ytzi/the-stack-dedup-python-filtered · Datasets at Hugging Face

3e105caf515da97595cf131c9228511ab5a47c2b

313

py

Python

2-mouth02/socket/communnication.py

gary-gggggg/gary

d8ba30ea4bc2b662a2d6a87d247f813e5680d63e

[ "Apache-2.0" ]

4

2021-02-01T10:28:11.000Z

2021-02-01T10:34:40.000Z

2-mouth02/socket/communnication.py

gary-gggggg/gary

d8ba30ea4bc2b662a2d6a87d247f813e5680d63e

[ "Apache-2.0" ]

null

2-mouth02/socket/communnication.py

gary-gggggg/gary

d8ba30ea4bc2b662a2d6a87d247f813e5680d63e

[ "Apache-2.0" ]

null

from socket import * a=input("IP") b=input("") ADDR = ("176.17.12.178", 31414) giao = socket(AF_INET, SOCK_DGRAM) while 1: m = input(":") if not m: break else: giao.sendto(m.encode(), ADDR) d, a = giao.recvfrom(1024) print("", d.decode()) giao.close()

18.411765

37

0.5623

3e11bd4f5fe50f533d78b84480d62520eb696807

151

py

Python

NhMedicalSite/panel/urls.py

Dogruyer/ecommerce

aa505b401e42882a96e6ef6375bd1a1ed95c5b85

[ "Apache-2.0" ]

null

NhMedicalSite/panel/urls.py

Dogruyer/ecommerce

aa505b401e42882a96e6ef6375bd1a1ed95c5b85

[ "Apache-2.0" ]

null

NhMedicalSite/panel/urls.py

Dogruyer/ecommerce

aa505b401e42882a96e6ef6375bd1a1ed95c5b85

[ "Apache-2.0" ]

1

2018-11-01T11:10:58.000Z

from django.conf.urls import url from django.conf import settings from panel.views import * urlpatterns = [ url(r'^$', index, name='index'), ]

13.727273

36

0.688742

3e11beb96e30d1e453934e9af1acf5d6478cd742

244

py

Python

nice_paintig.py

rushdi21-meet/meet2019y1lab6

e87c2f04593c8f7e3a5c1c66260c49a3690db90c

[ "MIT" ]

null

nice_paintig.py

rushdi21-meet/meet2019y1lab6

e87c2f04593c8f7e3a5c1c66260c49a3690db90c

[ "MIT" ]

null

nice_paintig.py

rushdi21-meet/meet2019y1lab6

e87c2f04593c8f7e3a5c1c66260c49a3690db90c

[ "MIT" ]

null

import turtle color=["green", "yellow",'orange','blue','pruple','red','pink'] x=10 y= 270 i=0 turtle.bgcolor("black") while True: turtle.color(color[0]) turtle.forward(x) turtle.left(y) x+=10 y-=1 i+=1 turtle.mainloop()

16.266667

63

0.614754

3e1247da76756de4876b84765ac8609022ba7513

2,446

py

Python

enzynet/models.py

gdarkwah/enzynet

7367635ae73595822133577054743a4c4c327cf3

[ "MIT" ]

189

2017-07-20T22:16:22.000Z

2022-02-21T17:57:41.000Z

enzynet/models.py

gdarkwah/enzynet

7367635ae73595822133577054743a4c4c327cf3

[ "MIT" ]

16

2019-05-09T14:47:44.000Z

2021-09-19T00:25:59.000Z

enzynet/models.py

gdarkwah/enzynet

7367635ae73595822133577054743a4c4c327cf3

[ "MIT" ]

93

2017-07-20T22:55:41.000Z

2022-03-12T19:42:14.000Z

"""Model definitions.""" # Authors: Afshine Amidi <[email protected]> # Shervine Amidi <[email protected]> # MIT License import numpy as np from enzynet import constants from keras import initializers from keras import layers from keras.layers import advanced_activations from keras import models from keras import regularizers def enzynet(input_v_size: int, n_channels: int) -> models.Sequential: """Returns EnzyNet as a Keras model.""" # Parameters. stddev_conv3d = np.sqrt(2.0/n_channels) # Initialization. model = models.Sequential() # Add layers. model.add( layers.Conv3D( filters=32, kernel_size=9, strides=2, padding='valid', kernel_initializer=initializers.RandomNormal( mean=0.0, stddev=stddev_conv3d * 9 ** (-3 / 2)), bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=None, input_shape=(input_v_size,)*constants.N_DIMENSIONS + (n_channels,))) model.add(advanced_activations.LeakyReLU(alpha=0.1)) model.add(layers.Dropout(rate=0.2)) model.add( layers.Conv3D( filters=64, kernel_size=5, strides=1, padding='valid', kernel_initializer=initializers.RandomNormal( mean=0.0, stddev=stddev_conv3d * 5 ** (-3 / 2)), bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=None)) model.add(advanced_activations.LeakyReLU(alpha=0.1)) model.add(layers.MaxPooling3D(pool_size=(2, 2, 2))) model.add(layers.Dropout(rate=0.3)) model.add(layers.Flatten()) model.add( layers.Dense( units=128, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=None)) model.add(layers.Dropout(rate=0.4)) model.add( layers.Dense( units=constants.N_CLASSES, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=None)) model.add(layers.Activation('softmax')) return model

28.114943

80

0.618561

3e13f1a614fdcd99556bcda63d31e15a470031fa

998

py

Python

entity/address_entity.py

rpinaa/python-flask-microservice

3e479bd1653f8e846ae0b84ffb4f20481dfc7c5d

[ "MIT" ]

