Vedic Tone Candra Above ..Vedic Tone Candra Above (0x01cf8, 0x01cf9,), # Vedic Tone Ring Above ..Vedic Tone Double Ring A (0x01dc0, 0x01df9,), # Combining Dotted Grave A..Combining Wide Inverted (0x01dfb, 0x01dff,), # Combining Deletion Mark ..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02d7f, 0x02d7f,), # Tifinagh Consonant Joine..Tifinagh Consonant Joine (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302d,), # Ideographic Level Tone M..Ideographic Entering Ton (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a674, 0x0a67d,), # Combining Cyrillic Lette..Combining Cyrillic Payer (0x0a69e, 0x0a69f,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c5,), # Saurashtra Sign Virama ..Saurashtra Sign Candrabi (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a8ff, 0x0a8ff,), # Devanagari Vowel Sign Ay..Devanagari Vowel Sign Ay (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bd,), # Javanese Vowel Sign Pepe..Javanese Consonant Sign (0x0a9e5, 0x0a9e5,), # Myanmar Sign Shan Saw ..Myanmar Sign Shan Saw (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa7c, 0x0aa7c,), # Myanmar Sign Tai Laing T..Myanmar Sign Tai Laing T (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0aaec, 0x0aaed,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0aaf6, 0x0aaf6,), # Meetei Mayek Virama ..Meetei Mayek Virama (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe2f,), # Combining Ligature Left ..Combining Cyrillic Titlo (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x102e0, 0x102e0,), # Coptic Epact Thousands M..Coptic Epact Thousands M (0x10376, 0x1037a,), # Combining Old Permic Let..Combining Old Permic Let (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x10ae5, 0x10ae6,), # Manichaean Abbreviation ..Manichaean Abbreviation (0x10d24, 0x10d27,), # Hanifi Rohingya Sign Har..Hanifi Rohingya Sign Tas (0x10f46, 0x10f50,), # Sogdian Combining Dot Be..Sogdian Combining Stroke (0x11001, 0x11001,), # Brahmi Sign Anusvara ..Brahmi Sign Anusvara (0x11038, 0x11046,), # Brahmi Vowel Sign Aa ..Brahmi Virama (0x1107f, 0x11081,), # Brahmi Number Joiner ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x11100, 0x11102,), # Chakma Sign Candrabindu ..Chakma Sign Visarga (0x11127, 0x1112b,), # Chakma Vowel Sign A ..Chakma Vowel Sign Uu (0x1112d, 0x11134,), # Chakma Vowel Sign Ai ..Chakma Maayyaa (0x11173, 0x11173,), # Mahajani Sign Nukta ..Mahajani Sign Nukta (0x11180, 0x11181,), # Sharada Sign Candrabindu..Sharada Sign Anusvara (0x111b6, 0x111be,), # Sharada Vowel Sign U ..Sharada Vowel Sign O (0x111c9, 0x111cc,), # Sharada Sandhi Mark ..Sharada Extra Short Vowe (0x1122f, 0x11231,), # Khojki Vowel Sign U ..Khojki Vowel Sign Ai (0x11234, 0x11234,), # Khojki Sign Anusvara ..Khojki Sign Anusvara (0x11236, 0x11237,), # Khojki Sign Nukta ..Khojki Sign Shadda (0x1123e, 0x1123e,), # Khojki Sign Sukun ..Khojki Sign Sukun (0x112df, 0x112df,), # Khudawadi Sign Anusvara ..Khudawadi Sign Anusvara (0x112e3, 0x112ea,), # Khudawadi Vowel Sign U ..Khudawadi Sign Virama (0x11300, 0x11301,), # Grantha Sign Combining A..Grantha Sign Candrabindu (0x1133b, 0x1133c,), # Combining Bindu Below ..Grantha Sign Nukta (0x11340, 0x11340,), # Grantha Vowel Sign Ii ..Grantha Vowel Sign Ii (0x11366, 0x1136c,), # Combining Grantha Digit ..Combining Grantha Digit (0x11370, 0x11374,), # Combining Grantha Letter..Combining Grantha Letter (0x11438, 0x1143f,), # Newa Vowel Sign U ..Newa Vowel Sign Ai (0x11442, 0x11444,), # Newa Sign Virama ..Newa Sign Anusvara (0x11446, 0x11446,), # Newa Sign Nukta ..Newa Sign Nukta (0x1145e, 0x1145e,), # Newa Sandhi Mark ..Newa Sandhi Mark (0x114b3, 0x114b8,), # Tirhuta Vowel Sign U ..Tirhuta Vowel Sign Vocal (0x114ba, 0x114ba,), # Tirhuta Vowel Sign Short..Tirhuta Vowel Sign Short (0x114bf, 0x114c0,), # Tirhuta Sign Candrabindu..Tirhuta Sign Anusvara (0x114c2, 0x114c3,), # Tirhuta Sign Virama ..Tirhuta Sign Nukta (0x115b2, 0x115b5,), # Siddham Vowel Sign U ..Siddham Vowel Sign Vocal (0x115bc, 0x115bd,), # Siddham Sign Candrabindu..Siddham Sign Anusvara (0x115bf, 0x115c0,), # Siddham Sign Virama ..Siddham Sign Nukta (0x115dc, 0x115dd,), # Siddham Vowel Sign Alter..Siddham Vowel Sign Alter (0x11633, 0x1163a,), # Modi Vowel Sign U ..Modi Vowel Sign Ai (0x1163d, 0x1163d,), # Modi Sign Anusvara ..Modi Sign Anusvara (0x1163f, 0x11640,), # Modi Sign Virama ..Modi Sign Ardhacandra (0x116ab, 0x116ab,), # Takri Sign Anusvara ..Takri Sign Anusvara (0x116ad, 0x116ad,), # Takri Vowel Sign Aa ..Takri Vowel Sign Aa (0x116b0, 0x116b5,), # Takri Vowel Sign U ..Takri Vowel Sign Au (0x116b7, 0x116b7,), # Takri Sign Nukta ..Takri Sign Nukta (0x1171d, 0x1171f,), # Ahom Consonant Sign Medi..Ahom Consonant Sign Medi (0x11722, 0x11725,), # Ahom Vowel Sign I ..Ahom Vowel Sign Uu (0x11727, 0x1172b,), # Ahom Vowel Sign Aw ..Ahom Sign Killer (0x1182f, 0x11837,), # Dogra Vowel Sign U ..Dogra Sign Anusvara (0x11839, 0x1183a,), # Dogra Sign Virama ..Dogra Sign Nukta (0x119d4, 0x119d7,), # Nandinagari Vowel Sign U..Nandinagari Vowel Sign V (0x119da, 0x119db,), # Nandinagari Vowel Sign E..Nandinagari Vowel Sign A (0x119e0, 0x119e0,), # Nandinagari Sign Virama ..Nandinagari Sign Virama (0x11a01, 0x11a0a,), # Zanabazar Square Vowel S..Zanabazar Square Vowel L (0x11a33, 0x11a38,), # Zanabazar Square Final C..Zanabazar Square Sign An (0x11a3b, 0x11a3e,), # Zanabazar Square Cluster..Zanabazar Square Cluster (0x11a47, 0x11a47,), # Zanabazar Square Subjoin..Zanabazar Square Subjoin (0x11a51, 0x11a56,), # Soyombo Vowel Sign I ..Soyombo Vowel Sign Oe (0x11a59, 0x11a5b,), # Soyombo Vowel Sign Vocal..Soyombo Vowel Length Mar (0x11a8a, 0x11a96,), # Soyombo Final Consonant ..Soyombo Sign Anusvara (0x11a98, 0x11a99,), # Soyombo Gemination Mark ..Soyombo Subjoiner (0x11c30, 0x11c36,), # Bhaiksuki Vowel Sign I ..Bhaiksuki Vowel Sign Voc (0x11c38, 0x11c3d,), # Bhaiksuki Vowel Sign E ..Bhaiksuki Sign Anusvara (0x11c3f, 0x11c3f,), # Bhaiksuki Sign Virama ..Bhaiksuki Sign Virama (0x11c92, 0x11ca7,), # Marchen Subjoined Letter..Marchen Subjoined Letter (0x11caa, 0x11cb0,), # Marchen Subjoined Letter..Marchen Vowel Sign Aa (0x11cb2, 0x11cb3,), # Marchen Vowel Sign U ..Marchen Vowel Sign E (0x11cb5, 0x11cb6,), # Marchen Sign Anusvara ..Marchen Sign Candrabindu (0x11d31, 0x11d36,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3a, 0x11d3a,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3c, 0x11d3d,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3f, 0x11d45,), # Masaram Gondi Vowel Sign..Masaram Gondi Virama (0x11d47, 0x11d47,), # Masaram Gondi Ra-kara ..Masaram Gondi Ra-kara (0x11d90, 0x11d91,), # Gunjala Gondi Vowel Sign..Gunjala Gondi Vowel Sign (0x11d95, 0x11d95,), # Gunjala Gondi Sign Anusv..Gunjala Gondi Sign Anusv (0x11d97, 0x11d97,), # Gunjala Gondi Virama ..Gunjala Gondi Virama (0x11ef3, 0x11ef4,), # Makasar Vowel Sign I ..Makasar Vowel Sign U (0x16af0, 0x16af4,), # Bassa Vah Combining High..Bassa Vah Combining High (0x16b30, 0x16b36,), # Pahawh Hmong Mark Cim Tu..Pahawh Hmong Mark Cim Ta (0x16f4f, 0x16f4f,), # Miao Sign Consonant Modi..Miao Sign Consonant Modi (0x16f8f, 0x16f92,), # Miao Tone Right ..Miao Tone Below (0x1bc9d, 0x1bc9e,), # Duployan Thick Letter Se..Duployan Double Mark (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0x1da00, 0x1da36,), # Signwriting Head Rim ..Signwriting Air Sucking (0x1da3b, 0x1da6c,), # Signwriting Mouth Closed..Signwriting Excitement (0x1da75, 0x1da75,), # Signwriting Upper Body T..Signwriting Upper Body T (0x1da84, 0x1da84,), # Signwriting Location Hea..Signwriting Location Hea (0x1da9b, 0x1da9f,), # Signwriting Fill Modifie..Signwriting Fill Modifie (0x1daa1, 0x1daaf,), # Signwriting Rotation Mod..Signwriting Rotation Mod (0x1e000, 0x1e006,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e008, 0x1e018,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e01b, 0x1e021,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e023, 0x1e024,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e026, 0x1e02a,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e130, 0x1e136,), # Nyiakeng Puachue Hmong T..Nyiakeng Puachue Hmong T (0x1e2ec, 0x1e2ef,), # Wancho Tone Tup ..Wancho Tone Koini (0x1e8d0, 0x1e8d6,), # Mende Kikakui Combining ..Mende Kikakui Combining (0x1e944, 0x1e94a,), # Adlam Alif Lengthener ..Adlam Nukta (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '12.1.0': ( # Source: DerivedGeneralCategory-12.1.0.txt # Date: 2019-03-10, 10:53:08 GMT # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065f,), # Arabic Fathatan ..Arabic Wavy Hamza Below (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006df, 0x006e4,), # Arabic Small High Rounde..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x007fd, 0x007fd,), # Nko Dantayalan ..Nko Dantayalan (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00859, 0x0085b,), # Mandaic Affrication Mark..Mandaic Gemination Mark (0x008d3, 0x008e1,), # Arabic Small Low Waw ..Arabic Small High Sign S (0x008e3, 0x00902,), # Arabic Turned Damma Belo..Devanagari Sign Anusvara (0x0093a, 0x0093a,), # Devanagari Vowel Sign Oe..Devanagari Vowel Sign Oe (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00957,), # Devanagari Stress Sign U..Devanagari Vowel Sign Uu (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x009fe, 0x009fe,), # Bengali Sandhi Mark ..Bengali Sandhi Mark (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00afa, 0x00aff,), # Gujarati Sign Sukun ..Gujarati Sign Two-circle (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b56, 0x00b56,), # Oriya Ai Length Mark ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c00, 0x00c00,), # Telugu Sign Combining Ca..Telugu Sign Combining Ca (0x00c04, 0x00c04,), # Telugu Sign Combining An..Telugu Sign Combining An (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00c81, 0x00c81,), # Kannada Sign Candrabindu..Kannada Sign Candrabindu (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d00, 0x00d01,), # Malayalam Sign Combining..Malayalam Sign Candrabin (0x00d3b, 0x00d3c,), # Malayalam Sign Vertical ..Malayalam Sign Circular (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00ebc,), # Lao Vowel Sign I ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f8d, 0x00f97,), # Tibetan Subjoined Sign L..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135d, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b4, 0x017b5,), # Khmer Vowel Inherent Aq ..Khmer Vowel Inherent Aa (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x01885, 0x01886,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a1b, 0x01a1b,), # Buginese Vowel Sign Ae ..Buginese Vowel Sign Ae (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01ab0, 0x01abe,), # Combining Doubled Circum..Combining Parentheses Ov (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01bab, 0x01bad,), # Sundanese Sign Virama ..Sundanese Consonant Sign (0x01be6, 0x01be6,), # Batak Sign Tompi ..Batak Sign Tompi (0x01be8, 0x01be9,), # Batak Vowel Sign Pakpak ..Batak Vowel Sign Ee (0x01bed, 0x01bed,), # Batak Vowel Sign Karo O ..Batak Vowel Sign Karo O (0x01bef, 0x01bf1,), # Batak Vowel Sign U For S..Batak Consonant Sign H (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01cf4, 0x01cf4,), # Vedic Tone Candra Above ..Vedic Tone Candra Above (0x01cf8, 0x01cf9,), # Vedic Tone Ring Above ..Vedic Tone Double Ring A (0x01dc0, 0x01df9,), # Combining Dotted Grave A..Combining Wide Inverted (0x01dfb, 0x01dff,), # Combining Deletion Mark ..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02d7f, 0x02d7f,), # Tifinagh Consonant Joine..Tifinagh Consonant Joine (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302d,), # Ideographic Level Tone M..Ideographic Entering Ton (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a674, 0x0a67d,), # Combining Cyrillic Lette..Combining Cyrillic Payer (0x0a69e, 0x0a69f,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a8c4, 0x0a8c5,), # Saurashtra Sign Virama ..Saurashtra Sign Candrabi (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a8ff, 0x0a8ff,), # Devanagari Vowel Sign Ay..Devanagari Vowel Sign Ay (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bd,), # Javanese Vowel Sign Pepe..Javanese Consonant Sign (0x0a9e5, 0x0a9e5,), # Myanmar Sign Shan Saw ..Myanmar Sign Shan Saw (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa7c, 0x0aa7c,), # Myanmar Sign Tai Laing T..Myanmar Sign Tai Laing T (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0aaec, 0x0aaed,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0aaf6, 0x0aaf6,), # Meetei Mayek Virama ..Meetei Mayek Virama (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe2f,), # Combining Ligature Left ..Combining Cyrillic Titlo (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x102e0, 0x102e0,), # Coptic Epact Thousands M..Coptic Epact Thousands M (0x10376, 0x1037a,), # Combining Old Permic Let..Combining Old Permic Let (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x10ae5, 0x10ae6,), # Manichaean Abbreviation ..Manichaean Abbreviation (0x10d24, 0x10d27,), # Hanifi Rohingya Sign Har..Hanifi Rohingya Sign Tas (0x10f46, 0x10f50,), # Sogdian Combining Dot Be..Sogdian Combining Stroke (0x11001, 0x11001,), # Brahmi Sign Anusvara ..Brahmi Sign Anusvara (0x11038, 0x11046,), # Brahmi Vowel Sign Aa ..Brahmi Virama (0x1107f, 0x11081,), # Brahmi Number Joiner ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x11100, 0x11102,), # Chakma Sign Candrabindu ..Chakma Sign Visarga (0x11127, 0x1112b,), # Chakma Vowel Sign A ..Chakma Vowel Sign Uu (0x1112d, 0x11134,), # Chakma Vowel Sign Ai ..Chakma Maayyaa (0x11173, 0x11173,), # Mahajani Sign Nukta ..Mahajani Sign Nukta (0x11180, 0x11181,), # Sharada Sign Candrabindu..Sharada Sign Anusvara (0x111b6, 0x111be,), # Sharada Vowel Sign U ..Sharada Vowel Sign O (0x111c9, 0x111cc,), # Sharada Sandhi Mark ..Sharada Extra Short Vowe (0x1122f, 0x11231,), # Khojki Vowel Sign U ..Khojki Vowel Sign Ai (0x11234, 0x11234,), # Khojki Sign Anusvara ..Khojki Sign Anusvara (0x11236, 0x11237,), # Khojki Sign Nukta ..Khojki Sign Shadda (0x1123e, 0x1123e,), # Khojki Sign Sukun ..Khojki Sign Sukun (0x112df, 0x112df,), # Khudawadi Sign Anusvara ..Khudawadi Sign Anusvara (0x112e3, 0x112ea,), # Khudawadi Vowel Sign U ..Khudawadi Sign Virama (0x11300, 0x11301,), # Grantha Sign Combining A..Grantha Sign Candrabindu (0x1133b, 0x1133c,), # Combining Bindu Below ..Grantha Sign Nukta (0x11340, 0x11340,), # Grantha Vowel Sign Ii ..Grantha Vowel Sign Ii (0x11366, 0x1136c,), # Combining Grantha Digit ..Combining Grantha Digit (0x11370, 0x11374,), # Combining Grantha Letter..Combining Grantha Letter (0x11438, 0x1143f,), # Newa Vowel Sign U ..Newa Vowel Sign Ai (0x11442, 0x11444,), # Newa Sign Virama ..Newa Sign Anusvara (0x11446, 0x11446,), # Newa Sign Nukta ..Newa Sign Nukta (0x1145e, 0x1145e,), # Newa Sandhi Mark ..Newa Sandhi Mark (0x114b3, 0x114b8,), # Tirhuta Vowel Sign U ..Tirhuta Vowel Sign Vocal (0x114ba, 0x114ba,), # Tirhuta Vowel Sign Short..Tirhuta Vowel Sign Short (0x114bf, 0x114c0,), # Tirhuta Sign Candrabindu..Tirhuta Sign Anusvara (0x114c2, 0x114c3,), # Tirhuta Sign Virama ..Tirhuta Sign Nukta (0x115b2, 0x115b5,), # Siddham Vowel Sign U ..Siddham Vowel Sign Vocal (0x115bc, 0x115bd,), # Siddham Sign Candrabindu..Siddham Sign Anusvara (0x115bf, 0x115c0,), # Siddham Sign Virama ..Siddham Sign Nukta (0x115dc, 0x115dd,), # Siddham Vowel Sign Alter..Siddham Vowel Sign Alter (0x11633, 0x1163a,), # Modi Vowel Sign U ..Modi Vowel Sign Ai (0x1163d, 0x1163d,), # Modi Sign Anusvara ..Modi Sign Anusvara (0x1163f, 0x11640,), # Modi Sign Virama ..Modi Sign Ardhacandra (0x116ab, 0x116ab,), # Takri Sign Anusvara ..Takri Sign Anusvara (0x116ad, 0x116ad,), # Takri Vowel Sign Aa ..Takri Vowel Sign Aa (0x116b0, 0x116b5,), # Takri Vowel Sign U ..Takri Vowel Sign Au (0x116b7, 0x116b7,), # Takri Sign Nukta ..Takri Sign Nukta (0x1171d, 0x1171f,), # Ahom Consonant Sign Medi..Ahom Consonant Sign Medi (0x11722, 0x11725,), # Ahom Vowel Sign I ..Ahom Vowel Sign Uu (0x11727, 0x1172b,), # Ahom Vowel Sign Aw ..Ahom Sign Killer (0x1182f, 0x11837,), # Dogra Vowel Sign U ..Dogra Sign Anusvara (0x11839, 0x1183a,), # Dogra Sign Virama ..Dogra Sign Nukta (0x119d4, 0x119d7,), # Nandinagari Vowel Sign U..Nandinagari Vowel Sign V (0x119da, 0x119db,), # Nandinagari Vowel Sign E..Nandinagari Vowel Sign A (0x119e0, 0x119e0,), # Nandinagari Sign Virama ..Nandinagari Sign Virama (0x11a01, 0x11a0a,), # Zanabazar Square Vowel S..Zanabazar Square Vowel L (0x11a33, 0x11a38,), # Zanabazar Square Final C..Zanabazar Square Sign An (0x11a3b, 0x11a3e,), # Zanabazar Square Cluster..Zanabazar Square Cluster (0x11a47, 0x11a47,), # Zanabazar Square Subjoin..Zanabazar Square Subjoin (0x11a51, 0x11a56,), # Soyombo Vowel Sign I ..Soyombo Vowel Sign Oe (0x11a59, 0x11a5b,), # Soyombo Vowel Sign Vocal..Soyombo Vowel Length Mar (0x11a8a, 0x11a96,), # Soyombo Final Consonant ..Soyombo Sign Anusvara (0x11a98, 0x11a99,), # Soyombo Gemination Mark ..Soyombo Subjoiner (0x11c30, 0x11c36,), # Bhaiksuki Vowel Sign I ..Bhaiksuki Vowel Sign Voc (0x11c38, 0x11c3d,), # Bhaiksuki Vowel Sign E ..Bhaiksuki Sign Anusvara (0x11c3f, 0x11c3f,), # Bhaiksuki Sign Virama ..Bhaiksuki Sign Virama (0x11c92, 0x11ca7,), # Marchen Subjoined Letter..Marchen Subjoined Letter (0x11caa, 0x11cb0,), # Marchen Subjoined Letter..Marchen Vowel Sign Aa (0x11cb2, 0x11cb3,), # Marchen Vowel Sign U ..Marchen Vowel Sign E (0x11cb5, 0x11cb6,), # Marchen Sign Anusvara ..Marchen Sign Candrabindu (0x11d31, 0x11d36,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3a, 0x11d3a,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3c, 0x11d3d,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3f, 0x11d45,), # Masaram Gondi Vowel Sign..Masaram Gondi Virama (0x11d47, 0x11d47,), # Masaram Gondi Ra-kara ..Masaram Gondi Ra-kara (0x11d90, 0x11d91,), # Gunjala Gondi Vowel Sign..Gunjala Gondi Vowel Sign (0x11d95, 0x11d95,), # Gunjala Gondi Sign Anusv..Gunjala Gondi Sign Anusv (0x11d97, 0x11d97,), # Gunjala Gondi Virama ..Gunjala Gondi Virama (0x11ef3, 0x11ef4,), # Makasar Vowel Sign I ..Makasar Vowel Sign U (0x16af0, 0x16af4,), # Bassa Vah Combining High..Bassa Vah Combining High (0x16b30, 0x16b36,), # Pahawh Hmong Mark Cim Tu..Pahawh Hmong Mark Cim Ta (0x16f4f, 0x16f4f,), # Miao Sign Consonant Modi..Miao Sign Consonant Modi (0x16f8f, 0x16f92,), # Miao Tone Right ..Miao Tone Below (0x1bc9d, 0x1bc9e,), # Duployan Thick Letter Se..Duployan Double Mark (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0x1da00, 0x1da36,), # Signwriting Head Rim ..Signwriting Air Sucking (0x1da3b, 0x1da6c,), # Signwriting Mouth Closed..Signwriting Excitement (0x1da75, 0x1da75,), # Signwriting Upper Body T..Signwriting Upper Body T (0x1da84, 0x1da84,), # Signwriting Location Hea..Signwriting Location Hea (0x1da9b, 0x1da9f,), # Signwriting Fill Modifie..Signwriting Fill Modifie (0x1daa1, 0x1daaf,), # Signwriting Rotation Mod..Signwriting Rotation Mod (0x1e000, 0x1e006,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e008, 0x1e018,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e01b, 0x1e021,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e023, 0x1e024,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e026, 0x1e02a,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e130, 0x1e136,), # Nyiakeng Puachue Hmong T..Nyiakeng Puachue Hmong T (0x1e2ec, 0x1e2ef,), # Wancho Tone Tup ..Wancho Tone Koini (0x1e8d0, 0x1e8d6,), # Mende Kikakui Combining ..Mende Kikakui Combining (0x1e944, 0x1e94a,), # Adlam Alif Lengthener ..Adlam Nukta (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), '13.0.0': ( # Source: DerivedGeneralCategory-13.0.0.txt # Date: 2019-10-21, 14:30:32 GMT # (0x00300, 0x0036f,), # Combining Grave Accent ..Combining Latin Small Le (0x00483, 0x00489,), # Combining Cyrillic Titlo..Combining Cyrillic Milli (0x00591, 0x005bd,), # Hebrew Accent Etnahta ..Hebrew Point Meteg (0x005bf, 0x005bf,), # Hebrew Point Rafe ..Hebrew Point Rafe (0x005c1, 0x005c2,), # Hebrew Point Shin Dot ..Hebrew Point Sin Dot (0x005c4, 0x005c5,), # Hebrew Mark Upper Dot ..Hebrew Mark Lower Dot (0x005c7, 0x005c7,), # Hebrew Point Qamats Qata..Hebrew Point Qamats Qata (0x00610, 0x0061a,), # Arabic Sign Sallallahou ..Arabic Small Kasra (0x0064b, 0x0065f,), # Arabic Fathatan ..Arabic Wavy Hamza Below (0x00670, 0x00670,), # Arabic Letter Superscrip..Arabic Letter Superscrip (0x006d6, 0x006dc,), # Arabic Small High Ligatu..Arabic Small High Seen (0x006df, 0x006e4,), # Arabic Small High Rounde..Arabic Small High Madda (0x006e7, 0x006e8,), # Arabic Small High Yeh ..Arabic Small High Noon (0x006ea, 0x006ed,), # Arabic Empty Centre Low ..Arabic Small Low Meem (0x00711, 0x00711,), # Syriac Letter Superscrip..Syriac Letter Superscrip (0x00730, 0x0074a,), # Syriac Pthaha Above ..Syriac Barrekh (0x007a6, 0x007b0,), # Thaana Abafili ..Thaana Sukun (0x007eb, 0x007f3,), # Nko Combining Short High..Nko Combining Double Dot (0x007fd, 0x007fd,), # Nko Dantayalan ..Nko Dantayalan (0x00816, 0x00819,), # Samaritan Mark In ..Samaritan Mark Dagesh (0x0081b, 0x00823,), # Samaritan Mark Epentheti..Samaritan Vowel Sign A (0x00825, 0x00827,), # Samaritan Vowel Sign Sho..Samaritan Vowel Sign U (0x00829, 0x0082d,), # Samaritan Vowel Sign Lon..Samaritan Mark Nequdaa (0x00859, 0x0085b,), # Mandaic Affrication Mark..Mandaic Gemination Mark (0x008d3, 0x008e1,), # Arabic Small Low Waw ..Arabic Small High Sign S (0x008e3, 0x00902,), # Arabic Turned Damma Belo..Devanagari Sign Anusvara (0x0093a, 0x0093a,), # Devanagari Vowel Sign Oe..Devanagari Vowel Sign Oe (0x0093c, 0x0093c,), # Devanagari Sign Nukta ..Devanagari Sign Nukta (0x00941, 0x00948,), # Devanagari Vowel Sign U ..Devanagari Vowel Sign Ai (0x0094d, 0x0094d,), # Devanagari Sign Virama ..Devanagari Sign Virama (0x00951, 0x00957,), # Devanagari Stress Sign U..Devanagari Vowel Sign Uu (0x00962, 0x00963,), # Devanagari Vowel Sign Vo..Devanagari Vowel Sign Vo (0x00981, 0x00981,), # Bengali Sign Candrabindu..Bengali Sign Candrabindu (0x009bc, 0x009bc,), # Bengali Sign Nukta ..Bengali Sign Nukta (0x009c1, 0x009c4,), # Bengali Vowel Sign U ..Bengali Vowel Sign Vocal (0x009cd, 0x009cd,), # Bengali Sign Virama ..Bengali Sign Virama (0x009e2, 0x009e3,), # Bengali Vowel Sign Vocal..Bengali Vowel Sign Vocal (0x009fe, 0x009fe,), # Bengali Sandhi Mark ..Bengali Sandhi Mark (0x00a01, 0x00a02,), # Gurmukhi Sign Adak Bindi..Gurmukhi Sign Bindi (0x00a3c, 0x00a3c,), # Gurmukhi Sign Nukta ..Gurmukhi Sign Nukta (0x00a41, 0x00a42,), # Gurmukhi Vowel Sign U ..Gurmukhi Vowel Sign Uu (0x00a47, 0x00a48,), # Gurmukhi Vowel Sign Ee ..Gurmukhi Vowel Sign Ai (0x00a4b, 0x00a4d,), # Gurmukhi Vowel Sign Oo ..Gurmukhi Sign Virama (0x00a51, 0x00a51,), # Gurmukhi Sign Udaat ..Gurmukhi Sign Udaat (0x00a70, 0x00a71,), # Gurmukhi Tippi ..Gurmukhi Addak (0x00a75, 0x00a75,), # Gurmukhi Sign Yakash ..Gurmukhi Sign Yakash (0x00a81, 0x00a82,), # Gujarati Sign Candrabind..Gujarati Sign Anusvara (0x00abc, 0x00abc,), # Gujarati Sign Nukta ..Gujarati Sign Nukta (0x00ac1, 0x00ac5,), # Gujarati Vowel Sign U ..Gujarati Vowel Sign Cand (0x00ac7, 0x00ac8,), # Gujarati Vowel Sign E ..Gujarati Vowel Sign Ai (0x00acd, 0x00acd,), # Gujarati Sign Virama ..Gujarati Sign Virama (0x00ae2, 0x00ae3,), # Gujarati Vowel Sign Voca..Gujarati Vowel Sign Voca (0x00afa, 0x00aff,), # Gujarati Sign Sukun ..Gujarati Sign Two-circle (0x00b01, 0x00b01,), # Oriya Sign Candrabindu ..Oriya Sign Candrabindu (0x00b3c, 0x00b3c,), # Oriya Sign Nukta ..Oriya Sign Nukta (0x00b3f, 0x00b3f,), # Oriya Vowel Sign I ..Oriya Vowel Sign I (0x00b41, 0x00b44,), # Oriya Vowel Sign U ..Oriya Vowel Sign Vocalic (0x00b4d, 0x00b4d,), # Oriya Sign Virama ..Oriya Sign Virama (0x00b55, 0x00b56,), # (nil) ..Oriya Ai Length Mark (0x00b62, 0x00b63,), # Oriya Vowel Sign Vocalic..Oriya Vowel Sign Vocalic (0x00b82, 0x00b82,), # Tamil Sign Anusvara ..Tamil Sign Anusvara (0x00bc0, 0x00bc0,), # Tamil Vowel Sign Ii ..Tamil Vowel Sign Ii (0x00bcd, 0x00bcd,), # Tamil Sign Virama ..Tamil Sign Virama (0x00c00, 0x00c00,), # Telugu Sign Combining Ca..Telugu Sign Combining Ca (0x00c04, 0x00c04,), # Telugu Sign Combining An..Telugu Sign Combining An (0x00c3e, 0x00c40,), # Telugu Vowel Sign Aa ..Telugu Vowel Sign Ii (0x00c46, 0x00c48,), # Telugu Vowel Sign E ..Telugu Vowel Sign Ai (0x00c4a, 0x00c4d,), # Telugu Vowel Sign O ..Telugu Sign Virama (0x00c55, 0x00c56,), # Telugu Length Mark ..Telugu Ai Length Mark (0x00c62, 0x00c63,), # Telugu Vowel Sign Vocali..Telugu Vowel Sign Vocali (0x00c81, 0x00c81,), # Kannada Sign Candrabindu..Kannada Sign Candrabindu (0x00cbc, 0x00cbc,), # Kannada Sign Nukta ..Kannada Sign Nukta (0x00cbf, 0x00cbf,), # Kannada Vowel Sign I ..Kannada Vowel Sign I (0x00cc6, 0x00cc6,), # Kannada Vowel Sign E ..Kannada Vowel Sign E (0x00ccc, 0x00ccd,), # Kannada Vowel Sign Au ..Kannada Sign Virama (0x00ce2, 0x00ce3,), # Kannada Vowel Sign Vocal..Kannada Vowel Sign Vocal (0x00d00, 0x00d01,), # Malayalam Sign Combining..Malayalam Sign Candrabin (0x00d3b, 0x00d3c,), # Malayalam Sign Vertical ..Malayalam Sign Circular (0x00d41, 0x00d44,), # Malayalam Vowel Sign U ..Malayalam Vowel Sign Voc (0x00d4d, 0x00d4d,), # Malayalam Sign Virama ..Malayalam Sign Virama (0x00d62, 0x00d63,), # Malayalam Vowel Sign Voc..Malayalam Vowel Sign Voc (0x00d81, 0x00d81,), # (nil) ..(nil) (0x00dca, 0x00dca,), # Sinhala Sign Al-lakuna ..Sinhala Sign Al-lakuna (0x00dd2, 0x00dd4,), # Sinhala Vowel Sign Ketti..Sinhala Vowel Sign Ketti (0x00dd6, 0x00dd6,), # Sinhala Vowel Sign Diga ..Sinhala Vowel Sign Diga (0x00e31, 0x00e31,), # Thai Character Mai Han-a..Thai Character Mai Han-a (0x00e34, 0x00e3a,), # Thai Character Sara I ..Thai Character Phinthu (0x00e47, 0x00e4e,), # Thai Character Maitaikhu..Thai Character Yamakkan (0x00eb1, 0x00eb1,), # Lao Vowel Sign Mai Kan ..Lao Vowel Sign Mai Kan (0x00eb4, 0x00ebc,), # Lao Vowel Sign I ..Lao Semivowel Sign Lo (0x00ec8, 0x00ecd,), # Lao Tone Mai Ek ..Lao Niggahita (0x00f18, 0x00f19,), # Tibetan Astrological Sig..Tibetan Astrological Sig (0x00f35, 0x00f35,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f37, 0x00f37,), # Tibetan Mark Ngas Bzung ..Tibetan Mark Ngas Bzung (0x00f39, 0x00f39,), # Tibetan Mark Tsa -phru ..Tibetan Mark Tsa -phru (0x00f71, 0x00f7e,), # Tibetan Vowel Sign Aa ..Tibetan Sign Rjes Su Nga (0x00f80, 0x00f84,), # Tibetan Vowel Sign Rever..Tibetan Mark Halanta (0x00f86, 0x00f87,), # Tibetan Sign Lci Rtags ..Tibetan Sign Yang Rtags (0x00f8d, 0x00f97,), # Tibetan Subjoined Sign L..Tibetan Subjoined Letter (0x00f99, 0x00fbc,), # Tibetan Subjoined Letter..Tibetan Subjoined Letter (0x00fc6, 0x00fc6,), # Tibetan Symbol Padma Gda..Tibetan Symbol Padma Gda (0x0102d, 0x01030,), # Myanmar Vowel Sign I ..Myanmar Vowel Sign Uu (0x01032, 0x01037,), # Myanmar Vowel Sign Ai ..Myanmar Sign Dot Below (0x01039, 0x0103a,), # Myanmar Sign Virama ..Myanmar Sign Asat (0x0103d, 0x0103e,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01058, 0x01059,), # Myanmar Vowel Sign Vocal..Myanmar Vowel Sign Vocal (0x0105e, 0x01060,), # Myanmar Consonant Sign M..Myanmar Consonant Sign M (0x01071, 0x01074,), # Myanmar Vowel Sign Geba ..Myanmar Vowel Sign Kayah (0x01082, 0x01082,), # Myanmar Consonant Sign S..Myanmar Consonant Sign S (0x01085, 0x01086,), # Myanmar Vowel Sign Shan ..Myanmar Vowel Sign Shan (0x0108d, 0x0108d,), # Myanmar Sign Shan Counci..Myanmar Sign Shan Counci (0x0109d, 0x0109d,), # Myanmar Vowel Sign Aiton..Myanmar Vowel Sign Aiton (0x0135d, 0x0135f,), # Ethiopic Combining Gemin..Ethiopic Combining Gemin (0x01712, 0x01714,), # Tagalog Vowel Sign I ..Tagalog Sign Virama (0x01732, 0x01734,), # Hanunoo Vowel Sign I ..Hanunoo Sign Pamudpod (0x01752, 0x01753,), # Buhid Vowel Sign I ..Buhid Vowel Sign U (0x01772, 0x01773,), # Tagbanwa Vowel Sign I ..Tagbanwa Vowel Sign U (0x017b4, 0x017b5,), # Khmer Vowel Inherent Aq ..Khmer Vowel Inherent Aa (0x017b7, 0x017bd,), # Khmer Vowel Sign I ..Khmer Vowel Sign Ua (0x017c6, 0x017c6,), # Khmer Sign Nikahit ..Khmer Sign Nikahit (0x017c9, 0x017d3,), # Khmer Sign Muusikatoan ..Khmer Sign Bathamasat (0x017dd, 0x017dd,), # Khmer Sign Atthacan ..Khmer Sign Atthacan (0x0180b, 0x0180d,), # Mongolian Free Variation..Mongolian Free Variation (0x01885, 0x01886,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x018a9, 0x018a9,), # Mongolian Letter Ali Gal..Mongolian Letter Ali Gal (0x01920, 0x01922,), # Limbu Vowel Sign A ..Limbu Vowel Sign U (0x01927, 0x01928,), # Limbu Vowel Sign E ..Limbu Vowel Sign O (0x01932, 0x01932,), # Limbu Small Letter Anusv..Limbu Small Letter Anusv (0x01939, 0x0193b,), # Limbu Sign Mukphreng ..Limbu Sign Sa-i (0x01a17, 0x01a18,), # Buginese Vowel Sign I ..Buginese Vowel Sign U (0x01a1b, 0x01a1b,), # Buginese Vowel Sign Ae ..Buginese Vowel Sign Ae (0x01a56, 0x01a56,), # Tai Tham Consonant Sign ..Tai Tham Consonant Sign (0x01a58, 0x01a5e,), # Tai Tham Sign Mai Kang L..Tai Tham Consonant Sign (0x01a60, 0x01a60,), # Tai Tham Sign Sakot ..Tai Tham Sign Sakot (0x01a62, 0x01a62,), # Tai Tham Vowel Sign Mai ..Tai Tham Vowel Sign Mai (0x01a65, 0x01a6c,), # Tai Tham Vowel Sign I ..Tai Tham Vowel Sign Oa B (0x01a73, 0x01a7c,), # Tai Tham Vowel Sign Oa A..Tai Tham Sign Khuen-lue (0x01a7f, 0x01a7f,), # Tai Tham Combining Crypt..Tai Tham Combining Crypt (0x01ab0, 0x01ac0,), # Combining Doubled Circum..(nil) (0x01b00, 0x01b03,), # Balinese Sign Ulu Ricem ..Balinese Sign Surang (0x01b34, 0x01b34,), # Balinese Sign Rerekan ..Balinese Sign Rerekan (0x01b36, 0x01b3a,), # Balinese Vowel Sign Ulu ..Balinese Vowel Sign Ra R (0x01b3c, 0x01b3c,), # Balinese Vowel Sign La L..Balinese Vowel Sign La L (0x01b42, 0x01b42,), # Balinese Vowel Sign Pepe..Balinese Vowel Sign Pepe (0x01b6b, 0x01b73,), # Balinese Musical Symbol ..Balinese Musical Symbol (0x01b80, 0x01b81,), # Sundanese Sign Panyecek ..Sundanese Sign Panglayar (0x01ba2, 0x01ba5,), # Sundanese Consonant Sign..Sundanese Vowel Sign Pan (0x01ba8, 0x01ba9,), # Sundanese Vowel Sign Pam..Sundanese Vowel Sign Pan (0x01bab, 0x01bad,), # Sundanese Sign Virama ..Sundanese Consonant Sign (0x01be6, 0x01be6,), # Batak Sign Tompi ..Batak Sign Tompi (0x01be8, 0x01be9,), # Batak Vowel Sign Pakpak ..Batak Vowel Sign Ee (0x01bed, 0x01bed,), # Batak Vowel Sign Karo O ..Batak Vowel Sign Karo O (0x01bef, 0x01bf1,), # Batak Vowel Sign U For S..Batak Consonant Sign H (0x01c2c, 0x01c33,), # Lepcha Vowel Sign E ..Lepcha Consonant Sign T (0x01c36, 0x01c37,), # Lepcha Sign Ran ..Lepcha Sign Nukta (0x01cd0, 0x01cd2,), # Vedic Tone Karshana ..Vedic Tone Prenkha (0x01cd4, 0x01ce0,), # Vedic Sign Yajurvedic Mi..Vedic Tone Rigvedic Kash (0x01ce2, 0x01ce8,), # Vedic Sign Visarga Svari..Vedic Sign Visarga Anuda (0x01ced, 0x01ced,), # Vedic Sign Tiryak ..Vedic Sign Tiryak (0x01cf4, 0x01cf4,), # Vedic Tone Candra Above ..Vedic Tone Candra Above (0x01cf8, 0x01cf9,), # Vedic Tone Ring Above ..Vedic Tone Double Ring A (0x01dc0, 0x01df9,), # Combining Dotted Grave A..Combining Wide Inverted (0x01dfb, 0x01dff,), # Combining Deletion Mark ..Combining Right Arrowhea (0x020d0, 0x020f0,), # Combining Left Harpoon A..Combining Asterisk Above (0x02cef, 0x02cf1,), # Coptic Combining Ni Abov..Coptic Combining Spiritu (0x02d7f, 0x02d7f,), # Tifinagh Consonant Joine..Tifinagh Consonant Joine (0x02de0, 0x02dff,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0302a, 0x0302d,), # Ideographic Level Tone M..Ideographic Entering Ton (0x03099, 0x0309a,), # Combining Katakana-hirag..Combining Katakana-hirag (0x0a66f, 0x0a672,), # Combining Cyrillic Vzmet..Combining Cyrillic Thous (0x0a674, 0x0a67d,), # Combining Cyrillic Lette..Combining Cyrillic Payer (0x0a69e, 0x0a69f,), # Combining Cyrillic Lette..Combining Cyrillic Lette (0x0a6f0, 0x0a6f1,), # Bamum Combining Mark Koq..Bamum Combining Mark Tuk (0x0a802, 0x0a802,), # Syloti Nagri Sign Dvisva..Syloti Nagri Sign Dvisva (0x0a806, 0x0a806,), # Syloti Nagri Sign Hasant..Syloti Nagri Sign Hasant (0x0a80b, 0x0a80b,), # Syloti Nagri Sign Anusva..Syloti Nagri Sign Anusva (0x0a825, 0x0a826,), # Syloti Nagri Vowel Sign ..Syloti Nagri Vowel Sign (0x0a82c, 0x0a82c,), # (nil) ..(nil) (0x0a8c4, 0x0a8c5,), # Saurashtra Sign Virama ..Saurashtra Sign Candrabi (0x0a8e0, 0x0a8f1,), # Combining Devanagari Dig..Combining Devanagari Sig (0x0a8ff, 0x0a8ff,), # Devanagari Vowel Sign Ay..Devanagari Vowel Sign Ay (0x0a926, 0x0a92d,), # Kayah Li Vowel Ue ..Kayah Li Tone Calya Plop (0x0a947, 0x0a951,), # Rejang Vowel Sign I ..Rejang Consonant Sign R (0x0a980, 0x0a982,), # Javanese Sign Panyangga ..Javanese Sign Layar (0x0a9b3, 0x0a9b3,), # Javanese Sign Cecak Telu..Javanese Sign Cecak Telu (0x0a9b6, 0x0a9b9,), # Javanese Vowel Sign Wulu..Javanese Vowel Sign Suku (0x0a9bc, 0x0a9bd,), # Javanese Vowel Sign Pepe..Javanese Consonant Sign (0x0a9e5, 0x0a9e5,), # Myanmar Sign Shan Saw ..Myanmar Sign Shan Saw (0x0aa29, 0x0aa2e,), # Cham Vowel Sign Aa ..Cham Vowel Sign Oe (0x0aa31, 0x0aa32,), # Cham Vowel Sign Au ..Cham Vowel Sign Ue (0x0aa35, 0x0aa36,), # Cham Consonant Sign La ..Cham Consonant Sign Wa (0x0aa43, 0x0aa43,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa4c, 0x0aa4c,), # Cham Consonant Sign Fina..Cham Consonant Sign Fina (0x0aa7c, 0x0aa7c,), # Myanmar Sign Tai Laing T..Myanmar Sign Tai Laing T (0x0aab0, 0x0aab0,), # Tai Viet Mai Kang ..Tai Viet Mai Kang (0x0aab2, 0x0aab4,), # Tai Viet Vowel I ..Tai Viet Vowel U (0x0aab7, 0x0aab8,), # Tai Viet Mai Khit ..Tai Viet Vowel Ia (0x0aabe, 0x0aabf,), # Tai Viet Vowel Am ..Tai Viet Tone Mai Ek (0x0aac1, 0x0aac1,), # Tai Viet Tone Mai Tho ..Tai Viet Tone Mai Tho (0x0aaec, 0x0aaed,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0aaf6, 0x0aaf6,), # Meetei Mayek Virama ..Meetei Mayek Virama (0x0abe5, 0x0abe5,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abe8, 0x0abe8,), # Meetei Mayek Vowel Sign ..Meetei Mayek Vowel Sign (0x0abed, 0x0abed,), # Meetei Mayek Apun Iyek ..Meetei Mayek Apun Iyek (0x0fb1e, 0x0fb1e,), # Hebrew Point Judeo-spani..Hebrew Point Judeo-spani (0x0fe00, 0x0fe0f,), # Variation Selector-1 ..Variation Selector-16 (0x0fe20, 0x0fe2f,), # Combining Ligature Left ..Combining Cyrillic Titlo (0x101fd, 0x101fd,), # Phaistos Disc Sign Combi..Phaistos Disc Sign Combi (0x102e0, 0x102e0,), # Coptic Epact Thousands M..Coptic Epact Thousands M (0x10376, 0x1037a,), # Combining Old Permic Let..Combining Old Permic Let (0x10a01, 0x10a03,), # Kharoshthi Vowel Sign I ..Kharoshthi Vowel Sign Vo (0x10a05, 0x10a06,), # Kharoshthi Vowel Sign E ..Kharoshthi Vowel Sign O (0x10a0c, 0x10a0f,), # Kharoshthi Vowel Length ..Kharoshthi Sign Visarga (0x10a38, 0x10a3a,), # Kharoshthi Sign Bar Abov..Kharoshthi Sign Dot Belo (0x10a3f, 0x10a3f,), # Kharoshthi Virama ..Kharoshthi Virama (0x10ae5, 0x10ae6,), # Manichaean Abbreviation ..Manichaean Abbreviation (0x10d24, 0x10d27,), # Hanifi Rohingya Sign Har..Hanifi Rohingya Sign Tas (0x10eab, 0x10eac,), # (nil) ..(nil) (0x10f46, 0x10f50,), # Sogdian Combining Dot Be..Sogdian Combining Stroke (0x11001, 0x11001,), # Brahmi Sign Anusvara ..Brahmi Sign Anusvara (0x11038, 0x11046,), # Brahmi Vowel Sign Aa ..Brahmi Virama (0x1107f, 0x11081,), # Brahmi Number Joiner ..Kaithi Sign Anusvara (0x110b3, 0x110b6,), # Kaithi Vowel Sign U ..Kaithi Vowel Sign Ai (0x110b9, 0x110ba,), # Kaithi Sign Virama ..Kaithi Sign Nukta (0x11100, 0x11102,), # Chakma Sign Candrabindu ..Chakma Sign Visarga (0x11127, 0x1112b,), # Chakma Vowel Sign A ..Chakma Vowel Sign Uu (0x1112d, 0x11134,), # Chakma Vowel Sign Ai ..Chakma Maayyaa (0x11173, 0x11173,), # Mahajani Sign Nukta ..Mahajani Sign Nukta (0x11180, 0x11181,), # Sharada Sign Candrabindu..Sharada Sign Anusvara (0x111b6, 0x111be,), # Sharada Vowel Sign U ..Sharada Vowel Sign O (0x111c9, 0x111cc,), # Sharada Sandhi Mark ..Sharada Extra Short Vowe (0x111cf, 0x111cf,), # (nil) ..(nil) (0x1122f, 0x11231,), # Khojki Vowel Sign U ..Khojki Vowel Sign Ai (0x11234, 0x11234,), # Khojki Sign Anusvara ..Khojki Sign Anusvara (0x11236, 0x11237,), # Khojki Sign Nukta ..Khojki Sign Shadda (0x1123e, 0x1123e,), # Khojki Sign Sukun ..Khojki Sign Sukun (0x112df, 0x112df,), # Khudawadi Sign Anusvara ..Khudawadi Sign Anusvara (0x112e3, 0x112ea,), # Khudawadi Vowel Sign U ..Khudawadi Sign Virama (0x11300, 0x11301,), # Grantha Sign Combining A..Grantha Sign Candrabindu (0x1133b, 0x1133c,), # Combining Bindu Below ..Grantha Sign Nukta (0x11340, 0x11340,), # Grantha Vowel Sign Ii ..Grantha Vowel Sign Ii (0x11366, 0x1136c,), # Combining Grantha Digit ..Combining Grantha Digit (0x11370, 0x11374,), # Combining Grantha Letter..Combining Grantha Letter (0x11438, 0x1143f,), # Newa Vowel Sign U ..Newa Vowel Sign Ai (0x11442, 0x11444,), # Newa Sign Virama ..Newa Sign Anusvara (0x11446, 0x11446,), # Newa Sign Nukta ..Newa Sign Nukta (0x1145e, 0x1145e,), # Newa Sandhi Mark ..Newa Sandhi Mark (0x114b3, 0x114b8,), # Tirhuta Vowel Sign U ..Tirhuta Vowel Sign Vocal (0x114ba, 0x114ba,), # Tirhuta Vowel Sign Short..Tirhuta Vowel Sign Short (0x114bf, 0x114c0,), # Tirhuta Sign Candrabindu..Tirhuta Sign Anusvara (0x114c2, 0x114c3,), # Tirhuta Sign Virama ..Tirhuta Sign Nukta (0x115b2, 0x115b5,), # Siddham Vowel Sign U ..Siddham Vowel Sign Vocal (0x115bc, 0x115bd,), # Siddham Sign Candrabindu..Siddham Sign Anusvara (0x115bf, 0x115c0,), # Siddham Sign Virama ..Siddham Sign Nukta (0x115dc, 0x115dd,), # Siddham Vowel Sign Alter..Siddham Vowel Sign Alter (0x11633, 0x1163a,), # Modi Vowel Sign U ..Modi Vowel Sign Ai (0x1163d, 0x1163d,), # Modi Sign Anusvara ..Modi Sign Anusvara (0x1163f, 0x11640,), # Modi Sign Virama ..Modi Sign Ardhacandra (0x116ab, 0x116ab,), # Takri Sign Anusvara ..Takri Sign Anusvara (0x116ad, 0x116ad,), # Takri Vowel Sign Aa ..Takri Vowel Sign Aa (0x116b0, 0x116b5,), # Takri Vowel Sign U ..Takri Vowel Sign Au (0x116b7, 0x116b7,), # Takri Sign Nukta ..Takri Sign Nukta (0x1171d, 0x1171f,), # Ahom Consonant Sign Medi..Ahom Consonant Sign Medi (0x11722, 0x11725,), # Ahom Vowel Sign I ..Ahom Vowel Sign Uu (0x11727, 0x1172b,), # Ahom Vowel Sign Aw ..Ahom Sign Killer (0x1182f, 0x11837,), # Dogra Vowel Sign U ..Dogra Sign Anusvara (0x11839, 0x1183a,), # Dogra Sign Virama ..Dogra Sign Nukta (0x1193b, 0x1193c,), # (nil) ..(nil) (0x1193e, 0x1193e,), # (nil) ..(nil) (0x11943, 0x11943,), # (nil) ..(nil) (0x119d4, 0x119d7,), # Nandinagari Vowel Sign U..Nandinagari Vowel Sign V (0x119da, 0x119db,), # Nandinagari Vowel Sign E..Nandinagari Vowel Sign A (0x119e0, 0x119e0,), # Nandinagari Sign Virama ..Nandinagari Sign Virama (0x11a01, 0x11a0a,), # Zanabazar Square Vowel S..Zanabazar Square Vowel L (0x11a33, 0x11a38,), # Zanabazar Square Final C..Zanabazar Square Sign An (0x11a3b, 0x11a3e,), # Zanabazar Square Cluster..Zanabazar Square Cluster (0x11a47, 0x11a47,), # Zanabazar Square Subjoin..Zanabazar Square Subjoin (0x11a51, 0x11a56,), # Soyombo Vowel Sign I ..Soyombo Vowel Sign Oe (0x11a59, 0x11a5b,), # Soyombo Vowel Sign Vocal..Soyombo Vowel Length Mar (0x11a8a, 0x11a96,), # Soyombo Final Consonant ..Soyombo Sign Anusvara (0x11a98, 0x11a99,), # Soyombo Gemination Mark ..Soyombo Subjoiner (0x11c30, 0x11c36,), # Bhaiksuki Vowel Sign I ..Bhaiksuki Vowel Sign Voc (0x11c38, 0x11c3d,), # Bhaiksuki Vowel Sign E ..Bhaiksuki Sign Anusvara (0x11c3f, 0x11c3f,), # Bhaiksuki Sign Virama ..Bhaiksuki Sign Virama (0x11c92, 0x11ca7,), # Marchen Subjoined Letter..Marchen Subjoined Letter (0x11caa, 0x11cb0,), # Marchen Subjoined Letter..Marchen Vowel Sign Aa (0x11cb2, 0x11cb3,), # Marchen Vowel Sign U ..Marchen Vowel Sign E (0x11cb5, 0x11cb6,), # Marchen Sign Anusvara ..Marchen Sign Candrabindu (0x11d31, 0x11d36,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3a, 0x11d3a,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3c, 0x11d3d,), # Masaram Gondi Vowel Sign..Masaram Gondi Vowel Sign (0x11d3f, 0x11d45,), # Masaram Gondi Vowel Sign..Masaram Gondi Virama (0x11d47, 0x11d47,), # Masaram Gondi Ra-kara ..Masaram Gondi Ra-kara (0x11d90, 0x11d91,), # Gunjala Gondi Vowel Sign..Gunjala Gondi Vowel Sign (0x11d95, 0x11d95,), # Gunjala Gondi Sign Anusv..Gunjala Gondi Sign Anusv (0x11d97, 0x11d97,), # Gunjala Gondi Virama ..Gunjala Gondi Virama (0x11ef3, 0x11ef4,), # Makasar Vowel Sign I ..Makasar Vowel Sign U (0x16af0, 0x16af4,), # Bassa Vah Combining High..Bassa Vah Combining High (0x16b30, 0x16b36,), # Pahawh Hmong Mark Cim Tu..Pahawh Hmong Mark Cim Ta (0x16f4f, 0x16f4f,), # Miao Sign Consonant Modi..Miao Sign Consonant Modi (0x16f8f, 0x16f92,), # Miao Tone Right ..Miao Tone Below (0x16fe4, 0x16fe4,), # (nil) ..(nil) (0x1bc9d, 0x1bc9e,), # Duployan Thick Letter Se..Duployan Double Mark (0x1d167, 0x1d169,), # Musical Symbol Combining..Musical Symbol Combining (0x1d17b, 0x1d182,), # Musical Symbol Combining..Musical Symbol Combining (0x1d185, 0x1d18b,), # Musical Symbol Combining..Musical Symbol Combining (0x1d1aa, 0x1d1ad,), # Musical Symbol Combining..Musical Symbol Combining (0x1d242, 0x1d244,), # Combining Greek Musical ..Combining Greek Musical (0x1da00, 0x1da36,), # Signwriting Head Rim ..Signwriting Air Sucking (0x1da3b, 0x1da6c,), # Signwriting Mouth Closed..Signwriting Excitement (0x1da75, 0x1da75,), # Signwriting Upper Body T..Signwriting Upper Body T (0x1da84, 0x1da84,), # Signwriting Location Hea..Signwriting Location Hea (0x1da9b, 0x1da9f,), # Signwriting Fill Modifie..Signwriting Fill Modifie (0x1daa1, 0x1daaf,), # Signwriting Rotation Mod..Signwriting Rotation Mod (0x1e000, 0x1e006,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e008, 0x1e018,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e01b, 0x1e021,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e023, 0x1e024,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e026, 0x1e02a,), # Combining Glagolitic Let..Combining Glagolitic Let (0x1e130, 0x1e136,), # Nyiakeng Puachue Hmong T..Nyiakeng Puachue Hmong T (0x1e2ec, 0x1e2ef,), # Wancho Tone Tup ..Wancho Tone Koini (0x1e8d0, 0x1e8d6,), # Mende Kikakui Combining ..Mende Kikakui Combining (0x1e944, 0x1e94a,), # Adlam Alif Lengthener ..Adlam Nukta (0xe0100, 0xe01ef,), # Variation Selector-17 ..Variation Selector-256 ), }
[ "kd619@ic.ac.uk" ]
kd619@ic.ac.uk
7370dbee5c5d4f5920e1dc5ef94adebfd991c9f7
4774ce4e23a19827f0fbf6baa71bbb8cab98b4de
/imagespace/server/imagebackgroundsearch_rest.py
eae3a18eb28023f8c75fd155d13e7765ebbd71fa
[ "Apache-2.0" ]
permissive
lewismc/image_space
a4bd8494fbc04f8cb71256bb5918786aea9259b9
4164523b573be364661809f5971946c65570e899
refs/heads/master
2021-01-18T11:22:37.363303
2015-10-15T14:50:21
2015-10-15T14:50:21
39,895,591
0
0
null
2015-07-29T13:43:50
2015-07-29T13:43:50
null
UTF-8
Python
false
false
1,778
py
#!/usr/bin/env python # -*- coding: utf-8 -*- ############################################################################### # Copyright Kitware Inc. # # Licensed under the Apache License, Version 2.0 ( the "License" ); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### from girder.api import access from girder.api.describe import Description from girder.api.rest import Resource from girder import logger import requests import os class ImageBackgroundSearch(Resource): def __init__(self): self.resourceName = 'imagebackgroundsearch' self.route('GET', (), self.getImageSearch) @access.public def getImageSearch(self, params): return self._imageSearch(params) @access.public def postImageSearch(self, params): return self._imageSearch(params) def _imageSearch(self, params): return [{'id': d[0], 'score': d[1]} for d in requests.post( os.environ['IMAGE_SPACE_CMU_BACKGROUND_SEARCH'], data=params['url'], headers={ 'Content-type': 'text', 'Content-length': str(len(params['url'])) }, verify=False) .json()] getImageSearch.description = Description('Searches images by background')
[ "jeff.baumes@kitware.com" ]
jeff.baumes@kitware.com
83b12f7616cc9f2600fc279082ca09056457ba30
a071ceda7a4f7327f2e0ddbe87d431b69e2cdf6f
/Lab1/ex5.py
11bd8fc7bf3f262b740f22b8eb23727b8b728a78
[]
no_license
thomasolsen/IS105
e599e5992d4f0cb93a3052f911d98137929792d9
2c4537c42c76a59fc2fe1a22ce32e5ac7ffbe89f
refs/heads/master
2020-04-19T13:48:28.445758
2014-01-29T09:50:18
2014-01-29T09:50:18
null
0
0
null
null
null
null
UTF-8
Python
false
false
804
py
my_name = 'Zed A. Shaw' # No, it's really Thomas. my_age = 35 # Actually, i'm 26. my_height = 74 # inches, but 72,5 in reality. my_weight = 180 # lbs but really 190. my_eyes = 'Blue' # Green actually. my_teeth = 'White' # Teeth colour. my_hair = 'Brown' # Blonde brown really. # The text that shows everything in a proper manner. print "Let's talk about %s." % my_name print "He's %d inches tall." % my_height print "He's %d pounds heavy." % my_weight print "Actually that's not too heavy." print "He's got %s eyes and %s hair." % (my_eyes, my_hair) print "His teeth are usually %s depending on the coffee." % my_teeth # This is where we sum up all my numbers and get a total sum for no reason. print "If I add %d, %d, and %d I get %d." % (my_age, my_height, my_weight, my_age + my_height + my_weight)
[ "thomas.pedersen@live.com" ]
thomas.pedersen@live.com
76b7cc09b0866a058e802ecbad3ff7e42ba9b7a1
4ecedf7d7801e25c60ea3098a63ea96056b6fe20
/matrix_addition_ii.py
01377ea050a419f99ac13065fb407fdd0039ed91
[]
no_license
bengovernali/list_exercises
1b86d5df1c3c002d72c156e5047422af417317bf
00cd186fe34c4abec53dd8c020ce190464ae893d
refs/heads/master
2020-05-15T12:29:25.523641
2019-04-21T23:47:56
2019-04-21T23:47:56
182,266,111
0
0
null
null
null
null
UTF-8
Python
false
false
401
py
mat1 = [[1, 3], [2, 4]] mat2 = [[5, 2], [1, 0]] def matrix_addition(matrix1, matrix2): result = [] row_count = 0 for row in matrix1: col_count = 0 for col in row: result[row_count][col_count] = matrix1[row_count][col_count] + matrix2[row_count][col_count] col_count += 1 row_count += 1 return result print(matrix_addition(mat1, mat2))
[ "bgovern93@gmail.com" ]
bgovern93@gmail.com
a5f7ee66bb01870a20c98e9c3f0bb59c42c5afc2
ee09846576fbd3b75978db60e6ea4940c40cfa49
/Python/Aula15c.py
48f16b23f8fc2eeb612585d874145ac08fe7bacc
[]
no_license
MariVilas/teste
f59758277957f6f6335aa8f8ff8d608ce4b43358
0ce8e1ee01b07a814f1603b971d2d60f3796b645
refs/heads/master
2023-07-14T20:48:52.991477
2021-08-28T05:42:35
2021-08-28T05:42:35
275,003,096
0
0
null
null
null
null
UTF-8
Python
false
false
762
py
from random import randint from time import sleep resp='S' jogo = resultado = 0 while resp in 'Ss': lista = ("PAR","ÍMPAR") computador = randint(0, 1) perguntar = int(input('''Escolha uma opcao para se jogar: [0] PAR [1] ÍMPAR Digite sua escolha: ''')) print("ÍMPAR\n") sleep(1) print("OU\n") sleep(1) print("PAR!!!\n") print("-=" * 20) print("O computador escolheu: {}".format(lista[computador])) print("O jogador escolheu: {}".format(lista[perguntar])) print("-=" * 20) if computador == 0: if perguntar == 0: print("Empate!") if computador == 1: if perguntar == 1: print("Empate!") resultado += 1 resp = str(input('Quer continuar?[S/N]')).upper().strip()[0] print(resultado)
[ "mari_jvs@homail.com" ]
mari_jvs@homail.com
67cc8ae0e9b2a05fb85c2ca88cb58905348d3cf1
141545126466a00f32247dfa40e067ec049b0fa4
/Programming Basics Python/Exam Problems 20042019/Easter Shop.py
24f0f9d5278dd658f8d045e6e6d0eab1bb4ce257
[]
no_license
RadkaValkova/SoftUni-Web-Developer
83314367172a18f001e182b4e57f7ca0502ad1fc
61d3414373498bb6009ae70e8d17f26cd2d88ea5
refs/heads/main
2023-06-01T02:11:06.606370
2021-06-29T19:39:19
2021-06-29T19:39:19
325,611,606
1
0
null
null
null
null
UTF-8
Python
false
false
629
py
start_eggs = int(input()) sold_eggs = 0 fill_eggs = 0 available_eggs = start_eggs while True: command = input() if command == 'Close': print('Store is closed!') print(f'{sold_eggs} eggs sold.') break eggs_number = int(input()) if command == 'Buy': if available_eggs < eggs_number: print('Not enough eggs in store!') print(f'You can buy only {available_eggs}.') break else: sold_eggs += eggs_number available_eggs -= eggs_number else: fill_eggs += eggs_number available_eggs += eggs_number
[ "radka_valkova@abv.bg" ]
radka_valkova@abv.bg
76586b973fc183aca6038075ca9d1fedb6047377
7b4fedb81a42d737007b4602eee035916be39bcd
/day7/day7.py
61f847d64f3c0b640b3dd2b0e6c74583dde710dd
[]
no_license
garymjr/adventofcode
51fbbcc01d0d3806f039475baa62a2e6170af7a9
0e2550123efa2bff216b857f2e7f47c4f2d0bebd
refs/heads/master
2021-01-21T04:48:06.013313
2016-06-14T15:34:47
2016-06-14T15:34:47
48,621,126
0
0
null
null
null
null
UTF-8
Python
false
false
1,311
py
import re f = open("./input.txt") lines = f.readlines() wires = {} def get_val(wire): if wire.isdigit(): val = int(wire) elif wire in wires.keys(): val = wires[wire] else: val = find_wire_val(wire) return val def find_wire_val(wire): if wire in wires.keys(): return wires[wire] for line in lines: line = line.split() output_wire = line[-1] if wire == output_wire: if len(line) == 3: wires[output_wire] = get_val(line[0]) elif line[0] == "NOT": wires[output_wire] = 65535 - get_val(line[1]) elif line[1] == "AND": wires[output_wire] = get_val(line[0]) & get_val(line[2]) elif line[1] == "OR": wires[output_wire] = get_val(line[0]) | get_val(line[2]) elif line[1] == "LSHIFT": shift = int(line[2]) wires[output_wire] = get_val(line[0]) << shift elif line[1] == "RSHIFT": shift = int(line[2]) wires[output_wire] = get_val(line[0]) >> shift return wires[wire] # part 1 print(find_wire_val("a")) # part 2 b = wires['a'] wires.clear() wires['b'] = b print(find_wire_val("a"))
[ "garymjr@gmail.com" ]
garymjr@gmail.com
4f46f1e8e5ff03b75d112f34a2a61c0445245336
a9378ff37b5efe805894a1ea3aab98c91e982635
/Microgrid/test.py
e4b362f9f0e0ed2184667b3530ca5e8ae0562e28
[ "MIT" ]
permissive
nudging-SMDP/nudging-supplementary-material
36fd91f248ba12df7aefbf4977198de5fde8fffe
94795abf1e26abab07389436fc737029e6a2a566
refs/heads/main
2023-07-05T22:25:23.254530
2021-08-10T23:37:20
2021-08-10T23:37:20
372,692,059
2
0
null
null
null
null
UTF-8
Python
false
false
1,752
py
import os # comment if not using GPU os.environ["CUDA_VISIBLE_DEVICES"]="0" from microgrid_env import Microgrid from stable_baselines import PPO2 from stable_baselines.common.policies import MlpPolicy from nudge.nudge_functions import * import numpy as np # path to final model path = f'./results_nudging/ppo_opt_nudge_final' # parameters for the recurrent state day = 2 month = 1 gas_enable = True battery_charge = 50 # number of episodes to test test_episodes = 100 # get estimated gain rhos = np.load(f'{path}/summary_rhos.npy') # create environment ugrid = Microgrid(day, month, gas_enable, battery_charge) ugrid.rho = rhos[-1] # reset environment and get recurrent state state_s0 = ugrid.reset() # load policy model model = PPO2.load(f'{path}') test_rewards = [] test_cost = [] test_rc = [] # ------------ TESTING ------------ for i in range(test_episodes): # reset environment state = ugrid.reset() done = False rewards = [] costs = [] while(not done): # apply policy action, value, _ = model.predict(observation=state_s0, deterministic=True) # get next state, nudged reward and done flag state, r, done, _ = ugrid.step(action) # save rewards and cost of the microgrid followinf the learned policy rewards.append(ugrid.record_rewards[-1]) costs.append(ugrid.record_cost[-1]) if done: test_rewards.append(np.mean(rewards)) test_cost.append(np.mean(costs)) rc = test_rewards[-1]/test_cost[-1] test_rc.append(rc) break # Report print(f'Rewards, r = {np.mean(test_rewards)}') print(f'Cost, c = {np.mean(test_cost)}') print(f'r/c = {np.mean(test_rc)}')
[ "optimalnudging@gmail.com" ]
optimalnudging@gmail.com
04a6f058f68d60e995b0e65725d2a95d224275ac
342fec10a5e75fa881413fadfb21c1e21021aa08
/todolist/todolist/settings.py
032b73a8d6672c3d0572ab87d394720d5f422eb9
[]
no_license
ELBEQQAL94/todo_app_django_python
9c915cdec634644a6e7e5281810ba9e9683fb2c8
7484b60ee1af6d65242b877c8b49f758ad902b6b
refs/heads/master
2021-01-14T18:56:02.047963
2020-02-24T11:58:52
2020-02-24T11:58:52
242,720,670
0
0
null
null
null
null
UTF-8
Python
false
false
3,094
py
""" Django settings for todolist project. Generated by 'django-admin startproject' using Django 3.0.3. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'tz%9v8paqsd53)@yt^d5ruy($!lnq)=#hpp1d0(heljcjto($j' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'todolist.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'todolist.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/'
[ "ELBEQQAL.youssef@gmail.com" ]
ELBEQQAL.youssef@gmail.com
cac5a5118c1caa62049a0352cf2b96d18328b009
068d271e241d8cdb46dbf4243166e4b8ee7025b2
/Django/rbac/day69/luffy_permission/rbac/middlewares/rbac.py
05e05715e769339255dc4cd45e4b8f209919f427
[]
no_license
caiqinxiong/python
f6e226e76cb62aac970bcfbcb6c8adfc64858b60
9029f6c528d2cb742b600af224e803baa74cbe6a
refs/heads/master
2023-05-26T19:41:34.911885
2020-05-15T09:02:08
2020-05-15T09:02:08
195,261,757
1
0
null
2021-06-10T23:33:33
2019-07-04T15:01:42
JavaScript
UTF-8
Python
false
false
1,122
py
from django.utils.deprecation import MiddlewareMixin from django.shortcuts import HttpResponse, redirect, reverse from django.conf import settings import re class RbacMiddleWare(MiddlewareMixin): def process_request(self, request): # 获取当前访问的页面 url = request.path_info # index # 白名单 for i in settings.WHITE_LIST: if re.match(i, url): return # 获取登录状态 is_login = request.session.get('is_login') # 没有登录跳转到登录页面 if not is_login: return redirect(reverse('login')) # 免认证 for i in settings.NO_PERMISSION_LIST: if re.match(i, url): return # 获取当前用户的权限 permission_list = request.session['permission'] print(permission_list) # 权限的校验 for i in permission_list: if re.match('^{}$'.format(i['permissions__url']), url): return # 没匹配成功 没有权限 return HttpResponse('没有访问的权限')
[ "13269469526@163.com" ]
13269469526@163.com
4586f31cad02d6a6be7b36c3b4163765c6399b35
c937baa378c59cbcaaf88ef26e652103f7bca5d1
/message.py
afa6e91a73a5dbe3e58e7ae5e2c7ac15a494680b
[]
no_license
gregorybrancq/pythonCommon
87f8026b5e11ad3218e6e24b21002d974f4079f4
e3aa7b22d60b760737cba618a7fbb81e1cee9c71
refs/heads/master
2023-03-23T06:17:56.294515
2021-03-09T13:42:42
2021-03-09T13:42:42
247,312,961
0
0
null
null
null
null
UTF-8
Python
false
false
5,192
py
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Open a window to print the status of the program """ # use for graphical interface import multiprocessing import gi gi.require_version('Gtk', '3.0') from gi.repository import Gtk ############################################## # Message Dialog Class ############################################## class MessageDialog(object): """Shows a message. The message type, title and the message to be displayed can be passed when initializing the class.""" def __init__(self, dialog_type, title, message1, message2=None): self.type = dialog_type if self.type == 'error': self.dialog = Gtk.MessageDialog(Gtk.Window(), Gtk.DialogFlags.MODAL, Gtk.MessageType.ERROR, Gtk.ButtonsType.CLOSE, message1, ) if message2 is not None: self.dialog.format_secondary_text(message2) elif self.type == 'info': self.dialog = Gtk.MessageDialog(Gtk.Window(), Gtk.DialogFlags.MODAL, Gtk.MessageType.INFO, Gtk.ButtonsType.CLOSE, message1, ) if message2 is not None: self.dialog.format_secondary_text(message2) elif self.type == 'question': self.dialog = Gtk.MessageDialog(Gtk.Window(), Gtk.DialogFlags.MODAL, Gtk.MessageType.QUESTION, Gtk.ButtonsType.YES_NO, message1, ) if message2 is not None: self.dialog.format_secondary_text(message2) elif self.type == 'entry': self.dialog = Gtk.MessageDialog(Gtk.Window(), Gtk.DialogFlags.MODAL, Gtk.MessageType.QUESTION, Gtk.ButtonsType.YES_NO, message1, ) if message2 is not None: self.dialog.format_secondary_text(message2) dialog_box = self.dialog.get_content_area() self.userEntry = Gtk.Entry() self.userEntry.show() # the following will trigger OK response when enter is hit in the entry self.userEntry.connect("activate", lambda w: self.dialog.response(Gtk.ResponseType.YES)) dialog_box.pack_end(self.userEntry, False, False, 0) self.dialog.set_title(title) self.dialog.show_all() def run(self): """Runs the dialog and closes it afterwards.""" response = self.dialog.run() if (response == Gtk.ResponseType.YES) and (self.type == 'entry'): user_text = self.userEntry.get_text() self.dialog.destroy() return user_text else: self.dialog.destroy() return response class KillQuestionAfterDelay(object): """ Shows a question and wait a delay before killing it if user hasn't give an answer. """ def __init__(self, delay, question, message=None): self.delay = delay self.question = question self.message = message def ask_question(self, answer): response = MessageDialog('question', self.question, self.message).run() if response == Gtk.ResponseType.YES: answer.value = True else: answer.value = False def run(self): # Shared memory user_answer = multiprocessing.Value('b', True) # Start question as a process p = multiprocessing.Process(target=self.ask_question, args=(user_answer,)) p.start() p.join(self.delay) # Terminate answer after delay if p.is_alive(): p.terminate() p.join() # return answer return user_answer.value # End dialog def MessageDialogEnd(error, log_file, title, msg1, msg2): if error: msg2 += "\nLog file = " + str(log_file) MessageDialog(dialog_type='error', title=title, message1=msg1, message2=msg2).run() else: msg2 += "\nLog file = " + str(log_file) MessageDialog(dialog_type='info', title=title, message1=msg1, message2=msg2).run() # 'application' code # print(MessageDialog('error', "Error !", "Le programme a planté", "comme une grosse ...").run()) # print(MessageDialog('info', "Info", "C'est une information importante").run()) # print(MessageDialog('question', "Question ?", "Irais-tu sur la lune si tu le pouvais ?", "une petite aide : pourquoi pas...").run()) # print(MessageDialog('entry', "Entry", "Question importante", "Donne moi un chiffre").run())
[ "gregory.brancq@free.fr" ]
gregory.brancq@free.fr
5e5e921bd20b358220b07bd22aafc99e675ffafa
62a7b1a4569f519554f7e68e73115e87d4102c39
/wyklad_1/file_hasher.py
895d6149f422c3f4a90f553d2b32c556dffdd547
[]
no_license
DaftAcademy/zajecia_python_mini_edycja4
ff491b3dcdf4f84ea9b01edc6cb9094235bcb7df
f83d7b31521cb5a3248fb17a3640840d8f77a84a
refs/heads/master
2022-08-01T02:45:28.471046
2017-05-15T11:25:09
2017-05-15T11:25:09
null
0
0
null
null
null
null
UTF-8
Python
false
false
714
py
import hashlib # https://en.wikipedia.org/wiki/Pigeonhole_principle # https://pl.wikipedia.org/wiki/Zasada_szufladkowa_Dirichleta def get_hash(f_path, mode='md5'): h = hashlib.new(mode) # TODO: PRACA DOMOWA: Nie czytać całego pliku na raz tylko po kawałku f = open(f_path, 'rb') # otwiera plik w funkcji hashującej, co z obsługą błedów? h.update(f.read()) # czyta cały plik na raz! hash_text = h.hexdigest() f.close() return hash_text #print(get_hash('plik_testowy')) #print(get_hash('sha1_collisions/shattered-1.pdf', mode='sha1')) #print(get_hash('sha1_collisions/shattered-2.pdf', mode='sha1')) # eb63071881718ed66bb75ce670e65b9e # eb63071881718ed66bb75ce670e65b9e
[ "marcin.jaroszewski@dcx.bz" ]
marcin.jaroszewski@dcx.bz
6a7a27fa1290cb3c81ae1d132dbf45749bc38835
efd2781b5da3d118ea0bc56aa947193b500e0299
/newapi/urls.py
fed203a45232f8955249d7e79931774157fd3799
[]
no_license
Hari635/newsApI
9cdb58cea8a1f3e776c18dc8342f3ffd53651c49
be03c0632a6875b2440156ff66d2e519d7d5bd76
refs/heads/master
2023-07-06T02:16:15.842751
2021-07-25T13:03:39
2021-07-25T13:03:39
389,348,779
0
0
null
null
null
null
UTF-8
Python
false
false
1,473
py
"""newapi URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from rest_framework import permissions from drf_yasg.views import get_schema_view from drf_yasg import openapi from django.contrib import admin from django.urls import path,include schema_view = get_schema_view( openapi.Info( title="news API", default_version='v1', description="News description", terms_of_service="https://www.google.com/policies/terms/", contact=openapi.Contact(email="contact@snippets.local"), license=openapi.License(name="BSD License"), ), public=True, permission_classes=(permissions.AllowAny,), ) urlpatterns = [ path("", schema_view.with_ui('swagger', cache_timeout=0), name='schema-swagger-ui'), path("redoc", schema_view.with_ui('redoc', cache_timeout=0), name='schema-redoc'), path('admin/', admin.site.urls), path('api/',include("news.urls")) ]
[ "hariharasuthan81@gmail.com" ]
hariharasuthan81@gmail.com
56217e6cc807440450ad8316fdd6c63956a56e05
0c68fad8b6e8375f17ad2e10a2a74e6181d7b55c
/LV_cavity.py
f1349c229cfd769ffca82ee4f70a64e24967dd18
[]
no_license
Wenping-Cui/Eco_functions
0e92ad993bff6b9a83d9220ecf908155f7f46a62
1598e981c8eacfa37c9f202f28e6762fcc42778a
refs/heads/master
2021-07-03T09:06:21.443023
2020-08-24T03:45:41
2020-08-24T03:45:41
150,139,810
1
1
null
null
null
null
UTF-8
Python
false
false
3,636
py
# -*- coding: utf-8 -*- """ Created on Thu 03/31/2019 @author: Wenping Cui """ import time import pandas as pd import pdb import matplotlib from matplotlib import cm from matplotlib import colors import matplotlib.ticker as ticker import matplotlib.pyplot as plt import numpy as np import itertools from Eco_function.eco_lib import * from Eco_function.Model_cavity_LV import * import pdb import os.path import pickle from scipy.integrate import odeint from multiprocessing import Pool import multiprocessing import argparse #pdb.set_trace() parser = argparse.ArgumentParser(description='Process types and dynamics') parser.add_argument('--A', default='gaussian') parser.add_argument('--B', default='identity') args = parser.parse_args() A_type = args.A #'gaussian',‘binomial' B_type = args.B #'identity', 'null', 'circulant' and 'block' start_time = time.time() Pool_num=10 file_name='LV_'+A_type+'_sig_1.csv' parameters = {} parameters['sample_size']=10 parameters['S'] =100 parameters['A_type']=A_type parameters['B_type']=B_type parameters['k']=1.0; parameters['sigma_k']=0.1; parameters['mu']=1.0; parameters['epsilon'] =0.1 parameters['g']=1.; parameters['sigma_g']=0.; parameters['B']=0 parameters['ODE_Time']=[0,500, 2000] parameters['mapping_CR']=False parameters['sys_gamma']=0 def func_parallel(para): index=para[0] paras={} paras={**para[1],**paras} start_time0=time.time() S=paras['S'] assert paras['A_type'] in ['binomial','gamma', 'gaussian','uniform'], \ "A type must be 'binomial','gamma' ,'gaussian' or 'uniform'" assert paras['B_type'] in ['identity','null', 'circulant','block'], \ "B type must be 'identity','null', 'circulant','block'" if paras['B_type']=='identity': #'diag', 'null', 'circulant' and 'block' B=np.identity(S) elif paras['B_type']=='null': B=0 elif paras['B_type']=='circulant': D = [7, 1] # generalist, specialist B=circ(S, D[1])+np.identity(S) elif paras['B_type']=='block': B=block(int(S/10), 10)+np.identity(S) paras['B']=B Model=LV_Cavity_simulation(paras) mean_var=Model._simulation(dynamics="ODE") epsilon, mu, gamma = paras['epsilon'], paras['mu'], paras['sys_gamma'] save_pkl =1 if save_pkl: filename='eigenvalues'+'_'+A_type +'_'+B_type+'_sigc_'+str(round(epsilon,2))+'_mu_'+str(round(mu,2))+"gamma_"+str(round(gamma,2))+'.pkl' with open(filename, 'wb') as f: # Python 3: open(..., 'wb') pickle.dump((Model.lams, Model.lams_org, Model.phin_list, Model.col_N), f) paras.pop("B", None) paras.pop("ODE_Time", None) data= { **paras,**mean_var} para_df = pd.DataFrame(data, index=[index]) print("index", index) print("*"*20) print("finished time: ", time.time()-start_time0) return para_df jobs=[]; index=0 for B_type in ['identity']: for S in [100]: for epsilon in np.arange(0.4, 1., 0.1): for mu in [0]: #np.arange(-1, 1.1, 0.1) for sys_gamma in [1.,0.]: parameters['S'] =S parameters['sys_gamma'] = sys_gamma parameters['sample_size']=int(1000) parameters['B_type']=B_type parameters['mu']=mu parameters['epsilon'] =epsilon parameters['sys_gamma']= sys_gamma var=parameters.copy() jobs.append([index, var]) index=index+1 pool = Pool(processes=Pool_num) results = pool.map(func_parallel, jobs) pool.close() pool.join() results_df = pd.concat(results) with open(file_name, 'a', newline = '\n') as f: results_df.to_csv(f, index=False,encoding='utf-8') print ('finish time',int(time.time() - start_time))
[ "wenpingcui@gmail.com" ]
wenpingcui@gmail.com
a916c65dd16e574dda4caae3acfe79ca554e7832
6cb6f0d5213d2af70793bca7f9429cfe878630fa
/sovryn_bridge_rewarder/utils.py
51ffe105f3994d7b1cb998d438492ea61802537c
[]
no_license
DistributedCollective/sovryn-bridge-rewarder
90812926a50a855ee925f6cdea6f3d0e75533838
1286fb5bed02c67474bd1a105c2e6fb664b52046
refs/heads/master
2023-07-17T08:34:28.444152
2021-08-26T12:46:47
2021-08-26T12:46:47
366,236,486
0
1
null
null
null
null
UTF-8
Python
false
false
4,243
py
from datetime import datetime, timezone import functools import json import logging import os from time import sleep from typing import Dict, Any, Union from eth_abi import decode_single from eth_abi.exceptions import DecodingError from eth_typing import AnyAddress from eth_utils import to_checksum_address, to_hex from web3 import Web3 from web3.contract import Contract, ContractEvent THIS_DIR = os.path.dirname(__file__) logger = logging.getLogger(__name__) def utcnow() -> datetime: return datetime.now(timezone.utc) def load_abi(name: str) -> Dict[str, Any]: with open(os.path.join(THIS_DIR, 'abi', name)) as f: return json.load(f) def address(a: Union[bytes, str]) -> AnyAddress: # Web3.py expects checksummed addresses, but has no support for EIP-1191, # so RSK-checksummed addresses are broken # Should instead fix web3, but meanwhile this wrapper will help us return to_checksum_address(a) # Alias, better name... to_address = address @functools.lru_cache() def get_erc20_contract(*, token_address: Union[str, AnyAddress], web3: Web3) -> Contract: return web3.eth.contract( address=address(token_address), abi=ERC20_ABI, ) ERC20_ABI = load_abi('IERC20.json') def get_events( *, event: ContractEvent, from_block: int, to_block: int, batch_size: int = 100 ): """Load events in batches""" if to_block < from_block: raise ValueError(f'to_block {to_block} is smaller than from_block {from_block}') logger.info('fetching events from %s to %s with batch size %s', from_block, to_block, batch_size) ret = [] batch_from_block = from_block while batch_from_block <= to_block: batch_to_block = min(batch_from_block + batch_size, to_block) logger.info('fetching batch from %s to %s (up to %s)', batch_from_block, batch_to_block, to_block) event_filter = event.createFilter( fromBlock=batch_from_block, toBlock=batch_to_block, ) events = get_event_batch_with_retries( event=event, from_block=batch_from_block, to_block=batch_to_block, ) if len(events) > 0: logger.info(f'found %s events in batch', len(events)) ret.extend(events) batch_from_block = batch_to_block + 1 return ret def get_event_batch_with_retries(event, from_block, to_block, *, retries=3): while True: try: return event.getLogs( fromBlock=from_block, toBlock=to_block, ) except ValueError as e: if retries <= 0: raise e logger.warning('error in get_all_entries: %s, retrying (%s)', e, retries) retries -= 1 def exponential_sleep(attempt, max_sleep_time=256.0): sleep_time = min(2 ** attempt, max_sleep_time) sleep(sleep_time) def retryable(*, max_attempts: int = 10): def decorator(func): @functools.wraps(func) def wrapped(*args, **kwargs): attempt = 0 while True: try: return func(*args, **kwargs) except Exception as e: if attempt >= max_attempts: logger.warning('max attempts (%s) exchusted for error: %s', max_attempts, e) raise logger.warning( 'Retryable error (attempt: %s/%s): %s', attempt + 1, max_attempts, e, ) exponential_sleep(attempt) attempt += 1 return wrapped return decorator class UserDataNotAddress(Exception): def __init__(self, userdata: bytes): super().__init__(f'userdata {userdata!r} cannot be decoded to an address') def decode_address_from_userdata(userdata: bytes) -> str: try: return decode_single('address', userdata) except DecodingError as e: raise UserDataNotAddress(userdata) from e @functools.lru_cache() def is_contract(*, web3: Web3, address: str) -> bool: code = web3.eth.get_code(to_address(address)) return code != b'\x00'
[ "rainer@koirikivi.fi" ]
rainer@koirikivi.fi
7bdbb45c2a647db014716b1afeeb24c732579da3
7a73fef9ae426c48573bae41447cef7cb2b97bf6
/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/__init__.py
e1e5fcfbdc6f395fcaab59c2371def4edec4459b
[ "LicenseRef-scancode-public-domain", "BSD-3-Clause" ]
permissive
mjames-upc/python-awips
7f0a80a04457224c9e195b82a95eef4d9b2b3091
e2b05f5587b02761df3b6dd5c6ee1f196bd5f11c
refs/heads/master
2020-03-31T03:00:49.540816
2018-10-05T23:15:42
2018-10-05T23:15:42
53,707,817
0
0
null
2017-04-12T18:00:59
2016-03-12T01:46:57
Python
UTF-8
Python
false
false
104
py
## ## # File auto-generated by PythonFileGenerator __all__ = [ 'diagnostic' ]
[ "mjames@unidata.ucar.edu" ]
mjames@unidata.ucar.edu
2cd69de0ed6caab0657b020ebf567283aa2001c4
52a32a93942b7923b7c0c6ca5a4d5930bbba384b
/dojo/db_migrations/0051_regulation_categories.py
2fe6ff5d352fd9d2bde9377ad6d5aaf8ae2c4bdb
[ "MIT-open-group", "GCC-exception-2.0", "BSD-3-Clause", "LicenseRef-scancode-free-unknown", "LGPL-3.0-only", "GPL-3.0-or-later", "LicenseRef-scancode-warranty-disclaimer", "LGPL-3.0-or-later", "IJG", "Zlib", "LicenseRef-scancode-proprietary-license", "PSF-2.0", "LicenseRef-scancode-python-cwi", "GPL-2.0-or-later", "HPND", "libtiff", "LGPL-2.1-or-later", "EPL-2.0", "GPL-3.0-only", "MIT", "BSD-3-Clause-Modification", "LicenseRef-scancode-public-domain-disclaimer", "HPND-Markus-Kuhn", "CC-BY-SA-4.0", "LicenseRef-scancode-secret-labs-2011", "LicenseRef-scancode-other-copyleft", "LicenseRef-scancode-unknown-license-reference", "LGPL-2.1-only", "LicenseRef-scancode-openssl-exception-lgpl3.0plus", "Libpng", "LicenseRef-scancode-other-permissive", "Python-2.0", "BSD-Advertising-Acknowledgement", "LGPL-2.0-or-later", "GPL-1.0-or-later", "MPL-2.0", "MIT-Modern-Variant", "ISC", "GPL-2.0-only", "LicenseRef-scancode-xfree86-1.0", "Apache-2.0", "LicenseRef-scancode-public-domain", "LicenseRef-scancode-unknown", "BSD-2-Clause" ]
permissive
DefectDojo/django-DefectDojo
43bfb1c728451335661dadc741be732a50cd2a12
b98093dcb966ffe972f8719337de2209bf3989ec
refs/heads/master
2023-08-21T13:42:07.238370
2023-08-14T18:00:34
2023-08-14T18:00:34
31,028,375
2,719
1,666
BSD-3-Clause
2023-09-14T19:46:49
2015-02-19T17:53:47
HTML
UTF-8
Python
false
false
810
py
# Generated by Django 2.2.15 on 2020-08-30 21:49 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('dojo', '0050_deduplication_on_engagement'), ] operations = [ migrations.AlterField( model_name='regulation', name='category', field=models.CharField(choices=[('privacy', 'Privacy'), ('finance', 'Finance'), ('education', 'Education'), ('medical', 'Medical'), ('corporate', 'Corporate'), ('other', 'Other')], help_text='The subject of the regulation.', max_length=9), ), migrations.AlterField( model_name='regulation', name='name', field=models.CharField(help_text='The name of the regulation.', max_length=128, unique=True), ), ]
[ "noreply@github.com" ]
DefectDojo.noreply@github.com
0297d348ab4dfb6710e82a3926547044c9ca90ac
1aa4ef1dc6052e763dc83bf1aadfd7d02ac183a6
/assignment12new 202020/products/migrations/0002_offer.py
a0ab111969653c50669d7206e72b68768abc66ca
[]
no_license
berves50/Final-project-
b65b86f4b1a36b49585960fbc0fd44b4bf5563ec
514778844d1306fcea92021d062e230495c43553
refs/heads/main
2023-01-15T22:56:05.500769
2020-11-26T05:44:49
2020-11-26T05:44:49
316,122,814
0
0
null
null
null
null
UTF-8
Python
false
false
623
py
# Generated by Django 3.0.10 on 2020-11-20 16:23 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('products', '0001_initial'), ] operations = [ migrations.CreateModel( name='Offer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('code', models.CharField(max_length=100)), ('discount', models.FloatField()), ('description', models.CharField(max_length=2000)), ], ), ]
[ "noreply@github.com" ]
berves50.noreply@github.com
0957c3b078d721491faabeeda539ea760c7bf54c
0205e04f1c47b3a34da20e938a4dab7640df89ad
/ics_demo/dao/interfaces/demo/carrot.py
1db4747531680bc6dafe8feaca0ac9d5ddf6e24a
[ "MIT" ]
permissive
Mahuaixu/ics_demo
e765c06aa68be77c5886d962ed0266aee04ad551
21a08945f3983eb409916a7380549f74e3ba5171
refs/heads/master
2021-01-23T20:56:45.619488
2016-04-01T11:49:57
2016-04-01T11:49:57
null
0
0
null
null
null
null
UTF-8
Python
false
false
620
py
from ics_demo.dao.interfaces import base from ics_demo.dao.orm.demo import Carrot from ics_demo.helpers import uuidgen def get_all(): return base.class_get_all_to_dict(Carrot) def get_one(uuid): return base.class_get_one_by_uuid_to_dict(Carrot, uuid) def get_obj(uuid): return base.class_get_one_by_uuid_to_obj(Carrot, uuid) def get_obj_by_ID(ID): return base.class_get_one_by_ID_to_obj(Carrot, ID) def get_keys(): return base.class_get_keys(Carrot) def save(post_dict): name = post_dict['name'] rabbit = post_dict['rabbit'] return Carrot(uuid=uuidgen(), name=name, rabbit=rabbit)
[ "zhaozhl@inspur.com" ]
zhaozhl@inspur.com
73c13eff80b7d3a220922d5ee5f7cf12283fc69f
b966c155f16ed58942e6af3660102c0bb222faa3
/tools/config_manager.py
b9508c9a6664985f0508b8de15bb8a4c80fa07a3
[]
no_license
tungit980/docbao
9e9a8837935168e69f5abfa9a5966b94305d94f0
0ecd9696ca2b7b6b4ed17f3996cb0ab4400efbb3
refs/heads/master
2022-12-05T10:41:46.326095
2020-08-30T09:52:22
2020-08-30T09:52:22
null
0
0
null
null
null
null
UTF-8
Python
false
false
32,086
py
header = ''' ############################################################################### # program : config manager # function: help create, edit web configs easily for crawling # author : hailoc12 # created : 2019-06-14 ############################################################################### ''' import copy from os import system import os from src.backend.lib.config import ConfigManager, WebConfig from src.backend.lib.utils import get_independent_os_path from src.backend.lib.data import Article, ArticleManager from src.backend.lib.browser_crawler import BrowserWrapper, BrowserCrawler HELP_STRING = 'Type "help" for help, "quit" or "exit" to quit app' MAX_DISPLAY = 10 mem_list = None # memorized list use for chaining commands # UTILITY def display_multiple_input(message, answer_message="Input: "): answer = None print(message) count=0 result = [] while answer != '': count+=1 answer = input(str(count) + '. ' + answer_message) if answer !='': result.append(answer) return result def display_choice_dialog(message, choices, answer_message="Your Answer: "): ''' output ====== None if cancel 0..len(choices) if choice ''' print(message) for i in range(0, len(choices)): print("%s. %s" % (str(i+1), choices[i])) choice = 'a' while not choice.isdigit(): try: print() choice = input(answer_message) if choice.strip().isdigit(): choice = int(choice) if choice <= 0 or choice > len(choices): print("You must input a number between 1..%s" % str(len(choices))) choice = 'a' return choice-1 else: if choice.strip() == '': return None else: print("You must input a number between 1..%s" % str(len(choices))) choice = 'a' except: print("You must input a number between 1..%s" % str(len(choices))) choice = 'a' def extract_args(args): ''' extract index or keyword from arguments arg: - args: list of arguments of command output: index (int) or keyword (str) or None if fail ''' arg = " ".join(args).strip() try: index = int(arg) - 1 #index start from 0 if mem_list is not None: if index >=0 and index < len(mem_list): return index else: print("Must provide an index between 1..%s" % str(len(mem_list))) else: print('There are no list to use index. Use command "show" to create list') except: keyword = arg return keyword return None def display_menu_dialog(menu_message,input_message, menu=[]): ''' function -------- display menu and get user choice input ----- menu: list of menu output ------ return user choice in number (0..len(menu)-1) ''' print(menu_message) choice = display_choice_dialog(input_message, menu) return choice def clear_screen(): system('clear') def process_edit_config(webconfig): if webconfig is None: input("There isn't any web config yet. Create a new one or load from resources") return None print_config_header = ''' ########################################### # PRINT WEB CONFIG # ########################################### ''' while True: clear_screen() print(print_config_header) config_list = webconfig.print_config() number_of_config = len(config_list) choice = input("Press ENTER to return, LINE NUMBER <=%s to edit, -LINENUMBER <=%s to remove, or >%s to add new config: " % (number_of_config, number_of_config, number_of_config)) is_digit = False try: line = int(choice) is_digit = True except: pass if is_digit: if line <= number_of_config: # edit config if line > 0: # edit old config if line != 1: key, value = webconfig.get_config_by_index(line-2) else: key = 'Webname' value = webconfig.get_webname() print() print("Editing key: %s" % key) print("Old value: %s" % str(value)) validated = False while not validated: try: new_value = eval(input("New Value: ")) # note: eval is important here to get right data type validated = True except: print("Input value in wrong format. Remember to input like Python code (eg. 'money' not money. [a, b, c] not '[a, b, c]'") if line != 1: webconfig.set_config(key, new_value) else: webconfig.set_webname(new_value) input("Change OK. Press ENTER to continue") elif line < 0: # delete config if line == -1: # delete webname input("Sorry, you can't delete site name") else: key, value = webconfig.get_config_by_index(-line-2) webconfig.delete_config(key) input("Succesfully remove %s from site config" % key) else: # add new config key = input("Enter new config key: ") validated = False while not validated: try: value = eval(input("Enter config value: ")) validated = True except: print("Input value in wrong format. Remember to input like Python code (eg. 'money' not money. [a, b, c] not '[a, b, c]'") webconfig.set_config(key, value) input("Sucessfully add %s to config" % key) else: break return webconfig def display_yes_no_dialog(message): choice = input(message) if 'y' == choice.lower().strip(): return True elif 'n' == choice.lower().strip(): return False else: return None def process_create_blank_newspaper(): webconfig = WebConfig() webconfig.load_default_config('newspaper') print() # basic config newspaper_name = input("Please enter newspaper name: ") newspaper_url = input("Please enter newspaper base url: ") choice = display_yes_no_dialog("Is crawl url the same as base url (y/n) ?") if not choice: crawl_url = input("Please enter crawl url: ") else: crawl_url = newspaper_url use_browser = display_yes_no_dialog("Do you want to crawl using browser or not (y/n) ?") if use_browser: webconfig.set_config("use_browser", True) else: webconfig.set_config("use_browser", False) #date_xpath = input("Please enter xpath to get publish date in detail page: ") menu = ['Auto Find', 'Class name', 'Xpath'] date_extract_type = display_choice_dialog('Please choose one way to extract publish date in detail page: ', menu) if date_extract_type == 1: # CSS selection css = input("Please enter class name of html tag that contain publish date: ") date_xpath = '//node()[@class="' + css + '"]' elif date_extract_type == 2: date_xpath = input("Please enter single xpath that extract html tag containing publish date: ") xpath_count = len(webconfig.get_topics_xpath()) remove_date_tag_html = display_yes_no_dialog('Does it need to remove html tag to extract publish date (y/n)?: ') webconfig.set_config('remove_date_tag_html', remove_date_tag_html) ignore_topic_menu_choice = ['Topic is invalid', 'Use current time as publish date'] choice = display_choice_dialog("How to treat topic that can't find its publish date ?", ignore_topic_menu_choice) if choice == 0: ignore_topic_not_have_publish_date = True else: ignore_topic_not_have_publish_date = False webconfig.set_config('ignore_topic_not_have_publish_date', ignore_topic_not_have_publish_date) crawl_detail_choice = display_yes_no_dialog('Do you want to crawl detail content (sapo, content, img) ?: ') if crawl_detail_choice: sapo_xpath = input("Please enter xpath to extract sapo text: ") content_xpath = input("Please enter xpath to extract main content: ") remove_content_html = True remove_content_html_xpaths = [] answer = None count = 1 while answer != '': print("Please input xpaths to remove tags (ENTER=Finish): ") answer = input("Xpath %s: " % str(count)) if answer != '': remove_content_html_xpaths.append(answer) count+=1 feature_image_xpath = input("Please enter xpath to extract feature images url: ") text_xpath = display_multiple_input("Please input xpaths to get text element: ") image_box_xpath = display_multiple_input("Please input xpaths to get image box element: ") image_title_xpath = display_multiple_input("Please input xpaths to get title element from image box (ENTER=finish): ") video_box_xpath = display_multiple_input("Please input xpaths to get video box element (ENTER=Finish)") video_title_xpath = display_multiple_input("Please input xpaths to get title element from video box (ENTER=finish): ") audio_box_xpath = display_multiple_input("Please input xpaths to get audio box element (ENTER=Finish)") audio_title_xpath = display_multiple_input("Please input xpaths to get title element from audio box (ENTER=finish): ") avatar_choice_menu = ['Provide logo link', 'Xpath to get logo url'] avatar_choice = display_choice_dialog("How do you want to extract logo url ?", avatar_choice_menu) avatar_url = '' avatar_xpath = '' if avatar_choice == 0: # provide linke avatar_type = 'url' avatar_url = input("Please enter logo absolute url: ") else: avatar_xpath = input("Please enter xpath to extract avatar/logo url: ") avatar_type = 'xpath' sapo_xpath_list = [] content_xpath_list = [] feature_image_xpath_list = [] for i in range(0, xpath_count): sapo_xpath_list.append(sapo_xpath) content_xpath_list.append(content_xpath) feature_image_xpath_list.append(feature_image_xpath) webconfig.set_config('sapo_xpath', sapo_xpath_list) webconfig.set_config('content_xpath', content_xpath_list) webconfig.set_config('text_xpath', text_xpath) webconfig.set_config('feature_image_xpath', feature_image_xpath_list) webconfig.set_config('get_detail_content', crawl_detail_choice) webconfig.set_config('remove_content_html', remove_content_html) webconfig.set_config('remove_content_html_xpaths', remove_content_html_xpaths) webconfig.set_config('image_box_xpath', image_box_xpath) webconfig.set_config('image_title_xpath', image_title_xpath) webconfig.set_config('video_box_xpath', video_box_xpath) webconfig.set_config('video_title_xpath', video_title_xpath) webconfig.set_config('audio_box_xpath', audio_box_xpath) webconfig.set_config('audio_title_xpath', audio_title_xpath) webconfig.set_config('avatar_type', avatar_type) webconfig.set_config('avatar_xpath', avatar_xpath) webconfig.set_config('avatar_url', avatar_url) tags = display_multiple_input("Please input metadata to label article crawled from this config (ENTER=finish): ") domain_re = newspaper_url webconfig.set_webname(newspaper_name) webconfig.set_config('web_url', newspaper_url) webconfig.set_config('crawl_url', crawl_url) webconfig.set_config('url_pattern_re', domain_re) webconfig.set_tags(tags) date_xpath_list = [] # use the same date_xpath for every topic_xpath for i in range(0, xpath_count): date_xpath_list.append(date_xpath) webconfig.set_config('date_xpath', date_xpath_list) # topic type choice = display_yes_no_dialog("Do you want to run test with default config (y/n) ?") if choice: webconfig = process_test_crawl_web_config(webconfig) is_ok = True input("Successfully createn %s site config" % webconfig.get_webname()) return webconfig def process_create_blank_web_config(): clear_screen() header = ''' ############################################## CREATE BLANK WEB CONFIG ############################################## ''' print(header) menu = ['Newspaper', 'Wordpress Blog', 'Facebook Account', 'Facebook Page', 'Facebook Group', 'Other'] # choose template user_choice = display_menu_dialog('What type of site: ', 'Choice: ', menu) webconfig = WebConfig() if user_choice == 0: # newspaper webconfig = process_create_blank_newspaper() elif user_choice == 1: # wordpress webconfig.load_default_config('wordpress') elif user_choice == 2: # facebook user webconfig.load_default_config('facebook user') fb_name = input("Please input FB Account name: ") fb_username = input("Please input FB username (or ENTER to leave it): ") fb_userid = input("Please input FB user ID (or ENTER to leave it): ") if fb_username.strip() != '': url = "https://www.facebook.com/" + fb_username.strip() else: url = "https://www.facebook.com/profile.php?id=" + fb_userid.strip() webconfig.set_webname(fb_name) webconfig.set_config('web_url', url) webconfig.set_config('crawl_url',url) webconfig.set_config('url_pattern_re', url) elif user_choice == 3: # facebook fanpage webconfig.load_default_config('facebook fanpage') fb_name = input("Please input Fanpage name: ") fb_id = input("Please input Fanpage id: ") url = "https://www.facebook.com/pg/" + fb_id.strip() + "/posts/?ref=page_internal" webconfig.set_webname(fb_name) webconfig.set_config('web_url', url) webconfig.set_config('crawl_url',url) webconfig.set_config('url_pattern_re', url) elif user_choice == 4: # facebook group webconfig.load_default_config('facebook fanpage') fb_name = input("Please input FB Group name (eg Page Hải Phòng): ") fb_id = input("Please input Group id (eg page.haiphong): ") url = "https://www.facebook.com/groups/" + fb_id.strip() + "/" webconfig.set_webname(fb_name) webconfig.set_config('web_url', url) webconfig.set_config('crawl_url',url) webconfig.set_config('url_pattern_re', url) else: webconfig.load_default_config() # what to do next menu = ['Edit created config', 'Test crawling this config', 'Save config', "Return"] user_choice = -1 while user_choice != 3: # finish clear_screen() print(header) user_choice = display_menu_dialog('What do you want to do next ? ', 'Choice: ', menu) if user_choice == 0: # edit config process_edit_config(webconfig) elif user_choice == 1: # test crawl webconfig = process_test_crawl_web_config(webconfig) elif user_choice == 2: # save config process_save_webconfig(webconfig) return webconfig def process_test_crawl_web_config(webconfig): ''' function -------- try to crawl with webconfig only return ------ modified webconfig ''' test_crawl_header = ''' ########################################### # TEST CRAWLING SITE CONFIG # ########################################### ''' has_change_dir = False try: os.chdir("backend") has_change_dir = True except: pass continue_test = True while continue_test: clear_screen() print(test_crawl_header) # prepare webconfig for test minimum_duration_old_value = webconfig.get_minimum_duration_between_crawls() webconfig.set_minimum_duration_between_crawls(-5) # mean always crawl this config maximum_url_old_value = webconfig.get_config('maximum_url', 10) webconfig.set_config('maximum_url',50) # ask for edit choice = display_yes_no_dialog("Is there anything to edit before test crawling (y/n) ?") if choice: webconfig = process_edit_config(webconfig) maximum_url_old_value = webconfig.get_maximum_url() # test config_manager = ConfigManager(get_independent_os_path(['src', 'backend','input', 'test.yaml']), get_independent_os_path(['input', 'kols_list.txt']), get_independent_os_path(['input', 'fb_list.txt'])) config_manager.load_data() config_manager.replace_crawl_list([webconfig]) data_filename = get_independent_os_path(['src', 'backend', 'data','test_article.dat']) blacklist_filename = get_independent_os_path(['src', 'backend','data','test_blacklist.dat']) data_manager = ArticleManager(config_manager, data_filename, blacklist_filename) data_manager.reset_data() # test crawl my_pid = 1 browser = BrowserWrapper() if webconfig.get_crawl_type() == 'newspaper': data_manager.add_articles_from_newspaper(my_pid, webconfig, browser) elif 'facebook' in webconfig.get_crawl_type(): data_manager.add_articles_from_facebook(my_pid, webconfig, browser) # report # continue_test = display_yes_no_dialog('Do you want to test again (y/n) ?: ') # return back webconfig.set_config('maximum_url', maximum_url_old_value) webconfig.set_minimum_duration_between_crawls(minimum_duration_old_value) if has_change_dir: os.chdir("..") return webconfig def process_create_web_config_from_existing_one(): ''' return ------ webconfig object that contain config load from file ''' load_config_header = ''' ########################################### # PRINT WEB CONFIG # ########################################### ''' load_config_menu = ['Load from local config files', 'Load from online config database', 'Return to main menu' ] choice = -1 webconfig = None while choice != 2: clear_screen() print(load_config_header) choice = display_menu_dialog('What do you want to do ?', "Choice (ENTER=cancel): ", load_config_menu) if choice == 0: # display config file list file_base_path = get_independent_os_path(['resources','configs', 'newspaper']) filepath = display_choose_file_dialog(file_base_path) if filepath is not None: webconfig = WebConfig() webconfig.load_config_from_file(filepath) input("Successfully load site config from %s" % filepath) elif choice == 1: pass return webconfig def process_manage_other_config(config_manager): ''' return ------ None ''' manage_other_config_header = ''' ########################################### # MANAGE PROPERTIES IN CONFIG.YAML FILE # ########################################### ''' while True: clear_screen() print(manage_other_config_header) config_list = config_manager.print_config() choice = input("Press ENTER to return or LINE NUMBER to edit: ") if choice.isdigit(): index = int(choice) - 1 key, value = config_list[index] print() print("Editing key: %s" % key) print("Old value: %s" % str(value)) new_value = eval(input("New Value: ")) config_manager.set_config(key, new_value) input("Change OK. Press ENTER to continue") else: break config_manager.save_data() def display_choose_file_dialog(file_base_path): header = ''' ########################################### # LOAD SITE CONFIG # ########################################### ''' ok = False while not ok: clear_screen() print(header) search = input("Enter keyword to find config file or ENTER to display all: ") file_list = [] for root,directory,files in os.walk(file_base_path): for item in files: if search in item: file_list.append(item) # choose file config_file_index = display_menu_dialog('Which config do you want to load ?', 'Choice (ENTER=Cancel): ', file_list) if config_file_index is None: #cancel return None config_file_name = file_list[int(config_file_index)] # make filepath to load filepath = get_independent_os_path([file_base_path, config_file_name]) answer = input("Are you sure to load site config from %s ? (ENTER=ok, anything=repeat)" % config_file_name) if answer.strip() == '': ok = True return filepath def process_manage_crawl_list(config_manager): ''' output ====== - config_manager with modified data - None or webconfig loaded from current crawl list ''' manage_crawl_list_header = ''' ########################################### # MANAGE CRAWL LIST # ########################################### ''' # what to do next menu = ['Add site config file to list', 'Remove newspaper from list', 'Edit site config in list', 'Load site config in list to working config', 'Edit config of all site in list', 'Add working site config to list', 'Load working site config from list', 'Return', ] user_choice = -1 webconfig = None while user_choice != len(menu)-1: # finish clear_screen() print(manage_crawl_list_header) newspaper_list = config_manager.print_crawl_list() # newspaper_list contain all crawl config. All edits will be made on newspaper_list then merge back into config_manager print() user_choice = display_menu_dialog('What do you want to do next ? ', 'Choice: ', menu) if user_choice == 0: # add config file to list filepath = display_choose_file_dialog(get_independent_os_path(['resources', 'configs', 'newspaper'])) if filepath is not None: new_webconfig = WebConfig() new_webconfig.load_config_from_file(filepath) newspaper_list.append(new_webconfig) input("Successfully add %s to crawl list" % new_webconfig.get_webname()) config_manager.replace_crawl_list(newspaper_list) #save all changes to config_manager config_manager.save_data() elif user_choice == 1: # remove newspaper from list choice = input("Please input LINE NUMBER to remove or ENTER to cancel: ") if choice.strip() != '' and choice.isdigit(): remove_webconfig = newspaper_list.pop(int(choice) -1) input("Successfuly remove %s from crawl list" % remove_webconfig.get_webname()) config_manager.replace_crawl_list(newspaper_list) #save all changes to config_manager config_manager.save_data() elif user_choice == 2: # edit site config in list choice = input("Please input LINE NUMBER to edit or ENTER to cancel: ") if choice.strip() != '' and choice.isdigit(): choose_webconfig = newspaper_list[int(choice) -1] choose_webconfig = process_edit_config(choose_webconfig) config_manager.add_newspaper(choose_webconfig) # update config config_manager.save_data() elif user_choice == 3: # load site config to working config choice = input("Please input LINE NUMBER to load or ENTER to cancel: ") if choice.strip() != '' and choice.isdigit(): choose_webconfig = newspaper_list[int(choice) -1] webconfig = choose_webconfig input("Successfuly load %s config to working config" % choose_webconfig.get_webname()) elif user_choice == 4: # edit single config of all sites in list print() newspaper_list = config_manager.get_newspaper_list() if len(newspaper_list) > 0: print("Sample of a site config:") sample_site = newspaper_list[0] sample_site.print_config() print() key = input('Enter config property to edit: (ENTER=cancel, -config_name=remove)').strip() key = key.strip() if key != '': if key[0] == '-': # remove config key = key[1:] count = 0 for newspaper in config_manager.get_newspaper_list(): count+=1 newspaper.delete_config(key) input("Successfully remove %s of %s site" % (key, str(count))) config_manager.save_data() else: # edit all new_value = eval(input('Enter new value of %s: ' % key)) count = 0 for newspaper in config_manager.get_newspaper_list(): count+=1 newspaper.set_config(key, new_value) input("Successfully change %s of %s site to new value" % (key, str(count))) config_manager.save_data() else: print("There haven't been any site config in crawling list") elif user_choice == 5: # add working site config to list if webconfig is not None: config_manager.add_newspaper(webconfig) input("Succesfully add %s to crawl list" % webconfig.get_webname()) config_manager.save_data() else: input("No working site config. Please go to site config manager to create/load one") elif user_choice == 6: # load newspaper to current webconfig choice = input("Please input LINE NUMBER to load or ENTER to cancel: ") if choice.strip() != '' and choice.isdigit(): webconfig = copy.copy(newspaper_list[int(choice) -1]) input("Successfuly load %s from crawl list" % webconfig.get_webname()) return webconfig def process_manage_config(webconfig=None, config_manager=None): ''' return ------ None or webconfig loaded from crawling list ''' manage_config_header = ''' ########################################### # MANAGE CONFIG.YAML FILE # ########################################### ''' manage_config_menu = [ 'Edit crawl list', 'Edit program settings', 'Go to site config manager', 'Return to main menu' ] choice = -1 while choice != len(manage_config_menu) -1: clear_screen() print(manage_config_header) choice = display_menu_dialog('What do you want to do ?', "Choice: ", manage_config_menu) if choice == 0: # manage crawl list load_web_config = process_manage_crawl_list(config_manager) if load_web_config is not None: webconfig = load_web_config elif choice == 1: # manage other config process_manage_other_config(config_manager) elif choice == 2: # go to site config manager process_webconfig_manager(webconfig, config_manager) return webconfig def process_save_webconfig(webconfig): menu = ['Save in a new file', 'Update an existing file'] choice = display_choice_dialog('How do you want to save: ', menu) file_base_path = get_independent_os_path(['resources','configs','newspaper']) if choice == 0: print("Working config will be saved in ./resource/config/newspaper/") filename = input("Filename: ") filepath = get_independent_os_path([file_base_path, filename]) elif choice == 1: #update existing file filepath = display_choose_file_dialog(file_base_path) webconfig.export(filepath) print("File is save OK") def process_webconfig_manager(webconfig=None, config_manager=None): ''' return ------ None ''' webconfig_header = ''' ########################################### # SITE CONFIG MANAGER # ########################################### ''' manage_webconfig_menu = ['Create new blank config', 'Load config from file', 'Edit working site config', 'Save working site config to file', 'Test crawling working site config', 'Add/Update working site config to crawl list', 'Move to program setting', 'Return to main menu' ] choice = -1 while choice != len(manage_webconfig_menu) -1 : clear_screen() print(webconfig_header) choice = display_menu_dialog('What do you want to do ?', "Choice: ", manage_webconfig_menu) if choice == 0: # add current webconfig to crawl list webconfig = process_create_blank_web_config() if choice == 1: # create webconfig from existing one webconfig = process_create_web_config_from_existing_one() elif choice == 2: # editing working webconfig webconfig = process_edit_config(webconfig) elif choice == 3: # save working site config to file process_save_webconfig(webconfig) elif choice == 4: # test crawling process_test_crawl_web_config(webconfig) elif choice == 5: # add/update working site config to crawl list config_manager.add_newspaper(webconfig) config_manager.save_data() print("Successfully add/update %s to crawl list" % webconfig.get_webname()) elif choice == 6: # move to program setting process_manage_config(webconfig, config_manager) return webconfig # MAIN PROGRAM config_manager = ConfigManager(get_independent_os_path(['src', 'backend', 'input', 'config.yaml']), get_independent_os_path(['src, ','backend', 'input', 'kols_list.txt']), get_independent_os_path(['src', 'backend', 'input', 'fb_list.txt'])) #config object config_manager.load_data(True, False, False, 30, '.') main_menu_choice = -1 while main_menu_choice != 2: clear_screen() print(header) main_menu = ['Manage site config', 'Manage program settings', 'Quit'] main_menu_choice = display_menu_dialog('WELCOME TO CONFIG MANAGER', 'Your choice: ', main_menu) webconfig = None if main_menu_choice == 0: # crawl new site webconfig = process_webconfig_manager(webconfig, config_manager) elif main_menu_choice == 1: # manage config.yaml webconfig = process_manage_config(webconfig, config_manager) print("Goodbye")