1

2019-09-23T20:00:52.000Z

entity/address_entity.py

rpinaa/python-flask-microservice

3e479bd1653f8e846ae0b84ffb4f20481dfc7c5d

[ "MIT" ]

null

entity/address_entity.py

rpinaa/python-flask-microservice

3e479bd1653f8e846ae0b84ffb4f20481dfc7c5d

[ "MIT" ]

null

from db import db

39.92

99

0.700401

3e140c63bd33992dd5d90e07a79edb1db5f260ce

10,357

py

Python

FeatureCloud/api/cli/test/commands.py

FeatureCloud/FeatureCloud

3421bc9621201ae4a888192f09886122b0cb571a

[ "Apache-2.0" ]

null

FeatureCloud/api/cli/test/commands.py

FeatureCloud/FeatureCloud

3421bc9621201ae4a888192f09886122b0cb571a

[ "Apache-2.0" ]

null

FeatureCloud/api/cli/test/commands.py

FeatureCloud/FeatureCloud

3421bc9621201ae4a888192f09886122b0cb571a

[ "Apache-2.0" ]

null

import os import click import requests from FeatureCloud.api.imp.exceptions import FCException from FeatureCloud.api.imp.test import commands from FeatureCloud.api.cli.test.workflow.commands import workflow test.add_command(workflow) if __name__ == "__main__": test()

51.527363

220

0.663609

3e14c4fe464f76c3e655c88c87bd66bc84933f25

4,188

py

Python

axi_plot/utils.py

zoso95/axi_plot

1a8c1f601c75e149d60377ccc4a437c33b3620bb

[ "MIT" ]

null

axi_plot/utils.py

zoso95/axi_plot

1a8c1f601c75e149d60377ccc4a437c33b3620bb

[ "MIT" ]

null

axi_plot/utils.py

zoso95/axi_plot

1a8c1f601c75e149d60377ccc4a437c33b3620bb

[ "MIT" ]

null

import subprocess import logging import os, time from pathlib import Path from shutil import copyfile import pandas as pd from datetime import datetime def res_plot(filename, config, checkpoint_file): """ base_commands = ['axicli', filename, '--config', config, '--mode', 'res_plot'] end_commands = ['-o', checkpoint_file] commands = base_commands + end_commands process = subprocess.run(commands, stdout=subprocess.PIPE, universal_newlines=True) return process.stdout """ raise Exception() def backup_drawing(file): """ Check to see if $PLOTTER_BACKUP exists. If it does, then copy over the file if it doesnt exist, and add to the print logs that we are printing it. """ if 'PLOTTER_BACKUP' in os.environ: logging.info("backing up {}".format(file)) filename = os.path.basename(file) backup_dir = os.path.join(os.environ.get('PLOTTER_BACKUP')) backup_path = os.path.join(backup_dir, filename) if not os.path.exists(backup_path): copyfile(file, backup_path) print_logs = os.path.join(backup_dir, "print_logs.csv") if os.path.exists(print_logs): logs = pd.read_csv(print_logs) else: logs = pd.DataFrame({}) df = pd.DataFrame([{'name':filename, 'time_printed':datetime.now().strftime('%Y-%m-%d %H:%M')}], columns=['name', 'time_printed']) logs = logs.append(df, sort=False) logs.to_csv(print_logs, index=False) else: logging.info("Skipping backup for {}, no $PLOTTER_BACKUP path given".format(file))

36.417391

138

0.680755

3e14f76f2adf0f315a94c191c5946f1de65d9fa9

5,258

py

Python

scripts/regions_optimize.py

jason-neal/Starfish

4ffa45e0190fb6f3262511d57d1a563e5ee711de

[ "BSD-3-Clause" ]

1

2017-07-10T00:06:36.000Z

scripts/regions_optimize.py

jason-neal/Starfish

4ffa45e0190fb6f3262511d57d1a563e5ee711de

[ "BSD-3-Clause" ]

null

scripts/regions_optimize.py

jason-neal/Starfish

4ffa45e0190fb6f3262511d57d1a563e5ee711de

[ "BSD-3-Clause" ]