[ "hailoc12@gmail.com" ]
hailoc12@gmail.com
d8378edcd6198ed26579441c3a40b78c8cf15001
bc233c24523f05708dd1e091dca817f9095e6bb5
/bitmovin_api_sdk/encoding/manifests/dash/periods/adaptationsets/representations/__init__.py
bf71c01861311a939e78407acf62aa9512c59a19
[ "MIT" ]
permissive
bitmovin/bitmovin-api-sdk-python
e3d6cf8eb8bdad62cb83ec77c0fc4950b06b9cdd
b0860c0b1be7747cf22ad060985504da625255eb
refs/heads/main
2023-09-01T15:41:03.628720
2023-08-30T10:52:13
2023-08-30T10:52:13
175,209,828
13
14
MIT
2021-04-29T12:30:31
2019-03-12T12:47:18
Python
UTF-8
Python
false
false
1,493
py
from bitmovin_api_sdk.encoding.manifests.dash.periods.adaptationsets.representations.representations_api import RepresentationsApi from bitmovin_api_sdk.encoding.manifests.dash.periods.adaptationsets.representations.type.type_api import TypeApi from bitmovin_api_sdk.encoding.manifests.dash.periods.adaptationsets.representations.vtt.vtt_api import VttApi from bitmovin_api_sdk.encoding.manifests.dash.periods.adaptationsets.representations.imsc.imsc_api import ImscApi from bitmovin_api_sdk.encoding.manifests.dash.periods.adaptationsets.representations.sprite.sprite_api import SpriteApi from bitmovin_api_sdk.encoding.manifests.dash.periods.adaptationsets.representations.fmp4.fmp4_api import Fmp4Api from bitmovin_api_sdk.encoding.manifests.dash.periods.adaptationsets.representations.chunked_text.chunked_text_api import ChunkedTextApi from bitmovin_api_sdk.encoding.manifests.dash.periods.adaptationsets.representations.cmaf.cmaf_api import CmafApi from bitmovin_api_sdk.encoding.manifests.dash.periods.adaptationsets.representations.mp4.mp4_api import Mp4Api from bitmovin_api_sdk.encoding.manifests.dash.periods.adaptationsets.representations.webm.webm_api import WebmApi from bitmovin_api_sdk.encoding.manifests.dash.periods.adaptationsets.representations.progressive_webm.progressive_webm_api import ProgressiveWebmApi from bitmovin_api_sdk.encoding.manifests.dash.periods.adaptationsets.representations.dash_representation_list_query_params import DashRepresentationListQueryParams
[ "openapi@bitmovin.com" ]
openapi@bitmovin.com
513cd74af6ff93ef148488fe794428acc09017a1
0c17c0eae2f1b616dce500315b35167fda6c0828
/GUI/Source_Code_OfficePortal-0.1/Make_Config_File.py
ec10d17c4e4a34e8da28154721acd64d47dba3ac
[]
no_license
merijndewit/Office_Portal
e32f6e74be7316783f44f1c89a4a46a3442d3157
f58589589f0b94839eba222558e7af2075e4856c
refs/heads/main
2023-06-19T08:33:55.301685
2021-07-14T10:02:31
2021-07-14T10:02:31
369,529,388
8
0
null
null
null
null
UTF-8
Python
false
false
1,201
py
import sys,os import json if getattr(sys, 'frozen', False): cd = os.path.dirname(sys.executable) else: cd = os.path.dirname(os.path.abspath(__file__)) def makeConfig(values, configspecs): config = {} config['streamInfo'] = [] config = { 'ip': values[configspecs[0]], 'pixelWidth': values[configspecs[1]], 'pixelHeight': values[configspecs[2]], 'framerate': values[configspecs[3]], 'ledStrip': values[configspecs[4]], 'ledTexture': values[configspecs[5]], 'none': values[configspecs[6]], 'bitrate': values[configspecs[7]], 'portSend': values[configspecs[8]], 'portReceive': values[configspecs[9]], 'blue': values[configspecs[10]], 'orange': values[configspecs[11]], 'ring1080': values[configspecs[12]], 'ring720': values[configspecs[13]], 'customcolor': values[configspecs[14]], 'brightness': values[configspecs[15]], 'Rvalue': values[configspecs[16]], 'Gvalue': values[configspecs[17]], 'Bvalue': values[configspecs[18]], 'customTexture': values[configspecs[19]], 'customPath': values[configspecs[20]] } with open(cd + '/config.txt', 'w+') as outfile: json.dump(config, outfile)
[ "merijn.de.wit@gmail.com" ]
merijn.de.wit@gmail.com
5cfb969dbad69601a0a0ad1f17caf3bd457ffee9
6cb4f94be34451bd51d9f1585ea3bd252e60d5e0
/fresh_day_back/fresh_day_back/urls.py
b40e8fd3f5f744144d5ed48abf343b91f66ad978
[]
no_license
1032524125/django
b3f7b825fd8cacf268fbaab8482d142599bda275
b57ff20c45032092e295434b8e1b60bce027d2f1
refs/heads/master
2020-04-04T10:03:22.279642
2018-11-15T09:55:42
2018-11-15T09:55:42
155,841,146
0
0
null
null
null
null
UTF-8
Python
false
false
1,118
py
"""fresh_day_back URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.11/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url,include from django.contrib import admin from django.contrib.staticfiles.urls import static from fresh_day_back import settings urlpatterns = [ url(r'^admin/', admin.site.urls), #app模块 url(r'^app/',include('app.urls',namespace='app')), #商品模块 url(r'^goods/',include('goods.urls',namespace='goods')) ] urlpatterns += static(settings.MEDIA_URL,document_root = settings.MEDIA_ROOT)
[ "1032524125@qq.com" ]
1032524125@qq.com
ca40498aa30d2a1e5baee83adfbb27bbb25c2bfa
f18125b848e37a64e35136a90cf4694e52eb9fcc
/teek/_timeouts.py
13563144be59e5b03aa5f93e5c8b5b14d4b72708
[ "MIT" ]
permissive
carlbordum/teek
d19271dfe11e3e77052b1a3c215ddf6a9d50e440
a931b468744c8236fd4ce6f1dc3a8c4829d59db3
refs/heads/master
2020-04-16T11:41:10.909230
2019-01-13T19:24:16
2019-01-13T19:24:16
165,547,247
0
0
null
2019-01-13T19:48:26
2019-01-13T19:48:26
null
UTF-8
Python
false
false
2,489
py
import teek as tk from teek._tcl_calls import make_thread_safe # there's no after_info because i don't see how it would be useful in # teek class _Timeout: def __init__(self, after_what, callback, args, kwargs): if kwargs is None: kwargs = {} self._callback = callback self._args = args self._kwargs = kwargs self._state = 'pending' # just for __repr__ and error messages self._tcl_command = tk.create_command(self._run) self._id = tk.tcl_call(str, 'after', after_what, self._tcl_command) def __repr__(self): name = getattr(self._callback, '__name__', self._callback) return '<%s %r timeout %r>' % (self._state, name, self._id) def _run(self): needs_cleanup = True # this is important, thread tests freeze without this special # case for some reason def quit_callback(): nonlocal needs_cleanup needs_cleanup = False tk.before_quit.connect(quit_callback) try: self._callback(*self._args, **self._kwargs) self._state = 'successfully completed' except Exception as e: self._state = 'failed' raise e finally: tk.before_quit.disconnect(quit_callback) if needs_cleanup: tk.delete_command(self._tcl_command) @make_thread_safe def cancel(self): """Prevent this timeout from running as scheduled. :exc:`RuntimeError` is raised if the timeout has already ran or it has been cancelled. """ if self._state != 'pending': raise RuntimeError("cannot cancel a %s timeout" % self._state) tk.tcl_call(None, 'after', 'cancel', self._id) self._state = 'cancelled' tk.delete_command(self._tcl_command) @make_thread_safe def after(ms, callback, args=(), kwargs=None): """Run ``callback(*args, **kwargs)`` after waiting for the given time. The *ms* argument should be a waiting time in milliseconds, and *kwargs* defaults to ``{}``. This returns a timeout object with a ``cancel()`` method that takes no arguments; you can use that to cancel the timeout before it runs. """ return _Timeout(ms, callback, args, kwargs) @make_thread_safe def after_idle(callback, args=(), kwargs=None): """Like :func:`after`, but runs the timeout as soon as possible.""" return _Timeout('idle', callback, args, kwargs)
[ "akuviljanen17@gmail.com" ]
akuviljanen17@gmail.com
84eaed61749bc550b3f113ebdf7b48e5b356ceed
cd9702fdcfd36b26fcd639fb4d0be8019c4c21e0
/src/getEcommence_yhd.py
b469fe92a4f6abf2061ff8a23e7533a675ba3408
[]
no_license
Neilfu/NLP
0d467b860061d2fd2e2aaa8571f391f183158e32
7ebfc2264761b83174f8ad47e0fce11fdff7e322
refs/heads/master
2020-04-06T07:04:54.755915
2016-02-26T13:38:49
2016-02-26T13:38:49
15,428,306
0
0
null
null
null
null
UTF-8
Python
false
false
12,456
py
#coding=utf-8 import re import sys, getopt import datetime import requests from bs4 import BeautifulSoup import logging import json from pymongo import MongoClient,ASCENDING import time LEVELS={'DEBUG':logging.DEBUG, 'INFO':logging.INFO, 'WARNING':logging.WARNING, 'ERROR':logging.ERROR, 'CRITICAL':logging.CRITICAL, } def setLog(strLevel='INFO',logfile='../log.txt'): level=LEVELS[strLevel] logging.basicConfig( format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s', datefmt='%a, %d %b %Y %H:%M:%S', ) logger = logging.getLogger() handler = logging.FileHandler(logfile) logger.addHandler(handler) console = logging.StreamHandler() logger.addHandler(console) logger.setLevel(level) return logger def progressBar(strTitle,num=1, total=100): rate = int(float(num) / float(total)*1000) sys.stdout.write(strTitle+"\t=>\t"+str(rate/10.0)+"%\r") sys.stdout.flush() def openTable(tableName=''): strConn = "mongodb://" + global_setting['host'] + ":" + global_setting['port'] con = MongoClient(strConn) db = con[global_setting['database']] return db[tableName] def getCategoryUrl(site="",url=""): catDb = openTable(tableName=global_setting['catTable']) r = session.get(url) if not r.text: return False soup = BeautifulSoup(r.text) # for level1 in soup.select('.alonesort'): curLevel1 = level1.select('.mt')[0].text curLevel1 = re.sub('\s', '', curLevel1) for level2 in level1.select('dl'): curLevel2 = level2.select('dt')[0].text curLevel2 = re.sub('\s', '', curLevel2) for level3 in level2.select('dd a'): curLevel3 = re.sub('\s', '', level3.text) curlUrl = level3['href'] retFind = re.findall(r'com\/(.*)\/',curlUrl) if retFind: curCatID = retFind[0] if catDb.find({'catId':curCatID}).count() >0: logger.debug('catetogy %s exists,skip\n'%(curCatID)) else: catDb.insert({'catId':curCatID,'level1':curLevel1, 'level2':curLevel2, 'level3':curLevel3, 'catUrl':curlUrl, 'site':site}) return True def getPidList4Cat(): level1Filter = global_setting['level1'] level2Filter = global_setting['level2'] level3Filter = global_setting['level3'] catDb = openTable(tableName=global_setting['catTable']) dbProductList = openTable(tableName=global_setting['productTable']) #支持Ctrl+c 中断 ctlC = False #优先扫描最久没更新的类别 for cat in catDb.find({'site':global_setting['site']}).sort('lasttime',ASCENDING): if ctlC: break if (global_setting['site']==cat['site']) and cat['catUrl'] and ((level1Filter and cat['level1'] in level1Filter) \ or (level2Filter and cat['level2'] in level2Filter) \ or level3Filter and cat['level3'] in level3Filter): #产品的类别属性 CATLIST = ('catId','catUrl','site','level1','level2','level3') paraCat = {} for li in CATLIST: if li in cat: paraCat[li] = cat[li] #获取产品列表 ctlC = getProduct(dbProductList, **paraCat) #记录最近更新时间及品类库存量 catCount = dbProductList.find({'$and':[{'catId':cat['catId']},{'site':cat['site']}]}).count() lastFreshDate = datetime.datetime.utcnow() catDb.update({'catId':cat['catId']},{'$set':{'lasttime':lastFreshDate}}) catDb.update({'catId':cat['catId']},{'$push':{'timeline':{'lasttime':lastFreshDate,'count':catCount}}}) def getCatPageNum(url): r = session.get(url) soup = BeautifulSoup(r.text) strPages = soup.select('.select_page_num') if strPages: pages = re.findall('\/(\d+)',strPages[0].text)[0] if pages: pages = int(pages) else: pages = 0 return pages def getProduct(dbProductList,**cat): SUFFIX = '/b/a-s1-v4-p%s-price-d0-f0d-m1-rt0-pid-mid0-k/' catUrl = cat['catUrl'] totalPages = getCatPageNum(catUrl) logger.info("begin:%s\t->%s\t->%s,\ttotal %d page" %(cat['level1'],cat['level2'],cat['level3'], totalPages) ) rule = re.compile(r'id=\"plist\".*?>(.*?)<div class=\"clr\"',re.S) Skip = False for page in range(totalPages): if Skip: break try: progressBar("getting pages",page,totalPages) urlPage = catUrl + SUFFIX %(page+1) time.sleep(0.1) r = session.get(urlPage) #listUls = re.findall(rule,r.text) soup = BeautifulSoup(r.text) skuLists=[] for li in soup.select('.mod_search_pro'): product = {} product.update(cat) item = li.select('.proName')[0].a product['sku'] = item['pmid'] skuLists.append(product['sku']) product['url'] = item['href'] product['name'] = re.sub('[\r\n]','',item.text) reBrand = re.findall(r'^(.*?)[\s（]',product['name']) if reBrand: product['brand'] = reBrand[0] strPrice = li.select('.proPrice')[0] product['price'] = strPrice.em['yhdprice'] try: if dbProductList.find({u'sku':product['sku']}).count() >0: if global_setting['delta']: logger.debug('Delta:on, category %s scanning finished' %(cat['catId']) ) Skip = True break else: logger.debug('%s exist,skip' %(product['sku'])) else: dbProductList.insert(product) if global_setting['spec']: getProductDetail(product['sku'],product['url'],dbProductList) except Exception, e: logger.exception("error in Page:%d, skuid:%s, reason:%s" %(page, product['sku'], str(e))) continue if global_setting['price']: updatePrice(skuLists,dbProductList) except (KeyboardInterrupt, SystemExit), e: logger.critical("app is interrupted, finished pages:%d" %(page)) Skip = True return Skip except Exception,e: logger.exception("error in Page:%d, reason:%s" %(page,str(e))) logger.debug('category %s getting finished'%(cat['level3'])) def getProductDetail(sku, url, db): tableRule=re.compile(r'<table.*?class="Ptable".*?<\/table>',re.S) if not url: return False productDetail = {} time.sleep(0.5) r = session.get(url) try: table = re.findall(tableRule,r.text)[0] if not table: return productDetail soup = BeautifulSoup(table) trs = soup('tr') for tr in trs: if len(tr('td')) == 2: productDetail[tr('td')[0].text.replace('.','')] = tr('td')[1].text #product[tr('td')[0].text.replace('.','').encode('utf-8')] = tr('td')[1].text.encode('utf-8') db.update({'sku':sku},{'$set':{'spec':productDetail}}) except Exception,e: logger.exception("error in parsing sku:%s\t page:%s,reson:%s" %(sku, url,str(e))) return False return True def updatePrice(skuLists,db): priceUrl = 'http://p.3.cn/prices/mgets?skuIds=J_%s&type=1' time.sleep(0.5) strSku = ",J_".join(skuLists) r = session.get(priceUrl %(strSku)) if not r.text: return False jsonPriceLists = json.loads(r.text) for price in jsonPriceLists: if price['p'] and price['id']: skuid = price['id'].replace('J_','') price = price['p'] curTime = datetime.datetime.utcnow() db.update({'sku':skuid},{'$set':{'price':price}}) db.update({'sku':skuid},{'$push':{'timeline':{'date':curTime,'price':price}}}) return True def parseCommandLine(): para = {} options,args = getopt.getopt(sys.argv[1:],"h",['site=', 'level1=', 'level2=', 'level3=', 'host=', 'port=', 'database=','productTable=','catTable=','pagesize=', 'hasPrice','batchUpdate', 'hasSpec','delta', 'help','catUpdate']) for opt, value in options: if opt in ['--level1','--level2','--level3']: strKey = re.sub('-','',opt) para[strKey] = value.decode('gb2312').split(',') elif opt in ['--site','--database','--catTable','--productTable']: strKey = re.sub('-','',opt) para[strKey] = value.decode('gb2312') elif opt in ['--host','--port','--pagesize']: strKey = re.sub('-','',opt) para[strKey] = value elif opt in ['--hasPrice','--hasSpec','--delta','--batchUpdate','--catUpdate']: strKey = re.sub('-','',opt) para[strKey] = True if opt in ['-h','--help']: usage() sys.exit() return para def updateBactch(): dbProductList = openTable(tableName=global_setting['productTable']) updateCatids = getUpdateCat() for catid,catname in updateCatids: skuList = [] logger.info('price updating to categary %s(%s) '%(catname,catid)) for product in dbProductList.find({'catId':catid}): try: if global_setting['spec']: getProductDetail(product['sku'],product['url'],dbProductList) skuList.append(product['sku']) if len(skuList) > global_setting['pagesize']: updatePrice(skuList, dbProductList); skuList = [] except Exception, e: logger.exception("error in updating pricing:%s %s " %(catname, str(e))) continue return True def getUpdateCat(): level1Filter = global_setting['level1'] level2Filter = global_setting['level2'] level3Filter = global_setting['level3'] retCat = [] catDb = openTable(tableName=global_setting['catTable']) for cat in catDb.find({'site':global_setting['site']}).sort('lasttime',ASCENDING): if (global_setting['site']==cat['site']) and cat['catUrl'] and ((level1Filter and cat['level1'] in level1Filter) \ or (level2Filter and cat['level2'] in level2Filter) \ or level3Filter and cat['level3'] in level3Filter): retCat.append((cat['catId'],cat['level3'])) return retCat def usage(): print "Usage: python getCategory.py [--help] [--site] [--hasPrice] [--hasSpec] [--homeUrl] [--host], [--port], [--database],[--productTable], [--catTable],[--level1] [--level2] [--level3] [--delta] [--batchUpdate] [--catUpdate]\n" global_setting = {} global session global logger if __name__ == '__main__': logger = setLog('INFO') logger.debug('log level, %d' %(logger.level)) session = requests.Session() retPara = parseCommandLine() global_setting['site'] = retPara.get('site',u'京东') global_setting['targetUrl'] = retPara.get('homeUrl','http://www.yhd.com/marketing/allproduct.html') global_setting['level1'] = retPara.get('level1',None) global_setting['level2'] = retPara.get('level2',None) global_setting['level3'] = retPara.get('level3',None) global_setting['spec'] = retPara.get('hasSpec',False) global_setting['price'] = retPara.get('hasPrice',False) global_setting['delta'] = retPara.get('delta',False) global_setting['host'] = retPara.get('host','127.0.0.1') global_setting['port'] = retPara.get('port','27017') global_setting['database'] = retPara.get('database','productKB') global_setting['catTable'] = retPara.get('catTable','catdb') global_setting['productTable'] = retPara.get('productTable','productdb') global_setting['pagesize'] = retPara.get('pagesize',60) global_setting['batchUpdate'] = retPara.get('batchUpdate',False) global_setting['catUpdate'] = retPara.get('catUpdate',False) #import pdb;pdb.set_trace() if global_setting['catUpdate']: getCategoryUrl(site=global_setting['site'],url=global_setting['targetUrl']) if global_setting['batchUpdate']: updateBactch() else: getPidList4Cat()
[ "neil@local.com" ]
neil@local.com
5bcb14b9a77abb06d694d912c1001a113b902a97
a901e62765acf49238fcff53c92dacfb3ac806dd
/app/gbd/core/util.py
aebf5c44d23becf0d83f3d6a1a5c0d8214d39602
[ "MIT" ]
permissive
gbd-consult/qwebgisapp
d0834492a301395d18fb5867f2fef485ac032c14
86318f42d2e931ab3a6ea4e5108b9e0b0095156b
refs/heads/master
2021-03-19T14:21:57.993770
2018-02-27T12:11:17
2018-02-27T12:11:17
94,889,360
0
0
null
null
null
null
UTF-8
Python
false
false
4,352
py
"""Misc utilities.""" import sys, string, time, os, math, itertools, random, datetime import mako.template, mako.exceptions from gbd.core import config, log, templatetools def f(s, *args, **kwargs): frame = sys._getframe(1) d = {} d.update(frame.f_globals) d.update(frame.f_locals) d.update(kwargs) return string.Formatter().vformat(s, args, d) def inline_format(s, *args, **kwargs): frame = sys._getframe(1) d = {} d.update(frame.f_globals) d.update(frame.f_locals) d.update(kwargs) return string.Formatter().vformat(s, args, d) class Struct(object): def __init__(self, d): if isinstance(d, dict): for k, v in d.items(): setattr(self, k, v) elif isinstance(d, (list, tuple)): for k in d: setattr(self, k, None) def struct(d): return Struct(d) def encode_all(d, coding='utf8'): if isinstance(d, unicode): return d.encode(coding) if isinstance(d, dict): return dict((encode_all(k, coding), encode_all(v, coding)) for k, v in d.items()) if isinstance(d, list): return [encode_all(v, coding) for v in d] if isinstance(d, tuple): return tuple(encode_all(v, coding) for v in d) return d def render_from_string(src, args=None): try: # we use 'disable_unicode' to avoid u"" strings in the template # so all input must be preencoded to utf8 tpl = mako.template.Template(encode_all(src), disable_unicode=True, input_encoding='utf-8') args = encode_all(args or {}) args['_'] = templatetools return tpl.render(**args) except: err = mako.exceptions.text_error_template().render() for s in err.strip().splitlines(): log.error(s) raise def render_template(path, args=None): with open(path) as fp: return render_from_string(fp.read(), args) _app_version = None def app_version(as_number=False): global _app_version if _app_version is None: with open(os.path.join(config.app_root(), 'VERSION')) as fp: _app_version = fp.read().strip() if as_number: v = _app_version.split('.') return int(v[0]) * 1000000 + int(v[1]) * 1000 + int(v[2]) return _app_version def strip_none(d): return dict((k, v) for k, v in d.items() if v is not None and v != [None]) def split_dict(d): r = {} for k, v in d.items(): k = k.split('.') q = r for sub in k[:-1]: if sub not in q: q[sub] = {} q = q[sub] q[k[-1]] = v return r def pick(d, keys, skip_none=False): r = {} is_dict = isinstance(keys, dict) for k in keys: p = keys[k] if is_dict else k if d.get(k) is not None: r[p] = d[k] elif not skip_none: r[p] = None return r def chunked(it, size): it = iter(it) while True: p = tuple(itertools.islice(it, size)) if not p: break yield p def randstr(chars, size): return ''.join(chars[random.randint(0, len(chars) - 1)] for _ in range(size)) def uid(prefix=''): r = randstr('0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz', 64) return prefix + r def now(): return datetime.datetime.now() class ProgressIndicator(object): def __init__(self, title, total, resolution=10): self.console = sys.stderr.isatty() self.resolution = resolution self.title = title self.total = total self.progress = 0 self.lastd = 0 def __enter__(self): self.write('START (total=%d)' % self.total) self.starttime = time.time() return self def __exit__(self, exc_type, exc_val, exc_tb): if exc_type: self.write('\n') else: t = time.time() - self.starttime self.write('END (time=%.2f rps=%.1f)' % (t, self.total / t)) def update(self, add=1): self.progress += add p = math.floor(self.progress * 100.0 / self.total) if p > 100: p = 100 d = round(p / self.resolution) * self.resolution if d > self.lastd: self.write('%d%%' % d) self.lastd = d def write(self, s): log.info(self.title + ': ' + s)
[ "gebrkn@gmail.com" ]
gebrkn@gmail.com
12e36e9537e9bd48715cc87299c6710dcc8d2484
4341c1c4fbf30032c50b66ca6ac2d4a2bfc0e83a
/translate/models.py
6d1a98c568bcfc0c779f1c1ebfd40552bd2fb9a1
[]
no_license
a574751346/transfer2nl
fec566835a62ebdc5388fcfef7526dbe72bf78d7
0251655603e2da0c3ca7cf597b2d7c10060804ba
refs/heads/master
2021-05-23T17:44:56.836659
2020-04-09T05:33:57
2020-04-09T05:33:57
253,404,966
0
0
null
null
null
null
UTF-8
Python
false
false
47,574
py
import tensorflow as tf import math from tensorflow.contrib.rnn import BasicLSTMCell, RNNCell, DropoutWrapper, MultiRNNCell from rnn import stack_bidirectional_dynamic_rnn, CellInitializer, GRUCell, DropoutGRUCell import utils, beam_search def auto_reuse(fun): """ Wrapper that automatically handles the `reuse' parameter. This is rather risky, as it can lead to reusing variables by mistake. """ def fun_(*args, **kwargs): try: return fun(*args, **kwargs) except ValueError as e: if 'reuse' in str(e): with tf.variable_scope(tf.get_variable_scope(), reuse=True): return fun(*args, **kwargs) else: raise e return fun_ get_variable = auto_reuse(tf.get_variable) dense = auto_reuse(tf.layers.dense) class CellWrapper(RNNCell): """ Wrapper around LayerNormBasicLSTMCell, BasicLSTMCell and MultiRNNCell, to keep the state_is_tuple=False behavior (soon to be deprecated). """ def __init__(self, cell): super(CellWrapper, self).__init__() self.cell = cell self.num_splits = len(cell.state_size) if isinstance(cell.state_size, tuple) else 1 @property def state_size(self): return sum(self.cell.state_size) @property def output_size(self): return self.cell.output_size def __call__(self, inputs, state, scope=None): state = tf.split(value=state, num_or_size_splits=self.num_splits, axis=1) new_h, new_state = self.cell(inputs, state, scope=scope) return new_h, tf.concat(new_state, 1) def multi_encoder(encoder_inputs, encoders, encoder_input_length, other_inputs=None, **kwargs): """ Build multiple encoders according to the configuration in `encoders`, reading from `encoder_inputs`. The result is a list of the outputs produced by those encoders (for each time-step), and their final state. :param encoder_inputs: list of tensors of shape (batch_size, input_length), one tensor for each encoder. :param encoders: list of encoder configurations :param encoder_input_length: list of tensors of shape (batch_size,) (one tensor for each encoder) :return: encoder outputs: a list of tensors of shape (batch_size, input_length, encoder_cell_size), hidden states of the encoders. encoder state: concatenation of the final states of all encoders, tensor of shape (batch_size, sum_of_state_sizes) new_encoder_input_length: list of tensors of shape (batch_size,) with the true length of the encoder outputs. May be different than `encoder_input_length` because of maxout strides, and time pooling. """ encoder_states = [] encoder_outputs = [] # create embeddings in the global scope (allows sharing between encoder and decoder) embedding_variables = [] for encoder in encoders: if encoder.binary: embedding_variables.append(None) continue # inputs are token ids, which need to be mapped to vectors (embeddings) embedding_shape = [encoder.vocab_size, encoder.embedding_size] if encoder.embedding_initializer == 'sqrt3': initializer = tf.random_uniform_initializer(-math.sqrt(3), math.sqrt(3)) else: initializer = None device = '/cpu:0' if encoder.embeddings_on_cpu else None with tf.device(device): # embeddings can take a very large amount of memory, so # storing them in GPU memory can be impractical embedding = get_variable('embedding_{}'.format(encoder.name), shape=embedding_shape, initializer=initializer) embedding_variables.append(embedding) new_encoder_input_length = [] for i, encoder in enumerate(encoders): if encoder.use_lstm is False: encoder.cell_type = 'GRU' with tf.variable_scope('encoder_{}'.format(encoder.name)): encoder_inputs_ = encoder_inputs[i] encoder_input_length_ = encoder_input_length[i] def get_cell(input_size=None, reuse=False): if encoder.cell_type.lower() == 'lstm': cell = CellWrapper(BasicLSTMCell(encoder.cell_size, reuse=reuse)) elif encoder.cell_type.lower() == 'dropoutgru': cell = DropoutGRUCell(encoder.cell_size, reuse=reuse, layer_norm=encoder.layer_norm, input_size=input_size, input_keep_prob=encoder.rnn_input_keep_prob, state_keep_prob=encoder.rnn_state_keep_prob) elif encoder.cell_type.lower() == 'treelstm': # TODO cell = None return else: cell = GRUCell(encoder.cell_size, reuse=reuse, layer_norm=encoder.layer_norm) if encoder.use_dropout and encoder.cell_type.lower() != 'dropoutgru': cell = DropoutWrapper(cell, input_keep_prob=encoder.rnn_input_keep_prob, output_keep_prob=encoder.rnn_output_keep_prob, state_keep_prob=encoder.rnn_state_keep_prob, variational_recurrent=encoder.pervasive_dropout, dtype=tf.float32, input_size=input_size) return cell embedding = embedding_variables[i] batch_size = tf.shape(encoder_inputs_)[0] time_steps = tf.shape(encoder_inputs_)[1] if embedding is not None: flat_inputs = tf.reshape(encoder_inputs_, [tf.multiply(batch_size, time_steps)]) flat_inputs = tf.nn.embedding_lookup(embedding, flat_inputs) encoder_inputs_ = tf.reshape(flat_inputs, tf.stack([batch_size, time_steps, flat_inputs.get_shape()[1].value])) if other_inputs is not None: encoder_inputs_ = tf.concat([encoder_inputs_, other_inputs], axis=2) if encoder.use_dropout: noise_shape = [1, time_steps, 1] if encoder.pervasive_dropout else [batch_size, time_steps, 1] encoder_inputs_ = tf.nn.dropout(encoder_inputs_, keep_prob=encoder.word_keep_prob, noise_shape=noise_shape) size = tf.shape(encoder_inputs_)[2] noise_shape = [1, 1, size] if encoder.pervasive_dropout else [batch_size, time_steps, size] encoder_inputs_ = tf.nn.dropout(encoder_inputs_, keep_prob=encoder.embedding_keep_prob, noise_shape=noise_shape) if encoder.input_layers: for j, layer_size in enumerate(encoder.input_layers): if encoder.input_layer_activation is not None and encoder.input_layer_activation.lower() == 'relu': activation = tf.nn.relu else: activation = tf.tanh encoder_inputs_ = dense(encoder_inputs_, layer_size, activation=activation, use_bias=True, name='layer_{}'.format(j)) if encoder.use_dropout: encoder_inputs_ = tf.nn.dropout(encoder_inputs_, keep_prob=encoder.input_layer_keep_prob) # Contrary to Theano's RNN implementation, states after the sequence length are zero # (while Theano repeats last state) inter_layer_keep_prob = None if not encoder.use_dropout else encoder.inter_layer_keep_prob parameters = dict( inputs=encoder_inputs_, sequence_length=encoder_input_length_, dtype=tf.float32, parallel_iterations=encoder.parallel_iterations ) input_size = encoder_inputs_.get_shape()[2].value state_size = (encoder.cell_size * 2 if encoder.cell_type.lower() == 'lstm' else encoder.cell_size) def get_initial_state(name='initial_state'): if encoder.train_initial_states: initial_state = get_variable(name, initializer=tf.zeros(state_size)) return tf.tile(tf.expand_dims(initial_state, axis=0), [batch_size, 1]) else: return None if encoder.bidir: rnn = lambda reuse: stack_bidirectional_dynamic_rnn( cells_fw=[get_cell(input_size if j == 0 else 2 * encoder.cell_size, reuse=reuse) for j in range(encoder.layers)], cells_bw=[get_cell(input_size if j == 0 else 2 * encoder.cell_size, reuse=reuse) for j in range(encoder.layers)], initial_states_fw=[get_initial_state('initial_state_fw')] * encoder.layers, initial_states_bw=[get_initial_state('initial_state_bw')] * encoder.layers, time_pooling=encoder.time_pooling, pooling_avg=encoder.pooling_avg, **parameters) initializer = CellInitializer(encoder.cell_size) if encoder.orthogonal_init else None with tf.variable_scope(tf.get_variable_scope(), initializer=initializer): try: encoder_outputs_, _, encoder_states_ = rnn(reuse=False) except ValueError: # Multi-task scenario where we're reusing the same RNN parameters encoder_outputs_, _, encoder_states_ = rnn(reuse=True) else: if encoder.time_pooling or encoder.final_state == 'concat_last': raise NotImplementedError if encoder.layers > 1: cell = MultiRNNCell([get_cell(input_size if j == 0 else encoder.cell_size) for j in range(encoder.layers)]) initial_state = (get_initial_state(),) * encoder.layers else: cell = get_cell(input_size) initial_state = get_initial_state() encoder_outputs_, encoder_states_ = auto_reuse(tf.nn.dynamic_rnn)(cell=cell, initial_state=initial_state, **parameters) last_backward = encoder_outputs_[:, 0, encoder.cell_size:] indices = tf.stack([tf.range(batch_size), encoder_input_length_ - 1], axis=1) last_forward = tf.gather_nd(encoder_outputs_[:, :, :encoder.cell_size], indices) last_forward.set_shape([None, encoder.cell_size]) if encoder.final_state == 'concat_last': # concats last states of all backward layers (full LSTM states) encoder_state_ = tf.concat(encoder_states_, axis=1) elif encoder.final_state == 'average': mask = tf.sequence_mask(encoder_input_length_, maxlen=tf.shape(encoder_outputs_)[1], dtype=tf.float32) mask = tf.expand_dims(mask, axis=2) encoder_state_ = tf.reduce_sum(mask * encoder_outputs_, axis=1) / tf.reduce_sum(mask, axis=1) elif encoder.final_state == 'average_inputs': mask = tf.sequence_mask(encoder_input_length_, maxlen=tf.shape(encoder_inputs_)[1], dtype=tf.float32) mask = tf.expand_dims(mask, axis=2) encoder_state_ = tf.reduce_sum(mask * encoder_inputs_, axis=1) / tf.reduce_sum(mask, axis=1) elif encoder.bidir and encoder.final_state == 'last_both': encoder_state_ = tf.concat([last_forward, last_backward], axis=1) elif encoder.bidir and not encoder.final_state == 'last_forward': # last backward hidden state encoder_state_ = last_backward else: # last forward hidden state encoder_state_ = last_forward if encoder.bidir and encoder.bidir_projection: encoder_outputs_ = dense(encoder_outputs_, encoder.cell_size, use_bias=False, name='bidir_projection') encoder_outputs.append(encoder_outputs_) encoder_states.append(encoder_state_) new_encoder_input_length.append(encoder_input_length_) encoder_state = tf.concat(encoder_states, 1) return encoder_outputs, encoder_state, new_encoder_input_length def compute_energy(hidden, state, attn_size, attn_keep_prob=None, pervasive_dropout=False, layer_norm=False, mult_attn=False, **kwargs): if attn_keep_prob is not None: state_noise_shape = [1, tf.shape(state)[1]] if pervasive_dropout else None state = tf.nn.dropout(state, keep_prob=attn_keep_prob, noise_shape=state_noise_shape) hidden_noise_shape = [1, 1, tf.shape(hidden)[2]] if pervasive_dropout else None hidden = tf.nn.dropout(hidden, keep_prob=attn_keep_prob, noise_shape=hidden_noise_shape) if mult_attn: state = dense(state, attn_size, use_bias=False, name='state') hidden = dense(hidden, attn_size, use_bias=False, name='hidden') return tf.einsum('ijk,ik->ij', hidden, state) else: y = dense(state, attn_size, use_bias=not layer_norm, name='W_a') y = tf.expand_dims(y, axis=1) if layer_norm: y = tf.contrib.layers.layer_norm(y, scope='layer_norm_state') hidden = tf.contrib.layers.layer_norm(hidden, center=False, scope='layer_norm_hidden') f = dense(hidden, attn_size, use_bias=False, name='U_a') v = get_variable('v_a', [attn_size]) s = f + y return tf.reduce_sum(v * tf.tanh(s), axis=2) def compute_energy_with_filter(hidden, state, prev_weights, attn_filters, attn_filter_length, **kwargs): hidden = tf.expand_dims(hidden, 2) batch_size = tf.shape(hidden)[0] time_steps = tf.shape(hidden)[1] attn_size = hidden.get_shape()[3].value filter_shape = [attn_filter_length * 2 + 1, 1, 1, attn_filters] filter_ = get_variable('filter', filter_shape) u = get_variable('U', [attn_filters, attn_size]) prev_weights = tf.reshape(prev_weights, tf.stack([batch_size, time_steps, 1, 1])) conv = tf.nn.conv2d(prev_weights, filter_, [1, 1, 1, 1], 'SAME') shape = tf.stack([tf.multiply(batch_size, time_steps), attn_filters]) conv = tf.reshape(conv, shape) z = tf.matmul(conv, u) z = tf.reshape(z, tf.stack([batch_size, time_steps, 1, attn_size])) y = dense(state, attn_size, use_bias=True, name='y') y = tf.reshape(y, [-1, 1, 1, attn_size]) k = get_variable('W', [attn_size, attn_size]) # dot product between tensors requires reshaping hidden = tf.reshape(hidden, tf.stack([tf.multiply(batch_size, time_steps), attn_size])) f = tf.matmul(hidden, k) f = tf.reshape(f, tf.stack([batch_size, time_steps, 1, attn_size])) v = get_variable('V', [attn_size]) s = f + y + z return tf.reduce_sum(v * tf.tanh(s), [2, 3]) def global_attention(state, hidden_states, encoder, encoder_input_length, scope=None, context=None, **kwargs): with tf.variable_scope(scope or 'attention_{}'.format(encoder.name)): if context is not None and encoder.use_context: state = tf.concat([state, context], axis=1) if encoder.attn_filters: e = compute_energy_with_filter(hidden_states, state, attn_size=encoder.attn_size, attn_filters=encoder.attn_filters, attn_filter_length=encoder.attn_filter_length, **kwargs) else: e = compute_energy(hidden_states, state, attn_size=encoder.attn_size, attn_keep_prob=encoder.attn_keep_prob, pervasive_dropout=encoder.pervasive_dropout, layer_norm=encoder.layer_norm, mult_attn=encoder.mult_attn, **kwargs) e -= tf.reduce_max(e, axis=1, keep_dims=True) mask = tf.sequence_mask(encoder_input_length, maxlen=tf.shape(hidden_states)[1], dtype=tf.float32) T = encoder.attn_temperature or 1.0 exp = tf.exp(e / T) * mask weights = exp / tf.reduce_sum(exp, axis=-1, keep_dims=True) weighted_average = tf.reduce_sum(tf.expand_dims(weights, 2) * hidden_states, axis=1) return weighted_average, weights def no_attention(state, hidden_states, *args, **kwargs): batch_size = tf.shape(state)[0] weighted_average = tf.zeros(shape=tf.stack([batch_size, 0])) weights = tf.zeros(shape=[batch_size, tf.shape(hidden_states)[1]]) return weighted_average, weights def average_attention(hidden_states, encoder_input_length, *args, **kwargs): # attention with fixed weights (average of all hidden states) lengths = tf.to_float(tf.expand_dims(encoder_input_length, axis=1)) mask = tf.sequence_mask(encoder_input_length, maxlen=tf.shape(hidden_states)[1]) weights = tf.to_float(mask) / lengths weighted_average = tf.reduce_sum(hidden_states * tf.expand_dims(weights, axis=2), axis=1) return weighted_average, weights def last_state_attention(hidden_states, encoder_input_length, *args, **kwargs): weights = tf.one_hot(encoder_input_length - 1, tf.shape(hidden_states)[1]) weights = tf.to_float(weights) weighted_average = tf.reduce_sum(hidden_states * tf.expand_dims(weights, axis=2), axis=1) return weighted_average, weights def local_attention(state, hidden_states, encoder, encoder_input_length, pos=None, scope=None, context=None, **kwargs): batch_size = tf.shape(state)[0] attn_length = tf.shape(hidden_states)[1] if context is not None and encoder.use_context: state = tf.concat([state, context], axis=1) state_size = state.get_shape()[1].value with tf.variable_scope(scope or 'attention_{}'.format(encoder.name)): encoder_input_length = tf.to_float(tf.expand_dims(encoder_input_length, axis=1)) if pos is not None: pos = tf.reshape(pos, [-1, 1]) pos = tf.minimum(pos, encoder_input_length - 1) if pos is not None and encoder.attn_window_size > 0: # `pred_edits` scenario, where we know the aligned pos # when the windows size is non-zero, we concatenate consecutive encoder states # and map it to the right attention vector size. weights = tf.to_float(tf.one_hot(tf.to_int32(tf.squeeze(pos, axis=1)), depth=attn_length)) weighted_average = [] for offset in range(-encoder.attn_window_size, encoder.attn_window_size + 1): pos_ = pos + offset pos_ = tf.minimum(pos_, encoder_input_length - 1) pos_ = tf.maximum(pos_, 0) # TODO: when pos is < 0, use <S> or </S> weights_ = tf.to_float(tf.one_hot(tf.to_int32(tf.squeeze(pos_, axis=1)), depth=attn_length)) weighted_average_ = tf.reduce_sum(tf.expand_dims(weights_, axis=2) * hidden_states, axis=1) weighted_average.append(weighted_average_) weighted_average = tf.concat(weighted_average, axis=1) weighted_average = dense(weighted_average, encoder.attn_size) elif pos is not None: weights = tf.to_float(tf.one_hot(tf.to_int32(tf.squeeze(pos, axis=1)), depth=attn_length)) weighted_average = tf.reduce_sum(tf.expand_dims(weights, axis=2) * hidden_states, axis=1) else: # Local attention of Luong et al. (http://arxiv.org/abs/1508.04025) wp = get_variable('Wp', [state_size, state_size]) vp = get_variable('vp', [state_size, 1]) pos = tf.nn.sigmoid(tf.matmul(tf.nn.tanh(tf.matmul(state, wp)), vp)) pos = tf.floor(encoder_input_length * pos) pos = tf.reshape(pos, [-1, 1]) pos = tf.minimum(pos, encoder_input_length - 1) idx = tf.tile(tf.to_float(tf.range(attn_length)), tf.stack([batch_size])) idx = tf.reshape(idx, [-1, attn_length]) low = pos - encoder.attn_window_size high = pos + encoder.attn_window_size mlow = tf.to_float(idx < low) mhigh = tf.to_float(idx > high) m = mlow + mhigh m += tf.to_float(idx >= encoder_input_length) mask = tf.to_float(tf.equal(m, 0.0)) e = compute_energy(hidden_states, state, attn_size=encoder.attn_size, **kwargs) weights = softmax(e, mask=mask) sigma = encoder.attn_window_size / 2 numerator = -tf.pow((idx - pos), tf.convert_to_tensor(2, dtype=tf.float32)) div = tf.truediv(numerator, 2 * sigma ** 2) weights *= tf.exp(div) # result of the truncated normal distribution # normalize to keep a probability distribution # weights /= (tf.reduce_sum(weights, axis=1, keep_dims=True) + 10e-12) weighted_average = tf.reduce_sum(tf.expand_dims(weights, axis=2) * hidden_states, axis=1) return weighted_average, weights def attention(encoder, **kwargs): attention_functions = { 'global': global_attention, 'local': local_attention, 'none': no_attention, 'average': average_attention, 'last_state': last_state_attention } attention_function = attention_functions.get(encoder.attention_type, global_attention) return attention_function(encoder=encoder, **kwargs) def multi_attention(state, hidden_states, encoders, encoder_input_length, pos=None, aggregation_method='sum', prev_weights=None, **kwargs): attns = [] weights = [] context_vector = None for i, (hidden, encoder, input_length) in enumerate(zip(hidden_states, encoders, encoder_input_length)): pos_ = pos[i] if pos is not None else None prev_weights_ = prev_weights[i] if prev_weights is not None else None hidden = beam_search.resize_like(hidden, state) input_length = beam_search.resize_like(input_length, state) context_vector, weights_ = attention(state=state, hidden_states=hidden, encoder=encoder, encoder_input_length=input_length, pos=pos_, context=context_vector, prev_weights=prev_weights_, **kwargs) attns.append(context_vector) weights.append(weights_) if aggregation_method == 'sum': context_vector = tf.reduce_sum(tf.stack(attns, axis=2), axis=2) else: context_vector = tf.concat(attns, axis=1) return context_vector, weights def attention_decoder(decoder_inputs, initial_state, attention_states, encoders, decoder, encoder_input_length, feed_previous=0.0, align_encoder_id=0, feed_argmax=True, **kwargs): """ :param decoder_inputs: int32 tensor of shape (batch_size, output_length) :param