5

2016-06-11T09:48:16.000Z

2019-08-07T19:52:41.000Z

#!/usr/bin/env python import argparse parser = argparse.ArgumentParser(prog="region_optimize.py", description="Find the kernel parameters for Gaussian region zones.") parser.add_argument("spectrum", help="JSON file containing the data, model, and residual.") parser.add_argument("--sigma0", type=float, default=2, help="(AA) to use in fitting") args = parser.parse_args() import json import numpy as np from scipy.optimize import fmin from scipy.linalg import cho_factor, cho_solve from numpy.linalg import slogdet import Starfish from Starfish.model import PhiParam from Starfish.covariance import get_dense_C, make_k_func from Starfish import constants as C # Load the spectrum and then take the data products. f = open(args.spectrum, "r") read = json.load(f) # read is a dictionary f.close() wl = np.array(read["wl"]) # data_full = np.array(read["data"]) # model = np.array(read["model"]) resid = np.array(read["resid"]) sigma = np.array(read["sigma"]) spectrum_id = read["spectrum_id"] order = read["order"] fname = Starfish.specfmt.format(spectrum_id, order) + "regions.json" f = open(fname, "r") read = json.load(f) # read is a dictionary f.close() mus = np.array(read["mus"]) assert spectrum_id == read["spectrum_id"], "Spectrum/Order mismatch" assert order == read["order"], "Spectrum/Order mismatch" # Load the guesses for the global parameters from the .json # If the file exists, optionally initiliaze to the chebyshev values fname = Starfish.specfmt.format(spectrum_id, order) + "phi.json" try: phi = PhiParam.load(fname) except FileNotFoundError: print("No order parameter file found (e.g. sX_oXXphi.json), please run `star.py --initPhi` first.") raise # Puposely set phi.regions to none for this exercise, since we don't care about existing regions, and likely we want to overwrite them. phi.regions = None def optimize_region_residual(wl, residuals, sigma, mu): ''' Determine the optimal parameters for the line kernels by fitting a Gaussian directly to the residuals. ''' # Using sigma0, truncate the wavelength vector and residulas to include # only those portions that fall in the range [mu - sigma, mu + sigma] ind = (wl > mu - args.sigma0) & (wl < mu + args.sigma0) wl = wl[ind] R = residuals[ind] sigma = sigma[ind] sigma_mat = phi.sigAmp * sigma**2 * np.eye(len(wl)) max_r = 6.0 * phi.l # [km/s] k_func = make_k_func(phi) # Use the full covariance matrix when doing the likelihood eval CC = get_dense_C(wl, k_func=k_func, max_r=max_r) + sigma_mat factor, flag = cho_factor(CC) logdet = np.sum(2 * np.log((np.diag(factor)))) rr = C.c_kms/mu * np.abs(mu - wl) # Km/s par = Starfish.config["region_params"] p0 = np.array([10**par["logAmp"], par["sigma"]]) f = lambda x: -fprob(x) try: p = fmin(f, p0, maxiter=10000, maxfun=10000, disp=False) # print(p) return p except np.linalg.linalg.LinAlgError: return p0 def optimize_region_covariance(wl, residuals, sigma, mu): ''' Determine the optimal parameters for the line kernels by actually using a chunk of the covariance matrix. Note this actually uses the assumed global parameters. ''' # Using sigma0, truncate the wavelength vector and residulas to include # only those portions that fall in the range [mu - sigma, mu + sigma] ind = (wl > mu - args.sigma0) & (wl < mu + args.sigma0) wl = wl[ind] R = residuals[ind] sigma = sigma[ind] sigma_mat = phi.sigAmp * sigma**2 * np.eye(len(wl)) max_rl = 6.0 * phi.l # [km/s] # Define a probability function for the residuals par = Starfish.config["region_params"] p0 = np.array([par["logAmp"], par["sigma"]]) f = lambda x: -fprob(x) try: p = fmin(f, p0, maxiter=10000, maxfun=10000) print(p) return p except np.linalg.linalg.LinAlgError: return p0 # Regions will be a 2D array with shape (nregions, 3) regions = [] for mu in mus: # amp, sig = optimize_region_residual(wl, resid, sigma, mu) # regions.append([np.log10(np.abs(amp)), mu, sig]) logAmp, sig = optimize_region_covariance(wl, resid, sigma, mu) regions.append([logAmp, mu, sig]) # Add these values back to the phi parameter file and save phi.regions = np.array(regions) phi.save()

30.929412

135

0.659947

3e15b565f2c5c8e4188c7106981c4468935c3719

2,261

py

Python

Bases/download_bases.py

lucas26xd/Estudo-Dados-COVID19-BR

cba0278e1cbd2464b4b4c7faa866d05d9968247d

[ "MIT" ]

null

Bases/download_bases.py

lucas26xd/Estudo-Dados-COVID19-BR

cba0278e1cbd2464b4b4c7faa866d05d9968247d

[ "MIT" ]

null

Bases/download_bases.py

lucas26xd/Estudo-Dados-COVID19-BR

cba0278e1cbd2464b4b4c7faa866d05d9968247d

[ "MIT" ]

null

import requests from urllib.request import urlopen from bs4 import BeautifulSoup print('Pegando informaes para download das bases...') urls_bases, last_updates = get_urls_and_last_updates() if len(urls_bases) > 0: print('Iniciando Downloads...') progress = 0 for url in urls_bases: print(f'Baixando {url[url.rfind("/") + 1:]} - {last_updates[progress]} - ({progress + 1:0>2}/{len(urls_bases)})') download(url) progress += 1 else: print('Problema ao resgatar as URLs das bases!')

39.666667

132

0.587793

3e16ddbf593ddf87a424ef3546058ed337f938d3

10,699

py

Python

rax/_src/utils_test.py

google/rax

d6370d574246db9fb0566317f7cac8cd331526d7

[ "Apache-2.0" ]

19

2022-01-25T12:37:51.000Z

2022-03-30T17:12:45.000Z

rax/_src/utils_test.py

google/rax

d6370d574246db9fb0566317f7cac8cd331526d7

[ "Apache-2.0" ]

1

2022-02-08T23:02:42.000Z

rax/_src/utils_test.py

google/rax

d6370d574246db9fb0566317f7cac8cd331526d7

[ "Apache-2.0" ]

null

# Copyright 2022 Google LLC. # # 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. # pytype: skip-file """Tests for rax._src.utils.""" import doctest from absl.testing import absltest import jax import jax.numpy as jnp import numpy as np import rax from rax._src import utils def load_tests(loader, tests, ignore): del loader, ignore # Unused. tests.addTests( doctest.DocTestSuite( utils, extraglobs={ "jax": jax, "jnp": jnp, "rax": rax })) return tests if __name__ == "__main__": absltest.main()

34.291667

79

0.639873

3e182689577a11bad1e8f7437a3d622ced715f94

427

py

Python

examples/decorators.py

FusionSid/FusionSidAPI.py

e1b50622bf4fcec8265f8fd4e9b3ac79b580d286

[ "MIT" ]

5

2022-03-05T23:29:33.000Z

2022-03-20T07:44:20.000Z

examples/decorators.py

FusionSid/FusionSidAPI.py

e1b50622bf4fcec8265f8fd4e9b3ac79b580d286

[ "MIT" ]

null

examples/decorators.py

FusionSid/FusionSidAPI.py

e1b50622bf4fcec8265f8fd4e9b3ac79b580d286

[ "MIT" ]

null

import asyncio from fusionsid import Decorators deco = Decorators do_roast = deco.roast loop = asyncio.new_event_loop() loop.run_until_complete(main())

23.722222

97

0.744731

3e188c93ed7a3552c4548ac6fc5970107dcdbcdb

2,303

py

Python

configs/raubtierv2b/centripetalnet_hourglass104_mstest_16x6_210e_coco_raubtierv2b_2gpu.py

esf-bt2020/mmdetection

abc5fe060e0fcb716f845c85441be3741b22d3cf

[ "Apache-2.0" ]

null

configs/raubtierv2b/centripetalnet_hourglass104_mstest_16x6_210e_coco_raubtierv2b_2gpu.py

esf-bt2020/mmdetection

abc5fe060e0fcb716f845c85441be3741b22d3cf

[ "Apache-2.0" ]

null

configs/raubtierv2b/centripetalnet_hourglass104_mstest_16x6_210e_coco_raubtierv2b_2gpu.py

esf-bt2020/mmdetection

abc5fe060e0fcb716f845c85441be3741b22d3cf

[ "Apache-2.0" ]

null

# Basiskonfigurationsfile _base_ = '../centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py' model = dict( type='CornerNet', backbone=dict( type='HourglassNet', downsample_times=5, num_stacks=2, stage_channels=[256, 256, 384, 384, 384, 512], stage_blocks=[2, 2, 2, 2, 2, 4], norm_cfg=dict(type='BN', requires_grad=True)), neck=None, bbox_head=dict( type='CentripetalHead', num_classes=3, in_channels=256, num_feat_levels=2, corner_emb_channels=0, loss_heatmap=dict( type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1), loss_guiding_shift=dict( type='SmoothL1Loss', beta=1.0, loss_weight=0.05), loss_centripetal_shift=dict( type='SmoothL1Loss', beta=1.0, loss_weight=1)) ) dataset_type = 'COCODataset' classes = ('luchs', 'rotfuchs', 'wolf') data = dict( samples_per_gpu=3, #default 6 workers_per_gpu=1, #default 3 train=dict( img_prefix='customData/train/', classes=classes, ann_file='customData/train/_annotations.coco.json'), val=dict( img_prefix='customData/valid/', classes=classes, ann_file='customData/valid/_annotations.coco.json'), test=dict( img_prefix='customData/test/', classes=classes, ann_file='customData/test/_annotations.coco.json')) #optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) #8 GPUs => 8*6=48 optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001) #2 GPUs => 2*3=6 => 6/48= 1/8 cheetah #optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001) #(1x6=6) evaluation = dict(classwise=True, interval=4, metric='bbox') load_from = 'checkpoints/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth' work_dir = '/media/storage1/projects/WilLiCam/checkpoint_workdir/centripetalnet_hourglass104_mstest_16x6_210e_coco_raubtierv2b_2gpu' #http://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth

37.145161

191

0.691706

3e1c92be5d3fa432577c6a625de6487e656413d6

3,175

py

Python

firecares/firestation/tests/test_feedback.py

FireCARES/firecares

aa708d441790263206dd3a0a480eb6ca9031439d

[ "MIT" ]

12

2016-01-30T02:28:35.000Z

2019-05-29T15:49:56.000Z

firecares/firestation/tests/test_feedback.py

FireCARES/firecares

aa708d441790263206dd3a0a480eb6ca9031439d

[ "MIT" ]

455

2015-07-27T20:21:56.000Z

2022-03-11T23:26:20.000Z

firecares/firestation/tests/test_feedback.py

FireCARES/firecares

aa708d441790263206dd3a0a480eb6ca9031439d

[ "MIT" ]

14

2015-07-29T09:45:53.000Z

2020-10-21T20:03:17.000Z

import json import mock import os from django.contrib.auth import get_user_model from django.core import mail from django.core.urlresolvers import reverse from django.test import Client from firecares.firestation.models import FireDepartment, FireStation, DataFeedback from firecares.firecares_core.tests.base import BaseFirecaresTestcase User = get_user_model()

42.333333

110

0.627402

3e1d0a3af98bd0674017ebccc343c54e9d715f7e

325

py

Python

reactivex/scheduler/mainloop/__init__.py

christiansandberg/RxPY

036027d2858ea6c9d45839c863bd791e5bb50c36

[ "MIT" ]

null

reactivex/scheduler/mainloop/__init__.py

christiansandberg/RxPY

036027d2858ea6c9d45839c863bd791e5bb50c36

[ "MIT" ]

null

reactivex/scheduler/mainloop/__init__.py

christiansandberg/RxPY

036027d2858ea6c9d45839c863bd791e5bb50c36

[ "MIT" ]

null

from .gtkscheduler import GtkScheduler from .pygamescheduler import PyGameScheduler from .qtscheduler import QtScheduler from .tkinterscheduler import TkinterScheduler from .wxscheduler import WxScheduler __all__ = [ "GtkScheduler", "PyGameScheduler", "QtScheduler", "TkinterScheduler", "WxScheduler", ]

23.214286

46

0.775385

3e1d352c7922c834041d16e3fdaa531ed2d9b63c

75

py

Python

map2annotation/__main__.py

aas-integration/integration-test2

dc9a9b4593cd59841f0d8348056cbff80a9c2a21

[ "MIT" ]

3

2016-10-10T20:18:51.000Z

2018-05-01T19:42:10.000Z

map2annotation/__main__.py

aas-integration/integration-test2

dc9a9b4593cd59841f0d8348056cbff80a9c2a21

[ "MIT" ]

38

2016-08-22T03:20:25.000Z

2018-06-11T19:13:05.000Z

map2annotation/__main__.py

aas-integration/integration-test2

dc9a9b4593cd59841f0d8348056cbff80a9c2a21

[ "MIT" ]

7

2016-08-29T17:37:42.000Z

2022-01-28T00:30:10.000Z

import map2annotation if __name__ == '__main__': map2annotation.main()