initial_state: initial state of the decoder (usually the final state of the encoder), as a float32 tensor of shape (batch_size, initial_state_size). This state is mapped to the correct state size for the decoder. :param attention_states: list of tensors of shape (batch_size, input_length, encoder_cell_size), the hidden states of the encoder(s) (one tensor for each encoder). :param encoders: configuration of the encoders :param decoder: configuration of the decoder :param encoder_input_length: list of int32 tensors of shape (batch_size,), tells for each encoder, the true length of each sequence in the batch (sequences in the same batch are padded to all have the same length). :param feed_previous: scalar tensor corresponding to the probability to use previous decoder output instead of the ground truth as input for the decoder (1 when decoding, between 0 and 1 when training) :param feed_argmax: boolean tensor, when True the greedy decoder outputs the word with the highest probability (argmax). When False, it samples a word from the probability distribution (softmax). :param align_encoder_id: outputs attention weights for this encoder. Also used when predicting edit operations (pred_edits), to specifify which encoder reads the sequence to post-edit (MT). :return: outputs of the decoder as a tensor of shape (batch_size, output_length, decoder_cell_size) attention weights as a tensor of shape (output_length, encoders, batch_size, input_length) """ assert not decoder.pred_maxout_layer or decoder.cell_size % 2 == 0, 'cell size must be a multiple of 2' if decoder.use_lstm is False: decoder.cell_type = 'GRU' embedding_shape = [decoder.vocab_size, decoder.embedding_size] if decoder.embedding_initializer == 'sqrt3': initializer = tf.random_uniform_initializer(-math.sqrt(3), math.sqrt(3)) else: initializer = None device = '/cpu:0' if decoder.embeddings_on_cpu else None with tf.device(device): embedding = get_variable('embedding_{}'.format(decoder.name), shape=embedding_shape, initializer=initializer) input_shape = tf.shape(decoder_inputs) batch_size = input_shape[0] time_steps = input_shape[1] scope_name = 'decoder_{}'.format(decoder.name) scope_name += '/' + '_'.join(encoder.name for encoder in encoders) def embed(input_): embedded_input = tf.nn.embedding_lookup(embedding, input_) if decoder.use_dropout and decoder.word_keep_prob is not None: noise_shape = [1, 1] if decoder.pervasive_dropout else [batch_size, 1] embedded_input = tf.nn.dropout(embedded_input, keep_prob=decoder.word_keep_prob, noise_shape=noise_shape) if decoder.use_dropout and decoder.embedding_keep_prob is not None: size = tf.shape(embedded_input)[1] noise_shape = [1, size] if decoder.pervasive_dropout else [batch_size, size] embedded_input = tf.nn.dropout(embedded_input, keep_prob=decoder.embedding_keep_prob, noise_shape=noise_shape) return embedded_input def get_cell(input_size=None, reuse=False): cells = [] for j in range(decoder.layers): input_size_ = input_size if j == 0 else decoder.cell_size if decoder.cell_type.lower() == 'lstm': cell = CellWrapper(BasicLSTMCell(decoder.cell_size, reuse=reuse)) elif decoder.cell_type.lower() == 'dropoutgru': cell = DropoutGRUCell(decoder.cell_size, reuse=reuse, layer_norm=decoder.layer_norm, input_size=input_size_, input_keep_prob=decoder.rnn_input_keep_prob, state_keep_prob=decoder.rnn_state_keep_prob) else: cell = GRUCell(decoder.cell_size, reuse=reuse, layer_norm=decoder.layer_norm) if decoder.use_dropout and decoder.cell_type.lower() != 'dropoutgru': cell = DropoutWrapper(cell, input_keep_prob=decoder.rnn_input_keep_prob, output_keep_prob=decoder.rnn_output_keep_prob, state_keep_prob=decoder.rnn_state_keep_prob, variational_recurrent=decoder.pervasive_dropout, dtype=tf.float32, input_size=input_size_) cells.append(cell) if len(cells) == 1: return cells[0] else: return CellWrapper(MultiRNNCell(cells)) def look(state, input_, prev_weights=None, pos=None): prev_weights_ = [prev_weights if i == align_encoder_id else None for i in range(len(encoders))] pos_ = None if decoder.pred_edits: pos_ = [pos if i == align_encoder_id else None for i in range(len(encoders))] if decoder.attn_prev_word: state = tf.concat([state, input_], axis=1) parameters = dict(hidden_states=attention_states, encoder_input_length=encoder_input_length, encoders=encoders, aggregation_method=decoder.aggregation_method) context, new_weights = multi_attention(state, pos=pos_, prev_weights=prev_weights_, **parameters) if decoder.context_mapping: with tf.variable_scope(scope_name): activation = tf.nn.tanh if decoder.context_mapping_activation == 'tanh' else None use_bias = not decoder.context_mapping_no_bias context = dense(context, decoder.context_mapping, use_bias=use_bias, activation=activation, name='context_mapping') return context, new_weights[align_encoder_id] def update(state, input_, context=None, symbol=None): if context is not None and decoder.rnn_feed_attn: input_ = tf.concat([input_, context], axis=1) input_size = input_.get_shape()[1].value initializer = CellInitializer(decoder.cell_size) if decoder.orthogonal_init else None with tf.variable_scope(tf.get_variable_scope(), initializer=initializer): try: output, new_state = get_cell(input_size)(input_, state) except ValueError: # auto_reuse doesn't work with LSTM cells output, new_state = get_cell(input_size, reuse=True)(input_, state) if decoder.skip_update and decoder.pred_edits and symbol is not None: is_del = tf.equal(symbol, utils.DEL_ID) new_state = tf.where(is_del, state, new_state) if decoder.cell_type.lower() == 'lstm' and decoder.use_lstm_full_state: output = new_state return output, new_state def update_pos(pos, symbol, max_pos=None): if not decoder.pred_edits: return pos is_keep = tf.equal(symbol, utils.KEEP_ID) is_del = tf.equal(symbol, utils.DEL_ID) is_not_ins = tf.logical_or(is_keep, is_del) pos = beam_search.resize_like(pos, symbol) max_pos = beam_search.resize_like(max_pos, symbol) pos += tf.to_float(is_not_ins) if max_pos is not None: pos = tf.minimum(pos, tf.to_float(max_pos)) return pos def generate(state, input_, context): if decoder.pred_use_lstm_state is False: # for back-compatibility state = state[:,-decoder.cell_size:] projection_input = [state, context] if decoder.use_previous_word: projection_input.insert(1, input_) # for back-compatibility output_ = tf.concat(projection_input, axis=1) if decoder.pred_deep_layer: deep_layer_size = decoder.pred_deep_layer_size or decoder.embedding_size if decoder.layer_norm: output_ = dense(output_, deep_layer_size, use_bias=False, name='deep_output') output_ = tf.contrib.layers.layer_norm(output_, activation_fn=tf.nn.tanh, scope='output_layer_norm') else: output_ = dense(output_, deep_layer_size, activation=tf.tanh, use_bias=True, name='deep_output') if decoder.use_dropout: size = tf.shape(output_)[1] noise_shape = [1, size] if decoder.pervasive_dropout else None output_ = tf.nn.dropout(output_, keep_prob=decoder.deep_layer_keep_prob, noise_shape=noise_shape) else: if decoder.pred_maxout_layer: maxout_size = decoder.maxout_size or decoder.cell_size output_ = dense(output_, maxout_size, use_bias=True, name='maxout') if decoder.old_maxout: # for back-compatibility with old models output_ = tf.nn.pool(tf.expand_dims(output_, axis=2), window_shape=[2], pooling_type='MAX', padding='SAME', strides=[2]) output_ = tf.squeeze(output_, axis=2) else: output_ = tf.maximum(*tf.split(output_, num_or_size_splits=2, axis=1)) if decoder.pred_embed_proj: # intermediate projection to embedding size (before projecting to vocabulary size) # this is useful to reduce the number of parameters, and # to use the output embeddings for output projection (tie_embeddings parameter) output_ = dense(output_, decoder.embedding_size, use_bias=False, name='softmax0') if decoder.tie_embeddings and (decoder.pred_embed_proj or decoder.pred_deep_layer): bias = get_variable('softmax1/bias', shape=[decoder.vocab_size]) output_ = tf.matmul(output_, tf.transpose(embedding)) + bias else: output_ = dense(output_, decoder.vocab_size, use_bias=True, name='softmax1') return output_ state_size = (decoder.cell_size * 2 if decoder.cell_type.lower() == 'lstm' else decoder.cell_size) * decoder.layers if decoder.use_dropout: initial_state = tf.nn.dropout(initial_state, keep_prob=decoder.initial_state_keep_prob) with tf.variable_scope(scope_name): if decoder.layer_norm: initial_state = dense(initial_state, state_size, use_bias=False, name='initial_state_projection') initial_state = tf.contrib.layers.layer_norm(initial_state, activation_fn=tf.nn.tanh, scope='initial_state_layer_norm') else: initial_state = dense(initial_state, state_size, use_bias=True, name='initial_state_projection', activation=tf.nn.tanh) if decoder.cell_type.lower() == 'lstm' and decoder.use_lstm_full_state: initial_output = initial_state else: initial_output = initial_state[:, -decoder.cell_size:] time = tf.constant(0, dtype=tf.int32, name='time') outputs = tf.TensorArray(dtype=tf.float32, size=time_steps) samples = tf.TensorArray(dtype=tf.int64, size=time_steps) inputs = tf.TensorArray(dtype=tf.int64, size=time_steps).unstack(tf.to_int64(tf.transpose(decoder_inputs))) states = tf.TensorArray(dtype=tf.float32, size=time_steps) weights = tf.TensorArray(dtype=tf.float32, size=time_steps) attns = tf.TensorArray(dtype=tf.float32, size=time_steps) initial_symbol = inputs.read(0) # first symbol is BOS initial_input = embed(initial_symbol) initial_pos = tf.zeros([batch_size], tf.float32) initial_weights = tf.zeros(tf.shape(attention_states[align_encoder_id])[:2]) initial_context, _ = look(initial_output, initial_input, pos=initial_pos, prev_weights=initial_weights) initial_data = tf.concat([initial_state, initial_context, tf.expand_dims(initial_pos, axis=1), initial_weights], axis=1) context_size = initial_context.shape[1].value def get_logits(state, ids, time): # for beam-search decoding with tf.variable_scope('decoder_{}'.format(decoder.name)): state, context, pos, prev_weights = tf.split(state, [state_size, context_size, 1, -1], axis=1) input_ = embed(ids) pos = tf.squeeze(pos, axis=1) pos = tf.cond(tf.equal(time, 0), lambda: pos, lambda: update_pos(pos, ids, encoder_input_length[align_encoder_id])) if decoder.cell_type.lower() == 'lstm' and decoder.use_lstm_full_state: output = state else: # output is always the right-most part of state. However, this only works at test time, # because different dropout operations can be used on state and output. output = state[:, -decoder.cell_size:] if decoder.conditional_rnn: with tf.variable_scope('conditional_1'): output, state = update(state, input_) elif decoder.update_first: output, state = update(state, input_, None, ids) elif decoder.generate_first: output, state = tf.cond(tf.equal(time, 0), lambda: (output, state), lambda: update(state, input_, context, ids)) context, new_weights = look(output, input_, pos=pos, prev_weights=prev_weights) if decoder.conditional_rnn: with tf.variable_scope('conditional_2'): output, state = update(state, context) elif not decoder.generate_first: output, state = update(state, input_, context, ids) logits = generate(output, input_, context) pos = tf.expand_dims(pos, axis=1) state = tf.concat([state, context, pos, new_weights], axis=1) return state, logits def _time_step(time, input_, input_symbol, pos, state, output, outputs, states, weights, attns, prev_weights, samples): if decoder.conditional_rnn: with tf.variable_scope('conditional_1'): output, state = update(state, input_) elif decoder.update_first: output, state = update(state, input_, None, input_symbol) context, new_weights = look(output, input_, pos=pos, prev_weights=prev_weights) if decoder.conditional_rnn: with tf.variable_scope('conditional_2'): output, state = update(state, context) elif not decoder.generate_first: output, state = update(state, input_, context, input_symbol) output_ = generate(output, input_, context) argmax = lambda: tf.argmax(output_, 1) target = lambda: inputs.read(time + 1) softmax = lambda: tf.squeeze(tf.multinomial(tf.log(tf.nn.softmax(output_)), num_samples=1), axis=1) use_target = tf.logical_and(time < time_steps - 1, tf.random_uniform([]) >= feed_previous) predicted_symbol = tf.case([ (use_target, target), (tf.logical_not(feed_argmax), softmax)], default=argmax) # default case is useful for beam-search predicted_symbol.set_shape([None]) predicted_symbol = tf.stop_gradient(predicted_symbol) samples = samples.write(time, predicted_symbol) input_ = embed(predicted_symbol) pos = update_pos(pos, predicted_symbol, encoder_input_length[align_encoder_id]) attns = attns.write(time, context) weights = weights.write(time, new_weights) states = states.write(time, state) outputs = outputs.write(time, output_) if not decoder.conditional_rnn and not decoder.update_first and decoder.generate_first: output, state = update(state, input_, context, predicted_symbol) return (time + 1, input_, predicted_symbol, pos, state, output, outputs, states, weights, attns, new_weights, samples) with tf.variable_scope('decoder_{}'.format(decoder.name)): _, _, _, new_pos, new_state, _, outputs, states, weights, attns, new_weights, samples = tf.while_loop( cond=lambda time, *_: time < time_steps, body=_time_step, loop_vars=(time, initial_input, initial_symbol, initial_pos, initial_state, initial_output, outputs, weights, states, attns, initial_weights, samples), parallel_iterations=decoder.parallel_iterations, swap_memory=decoder.swap_memory) outputs = outputs.stack() weights = weights.stack() # batch_size, encoders, output time, input time states = states.stack() attns = attns.stack() samples = samples.stack() # put batch_size as first dimension outputs = tf.transpose(outputs, perm=(1, 0, 2)) weights = tf.transpose(weights, perm=(1, 0, 2)) states = tf.transpose(states, perm=(1, 0, 2)) attns = tf.transpose(attns, perm=(1, 0, 2)) samples = tf.transpose(samples) return outputs, weights, states, attns, samples, get_logits, initial_data def encoder_decoder(encoders, decoders, encoder_inputs, targets, feed_previous, align_encoder_id=0, encoder_input_length=None, feed_argmax=True, **kwargs): decoder = decoders[0] targets = targets[0] # single decoder if encoder_input_length is None: encoder_input_length = [] for encoder_inputs_ in encoder_inputs: weights = get_weights(encoder_inputs_, utils.EOS_ID, include_first_eos=True) encoder_input_length.append(tf.to_int32(tf.reduce_sum(weights, axis=1))) parameters = dict(encoders=encoders, decoder=decoder, encoder_inputs=encoder_inputs, feed_argmax=feed_argmax) target_weights = get_weights(targets[:, 1:], utils.EOS_ID, include_first_eos=True) attention_states, encoder_state, encoder_input_length = multi_encoder( encoder_input_length=encoder_input_length, **parameters) outputs, attention_weights, _, _, samples, beam_fun, initial_data = attention_decoder( attention_states=attention_states, initial_state=encoder_state, feed_previous=feed_previous, decoder_inputs=targets[:, :-1], align_encoder_id=align_encoder_id, encoder_input_length=encoder_input_length, **parameters ) xent_loss = sequence_loss(logits=outputs, targets=targets[:, 1:], weights=target_weights) losses = xent_loss return losses, [outputs], encoder_state, attention_states, attention_weights, samples, beam_fun, initial_data def chained_encoder_decoder(encoders, decoders, encoder_inputs, targets, feed_previous, chaining_strategy=None, align_encoder_id=0, chaining_non_linearity=False, chaining_loss_ratio=1.0, chaining_stop_gradient=False, **kwargs): decoder = decoders[0] targets = targets[0] # single decoder assert len(encoders) == 2 encoder_input_length = [] input_weights = [] for encoder_inputs_ in encoder_inputs: weights = get_weights(encoder_inputs_, utils.EOS_ID, include_first_eos=True) input_weights.append(weights) encoder_input_length.append(tf.to_int32(tf.reduce_sum(weights, axis=1))) target_weights = get_weights(targets[:, 1:], utils.EOS_ID, include_first_eos=True) parameters = dict(encoders=encoders[1:], decoder=encoders[0]) attention_states, encoder_state, encoder_input_length[1:] = multi_encoder( encoder_inputs[1:], encoder_input_length=encoder_input_length[1:], **parameters) decoder_inputs = encoder_inputs[0][:, :-1] batch_size = tf.shape(decoder_inputs)[0] pad = tf.ones(shape=tf.stack([batch_size, 1]), dtype=tf.int32) * utils.BOS_ID decoder_inputs = tf.concat([pad, decoder_inputs], axis=1) outputs, _, states, attns, _, _, _ = attention_decoder( attention_states=attention_states, initial_state=encoder_state, decoder_inputs=decoder_inputs, encoder_input_length=encoder_input_length[1:], **parameters ) chaining_loss = sequence_loss(logits=outputs, targets=encoder_inputs[0], weights=input_weights[0]) if decoder.cell_type.lower() == 'lstm': size = states.get_shape()[2].value decoder_outputs = states[:, :, size // 2:] else: decoder_outputs = states if chaining_strategy == 'share_states': other_inputs = states elif chaining_strategy == 'share_outputs': other_inputs = decoder_outputs else: other_inputs = None if other_inputs is not None and chaining_stop_gradient: other_inputs = tf.stop_gradient(other_inputs) parameters = dict(encoders=encoders[:1], decoder=decoder, encoder_inputs=encoder_inputs[:1], other_inputs=other_inputs) attention_states, encoder_state, encoder_input_length[:1] = multi_encoder( encoder_input_length=encoder_input_length[:1], **parameters) if chaining_stop_gradient: attns = tf.stop_gradient(attns) states = tf.stop_gradient(states) decoder_outputs = tf.stop_gradient(decoder_outputs) if chaining_strategy == 'concat_attns': attention_states[0] = tf.concat([attention_states[0], attns], axis=2) elif chaining_strategy == 'concat_states': attention_states[0] = tf.concat([attention_states[0], states], axis=2) elif chaining_strategy == 'sum_attns': attention_states[0] += attns elif chaining_strategy in ('map_attns', 'map_states', 'map_outputs'): if chaining_strategy == 'map_attns': x = attns elif chaining_strategy == 'map_outputs': x = decoder_outputs else: x = states shape = [x.get_shape()[-1], attention_states[0].get_shape()[-1]] w = tf.get_variable("map_attns/matrix", shape=shape) b = tf.get_variable("map_attns/bias", shape=shape[-1:]) x = tf.einsum('ijk,kl->ijl', x, w) + b if chaining_non_linearity: x = tf.nn.tanh(x) attention_states[0] += x outputs, attention_weights, _, _, samples, beam_fun, initial_data = attention_decoder( attention_states=attention_states, initial_state=encoder_state, feed_previous=feed_previous, decoder_inputs=targets[:,:-1], align_encoder_id=align_encoder_id, encoder_input_length=encoder_input_length[:1], **parameters ) xent_loss = sequence_loss(logits=outputs, targets=targets[:, 1:], weights=target_weights) if chaining_loss is not None and chaining_loss_ratio: xent_loss += chaining_loss_ratio * chaining_loss losses = [xent_loss, None, None] return losses, [outputs], encoder_state, attention_states, attention_weights, samples, beam_fun, initial_data def softmax(logits, dim=-1, mask=None): e = tf.exp(logits) if mask is not None: e *= mask return e / tf.clip_by_value(tf.reduce_sum(e, axis=dim, keep_dims=True), 10e-37, 10e+37) def sequence_loss(logits, targets, weights, average_across_timesteps=False, average_across_batch=True): batch_size = tf.shape(targets)[0] time_steps = tf.shape(targets)[1] logits_ = tf.reshape(logits, tf.stack([time_steps * batch_size, logits.get_shape()[2].value])) targets_ = tf.reshape(targets, tf.stack([time_steps * batch_size])) crossent = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_, labels=targets_) crossent = tf.reshape(crossent, tf.stack([batch_size, time_steps])) log_perp = tf.reduce_sum(crossent * weights, axis=1) if average_across_timesteps: total_size = tf.reduce_sum(weights, axis=1) total_size += 1e-12 # just to avoid division by 0 for all-0 weights log_perp /= total_size cost = tf.reduce_sum(log_perp) if average_across_batch: return cost / tf.to_float(batch_size) else: return cost def get_weights(sequence, eos_id, include_first_eos=True): cumsum = tf.cumsum(tf.to_float(tf.not_equal(sequence, eos_id)), axis=1) range_ = tf.range(start=1, limit=tf.shape(sequence)[1] + 1) range_ = tf.tile(tf.expand_dims(range_, axis=0), [tf.shape(sequence)[0], 1]) weights = tf.to_float(tf.equal(cumsum, tf.to_float(range_))) if include_first_eos: weights = weights[:,:-1] shape = [tf.shape(weights)[0], 1] weights = tf.concat([tf.ones(tf.stack(shape)), weights], axis=1) return tf.stop_gradient(weights)
[ "574751346@qq.com" ]
574751346@qq.com
cccdf851ec9f9a96a2225d8bfbd7e62cfcd1b59a
0c01f24c7d2e2433ad999358bcb5cd246d824c64
/Settings/Lopy/Lopy_device/flash/main.py
d577883725a19488191f6648b511b6abb6858500
[]
no_license
jemesmain/openenergy
368b2b2dc4cee6e3b0ea93f9d0029572b004983f
3d0c7d0892336a0a0f0a880d53561079f9ef2637
refs/heads/master
2023-05-11T19:09:00.011456
2023-04-30T21:29:02
2023-04-30T21:29:02
251,673,421
0
0
null
null
null
null
UTF-8
Python
false
false
3,112
py
""" OTAA Node example compatible with the LoPy Nano Gateway """ from network import LoRa import socket import binascii import struct import time import config # initialize LoRa in LORAWAN mode. lora = LoRa(mode=LoRa.LORAWAN) # create an OTA authentication params #setting found in ttn device / name of device with activation method OTAA #ttn dev_eui 70B3D54995E574C6 #const char *appEui = "70B3D59BA0000114"; #const char *appKey = "65240933ADE876AB9AD5990DB003B6E9"; #ttn configuration #print('ttn config') #dev_eui = binascii.unhexlify('70B3D54985E573F7'.replace(' ','')) # these settings can be found from TTN #app_eui = binascii.unhexlify('70B3D59CC0000004'.replace(' ','')) # these settings can be found from TTN #app_key = binascii.unhexlify('65240932ADE876AB9AD5990DB004C2E9'.replace(' ','')) # these settings can be found from TTN #chirpstack configuration print('chirpstack config') dev_eui = binascii.unhexlify('db2b4bbc2cf73c6'.replace(' ','')) # these settings can be found from Chirpstack app_eui = binascii.unhexlify('db2b4bbc2cf743c6'.replace(' ','')) # these settings can be found from Chirpstack app_key = binascii.unhexlify('f5d90236196aef9d6d39c690b5b8a317'.replace(' ','')) # these settings can be found from Chirpstack # set the 3 default channels to the same frequency (must be before sending the OTAA join request) lora.add_channel(0, frequency=config.LORA_FREQUENCY, dr_min=0, dr_max=5) lora.add_channel(1, frequency=config.LORA_FREQUENCY, dr_min=0, dr_max=5) lora.add_channel(2, frequency=config.LORA_FREQUENCY, dr_min=0, dr_max=5) # join a network using OTAA lora.join(activation=LoRa.OTAA, auth=(dev_eui, app_eui, app_key), timeout=0, dr=config.LORA_NODE_DR) # wait until the module has joined the network i=1 while not lora.has_joined(): time.sleep(2.5) print('Not joined yet...') print(i) i+=1 print('device connected') # remove all the non-default channels for i in range(3, 16): lora.remove_channel(i) # create a LoRa socket s = socket.socket(socket.AF_LORA, socket.SOCK_RAW) # set the LoRaWAN data rate s.setsockopt(socket.SOL_LORA, socket.SO_DR, config.LORA_NODE_DR) # make the socket blocking s.setblocking(False) time.sleep(5.0) for i in range (60): #envoi des information toutes les 2 minutes donc pendant 2h value = 22.5 # Nombre mis dans un tableau de flottants et on l'affiche avec formattage myArrayOfFloat = bytes(struct.pack("f", value)) print('myArrayOfFloat') print(myArrayOfFloat) print([ "0x%02x" % item for item in myArrayOfFloat ]) #s.send(b'0001' + myArrayOfFloat) s.send(myArrayOfFloat) # send some data ("hello") #s.send(bytes([0x48, 0x65, 0x6C, 0x6C, 0x6F])) #print('hello is sent') # send some data ("h") #s.send(bytes([0x48])) #print('h is sent') # send some data ("20.5") #s.send(bytes([0x32, 0x30, 0x2e, 0x35])) #print('20.5 is sent') #s.send(b'PKT #' + myArrayOfFloat) #s.send(b'PKT #' + bytes([i])) time.sleep(118) rx, port = s.recvfrom(256) if rx: print('Received: {}, on port: {}'.format(rx, port)) time.sleep(2)
[ "jeaneric.mesmain@gmail.com" ]
jeaneric.mesmain@gmail.com
3c2f70f7d8cb1b239b139682b5e5bfd6579d4cb8
02c4c52f2f630df77bc25994aee13a7530e3136d
/012016/python/xmlParsing.py
ec2f58145a53178b29ba7dc467dc309ce4c9fc3c
[]
no_license
abgoswam/2016
ac7d61828bc5fda8c003f135bc250ed57d03ae4c
307844ba74bf302c290a4e2b4792a096e0c3cb56
refs/heads/master
2020-04-15T14:03:49.674650
2016-12-09T02:19:44
2016-12-09T02:19:44
57,982,844
1
0
null
null
null
null
UTF-8
Python
false
false
3,555
py
# -*- coding: utf-8 -*- """ Created on Sun Jan 10 07:38:36 2016 @author: agoswami Code from : http://www.blog.pythonlibrary.org/2013/04/30/python-101-intro-to-xml-parsing-with-elementtree/ """ import time import xml.etree.ElementTree as xml import xml.etree.cElementTree as ET import xml.dom.minidom as minidom #http://stackoverflow.com/questions/17402323/use-xml-etree-elementtree-to-write-out-nicely-formatted-xml-files def prettify(elem): """Return a pretty-printed XML string for the Element. """ "The idea is to print your Element in a string, parse it using minidom and convert it again in XML using the toprettyxml function" rough_string = xml.tostring(elem, 'utf-8') reparsed = minidom.parseString(rough_string) return reparsed.toprettyxml(indent="\t") def createXML(filename): root = xml.Element("zAppointments") appt = xml.Element("appointment") root.append(appt) # adding appt children begin = xml.SubElement(appt, "begin") begin.text = "12345678" uid = xml.SubElement(appt, "uid") uid.text = "040000008200E000" alarmTime = xml.SubElement(appt, "alarmTime") alarmTime.text = "1181572063" state = xml.SubElement(appt, "state") location = xml.SubElement(appt, "location") duration = xml.SubElement(appt, "duration") duration.text = "1800" subject = xml.SubElement(appt, "subject") tree = xml.ElementTree(root) with open(filename, "w") as fh: tree.write(fh) def editXML(filename, updatedfilename): tree = ET.ElementTree(file=filename) root = tree.getroot() for begin_time in root.iter("begin"): begin_time.text = time.ctime(int(begin_time.text)) s = prettify(root) print s # tree = ET.ElementTree(root) with open(updatedfilename, "w") as f: # tree.write(f) f.write(s) def parseXML(xml_file): """ Parse XML with ElementTree """ tree = ET.ElementTree(file=xml_file) print tree.getroot() root = tree.getroot() print "tag=%s, attrib=%s" % (root.tag, root.attrib) for child in root: print child.tag, child.attrib if child.tag == "appointment": for step_child in child: print step_child.tag # iterate over the entire tree print "-" * 40 print "Iterating using a tree iterator" print "-" * 40 iter_ = tree.getiterator() for elem in iter_: print elem.tag # get the information via the children! print "-" * 40 print "Iterating using getchildren()" print "-" * 40 appointments = root.getchildren() for appointment in appointments: appt_children = appointment.getchildren() for appt_child in appt_children: print "%s=%s" % (appt_child.tag, appt_child.text) #---------------------------------------------------------------------- if __name__ == "__main__": filename = "appt.xml" updatedfilename = "updated.xml" createXML(filename) ## just playing around with how to read / write text to files in python # f = open(filename, "ab") # f.writelines("abhishek\n") # f.writelines("goswami\n") # f.writelines("microsoft\n") # f.close() # # with open(filename, "rb") as fh: # s = fh.read() # print "++ line:{0}".format(s) # for line in fh: # print "-- line:{0}".format(line) editXML(filename, updatedfilename) parseXML(updatedfilename)
[ "abgoswam@gmail.com" ]
abgoswam@gmail.com
17f286bd52e1fda213acc5e1347e4d32bd730c24
de24f83a5e3768a2638ebcf13cbe717e75740168
/moodledata/vpl_data/389/usersdata/346/73669/submittedfiles/poligono.py
2d92908686c389fe16d305bc44eb0541a9bdb9f1
[]
no_license
rafaelperazzo/programacao-web
95643423a35c44613b0f64bed05bd34780fe2436
170dd5440afb9ee68a973f3de13a99aa4c735d79
refs/heads/master
2021-01-12T14:06:25.773146
2017-12-22T16:05:45
2017-12-22T16:05:45
69,566,344
0
0
null
null
null
null
UTF-8
Python
false
false
156
py
# -*- coding: utf-8 -*- #ENTRADA n= int(input('Digite quantos lados deve ter o seu polígono: ') #PROCESSAMENTO nd = (n*(n-3))/2 #SAÍDA print('%.1f' % nd)
[ "rafael.mota@ufca.edu.br" ]
rafael.mota@ufca.edu.br
3af5a5a3567f1a8fedae3b522ec01b68b906db07
456219b19249259643810cca5f92ce4c9a26c947
/Problem 07/advent7b.py
0d4c68181df187231c7452e046bd203646f84ebf
[ "Apache-2.0" ]
permissive
mankybansal/advent-of-code-2020
04a10a874a8444a7ef34f8d105050f574fdf0f3d
62b389c52d488ea88443b564b6b6b89fd7b5290b
refs/heads/main
2023-02-01T16:38:49.004717
2020-12-23T11:27:15
2020-12-23T11:27:15
318,087,339
0
0
null
null
null
null
UTF-8
Python
false
false
733
py
ad_list = {} for line in open('input.txt', 'r').readlines(): rules = line.strip().replace('.', '').replace('contain', '').replace(',', ' ').split(' ') master_key = None for i in range(len(rules)): if i == 0: split_rule = rules[i].split(' ') key = split_rule[0] + split_rule[1] if key not in ad_list: ad_list[key] = [] master_key = key else: split_rule = rules[i].split(' ') number_key = 0 if split_rule[0] == 'no' else int(split_rule[0]) ad_list[master_key].append((number_key, split_rule[1] + split_rule[2])) def recurse(key): if key == 'otherbags': return 0 total = 0 for q_num, q_key in ad_list[key]: total += (q_num * recurse(q_key)) + q_num return total print(recurse('shinygold'))
[ "sunny.bansal@gmail.com" ]
sunny.bansal@gmail.com
6e3a536375721655c5f4134ba620f4f7a4d0bbb6
b6a9268bcb333ec3d873cf28caef38c12de55e00
/ch7_functions/anonymous_functions.py
99fafbc2095ac3013dd4a9474d109961f183bd61
[]
no_license
bobo333/python_cookbook
eb6fff82e1f5dc90963c8798cf88ffdcc3195440
e0a022ecfe5abbcd074f90be9b274f6d29fe4362
refs/heads/master
2020-07-03T09:20:37.808762
2017-03-17T08:29:52
2017-03-17T08:29:52
66,986,155
0
0
null
null
null
null
UTF-8
Python
false
false
290
py
# lambdas are anonymous functions add = lambda x, y: x + y print(add(5, 3)) # usually used in combination with some other operation, like sort names = ['David Beazley', 'Brian Jones', 'Raymond Hettinger', 'Ned Batchelder'] print(sorted(names, key=lambda name: name.split()[-1].lower()))
[ "redsox333@gmail.com" ]
redsox333@gmail.com
e9bc9aded9058951ba4a02e774a00a8f7b8b97b0
1ed7d8d0490c90704994863daf6e6517717b17cd
/programs/unsupervised_category_learning/domain_to_category.py
f8d6e3ebb76e32bebdc0c785c97f2adb7750d902
[]
no_license
ankuragrawal70/KenSor
61ca59ee61a374eb5efcabf742c05f1b67000bd0
08cc378aaf5087a7990c1d3bfc2962b8334a0de7
refs/heads/master
2020-03-27T05:09:41.046750
2018-08-31T12:44:44
2018-08-31T12:44:44
145,998,044
0
0
null
null
null
null
UTF-8
Python
false
false
18,491
py
#import MySQLdb import gexf import networkx as nx import wikipedia import difflib import operator import socket import MySQLdb import os import urllib2, httplib import category_from_urls as cat_class import wiki_classification_cat_relation as wiki from bs4 import BeautifulSoup #import matplotlib.pyplot as plt def loose_match(s1,s2): seq = difflib.SequenceMatcher() try: seq.set_seqs(s1, s2) d=seq.ratio()*100 d=int(d) return d except: return 0 """def source_fetcher(): db = MySQLdb.connect("localhost",user="root",db="temporary_category_classification") cursor = db.cursor() sql="select * from level_1" url_list=[] try: cursor.execute(sql) result=cursor.fetchall() url_list=list(result) except: print "error in selection" db.close() print len(url_list) for e in url_list: if e[2]>=3: #if e[1]=='http://timesofindia.indiatimes.com/techspeciallisting/35626913.cms': # print 'yes' u=e[1] #cat_list=url_category_list(u) #print u,cat_list if domain.has_key(e[0]): info=domain[e[0]] category_domain_info(u,info) domain[e[0]]=info else: info={} category_domain_info(u,info) domain[e[0]]=info""" def gdelt_source_fetcher(): gdelt_path='D://Thesis//data//domain_name//category_gdelt_valid_source//' file_list=os.listdir(gdelt_path) for i in range(1,len(file_list)): p=gdelt_path+file_list[i] f=open(p,'r') c_info=eval(f.read()) if len(c_info)>0: f_name='http://'+file_list[i].rstrip('.txt') #print f_name for e in c_info: cat_class.category_domain_info(e,f_name,category) f.close() """gdelt_path='D://Thesis//data//domain_name//gdelt_heuristic_approach_1//' file_list=os.listdir(gdelt_path) for i in range(0,len(file_list)): #for i in range(0,500): p=gdelt_path+file_list[i] f=open(p,'r') category_url=f.read().split('\n') f_name='http://'+file_list[i].rstrip('.txt') #print f_name i=0 for e in category_url: cat_class.category_domain_info(e,f_name,category) i=i+1 #gdelt_cat[f_name]=cat if i==len(category_url)-1: break f.close()""" """gdelt_path='D://Thesis//data//domain_name//special_sources_not_in_gdelt//output//' file_list=os.listdir(gdelt_path) for i in range(0,len(file_list)): #for i in range(0,500): p=gdelt_path+file_list[i] f=open(p,'r') category_url=f.read().split('\n') f_name='http://'+file_list[i].rstrip('.txt') #print f_name i=0 for e in category_url: cat_class.category_domain_info(e,f_name,category) i=i+1 #gdelt_cat[f_name]=cat if i==len(category_url)-1: break f.close()""" def wiki_check(cat): #print len(list1) cat_distri={} cat=clean(cat) try: #ny = wikipedia.page(cat) wiki_text=wikipedia.summary(cat) elements=wiki_text.split(" ") count=0 for e in elements: e=e.lower() e=e.encode('utf-8') try: for ele in list2: ele=ele.encode('utf-8') ele=ele.lower() #print ele if loose_match(ele,e)>=80: #if loose_match1(ele,e): #print 'hello' count=count+1 if cat_distri.has_key(ele): cat_distri[ele]=cat_distri[ele]+1 else: f=0 for ca in cat_distri: if ca in ele or ele in ca: f=1 cat_distri[ca]=cat_distri[ca]+1 if ca=='chile': print 'elemenmt is' ,ele else: if loose_match(ca,ele)>=80: f=1 cat_distri[ca]=cat_distri[ca]+1 if f==0: cat_distri[ele]=1 except: pass print 'delhi in cat does not exists\n delhi',cat_distri['delhi'] print cat_distri ch=len(elements) x=sorted(cat_distri.iteritems(), key=lambda (k,v): (v,k),reverse=True) """for w in sorted(cat_distri, key=cat_distri.get, reverse=True): c=sum(cat_distri.values()) print c x=((float)(cat_distri[w]))/c print w,x,sub_cat[w].parent""" k=0 for re in x: print re[0], float(re[1])/ch k=k+1 if k>6: break except: print "no result" def check_validity(h_link,domain_name,level): if domain_name in h_link: level.append(h_link) else: if h_link[0]=='/': if domain_name[len(domain_name)-1]!='/': level.append(domain_name+h_link) else: l=len(domain_name)-1 x=domain_name[:l] level.append(x+h_link) def top_news_articles(d_name): level=[] try : web_page = urllib2.urlopen(d_name,timeout=4) soup = BeautifulSoup(web_page) c=soup.find_all('a') for e in c: #print e try: l=e['href'] if l!=d_name: check_validity(l,d_name,level) except: #print 'error after parsing links' pass #return level return level except: print 'error in main link' #return level return level pass def remove_redundant_category(category): for cat in (category.keys()): cat_object=category[cat] #print cat,cat_object #for ele in (cat_object.references.keys()): #if cat_object.references[ele]<=2: if len(category[cat].news_sources)<6: cat_delete=category[cat] if len(cat_delete.parent)>0: for ele in cat_delete.parent: del category[ele].references[cat] if len(cat_delete.references)>0: for e in cat_delete.references: category[e].parent.remove(cat) del category[cat] #category={} def write_to_db(category): db = MySQLdb.connect("localhost",user="root",db="web_categorization") cursor = db.cursor() p_id=0 for e in (category.keys()): c_info=category[e] name=c_info.c_name c=c_info.count p_id=p_id+1 length=len(category[name].news_sources) sql = ("insert into category(category_name,count,sources_count) values ('%s','%s','%s')"%(name,c,length)) try: # Execute the SQL command cursor.execute(sql) db.commit() print 'inserted category is',name, pid except: print "error in main" # Rollback in case there is any error db.rollback() db.close() def write_children(category): db = MySQLdb.connect("localhost",user="root",db="web_categorization") cursor = db.cursor() #for el in c_info.references: for e in category: #e="business" c_id=0 #print e sql="select category_id from category where category_name='%s'"%(e) try: cursor.execute(sql) row=cursor.fetchone() c_id=row[0] #print "c_id is",c_id db.commit() except: print "error in selection" db.rollback() obj=category[e] children=obj.references for child in children: cnt=children[child] print "category and c_id ",e,c_id sql = ("INSERT INTO CHILDREN(CHILD_NAME,COUNT,PARENT_ID) VALUES('%s','%s','%s')" %(child, cnt,c_id)) try: cursor.execute(sql) db.commit() except: print "error in insertion" db.rollback() """url_info=obj.news_sources for ele in url_info: sql = ("INSERT INTO url_info(url,cat_id) VALUES('%s','%s')" %(ele,c_id)) try: cursor.execute(sql) print "inserting url" db.commit() except: print "error in insertion" db.rollback()""" db.close() def write_sources(category): db = MySQLdb.connect("localhost",user="root",db="web_categorization") cursor = db.cursor() for element in category: sou=category[element].news_sources for child in sou: print "category and source ",element,child sql = ("INSERT INTO DOMAIN_CATEGORY_PATH(CATEGORY_NAME,DOMAIN_PATH) VALUES('%s','%s')" %(element,child)) try: cursor.execute(sql) db.commit() except: print "error in insertion" db.rollback() db.close() domain={} category={} #source_fetcher() gdelt_source_fetcher() remove_redundant_category(category) #print 'length of the category is',len(category),"\n".join(category.keys()) #write_sources(category) #write_to_db(category) #write_children(category) #y=sorted(domain.items(), key=lambda kv: len(kv[1]), reverse=True) """for i in range(0,20): print y[i][0],len(y[i][1])""" #source_fetcher2() #print domain.keys() #write sources category_wise #path="D://Thesis//data//domain_name//domains_related_to_category_all_5//" #source_file="D://Thesis//data//domain_name//special_sources_not_in_gdelt//sources.txt" #file_o=open(source_file,"a+") #code to find domains for categories """exist=os.listdir(path) for e in category: if len(category[e].news_sources)>1: cat=e+'.txt' #if cat not in exist: #print e path1=path+cat try: sources=category[e].news_sources f1=open(path1,'w') str1="\n".join(sources.keys()) f1.write(str1) file_o.write("\n"+str1) f1.close() except: pass else: path2=path+cat print e sources=category[e].news_sources str2="\n".join(sources.keys()) f2=open(path2,'a+') f2.write(str2) f2.close() #used for wikipedia distribution identification""" """list1=os.listdir(path) list2=[] for e in list1: path1=path+e f=open(path1,'r') sources=f.read().split("\n") if len(sources)>9: list2.append(e.rstrip('.txt'))""" """while True: categ=raw_input("input category") if categ+'.txt' in list1: path1=path+categ+'.txt' f1=open(path1,'r') sources=f1.read().split("\n") for s in sources: print s print len(sources) else: print 'category not found'""" #wiki_check('Arvind Kejriwal') def suggest(inp): suggessions="" for element in category: if element!=inp: if inp in element or element in inp: suggessions=suggessions+element+'\n' else: if loose_match(element,inp)>80: suggessions=suggessions+element+'\n' return suggessions """while True: try: sou=raw_input('enter source') #d=domain[sou] for v in d: #if len(d[v].references)>0: print v,d[v].count,d[v].references #print d[v].news_sources print '\n' #print d['music'].news_sources print 'no of categories obtained are',len(d) except: pass""" # suggessions function to get suggessions for a particular category # working synario""" def result(c_name): d=category output=[[],[],[],[]] #while True: try: #i=raw_input('enter category\n') #inp=i.lower() inp=c_name.lower() print d[inp].count if len(d[inp].references)>0 or len(d[inp].parent)>0: print 'related categories are' if len(d[inp].references)>0: #children sorted in reverse order of count print 'childrens are',len(d[inp].references) x=sorted(d[inp].references.iteritems(), key=lambda (k,v): (v,k),reverse=True) rel="" for r in x: #print r[0] rel=rel+r[0]+'\n' output[0]=rel #output[0]="\n".join(x) if len(d[inp].parent)>0: print 'parents',len(d[inp].parent) #reverse sorted the map map={} p_list="" for e in d[inp].parent: map[e]=category[e].references[inp] y=sorted(map.iteritems(), key=lambda (k,v): (v,k),reverse=True) #ou= d[inp].parent #print 'parents info is',y for el in y: #output[1]="\n".join(ou) p_list=p_list+el[0]+'\n' output[1]=p_list #print p_list j=0 print 'total sources published in the categories are',len(d[inp].news_sources) str1="" for ns in d[inp].news_sources: str1=str1+ ns+'\n' j=j+1 #if j>20: # break output[2]=str1 #x=suggest(inp) #print 'other categories)',len(x) #if len(x)>0: # print 'other similiar categories may be\n',x #print 'news sources are\n' """min1=1000 source='' for e in d[inp].news_sources.keys(): if len(e)<min1: source=e min1=len(e) if source !='': sour=source.split(inp)[0]+inp+'/' print 'category path is',sour x=top_news_articles(sour) # x containes list of links specified by the path x.sort(key = lambda s: len(s),reverse=True) if len(x)>0: print 'latest news events are ' for i in range(0,5): print x[i] #print source else: print d[inp].news_sources.keys()""" su=suggest(inp) #sug="\n".join(su) output[3]=su #if len(su)>0: # print " Please enter correct one, Suggessions are\n",su #else: # print 'no category found. Please enter correct one' #continue except: s=suggest(inp) output[3]=s return output def wiki_result(c_name): result=wiki.wiki_check(c_name,category) return result #function is used to rank news sources based on global ranking of top 200 news sources def sources_based_on_ranking(): world_ranking="D://Thesis//data//domain_name//news_sources_ranking//based_on_4inm_website//" direc=os.listdir(world_ranking) print direc r_sources=[] ranked_sources=[] for i in range(14,15): print direc[i] f=open(world_ranking+direc[i],'r') #ranked_sources are 200 sources containing global ranking r_sources.append(f.read().split("\n")) print r_sources for sou in r_sources: ranked_sources=ranked_sources+sou print '200 ranked sources are',ranked_sources f.close() while True: try: cat=raw_input("Please enter the category") #category specific sources print 'related categories are',category[cat].references.keys() print category[cat].parent category_sources=category[cat].news_sources.keys() print 'length of category sources is',len(category_sources) print 'sources without rankings are',category_sources #sorting index is used to sort category mnews sources sorting_index=[0]*len(category_sources) count=0 for i in range(0,len(category_sources)): domain_name='http://'+(category_sources[i].split("//")[1]).split('/')[0] if domain_name in ranked_sources: index=ranked_sources.index(domain_name) count=count+1 #print index sorting_index[i]=index else: sorting_index[i]=50000 #sort category specific source based on ranks of top 200 news sources #print sorting_index print 'sources is top 200 are',count category_sources.sort(key=dict(zip(category_sources, sorting_index)).get) #[x for (y,x) in sorted(zip(sorting_index,category_sources))] print "\n sources after rankings are\n" for source in category_sources: print source except: pass #sources_based_on_ranking()
[ "ankuragrawal70@gmail.com" ]
ankuragrawal70@gmail.com
49ac5ef41b8dac8841016e8ef4956958958458a3
585a068c651d85e6f02b18686ad95ec0671fbdfb
/habilidades.py
bdf134c919923b842545c8815dde37f379f18fe9
[]
no_license
FlavioSG-BR/Flask-RestfulAPI
f9b1361b3e2ba4b4436bc656a0b534418ecbe4c6
7380143a4d801f758ab59779287f443c1f1ad13b
refs/heads/master
2022-04-17T18:41:18.508258
2020-04-16T18:10:40
2020-04-16T18:10:40
256,292,424
0
0
null
null
null
null
UTF-8
Python
false
false
172
py
from flask_restful import Resource lista_habilidades = ['Python', 'Java', 'Flask', 'PHP'] class Habilidades(Resource): def get(self): return lista_habilidades
[ "flavio_godoy@yahoo.com.br" ]
flavio_godoy@yahoo.com.br
0fe60563da73b0f964c80040f248e1ff90390f63
b57414bc300636cec239e81bc2cf0d1b1c170d21
/Main.py
d1827d7ef168205a2e0c07dcfe867316e6130d59
[]
no_license
TaraRosen/mn_primary_election_day_tracking
7abcd0f4bcf56f196aef1b865f17129501aeb6f7
db6e73f6023b2432530d4664cdba3e1fa5f6eaab
refs/heads/main
2023-01-15T19:23:01.755729
2020-11-06T15:13:49
2020-11-06T15:13:49
310,629,807
0
0
null
null
null
null
UTF-8
Python
false
false
6,681
py
import time from time import sleep from warnings import warn import urllib.parse as up from requests import get from bs4 import BeautifulSoup import re import datetime import psycopg2 def resultsScraper(countyCode, precinctCodes): precinct_entry = "" precincts = "" for precinct in precinctCodes: precincts += precinct precincts += "," precincts = precincts[:-1] # specify the url with 'countyid' and 'precincts' response = get( 'https://electionresults.sos.state.mn.us/Results/PrecinctListResults/115?countyid=' + countyCode + '&precincts=' + precincts) # parse the html using beautiful soup and store in variable `soup` soup = BeautifulSoup(response.text, 'html.parser') # Selects starting area in html at 'center' center = soup.find('center') # Creates list of precinct names, # sets iterator at 1 to skip "Results for Selected Precincts in Hennepin County" precinct_containers = soup.find_all('div', class_='resultgroupheader') pnum = 1 # Creates list of all tables which is where results are stored for each precinct tables = center.find_all('table') # Iterates through table for ptable in tables: # Holds the name of the office_name candidates are running for i.e. U.S. Senator office_name = "" # Creates list of all rows which is where each candidates results are stored rows = ptable.find_all('tr') # Iterates through candidates for row in rows: # Initializes the string that holds the row for each candidate result in table # with precinct name and office name rowentry = "('" + precinct_containers[0].text.strip()[34:-7].replace("'", "") + "','" + precinct_containers[pnum].text.strip().replace("'", "") + "','" + office_name.replace("'", "") + "'" # Check if the row has 'class' so it doesn't error, skips if doesn't if row.has_attr('class'): # Updates the 'office_name' variable to whichever seat candidates are running for if row['class'] == ['resultofficeheader']: # Generates and cleans the office name office_name = row.find('div', class_='resultoffice') office_name = office_name.text.strip() office_name = re.sub(r"\s+", " ", office_name) # If not a new office, check if a candidate result elif row['class'] == ['resultcandidates']: # Selects appropriate entries, cleans extra empty field, cleans text cols = row.find_all('td')[:4] cols = [ele.text.strip() for ele in cols] if cols: for ele in cols: rowentry += ",'" rowentry += ele.replace("'", "") + "'" rowentry += ")," precinct_entry += rowentry # Updates to next precinct once iterated through entire table pnum += 1 return precinct_entry def precinctCodes(countyCode, reportedPrecincts): # List to store codes in newPrecincts = [] precinct_codes = "" precinct_counter = 0 # Specificy URL response = get('https://electionresults.sos.state.mn.us/Select/CountyPrecinctSelect/115?districtid=' + countyCode) # Parse the html using beautiful soup and store in variable `soup` soup = BeautifulSoup(response.text, 'html.parser') # Precinct list precinct_list = soup.find_all('option', class_='selOptReported', limit=253) # Change back to 'selOptReported' when actually using, and limit 253 # Check all precinct codes for precinct in precinct_list: precinctCode = precinct.attrs['value'] # Compile precincts that reported since last check if precinctCode not in reportedPrecincts: newPrecincts.append(precinctCode) precinct_counter += 1 precinct_codes += precinctCode precinct_codes += "," return newPrecincts #-- Main ------------------------------------------------------------------------------- conn = psycopg2.connect(dbname="results18", user="dflvictory", password="dflguest18", host="dfl-election-returns.cmycsq7ldygm.us-east-2.rds.amazonaws.com") cur = conn.cursor() cur.execute("set time zone 'America/Chicago'") # while True: # precinctsReported = numpy.empty(88, dtype=object) precinctsReported = [[] for i in range(88)] # print(precinctsReported) # URL URL = 'https://electionresults.sos.state.mn.us/Results/CountyStatistics/115' # Open URL response = get(URL) # Monitor Loop start_time = time.time() requests = 0 # Throw a warning for non-200 status codes if response.status_code != 200: requests += 1 warn('Request: {}; Status code: []'.format(requests, response.status_code)) # Slow the loop sleep(1) # Parse the html using beautiful soup and store in variable `soup` soup = BeautifulSoup(response.text, 'html.parser') # Precinct list counties_reported = soup.find_all('tr') # Finding County Results for county in counties_reported: county_entry = "INSERT INTO results18 (county, precinct, office, party, candidate, raw_votes, percentage) values " # Removes nulls if county.find('a', href=True) is not None: # Get County Code row = county.find('a', href=True) url = row.get('href') parsed = up.urlparse(url) code = up.parse_qs(parsed.query)['countyId'] countyCode = code[0] # Get Precincts Reported reported = county.find('td', class_='statscell statscellnumber').text numReported = int(reported) # Compared # of precincts currently reported to # previously reported if numReported > len(precinctsReported[int(countyCode)]): # look up syntax for this # Call PrecinctCodes helper function precinctsUpdated = precinctCodes(countyCode, precinctsReported[int(countyCode)]) # Call MyScraper if(len(precinctsUpdated) > 0): county_entry += resultsScraper(countyCode, precinctsUpdated) # Append new list of precincts to array # numpy.insert(precinctsReported, countyCode, precinctsUpdated) # if county_entry != "INSERT INTO results18 (county, precinct, office, party, candidate, raw_votes, percentage) values ": county_entry = county_entry[:-1] cur.execute(county_entry) conn.commit() # else: # print(countyCode) print("Time:", time.time() - start_time) cur.close() conn.close()
[ "noreply@github.com" ]
TaraRosen.noreply@github.com
a2d16a5c6608a02c2ad6b9f34f6a09e60da23d96
b95bf1219a8a35e9991e965b5aa2df6e1ed9e1d8
/Variables/variable.py
bdab6666729db6f6c1e979141065e0c563662117
[]
no_license
cosmolgj/charm_private_python
73738b8c53909ccf376cd0582590101d340ccc52
85c87f6922d4dcb08c41ac10d990b8a741958a13
refs/heads/master
2023-08-28T07:43:57.471238
2021-10-21T03:54:51
2021-10-21T03:54:51
417,421,863
0
0
null
null
null
null
UTF-8
Python
false
false
677
py
# 리스트 참조하는 새로운 변수할당 a = [1, 2, 3] b = a print(id(a)) print(id(b)) # 리스트 복사 a = [1, 2, 3] b = a[:] # list copy 1 a[1] = 4 print(a) print(b) from copy import copy b = copy(a) # list copy 2 b[1] = 7 print(a) print(b) b = a.copy() # 리스트의 자체 함수 print(a) print(b) """ def print_n_times(n, *values): # n번 반복합니다. for i in range(n): # values는 리스트처럼 활용합니다. for value in values: print(value) # 단순한 줄바꿈 print() # 함수를 호출합니다. print_n_times(3, "안녕하세요", "즐거운", "파이썬 프로그래밍") """
[ "cosmolgj@empal.com" ]
cosmolgj@empal.com
b97bdf194a8fbf05e9d4d3cc5fcabae4813e1dcc
ec02650631ee787fa2518d4b12febbd372fe64c3
/src/statistics_calculation.py
81947f9231555030fcdb4363d0f370a6fa229f53
[]
no_license
VitalyCherkov/state_exam_statistics
d889f95c20c86e4cd2b4aaad9f9f57c367cd030b
f499363e3bf1a8d7c96d5e5aee019ba99fdefd23
refs/heads/master
2020-03-23T05:13:27.771935
2019-07-10T10:00:56
2019-07-10T10:00:56
140,887,621
0
0
null
null
null
null
UTF-8
Python
false
false
2,152
py
import functools import operator from src.constants.common_parameter_names import ACADEMIC_PERFORMANCE, QUALITY, AVERAGE_GRADE from src.weight_functions import ( get_academic_performance_weight, get_quality_weight, get_average_grade_weight ) def calc_percentages_per_task(columns, max_results): """ возвращает массив суммарных баллов по каждой задаче и массив процентов решений каждой задачи """ rows_number = columns.shape[0] total_by_task = columns.sum(axis=0) total_by_task = [value for index, value in total_by_task.iteritems()] percentages_by_task = [ round(value / (max_results[index] * rows_number) * 100) for index, value in enumerate(total_by_task) ] return percentages_by_task def calc_common_statistics_via_func(marks_column, weight_func): """ подсчитывет статистику для данной задачи по переданной весовой функции """ total_count = len(marks_column) weighted_sequence = [ mark for index, mark in marks_column.apply(weight_func).iteritems() ] total_weight = functools.reduce(operator.add, weighted_sequence, 0) return round(total_weight / total_count * 100) def get_common_statistics(table, marks_column_name): """ подсчитывает статистики для девятых классов """ marks_column = table[marks_column_name]\ .apply(lambda x: int(x)) academic_performance = calc_common_statistics_via_func( marks_column=marks_column, weight_func=get_academic_performance_weight ) quality = calc_common_statistics_via_func( marks_column=marks_column, weight_func=get_quality_weight ) average_grade = calc_common_statistics_via_func( marks_column=marks_column, weight_func=get_average_grade_weight ) return { ACADEMIC_PERFORMANCE: str(academic_performance), QUALITY: str(quality), AVERAGE_GRADE: str(average_grade), }
[ "vv-ch@bk.ru" ]
vv-ch@bk.ru
5c9ac3b06991bb157e92c298215e3f2f75df99ef
911d961e2dfe786ca163d0eb5016e3edd7f909b8
/backjoon/11399.py
0bb55a3a817b59f88d05e760d6d1fcecc561487f
[]
no_license
kkhhkk/Study-Algorithms
abf8f4897c8ffc33d149930631275851fecb282d
28a69c71be8dd526c93e1414bc7531c9f4bb1710
refs/heads/master
2023-05-07T23:55:35.817235
2021-06-01T15:11:49
2021-06-01T15:11:49
335,855,333
0
0
null
null
null
null
UTF-8
Python
false
false
2,108
py
# 11399번 # 문제 # 인하은행에는 ATM이 1대밖에 없다. 지금 이 ATM앞에 N명의 사람들이 줄을 서있다. 사람은 1번부터 N번까지 번호가 매겨져 있으며, # i번 사람이 돈을 인출하는데 걸리는 시간은 Pi분이다. # 사람들이 줄을 서는 순서에 따라서, 돈을 인출하는데 필요한 시간의 합이 달라지게 된다. 예를 들어, 총 5명이 있고, # P1 = 3, P2 = 1, P3 = 4, P4 = 3, P5 = 2 인 경우를 생각해보자. [1, 2, 3, 4, 5] 순서로 줄을 선다면, 1번 사람은 3분만에 돈을 뽑을 수 있다. # 2번 사람은 1번 사람이 돈을 뽑을 때 까지 기다려야 하기 때문에, 3+1 = 4분이 걸리게 된다. 3번 사람은 1번, 2번 사람이 돈을 뽑을 때까지 기다려야 하기 때문에, # 총 3+1+4 = 8분이 필요하게 된다. 4번 사람은 3+1+4+3 = 11분, 5번 사람은 3+1+4+3+2 = 13분이 걸리게 된다. # 이 경우에 각 사람이 돈을 인출하는데 필요한 시간의 합은 3+4+8+11+13 = 39분이 된다. # 줄을[2, 5, 1, 4, 3] 순서로 줄을 서면, 2번 사람은 1분만에, 5번 사람은 1+2 = 3분, 1번 사람은 1+2+3 = 6분, 4번 사람은 1+2+3+3 = 9분, # 3번 사람은 1+2+3+3+4 = 13분이 걸리게 된다. 각 사람이 돈을 인출하는데 필요한 시간의 합은 1+3+6+9+13 = 32분이다. # 이 방법보다 더 필요한 시간의 합을 최소로 만들 수는 없다. # 줄을 서 있는 사람의 수 N과 각 사람이 돈을 인출하는데 걸리는 시간 Pi가 주어졌을 때, 각 사람이 돈을 인출하는데 필요한 시간의 합의 최솟값을 구하는 프로그램을 작성하시오. # 입력 # 첫째 줄에 사람의 수 N(1 ≤ N ≤ 1, 000)이 주어진다. 둘째 줄에는 각 사람이 돈을 인출하는데 걸리는 시간 Pi가 주어진다. (1 ≤ Pi ≤ 1, 000) # 출력 # 첫째 줄에 각 사람이 돈을 인출하는데 필요한 시간의 합의 최솟값을 출력한다. n = int(input()) arr = [int(x) for x in input().split()] arr.sort() cnt = 0 for i in range(n): cnt += (n-i) * arr[i] print(cnt)
[ "78129787+kkhhkk@users.noreply.github.com" ]
78129787+kkhhkk@users.noreply.github.com
a23a188a9a220ca25ffaec14f74dd0d311ecb76a
ba3be84d355e90860479fc2a0d92b536c377c643
/PYTHON/Advance/Python Controls/py_Frame2.py
d9e598e1135bffa05661b601196ac36c71774648
[]
no_license
vipin26/python
c62a3427511658ff292085fc382c5b3b3ff4d609
e8442b84c385ddef972d6a514e471d8eba8af0a3
refs/heads/master
2020-09-29T12:39:52.775466
2019-12-15T05:44:31
2019-12-15T05:44:31
227,034,374
0
0
null
null
null
null
UTF-8
Python
false
false
348
py
from Tkinter import * top = Tk() top.geometry("500x500") #width="200", height="200" f1 = Frame(top,bg="green") f1.pack(side="left",fill=BOTH,expand=1) f2 = Frame(top,bg="Red", width="200", height="200") f2.pack(side="right",fill=BOTH,expand=1) b1= Button(f1,text="Button 1"); b1.pack() b2= Button(f2,text="Button 2"); b2.pack() top.mainloop()
[ "58661640+vipin26@users.noreply.github.com" ]
58661640+vipin26@users.noreply.github.com
3f9c3e9ffc8803e6cd6e39ae498eaed82dd43a2d
a9cb028a29388ef97614faca6405985584e508c0
/CorpRoomApp/views/__init__.py
0202939f5240055387b68e169d950ae50c230c2f
[]
no_license
tungsten-manojb/DialARoomProject
f18cb0d71dbe5a94818aa7d6ec015c57580d83db
d5f8bb6a4611d6c452db65e782f0029b19081d13
refs/heads/master
2021-01-10T02:25:14.389591
2015-12-22T07:30:48
2015-12-22T07:30:48
48,413,955
0
0
null
null
null
null
UTF-8
Python
false
false
39
py
from CorpRoomApp.views import portal
[ "manoj.bawane@tungstenbigdata.com" ]
manoj.bawane@tungstenbigdata.com
5d7f25c116d2a9fa1d5af893930135ce01f01bff
f07a42f652f46106dee4749277d41c302e2b7406
/Data Set/bug-fixing-5/d3d1aa2dca4988bb7a232458338d86a0c9fef06e-<main>-fix.py
2ca55744f3a4a48e7dd7ef030d5aae4df1a066eb
[]
no_license
wsgan001/PyFPattern
e0fe06341cc5d51b3ad0fe29b84098d140ed54d1
cc347e32745f99c0cd95e79a18ddacc4574d7faa
refs/heads/main
2023-08-25T23:48:26.112133
2021-10-23T14:11:22
2021-10-23T14:11:22
null
0
0
null
null
null
null
UTF-8
Python
false
false
6,010
py
def main(): module = AnsibleModule(argument_spec=dict(src=dict(required=True), dest=dict(required=True), dest_port=dict(default=None, type='int'), delete=dict(default='no', type='bool'), private_key=dict(default=None), rsync_path=dict(default=None), _local_rsync_path=dict(default='rsync', type='path'), _substitute_controller=dict(default='no', type='bool'), archive=dict(default='yes', type='bool'), checksum=dict(default='no', type='bool'), compress=dict(default='yes', type='bool'), existing_only=dict(default='no', type='bool'), dirs=dict(default='no', type='bool'), recursive=dict(type='bool'), links=dict(type='bool'), copy_links=dict(type='bool'), perms=dict(type='bool'), times=dict(type='bool'), owner=dict(type='bool'), group=dict(type='bool'), set_remote_user=dict(default='yes', type='bool'), rsync_timeout=dict(type='int', default=0), rsync_opts=dict(type='list'), ssh_args=dict(type='str'), partial=dict(default='no', type='bool'), verify_host=dict(default='no', type='bool'), mode=dict(default='push', choices=['push', 'pull'])), supports_check_mode=True) if module.params['_substitute_controller']: try: source = (('"' + substitute_controller(module.params['src'])) + '"') dest = (('"' + substitute_controller(module.params['dest'])) + '"') except ValueError: module.fail_json(msg='Could not determine controller hostname for rsync to send to') else: source = (('"' + module.params['src']) + '"') dest = (('"' + module.params['dest']) + '"') dest_port = module.params['dest_port'] delete = module.params['delete'] private_key = module.params['private_key'] rsync_path = module.params['rsync_path'] rsync = module.params.get('_local_rsync_path', 'rsync') rsync_timeout = module.params.get('rsync_timeout', 'rsync_timeout') archive = module.params['archive'] checksum = module.params['checksum'] compress = module.params['compress'] existing_only = module.params['existing_only'] dirs = module.params['dirs'] partial = module.params['partial'] recursive = module.params['recursive'] links = module.params['links'] copy_links = module.params['copy_links'] perms = module.params['perms'] times = module.params['times'] owner = module.params['owner'] group = module.params['group'] rsync_opts = module.params['rsync_opts'] ssh_args = module.params['ssh_args'] verify_host = module.params['verify_host'] if ('/' not in rsync): rsync = module.get_bin_path(rsync, required=True) ssh = module.get_bin_path('ssh', required=True) cmd = ('%s --delay-updates -F' % rsync) if compress: cmd = (cmd + ' --compress') if rsync_timeout: cmd = (cmd + (' --timeout=%s' % rsync_timeout)) if module.check_mode: cmd = (cmd + ' --dry-run') if delete: cmd = (cmd + ' --delete-after') if existing_only: cmd = (cmd + ' --existing') if checksum: cmd = (cmd + ' --checksum') if archive: cmd = (cmd + ' --archive') if (recursive is False): cmd = (cmd + ' --no-recursive') if (links is False): cmd = (cmd + ' --no-links') if (copy_links is True): cmd = (cmd + ' --copy-links') if (perms is False): cmd = (cmd + ' --no-perms') if (times is False): cmd = (cmd + ' --no-times') if (owner is False): cmd = (cmd + ' --no-owner') if (group is False): cmd = (cmd + ' --no-group') else: if (recursive is True): cmd = (cmd + ' --recursive') if (links is True): cmd = (cmd + ' --links') if (copy_links is True): cmd = (cmd + ' --copy-links') if (perms is True): cmd = (cmd + ' --perms') if (times is True): cmd = (cmd + ' --times') if (owner is True): cmd = (cmd + ' --owner') if (group is True): cmd = (cmd + ' --group') if dirs: cmd = (cmd + ' --dirs') if (private_key is None): private_key = '' else: private_key = ('-i "%s"' % private_key) ssh_opts = '-S none' if (not verify_host): ssh_opts = ('%s -o StrictHostKeyChecking=no' % ssh_opts) if ssh_args: ssh_opts = ('%s %s' % (ssh_opts, ssh_args)) if (source.startswith('"rsync://') and dest.startswith('"rsync://')): module.fail_json(msg='either src or dest must be a localhost', rc=1) if ((not source.startswith('"rsync://')) and (not dest.startswith('"rsync://'))): if (dest_port is not None): cmd += (" --rsh 'ssh %s %s -o Port=%s'" % (private_key, ssh_opts, dest_port)) else: cmd += (" --rsh 'ssh %s %s'" % (private_key, ssh_opts)) if rsync_path: cmd = (cmd + (' --rsync-path=%s' % rsync_path)) if rsync_opts: cmd = ((cmd + ' ') + ' '.join(rsync_opts)) if partial: cmd = (cmd + ' --partial') changed_marker = '<<CHANGED>>' cmd = (((cmd + " --out-format='") + changed_marker) + "%i %n%L'") if ('@' not in source): source = os.path.expanduser(source) if ('@' not in dest): dest = os.path.expanduser(dest) cmd = ' '.join([cmd, source, dest]) cmdstr = cmd (rc, out, err) = module.run_command(cmd) if rc: return module.fail_json(msg=err, rc=rc, cmd=cmdstr) else: changed = (changed_marker in out) out_clean = out.replace(changed_marker, '') out_lines = out_clean.split('\n') while ('' in out_lines): out_lines.remove('') if module._diff: diff = { 'prepared': out_clean, } return module.exit_json(changed=changed, msg=out_clean, rc=rc, cmd=cmdstr, stdout_lines=out_lines, diff=diff) else: return module.exit_json(changed=changed, msg=out_clean, rc=rc, cmd=cmdstr, stdout_lines=out_lines)
[ "dg1732004@smail.nju.edu.cn" ]
dg1732004@smail.nju.edu.cn
98a9eb880eda3ee7f1c46c6d7d034777ec672a3c
71f36e89cef2f7739f6a19e77ec94649cf408297
/CodeIsFun/manage.py
9a2b1882e4285baba6d0d67a98b347aa4e0d2beb
[]
no_license
ansh9690/Django-Blog
bdbccf4cb6310ec9b325afdaf2402e319751b287
845c0d30381fb7248756d3617b30647e680884a3
refs/heads/master
2022-12-08T01:42:49.453597
2020-08-25T17:13:59
2020-08-25T17:13:59
258,409,983
0
2
null
null
null
null
UTF-8
Python
false
false
629
py
#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'CodeIsFun.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()
[ "anshuprakash8445@gmail.com" ]
anshuprakash8445@gmail.com
57f400f6021873fa23f9efa7d91a3e2a001f4aba
6ca12dab48862659c1426e5e245f961c661e5c42
/check_gibdd_fines.py
9c9d377cda88eb8eb9887b55bb395acfaac997d0
[ "MIT" ]
permissive
Wolfram-180/check_gibdd_fines
0d69b7269fe7b47fbf59e4a8091efa96c2cdd3f2
e94a9368fba549fb61387859ba1ea92747bda536
refs/heads/main
2023-09-02T12:43:58.745457
2021-11-01T10:21:02
2021-11-01T10:21:02
423,421,487
0
0
null
null
null
null
UTF-8
Python
false
false
2,021
py
import time from selenium import webdriver from selenium.webdriver.firefox.firefox_binary import FirefoxBinary from selenium.webdriver.firefox.options import Options from selenium.webdriver.common.desired_capabilities import DesiredCapabilities import datetime import emailwork import re debug = True WebDriverWaitInSec = 30 binary = FirefoxBinary('c:\geckodriver\geckodriver.exe') def init_driver(): binary = r'c:\Program Files (x86)\Mozilla Firefox\firefox.exe' options = Options() options.binary = binary cap = DesiredCapabilities().FIREFOX cap["marionette"] = True #optional return webdriver.Firefox(options=options, capabilities=cap, executable_path="c:\\geckodriver\\geckodriver.exe") def sleep(secs, place='whatever'): ttlsecs = secs while secs > 0: time.sleep(1) print('now: {} - {} of {} in {}'.format(datetime.datetime.now(), secs, ttlsecs, place)) secs -= 1 cars = {'х988то750': 'https://xn--90adear.xn--p1ai/check/fines#%D1%85988%D1%82%D0%BE+750+9907379357', 'в691ем777': 'https://xn--90adear.xn--p1ai/check/fines#%D0%B2691%D0%B5%D0%BC+777+5047741110'} if __name__ == "__main__": for car, link in cars.items(): browser = init_driver() sleep(10, 'стартуем') browser.get(link) sleep(20, 'открываем сайт гибдд') btn_check = browser.find_element_by_xpath('//*[@id="checkFines"]/p[4]/a') btn_check.click() sleep(180, 'ждем проверку') src = browser.page_source text_found = re.search(r'В результате проверки не были найдены сведения о неуплаченных штрафах', src) if text_found is None: emailwork.send_mail('user@gmail.com', car + ' - есть штраф', link) else: emailwork.send_mail('user@gmail.com', car + ' - нет штрафа', link) browser.quit()
[ "noreply@github.com" ]
Wolfram-180.noreply@github.com
f1984d621cb9dff5c7d33467c52fd530da9da790
d78d214c1040865d1d054cc034ff04e3cb025549
/bdgtools/io.py
526a76e4e092697ed3e067aaa8e3ee8588a8646d
[ "MIT" ]
permissive
knutdrand/bdgtools
922685c6225bb0dc458d3f4b9dc6985aee0b9e51
18d21586515ec03e5fb96e959447f6b35e5350de
refs/heads/master
2023-01-03T14:27:05.855074
2020-10-21T12:30:12
2020-10-21T12:30:12
301,361,372
0
0
null
null
null
null
UTF-8
Python
false
false
8,279
py
import logging from itertools import chain, groupby from more_itertools import pairwise from operator import itemgetter import pandas as pd import numpy as np from .regions import Regions from .splitregions import SplitRegions, Genes from .bedgraph import BedGraph, broadcast log = logging def _peek_line(f): pos = f.tell() line = f.readline() f.seek(pos) return line def read_bedfile(file_obj): n_cols = len(_peek_line(file_obj).split("\t")) assert n_cols >=3, n_cols if n_cols < 6: table = pd.read_table(file_obj, names=["chrom", "start", "end"], usecols=[0, 1, 2]) else: table = pd.read_table(file_obj, names=["chrom", "start", "end", "direction"], usecols=[0, 1, 2, 5]) table = table.sort_values(["chrom", "start"]) changes = np.flatnonzero(table["chrom"].values[:-1] != table["chrom"].values[1:])+1 changes = np.concatenate(([0], changes, [table["chrom"].values.size])) chrom_split = (table.iloc[start:end] for start, end in zip(changes[:-1], changes[1:])) r = {t["chrom"].iloc[0]: Regions(t["start"].values, t["end"].values, np.where(t["direction"].values=="+", 1, -1) if n_cols>=6 else 1) for t in chrom_split} return r def _fix_bedgraph(starts, ends, values): ends_w_zero = np.insert(ends[:-1], 0, 0) missing = np.flatnonzero(starts != ends_w_zero) new_starts = ends_w_zero[missing] new_ends = starts[missing] all_starts = np.empty(starts.size+missing.size, dtype=starts.dtype) all_ends = np.empty(starts.size+missing.size,dtype=ends.dtype) all_values = np.empty(starts.size+missing.size, dtype=values.dtype) new_missing = missing + np.arange(missing.size) all_starts[new_missing] = ends_w_zero[missing] all_ends[new_missing] = starts[missing] all_values[new_missing] = 0 for i, (a, b) in enumerate(pairwise(chain([0], missing, [starts.size]))): all_starts[a+i:b+i] = starts[a:b] all_ends[a+i:b+i] = ends[a:b] all_values[a+i:b+i] = values[a:b] return all_starts, all_ends, all_values def _get_bedgraph(chunks): chunks = list(chunks) cur_chrom = chunks[0]["chrom"].iloc[0] starts = np.concatenate([c["start"].values for c in chunks]) ends = np.concatenate([c["end"].values for c in chunks]) values = np.concatenate([c["value"].values for c in chunks]) if starts[0] != 0 or not np.all(starts[1:] == ends[:-1]): logging.warning(f"Uncomplete bedfile, fixing %s", starts[0]) starts, ends, values = _fix_bedgraph(starts, ends, values) assert np.all(starts[1:] == ends[:-1]), f"Begraph is not continous on {cur_chrom}, {starts[1:]}, {ends[:-1]}\n{np.flatnonzero(starts[1:]!=ends[:-1])}, {starts.size}" log.info("Read chromosome", cur_chrom) return BedGraph(starts, values, chunks[-1]["end"].values[-1]) def read_bedgraph(file_obj, size_hint=1000000): reader = pd.read_table(file_obj, names=["chrom", "start", "end", "value"], usecols=[0, 1, 2, 3], chunksize=size_hint) grouped = groupby(chain.from_iterable(chunk.groupby("chrom", sort=False) for chunk in reader), itemgetter(0)) grouped = ((chrom, map(itemgetter(1), group)) for chrom, group in grouped) return ((chrom, _get_bedgraph(group)) for chrom, group in grouped) def _get_bedfile(chunks, with_strand=False): chunks = list(chunks) cur_chrom = chunks[0]["chrom"].iloc[0] starts = np.concatenate([c["start"].values for c in chunks]) ends = np.concatenate([c["end"].values for c in chunks]) if with_strand: strands = np.concatenate([np.where(c["strand"].values=="+", 1, -1) for c in chunks]) else: strands=1 log.info("Read chromosome", cur_chrom) return Regions(starts, ends, strands) def read_large_bedfile(file_obj, size_hint=1000000): n_cols = len(_peek_line(file_obj).split("\t")) assert n_cols >=3, n_cols names=["chrom", "start", "end"] cols = [0, 1, 2] if n_cols>=6: names.append("strand") cols.append(5) reader = pd.read_table(file_obj, names=names, usecols=cols, chunksize=size_hint) grouped = groupby(chain.from_iterable(chunk.groupby("chrom", sort=False) for chunk in reader), itemgetter(0)) grouped = ((chrom, map(itemgetter(1), group)) for chrom, group in grouped) return ((chrom, _get_bedfile(group)) for chrom, group in grouped) def _filter_coding(df): s = np.array([starts[0] for starts in df["exon_starts"]]) e = np.array([ends[-1] for ends in df["exon_ends"]]) mask = df["cds_start"] > s mask &= df["cds_end"] > df["cds_start"] mask &= e > df["cds_end"] if not np.any(mask): return None return df.loc[mask] def _get_genes(df): df = _filter_coding(df) if df is None: return None directions = np.where(df["direction"].values=="+", 1, -1) starts = np.concatenate([s[::d] for s, d in zip(df["exon_starts"].values, directions)]) ends = np.concatenate([s[::d] for s, d in zip(df["exon_ends"].values, directions)]) lens = [len(starts) for starts in df["exon_starts"]] offsets=np.cumsum([0]+lens) all_directions = broadcast(directions, offsets) regions = Regions(starts, ends, all_directions) coding_offsets = _find_coding_offsets(df["cds_start"].values, df["cds_end"].values, regions, offsets, all_directions) return Genes(regions, offsets, coding_regions=Regions(coding_offsets[0], coding_offsets[1])) def _find_coding_offsets(cds_starts, cds_ends, regions, offsets, directions): cum_sizes = np.insert(np.cumsum(regions.sizes()), 0, 0) starts = broadcast(cds_starts, offsets) start_idxs = np.flatnonzero((regions.ends>=starts) & (regions.starts<=starts)) local_starts = np.where(directions[start_idxs]==1, cds_starts-regions.starts[start_idxs], regions.ends[start_idxs]-cds_starts) local_starts += cum_sizes[start_idxs]-cum_sizes[offsets[:-1]] ends = broadcast(cds_ends, offsets) end_idxs = np.flatnonzero((regions.ends>=ends) & (regions.starts<=ends)) local_ends = np.where(directions[end_idxs]==1, cds_ends-regions.starts[end_idxs], regions.ends[end_idxs]-cds_ends) local_ends += cum_sizes[end_idxs]-cum_sizes[offsets[:-1]] return np.where(directions[start_idxs]==1, local_starts, local_ends), np.where(directions[start_idxs]==-1, local_starts, local_ends) def read_refseq(file_obj): get_ints = lambda x: [int(r) for r in x.split(",") if r] df = pd.read_table(file_obj, names=["chrom", "direction", "start", "end","cds_start","cds_end", "exon_starts", "exon_ends"], usecols=[2,3,4,5,6,7,9,10], converters={"exon_starts": get_ints, "exon_ends": get_ints}) grouped = df.sort_values(["chrom", "start"]).groupby("chrom", sort=False) d = {chrom: _get_genes(df) for chrom, df in grouped} return {chrom: genes for chrom, genes in d.items() if genes is not None} def write_bedgraph(bedgraphs, f): for chrom, bedgraph in bedgraphs: if bedgraph._size is not None: df = pd.DataFrame({"chrom": chrom, "start": bedgraph._indices, "end": np.append(bedgraph._indices, bedgraph._size), "value": bedgraph._values}) else: df = pd.DataFrame({"chrom": chrom, "start": bedgraph._indices[:-1], "end": bedgraph._indices[1:], "value": bedgraph._values[:-1]}) df.to_csv(f, sep="\t", header=False, index=False) def write_bedfile(regions_dict, f): for chrom, regions in regions_dict.items(): df = pd.DataFrame({"chrom": chrom, "start": regions.starts, "end": regions.ends}) df.to_csv(f, sep="\t", header=False, index=False)
[ "knutdrand@gmail.com" ]
knutdrand@gmail.com
7d31daeee7daa90b1dc6e4d1fb65142ca9d46562
05c6023c8bc3a495d27ea8028a236aefc0324e31
/src/utils/db/matches/DBMatchesHandler.py
f415677526b5247e8b46fc975f7abbdcb5fc5c31
[]
no_license
huyleminh/network-socket-livescore
c3213c22c38a225f0f8da46a8cd8a481afcde8f2
abd347dac645aa037149fdb03285db4b71d00597
refs/heads/master
2023-04-19T05:38:58.370316
2021-05-09T02:13:27
2021-05-09T02:13:27
null
0
0
null
null
null
null
UTF-8
Python
false
false
3,476
py
import json import shortuuid as shortId import sys from pathlib import Path pathfile = Path(__file__).resolve() dbRoot = pathfile.parents[1] sys.path.append(str(dbRoot)) from details.DBDetailsHandler import DBDetailsHandler class DBMatchesHandler: @staticmethod def getAllMatches(): try: fMatches = open("database/Matches.json") matches = fMatches.read() fMatches.close() except: return { "status": 500 } matchesJSON = json.loads(matches) return { "status": 200, "data": matchesJSON } @staticmethod def getMatchById(idMatch): if idMatch == "": return { "status": 404 } res = DBMatchesHandler.getAllMatches() if res["status"] == 500: return { "status": 500 } if res["status"] == 200: dataMatches = res["data"] for i in range(0, len(dataMatches)): if dataMatches[i]["idMatch"] == idMatch: matchDetailRes = DBDetailsHandler.getDetailsById(dataMatches[i]["details"], dataMatches[i]["idMatch"]) if matchDetailRes["status"] == 200: matchDetail = matchDetailRes["data"] return { "status": 200, "data": { "match": dataMatches[i], "details": matchDetail } } elif matchDetailRes["status"] == 404: return { "status": 200, "data": { "match": dataMatches[i], "details": {} } } return { "status": 404 } @staticmethod def writeAllMatches(matches): with open("database/Matches.json", "w") as writeFile: json.dumps(matches, writeFile) writeFile.close() @staticmethod def createNewMatch(matchInfo): """ matchInfo: dict type - home and away: required - status: FT, time (minutes), HT, Pospt, time (begin time: date type), default is current date - homeScore and awayScore: default is ? """ if not isinstance(matchInfo, dict): return { "status": 400, "text": "Invalid type" } if "home" not in matchInfo or "away" not in matchInfo: return { "status": 400, "text": "Missing attribute" } newMatch = { "idMatch": shortId.random(length=10), "home": matchInfo["home"], "away": matchInfo["away"], "status": matchInfo["status"], "homeScore": matchInfo["homeScore"], "awayScore": matchInfo["awayScore"], "details": shortId.random(length=10) } # Create new details for this match: newDetail = { "idDetails": newMatch["details"], "idMatch": newMatch["idMatch"] } # Insert to database res = DBMatchesHandler.getAllMatches() if res["status"] == 500: return { "status": 500, "text": "Internal error"} if res["status"] == 200: dataMatches = res["data"] dataMatches.append(newMatch) DBMatchesHandler.writeAllMatches(dataMatches) return { "status": 201, "text": "Create new ok"}
[ "leminhhuy.hcmus@gmail.com" ]
leminhhuy.hcmus@gmail.com
2749a89da17d91be60716423a5b52e513374404b
7e325da0ec25a56158f4a47acf6f594548a72384
/users/migrations/0005_profile_stripe_customer_id.py
97a12f3ccd87c340090928f9137cccccc4257501
[ "MIT" ]
permissive
drewvpham/xclude.com
5102a921721c508552648ee03f5a8e1b0bafb6e8
103e89e2326c4c6fbfab819c43bc4e4634913bc9
refs/heads/master
2022-12-16T06:36:00.631404
2019-12-29T20:35:16
2019-12-29T20:35:16
222,317,889
0
0
MIT
2022-12-07T23:54:06
2019-11-17T21:58:47
Python
UTF-8
Python
false
false
428
py
# Generated by Django 2.2.7 on 2019-12-24 16:01 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('users', '0004_profile_one_click_purchasing'), ] operations = [ migrations.AddField( model_name='profile', name='stripe_customer_id', field=models.CharField(blank=True, max_length=50, null=True), ), ]
[ "drewvpham@gmail.com" ]
drewvpham@gmail.com
aa6c6881d7565c65dcc402b478b85a6aaab77318
8410bb5a2e8849bb3a554b95ddc713d88f3440c4
/aws-dev/awsdev8/venv/Lib/site-packages/amazondax/grammar/DynamoDbGrammarListener.py
e8bd3845c650ff1693a6d39c27674112f67b43c3
[ "MIT" ]
permissive
PacktPublishing/-AWS-Certified-Developer---Associate-Certification
ae99b6c1efb30e8fab5b76e3d8c821823a4cd852
b9838b4e038b42ad1813a296379cbbc40cab6286
refs/heads/master
2022-11-03T04:37:49.014335
2022-10-31T05:42:19
2022-10-31T05:42:19
219,964,717
13
11
MIT
2021-06-02T00:57:45
2019-11-06T09:54:09
Python
UTF-8
Python
false
false
13,172
py
# Generated from grammar/DynamoDbGrammar.g4 by ANTLR 4.7 from antlr4 import * if __name__ is not None and "." in __name__: from .DynamoDbGrammarParser import DynamoDbGrammarParser else: from DynamoDbGrammarParser import DynamoDbGrammarParser # This class defines a complete listener for a parse tree produced by DynamoDbGrammarParser. class DynamoDbGrammarListener(ParseTreeListener): # Enter a parse tree produced by DynamoDbGrammarParser#projection_. def enterProjection_(self, ctx:DynamoDbGrammarParser.Projection_Context): pass # Exit a parse tree produced by DynamoDbGrammarParser#projection_. def exitProjection_(self, ctx:DynamoDbGrammarParser.Projection_Context): pass # Enter a parse tree produced by DynamoDbGrammarParser#projection. def enterProjection(self, ctx:DynamoDbGrammarParser.ProjectionContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#projection. def exitProjection(self, ctx:DynamoDbGrammarParser.ProjectionContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#condition_. def enterCondition_(self, ctx:DynamoDbGrammarParser.Condition_Context): pass # Exit a parse tree produced by DynamoDbGrammarParser#condition_. def exitCondition_(self, ctx:DynamoDbGrammarParser.Condition_Context): pass # Enter a parse tree produced by DynamoDbGrammarParser#Or. def enterOr(self, ctx:DynamoDbGrammarParser.OrContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#Or. def exitOr(self, ctx:DynamoDbGrammarParser.OrContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#Negation. def enterNegation(self, ctx:DynamoDbGrammarParser.NegationContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#Negation. def exitNegation(self, ctx:DynamoDbGrammarParser.NegationContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#In. def enterIn(self, ctx:DynamoDbGrammarParser.InContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#In. def exitIn(self, ctx:DynamoDbGrammarParser.InContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#And. def enterAnd(self, ctx:DynamoDbGrammarParser.AndContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#And. def exitAnd(self, ctx:DynamoDbGrammarParser.AndContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#Between. def enterBetween(self, ctx:DynamoDbGrammarParser.BetweenContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#Between. def exitBetween(self, ctx:DynamoDbGrammarParser.BetweenContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#FunctionCondition. def enterFunctionCondition(self, ctx:DynamoDbGrammarParser.FunctionConditionContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#FunctionCondition. def exitFunctionCondition(self, ctx:DynamoDbGrammarParser.FunctionConditionContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#Comparator. def enterComparator(self, ctx:DynamoDbGrammarParser.ComparatorContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#Comparator. def exitComparator(self, ctx:DynamoDbGrammarParser.ComparatorContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#ConditionGrouping. def enterConditionGrouping(self, ctx:DynamoDbGrammarParser.ConditionGroupingContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#ConditionGrouping. def exitConditionGrouping(self, ctx:DynamoDbGrammarParser.ConditionGroupingContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#comparator_symbol. def enterComparator_symbol(self, ctx:DynamoDbGrammarParser.Comparator_symbolContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#comparator_symbol. def exitComparator_symbol(self, ctx:DynamoDbGrammarParser.Comparator_symbolContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#update_. def enterUpdate_(self, ctx:DynamoDbGrammarParser.Update_Context): pass # Exit a parse tree produced by DynamoDbGrammarParser#update_. def exitUpdate_(self, ctx:DynamoDbGrammarParser.Update_Context): pass # Enter a parse tree produced by DynamoDbGrammarParser#update. def enterUpdate(self, ctx:DynamoDbGrammarParser.UpdateContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#update. def exitUpdate(self, ctx:DynamoDbGrammarParser.UpdateContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#set_section. def enterSet_section(self, ctx:DynamoDbGrammarParser.Set_sectionContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#set_section. def exitSet_section(self, ctx:DynamoDbGrammarParser.Set_sectionContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#set_action. def enterSet_action(self, ctx:DynamoDbGrammarParser.Set_actionContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#set_action. def exitSet_action(self, ctx:DynamoDbGrammarParser.Set_actionContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#add_section. def enterAdd_section(self, ctx:DynamoDbGrammarParser.Add_sectionContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#add_section. def exitAdd_section(self, ctx:DynamoDbGrammarParser.Add_sectionContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#add_action. def enterAdd_action(self, ctx:DynamoDbGrammarParser.Add_actionContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#add_action. def exitAdd_action(self, ctx:DynamoDbGrammarParser.Add_actionContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#delete_section. def enterDelete_section(self, ctx:DynamoDbGrammarParser.Delete_sectionContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#delete_section. def exitDelete_section(self, ctx:DynamoDbGrammarParser.Delete_sectionContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#delete_action. def enterDelete_action(self, ctx:DynamoDbGrammarParser.Delete_actionContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#delete_action. def exitDelete_action(self, ctx:DynamoDbGrammarParser.Delete_actionContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#remove_section. def enterRemove_section(self, ctx:DynamoDbGrammarParser.Remove_sectionContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#remove_section. def exitRemove_section(self, ctx:DynamoDbGrammarParser.Remove_sectionContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#remove_action. def enterRemove_action(self, ctx:DynamoDbGrammarParser.Remove_actionContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#remove_action. def exitRemove_action(self, ctx:DynamoDbGrammarParser.Remove_actionContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#OperandValue. def enterOperandValue(self, ctx:DynamoDbGrammarParser.OperandValueContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#OperandValue. def exitOperandValue(self, ctx:DynamoDbGrammarParser.OperandValueContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#ArithmeticValue. def enterArithmeticValue(self, ctx:DynamoDbGrammarParser.ArithmeticValueContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#ArithmeticValue. def exitArithmeticValue(self, ctx:DynamoDbGrammarParser.ArithmeticValueContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#PlusMinus. def enterPlusMinus(self, ctx:DynamoDbGrammarParser.PlusMinusContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#PlusMinus. def exitPlusMinus(self, ctx:DynamoDbGrammarParser.PlusMinusContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#ArithmeticParens. def enterArithmeticParens(self, ctx:DynamoDbGrammarParser.ArithmeticParensContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#ArithmeticParens. def exitArithmeticParens(self, ctx:DynamoDbGrammarParser.ArithmeticParensContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#PathOperand. def enterPathOperand(self, ctx:DynamoDbGrammarParser.PathOperandContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#PathOperand. def exitPathOperand(self, ctx:DynamoDbGrammarParser.PathOperandContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#LiteralOperand. def enterLiteralOperand(self, ctx:DynamoDbGrammarParser.LiteralOperandContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#LiteralOperand. def exitLiteralOperand(self, ctx:DynamoDbGrammarParser.LiteralOperandContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#FunctionOperand. def enterFunctionOperand(self, ctx:DynamoDbGrammarParser.FunctionOperandContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#FunctionOperand. def exitFunctionOperand(self, ctx:DynamoDbGrammarParser.FunctionOperandContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#ParenOperand. def enterParenOperand(self, ctx:DynamoDbGrammarParser.ParenOperandContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#ParenOperand. def exitParenOperand(self, ctx:DynamoDbGrammarParser.ParenOperandContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#FunctionCall. def enterFunctionCall(self, ctx:DynamoDbGrammarParser.FunctionCallContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#FunctionCall. def exitFunctionCall(self, ctx:DynamoDbGrammarParser.FunctionCallContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#path. def enterPath(self, ctx:DynamoDbGrammarParser.PathContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#path. def exitPath(self, ctx:DynamoDbGrammarParser.PathContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#id_. def enterId_(self, ctx:DynamoDbGrammarParser.Id_Context): pass # Exit a parse tree produced by DynamoDbGrammarParser#id_. def exitId_(self, ctx:DynamoDbGrammarParser.Id_Context): pass # Enter a parse tree produced by DynamoDbGrammarParser#MapAccess. def enterMapAccess(self, ctx:DynamoDbGrammarParser.MapAccessContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#MapAccess. def exitMapAccess(self, ctx:DynamoDbGrammarParser.MapAccessContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#ListAccess. def enterListAccess(self, ctx:DynamoDbGrammarParser.ListAccessContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#ListAccess. def exitListAccess(self, ctx:DynamoDbGrammarParser.ListAccessContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#LiteralSub. def enterLiteralSub(self, ctx:DynamoDbGrammarParser.LiteralSubContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#LiteralSub. def exitLiteralSub(self, ctx:DynamoDbGrammarParser.LiteralSubContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#expression_attr_names_sub. def enterExpression_attr_names_sub(self, ctx:DynamoDbGrammarParser.Expression_attr_names_subContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#expression_attr_names_sub. def exitExpression_attr_names_sub(self, ctx:DynamoDbGrammarParser.Expression_attr_names_subContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#expression_attr_values_sub. def enterExpression_attr_values_sub(self, ctx:DynamoDbGrammarParser.Expression_attr_values_subContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#expression_attr_values_sub. def exitExpression_attr_values_sub(self, ctx:DynamoDbGrammarParser.Expression_attr_values_subContext): pass # Enter a parse tree produced by DynamoDbGrammarParser#unknown. def enterUnknown(self, ctx:DynamoDbGrammarParser.UnknownContext): pass # Exit a parse tree produced by DynamoDbGrammarParser#unknown. def exitUnknown(self, ctx:DynamoDbGrammarParser.UnknownContext): pass
[ "sonalis@packtpub.com" ]
sonalis@packtpub.com
32217a6f79616909f2a98f30ead7693f6c395512
ec5db30f321fc0c77adc7ec0d567cb0b8c723cef
/OpenCV section/face_detector.py
a801bfa8f21ffbae648a2c9d87290e291f684f5c
[]
no_license
LingzeHu/python-projects
41b3fc19544232dcd715a162f6a457dbfd5a3371
1271c0c7bc3e58ad21f9a4cecd629be10618ee1a
refs/heads/master
2022-07-28T11:06:36.362649
2020-05-26T01:49:42
2020-05-26T01:49:42
266,919,231
0
0
null
null
null
null
UTF-8
Python
false
false
507
py
import cv2 face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") img = cv2.imread("news.jpg") gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray_img, scaleFactor=1.1, minNeighbors=5) for x, y, w, h in faces: img = cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 3) print(faces) print(type(faces)) resized = cv2.resize(img, (int(img.shape[1]/3), int(img.shape[0]/3))) cv2.imshow("Gray", img) cv2.waitKey(0) cv2.destroyAllWindows()
[ "lingzehu@gmail.com" ]
lingzehu@gmail.com
1c482da35a8e578fdc4f0045a27fb730c1b5717e
45d4e36581bfe9982619aa13c255e703d50e77da
/blog/forms.py
2ff9e417e2b1c65f9e98e25ddfd6e1ab12aedf29
[]
no_license
Sagar746/Project_blog
b93cedb08263e188f003d3af3462ea4273d1dec8
5650f5eca9cce6a011992c8c651ffb420bd3ff9e
refs/heads/master
2023-03-08T04:41:43.621844
2021-02-27T17:53:37
2021-02-27T17:53:37
342,921,417
0
0
null
null
null
null
UTF-8
Python
false
false
551
py
from django import forms from .models import Post from idea.models import Idea class PostCreateForm(forms.ModelForm): class Meta: model=Post fields=['title','description','category','image'] class PostUpdateForm(forms.ModelForm): class Meta: model=Post fields=['title','description','category','image'] class IdeaCreateForm(forms.ModelForm): class Meta: model=Idea fields=['author','title','description','photo'] class IdeaUpdateForm(forms.ModelForm): class Meta: model=Idea fields=['author','title','description','photo']
[ "saagartiwari722427@gmail.com" ]
saagartiwari722427@gmail.com
e9690e73f0b4e1dc083c4d6cc4fcf8ffe8143425
a3984032b1f20c38b4dee1809326230fa828287a
/manage.py
d960e32230106c88c173a3a199a6479c23c5ba5b
[ "MIT" ]
permissive
tomi77/django-auth-role
c5acccae5647578fffa938e5cba2f06a4eba5f7f
8fb3bf8940856a13ca7c7265c4670e4359dcb638
refs/heads/master
2021-01-19T21:50:33.773375
2017-05-10T16:48:39
2017-05-10T16:48:39
88,711,319
1
0
null
null
null
null
UTF-8
Python
false
false
195
py
#!/usr/bin/env python import os os.environ['DJANGO_SETTINGS_MODULE'] = 'tests.settings' from django.core import management if __name__ == "__main__": management.execute_from_command_line()
[ "tomasz.rup@gmail.com" ]
tomasz.rup@gmail.com
0e928393f712c9aa0b389fc8e611da0166635eb7
a3d6eb92c6fc6ed6095d10bc7b329cb8b4a8b166
/src/config.py
9f147f5f9b4dfea32202702abb0161c77899e708
[]
no_license
hgiesel/anki_straight_reward
ab29e6f154beba7c5f2f9bd9579c21cdd942b218
3fe72255f667d2eb544afb2541a1eb974c23eede
refs/heads/master
2022-12-23T22:38:28.432113
2022-12-12T13:46:36
2022-12-12T13:46:36
245,190,627
19
4
null
2023-08-28T16:01:55
2020-03-05T14:53:08
Python
UTF-8
Python
false
false
2,381
py
from anki.cards import Card from aqt import mw from .types import StraightSetting DEFAULT_SETTINGS = StraightSetting(2, True, 5, 5, 130, 250) KEYWORD = "straightReward" def serialize_setting(setting: StraightSetting) -> dict: return { "enableNotifications": setting.enable_notifications, "straightLength": setting.straight_length, "baseEase": setting.base_ease, "stepEase": setting.step_ease, "startEase": setting.start_ease, "stopEase": setting.stop_ease, } def deserialize_setting( straight_length: int, enable_notifications: bool, base_ease: int, step_ease: int, start_ease: int, stop_ease: int, ) -> StraightSetting: return StraightSetting( straight_length, enable_notifications, base_ease, step_ease, start_ease, stop_ease, ) def deserialize_setting_from_dict(setting_data: dict) -> StraightSetting: return StraightSetting( setting_data["straightLength"] if "straightLength" in setting_data else DEFAULT_SETTINGS.straight_length, setting_data["enableNotifications"] if "enableNotifications" in setting_data else DEFAULT_SETTINGS.enable_notifications, setting_data["baseEase"] if "baseEase" in setting_data else DEFAULT_SETTINGS.base_ease, setting_data["stepEase"] if "stepEase" in setting_data else DEFAULT_SETTINGS.step_ease, setting_data["startEase"] if "startEase" in setting_data else DEFAULT_SETTINGS.start_ease, setting_data["stopEase"] if "stopEase" in setting_data else DEFAULT_SETTINGS.stop_ease, ) def get_setting_from_config(config) -> StraightSetting: try: return deserialize_setting_from_dict(config[KEYWORD]) except: return get_default_setting() def get_setting_from_card(card: Card) -> StraightSetting: # confForDid did resort to conf for default deck if not available (TODO is this still true?) config = mw.col.decks.config_dict_for_deck_id(card.odid or card.did) return get_setting_from_config(config) def get_default_setting() -> StraightSetting: return DEFAULT_SETTINGS def write_setting(config, setting: StraightSetting): config[KEYWORD] = serialize_setting(setting) mw.col.decks.update_config(config)
[ "hengiesel@gmail.com" ]
hengiesel@gmail.com
ed2044a2c6ce156532db6f9abe972c1652e8e2d1
12c62359f011f92db8ec3649c6c23f9158482085
/components/ai.py
9d1c4e196d4101ac6ac8212529362a1afc4228a3
[]
no_license
Kuerschten/RoguelikeTutorial
7cab1274c7ffd936aed5192cb2aa1a9f36973e2a
f6e0c35011d8d039c2f8a1244738e925156a2ea0
refs/heads/main
2023-03-12T21:29:51.004921
2021-03-05T20:06:29
2021-03-05T20:06:29
338,453,931
0
0
null
null
null
null
UTF-8
Python
false
false
4,204
py