18.75

26

0.746667

3e1f67673a3fc56ac1f18b117525630eebc9c6b7

207

py

Python

convenient_ai/nlp/spacy/types/RulePattern.py

leftshiftone/convenient-nlp

8f24ff070ffaa2117af02ae16aaf00bc4d7cf32f

[ "MIT" ]

null

convenient_ai/nlp/spacy/types/RulePattern.py

leftshiftone/convenient-nlp

8f24ff070ffaa2117af02ae16aaf00bc4d7cf32f

[ "MIT" ]

null

convenient_ai/nlp/spacy/types/RulePattern.py

leftshiftone/convenient-nlp

8f24ff070ffaa2117af02ae16aaf00bc4d7cf32f

[ "MIT" ]

null

import dataclasses from dataclasses import dataclass

14.785714

39

0.695652

3e1ff4f3721e11c343d5fecb39e8c79bd6aafa15

1,014

py

Python

cocos2d/tools/cocos2d-console/plugins/plugin_run.py

meiry/Cocos2d-x-EarthWarrior3D-win-desktop-version

6611cf5bfac64e22f0b053681ef9bf563f93f06c

[ "MIT" ]

7

2015-05-19T07:54:48.000Z

2021-12-17T06:01:24.000Z

cocos2d/tools/cocos2d-console/plugins/plugin_run.py

meiry/Cocos2d-x-EarthWarrior3D-win-desktop-version

6611cf5bfac64e22f0b053681ef9bf563f93f06c

[ "MIT" ]

null

cocos2d/tools/cocos2d-console/plugins/plugin_run.py

meiry/Cocos2d-x-EarthWarrior3D-win-desktop-version

6611cf5bfac64e22f0b053681ef9bf563f93f06c

[ "MIT" ]

4

2015-11-24T06:27:16.000Z

2021-05-29T10:50:44.000Z

#!/usr/bin/python # ---------------------------------------------------------------------------- # cocos2d "install" plugin # # Authr: Luis Parravicini # # License: MIT # ---------------------------------------------------------------------------- ''' "run" plugin for cocos2d command line tool ''' __docformat__ = 'restructuredtext' import sys import os import cocos2d

24.142857

78

0.554241

3e22427e89b56fa4293c96f943f7ce0b77c3a1a7

2,759

py

Python

source/configuration.py

yux1991/PyRHEED

b39ad03651c92e3649069919ae48b1e5158cd3dd

[ "MIT" ]

14

2019-01-08T14:32:31.000Z

2021-11-17T21:07:10.000Z

source/configuration.py

yux1991/PyRHEED

b39ad03651c92e3649069919ae48b1e5158cd3dd

[ "MIT" ]

2

2019-05-14T08:56:36.000Z

2020-12-22T16:44:30.000Z

source/configuration.py

yux1991/PyRHEED

b39ad03651c92e3649069919ae48b1e5158cd3dd

[ "MIT" ]

4

2019-03-12T20:03:54.000Z

2022-03-08T14:24:46.000Z

import configparser

51.092593

66

0.303733

3e24e04ad5a6a1e6faafb25c71a578a2c2c42a6c

4,772

py

Python

api/api/endpoints/sensor_info.py

andschneider/ss_api

4ddf5cd60d5e0e87e7641e97c9fbe78965c4b522

[ "MIT" ]

null

api/api/endpoints/sensor_info.py

andschneider/ss_api

4ddf5cd60d5e0e87e7641e97c9fbe78965c4b522

[ "MIT" ]

2

2019-12-26T17:31:56.000Z

2020-01-06T19:45:05.000Z

api/api/endpoints/sensor_info.py

andschneider/soil_sense

4ddf5cd60d5e0e87e7641e97c9fbe78965c4b522

[ "MIT" ]

null

import datetime import json from flask import Response, request, Blueprint from flask_jwt_extended import jwt_required from flask_restplus import Api, Namespace, Resource, reqparse from sqlalchemy.exc import IntegrityError from api.core.db_execptions import bad_db_response from api.core.models import SensorInfoModel, SensorDataModel from api import db api = Namespace( "sensor_info", description="Sensor information: sensor id, plant name, and moisture alert level.", ) post_args = reqparse.RequestParser() post_args.add_argument("plant", type=str, required=True, help="Plant name.") post_args.add_argument( "alert_level", type=int, required=True, help="Alert level for moisture." )

33.843972

111

0.573135

3e24efcd76f37df58a59baf6375172236bdbc8f9

116

py

Python

notifications/utils.py

Natgeoed/django-notifications

a73c01f167bdf796b609d44a2be5323d08180b7f

[ "BSD-3-Clause" ]

1

2019-05-28T14:55:58.000Z

notifications/utils.py

Natgeoed/django-notifications

a73c01f167bdf796b609d44a2be5323d08180b7f

[ "BSD-3-Clause" ]

null

notifications/utils.py

Natgeoed/django-notifications

a73c01f167bdf796b609d44a2be5323d08180b7f

[ "BSD-3-Clause" ]

1

2020-03-29T10:13:28.000Z

# -*- coding: utf-8 -*-

14.5

30

0.603448

3e263e2d36efcfc4b3135f0a65636317114a2c8d

995

py

Python

hash calculator.py

Andrea1141/hash-calculator

182d2f9bcfa0227ad70f7fdb03dde4599717cafa

[ "MIT" ]

1

2021-10-02T12:48:25.000Z

hash calculator.py

Andrea1141/hash-calculator

182d2f9bcfa0227ad70f7fdb03dde4599717cafa

[ "MIT" ]

null

hash calculator.py

Andrea1141/hash-calculator

182d2f9bcfa0227ad70f7fdb03dde4599717cafa

[ "MIT" ]

1

2021-10-18T12:34:26.000Z

import tkinter, hashlib root = tkinter.Tk() root.title("Hash Calculator") label = tkinter.Label(text="Write the string to hash") label.pack() option = tkinter.StringVar() option.set("sha224") string = tkinter.StringVar() entry = tkinter.Entry(root, textvariable=string, width=150, justify="center") entry.pack() hexdigest = tkinter.StringVar() label = tkinter.Entry(text="", textvariable=hexdigest, width=150, justify="center", state="readonly") label.pack() string.trace_add("write", callback) option.trace_add("write", callback) algorithms = ["sha224", "sha1", "blake2s", "sha3_384", "sha256", "blake2b", "sha384", "sha3_256", "sha3_512", "md5", "sha512", "sha3_224"] menu = tkinter.OptionMenu(root, option, *algorithms) menu.pack() callback() root.mainloop()