from __future__ import annotations import random from typing import List, Optional, Tuple, TYPE_CHECKING import numpy as np # type: ignore import tcod from actions import Action, BumpAction, MeleeAction, MovementAction, WaitAction if TYPE_CHECKING: from entity import Actor class BaseAI(Action): def perform(self) -> None: raise NotImplementedError() def get_path_to(self, dest_x: int, dest_y: int) -> List[Tuple[int, int]]: """Compute and return a path to the target position. If there is no valid path then returns an empty list. """ # Copy the walkable array. cost = np.array(self.entity.gamemap.tiles["walkable"], dtype=np.int8) for entity in self.entity.gamemap.entities: # Check that an enitiy blocks movement and the cost isn't zero (blocking.) if entity.blocks_movement and cost[entity.x, entity.y]: # Add to the cost of a blocked position. # A lower number means more enemies will crowd behind each other in # hallways. A higher number means enemies will take longer paths in # order to surround the player. cost[entity.x, entity.y] += 10 # Create a graph from the cost array and pass that graph to a new pathfinder. graph = tcod.path.SimpleGraph(cost=cost, cardinal=2, diagonal=3) pathfinder = tcod.path.Pathfinder(graph) pathfinder.add_root((self.entity.x, self.entity.y)) # Start position. # Compute the path to the destination and remove the starting point. path: List[List[int]] = pathfinder.path_to((dest_x, dest_y))[1:].tolist() # Convert from List[List[int]] to List[Tuple[int, int]]. return [(index[0], index[1]) for index in path] class ConfusedEnemy(BaseAI): """ A confused enemy will stumble around aimlessly for a given number of turns, then revert back to its previous AI. If an actor occupies a tile it is randomly moving into, it will attack. """ def __init__( self, entity: Actor, previous_ai: Optional[BaseAI], turns_remaining: int ): super().__init__(entity) self.previous_ai = previous_ai self.turns_remaining = turns_remaining def perform(self) -> None: # Revert the AI back to the original state if the effect has run its course. if self.turns_remaining <= 0: self.engine.message_log.add_message( f"The {self.entity.name} is no longer confused." ) self.entity.ai = self.previous_ai else: # Pick a random direction direction_x, direction_y = random.choice( [ (-1, -1), # Northwest (0, -1), # North (1, -1), # Northeast (-1, 0), # West (1, 0), # East (-1, 1), # Southwest (0, 1), # South (1, 1), # Southeast ] ) self.turns_remaining -= 1 # The actor will either try to move or attack in the chosen random direction. # Its possible the actor will just bump into the wall, wasting a turn. return BumpAction(self.entity, direction_x, direction_y,).perform() class HostileEnemy(BaseAI): def __init__(self, entity: Actor): super().__init__(entity) self.path: List[Tuple[int, int]] = [] def perform(self) -> None: target = self.engine.player dx = target.x - self.entity.x dy = target.y - self.entity.y distance = max(abs(dx), abs(dy)) # Chebyshev distance. if self.engine.game_map.visible[self.entity.x, self.entity.y]: if distance <= 1: return MeleeAction(self.entity, dx, dy).perform() self.path = self.get_path_to(target.x, target.y) if self.path: dest_x, dest_y = self.path.pop(0) return MovementAction( self.entity, dest_x - self.entity.x, dest_y - self.entity.y, ).perform() return WaitAction(self.entity).perform()
[ "dk-siggi@web.de" ]
dk-siggi@web.de
a5c614cd15adeb9672fcd1647fab7785834fd360
af994d1163f445a79797186fae4c828670e575c7
/env/bin/pip2.7
eca04ab69df21074ce698efb4185e442cf920ea9
[ "MIT" ]
permissive
dhruvshah1214/jarvis-shah-nlp
03a80a9f8dbb0581549736b3cf9fd254b7a893f9
b720dc5ab3af7ed223483ce6919b84c4ad56e4a2
refs/heads/master
2021-01-12T15:34:57.388006
2016-10-24T23:51:26
2016-10-24T23:51:26
71,840,800
0
0
null
null
null
null
UTF-8
Python
false
false
244
7
#!/Users/hitesh/Documents/workspace/JARVIS/env/bin/python # -*- coding: utf-8 -*- import re import sys from pip import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())
[ "dhruv.shah@gmail.com" ]
dhruv.shah@gmail.com
841afbf9bb4cdfd5cafcbc0c6f3f11f329e527fc
24fe1f54fee3a3df952ca26cce839cc18124357a
/servicegraph/lib/python2.7/site-packages/acimodel-4.0_3d-py2.7.egg/cobra/modelimpl/eqptcapacity/l3v4usage32per.py
ec7d3ed093f8b0173f4a279c5813fc5c3027ba2f
[]
no_license
aperiyed/servicegraph-cloudcenter
4b8dc9e776f6814cf07fe966fbd4a3481d0f45ff
9eb7975f2f6835e1c0528563a771526896306392
refs/heads/master
2023-05-10T17:27:18.022381
2020-01-20T09:18:28
2020-01-20T09:18:28
235,065,676
0
0
null
2023-05-01T21:19:14
2020-01-20T09:36:37
Python
UTF-8
Python
false
false
12,181
py
# coding=UTF-8 # ********************************************************************** # Copyright (c) 2013-2019 Cisco Systems, Inc. All rights reserved # written by zen warriors, do not modify! # ********************************************************************** from cobra.mit.meta import ClassMeta from cobra.mit.meta import StatsClassMeta from cobra.mit.meta import CounterMeta from cobra.mit.meta import PropMeta from cobra.mit.meta import Category from cobra.mit.meta import SourceRelationMeta from cobra.mit.meta import NamedSourceRelationMeta from cobra.mit.meta import TargetRelationMeta from cobra.mit.meta import DeploymentPathMeta, DeploymentCategory from cobra.model.category import MoCategory, PropCategory, CounterCategory from cobra.mit.mo import Mo # ################################################## class L3v4Usage32Per(Mo): meta = StatsClassMeta("cobra.model.eqptcapacity.L3v4Usage32Per", "Layer3 v4 32 entries usage percentage") counter = CounterMeta("normalizedv4Total", CounterCategory.GAUGE, "percentage", "Total v4 32 L3 entries usage percentage") counter._propRefs[PropCategory.IMPLICIT_LASTREADING] = "normalizedv4TotalLast" counter._propRefs[PropCategory.IMPLICIT_MIN] = "normalizedv4TotalMin" counter._propRefs[PropCategory.IMPLICIT_MAX] = "normalizedv4TotalMax" counter._propRefs[PropCategory.IMPLICIT_AVG] = "normalizedv4TotalAvg" counter._propRefs[PropCategory.IMPLICIT_SUSPECT] = "normalizedv4TotalSpct" counter._propRefs[PropCategory.IMPLICIT_TOTAL] = "normalizedv4TotalTtl" counter._propRefs[PropCategory.IMPLICIT_THRESHOLDED] = "normalizedv4TotalThr" counter._propRefs[PropCategory.IMPLICIT_TREND_BASE] = "normalizedv4TotalTrBase" counter._propRefs[PropCategory.IMPLICIT_TREND] = "normalizedv4TotalTr" meta._counters.append(counter) meta.isAbstract = True meta.moClassName = "eqptcapacityL3v4Usage32Per" meta.moClassName = "eqptcapacityL3v4Usage32Per" meta.rnFormat = "" meta.category = MoCategory.STATS_CURRENT meta.label = "current Layer3 v4 32 entries usage percentage stats" meta.writeAccessMask = 0x1 meta.readAccessMask = 0x1 meta.isDomainable = False meta.isReadOnly = True meta.isConfigurable = False meta.isDeletable = False meta.isContextRoot = False meta.superClasses.add("cobra.model.stats.Item") meta.superClasses.add("cobra.model.stats.Curr") meta.concreteSubClasses.add("cobra.model.eqptcapacity.L3v4Usage32Per1d") meta.concreteSubClasses.add("cobra.model.eqptcapacity.L3v4Usage32Per5min") meta.concreteSubClasses.add("cobra.model.eqptcapacity.L3v4Usage32Per1qtr") meta.concreteSubClasses.add("cobra.model.eqptcapacity.L3v4Usage32Per1h") meta.concreteSubClasses.add("cobra.model.eqptcapacity.L3v4Usage32Per1mo") meta.concreteSubClasses.add("cobra.model.eqptcapacity.L3v4Usage32Per1year") meta.concreteSubClasses.add("cobra.model.eqptcapacity.L3v4Usage32Per1w") meta.concreteSubClasses.add("cobra.model.eqptcapacity.L3v4Usage32Per15min") meta.rnPrefixes = [ ] prop = PropMeta("str", "childAction", "childAction", 4, PropCategory.CHILD_ACTION) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop._addConstant("deleteAll", "deleteall", 16384) prop._addConstant("deleteNonPresent", "deletenonpresent", 8192) prop._addConstant("ignore", "ignore", 4096) meta.props.add("childAction", prop) prop = PropMeta("str", "cnt", "cnt", 16212, PropCategory.REGULAR) prop.label = "Number of Collections During this Interval" prop.isImplicit = True prop.isAdmin = True meta.props.add("cnt", prop) prop = PropMeta("str", "dn", "dn", 1, PropCategory.DN) prop.label = "None" prop.isDn = True prop.isImplicit = True prop.isAdmin = True prop.isCreateOnly = True meta.props.add("dn", prop) prop = PropMeta("str", "lastCollOffset", "lastCollOffset", 111, PropCategory.REGULAR) prop.label = "Collection Length" prop.isImplicit = True prop.isAdmin = True meta.props.add("lastCollOffset", prop) prop = PropMeta("str", "normalizedv4TotalAvg", "normalizedv4TotalAvg", 44076, PropCategory.IMPLICIT_AVG) prop.label = "Total v4 32 L3 entries usage percentage average value" prop.isOper = True prop.isStats = True meta.props.add("normalizedv4TotalAvg", prop) prop = PropMeta("str", "normalizedv4TotalLast", "normalizedv4TotalLast", 44073, PropCategory.IMPLICIT_LASTREADING) prop.label = "Total v4 32 L3 entries usage percentage current value" prop.isOper = True prop.isStats = True meta.props.add("normalizedv4TotalLast", prop) prop = PropMeta("str", "normalizedv4TotalMax", "normalizedv4TotalMax", 44075, PropCategory.IMPLICIT_MAX) prop.label = "Total v4 32 L3 entries usage percentage maximum value" prop.isOper = True prop.isStats = True meta.props.add("normalizedv4TotalMax", prop) prop = PropMeta("str", "normalizedv4TotalMin", "normalizedv4TotalMin", 44074, PropCategory.IMPLICIT_MIN) prop.label = "Total v4 32 L3 entries usage percentage minimum value" prop.isOper = True prop.isStats = True meta.props.add("normalizedv4TotalMin", prop) prop = PropMeta("str", "normalizedv4TotalSpct", "normalizedv4TotalSpct", 44077, PropCategory.IMPLICIT_SUSPECT) prop.label = "Total v4 32 L3 entries usage percentage suspect count" prop.isOper = True prop.isStats = True meta.props.add("normalizedv4TotalSpct", prop) prop = PropMeta("str", "normalizedv4TotalThr", "normalizedv4TotalThr", 44079, PropCategory.IMPLICIT_THRESHOLDED) prop.label = "Total v4 32 L3 entries usage percentage thresholded flags" prop.isOper = True prop.isStats = True prop.defaultValue = 0 prop.defaultValueStr = "unspecified" prop._addConstant("avgCrit", "avg-severity-critical", 2199023255552) prop._addConstant("avgHigh", "avg-crossed-high-threshold", 68719476736) prop._addConstant("avgLow", "avg-crossed-low-threshold", 137438953472) prop._addConstant("avgMajor", "avg-severity-major", 1099511627776) prop._addConstant("avgMinor", "avg-severity-minor", 549755813888) prop._addConstant("avgRecovering", "avg-recovering", 34359738368) prop._addConstant("avgWarn", "avg-severity-warning", 274877906944) prop._addConstant("cumulativeCrit", "cumulative-severity-critical", 8192) prop._addConstant("cumulativeHigh", "cumulative-crossed-high-threshold", 256) prop._addConstant("cumulativeLow", "cumulative-crossed-low-threshold", 512) prop._addConstant("cumulativeMajor", "cumulative-severity-major", 4096) prop._addConstant("cumulativeMinor", "cumulative-severity-minor", 2048) prop._addConstant("cumulativeRecovering", "cumulative-recovering", 128) prop._addConstant("cumulativeWarn", "cumulative-severity-warning", 1024) prop._addConstant("lastReadingCrit", "lastreading-severity-critical", 64) prop._addConstant("lastReadingHigh", "lastreading-crossed-high-threshold", 2) prop._addConstant("lastReadingLow", "lastreading-crossed-low-threshold", 4) prop._addConstant("lastReadingMajor", "lastreading-severity-major", 32) prop._addConstant("lastReadingMinor", "lastreading-severity-minor", 16) prop._addConstant("lastReadingRecovering", "lastreading-recovering", 1) prop._addConstant("lastReadingWarn", "lastreading-severity-warning", 8) prop._addConstant("maxCrit", "max-severity-critical", 17179869184) prop._addConstant("maxHigh", "max-crossed-high-threshold", 536870912) prop._addConstant("maxLow", "max-crossed-low-threshold", 1073741824) prop._addConstant("maxMajor", "max-severity-major", 8589934592) prop._addConstant("maxMinor", "max-severity-minor", 4294967296) prop._addConstant("maxRecovering", "max-recovering", 268435456) prop._addConstant("maxWarn", "max-severity-warning", 2147483648) prop._addConstant("minCrit", "min-severity-critical", 134217728) prop._addConstant("minHigh", "min-crossed-high-threshold", 4194304) prop._addConstant("minLow", "min-crossed-low-threshold", 8388608) prop._addConstant("minMajor", "min-severity-major", 67108864) prop._addConstant("minMinor", "min-severity-minor", 33554432) prop._addConstant("minRecovering", "min-recovering", 2097152) prop._addConstant("minWarn", "min-severity-warning", 16777216) prop._addConstant("periodicCrit", "periodic-severity-critical", 1048576) prop._addConstant("periodicHigh", "periodic-crossed-high-threshold", 32768) prop._addConstant("periodicLow", "periodic-crossed-low-threshold", 65536) prop._addConstant("periodicMajor", "periodic-severity-major", 524288) prop._addConstant("periodicMinor", "periodic-severity-minor", 262144) prop._addConstant("periodicRecovering", "periodic-recovering", 16384) prop._addConstant("periodicWarn", "periodic-severity-warning", 131072) prop._addConstant("rateCrit", "rate-severity-critical", 36028797018963968) prop._addConstant("rateHigh", "rate-crossed-high-threshold", 1125899906842624) prop._addConstant("rateLow", "rate-crossed-low-threshold", 2251799813685248) prop._addConstant("rateMajor", "rate-severity-major", 18014398509481984) prop._addConstant("rateMinor", "rate-severity-minor", 9007199254740992) prop._addConstant("rateRecovering", "rate-recovering", 562949953421312) prop._addConstant("rateWarn", "rate-severity-warning", 4503599627370496) prop._addConstant("trendCrit", "trend-severity-critical", 281474976710656) prop._addConstant("trendHigh", "trend-crossed-high-threshold", 8796093022208) prop._addConstant("trendLow", "trend-crossed-low-threshold", 17592186044416) prop._addConstant("trendMajor", "trend-severity-major", 140737488355328) prop._addConstant("trendMinor", "trend-severity-minor", 70368744177664) prop._addConstant("trendRecovering", "trend-recovering", 4398046511104) prop._addConstant("trendWarn", "trend-severity-warning", 35184372088832) prop._addConstant("unspecified", None, 0) meta.props.add("normalizedv4TotalThr", prop) prop = PropMeta("str", "normalizedv4TotalTr", "normalizedv4TotalTr", 44081, PropCategory.IMPLICIT_TREND) prop.label = "Total v4 32 L3 entries usage percentage trend" prop.isOper = True prop.isStats = True meta.props.add("normalizedv4TotalTr", prop) prop = PropMeta("str", "normalizedv4TotalTrBase", "normalizedv4TotalTrBase", 44080, PropCategory.IMPLICIT_TREND_BASE) prop.label = "Total v4 32 L3 entries usage percentage trend baseline" prop.isOper = True prop.isStats = True meta.props.add("normalizedv4TotalTrBase", prop) prop = PropMeta("str", "normalizedv4TotalTtl", "normalizedv4TotalTtl", 44078, PropCategory.IMPLICIT_TOTAL) prop.label = "Total v4 32 L3 entries usage percentage total sum" prop.isOper = True prop.isStats = True meta.props.add("normalizedv4TotalTtl", prop) prop = PropMeta("str", "repIntvEnd", "repIntvEnd", 110, PropCategory.REGULAR) prop.label = "Reporting End Time" prop.isImplicit = True prop.isAdmin = True meta.props.add("repIntvEnd", prop) prop = PropMeta("str", "repIntvStart", "repIntvStart", 109, PropCategory.REGULAR) prop.label = "Reporting Start Time" prop.isImplicit = True prop.isAdmin = True meta.props.add("repIntvStart", prop) prop = PropMeta("str", "rn", "rn", 2, PropCategory.RN) prop.label = "None" prop.isRn = True prop.isImplicit = True prop.isAdmin = True prop.isCreateOnly = True meta.props.add("rn", prop) prop = PropMeta("str", "status", "status", 3, PropCategory.STATUS) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop._addConstant("created", "created", 2) prop._addConstant("deleted", "deleted", 8) prop._addConstant("modified", "modified", 4) meta.props.add("status", prop) def __init__(self, parentMoOrDn, markDirty=True, **creationProps): namingVals = [] Mo.__init__(self, parentMoOrDn, markDirty, *namingVals, **creationProps) # End of package file # ##################################################
[ "rrishike@cisco.com" ]
rrishike@cisco.com
a75af14244df6896f34f5a5d274727df813bad8c
fc65e7c55058361edc163a82ea9d33d27d040268
/wsgi/pecan/golden/hooks.py
989933c7fe27c5ec77a72c8546846d75491f5fcb
[]
no_license
zhaozhilong1993/openstack_test
19a011029b4b9c9fa4411df793a1bd1e23e2da1d
a1153f4d258cc73e31f3e2604ab7309eb03879de
refs/heads/master
2021-01-21T14:44:48.605566
2016-07-16T03:53:21
2016-07-16T03:53:21
58,913,030
1
0
null
null
null
null
UTF-8
Python
false
false
220
py
from pecan import hooks from golden.db import api as db_api class DBHook(hooks.PecanHook): """Create a db connection instance.""" def before(self, state): state.request.db_conn = db_api.Connection()
[ "root@unitedstack.com" ]
root@unitedstack.com
86627084d51566bc70cecda01854f3e0851bcd6f
5be853e4bfe6b95c4e311af32e0c5cbf20218adc
/src/Kernel/FunctionsForLists.py
2c64500f279df13f20b5591fe6b9a060267e8d54
[]
no_license
SebastianDapra/KernelSimulator
ff89dfd40b91c6470ce7f654218ab33df8f753e9
ff14b82a2be30f84301efbba559d1bbd9883195d
refs/heads/master
2021-01-18T14:14:02.277544
2015-12-10T01:54:00
2015-12-10T01:54:00
42,142,128
0
0
null
null
null
null
UTF-8
Python
false
false
625
py
class FunctionsForLists: @staticmethod def filterList(functionForFilter,listF): return list(filter(functionForFilter, listF)) @staticmethod def mapList(function,listM): return list(map(function,listM)) @staticmethod def sum(function,listM): return sum(FunctionsForLists.mapList(function,listM)) @staticmethod def findFirst(functionForFilter,listF): return FunctionsForLists.filterList(functionForFilter, listF)[0] @staticmethod def exists(functionForFilter,listF): return FunctionsForLists.filterList(functionForFilter, listF).__len__() > 0
[ "luciano.federico.olmedo@gmail.com" ]
luciano.federico.olmedo@gmail.com
314181b6076dd4f353ab40ca9d5695c63949d5ba
c0a9460591dcb5a322c1c5ec8b67e1d775f8f4ba
/advec_1d/dg_modal_gpu.py
d4b79c2b6d4cba9f5cc88e1e69a5f264c41dfdf1
[]
no_license
wbkifun/my_research
aca8f5132d03de2e15adc3b0ded164fbd89e38a3
eb7e61f5405834dcbea240665bdc819f4b3f97bf
refs/heads/master
2020-12-24T16:40:29.722161
2016-03-07T00:31:15
2016-03-07T00:31:15
5,176,828
0
0
null
null
null
null
UTF-8
Python
false
false
3,676
py
#=============================================================================== # DG method with modal basis functions # 1-D advection equation # ------------------------------------------------------------------------------ # # Last update: 2012.4.26 # ------------------------------------------------------------------------------ # # <Description> # - basis function: Legendre polynomial # - boundary condition: periodic # - initial condition: Gaussian shape # - numerical integration: Gauss quadrature (Gauss-lubatto rules) # - time evolution: 4th-order Runge-Kutta # - Legendre polynomial calculations: Numpy module (numpy.polynomial.legendre) # # <Variables> # - ux solution u(x) at t in physical domain # - ul spectral components of u(x) in Legendre polynomial space # - fl spectral components of f(u), f=vu is used # - v fluid velociy # - ne # of elements # - nne # of gaussian quadrature nodes in a element # - nn # of total nodes # - x4n global coordinates for each nodes # - sle slice indices in a element # - sles list of sle s # # <History> # 2012.4.26 Class inheritance by Ki-Hwan Kim # Reduce number of kernels (4 -> 2) # 2012.4.25 fix dx -> de by Ki-Hwan Kim # 2012.4.24 CUDA version by Ki-Hwan Kim # 2012.4.14 Convert to object-oriented by Ki-Hwan Kim # 2012.4.13 Rewriten using Python by Ki-Hwan Kim # 2012.3.27 Matlab code by Shin-Hoo Kang #=============================================================================== from __future__ import division from dg_modal_base import DGModalBase import numpy as np import pycuda.driver as cuda class DGModalGpu(DGModalBase): def __init__(self, ne, p_degree, cfl=0.1, v=0.5, target_gpu=0): cuda.init() self.dev = cuda.Device(target_gpu) self.ctx = self.dev.make_context() import atexit atexit.register(self.ctx.pop) super(DGModalGpu, self).__init__(ne, p_degree, cfl, v) def allocation(self): super(DGModalGpu, self).allocation() self.ul_gpu = cuda.to_device(self.ul) self.ul_prev_gpu = cuda.to_device(self.ul) self.ul_tmp_gpu = cuda.to_device(self.ul) self.kl_gpu = cuda.to_device(self.ul) self.el_sum_gpu = cuda.to_device(np.zeros(self.ne)) def x2l(self): super(DGModalGpu, self).x2l() cuda.memcpy_htod(self.ul_gpu, self.ul) def l2x(self): cuda.memcpy_dtoh(self.ul, self.ul_gpu) super(DGModalGpu, self).l2x() def prepare_update(self): from pycuda.compiler import SourceModule import os src_path = '/'.join( os.path.abspath(__file__).split('/')[:-1] ) kernels = open(src_path + '/core.cu').read() mod = SourceModule(kernels) #mod = cuda.module_from_file('core.cubin') self.update_pre = mod.get_function('update_pre') self.update_ul = mod.get_function('update_ul') def update(self): nn, ne, nne = np.int32([self.nn, self.ne, self.nne]) dt, de, vf = np.float64([self.dt, self.de, self.vf]) bs, gs = (256,1,1), (self.nn//256+1,1) ul, ul_prev, ul_tmp = self.ul_gpu, self.ul_prev_gpu, self.ul_tmp_gpu kl = self.kl_gpu el_sum = self.el_sum_gpu c_ul_tmps = np.float32([0, 0.5, 0.5, 1]) c_uls = np.float32([1./6, 1./3, 1./3, 1./6]) cuda.memcpy_dtod(ul_prev, ul, self.ul.nbytes) for c_ul_tmp, c_ul in zip(c_ul_tmps, c_uls): self.update_pre(nn, nne, vf, c_ul_tmp, ul, ul_prev, ul_tmp, kl, el_sum, block=bs, grid=gs) self.update_ul(nn, ne, nne, dt, de, vf, c_ul, ul, ul_tmp, kl, el_sum, block=bs, grid=gs)
[ "kh.kim@kiaps.org" ]
kh.kim@kiaps.org
2ac42e0a73d94a3ea63cdde82603cce7f4879b66
b697b98db859c061c1174837deee1d6fc47d115e
/examples/spot/futures/futures_loan_interest_history.py
d11b8ced29894bbc52ba7c6b24d2585c250be296
[ "MIT" ]
permissive
leozaragoza/binance-connector-python
7e684d6e68ff7d580b7e3fa83f952540a79b1120
3311d102c9e788e3d71047f0af103c00d1ae2162
refs/heads/master
2023-07-15T12:27:50.041388
2021-08-22T17:08:38
2021-08-22T17:08:38
396,354,910
3
0
MIT
2021-08-22T17:08:38
2021-08-15T13:12:41
Python
UTF-8
Python
false
false
268
py
#!/usr/bin/env python import logging from binance.spot import Spot as Client from binance.lib.utils import config_logging config_logging(logging, logging.DEBUG) key = "" secret = "" client = Client(key, secret) logging.info(client.futures_loan_interest_history())
[ "liang.shi@protonmail.com" ]
liang.shi@protonmail.com
49df5a742852121ed1640fd82015e88245e9910f
98430ad712b7855b3ab9cdab1b31055d7f607d8e
/Airline Sentiment Analysis.py
0ac57428aec69e3209e8ed7f509c27cfdfdd9541
[]
no_license
adobbins1/US-Airline-Twitter-Sentiment-Analysis
cb6e7dc1709386a13b67c1b4273937003641dda9
db3908dd9d5c9f6f4e5763d7c2d57bed7228f0e0
refs/heads/master
2022-11-28T04:29:49.735077
2020-08-05T21:26:56
2020-08-05T21:26:56
272,076,410
0
0
null
null
null
null
UTF-8
Python
false
false
5,648
py
# Austin Dobbins # DSC 680 import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import re import numpy as np from nltk.corpus import stopwords from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.svm import SVC from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report, confusion_matrix, accuracy_score # Importing Airline Tweets Data Set df = pd.read_csv(r'C:\Users\austi\OneDrive\Desktop\Tweets.csv') print(df.head()) # Converting Data Type of Date Features df['tweet_created'] = pd.to_datetime(df['tweet_created']) df['date_created'] = df['tweet_created'].dt.date # Creating Data Frame Containing the Counts of Each Negative, Neutral, and Positive Tweet For Each Airline # Sorted by Date d = df.groupby(['date_created', 'airline']) d = d.airline_sentiment.value_counts() d.unstack() print(d) # Printing the number of Negative, Neutral, and Positive Tweet print(df.airline_sentiment.value_counts()) # Plotting the Percentage of Tweets for Each Airline df.airline.value_counts().plot(kind='pie', autopct='%1.0f%%') plt.title('Percentage of Tweets for Each Airline') plt.show() # Plotting Percentage of Positive, Negative, and Neutral Comments df.airline_sentiment.value_counts().plot(kind='pie', autopct='%1.0f%%', colors=['red', 'yellow', 'green']) plt.title('Percentage of Positive, Negative, and Neutral Comments') plt.ylabel('Airline Sentiment') plt.show() # Count of Negative Reasons print(df.negativereason.value_counts()) # Plotting Percentage of Reasons for Negative Comments df.negativereason.value_counts().plot(kind='pie', autopct='%1.0f%%') plt.title('Percentage of Reasons for Negative Comments') plt.ylabel('Negative Comment Reason') plt.show() # Plotting Counts of Positive, Neutral, and Negative Comments for Each Airline airlinesentiment = df.groupby(['airline', 'airline_sentiment']).airline_sentiment.count().unstack() airlinesentiment.plot(kind='bar') plt.title('Counts of Positive, Neutral, and Negative Comments for Each Airline') plt.xlabel('Airline') plt.show() # Plotting Confidence Level for Positive, Neutral, and Negative Tweets sns.barplot(x= 'airline_sentiment', y = 'airline_sentiment_confidence', data=df) plt.title('Confidence Level for Positive, Neutral, and Negative Tweets') plt.xlabel('Airline Sentiment') plt.ylabel('Airline Sentiment Confidence') plt.show() # Removing Unneeded Characters: 'RT' '@' words = ' '.join(df['text']) cleanedwords = " ".join([word for word in words.split() if 'http' not in word and not word.startswith('@') and word != 'RT' ]) # Calculating Frequency of Words In Tweets def freq(str): str = str.split() str2 = [] for i in str: if i not in str2: str2.append(i) for i in range(0, len(str2)): if str.count(str2[i]) > 50: print('Frequency of', str2[i], 'is :', str.count(str2[i])) # print(freq(cleanedwords)) # Cleaning the Dataset for Modeling # Dividing Dataset into Features and Labels features = df.iloc[:, 10].values labels = df.iloc[:, 1].values processed_features = [] for sentence in range(0, len(features)): # Remove special characters processed_feature = re.sub(r'\W', ' ', str(features[sentence])) # remove single characters processed_feature= re.sub(r'\s+[a-zA-Z]\s+', ' ', processed_feature) # Remove single characters from the start processed_feature = re.sub(r'\^[a-zA-Z]\s+', ' ', processed_feature) # Changing multiple spaces to single space processed_feature = re.sub(r'\s+', ' ', processed_feature, flags=re.I) # Removing prefixed 'b' processed_feature = re.sub(r'^b\s+', '', processed_feature) # Converting to Lowercase processed_feature = processed_feature.lower() processed_features.append(processed_feature) # Creating "Bag of Words" using the 2500 Most Frequently Occurring Words vectorizer = TfidfVectorizer (max_features=2500, min_df=7, max_df=0.8, stop_words=stopwords.words('english')) processed_features = vectorizer.fit_transform(processed_features).toarray() # Starting Model Creation # Splitting Data into Training and Testing Sets x_train, x_test, y_train, y_test = train_test_split(processed_features, labels, test_size=0.2, random_state=0) # Random Forest Classifier textclassifier = RandomForestClassifier(n_estimators=200) textclassifier.fit(x_train, y_train) # Random Forest Prediction predictions = textclassifier.predict(x_test) # Random Forest Accuracy Metrics print(confusion_matrix(y_test, predictions)) print(classification_report(y_test, predictions)) print(accuracy_score(y_test, predictions)) # SVM Classifier textclassifier2 = SVC(gamma='auto') textclassifier2.fit(x_train, y_train) # SVM Prediction predictions2 = textclassifier2.predict(x_test) # SVM Accuracy Metrics print(confusion_matrix(y_test, predictions2)) print(classification_report(y_test, predictions2)) print(accuracy_score(y_test, predictions2)) # Logistic Regression Model model = LogisticRegression() model.fit(x_train, y_train) # Logistic Regression Prediction predictions3 = model.predict(x_test) # Logistic Regression Accuracy Metrics print(confusion_matrix(y_test, predictions3)) print(classification_report(y_test, predictions3)) print(accuracy_score(y_test, predictions3))
[ "noreply@github.com" ]
adobbins1.noreply@github.com
4d16d2aff65c43ef586654078b8ea7389ab8be10
72eeea6b1d45faab32431d48bc2ab814724ed5d3
/model/modeling/necks/__init__.py
10ae8fdbda47bc933c0b86d13b98870e01caa66d
[]
no_license
wenjunyoung/TF_SAST_Paddle_OCR
15ba58be3b5aefefbbcda5e4dd02afa36aa5fbcf
60f94befe12cec6c6de72d4107bda7b109abdc6a
refs/heads/main
2023-07-24T14:47:01.210211
2021-09-01T07:02:18
2021-09-01T07:02:18
397,501,328
0
0
null
null
null
null
UTF-8
Python
false
false
1,185
py
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. __all__ = ['build_neck'] def build_neck(config): # from .db_fpn import DBFPN # from .east_fpn import EASTFPN # from .sast_fpn import SASTFPN from .sast_fpn import tf_SASTFPN # from .rnn import SequenceEncoder # from .pg_fpn import PGFPN support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'tf_SASTFPN', 'SequenceEncoder', 'PGFPN'] module_name = config.pop('name') assert module_name in support_dict, Exception('neck only support {}'.format( support_dict)) module_class = eval(module_name)(**config) return module_class
[ "i_yangwenjun@cvte.com" ]
i_yangwenjun@cvte.com
076a1e0256af4f30ffa33586bba32919f063825a
eac8a6ee0065627de15efe4e4f829c1489675879
/tests/test_problem01.py
d4f4b193638c61937bea711f33fae3f670ff8fa1
[ "MIT" ]
permissive
ishaansharma/blind-75-python
ff8163ae9f2757a3e69895b468531ac5ad0eaf05
b92ef3449eb0143c760ddd339897a3f0a2972830
refs/heads/master
2023-03-16T11:29:14.620223
2020-03-09T00:57:39
2020-03-09T00:57:39
null
0
0
null
null
null
null
UTF-8
Python
false
false
166
py
import unittest from problems.problem01 import solution class Test(unittest.TestCase): def test(self): self.assertListEqual(solution([2, 7, 11, 15], 9), [0, 1])
[ "nolan@nolanwright.dev" ]
nolan@nolanwright.dev
eba3614127140f9ea0fe64dd042430decb11b8c9
e4928b77dfc7c50fc2ba23a12a2c9201d08f1f67
/Chapter_Problems/Chapter4/4_1.py
84dee5fbe800aab6ad5af818625ce6eb12e32f22
[]
no_license
ishwarsaini10/Course-Work-Problems
02363624b5e43c629724751594fb180b05579aaf
fb4afc22319f7ebcd756c279022fdef2279571f9
refs/heads/main
2023-02-01T00:48:42.389969
2020-12-17T13:27:36
2020-12-17T13:27:36
309,957,963
0
0
null
null
null
null
UTF-8
Python
false
false
28
py
list a [] list :wq
[ "noreply@github.com" ]
ishwarsaini10.noreply@github.com
f121d704f462cbfcf6d8989a237fa08cedb0d211
e6a95ea97f285a992a8c39e1ec7dead6eec2e119
/src/Services/Singleton.py
7e88a2ff972dc150587ae7081796a3977bedd2fd
[ "MIT" ]
permissive
xclemence/pykube-switch
d132cfdd4bd91c30f5831824214b0362c49ae5cb
4b7ac4565cc15469c2109e1a400d78e1d2b14404
refs/heads/master
2022-12-28T00:39:18.692977
2020-10-15T19:48:20
2020-10-15T19:48:20
300,056,431
1
0
MIT
2020-10-14T20:48:22
2020-09-30T20:38:38
QML
UTF-8
Python
false
false
337
py
from PySide2.QtCore import QObject class Singleton(type): _instances = {} def __call__(cls, *args, **kwargs): if cls not in cls._instances: cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) return cls._instances[cls] class QObjectSingleton(type(QObject), Singleton): pass
[ "xavier.clemence@gmail.com" ]
xavier.clemence@gmail.com
ba1e1883e6c6bba64621e521b0b4aa974fe7895f
24fe1f54fee3a3df952ca26cce839cc18124357a
/servicegraph/lib/python2.7/site-packages/acimodel-4.0_3d-py2.7.egg/cobra/modelimpl/isis/isiscsnpstats1qtr.py
5e12cd0cc2e3b1aa65da434a7ed773805e4aebed
[]
no_license
aperiyed/servicegraph-cloudcenter
4b8dc9e776f6814cf07fe966fbd4a3481d0f45ff
9eb7975f2f6835e1c0528563a771526896306392
refs/heads/master
2023-05-10T17:27:18.022381
2020-01-20T09:18:28
2020-01-20T09:18:28
235,065,676
0
0
null
2023-05-01T21:19:14
2020-01-20T09:36:37
Python
UTF-8
Python
false
false
29,126
py
# coding=UTF-8 # ********************************************************************** # Copyright (c) 2013-2019 Cisco Systems, Inc. All rights reserved # written by zen warriors, do not modify! # ********************************************************************** from cobra.mit.meta import ClassMeta from cobra.mit.meta import StatsClassMeta from cobra.mit.meta import CounterMeta from cobra.mit.meta import PropMeta from cobra.mit.meta import Category from cobra.mit.meta import SourceRelationMeta from cobra.mit.meta import NamedSourceRelationMeta from cobra.mit.meta import TargetRelationMeta from cobra.mit.meta import DeploymentPathMeta, DeploymentCategory from cobra.model.category import MoCategory, PropCategory, CounterCategory from cobra.mit.mo import Mo # ################################################## class IsisCsnpStats1qtr(Mo): """ Mo doc not defined in techpub!!! """ meta = StatsClassMeta("cobra.model.isis.IsisCsnpStats1qtr", "ISIS CSNP Packets") counter = CounterMeta("fastCsnpPktsRx", CounterCategory.COUNTER, "packets", "Fast CSNP Packets Recevied") counter._propRefs[PropCategory.IMPLICIT_LASTREADING] = "fastCsnpPktsRxLast" counter._propRefs[PropCategory.IMPLICIT_CUMULATIVE] = "fastCsnpPktsRxCum" counter._propRefs[PropCategory.IMPLICIT_PERIODIC] = "fastCsnpPktsRxPer" counter._propRefs[PropCategory.IMPLICIT_MIN] = "fastCsnpPktsRxMin" counter._propRefs[PropCategory.IMPLICIT_MAX] = "fastCsnpPktsRxMax" counter._propRefs[PropCategory.IMPLICIT_AVG] = "fastCsnpPktsRxAvg" counter._propRefs[PropCategory.IMPLICIT_SUSPECT] = "fastCsnpPktsRxSpct" counter._propRefs[PropCategory.IMPLICIT_BASELINE] = "fastCsnpPktsRxBase" counter._propRefs[PropCategory.IMPLICIT_THRESHOLDED] = "fastCsnpPktsRxThr" counter._propRefs[PropCategory.IMPLICIT_TREND_BASE] = "fastCsnpPktsRxTrBase" counter._propRefs[PropCategory.IMPLICIT_TREND] = "fastCsnpPktsRxTr" counter._propRefs[PropCategory.IMPLICIT_RATE] = "fastCsnpPktsRxRate" meta._counters.append(counter) counter = CounterMeta("csnpPktsRx", CounterCategory.COUNTER, "packets", "CSNP Packets Recevied") counter._propRefs[PropCategory.IMPLICIT_LASTREADING] = "csnpPktsRxLast" counter._propRefs[PropCategory.IMPLICIT_CUMULATIVE] = "csnpPktsRxCum" counter._propRefs[PropCategory.IMPLICIT_PERIODIC] = "csnpPktsRxPer" counter._propRefs[PropCategory.IMPLICIT_MIN] = "csnpPktsRxMin" counter._propRefs[PropCategory.IMPLICIT_MAX] = "csnpPktsRxMax" counter._propRefs[PropCategory.IMPLICIT_AVG] = "csnpPktsRxAvg" counter._propRefs[PropCategory.IMPLICIT_SUSPECT] = "csnpPktsRxSpct" counter._propRefs[PropCategory.IMPLICIT_BASELINE] = "csnpPktsRxBase" counter._propRefs[PropCategory.IMPLICIT_THRESHOLDED] = "csnpPktsRxThr" counter._propRefs[PropCategory.IMPLICIT_TREND_BASE] = "csnpPktsRxTrBase" counter._propRefs[PropCategory.IMPLICIT_TREND] = "csnpPktsRxTr" counter._propRefs[PropCategory.IMPLICIT_RATE] = "csnpPktsRxRate" meta._counters.append(counter) counter = CounterMeta("csnpPktsTx", CounterCategory.COUNTER, "packets", "CSNP Packets Sent") counter._propRefs[PropCategory.IMPLICIT_LASTREADING] = "csnpPktsTxLast" counter._propRefs[PropCategory.IMPLICIT_CUMULATIVE] = "csnpPktsTxCum" counter._propRefs[PropCategory.IMPLICIT_PERIODIC] = "csnpPktsTxPer" counter._propRefs[PropCategory.IMPLICIT_MIN] = "csnpPktsTxMin" counter._propRefs[PropCategory.IMPLICIT_MAX] = "csnpPktsTxMax" counter._propRefs[PropCategory.IMPLICIT_AVG] = "csnpPktsTxAvg" counter._propRefs[PropCategory.IMPLICIT_SUSPECT] = "csnpPktsTxSpct" counter._propRefs[PropCategory.IMPLICIT_BASELINE] = "csnpPktsTxBase" counter._propRefs[PropCategory.IMPLICIT_THRESHOLDED] = "csnpPktsTxThr" counter._propRefs[PropCategory.IMPLICIT_TREND_BASE] = "csnpPktsTxTrBase" counter._propRefs[PropCategory.IMPLICIT_TREND] = "csnpPktsTxTr" counter._propRefs[PropCategory.IMPLICIT_RATE] = "csnpPktsTxRate" meta._counters.append(counter) meta.moClassName = "isisIsisCsnpStats1qtr" meta.rnFormat = "CDisisIsisCsnpStats1qtr" meta.category = MoCategory.STATS_CURRENT meta.label = "current ISIS CSNP Packets stats in 1 quarter" meta.writeAccessMask = 0x8008020040001 meta.readAccessMask = 0x8008020040001 meta.isDomainable = False meta.isReadOnly = True meta.isConfigurable = False meta.isDeletable = False meta.isContextRoot = True meta.parentClasses.add("cobra.model.isis.If") meta.superClasses.add("cobra.model.stats.Item") meta.superClasses.add("cobra.model.stats.Curr") meta.superClasses.add("cobra.model.isis.IsisCsnpStats") meta.rnPrefixes = [ ('CDisisIsisCsnpStats1qtr', False), ] prop = PropMeta("str", "childAction", "childAction", 4, PropCategory.CHILD_ACTION) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop._addConstant("deleteAll", "deleteall", 16384) prop._addConstant("deleteNonPresent", "deletenonpresent", 8192) prop._addConstant("ignore", "ignore", 4096) meta.props.add("childAction", prop) prop = PropMeta("str", "cnt", "cnt", 16212, PropCategory.REGULAR) prop.label = "Number of Collections During this Interval" prop.isImplicit = True prop.isAdmin = True meta.props.add("cnt", prop) prop = PropMeta("str", "csnpPktsRxAvg", "csnpPktsRxAvg", 46114, PropCategory.IMPLICIT_AVG) prop.label = "CSNP Packets Recevied average value" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsRxAvg", prop) prop = PropMeta("str", "csnpPktsRxBase", "csnpPktsRxBase", 46109, PropCategory.IMPLICIT_BASELINE) prop.label = "CSNP Packets Recevied baseline" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsRxBase", prop) prop = PropMeta("str", "csnpPktsRxCum", "csnpPktsRxCum", 46110, PropCategory.IMPLICIT_CUMULATIVE) prop.label = "CSNP Packets Recevied cumulative" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsRxCum", prop) prop = PropMeta("str", "csnpPktsRxLast", "csnpPktsRxLast", 46108, PropCategory.IMPLICIT_LASTREADING) prop.label = "CSNP Packets Recevied current value" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsRxLast", prop) prop = PropMeta("str", "csnpPktsRxMax", "csnpPktsRxMax", 46113, PropCategory.IMPLICIT_MAX) prop.label = "CSNP Packets Recevied maximum value" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsRxMax", prop) prop = PropMeta("str", "csnpPktsRxMin", "csnpPktsRxMin", 46112, PropCategory.IMPLICIT_MIN) prop.label = "CSNP Packets Recevied minimum value" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsRxMin", prop) prop = PropMeta("str", "csnpPktsRxPer", "csnpPktsRxPer", 46111, PropCategory.IMPLICIT_PERIODIC) prop.label = "CSNP Packets Recevied periodic" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsRxPer", prop) prop = PropMeta("str", "csnpPktsRxRate", "csnpPktsRxRate", 46119, PropCategory.IMPLICIT_RATE) prop.label = "CSNP Packets Recevied rate" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsRxRate", prop) prop = PropMeta("str", "csnpPktsRxSpct", "csnpPktsRxSpct", 46115, PropCategory.IMPLICIT_SUSPECT) prop.label = "CSNP Packets Recevied suspect count" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsRxSpct", prop) prop = PropMeta("str", "csnpPktsRxThr", "csnpPktsRxThr", 46116, PropCategory.IMPLICIT_THRESHOLDED) prop.label = "CSNP Packets Recevied thresholded flags" prop.isOper = True prop.isStats = True prop.defaultValue = 0 prop.defaultValueStr = "unspecified" prop._addConstant("avgCrit", "avg-severity-critical", 2199023255552) prop._addConstant("avgHigh", "avg-crossed-high-threshold", 68719476736) prop._addConstant("avgLow", "avg-crossed-low-threshold", 137438953472) prop._addConstant("avgMajor", "avg-severity-major", 1099511627776) prop._addConstant("avgMinor", "avg-severity-minor", 549755813888) prop._addConstant("avgRecovering", "avg-recovering", 34359738368) prop._addConstant("avgWarn", "avg-severity-warning", 274877906944) prop._addConstant("cumulativeCrit", "cumulative-severity-critical", 8192) prop._addConstant("cumulativeHigh", "cumulative-crossed-high-threshold", 256) prop._addConstant("cumulativeLow", "cumulative-crossed-low-threshold", 512) prop._addConstant("cumulativeMajor", "cumulative-severity-major", 4096) prop._addConstant("cumulativeMinor", "cumulative-severity-minor", 2048) prop._addConstant("cumulativeRecovering", "cumulative-recovering", 128) prop._addConstant("cumulativeWarn", "cumulative-severity-warning", 1024) prop._addConstant("lastReadingCrit", "lastreading-severity-critical", 64) prop._addConstant("lastReadingHigh", "lastreading-crossed-high-threshold", 2) prop._addConstant("lastReadingLow", "lastreading-crossed-low-threshold", 4) prop._addConstant("lastReadingMajor", "lastreading-severity-major", 32) prop._addConstant("lastReadingMinor", "lastreading-severity-minor", 16) prop._addConstant("lastReadingRecovering", "lastreading-recovering", 1) prop._addConstant("lastReadingWarn", "lastreading-severity-warning", 8) prop._addConstant("maxCrit", "max-severity-critical", 17179869184) prop._addConstant("maxHigh", "max-crossed-high-threshold", 536870912) prop._addConstant("maxLow", "max-crossed-low-threshold", 1073741824) prop._addConstant("maxMajor", "max-severity-major", 8589934592) prop._addConstant("maxMinor", "max-severity-minor", 4294967296) prop._addConstant("maxRecovering", "max-recovering", 268435456) prop._addConstant("maxWarn", "max-severity-warning", 2147483648) prop._addConstant("minCrit", "min-severity-critical", 134217728) prop._addConstant("minHigh", "min-crossed-high-threshold", 4194304) prop._addConstant("minLow", "min-crossed-low-threshold", 8388608) prop._addConstant("minMajor", "min-severity-major", 67108864) prop._addConstant("minMinor", "min-severity-minor", 33554432) prop._addConstant("minRecovering", "min-recovering", 2097152) prop._addConstant("minWarn", "min-severity-warning", 16777216) prop._addConstant("periodicCrit", "periodic-severity-critical", 1048576) prop._addConstant("periodicHigh", "periodic-crossed-high-threshold", 32768) prop._addConstant("periodicLow", "periodic-crossed-low-threshold", 65536) prop._addConstant("periodicMajor", "periodic-severity-major", 524288) prop._addConstant("periodicMinor", "periodic-severity-minor", 262144) prop._addConstant("periodicRecovering", "periodic-recovering", 16384) prop._addConstant("periodicWarn", "periodic-severity-warning", 131072) prop._addConstant("rateCrit", "rate-severity-critical", 36028797018963968) prop._addConstant("rateHigh", "rate-crossed-high-threshold", 1125899906842624) prop._addConstant("rateLow", "rate-crossed-low-threshold", 2251799813685248) prop._addConstant("rateMajor", "rate-severity-major", 18014398509481984) prop._addConstant("rateMinor", "rate-severity-minor", 9007199254740992) prop._addConstant("rateRecovering", "rate-recovering", 562949953421312) prop._addConstant("rateWarn", "rate-severity-warning", 4503599627370496) prop._addConstant("trendCrit", "trend-severity-critical", 281474976710656) prop._addConstant("trendHigh", "trend-crossed-high-threshold", 8796093022208) prop._addConstant("trendLow", "trend-crossed-low-threshold", 17592186044416) prop._addConstant("trendMajor", "trend-severity-major", 140737488355328) prop._addConstant("trendMinor", "trend-severity-minor", 70368744177664) prop._addConstant("trendRecovering", "trend-recovering", 4398046511104) prop._addConstant("trendWarn", "trend-severity-warning", 35184372088832) prop._addConstant("unspecified", None, 0) meta.props.add("csnpPktsRxThr", prop) prop = PropMeta("str", "csnpPktsRxTr", "csnpPktsRxTr", 46118, PropCategory.IMPLICIT_TREND) prop.label = "CSNP Packets Recevied trend" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsRxTr", prop) prop = PropMeta("str", "csnpPktsRxTrBase", "csnpPktsRxTrBase", 46117, PropCategory.IMPLICIT_TREND_BASE) prop.label = "CSNP Packets Recevied trend baseline" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsRxTrBase", prop) prop = PropMeta("str", "csnpPktsTxAvg", "csnpPktsTxAvg", 46135, PropCategory.IMPLICIT_AVG) prop.label = "CSNP Packets Sent average value" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsTxAvg", prop) prop = PropMeta("str", "csnpPktsTxBase", "csnpPktsTxBase", 46130, PropCategory.IMPLICIT_BASELINE) prop.label = "CSNP Packets Sent baseline" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsTxBase", prop) prop = PropMeta("str", "csnpPktsTxCum", "csnpPktsTxCum", 46131, PropCategory.IMPLICIT_CUMULATIVE) prop.label = "CSNP Packets Sent cumulative" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsTxCum", prop) prop = PropMeta("str", "csnpPktsTxLast", "csnpPktsTxLast", 46129, PropCategory.IMPLICIT_LASTREADING) prop.label = "CSNP Packets Sent current value" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsTxLast", prop) prop = PropMeta("str", "csnpPktsTxMax", "csnpPktsTxMax", 46134, PropCategory.IMPLICIT_MAX) prop.label = "CSNP Packets Sent maximum value" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsTxMax", prop) prop = PropMeta("str", "csnpPktsTxMin", "csnpPktsTxMin", 46133, PropCategory.IMPLICIT_MIN) prop.label = "CSNP Packets Sent minimum value" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsTxMin", prop) prop = PropMeta("str", "csnpPktsTxPer", "csnpPktsTxPer", 46132, PropCategory.IMPLICIT_PERIODIC) prop.label = "CSNP Packets Sent periodic" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsTxPer", prop) prop = PropMeta("str", "csnpPktsTxRate", "csnpPktsTxRate", 46140, PropCategory.IMPLICIT_RATE) prop.label = "CSNP Packets Sent rate" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsTxRate", prop) prop = PropMeta("str", "csnpPktsTxSpct", "csnpPktsTxSpct", 46136, PropCategory.IMPLICIT_SUSPECT) prop.label = "CSNP Packets Sent suspect count" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsTxSpct", prop) prop = PropMeta("str", "csnpPktsTxThr", "csnpPktsTxThr", 46137, PropCategory.IMPLICIT_THRESHOLDED) prop.label = "CSNP Packets Sent thresholded flags" prop.isOper = True prop.isStats = True prop.defaultValue = 0 prop.defaultValueStr = "unspecified" prop._addConstant("avgCrit", "avg-severity-critical", 2199023255552) prop._addConstant("avgHigh", "avg-crossed-high-threshold", 68719476736) prop._addConstant("avgLow", "avg-crossed-low-threshold", 137438953472) prop._addConstant("avgMajor", "avg-severity-major", 1099511627776) prop._addConstant("avgMinor", "avg-severity-minor", 549755813888) prop._addConstant("avgRecovering", "avg-recovering", 34359738368) prop._addConstant("avgWarn", "avg-severity-warning", 274877906944) prop._addConstant("cumulativeCrit", "cumulative-severity-critical", 8192) prop._addConstant("cumulativeHigh", "cumulative-crossed-high-threshold", 256) prop._addConstant("cumulativeLow", "cumulative-crossed-low-threshold", 512) prop._addConstant("cumulativeMajor", "cumulative-severity-major", 4096) prop._addConstant("cumulativeMinor", "cumulative-severity-minor", 2048) prop._addConstant("cumulativeRecovering", "cumulative-recovering", 128) prop._addConstant("cumulativeWarn", "cumulative-severity-warning", 1024) prop._addConstant("lastReadingCrit", "lastreading-severity-critical", 64) prop._addConstant("lastReadingHigh", "lastreading-crossed-high-threshold", 2) prop._addConstant("lastReadingLow", "lastreading-crossed-low-threshold", 4) prop._addConstant("lastReadingMajor", "lastreading-severity-major", 32) prop._addConstant("lastReadingMinor", "lastreading-severity-minor", 16) prop._addConstant("lastReadingRecovering", "lastreading-recovering", 