28.428571

139

0.684422

3e28aa85ecfaa56c22716b2abd9f954c4b0ab246

136

py

Python

tensorflow/__init__.py

vsilyaev/tensorflow

f41959ccb2d9d4c722fe8fc3351401d53bcf4900

[ "Apache-2.0" ]

4

2015-11-10T14:11:39.000Z

2021-11-17T11:11:25.000Z

tensorflow/__init__.py

TheRockStarDBA/tensorflow

db0b5da485e1d1f23003ee08ed2e191451ee0319

[ "Apache-2.0" ]

null

tensorflow/__init__.py

TheRockStarDBA/tensorflow

db0b5da485e1d1f23003ee08ed2e191451ee0319

[ "Apache-2.0" ]

2

2015-11-13T21:11:49.000Z

2015-11-29T04:13:49.000Z

# Bring in all of the public TensorFlow interface into this # module. # pylint: disable=wildcard-import from tensorflow.python import *

27.2

59

0.786765

3e28b00c8be476ae7052942943b00801e357b52e

4,337

py

Python

cpauto/objects/threat.py

krnnrt/cpauto

6076ee0e3f55769aac5b2480453d82f99371a31f

[ "Apache-2.0" ]

16

2016-12-07T02:45:31.000Z

2022-01-20T11:46:24.000Z

cpauto/objects/threat.py

krnnrt/cpauto

6076ee0e3f55769aac5b2480453d82f99371a31f

[ "Apache-2.0" ]

2

2017-07-20T21:12:27.000Z

2021-09-09T14:57:01.000Z

cpauto/objects/threat.py

krnnrt/cpauto

6076ee0e3f55769aac5b2480453d82f99371a31f

[ "Apache-2.0" ]

5

2017-07-28T14:06:25.000Z

2021-09-06T12:01:18.000Z

# -*- coding: utf-8 -*- # Copyright 2016 Dana James Traversie and Check Point Software Technologies, Ltd. 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. # cpauto.objects.threat # ~~~~~~~~~~~~~~~~~~~~~ """This module contains the classes needed to manage threat prevention objects.""" from ._common import _CommonClient

44.255102

113

0.679733

3e28e0f9797870a68b28678349b8f468bf2771ae

387

py

Python

src/tandlr/notifications/routing.py

shrmoud/schoolapp

7349ce18f56658d67daedf5e1abb352b5c15a029

[ "Apache-2.0" ]

null

src/tandlr/notifications/routing.py

shrmoud/schoolapp

7349ce18f56658d67daedf5e1abb352b5c15a029

[ "Apache-2.0" ]

null

src/tandlr/notifications/routing.py

shrmoud/schoolapp

7349ce18f56658d67daedf5e1abb352b5c15a029

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from channels.staticfiles import StaticFilesConsumer from tandlr.notifications import consumers channel_routing = { 'http.request': StaticFilesConsumer(), # Wire up websocket channels to our consumers: 'websocket.connect': consumers.ws_connect, 'websocket.receive': consumers.ws_receive, 'websocket.disconnect': consumers.ws_disconnect, }

25.8

52

0.74677

3e297317547f88cd2d57145599c9dcd9b0299b5a

646

py

Python

2018/d03.py

m1el/advent-of-code

0944579fd58c586ce5a72b4152c5105ec07846a1

[ "MIT" ]

null

2018/d03.py

m1el/advent-of-code

0944579fd58c586ce5a72b4152c5105ec07846a1

[ "MIT" ]

null

2018/d03.py

m1el/advent-of-code

0944579fd58c586ce5a72b4152c5105ec07846a1

[ "MIT" ]

null

from collections import defaultdict, Counter from itertools import product import re with open('03.txt') as fd: inp = [] for l in fd.readlines(): groups = re.findall(r'\d+', l) inp.append(list(map(int, groups))) claims = defaultdict(int) for (id, l,t, w,h) in inp: for y in range(t,t+h): for x in range(l,l+w): claims[(x,y)] += 1 c=0 for n in claims.values(): if n > 1: c+= 1 print(c) for (id, l,t, w,h) in inp: bad = False for y in range(t,t+h): for x in range(l,l+w): if claims[(x,y)] > 1: bad = True break if bad: break if not bad: print(id)

20.1875

45

0.547988

3e2a44b8d417cc833a2bb62cb532d7fa7ff0e6b8

2,591

py

Python

files/lambda/tagger.py

mbasri/generic-spot-cluster

cccfbee4660ae26742e1442f495dc9f523d0a2fd

[ "MIT" ]

1

2019-12-24T18:53:34.000Z

files/lambda/tagger.py

mbasri/generic-spot-cluster

cccfbee4660ae26742e1442f495dc9f523d0a2fd

[ "MIT" ]

null

files/lambda/tagger.py

mbasri/generic-spot-cluster

cccfbee4660ae26742e1442f495dc9f523d0a2fd

[ "MIT" ]

null

import os import sys import logging import boto3

22.530435

81

0.582015

3e2c2255a47604390ebe0475399a05f36907ad33

4,478

py

Python

Python/biopsy/data/bergman_fly_motifs.py

JohnReid/biopsy

1eeb714ba5b53f2ecf776d865d32e2078cbc0338

[ "MIT" ]

null

Python/biopsy/data/bergman_fly_motifs.py

JohnReid/biopsy

1eeb714ba5b53f2ecf776d865d32e2078cbc0338

[ "MIT" ]

null

Python/biopsy/data/bergman_fly_motifs.py

JohnReid/biopsy

1eeb714ba5b53f2ecf776d865d32e2078cbc0338

[ "MIT" ]

null

# # Copyright John Reid 2010 # """ Code to deal with the Bergman curated set of fly motifs. """ import os, biopsy.data as D, numpy as N import xml.etree.ElementTree as ET def xms_filename(): "@return: The filename of the XMS file where the motifs are stored." return os.path.join(D.data_dir(), "Bergman-Fly-Motifs", "SelexConsensus1.1.xms") def write_as_custom_pssm(f, id_, name, matrix, comments=None, url=None, field_width=3, scale=1): """ Write the motif as a custom PSSM to the file, f. # # Drosophila Hunchback from JASPAR # ID DN-000001 NA D$Hunchback WI 10 PO 01 02 03 04 05 06 07 08 09 10 CA 01 06 09 04 13 16 16 14 15 09 CC 05 08 03 03 01 00 00 00 01 02 CG 08 02 04 01 00 00 00 02 00 02 CT 02 00 00 08 02 00 00 00 00 03 IU G C A T A A A A A A UR None """ if None != comments: print >> f, '#' for comment in comments: print >> f, '# %s' % comment print >> f, '#' print >> f, 'ID %s' % id_ print >> f, 'NA %s' % name print >> f, 'WI %s' % len(matrix) print >> f, 'PO %s' % ' '.join('%*d' % (field_width, i+1) for i in xrange(len(matrix))) for b, tag in enumerate(('CA', 'CC', 'CG', 'CT')): print >> f, '%s %s' % (tag, ' '.join('%*d' % (field_width, int(v)) for v in matrix[:,b]*scale)) print >> f, 'UR %s' % (None != url and url or 'None') def normalise_matrix(matrix): "@return: A normalised version of the argument." return (matrix.T / matrix.sum(axis=1)).T def smooth_matrix_with_pseudo_count(matrix, pseudo_count): "@return: A smoothed version the matrix using the given pseudo counts." smoothed = matrix + pseudo_count return normalise_matrix(smoothed) def write_matrix_to_file(f, id_, name, alphabet, matrix, properties, threshold, scale=1): "Write the matrix to the file in the custom PSSM format." comments = [ 'PSSM parsed from set of fly TFs curated by Bergman.' ] comments.extend('%20s : %s' % (k, v) for k, v in properties.iteritems()) write_as_custom_pssm(f, id_, name, matrix, comments=comments, scale=scale) if '__main__' == __name__: import sys output_dir = '/home/john/Data/custom-pssms' pssm_set_tag = 'BG' scale = 30 pssm_set_f = open(os.path.join(output_dir, 'bergman-fly.pssm_set'), 'w') print >> pssm_set_f, '#' print >> pssm_set_f, '# Set of fly TFs curated by Bergman.' print >> pssm_set_f, '# PSSMs were scaled as if there were %d observations.' % scale print >> pssm_set_f, '#' for i, (name, alphabet, matrix, properties, threshold) in enumerate(parse_xms(open(xms_filename()))): id_ = '%s-%06d' % (pssm_set_tag, i+1) print id_, name print >> pssm_set_f, id_ f = open(os.path.join(output_dir, '%s.pssm' % id_), 'w') properties['Equivalent # observations'] = str(scale) write_matrix_to_file(f, id_, name, alphabet, matrix, properties, threshold, scale=scale) f.close() pssm_set_f.close()

33.41791

105

0.585306

3e2c4ce8c6ded9f25bc03ff3e20ecd6211356ad1

7,950

py

Python

addressbook/views.py

webskate101/django-polymer-addressbook

bf41b6a83e7b9228b383129958488f1c8075c728

[ "Apache-2.0" ]

null

addressbook/views.py

webskate101/django-polymer-addressbook

bf41b6a83e7b9228b383129958488f1c8075c728

[ "Apache-2.0" ]

null

addressbook/views.py

webskate101/django-polymer-addressbook

bf41b6a83e7b9228b383129958488f1c8075c728

[ "Apache-2.0" ]

null

"""Holds the HTTP handlers for the addressbook app.""" from django import db from django import http from django.views import generic import json from django.contrib.auth.decorators import login_required from django.utils.decorators import method_decorator from addressbook import models JSON_XSSI_PREFIX = ")]}'\n" class PersonListRESTHandler(LoginRequiredRESTHandler): """REST handler for multiple person requests.""" class PersonRESTHandler(LoginRequiredRESTHandler): """REST handler for single person requests.""" def _update_person(self, person, update_dict): person.first_name = update_dict['firstName'] person.last_name = update_dict['lastName'] _update_contact_details(person, update_dict) person.save()

33.544304

80

0.684528

3e2dc345629e84a8ce9faa979c3f69774ad29ef7

132

py

Python

backend/actions.py

HiroshiFuu/django-rest-drf-yasg-boilerplate

93221b2dbca0635eb42a18096e805b00f36ff9c1

[ "Apache-2.0" ]

null

backend/actions.py

HiroshiFuu/django-rest-drf-yasg-boilerplate

93221b2dbca0635eb42a18096e805b00f36ff9c1

[ "Apache-2.0" ]

null

backend/actions.py

HiroshiFuu/django-rest-drf-yasg-boilerplate

93221b2dbca0635eb42a18096e805b00f36ff9c1

[ "Apache-2.0" ]

null

from django.http import HttpResponseRedirect from django.urls import reverse from django.utils.translation import gettext_lazy as _