1) prop._addConstant("lastReadingWarn", "lastreading-severity-warning", 8) prop._addConstant("maxCrit", "max-severity-critical", 17179869184) prop._addConstant("maxHigh", "max-crossed-high-threshold", 536870912) prop._addConstant("maxLow", "max-crossed-low-threshold", 1073741824) prop._addConstant("maxMajor", "max-severity-major", 8589934592) prop._addConstant("maxMinor", "max-severity-minor", 4294967296) prop._addConstant("maxRecovering", "max-recovering", 268435456) prop._addConstant("maxWarn", "max-severity-warning", 2147483648) prop._addConstant("minCrit", "min-severity-critical", 134217728) prop._addConstant("minHigh", "min-crossed-high-threshold", 4194304) prop._addConstant("minLow", "min-crossed-low-threshold", 8388608) prop._addConstant("minMajor", "min-severity-major", 67108864) prop._addConstant("minMinor", "min-severity-minor", 33554432) prop._addConstant("minRecovering", "min-recovering", 2097152) prop._addConstant("minWarn", "min-severity-warning", 16777216) prop._addConstant("periodicCrit", "periodic-severity-critical", 1048576) prop._addConstant("periodicHigh", "periodic-crossed-high-threshold", 32768) prop._addConstant("periodicLow", "periodic-crossed-low-threshold", 65536) prop._addConstant("periodicMajor", "periodic-severity-major", 524288) prop._addConstant("periodicMinor", "periodic-severity-minor", 262144) prop._addConstant("periodicRecovering", "periodic-recovering", 16384) prop._addConstant("periodicWarn", "periodic-severity-warning", 131072) prop._addConstant("rateCrit", "rate-severity-critical", 36028797018963968) prop._addConstant("rateHigh", "rate-crossed-high-threshold", 1125899906842624) prop._addConstant("rateLow", "rate-crossed-low-threshold", 2251799813685248) prop._addConstant("rateMajor", "rate-severity-major", 18014398509481984) prop._addConstant("rateMinor", "rate-severity-minor", 9007199254740992) prop._addConstant("rateRecovering", "rate-recovering", 562949953421312) prop._addConstant("rateWarn", "rate-severity-warning", 4503599627370496) prop._addConstant("trendCrit", "trend-severity-critical", 281474976710656) prop._addConstant("trendHigh", "trend-crossed-high-threshold", 8796093022208) prop._addConstant("trendLow", "trend-crossed-low-threshold", 17592186044416) prop._addConstant("trendMajor", "trend-severity-major", 140737488355328) prop._addConstant("trendMinor", "trend-severity-minor", 70368744177664) prop._addConstant("trendRecovering", "trend-recovering", 4398046511104) prop._addConstant("trendWarn", "trend-severity-warning", 35184372088832) prop._addConstant("unspecified", None, 0) meta.props.add("csnpPktsTxThr", prop) prop = PropMeta("str", "csnpPktsTxTr", "csnpPktsTxTr", 46139, PropCategory.IMPLICIT_TREND) prop.label = "CSNP Packets Sent trend" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsTxTr", prop) prop = PropMeta("str", "csnpPktsTxTrBase", "csnpPktsTxTrBase", 46138, PropCategory.IMPLICIT_TREND_BASE) prop.label = "CSNP Packets Sent trend baseline" prop.isOper = True prop.isStats = True meta.props.add("csnpPktsTxTrBase", prop) prop = PropMeta("str", "dn", "dn", 1, PropCategory.DN) prop.label = "None" prop.isDn = True prop.isImplicit = True prop.isAdmin = True prop.isCreateOnly = True meta.props.add("dn", prop) prop = PropMeta("str", "fastCsnpPktsRxAvg", "fastCsnpPktsRxAvg", 46156, PropCategory.IMPLICIT_AVG) prop.label = "Fast CSNP Packets Recevied average value" prop.isOper = True prop.isStats = True meta.props.add("fastCsnpPktsRxAvg", prop) prop = PropMeta("str", "fastCsnpPktsRxBase", "fastCsnpPktsRxBase", 46151, PropCategory.IMPLICIT_BASELINE) prop.label = "Fast CSNP Packets Recevied baseline" prop.isOper = True prop.isStats = True meta.props.add("fastCsnpPktsRxBase", prop) prop = PropMeta("str", "fastCsnpPktsRxCum", "fastCsnpPktsRxCum", 46152, PropCategory.IMPLICIT_CUMULATIVE) prop.label = "Fast CSNP Packets Recevied cumulative" prop.isOper = True prop.isStats = True meta.props.add("fastCsnpPktsRxCum", prop) prop = PropMeta("str", "fastCsnpPktsRxLast", "fastCsnpPktsRxLast", 46150, PropCategory.IMPLICIT_LASTREADING) prop.label = "Fast CSNP Packets Recevied current value" prop.isOper = True prop.isStats = True meta.props.add("fastCsnpPktsRxLast", prop) prop = PropMeta("str", "fastCsnpPktsRxMax", "fastCsnpPktsRxMax", 46155, PropCategory.IMPLICIT_MAX) prop.label = "Fast CSNP Packets Recevied maximum value" prop.isOper = True prop.isStats = True meta.props.add("fastCsnpPktsRxMax", prop) prop = PropMeta("str", "fastCsnpPktsRxMin", "fastCsnpPktsRxMin", 46154, PropCategory.IMPLICIT_MIN) prop.label = "Fast CSNP Packets Recevied minimum value" prop.isOper = True prop.isStats = True meta.props.add("fastCsnpPktsRxMin", prop) prop = PropMeta("str", "fastCsnpPktsRxPer", "fastCsnpPktsRxPer", 46153, PropCategory.IMPLICIT_PERIODIC) prop.label = "Fast CSNP Packets Recevied periodic" prop.isOper = True prop.isStats = True meta.props.add("fastCsnpPktsRxPer", prop) prop = PropMeta("str", "fastCsnpPktsRxRate", "fastCsnpPktsRxRate", 46161, PropCategory.IMPLICIT_RATE) prop.label = "Fast CSNP Packets Recevied rate" prop.isOper = True prop.isStats = True meta.props.add("fastCsnpPktsRxRate", prop) prop = PropMeta("str", "fastCsnpPktsRxSpct", "fastCsnpPktsRxSpct", 46157, PropCategory.IMPLICIT_SUSPECT) prop.label = "Fast CSNP Packets Recevied suspect count" prop.isOper = True prop.isStats = True meta.props.add("fastCsnpPktsRxSpct", prop) prop = PropMeta("str", "fastCsnpPktsRxThr", "fastCsnpPktsRxThr", 46158, PropCategory.IMPLICIT_THRESHOLDED) prop.label = "Fast CSNP Packets Recevied thresholded flags" prop.isOper = True prop.isStats = True prop.defaultValue = 0 prop.defaultValueStr = "unspecified" prop._addConstant("avgCrit", "avg-severity-critical", 2199023255552) prop._addConstant("avgHigh", "avg-crossed-high-threshold", 68719476736) prop._addConstant("avgLow", "avg-crossed-low-threshold", 137438953472) prop._addConstant("avgMajor", "avg-severity-major", 1099511627776) prop._addConstant("avgMinor", "avg-severity-minor", 549755813888) prop._addConstant("avgRecovering", "avg-recovering", 34359738368) prop._addConstant("avgWarn", "avg-severity-warning", 274877906944) prop._addConstant("cumulativeCrit", "cumulative-severity-critical", 8192) prop._addConstant("cumulativeHigh", "cumulative-crossed-high-threshold", 256) prop._addConstant("cumulativeLow", "cumulative-crossed-low-threshold", 512) prop._addConstant("cumulativeMajor", "cumulative-severity-major", 4096) prop._addConstant("cumulativeMinor", "cumulative-severity-minor", 2048) prop._addConstant("cumulativeRecovering", "cumulative-recovering", 128) prop._addConstant("cumulativeWarn", "cumulative-severity-warning", 1024) prop._addConstant("lastReadingCrit", "lastreading-severity-critical", 64) prop._addConstant("lastReadingHigh", "lastreading-crossed-high-threshold", 2) prop._addConstant("lastReadingLow", "lastreading-crossed-low-threshold", 4) prop._addConstant("lastReadingMajor", "lastreading-severity-major", 32) prop._addConstant("lastReadingMinor", "lastreading-severity-minor", 16) prop._addConstant("lastReadingRecovering", "lastreading-recovering", 1) prop._addConstant("lastReadingWarn", "lastreading-severity-warning", 8) prop._addConstant("maxCrit", "max-severity-critical", 17179869184) prop._addConstant("maxHigh", "max-crossed-high-threshold", 536870912) prop._addConstant("maxLow", "max-crossed-low-threshold", 1073741824) prop._addConstant("maxMajor", "max-severity-major", 8589934592) prop._addConstant("maxMinor", "max-severity-minor", 4294967296) prop._addConstant("maxRecovering", "max-recovering", 268435456) prop._addConstant("maxWarn", "max-severity-warning", 2147483648) prop._addConstant("minCrit", "min-severity-critical", 134217728) prop._addConstant("minHigh", "min-crossed-high-threshold", 4194304) prop._addConstant("minLow", "min-crossed-low-threshold", 8388608) prop._addConstant("minMajor", "min-severity-major", 67108864) prop._addConstant("minMinor", "min-severity-minor", 33554432) prop._addConstant("minRecovering", "min-recovering", 2097152) prop._addConstant("minWarn", "min-severity-warning", 16777216) prop._addConstant("periodicCrit", "periodic-severity-critical", 1048576) prop._addConstant("periodicHigh", "periodic-crossed-high-threshold", 32768) prop._addConstant("periodicLow", "periodic-crossed-low-threshold", 65536) prop._addConstant("periodicMajor", "periodic-severity-major", 524288) prop._addConstant("periodicMinor", "periodic-severity-minor", 262144) prop._addConstant("periodicRecovering", "periodic-recovering", 16384) prop._addConstant("periodicWarn", "periodic-severity-warning", 131072) prop._addConstant("rateCrit", "rate-severity-critical", 36028797018963968) prop._addConstant("rateHigh", "rate-crossed-high-threshold", 1125899906842624) prop._addConstant("rateLow", "rate-crossed-low-threshold", 2251799813685248) prop._addConstant("rateMajor", "rate-severity-major", 18014398509481984) prop._addConstant("rateMinor", "rate-severity-minor", 9007199254740992) prop._addConstant("rateRecovering", "rate-recovering", 562949953421312) prop._addConstant("rateWarn", "rate-severity-warning", 4503599627370496) prop._addConstant("trendCrit", "trend-severity-critical", 281474976710656) prop._addConstant("trendHigh", "trend-crossed-high-threshold", 8796093022208) prop._addConstant("trendLow", "trend-crossed-low-threshold", 17592186044416) prop._addConstant("trendMajor", "trend-severity-major", 140737488355328) prop._addConstant("trendMinor", "trend-severity-minor", 70368744177664) prop._addConstant("trendRecovering", "trend-recovering", 4398046511104) prop._addConstant("trendWarn", "trend-severity-warning", 35184372088832) prop._addConstant("unspecified", None, 0) meta.props.add("fastCsnpPktsRxThr", prop) prop = PropMeta("str", "fastCsnpPktsRxTr", "fastCsnpPktsRxTr", 46160, PropCategory.IMPLICIT_TREND) prop.label = "Fast CSNP Packets Recevied trend" prop.isOper = True prop.isStats = True meta.props.add("fastCsnpPktsRxTr", prop) prop = PropMeta("str", "fastCsnpPktsRxTrBase", "fastCsnpPktsRxTrBase", 46159, PropCategory.IMPLICIT_TREND_BASE) prop.label = "Fast CSNP Packets Recevied trend baseline" prop.isOper = True prop.isStats = True meta.props.add("fastCsnpPktsRxTrBase", prop) prop = PropMeta("str", "lastCollOffset", "lastCollOffset", 111, PropCategory.REGULAR) prop.label = "Collection Length" prop.isImplicit = True prop.isAdmin = True meta.props.add("lastCollOffset", prop) prop = PropMeta("str", "modTs", "modTs", 7, PropCategory.REGULAR) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop.defaultValue = 0 prop.defaultValueStr = "never" prop._addConstant("never", "never", 0) meta.props.add("modTs", prop) prop = PropMeta("str", "repIntvEnd", "repIntvEnd", 110, PropCategory.REGULAR) prop.label = "Reporting End Time" prop.isImplicit = True prop.isAdmin = True meta.props.add("repIntvEnd", prop) prop = PropMeta("str", "repIntvStart", "repIntvStart", 109, PropCategory.REGULAR) prop.label = "Reporting Start Time" prop.isImplicit = True prop.isAdmin = True meta.props.add("repIntvStart", prop) prop = PropMeta("str", "rn", "rn", 2, PropCategory.RN) prop.label = "None" prop.isRn = True prop.isImplicit = True prop.isAdmin = True prop.isCreateOnly = True meta.props.add("rn", prop) prop = PropMeta("str", "status", "status", 3, PropCategory.STATUS) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop._addConstant("created", "created", 2) prop._addConstant("deleted", "deleted", 8) prop._addConstant("modified", "modified", 4) meta.props.add("status", prop) def __init__(self, parentMoOrDn, markDirty=True, **creationProps): namingVals = [] Mo.__init__(self, parentMoOrDn, markDirty, *namingVals, **creationProps) # End of package file # ##################################################
[ "rrishike@cisco.com" ]
rrishike@cisco.com
5994a372ecac4751d01b2d63d8cfe85ccff050c0
e9331ebf2d83184924ade1c9e4849c3ba0285bf2
/mutiple-devices/vgg16/8devices/node.py
8e3356581b54e55c63cec2cc97e4e45d94968fb4
[ "Apache-2.0" ]
permissive
parallel-ml/asplos2018-workshop
fcf7c13ff6723ac91f156377e27fc5acaf85367a
a05a63c17f43926d6411ada625b191db1abbee67
refs/heads/master
2022-12-02T02:12:34.450130
2022-11-29T16:06:36
2022-11-29T16:06:36
120,949,505
15
4
Apache-2.0
2022-11-29T16:06:37
2018-02-09T19:58:11
Python
UTF-8
Python
false
false
11,323
py
""" This module shows the node for 8 nodes distributed system setup. """ import argparse import os import time from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer from SocketServer import ThreadingMixIn from collections import deque from multiprocessing import Queue from threading import Thread, Lock import avro.ipc as ipc import avro.protocol as protocol import avro.schema as schema import numpy as np import tensorflow as tf import yaml import model as ml os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # read data packet format. PROTOCOL = protocol.parse(open('resource/image.avpr').read()) class Node(object): """ Singleton Node class. It will store data if necessary, record next layer response time, send data packet to next layer and store the loaded model in memory without reloading. Attributes: ip: A dictionary contains Queue of ip addresses for different models type. model: Loaded models associated to a node. graph: Default graph used by Tensorflow. debug: Flag for debugging. lock: Threading lock for safe usage of this class. The lock is used for safe models forwarding. If the models is processing input and it gets request from other devices, the new request will wait until the previous models forwarding finishes. name: Model name. total: Total time of getting frames. count: Total number of frames gets back. input: Store the input for last fully connected layer, it acts as a buffer that it will kick out extra data and store unused data. """ instance = None def __init__(self): self.ip = dict() self.model = None self.graph = tf.get_default_graph() self.debug = False self.lock = Lock() self.name = 'unknown' self.total = 0 self.count = 1 self.input = deque() def log(self, step, data=''): """ Log function for debug. Turn the flag on to show each step result. Args: step: Each step names. data: Data format or size. """ if self.debug: print '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++' for k in range(0, len(step), 68): print '+{:^68.68}+'.format(step[k:k + 68]) for k in range(0, len(data), 68): print '+{:^68.68}+'.format(data[k:k + 68]) print '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++' print def acquire_lock(self): self.lock.acquire() def release_lock(self): self.lock.release() def timer(self, interval): self.total += interval print '{:s}: {:.3f}'.format(self.name, self.total / self.count) self.count += 1 @classmethod def create(cls): if cls.instance is None: cls.instance = cls() return cls.instance class Responder(ipc.Responder): """ Responder called by handler when got request. """ def __init__(self): ipc.Responder.__init__(self, PROTOCOL) def invoke(self, msg, req): """ This functino is invoked by do_POST to handle the request. Invoke handles the request and get response for the request. This is the key of each node. All models forwarding and output redirect are done here. Because the invoke method of initializer only needs to receive the data packet, it does not do anything in the function and return None. Because this is a node class, it has all necessary code here for handling different inputs. Basically the logic is load model as the previous layer request and run model inference. And it will send the current layer output to next layer. We write different model's code all here for the sake of convenience. In order to avoid long waiting time of model reloading, we make sure each node is assigned to a unique job each time, so it does not need to reload the model. Args: msg: Meta data. req: Contains data packet. Returns: None: It just acts as confirmation for sender. Raises: AvroException: if the data does not have correct syntac defined in Schema """ node = Node.create() node.acquire_lock() if msg.name == 'forward': try: with node.graph.as_default(): bytestr = req['input'] if req['next'] == 'block1': node.log('block1 gets data') X = np.fromstring(bytestr, np.uint8).reshape(224, 224, 3) node.model = ml.block1() if node.model is None else node.model output = node.model.predict(np.array([X])) node.log('finish block1 forward') Thread(target=self.send, args=(output, 'block234', req['tag'])).start() elif req['next'] == 'block234': node.log('block234 gets data') X = np.fromstring(bytestr, np.float32).reshape(112, 112, 64) node.model = ml.block234() if node.model is None else node.model output = node.model.predict(np.array([X])) node.log('finish block234 forward') Thread(target=self.send, args=(output, 'block5', req['tag'])).start() elif req['next'] == 'block5': node.log('block5 gets data') X = np.fromstring(bytestr, np.float32).reshape(14, 14, 512) node.model = ml.block5() if node.model is None else node.model output = node.model.predict(np.array([X])) node.log('finish block5 forward') for _ in range(2): Thread(target=self.send, args=(output, 'fc1', req['tag'])).start() elif req['next'] == 'fc1': node.log('fc1 gets data') X = np.fromstring(bytestr, np.float32).reshape(25088) node.model = ml.fc1() if node.model is None else node.model output = node.model.predict(np.array([X])) node.log('finish block6 forward') Thread(target=self.send, args=(output, 'fc2', req['tag'])).start() elif req['next'] == 'fc2': node.log('fc2 gets data') X = np.fromstring(bytestr, np.float32).reshape(2048) node.input.append(X) node.log('input size', str(len(node.input))) # if the size is not enough, store in the queue and return. if len(node.input) < 2: node.release_lock() return # too many data packets, then drop some data. while len(node.input) > 2: node.input.popleft() X = np.concatenate(node.input) node.model = ml.fc2() if node.model is None else node.model output = node.model.predict(np.array([X])) node.log('finish model inference') Thread(target=self.send, args=(output, 'initial', req['tag'])).start() node.release_lock() return except Exception, e: node.log('Error', e.message) else: raise schema.AvroException('unexpected message:', msg.getname()) def send(self, X, name, tag): """ Send data to other devices. The data packet contains data and models name. Ip address of next device pop from Queue of a ip list. Args: X: numpy array name: next device models name tag: mark the current layer label """ node = Node.create() queue = node.ip[name] address = queue.get() # initializer use port 9999 to receive data port = 9999 if name == 'initial' else 12345 client = ipc.HTTPTransceiver(address, port) requestor = ipc.Requestor(PROTOCOL, client) node.name = name data = dict() data['input'] = X.tostring() data['next'] = name data['tag'] = tag node.log('finish assembly') start = time.time() requestor.request('forward', data) end = time.time() node.timer(end - start) node.log('node gets request back') client.close() queue.put(address) class Handler(BaseHTTPRequestHandler): def do_POST(self): """ do_POST is automatically called by ThreadedHTTPServer. It creates a new responder for each request. The responder generates response and write response to data sent back. """ self.responder = Responder() call_request_reader = ipc.FramedReader(self.rfile) call_request = call_request_reader.read_framed_message() resp_body = self.responder.respond(call_request) self.send_response(200) self.send_header('Content-Type', 'avro/binary') self.end_headers() resp_writer = ipc.FramedWriter(self.wfile) resp_writer.write_framed_message(resp_body) class ThreadedHTTPServer(ThreadingMixIn, HTTPServer): """ Handle requests in separate thread. """ def main(cmd): node = Node.create() node.debug = cmd.debug # read ip resources from config file with open('resource/ip') as file: address = yaml.safe_load(file) node.ip['block234'] = Queue() node.ip['block5'] = Queue() node.ip['fc1'] = Queue() node.ip['fc2'] = Queue() node.ip['initial'] = Queue() address = address['node'] for addr in address['block234']: if addr == '#': break node.ip['block234'].put(addr) for addr in address['block5']: if addr == '#': break node.ip['block5'].put(addr) for addr in address['fc1']: if addr == '#': break node.ip['fc1'].put(addr) for addr in address['fc2']: if addr == '#': break node.ip['fc2'].put(addr) for addr in address['initial']: if addr == '#': break node.ip['initial'].put(addr) server = ThreadedHTTPServer(('0.0.0.0', 12345), Handler) server.allow_reuse_address = True server.serve_forever() if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-d', '--debug', action='store_true', default=False, help='set to debug mode') cmd = parser.parse_args() main(cmd)
[ "caojiashen24@gmail.com" ]
caojiashen24@gmail.com
9c86ad961ad9aa88f17595d10f0dc2850ba5c90f
92e3652a7c0bc754a584a2fcde34d9b2f5c486b5
/py3file/downloadMp3.py
988de0e499a71d418391fda0ba24636aebe36baf
[]
no_license
scottph/phlib
57146be6ed7e22905e903034566747756584b042
537d15518cc342e476cc94a6190d78cdef2aa55b
refs/heads/master
2016-09-06T18:42:43.621587
2012-01-16T08:53:52
2012-01-16T08:53:52
3,189,120
0
0
null
null
null
null
UTF-8
Python
false
false
2,456
py
用法: 先将要下载的歌曲写在一个文本文件中，如song.txt,每首歌占一行. 然后运行： python getmp3.py song.txt 下载目录为当前目录下的"Music_" 代码: [ 下载 ] [ 隐藏 ] [ 选择 ] [ 收缩 ] 代码: [ 下载 ] [ 显示 ] 使用 python 语法高亮 #!/usr/bin/env python #-*- coding: utf-8 -*- import sys import os import re import urllib2 cwd = os.getcwd() lc = sys.getfilesystemencoding() downdir = cwd+os.sep+'Music_' print '\n下载目录为：',downdir,'\n' if not os.path.isdir(downdir): print '下载目录不存在，正在创建目录：',downdir os.mkdir(downdir) if os.path.isfile(sys.argv[1]): list_file = sys.argv[1] else: list_file = cwd + os.sep + sys.argv[1] try: f = file(list_file,'r') except IOError: print '歌典列表打开失败，请检查文件是否存在!' sys.exit() for eachLine in f: song = eachLine.strip() if not song: continue mp3file = downdir + os.sep + song + '.mp3' if os.path.isfile(mp3file): print '%s.mp3已经存在，转到下一首\n' % song continue url="http://box.zhangmen.baidu.com/x?op=12&count=1&title=%s$$" % urllib2.quote(song.decode(lc).encode('gbk')) xmlf = urllib2.urlopen(url) txt = xmlf.read() rex1 = u'(<encode>)(http://.+?/.+?\..+?)(</encode>)' rex2 = u'(<decode>)(.+?\..+?)(</decode>)' l1 = re.findall(rex1,txt) l2 = re.findall(rex2,txt) url_list = [] for i in range(len(l1)): temp_list = re.split('/',l1[i][1]) temp_list.pop() temp_list.append(l2[i][1]) down_url = '/'.join(temp_list) url_list.append(down_url) for i in range(len(url_list)): extname = url_list[i].split('.')[-1] #跳过非MP3的类型 if extname.upper() == 'MP3': print '\n正在下载歌曲：%s...\n' % song cmd = 'wget %s -c -t 3 -O "%s"' % (url_list[i],downdir+os.sep+song+'.mp3') os.system(cmd) #multGet.MyHttpGet(url_list[i],connections = 10) if os.path.getsize(mp3file) < 500000L: #小于500K的文件将被删除，并重新下载 print '\n文件过小，可能是目标网站有限制，将尝试下一个链接\n' os.remove(mp3file) else: print '《%s》下载完毕！' % song break print '全部下载完毕！'
[ "scottph@163.com" ]
scottph@163.com
c3fbe89b0c3818f26b5ad9deb4d5461a71acc106
97e306f5f0d4f87f762884522e1bb2a98b114ed6
/leetcode/1004/solution.py
daf59bdb6fea4cf0ba8ab14b7d8d49e5898cf58a
[]
no_license
yibei8811/algorithm
a81bd73c361a3ffc0c7c4f196e44fa6bda5d1d20
6b74260f81d67fc58f9e656afbe4eb7363c4901b
refs/heads/master
2023-04-19T10:28:00.114018
2021-05-01T12:11:27
2021-05-01T12:11:27
323,281,538
2
0
null
null
null
null
UTF-8
Python
false
false
1,425
py
from typing import List class Solution: # 如果 K = 3，题目可以转换为求每5个0中间的长度 def longestOnes(self, A: List[int], K: int) -> int: # if sum(A) + K >= len(A): # return len(A) A.insert(0,0) A.append(0) n = len(A) k = K p = 0 q = 1 result = 0 list0_index = [] for i in range(1, n): if A[i] == 0: list0_index.append(i) if k == 0: q = i result = max(result, q - p - 1) p = list0_index.pop(0) else: k -= 1 return max(result, i - p - 1) # right - left 路径上0的个数 > k 的时候 right++, left++ # right - left 路径上0的个数 <= k right++，left不变 # right++ 窗口才能变大, 则需要A[left]是0的时候， count-- def longestOnes2(self, A: List[int], K: int) -> int: left,right,count = 0,0,0 for right in range(len(A)): if A[right] == 0: count += 1 if count > K: if A[left] == 0: count -= 1 left += 1 return right - left + 1 print(Solution().longestOnes2([1,1,1,0,0,0,1,1,1,1,0],2)) print(Solution().longestOnes2([0,0,1,1,0,0,1,1,1,0,1,1,0,0,0,1,1,1,1],3)) print(Solution().longestOnes2([0,0,0,1],4))
[ "yibei8811@gmail.com" ]
yibei8811@gmail.com
1ecafdd4fbbaadd0e9d02fa7e8c3c083d19fa32e
31a39d7cd26856caaccead96d0fa9c7acd469792
/python/Tree/test.py
73dc011f02df5a8a453038131d4ae11f73806294
[]
no_license
lmy269/practices
b19fffb9f2aeae76c8941145353f0fd4f848835b
29dc5706ffc146c6c87b195fe94d60ae19e17297
refs/heads/master
2022-11-07T05:36:38.227660
2020-06-29T00:48:55
2020-06-29T00:48:55
271,398,204
0
0
null
null
null
null
UTF-8
Python
false
false
1,510
py
from typing import List import collections from collections import deque import unittest class Solution(unittest.TestCase): def getFolderNames(self, names: List[str]) -> List[str]: cache = collections.defaultdict(int) cacheDeque = collections.defaultdict(deque) output = [] for value in names: if cache[value] > 0: while cacheDeque[value] and cacheDeque[value][0] <= cache[value] + 1: na = cacheDeque[value].popleft() if na == cache[value] + 1: cache[value] += 1 output.append(value + f'({cache[value]})') cache[value] += 1 else: cache[value] += 1 output.append(value) if value.endswith(')'): values = value.split('(') if len(values) > 1: suffixNum = values[-1][0:-1] try: index = int(suffixNum) realvalue = value[0:-(len(values[-1])+1)] cacheDeque[realvalue].append(index) except ValueError: continue return output def test_case1(self): self.assertEqual(self.getFolderNames(["kaido","kaido(1)","kaido","kaido(1)"]), ["kaido","kaido(1)","kaido(2)","kaido(1)(1)"]) if __name__ == "__main__": unittest.main()
[ "miliu@microsoft.com" ]
miliu@microsoft.com
2c402ba9801ca89d32a15c68cd22e69faf89db6c
ae69ca518ba7069268be44e84d48405bf369820b
/py/tests/swagger_client/models/tx.py
5e59671f321ee5ba3703478f22bea6bc99271d8a
[ "ISC" ]
permissive
kxzy1990/epoch
3c92920ac0986c5096a4867e356b6d508084ae57
d40f331de8ef937872a7cc296ae72e5c0d0f98fe
refs/heads/master
2021-09-05T22:13:21.195641
2018-01-30T20:58:47
2018-01-30T20:58:47
null
0
0
null
null
null
null
UTF-8
Python
false
false
2,794
py
# coding: utf-8 """ Aeternity Epoch This is the [Aeternity](https://www.aeternity.com/) Epoch API. OpenAPI spec version: 1.0.0 Contact: apiteam@aeternity.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems import re class Tx(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'tx': 'str' } attribute_map = { 'tx': 'tx' } def __init__(self, tx=None): """ Tx - a model defined in Swagger """ self._tx = None if tx is not None: self.tx = tx @property def tx(self): """ Gets the tx of this Tx. :return: The tx of this Tx. :rtype: str """ return self._tx @tx.setter def tx(self, tx): """ Sets the tx of this Tx. :param tx: The tx of this Tx. :type: str """ self._tx = tx def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ if not isinstance(other, Tx): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other
[ "dimitar.p.ivanov@gmail.com" ]
dimitar.p.ivanov@gmail.com
b7b8bb8d391d2bf9c02e26c1ae9e2ca3a0ce590c
62ee285eb838551a92a9a3a3d6c39892a4eeba8b
/user.py
10686f1e0efce5a46a80870eb896d9fb0660fd6a
[]
no_license
masaki24k/masakiogawa
f1967e6639ef9ca60c704398617588c23ab2245b
39e64439eb8a5bce1b389f7310c0857c55193414
refs/heads/main
2023-03-26T17:56:47.797603
2021-03-29T13:48:20
2021-03-29T13:48:20
334,306,358
0
0
null
null
null
null
UTF-8
Python
false
false
188
py
class User(object): def __init__(self, id, username, email, password): self.id = id self.username = username self.email = email self.password = password
[ "noreply@github.com" ]
masaki24k.noreply@github.com
d2e49530b1207762fac4e9a833f1827195ccf153
7830d2bbd370202844b60238dd0a2d08105f9e19
/koch.py
dfe5cb8c1d0cc4d96547bd5a365a7cb4baf50a7e
[]
no_license
bchangip/DAE-Backend
cc4abf1ce253f46eab502c4870299ffec217c942
44fc7ba4901d5ec9522d9d550b0321f90837b6bc
refs/heads/master
2020-03-30T22:40:48.948142
2018-10-24T20:20:00
2018-10-24T20:20:00
151,674,837
0
0
null
null
null
null
UTF-8
Python
false
false
13,557
py
import time import multiprocessing import requests import os import xlrd import csv import sys import mysql.connector import pandas as pd import numpy as np import json import emotiv_dnn as dnn import sklearn from sklearn.model_selection import train_test_split from sklearn import preprocessing, neighbors, svm def getSensorArray(person, sensor): with open(person+'.csv') as csvfile: dataInput = csv.reader(csvfile, delimiter=' ', quotechar='|') print('Reading CSV', dataInput) array = [] count_array = 0 current_sec = 0 current_count = 0 current_sum = 0 for row in dataInput: line = ''.join(row) splitData = line.split('\t') #print 'Second of Data ' + str(splitData[0]) #Using sensors: AF3, AF4, F3 and F4 for i in range(1, len(splitData)-1): splitDots= splitData[i].split(":") splitCommas = splitDots[1].split(',') if splitDots[0] == sensor : if current_sec == int(splitData[0]) : current_sum = current_sum + int(splitCommas[1]) current_count += 1 else: average = current_sum / current_count current_sec = int(splitData[0]) current_sum = int(splitCommas[1]) current_count= 0 array.append(average) #af3 = af3 + str(count_af3) + ',' + str(average) + '\n' count_array = count_array+1 return array def getSex(sexo): if sexo == 'Masculino': return 0 else: return 1 def getDemographic( edad, pebl, dsmt, hare, ciep, cief, ciec, ciem, ciex, cies, cie ): demographics = [] demographics.append(edad) demographics.append(pebl) demographics.append(dsmt) demographics.append(hare) demographics.append(ciep) demographics.append(cief) demographics.append(ciec) demographics.append(ciem) demographics.append(ciex) demographics.append(cies) demographics.append(cie) return demographics def insert_BD ( second, sexo, edad, pebl, dsmt, hare, ciep, cief, ciec, ciem, ciex, cies, cie ): con = mysql.connector.connect( host="localhost", user="root", passwd="admin", database="megaproyecto" ) cur = con.cursor() person_count = 1 q_count = 1 var_count=1 meassure_count = 1 cur.execute('DELETE FROM `megaproyecto`.`medicion`;') con.commit() cur.execute('DELETE FROM `megaproyecto`.`pregunta`;') con.commit() cur.execute('DELETE FROM `megaproyecto`.`sensor`;') con.commit() cur.execute('DELETE FROM `megaproyecto`.`variable`;') con.commit() cur.execute('DELETE FROM `megaproyecto`.`variables`;') con.commit() cur.execute('DELETE FROM `megaproyecto`.`persona`;') con.commit() cur.execute('DELETE FROM `megaproyecto`.`campo`;') con.commit() cur.execute('INSERT INTO `megaproyecto`.`campo` VALUES (%s,%s);', (1, 'Sexo')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`campo` VALUES (%s,%s);', (2, 'Edad')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`campo` VALUES (%s,%s);', (3, 'Pebl')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`campo` VALUES (%s,%s);', (4, 'Dsmt')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`campo` VALUES (%s,%s);', (5, 'Hare')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`campo` VALUES (%s,%s);', (6, 'Ciep')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`campo` VALUES (%s,%s);', (7, 'Cief')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`campo` VALUES (%s,%s);', (8, 'Ciec')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`campo` VALUES (%s,%s);', (9, 'Ciem')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`campo` VALUES (%s,%s);', (10, 'Ciex')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`campo` VALUES (%s,%s);', (11, 'Cies')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`campo` VALUES (%s,%s);', (12, 'Cie')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`sensor` (`id`, `sensor`) VALUES (%s,%s);', (1, 'AF3')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`sensor` (`id`, `sensor`) VALUES (%s,%s);', (2, 'F3')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`sensor` (`id`, `sensor`) VALUES (%s,%s);', (3, 'AF4')) con.commit() cur.execute('INSERT INTO `megaproyecto`.`sensor` (`id`, `sensor`) VALUES (%s,%s);', (4, 'F4')) con.commit() person = 'person' af3 = getSensorArray(person, 'AF3') print('something') f3 = getSensorArray(person, 'F3') af4 = getSensorArray(person, 'AF4') f4 = getSensorArray(person, 'F4') if af3 != 0: person_count += 1 cur.execute('INSERT INTO `megaproyecto`.`persona` (`idpersona`,`codigo`) VALUES (%s, %s);', (int(person_count), str(person))) con.commit() cur.execute('INSERT INTO `megaproyecto`.`variables` (`id`,`id_persona`) VALUES (%s,%s);', (person_count-1, person_count)) con.commit() sex = getSex(sexo) if sex != 2: cur.execute('INSERT INTO `megaproyecto`.`variable` (`id`, `id_variable`, `id_campo`, `valor`, `variablecol`) VALUES (%s, %s , 1, %s,1);', (var_count, person_count-1,sex)) con.commit() var_count += 1 demographics = getDemographic(edad, pebl, dsmt, hare, ciep, cief, ciec, ciem, ciex, cies, cie) if demographics != 0: dem_count = 1 for demo in demographics: print(demo) cur.execute('INSERT INTO `megaproyecto`.`variable` (`id`, `id_variable`, `id_campo`, `valor`, `variablecol`) VALUES (%s, %s , %s, %s,1);', (var_count, person_count-1, 1+dem_count ,demo)) con.commit() var_count += 1 dem_count += 1 verdad = True contador = 1 local_q_count = 1 if verdad: cur.execute('INSERT INTO `megaproyecto`.`pregunta` (`id`, `id_persona`,`numero`,`veracidad`) VALUES (%s, %s,%s,%s);', (q_count, int(person_count), local_q_count, str(verdad))) con.commit() else: cur.execute('INSERT INTO `megaproyecto`.`pregunta` (`id`, `id_persona`,`numero`,`veracidad`) VALUES (%s, %s,%s,%s);', (q_count, int(person_count), local_q_count, str(verdad))) con.commit() local_q_count += 1 second = 4 print('Second:', second) print('LEN',len(af3)) if second <= 140 and len(af3)>second: print('INSIDE IF ') cur.execute('INSERT INTO `megaproyecto`.`medicion` (`id`, `id_sensor`, `id_pregunta`, `segundo`, `medicion`) VALUES (%s, %s, %s, %s, %s);', (meassure_count, 1, q_count, second, af3[second])) con.commit() meassure_count += 1 cur.execute('INSERT INTO `megaproyecto`.`medicion` (`id`, `id_sensor`, `id_pregunta`, `segundo`, `medicion`) VALUES (%s, %s, %s, %s, %s);', (meassure_count, 2, q_count, second, f3[second])) con.commit() meassure_count += 1 cur.execute('INSERT INTO `megaproyecto`.`medicion` (`id`, `id_sensor`, `id_pregunta`, `segundo`, `medicion`) VALUES (%s, %s, %s, %s, %s);', (meassure_count, 3, q_count, second, af4[second])) con.commit() meassure_count += 1 cur.execute('INSERT INTO `megaproyecto`.`medicion` (`id`, `id_sensor`, `id_pregunta`, `segundo`, `medicion`) VALUES (%s, %s, %s, %s, %s);', (meassure_count, 4, q_count, second, f4[second])) con.commit() meassure_count += 1 print('OUT OF IF ') q_count += 1 def getTableQuestionVera(veracidad, preguntaNum): con = mysql.connector.connect( host="localhost", user="root", passwd="admin", database="megaproyecto" ) cursor = con.cursor(buffered=True) af3 = [] f3 = [] af4 = [] f4 = [] sensores = [af3, f3,af4,f4] for sensorId in range(1,5): cursor.execute("SELECT medicion.medicion FROM medicion INNER JOIN pregunta ON pregunta.id = medicion.id_pregunta INNER JOIN sensor ON sensor.id = medicion.id_sensor INNER JOIN persona ON persona.idpersona = pregunta.id_persona WHERE pregunta.numero = "+str(preguntaNum)+" AND pregunta.veracidad = '"+veracidad+"' AND sensor.id = "+str(sensorId)+" ORDER BY persona.idpersona;") numrows = cursor.rowcount print (numrows) for x in range(0,numrows): row = cursor.fetchone() sensores[sensorId-1].append(row[0]) sexo = [] persona = [] cursor.execute("SELECT variable.valor, persona.codigo FROM variable INNER JOIN variables ON variable.id_variable = variables.id INNER JOIN campo ON variable.id_campo = campo.id INNER JOIN persona ON persona.idpersona = variables.id_persona WHERE campo.nombre = 'Sexo' ORDER BY persona.idpersona;") numrows = cursor.rowcount for y in range(0,numrows): row = cursor.fetchone() sexo.append(row[0]) persona.append(row[1]) demographics = [[],[],[],[],[],[],[],[],[],[],[]] for campo_id in range(2,13): cursor.execute("SELECT variable.valor FROM variable INNER JOIN variables ON variable.id_variable = variables.id INNER JOIN campo ON variable.id_campo = campo.id INNER JOIN persona ON persona.idpersona = variables.id_persona WHERE campo.id = "+str(campo_id)+" ORDER BY persona.idpersona;") numrows = cursor.rowcount for num in range(0,numrows): row = cursor.fetchone() demographics[campo_id-2].append(str(row[0])) #demographics_values = str.format('{},{},{},{},{},{},{},{},{},{}',) output = '' json_output = {"preguntas" : []} json_object = {} print(sexo) print(demographics) print() for medicion in range(0,len(sensores[0])): try: str_dem = "" for p in range(0, 11): if p == 0: str_dem = str(demographics[p][medicion]) else: str_dem = str(demographics[p][medicion])+ ','+ str_dem if veracidad == 'True': output = output+ persona[medicion]+','+str(sensores[0][medicion])+','+str(sensores[1][medicion])+','+str(sensores[2][medicion])+','+str(sensores[3][medicion])+','+str(sexo[medicion])+","+str(preguntaNum)+',1,'+'2,'+str_dem+'\n' json_object ['AF3'] = sensores[0][medicion] json_object ['F3']=sensores[1][medicion] json_object ['AF4']=sensores[2][medicion] json_object ['F4']=sensores[3][medicion] json_object ['sexo']=int(sexo[medicion]) json_object ['cief']=int(demographics[5][medicion]) json_object ['hare']=int(demographics[7][medicion]) json_object ['pebl']=int(demographics[9][medicion]) json_object ['edad']=int(demographics[10][medicion]) else: output = output+ persona[medicion]+','+str(sensores[0][medicion])+','+str(sensores[1][medicion])+','+str(sensores[2][medicion])+','+str(sensores[3][medicion])+','+str(sexo[medicion])+","+str(preguntaNum)+',0,'+'2,'+str_dem+'\n' except: print ('Oops') json_output["preguntas"].append(json_object) print (json_output) return output, json_output def generate_file(): csv_output, json_output = getTableQuestionVera('True',1) full_output = 'persona,AF3,F3,AF4,F4,sexo,numPregunta,veracidad,escolaridad,cie,cies,ciex,ciem,ciec,cief,ciep,hare,dsmt,pebl,edad\n' full_output = full_output + str(csv_output) with open('pregunta.json', 'w') as outfile: json.dump(json_output, outfile) with open('./result.csv', 'w') as file: print('writing') file.write(full_output) def get_knn(): df = pd.read_csv('./result.csv') df.drop(['persona','numPregunta','cie'], 1, inplace=True) x = np.array(df.drop(['veracidad'], 1)) y = np.array(df['veracidad']) clf = neighbors.KNeighborsClassifier(n_neighbors=19) x_train2 = np.loadtxt('./train/x_train.txt') y_train2 = np.loadtxt('./train/y_train.txt') one_value = x print(one_value) print(x_train2[0]) clf.fit(x_train2, y_train2) prediction = clf.predict(one_value) print('Accuracy of KNN: ',prediction) prediction = prediction[0] probability = clf.predict_proba(one_value)[0] return prediction, probability class EmotivRecorder(): def __init__(self, ): multiprocessing.Process.__init__(self) self.exit = multiprocessing.Event() def run(self): print("On emotiv") os.system('c:\\Python27\python.exe .\Emotiv\Emotrix\emotrix\my_recorder.py ') self.exit.set() global my_emotiv global t0 def koch(second,sexo, edad, pebl, dsmt, hare, ciep, cief, ciec, ciem, ciex, cies, cie): print('Running Koch') insert_BD(second,sexo, edad, pebl, dsmt, hare, ciep, cief, ciec, ciem, ciex, cies, cie) generate_file() value, confidence = get_knn() if value: category = 'true' confidence = confidence[1] * 100 else: category = 'false' confidence = confidence[0] * 100 time.sleep(3) dnn.main([]) print('category: ', category) print('confidence: ', confidence) requests.post('http://localhost:5000/send-koch-response', data={ "category": category, "confidence": confidence }) def startAnswer(): print('Starting answer') global t0 t0 = int(time.time()-t0) return 'OK' def finishAnswer(): global t0 koch(t0,'Masculino', 23, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) def startQuestion(): print('Starting question') global my_emotiv global t0 t0 = int(time.time()) my_emotiv = EmotivRecorder() my_emotiv.run() return 'OK' koch(2,'Masculino', 23, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
[ "pablo.koch26@gmail.com" ]
pablo.koch26@gmail.com
92f9238ad3092c97ec4b095701f67d1c55cfd079
5d302c38acd02d5af4ad7c8cfe244200f8e8f877
/String/1408. String Matching in an Array(Easy).py
adf72b4a7fde98423f8ff4a18cf2a13c73a92e79
[]
no_license
nerohuang/LeetCode
2d5214a2938dc06600eb1afd21686044fe5b6db0
f273c655f37da643a605cc5bebcda6660e702445
refs/heads/master
2023-06-05T00:08:41.312534
2021-06-21T01:03:40
2021-06-21T01:03:40
230,164,258
0
0
null
null
null
null
UTF-8
Python
false
false
725
py
class Solution: def stringMatching(self, words: List[str]) -> List[str]: store = set(); for i in range(len(words)): for j in range(len(words)): if len(words[i]) >= len(words[j]) and words[i] != words[j]: if words[i].find(words[j]) != -1: store.add(words[j]) return list(store) #class Solution: # def stringMatching(self, words: List[str]) -> List[str]: # res = [] # words.sort(key=len) # for i, word in enumerate(words): # for k in range(i+1, len(words)): # if word in words[k]: # res.append(word) # break # # return res
[ "huangxingyu00@gmail.com" ]
huangxingyu00@gmail.com
bfe55bf5237d1b834b0adcbbcf20d84565aa24f1
f87ce51b358caf2497fd0f6d0beb2e18ff13fac5
/TheNCollection/userlogin/forms.py
5cd0ce0cfe12c6104a68c730d572789dbf2d13ba
[]
no_license
naerae100/Droid-
035991b151ff0c40bc26668ca03e24d431f1c2c8
9f3a9487774b16ab632c5f1a9da7e3aa63617c00
refs/heads/main
2023-07-31T02:07:40.025592
2021-09-30T17:21:59
2021-09-30T17:21:59
397,072,559
0
0
null
null
null
null
UTF-8
Python
false
false
600
py
from django import forms from django.contrib.auth.models import User from django.contrib.auth.forms import UserCreationForm from .models import Profile from django.forms import ModelForm class LoginForm(forms.Form): username = forms.CharField() password = forms.CharField(widget=forms.PasswordInput) class CreateUserForm(UserCreationForm): class Meta: model= User fields = ['username', 'email', 'password1', 'password2'] class ProfileForm(ModelForm): class Meta: model = Profile fields = "__all__" exclude = ['user', 'username', 'email']
[ "naerae100@gmail.com" ]
naerae100@gmail.com
b24094d77418e88f7477458debc1594cdeb2b6fa
021ce16e42120246557dfa210bef6e96a34623b6
/tornado_sqlalchemy_login/sqla/models.py
6769f29cf020d69ba5c5480e1a770a37fb2ae347
[ "Apache-2.0" ]
permissive
timkpaine/tornado-sqlalchemy-login
f455b95d60d392202b46758ff230259785f7dc19
499bc7d79926b79352a3b9abdb864815e9896274
refs/heads/main
2022-12-01T22:28:06.620106
2022-11-22T01:46:21
2022-11-22T01:46:21
230,801,513
1
0
Apache-2.0
2023-09-04T13:35:46
2019-12-29T20:46:05
Python
UTF-8
Python
false
false
2,109
py
import secrets from sqlalchemy import Column, Integer, String, Boolean, ForeignKey from sqlalchemy.ext.hybrid import hybrid_property from sqlalchemy.orm import relationship from sqlalchemy.ext.declarative import declarative_base TOKEN_WIDTH = 64 Base = declarative_base() class User(Base): __tablename__ = "users" id = Column(Integer, primary_key=True) username = Column(String(100), nullable=False, unique=True) password = Column(String(100), nullable=False) _email = Column("email", String, nullable=False, unique=True) apikeys = relationship("APIKey", back_populates="user") admin = Column(Boolean, default=False) @hybrid_property def email(self): return self._email @email.setter def email(self, email): # TODO validate self._email = email def __repr__(self): return "<User(id='{}', username='{}')>".format(self.id, self.username) def to_dict(self): ret = {} for item in ("id", "username", "email"): ret[item] = getattr(self, item) return ret def from_dict(self, d): raise NotImplementedError() class APIKey(Base): __tablename__ = "apikeys" id = Column(Integer, primary_key=True) user_id = Column(Integer, ForeignKey("users.id", ondelete="cascade")) user = relationship("User", back_populates="apikeys") key = Column( String(100), nullable=False, default=lambda: secrets.token_urlsafe(TOKEN_WIDTH) ) secret = Column( String(100), nullable=False, default=lambda: secrets.token_urlsafe(TOKEN_WIDTH) ) @staticmethod def generateKey(): return { "key": secrets.token_urlsafe(TOKEN_WIDTH), "secret": secrets.token_urlsafe(TOKEN_WIDTH), } def __repr__(self): return "<Key(id='{}', key='{}', secret='***')>".format(self.id, self.key) def to_dict(self): ret = {} for item in ("id", "user_id", "key", "secret"): ret[item] = getattr(self, item) return ret def from_dict(self, d): raise NotImplementedError()
[ "t.paine154@gmail.com" ]
t.paine154@gmail.com
0305aa72da943843e6eb123959977d0d066a616e
9f044bfa3235a663e733e7a4c1c3476467de25a9
/apps/models.py
3a2b13933de4cc62924041b72b81be622e947904
[]
no_license
richard1230/flask175_Home_Dynamically_Obtain_Carousel_Data
60cf85b4834135c2608c6822afae3b1cfe7d1457
115d90dbfa22331802277c068aed24bb40f59573
refs/heads/master
2020-11-30T14:42:10.091896
2019-12-27T10:08:41
2019-12-27T10:08:41
230,420,691
0
0
null
null
null
null
UTF-8
Python
false
false
748
py
#encoding: utf-8 from exts import db from datetime import datetime from datetime import datetime class BannerModel(db.Model): __tablename__ = 'banner' id = db.Column(db.Integer,primary_key=True,autoincrement=True) name = db.Column(db.String(255),nullable=False) image_url = db.Column(db.String(255),nullable=False) link_url = db.Column(db.String(255),nullable=False) priority = db.Column(db.Integer,default=0) create_time = db.Column(db.DateTime,default=datetime.now) # # # class BoardModel(db.Model): # __tablename__ = 'board' # id = db.Column(db.Integer,primary_key=True,autoincrement=True) # name = db.Column(db.String(20),nullable=False) # create_time = db.Column(db.DateTime,default=datetime.now)
[ "huyongjinnuaa@163.com" ]
huyongjinnuaa@163.com
d6309335f610181b9f302cef7a872a2c304a842f
07392a13541ea574a9616f38977146bb6032cdb4
/board/views.py
c4ad0c2a65423b5f8add52e0ad13e81093f98053
[]
no_license
lutae2000/mysite3
9ae2205d3ecab66cf2734620a28da8132ec5979d
61670b82d657b9758aa2971f35439174675fd2c2
refs/heads/master
2020-03-22T09:02:16.407293
2018-07-05T07:21:15
2018-07-05T07:21:15
139,809,885
0
0
null
null
null
null
UTF-8
Python
false
false
936
py
from django.shortcuts import render from django.http import HttpResponseRedirect # Create your views here. from board.models import Board def list(request): board_list = Board.objects.all().order_by('-regdate') context = {'board_list':board_list} return render(request, 'board/list.html', context) #user #count def write(request): return render(request, 'board/write.html') def modify(request): content = Board.objects.filter(id=request.GET['id']) context = {'context',content} return render(request, 'board/modify.html',context) def add(request): board = Board() board.title = request.POST['title'] board.content = request.POST['content'] board.save() return HttpResponseRedirect('/board/list') def view(request): content = Board.objects.filter(id=request.GET['id']) context = {'content':content} print(context) return render(request, 'board/view.html',context)
[ "lutae2000@gmail.com" ]
lutae2000@gmail.com
71673848a7d58ab5d627f04535e6fa95bb7030d9
931a621adbfa598bd8740828a6d30d52e9821304
/tours/migrations/0012_auto_20190404_1849.py
91877263060bb2346c82fa0884fd459eb0ffc447
[]
no_license
PooyaAlamirpour/Zartour
d4102d7095b187e1eca1acc8e395bcb54d134e1c
f2a59a9e5ce64eee58615cf47d0c1d534c22b864
refs/heads/master
2020-05-24T17:28:38.456960
2019-05-23T01:11:50
2019-05-23T01:11:50
187,383,468
1
0
null
null
null
null
UTF-8
Python
false
false
713
py
# -*- coding: utf-8 -*- # Generated by Django 1.11.8 on 2019-04-04 18:49 from __future__ import unicode_literals import ckeditor.fields from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('tours', '0011_auto_20190404_1643'), ] operations = [ migrations.AddField( model_name='tour', name='description', field=ckeditor.fields.RichTextField(default='22'), preserve_default=False, ), migrations.AddField( model_name='tour', name='documents', field=ckeditor.fields.RichTextField(default='22'), preserve_default=False, ), ]
[ "P.Alamirpour@gmail.com" ]
P.Alamirpour@gmail.com
4dc8c53bfc1378aebcab4560c9d128583228ac0f
a50b9dfd5c3a703f0635350f93d5c1af62a6275c
/venv/bin/pip3
f9629ddc4ce228f5b980ff2c62da744da033cd74
[]
no_license
daniloaugusto0212/EstudoPython
8c37475f62adad9a77c7df1d5decd11136014b5c
2c3e4b523848cca946fc4fcc0946f3f45cd1f4ac
refs/heads/master
2021-06-27T12:52:07.765054
2021-03-09T23:53:33
2021-03-09T23:53:33
222,791,817
1
0
null
null
null
null
UTF-8
Python
false
false
399
#!/home/o1234/PycharmProjects/novo/venv/bin/python # EASY-INSTALL-ENTRY-SCRIPT: 'pip==19.0.3','console_scripts','pip3' __requires__ = 'pip==19.0.3' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('pip==19.0.3', 'console_scripts', 'pip3')() )
[ "daniloaugusto0212@hotmail.com" ]
daniloaugusto0212@hotmail.com
70dd220917460238b9c07da05f31e6a8feca9f20
a97658ae4119fa4753a25620b1c546a0619065a2
/helloworld/helloworld_project/settings.py
9fc4cf914eca32e3c23b8006c6371593e1152c58
[]
no_license
dhuert27/DjangoForBeginners_Exercises
b21d7ac2be9298287f4b4e40579f0fae30abf3a2
42e453217a9df03237e6031a70ec1418016c7fbf
refs/heads/main
2023-04-13T16:48:44.583218
2021-04-22T03:24:07
2021-04-22T03:24:07
356,119,317
0
0
null
null
null
null
UTF-8
Python
false
false
3,152
py
""" Django settings for helloworld_project project. Generated by 'django-admin startproject' using Django 2.1. For more information on this file, see https://docs.djangoproject.com/en/2.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.1/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '-sqmx3*48xixz9o#0w#1=$q@9u_%%*#mr08o*=40^+uqb2yr3x' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'pages.apps.PagesConfig', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'helloworld_project.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'helloworld_project.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/'
[ "daniel.huert2000@gmail.com" ]
daniel.huert2000@gmail.com
c85707eeb5aa3aeb170c7db7d126ea2bef1dd8b6
771cf512a2875b060faa5e4040aae3a8de654998
/python/strictConcaveArrayRecursion.py
2cd8e4310ad745583f6689bd230222fb6f39d3c7
[]
no_license
anirudhit/ab-projects
466e4e5b6fc3ad7ba0d5b2983567a2dc68a7c8df
bdf2030197d6e908e0db214d46ab1d674ee59449
refs/heads/master
2020-07-19T11:50:11.342317
2019-11-20T06:35:58
2019-11-20T06:35:58
206,443,584
0
0
null
null
null
null
UTF-8
Python
false
false
1,393
py
def findMaximum(arr, low, high): # Base Case: Only one element is present in arr[low..high]*/ if low == high: return arr[low] # If there are two elements and first is greater then # the first element is maximum */ if high == low + 1 and arr[low] >= arr[high]: return arr[low]; # If there are two elements and second is greater then # the second element is maximum */ if high == low + 1 and arr[low] < arr[high]: return arr[high] mid = (low + high)//2 #low + (high - low)/2;*/ # If we reach a point where arr[mid] is greater than both of # its adjacent elements arr[mid-1] and arr[mid+1], then arr[mid] # is the maximum element*/ if arr[mid] > arr[mid + 1] and arr[mid] > arr[mid - 1]: return arr[mid] # If arr[mid] is greater than the next element and smaller than the previous # element then maximum lies on left side of mid */ if arr[mid] > arr[mid + 1] and arr[mid] < arr[mid - 1]: return findMaximum(arr, low, mid-1) else: # when arr[mid] is greater than arr[mid-1] and smaller than arr[mid+1] return findMaximum(arr, mid + 1, high) # Driver program to check above functions */ # arr = [1, 3, 50, 10, 9, 7, 6] arr = [7, 5, 3, -1, -7, 5, 9, 100] n = len(arr) print ("The maximum element is %d"% findMaximum(arr, 0, n-1))
[ "anirudhit@outlook.com" ]
anirudhit@outlook.com
cc980694f1318e65adc551268f9f91e3059bed20
be5401cad765484d2971b160606ba89596364bb8
/cbsearch.py
c13796d0f61a58b8cf31a0121f44610ef8690b45
[]
no_license
Jashpatel1/Collaborative-Pathfinder
60117b1aa7baa802c2bda94f8c15aa5563492a90
24a6f7772997b509b6fc8a2101d9b50cbfba6d25
refs/heads/main
2023-01-08T13:02:16.370373
2020-11-16T03:39:10
2020-11-16T03:39:10
305,340,097
0
0
null
null
null
null
UTF-8
Python
false
false
7,264
py
from util import * from gworld import * from visualize import * import a_star_simple import a_star_constraint import random def get_constraint_astar_path(world, start, goal, constraints=None): ret_path = a_star_constraint.find_path(world.get_nbor_cells, start, goal, lambda cell: 1, lambda cell, constraints: world.passable( cell, constraints), world.tyx_dist_heuristic, constraints) return ret_path def get_astar_path(world, start, goal): ret_path, pathcost = a_star_simple.find_path(world.get_nbor_cells, start, goal, lambda cell: 1, lambda cell: world.passable(cell)) return ret_path, pathcost def path_spacetime_conv(path_yx, tstart=0): path_tyx = [] tcurr = tstart for step_yx in path_yx: step_tyx = (tcurr, step_yx[0], step_yx[1]) path_tyx.append(step_tyx) tcurr = tcurr + 1 return (tcurr - tstart), path_tyx def cell_spacetime_conv(cell, t): return ((t, cell[0], cell[1])) def get_max_pathlen(agents, path_seq): max_pathlen = 0 for agent in agents: pathlen = len(path_seq[agent]) max_pathlen = pathlen if pathlen > max_pathlen else max_pathlen return max_pathlen def path_equalize(agents, path_seq, max_pathlen=-1): if(max_pathlen < 0): max_pathlen = get_max_pathlen(agents, path_seq) for agent in agents: path = path_seq[agent] lstep = path[-1] for step in range(len(path), max_pathlen + TWAIT): path.append((step, lstep[1], lstep[2])) path_seq[agent] = path return path_seq def steptime_agtb(a, b): if(a[0] > b[0]): return True return False def tplusone(step): return ((step[0]+1, step[1], step[2])) def get_conflicts(agents, path_seq, conflicts_db=None): tyx_map = dict() if(not bool(conflicts_db)): conflicts_db = dict() random.shuffle(agents) for agent in agents: if(agent not in conflicts_db): conflicts_db[agent] = set() if(path_seq[agent]): pathlen = len(path_seq[agent]) for t, tstep in enumerate(path_seq[agent]): twosteps = [tstep] if(t > 0): twosteps.append(tplusone(path_seq[agent][t-1])) for step in twosteps: if(step not in tyx_map): tyx_map[step] = agent else: otheragent = tyx_map[step] if(step not in conflicts_db[agent] and agent != otheragent): conflicts_db[agent].update({step}) return conflicts_db def evaluate_path(path_seq, agent, conflicts_db): all_okay = True tpath = path_seq[agent] tconstraints = conflicts_db[agent] for constraint in tconstraints: if(constraint in tpath): all_okay = False def search(agents, world): path_seq = dict() pathcost = dict() agent_goal = dict() max_pathlen = 0 restart_loop = False for agent in agents: start = world.aindx_cpos[agent] goal = world.aindx_goal[agent] pathseq_yx, pathcost[agent] = get_astar_path(world, start, goal) pathlen, path_seq[agent] = path_spacetime_conv(pathseq_yx) max_pathlen = pathlen if pathlen > max_pathlen else max_pathlen conflicts_db = get_conflicts(agents, path_seq) iter_count = 1 pickd_agents = [] while(True): max_pathlen = get_max_pathlen(agents, path_seq) path_seq = path_equalize(agents, path_seq, max_pathlen) if(iter_count % 2 == 1): pickd_agents = [] nagents = len(agents) random.shuffle(agents) pickd_agents = agents[(nagents/2):] else: temp_pickd_agents = [] for agent in agents: if agent not in pickd_agents: temp_pickd_agents.append(agent) pickd_agents = temp_pickd_agents if(restart_loop): restart_loop = False print '\n\nStuck between a rock and a hard place?\nRapid Random Restart to the rescue!\n\n' for agent in agents: conflicts_db[agent] = set() start = world.aindx_cpos[agent] goal = world.aindx_goal[agent] pathseq_yx, pathcost[agent] = get_astar_path( world, start, goal) pathlen, path_seq[agent] = path_spacetime_conv(pathseq_yx) max_pathlen = pathlen if pathlen > max_pathlen else max_pathlen conflicts_db = get_conflicts(agents, path_seq, conflicts_db) for agent in pickd_agents: if (agent in conflicts_db): constraints = conflicts_db[agent] constraints.update({}) if(bool(constraints)): start = cell_spacetime_conv(world.aindx_cpos[agent], 0) goal = cell_spacetime_conv( world.aindx_goal[agent], SOMETIME) nw_path, nw_pathlen = get_constraint_astar_path( world, start, goal, constraints) if(nw_path): path_seq[agent] = nw_path evaluate_path(path_seq, agent, conflicts_db) else: path_seq[agent] = [start] restart_loop = True if not restart_loop: path_seq = path_equalize(agents, path_seq, SOMETIME) conflicts_db = get_conflicts(agents, path_seq, conflicts_db) break_loop = True for agent in agents: ubrokn_conflicts = [] constraints = conflicts_db[agent] for step in path_seq[agent]: if(step in constraints): ubrokn_conflicts.append(step) if(ubrokn_conflicts): print '## A', agent, 'UC:', ubrokn_conflicts print 'Yes, there are conflicts!' break_loop = False goal = cell_spacetime_conv(world.aindx_goal[agent], SOMETIME) if(path_seq[agent][-1] != goal): break_loop = False iter_count = iter_count + 1 if(break_loop and not restart_loop): print 'Loop break!' break for agent in agents: print '\nAgent ', agent, ' cost:', pathcost[agent], ' Path -- ', path_seq[agent] for agent in agents: if agent in conflicts_db: print '\nAgent ', agent, ' Conflicts -- ', conflicts_db[agent] return path_seq
[ "patel.5@iitj.ac.in" ]
patel.5@iitj.ac.in
6849e3aa107e6582238894eb5ace39b380d9102b
74ddb61b608bf47b1320a3a66a13c0896bff4444
/samples/openapi3/client/petstore/python-experimental/petstore_api/models/whale.py
f39c28174404f4f3c268c11125743f22f796eebc
[ "Apache-2.0" ]
permissive
ShakedH/openapi-generator
616f873dc29edf49e44c4685ebb7f46184ce62fd
07647b1a310410a28c95f8b4a9661c0ddeaf1db8
refs/heads/master
2022-09-08T07:40:44.550163
2020-05-28T13:43:38
2020-05-28T13:43:38
267,639,665
0
0
Apache-2.0
2020-05-28T16:26:44
2020-05-28T16:26:44
null
UTF-8
Python
false
false
6,430
py
# coding: utf-8 """ OpenAPI Petstore This spec is mainly for testing Petstore server and contains fake endpoints, models. Please do not use this for any other purpose. Special characters: \" \\ # noqa: E501 The version of the OpenAPI document: 1.0.0 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import re # noqa: F401 import sys # noqa: F401 import six # noqa: F401 import nulltype # noqa: F401 from petstore_api.model_utils import ( # noqa: F401 ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, int, none_type, str, validate_get_composed_info, ) class Whale(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } additional_properties_type = None @cached_property def openapi_types(): """ This must be a class method so a model may have properties that are of type self, this ensures that we don't create a cyclic import Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ return { 'class_name': (str,), # noqa: E501 'has_baleen': (bool,), # noqa: E501 'has_teeth': (bool,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'class_name': 'className', # noqa: E501 'has_baleen': 'hasBaleen', # noqa: E501 'has_teeth': 'hasTeeth', # noqa: E501 } _composed_schemas = {} required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, class_name, _check_type=True, _spec_property_naming=False, _path_to_item=(), _configuration=None, _visited_composed_classes=(), **kwargs): # noqa: E501 """whale.Whale - a model defined in OpenAPI Args: class_name (str): Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) has_baleen (bool): [optional] # noqa: E501 has_teeth (bool): [optional] # noqa: E501 """ self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.class_name = class_name for var_name, var_value in six.iteritems(kwargs): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value)
[ "noreply@github.com" ]
ShakedH.noreply@github.com
8df839776009a272be53927bd7b7708595455244
44a0a00037de8ec94fc1b7a47eb85ea160b047cd
/Algorithm/Programmers/STACK_QUE/기능개발.py
531fe6f98468f60d81107c5950202b3da36f420f
[]
no_license
a23822/TIL
ad7a7d7aee73a3f788affb90a00ca889b9630d8c
47a135ce85a61f01fad6de50b8fd701ebdbcb1b0
refs/heads/master
2020-04-14T11:16:18.395890
2020-03-01T14:42:19
2020-03-01T14:42:19
163,809,355
0
0
null
null
null
null
UTF-8
Python
false
false
2,511
py
""" 런타임 에러 30/100 """ def solution(progresses, speeds): answer = [] # 각 작업마다 걸리는 시간을 구해 배열 생성 complete = [] index = len(speeds) for i in range(index): cDay_mok = (100 - progresses[i])%speeds[i] if cDay_mok == 0: cDay = int((100 - progresses[i])/speeds[i]) else: cDay = int((100 - progresses[i])/speeds[i]) + 1 complete.append(cDay) print(complete) # 배포 배열에 하나씩 넣기 temp = 0 for i in range(index): if i == 0: answer.append(1) else: if complete[i] <= complete[temp]: answer[temp] += 1 ### else: answer.append(1) temp = i return answer """ 통과 , """ def solution2(progresses, speeds): answer = [] # 각 작업마다 걸리는 시간을 구해 배열 생성 complete = [] index = len(speeds) for i in range(index): cDay_mok = (100 - progresses[i])%speeds[i] if cDay_mok == 0: cDay = int((100 - progresses[i])/speeds[i]) else: cDay = int((100 - progresses[i])/speeds[i]) + 1 complete.append(cDay) # 배포 배열에 하나씩 넣기 temp = 0 for i in range(index): if i == 0: answer.append(1) else: if complete[i] <= complete[temp]: answer[-1] += 1 # 이 부분을 잘못 판단했었음. else: answer.append(1) temp = i return answer """ 다른사람풀이 ZIP 활용 """ def solution3(progresses, speeds): Q=[] for p, s in zip(progresses, speeds): if len(Q)==0 or Q[-1][0]<-((p-100)//s): Q.append([-((p-100)//s),1]) else: Q[-1][1]+=1 return [q[1] for q in Q] """ 다른사람풀이 Math ceil 이용 """ from math import ceil def solution4(progresses, speeds): daysLeft = list(map(lambda x: (ceil((100 - progresses[x]) / speeds[x])), range(len(progresses)))) count = 1 retList = [] for i in range(len(daysLeft)): try: if daysLeft[i] < daysLeft[i + 1]: retList.append(count) count = 1 else: daysLeft[i + 1] = daysLeft[i] count += 1 except IndexError: retList.append(count) return retList
[ "a23842@naver.com" ]
a23842@naver.com
7b6b855791edd206f5db635b20e927ab22d10a3d
d670ceefd62a673d7f071ad8255ea1ced2d1be1f
/python/example/udp_client.py
424b47e0953f4aab5a8c34f7f73840dff577d7b1
[]
no_license
wang-jinfeng/bigdata
2bfaecefd4aaac83c81e1dbce44cd596da717673
b031a5fb2d06c7a2c2942a19d8c818f50ec9b8f6
refs/heads/master
2022-12-01T22:55:23.425409
2021-01-20T10:08:56
2021-01-20T10:08:56
199,104,425
0
0
null
2022-11-16T01:56:42
2019-07-27T02:18:29
Scala
UTF-8
Python
false
false
294
py
#!/usr/bin/env python3 # -*- coding: utf-8 -*- import socket s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) for data in [b'Michael', b'Tracy', b'Sarah']: # 发送数据: s.sendto(data, ('127.0.0.1', 9998)) # 接收数据: print(s.recv(1024).decode('utf-8')) s.close()
[ "jinfeng.wang@mobvista.com" ]
jinfeng.wang@mobvista.com
4b0bb27aea8f07ee464581db2188e2705e358034
35b7b5c95320fd3548c7e929ea7191ff97f0008e
/tver.py
ffc58e05042ec645f128dcc8c550e4bfe82ff731
[]
no_license
gretastd/basik
2fcf08dff1c0f6b82e99b9b9439696392e26b8a2
a85fd993ce3aa7c15a6772df0d431e89366c7169
refs/heads/master
2020-09-13T20:21:31.493173
2019-11-20T09:26:12
2019-11-20T09:26:12
222,892,817
0
0
null
null
null
null
UTF-8
Python
false
false
874
py
def gumar(n): b=0 while n!=0: a=n%10 n=int(n/10) b=b+a print(b) def artadryal(n): b=1 while n!=0: a=n%10 n=int(n/10) b=b*a print(b) def agicdzax(n): while n!=0: a=n%10 b=int(n/10) b=a*10+b print(b) n=0 def dzaxicaj(n): while n!=0: a=n%10 b=int(n/10) b=b*10+a print(b) n=0 def erku(n): while n!=0: a=n%10 b=int(n/10) if a==2: print("true") else: print("false") n=0 def kent(n): while n!=0: a=n%10 b=int(n/10) if a%2!=0: print("true") else: print("false") n=0 def arjeq(n,x): import math k=1 while k<=n: a=math.pow(x,k) print(a) k=k+1
[ "stepanyangreta98@gmail.com" ]
stepanyangreta98@gmail.com
3bb9eaa46dd4fe663b19945b36fc8bb4842a88a2
9bf13d7bd0b9e48dc31f1fd6d3e14d3402386ee9
/lissandra/dto/league.py
3c9a09b5bad2c347b6a48747ec03ef22bda04e5c
[ "MIT" ]
permissive
Crimack/lissandra
04acd52c9b79e4d7946eef1e821bc8ce417a5a62
7f43ca70040ce054f335ad3b134e3bd4ee52ff3e
refs/heads/master
2022-12-05T01:01:15.825441
2020-08-31T23:06:06
2020-08-31T23:06:06
286,707,098
0
0
null
null
null
null
UTF-8
Python
false
false
369
py
from .common import DtoObject class LeagueEntryDto(DtoObject): pass class LeagueDto(DtoObject): pass class LeagueSummonerEntriesDto(DtoObject): pass class LeagueEntriesDto(DtoObject): pass class ChallengerLeagueListDto(DtoObject): pass class GrandmasterLeagueListDto(DtoObject): pass class MasterLeagueListDto(DtoObject): pass
[ "cmckee41@qub.ac.uk" ]
cmckee41@qub.ac.uk
f5586a51b83a59377fbf0ba3e1c1f7cbfca452e3
0fd0495f194bc22f0322d02ebabe8add9bf5814c
/python_concept_references/excel.py
a0cd93b32a6802edc9ce767d467a37f6082774d4
[]
no_license
WorldPierce/Automate_the_Boring_Stuff_With_Python
bd6982bddefa712598dc5e6eb4cf2a2aa87b2c1f
a0bc6ba49d92b25f4fbe8d4fdd9385f294567e4c
refs/heads/master
2020-12-30T09:58:38.341981
2017-08-05T19:51:10
2017-08-05T19:51:10
99,248,638
0
0
null
null
null
null
UTF-8
Python
false
false
579
py
# install openpyxl to open spreadsheets import openpyxl, os os.chdir('c:\\wffpi\\documents') workbook = openpyxl.load_workbook('example.xlsx') type(workbook) sheet = workbook.get_sheet_by_name('Sheet1') type(sheet) workbook.get_sheet_names() cell = sheet['A1'] # gets cell object for cell A 1 cell.value # gives you cell value str(sheet[A1].value) # prints string value of any cell sheet.cell(row=1, column=2) # returns cell object same as sheet['B1'] # useful for loops so you don't convert to A, B etc.. for i in range(1,8): print(i, sheet.cell(row=i, column=2).value)
[ "bildo316@gmail.com" ]
bildo316@gmail.com
3e2af68956ab395d7d6c3ee1a4238c837c4b51cc
b2472967910be9c12576f0f97d33bca0576a8667
/atcoder-old/2019/0901_abc139/d.py
0f4f619ee2e61c90c3c79b0e44b9d3a7b51c02c3
[]
no_license
ykmc/contest
85c3d1231e553d37d1235e1b0fd2c6c23f06c1e4
69a73da70f7f987eb3e85da503ea6da0744544bd
refs/heads/master
2020-09-01T22:56:10.444803
2020-07-14T11:36:43
2020-07-14T11:36:43
217,307,953
0
0
null
null
null
null
UTF-8
Python
false
false
417
py
# Python3 (3.4.3) import sys input = sys.stdin.readline # ------------------------------------------------------------- # function # ------------------------------------------------------------- # ------------------------------------------------------------- # main # ------------------------------------------------------------- N = int(input()) # i % i+1 = i が最善, あまりは 1 〜 N-1 print(N*(N-1)//2)
[ "34961813+ykmc@users.noreply.github.com" ]
34961813+ykmc@users.noreply.github.com
0ab766f09b072cf8494f45960dfbd183965b6d10
6b971e3401fba1498d8b4f3e1b4a46971ca6d0a9
/examples/scroll_area_dynamic_content_layouts.py
82af4fc9d3357c0a602b0d880ee3a28e95f86fec
[]
no_license
brent-stone/PySide6
1e41d76363343bfd238c60a93e11b4a9ac58d57c
2927ddbba51b677e4a0eb502b287b8a8d9e964ad
refs/heads/main
2023-08-14T23:09:29.669245
2021-09-05T00:50:41
2021-09-05T00:50:41
403,183,019
0
0
null
null
null
null
UTF-8
Python
false
false
1,731
py
from PySide6.QtWidgets import * import sys class MainWindow(QMainWindow): def __init__(self): QMainWindow.__init__(self, None) self.setWindowTitle("My GUI") self.resize(300, 400) self.scroll = QScrollArea() self.scroll.setWidgetResizable(True) # CRITICAL self.inner = QFrame(self.scroll) self.inner.setLayout(QVBoxLayout()) self.scroll.setWidget(self.inner) # CRITICAL self.scroll_layout_dict = {} b = QPushButton(self.inner) b.setText("Populate") b.clicked.connect(self.populate) self.inner.layout().addWidget(b) # When creating MainWindow() from scratch like this, # it's necessary to tell PySide6 which widget is # the 'central' one for the MainWindow(). self.setCentralWidget(self.scroll) self.show() def populate(self): for i in range(10): b = QPushButton(self.inner) b.setText(str(i)) b.clicked.connect(self.del_button) checkbox = QCheckBox(f"Check {i}!", self.inner) new_layout = QHBoxLayout(self.inner) new_layout.addWidget(b) new_layout.addWidget(checkbox) n = self.inner.layout().count() self.inner.layout().insertLayout(n, new_layout) self.scroll_layout_dict[b] = new_layout def del_button(self): button: QPushButton = self.sender() layout: QVBoxLayout = self.scroll_layout_dict[button] while layout.count() > 0: layout.takeAt(0).widget().deleteLater() if __name__ == "__main__": app = QApplication(sys.argv) window = MainWindow() window.show() sys.exit(app.exec())
[ "brent.jk.stone@gmail.com" ]
brent.jk.stone@gmail.com
92a78afdb24938e410a82db5c1f25bc42579d344
6c3d60415479708090a2e9760bb7b620458aa96d
/Python/django/belt_reviewer/apps/login/urls.py
4e095ee0ec0be9bb0445a4f3e9dd7843c4e431ed
[]
no_license
bopopescu/dojo_assignments
721868825268a242300edcb5f94b1ec32cbc6727
45cf4ecf1fcb513699f40d57437be7c9ab74800a
refs/heads/master
2022-11-18T09:36:37.527001
2018-04-11T19:40:55
2018-04-11T19:40:55
281,831,499
0
0
null
2020-07-23T02:34:45
2020-07-23T02:34:45
null
UTF-8
Python
false
false
254
py
from django.conf.urls import url from . import views urlpatterns = [ url(r'^$', views.index), url(r'process$', views.process), url(r'books$', views.success), url(r'login$', views.login), url(r'add$', views.add), ]
[ "victordevera24@gmail.com" ]
victordevera24@gmail.com
ad4994ace4456aa5f10d59b2268f5651f648d5f9
7af39ee40bd4df79ddf094fd3ecf59bedaed869f
/app.py
36affbfd05a1657139bb3b9f094c438c9e7234f9
[]
no_license
LeoKavanagh/sleepui
0b35def13c2f794c29a9c9ad1efeee45aed956ff
cf8c862c84283126538a32d8fbf25635fa32aa33
refs/heads/master
2020-04-04T17:32:45.594262
2018-11-05T22:07:37
2018-11-05T22:07:37
156,125,022
0
0
null
null
null
null
UTF-8
Python
false
false
1,701
py
from flask import Flask, request, render_template, url_for, redirect import datetime as dt app = Flask(__name__) # Database lol today = dt.date.today().strftime('%Y-%m-%d') latest_pred = [] def deep_sleep_pct(steps, mean_rate, sd_rate, dsp_lag): # fitbit - Sleep as Android adjustment dsp_lag /= 3 b0 = -0.0343 b1 = 0.00 b2 = 0.0039 b3 = -0.0006 # coef for (max - min) b4 = 0.2012 return b0 + b2 * mean_rate + b3 * sd_rate + b4 * dsp_lag def log_prediction(pred): latest_pred.append(pred) @app.route('/') @app.route('/index') def index(): return render_template('index.html', latest_pred=latest_pred) @app.route('/predict', methods=['GET', 'POST']) def predict(): if request.method == 'POST': data = request.form.to_dict() pred = deep_sleep_pct(float(data['steps']), float(data['mean_rate']), float(data['sd_rate']), float(data['dsp_lag'])) data['pred'] = pred # Log to the 'database' so that we can show something in the if statement # in the index.html template log_prediction(pred) return render_template('show_prediction.html', today=today, steps=float(data['steps']), mean_rate=float(data['mean_rate']), sd_rate=float(data['sd_rate']), dsp_lag=float(data['dsp_lag']), pred=float(data['pred'])) return render_template('input_data.html') if __name__ == '__main__': app.run(debug=True)
[ "leok90@gmail.com" ]
leok90@gmail.com
4c82d791ac005304fc15575acf85f39269284e1a
f39593e3d41150feaa45dea39050ce52d0f835a0
/crimeLA.py
40ff5a2de61213df34debd1e54f913da35f3fdb3
[]
no_license
jliellen/Vizathon
888ed41c87326af36c176981927b2f97bbdb9abf
5abb3624c334551cc9023f87c4fbe78808b2fc7b
refs/heads/main
2023-06-20T18:50:45.851710
2021-08-01T16:14:38
2021-08-01T16:14:38
390,940,580
0
1
null
null
null
null
UTF-8
Python
false
false
1,479
py
import math import numpy as np import pandas as pd import matplotlib.pyplot as plt import folium from folium import Marker from folium.plugins import MarkerCluster data = pd.read_csv(r'Crime_Data_from_2010_to_2019.csv') data = data[(data['LAT']!= 0) & (data['LON'] !=0)] data = data.drop(columns=['DATE OCC', 'TIME OCC', 'AREA ', 'Part 1-2', 'Crm Cd Desc', 'Mocodes', 'Premis Cd', 'Premis Desc', 'Weapon Used Cd', 'Weapon Desc', 'Status', 'Status Desc', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street']) data['Date Rptd'] = pd.to_datetime(data['Date Rptd'], errors='coerce') data['year']= data['Date Rptd'].dt.year # Get the latest date (we loop years later) freshDate = max(data.year) # Get the crime data of the year crimeByYear = data.loc[(data.year == freshDate)] # Create the map my_map = folium.Map(location=[34.052235,-118.243683], tiles='OpenStreetMap', zoom_start=4) # Add points to the map mc = MarkerCluster() for idx, row in crimeByYear.iterrows(): if not math.isnan(row['LON']) and not math.isnan(row['LAT']): # Create pop-up message for each point pop = ["Vict Age", "Vict Sex", "Vict Descent"] popmsg = [str(item) + ':' + str(row[item]) for item in pop] popmsg = '\n'.join(popmsg) # Add marker to mc mc.add_child(Marker(location=[row['LAT'], row['LON']], popup=popmsg, tooltip=str(row['LOCATION']))) # Add mc to map my_map.add_child(mc) # Save the map my_map.save('map_1.html')
[ "noreply@github.com" ]
jliellen.noreply@github.com
c5273fe33676e21cd6b6a67e55bd93d84921ca2d
b8d4d9169b6df138cb45d64b618812c29e03012c
/Promos_for_our_e_commerce.py
fd57e264af0826cb3a8af2d38fc72f6a95ad63ed
[]
no_license
mzfuadi97/Pythonq
5633057d351317d5156527fcf2cac288edec34cc
e64a180c9249650324d301a1d06407c8e01f3ea9
refs/heads/master
2023-08-03T07:19:12.543031
2021-09-21T04:34:21
2021-09-21T04:35:58
408,678,011
0
0
null
null
null
null
UTF-8
Python
false
false
407,128
py
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Promos for our e-commerce.ipynb", "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YD0d355VUQeb", "outputId": "b8034987-6295-4748-cf90-453ef771eca0" }, "source": [ "#import library \n", "import pandas as pd\n", "\n", "# Baca data 'ecommerce_banner_promo.csv'\n", "data = pd.read_csv('https://storage.googleapis.com/dqlab-dataset/pythonTutorial/ecommerce_banner_promo.csv')\n", "\n", "#1. Data eksplorasi dengan head(), info(), describe(), shape\n", "print(\"\\n[1] Data eksplorasi dengan head(), info(), describe(), shape\")\n", "print(\"Lima data teratas:\")\n", "print(data.head())\n", "print(\"Informasi dataset:\")\n", "print(data.info())\n", "print(\"Statistik deskriptif dataset:\")\n", "print(data.describe())\n", "print(\"Ukuran dataset:\")\n", "print(data.shape)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "\n", "[1] Data eksplorasi dengan head(), info(), describe(), shape\n", "Lima data teratas:\n", " Daily Time Spent on Site Age ... Timestamp Clicked on Ad\n", "0 68.95 35 ... 3/27/2016 0:53 0\n", "1 80.23 31 ... 4/4/2016 1:39 0\n", "2 69.47 26 ... 3/13/2016 20:35 0\n", "3 74.15 29 ... 1/10/2016 2:31 0\n", "4 68.37 35 ... 6/3/2016 3:36 0\n", "\n", "[5 rows x 10 columns]\n", "Informasi dataset:\n", "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 1000 entries, 0 to 999\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Daily Time Spent on Site 1000 non-null float64\n", " 1 Age 1000 non-null int64 \n", " 2 Area Income 1000 non-null float64\n", " 3 Daily Internet Usage 1000 non-null float64\n", " 4 Ad Topic Line 1000 non-null object \n", " 5 City 1000 non-null object \n", " 6 Male 1000 non-null int64 \n", " 7 Country 1000 non-null object \n", " 8 Timestamp 1000 non-null object \n", " 9 Clicked on Ad 1000 non-null int64 \n", "dtypes: float64(3), int64(3), object(4)\n", "memory usage: 78.2+ KB\n", "None\n", "Statistik deskriptif dataset:\n", " Daily Time Spent on Site Age ... Male Clicked on Ad\n", "count 1000.000000 1000.000000 ... 1000.000000 1000.00000\n", "mean 65.000200 36.009000 ... 0.481000 0.50000\n", "std 15.853615 8.785562 ... 0.499889 0.50025\n", "min 32.600000 19.000000 ... 0.000000 0.00000\n", "25% 51.360000 29.000000 ... 0.000000 0.00000\n", "50% 68.215000 35.000000 ... 0.000000 0.50000\n", "75% 78.547500 42.000000 ... 1.000000 1.00000\n", "max 91.430000 61.000000 ... 1.000000 1.00000\n", "\n", "[8 rows x 6 columns]\n", "Ukuran dataset:\n", "(1000, 10)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dHnrKWBfUSR9", "outputId": "009e3d75-9fbd-468e-bbf0-15712b1d3507" }, "source": [ "#2. Data eksplorasi dengan dengan mengecek korelasi dari setiap feature menggunakan fungsi corr()\n", "print(\"\\n[2] Data eksplorasi dengan dengan mengecek korelasi dari setiap feature menggunakan fungsi corr()\")\n", "print(data.corr())\n", "\n", "#3. Data eksplorasi dengan mengecek distribusi label menggunakan fungsi groupby() dan size()\n", "print(\"\\n[3] Data eksplorasi dengan mengecek distribusi label menggunakan fungsi groupby() dan size()\")\n", "print(data.groupby('Clicked on Ad').size())" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "\n", "[2] Data eksplorasi dengan dengan mengecek korelasi dari setiap feature menggunakan fungsi corr()\n", " Daily Time Spent on Site ... Clicked on Ad\n", "Daily Time Spent on Site 1.000000 ... -0.748117\n", "Age -0.331513 ... 0.492531\n", "Area Income 0.310954 ... -0.476255\n", "Daily Internet Usage 0.518658 ... -0.786539\n", "Male -0.018951 ... -0.038027\n", "Clicked on Ad -0.748117 ... 1.000000\n", "\n", "[6 rows x 6 columns]\n", "\n", "[3] Data eksplorasi dengan mengecek distribusi label menggunakan fungsi groupby() dan size()\n", "Clicked on Ad\n", "0 500\n", "1 500\n", "dtype: int64\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "tPJ9GKymUXHV", "outputId": "ae0a44b1-a774-4889-f8ce-ee96b2053377" }, "source": [ "#import library\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "# Seting: matplotlib and seaborn\n", "sns.set_style('whitegrid')\n", "plt.style.use('fivethirtyeight')\n", "#4. Data eksplorasi dengan visualisasi\n", "#4a. Visualisasi Jumlah user dibagi ke dalam rentang usia (Age) menggunakan histogram (hist()) plot\n", "plt.figure(figsize=(10, 5))\n", "plt.hist(data['Age'], bins = data.Age.nunique())\n", "plt.xlabel('Age')\n", "plt.tight_layout()\n", "plt.show()\n", "#4b. Gunakan pairplot() dari seaborn (sns) modul untuk menggambarkan hubungan setiap feature.\n", "plt.figure()\n", "sns.pairplot(data)\n", "plt.show()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": "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\n", "text/plain": [ "<Figure size 720x360 with 1 Axes>" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "image/png": "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\n", "text/plain": [ "<Figure size 1080x1080 with 42 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "q25Xy3DVUZ41", "outputId": "93c3289f-9251-4959-966a-3fd6481d311b" }, "source": [ "#5. Cek missing value\n", "print(\"\\n[5] Cek missing value\")\n", "print(data.isnull().sum().sum())" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "\n", "[5] Cek missing value\n", "0\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r_kGRlZdUeq5", "outputId": "1f5ecc58-11f1-408b-9ee6-4dac466ae186" }, "source": [ "#import library\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", "\n", "#6.Lakukan pemodelan dengan Logistic Regression, gunakan perbandingan 80:20 untuk training vs testing\n", "print(\"\\n[6] Lakukan pemodelan dengan Logistic Regression, gunakan perbandingan 80:20 untuk training vs testing\")\n", "\n", "#6a.Drop Non-Numerical (object type) feature from X, as Logistic Regression can only take numbers, and also drop Target/label, assign Target Variable to y.\n", "X = data.drop(['Ad Topic Line','City','Country','Timestamp','Clicked on Ad'], axis = 1)\n", "y = data['Clicked on Ad']\n", "\n", "#6b. splitting the data\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42)\n", "\n", "#6c. Modelling\n", "# Call the classifier\n", "logreg = LogisticRegression()\n", "\n", "# Fit the classifier to the training data\n", "logreg = logreg.fit(X_train,y_train)\n", "\n", "# Prediksi model\n", "y_pred = logreg.predict(X_test)\n", "\n", "#6d. Evaluasi Model Performance\n", "print(\"Evaluasi Model Performance:\")\n", "print(\"Training Accuracy :\", logreg.score(X_train, y_train))\n", "print(\"Testing Accuracy :\", logreg.score(X_test, y_test))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "\n", "[6] Lakukan pemodelan dengan Logistic Regression, gunakan perbandingan 80:20 untuk training vs testing\n", "Evaluasi Model Performance:\n", "Training Accuracy : 0.9\n", "Testing Accuracy : 0.9\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e_nJBVsUUons", "outputId": "5a78dd97-f503-4221-f7ff-0388d88591fb" }, "source": [ "# Import library\n", "from sklearn.metrics import confusion_matrix, classification_report\n", "\n", "#7. Print Confusion matrix dan classification report\n", "print(\"\\n[7] Print Confusion matrix dan classification report\")\n", "\n", "#apply confusion_matrix function to y_test and y_pred\n", "print(\"Confusion matrix:\")\n", "cm = confusion_matrix(y_test, y_pred)\n", "print(cm)\n", "\n", "#apply classification_report function to y_test and y_pred\n", "print(\"Classification report:\")\n", "cr = classification_report(y_test, y_pred)\n", "print(cr)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "\n", "[7] Print Confusion matrix dan classification report\n", "Confusion matrix:\n", "[[85 4]\n", " [16 95]]\n", "Classification report:\n", " precision recall f1-score support\n", "\n", " 0 0.84 0.96 0.89 89\n", " 1 0.96 0.86 0.90 111\n", "\n", " accuracy 0.90 200\n", " macro avg 0.90 0.91 0.90 200\n", "weighted avg 0.91 0.90 0.90 200\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "8dl68ec6U6FB" }, "source": [ "Model sudah sangat baik dalam memprediksi user yang akan mengklik website atau tidak, dapat dilihat dari nilai accuracy = 0.90; Dataset memiliki jumlah label yang seimbang (balance class), sehingga evaluasi performansi dapat menggunakan metrik Accuracy" ] }, { "cell_type": "code", "metadata": { "id": "VMV1h1fYUsKQ" }, "source": [ "" ], "execution_count": null, "outputs": [] } ] }
[ "mzfuadi@gmail.com" ]
mzfuadi@gmail.com
c7198b3709f26fddaf360a7db559d549f4583b89
a65e5dc54092a318fc469543c3b96f6699d0c60b
/Personel/Sandesh/Python/23feb/reverse_string.py
5666f30c06762d1e26e632121ba09969fd9dc1f1
[]
no_license
shankar7791/MI-10-DevOps
e15bfda460ffd0afce63274f2f430445d04261fe
f0b9e8c5be7b28298eb6d3fb6badf11cd033881d
refs/heads/main
2023-07-04T15:25:08.673757
2021-08-12T09:12:37
2021-08-12T09:12:37
339,016,230
1
0
null
2021-08-12T09:12:37
2021-02-15T08:50:08
JavaScript
UTF-8
Python
false
false
96
py
string = input("Plearse enter a string : ") print("Reverse of string is : ") print(string[::-1])
[ "sandeshpatekar20@gmail.com" ]
sandeshpatekar20@gmail.com
368d3a8e796916e2e6cadcf0e634c978a7ef2699
ae7ba9c83692cfcb39e95483d84610715930fe9e
/martinkersner/train-CRF-RNN/crfasrnn.py
472a86f853e687aa057de49dcc8aa8957733f63d
[]
no_license
xenron/sandbox-github-clone
364721769ea0784fb82827b07196eaa32190126b
5eccdd8631f8bad78eb88bb89144972dbabc109c
refs/heads/master
2022-05-01T21:18:43.101664
2016-09-12T12:38:32
2016-09-12T12:38:32
65,951,766
5
7
null
null
null
null
UTF-8
Python
false
false
3,462
py
#!/usr/bin/env python # Martin Kersner, m.kersner@gmail.com # 2016/03/03 from __future__ import print_function caffe_root = '../caffe-crfrnn/' import sys sys.path.insert(0, caffe_root + 'python') import os import cPickle import logging import numpy as np import pandas as pd from PIL import Image as PILImage import cStringIO as StringIO import caffe import matplotlib.pyplot as plt from utils import palette_demo # TODO concatenate input and output image def main(): iteration, image_paths = process_arguments(sys.argv) if iteration: prototxt = 'TVG_CRFRNN_COCO_VOC_TEST_3_CLASSES.prototxt' model = 'models/train_iter_{}.caffemodel'.format(iteration) else: prototxt = 'TVG_CRFRNN_COCO_VOC.prototxt' model = 'TVG_CRFRNN_COCO_VOC.caffemodel' if not exist_model(model, prototxt): help() # default images (part of http://www.cs.berkeley.edu/~bharath2/codes/SBD/download.html) if not image_paths: image_paths.append('images/2007_005844.png') # chair image_paths.append('images/2008_007811.png') # bottle image_paths.append('images/2007_002094.png') # bird palette = palette_demo() net = caffe.Segmenter(prototxt, model, True) for path in image_paths: image, cur_h, cur_w = preprocess_image(path) if image == None: print(path + ' does not exist! Skipping.' , file=sys.stderr) continue print('Processing ' + path + '...', end='') segmentation = net.predict([image]) segm_post = postprocess_label(segmentation, cur_h, cur_w, palette) plt.imshow(segm_post) plt.savefig(create_label_name(path)) print('finished.') def preprocess_image(image_path): if not os.path.exists(image_path): return None, 0, 0 input_image = 255 * caffe.io.load_image(image_path) image = PILImage.fromarray(np.uint8(input_image)) image = np.array(image) mean_vec = np.array([103.939, 116.779, 123.68], dtype=np.float32) reshaped_mean_vec = mean_vec.reshape(1, 1, 3); im = image[:,:,::-1] im = im - reshaped_mean_vec # Pad as necessary cur_h, cur_w, cur_c = im.shape pad_h = 500 - cur_h pad_w = 500 - cur_w im = np.pad(im, pad_width=((0, pad_h), (0, pad_w), (0, 0)), mode = 'constant', constant_values = 0) return im, cur_h, cur_w def postprocess_label(segmentation, cur_h, cur_w, palette): segmentation2 = segmentation[0:cur_h, 0:cur_w] output_im = PILImage.fromarray(segmentation2) output_im.putpalette(palette) return output_im def create_label_name(orig_path): return 'label_' + os.path.splitext(os.path.basename(orig_path))[0] + '.png' def exist_model(model, prototxt): if not os.path.exists(model): print('Model ' + model + ' does not exist! Exiting.', file=sys.stderr) return False elif not os.path.exists(prototxt): print('Prototxt' + prototxt + ' does not exist! Exiting.', file=sys.stderr) return False return True def process_arguments(argv): num_args = len(argv) iteration = None image_paths = [] if num_args == 2: iteration = argv[1] elif num_args > 2: iteration = argv[1] for name in argv[2:]: image_paths.append(name) return iteration, image_paths def help(): print('Usage: python crfasrnn.py [ITERATION_NUM [IMAGE, IMAGE, ...]\n' 'ITERATION_NUM denotes iteration number of model which shall be run.\n' 'IMAGE one or more images can be passed as arguments.' , file=sys.stderr) exit() if __name__ == '__main__': main()
[ "xenron@outlook.com" ]
xenron@outlook.com
fac531e71a08fa0485440c9f6fbb7d75d2bf50fd
2befe3929950fd5d6257ed6b1f1fd5751995f22f
/solutions/merge_sort.py
15489c9446b82baca70e9ed35dda41108dc2a0c8
[]
no_license
redpanda-ai/ctci
a493abda7d95639ab785199ac77dfdcf529a1dd6
a621b25b55c749a678577fad4658b0b9171f889f
refs/heads/master
2021-07-09T18:26:17.193734
2020-09-26T04:02:20
2020-09-26T04:02:20
200,296,580
2
1
null
2023-09-14T00:36:51
2019-08-02T20:52:51
Python
UTF-8
Python
false
false
909
py
def merge_sort(arr): if len(arr) > 1: mid = len(arr) // 2 l_arr = arr[:mid] r_arr = arr[mid:] merge_sort(l_arr) merge_sort(r_arr) i = j = k = 0 while i < len(l_arr) and j < len(r_arr): if l_arr[i] < r_arr[j]: arr[k] = l_arr[i] i += 1 else: arr[k] = r_arr[j] j += 1 k += 1 while i < len(l_arr): arr[k] = l_arr[i] i += 1 k += 1 while j < len(r_arr): arr[k] = r_arr[j] j += 1 k += 1 if __name__ == "__main__": tests = [ [2, 1, 3, 5, 4, 7], [2], [], [1, 1, 2, 1, 1, 1], [7, 6, 5, 4, 3, 2, 1] ] for test in tests: print(f"\nInput: {test}") merge_sort(test) print(f"Output: {test}")
[ "joeandrewkey@gmail.com" ]
joeandrewkey@gmail.com
8f948f5da2515448b700d72a799435448074270c
7eeb873b01b1da3401d5b8a802438bcc0e22de22
/61_Sets/main.py
447df8aecab79571c7d8207257ce7002d3b8894b
[]
no_license
Bhushan2581/Python-tuts
ed5f1606f23aa1d4370d4ed2e4171c25cfc1f206
4f2acc839874fcbc3407ba1e0dc6e2d44d6e6179
refs/heads/master
2022-07-29T22:38:17.831404
2020-05-17T03:48:24
2020-05-17T03:48:24
264,577,256
0
0
null
null
null
null
UTF-8
Python
false
false
393
py
pythonlist = ["C", "Python", "Java"] print(pythonlist[1]) pythonlist = ["C", "Python", "Java"] print(pythonlist[-1]) pythonlist = ["java", "C", "C++", "PHP", "HTML", "Ruby", "C#"] print(pythonlist[2:5]) pythonlist = ["java", "C", "C++", "PHP", "HTML", "Ruby", "C#"] pythonlist[1] = "Laravel" print(pythonlist) pythonlist = ["Java", "C", "Python"] for x in tpythonlistislist: print(x)
[ "noreply@github.com" ]
Bhushan2581.noreply@github.com
98648afb1168eb37ec156f7995980e16668f754b
276be25104b8dd68aa45fb6bb5d0ff289dcad539
/testing/Belinda/app2.py
9602db1abe2ffd2f7da83ed45f26ed450721f7d9
[]
no_license
CC196/Project-2
77ea2411607e84f9b75d55f1bfd0cd2b2240d553
fd3ca26703468443fec889e0e6e2d64dfd8214eb
refs/heads/main
2023-01-07T00:31:52.129842
2020-11-11T04:05:20
2020-11-11T04:05:20
302,524,477
0
0
null
null
null
null
UTF-8
Python
false
false
4,321
py
import numpy as np import sqlalchemy from sqlalchemy.ext.automap import automap_base from sqlalchemy.orm import Session from sqlalchemy import create_engine, func from flask import Flask, jsonify # from flask_cors import CORS ################################################# # Database Setup ################################################# engine = create_engine("sqlite:///D:/Course_work/Project2/Project-2/Resources/Spotify.sqlite") # reflect an existing database into a new model Base = automap_base() # reflect the tables Base.prepare(engine, reflect=True) # Save reference to the table Top_fifty = Base.classes.top50_detail All_year_top = Base.classes.top50_2019 world = Base.classes.SpotifyTopSongsByCountry ################################################# # Flask Setup ################################################# app = Flask(__name__) # cors = CORS(app) ################################################# # Flask Routes ################################################# @app.route("/") def welcome(): """List all available api routes.""" return ( f"Available Routes:<br/>" f"/api/v1.0/top_50_2020_May<br/>" f"/api/v1.0/top_50_2019<br>" f"/api/v1.0/top50_country_2020_May" ) @app.route("/api/v1.0/top_50_2020_May") def May(): session = Session(engine) results = session.query(Top_fifty.Title, Top_fifty.popularity,Top_fifty.acousticness,Top_fifty.danceability,Top_fifty.energy, Top_fifty.instrumentalness,Top_fifty.loudness,Top_fifty.speechiness,Top_fifty.valence, Top_fifty.tempo).all() session.close() all_song = [] count = 1 for Title,popularity,acousticness,danceability,energy,instrumentalness, loudness, speechiness, valence, tempo in results: if Title: song_detail = {} song_detail["Title"] = Title song_detail["popularity"] = popularity song_detail["acousticness"] = acousticness song_detail["danceability"] = danceability song_detail["energy"] = energy song_detail["instrumentalness"] = instrumentalness song_detail["loudness"] = loudness song_detail["speechiness"] = speechiness song_detail["valence"] = valence song_detail["tempo"] = tempo song_detail["rank_in_May"] = count count+=1 all_song.append(song_detail) return jsonify(all_song) @app.route("/api/v1.0/top_50_2019") def all_year(): session = Session(engine) result = session.query(All_year_top.Title, All_year_top.Popularity,All_year_top.Acousticness,All_year_top.Danceability,All_year_top.Energy, All_year_top.Liveness,All_year_top.Loudness,All_year_top.Speechiness,All_year_top.Valence, All_year_top.BPM, All_year_top.Rank).all() session.close() all_song = [] for Title,popularity,acousticness,danceability,energy,liveness, loudness, speechiness, valence, BPM, rank in result: if Title: song_detail = {} song_detail["Title"] = Title song_detail["popularity"] = popularity song_detail["acousticness"] = acousticness song_detail["danceability"] = danceability song_detail["energy"] = energy song_detail["liveness"] = liveness song_detail["loudness"] = loudness song_detail["speechiness"] = speechiness song_detail["valence"] = valence song_detail["BPM"] = BPM song_detail["Rank"] = rank all_song.append(song_detail) return jsonify(all_song) @app.route("/api/v1.0/top50_country_2020_May") def world_May(): session = Session(engine) world_top = session.query(world.Country, world.Rank, world.Title, world.Artists, world.Album).all() session.close() all_world = [] country_rank = {} for country, rank, title, artists, album in world_top: if country not in country_rank: country_rank[country] = [] song_detail = {} song_detail["Rank"] = rank song_detail["Title"] = title song_detail["Artists"] = artists song_detail["Album"] = album country_rank[country].append(song_detail) all_world.append(country_rank) return jsonify(all_world) if __name__ == '__main__': app.run(debug=True)
[ "qinqin.zha@gmail.com" ]
qinqin.zha@gmail.com
9e7202b2d72c068da23e38e2765dd64abbe128ce
52953b88fd65e64d13c0b8ebeae5d468305c01ea
/schema.py
cc90f9184c3d88b61fbb852d2641f19a86a0a955
[]
no_license
antdood/EmojiCounter
ed77a77d032bda93e4f4cffc1145a5f74d90d7d3
e3aace43cf7da57eb57f9603ab9c8d2c28762310
refs/heads/main
2023-03-28T16:26:11.846775
2021-04-10T23:59:08
2021-04-10T23:59:08
350,259,821
0
0
null
null
null
null
UTF-8
Python
false
false
151
py
CREATE TABLE emoji_usages ( channel BIGINT UNSIGNED, user BIGINT UNSIGNED, emoji VARCHAR(255), type ENUM('original', 'custom'), time TIMESTAMP );
[ "antawck@gmail.com" ]
antawck@gmail.com
d2d223dd5e9968b7af2c5e4232ccc82f887bf845
d3709ef033ddf881449c515936b448370a2625ee
/leetcode/058.Length of Last Word/common.py
58b6c30db0277230261076d8bd602b61eac1e818
[]
no_license
xjhuang1993/PythonExercise
e7d7b12497986798184fa78a4e44764961237a56
444db0876295ee2dca2bd2c6103eb5fca4d643be
refs/heads/master
2020-03-19T01:03:26.988279
2018-06-06T01:53:35
2018-06-06T01:53:35
135,518,973
0
0
null
null
null
null
UTF-8
Python
false
false
224
py
""" Accepted """ def func(s): if not s: return 0 result_list = s.strip().split(" ") result = len(result_list[-1]) return result if __name__ == "__main__": s = "Hello World" print(func(s))
[ "398478432@qq.com" ]
398478432@qq.com