33

54

0.863636

3e2de9f463b88672a9f0881711bb0f7f45018e12

1,124

py

Python

Housing Price/HouseRegression.py

anupriyamranjit/machinelearning

5e1deef38d356fddcedfe0a23094571500c1c82d

[ "MIT" ]

null

Housing Price/HouseRegression.py

anupriyamranjit/machinelearning

5e1deef38d356fddcedfe0a23094571500c1c82d

[ "MIT" ]

null

Housing Price/HouseRegression.py

anupriyamranjit/machinelearning

5e1deef38d356fddcedfe0a23094571500c1c82d

[ "MIT" ]

null

import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import tensorflow as tf import keras import os print(os.listdir("../input")) print("Success") # Any results you write to the current directory are saved as output. # importing models/layers from keras.models import Sequential from keras.layers import Dense print("Success") my_data = pd.read_csv('../input/kc_house_data.csv') my_data.head() #Splitting Data Up predictors = my_data.drop(columns=["price","date"]) output = my_data['price'] print("Success") model = Sequential() n_cols = predictors.shape[1] print("Success") #Dense Layers model.add(Dense(5,activation ="relu", input_shape=(n_cols,))) model.add(Dense(5,activation ="relu")) model.add(Dense(1)) print("Success") #Optimizer model.compile(optimizer="adam", loss ="mean_squared_error") print("Success") #fitting from keras.callbacks import EarlyStopping early_stopping_monitor = EarlyStopping(patience=3) model.fit(predictors,output,validation_split=0.2, epochs=30, callbacks=[early_stopping_monitor]) #prediction prediction = model.predict()

22.039216

96

0.758897

3e2e001920079b806a3731784374226e2f26379a

1,194

py

Python

migrations/versions/29e48091912e_remove_unique_constraint_from_user_table.py

GitauHarrison/somasoma_V1

2d74ad3b58f7e4ea5334e240d5bd30938f615e24

[ "MIT" ]

null

migrations/versions/29e48091912e_remove_unique_constraint_from_user_table.py

GitauHarrison/somasoma_V1

2d74ad3b58f7e4ea5334e240d5bd30938f615e24

[ "MIT" ]

2

2021-11-11T19:04:10.000Z

2021-11-11T19:08:42.000Z

migrations/versions/29e48091912e_remove_unique_constraint_from_user_table.py

GitauHarrison/somasoma_V1

2d74ad3b58f7e4ea5334e240d5bd30938f615e24

[ "MIT" ]

1

2021-09-09T13:44:26.000Z

"""remove unique constraint from user table Revision ID: 29e48091912e Revises: f73df8de1f1f Create Date: 2021-12-22 22:26:20.918461 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '29e48091912e' down_revision = 'f73df8de1f1f' branch_labels = None depends_on = None

30.615385

83

0.69598

3e2e6a8e43d315af581125fc3cb4dc17b915f7a7

6,065

py

Python

VBx/models/resnet.py

Jamiroquai88/VBx

35e7954ac0042ea445dcec657130e2c3c0b94ee0

[ "Apache-2.0" ]

145

2020-02-13T09:08:59.000Z

2022-03-28T02:05:38.000Z

VBx/models/resnet.py

Jamiroquai88/VBx

35e7954ac0042ea445dcec657130e2c3c0b94ee0

[ "Apache-2.0" ]

39

2021-01-12T02:49:37.000Z

2022-02-17T18:49:54.000Z

VBx/models/resnet.py

Jamiroquai88/VBx

35e7954ac0042ea445dcec657130e2c3c0b94ee0

[ "Apache-2.0" ]

44

2020-02-13T03:57:35.000Z

2022-03-31T07:05:09.000Z

'''ResNet in PyTorch. For Pre-activation ResNet, see 'preact_resnet.py'. Reference: [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Deep Residual Learning for Image Recognition. arXiv:1512.03385 ''' import torch import torch.nn as nn import torch.nn.functional as F import math def ResNet101(feat_dim, embed_dim, squeeze_excitation=False): return ResNet(Bottleneck, [3, 4, 23, 3], feat_dim=feat_dim, embed_dim=embed_dim, squeeze_excitation=squeeze_excitation)

40.433333

123

0.622754

3e345a0575b803502ed9bfed61051d0d9fb3fa57

5,159

py

Python

bc/recruitment/utils.py

Buckinghamshire-Digital-Service/buckinghamshire-council

bbbdb52b515bcdfc79a2bd9198dfa4828405370e

[ "BSD-3-Clause" ]

1

2021-02-27T07:27:17.000Z

bc/recruitment/utils.py

Buckinghamshire-Digital-Service/buckinghamshire-council

bbbdb52b515bcdfc79a2bd9198dfa4828405370e

[ "BSD-3-Clause" ]

null

bc/recruitment/utils.py

Buckinghamshire-Digital-Service/buckinghamshire-council

bbbdb52b515bcdfc79a2bd9198dfa4828405370e

[ "BSD-3-Clause" ]

1

2021-06-09T15:56:54.000Z

import json from django import forms from django.contrib.postgres.search import SearchQuery, SearchRank, SearchVector from django.core.exceptions import ValidationError from django.db.models import F from django.db.models.functions import ACos, Cos, Radians, Sin import requests from bc.recruitment.constants import JOB_FILTERS from bc.recruitment.models import JobCategory, RecruitmentHomePage, TalentLinkJob def get_current_search(querydict): """ Returns search query and filters in request.GET as json string """ search = {} if querydict.get("query", None): search["query"] = querydict["query"] if querydict.get("postcode", None): search["postcode"] = querydict["postcode"] # Loop through our filters so we don't just store any query params for filter in JOB_FILTERS: selected = querydict.getlist(filter["name"]) if selected: selected = list(dict.fromkeys(selected)) # Remove duplicate options search[filter["name"]] = sorted(selected) # Sort options alphabetically return json.dumps(search)

34.393333

107

0.652064

3e37452fb8273aa4b7fb354676b63c94081558fd

7,264

py

Python

classification/ClassificationModelLargeViz.py

geigerf/STAG_slim

391e7a8031a7e128509f276113b19fd7f13897ec

[ "Apache-2.0" ]

null

classification/ClassificationModelLargeViz.py

geigerf/STAG_slim

391e7a8031a7e128509f276113b19fd7f13897ec

[ "Apache-2.0" ]

null

classification/ClassificationModelLargeViz.py

geigerf/STAG_slim

391e7a8031a7e128509f276113b19fd7f13897ec

[ "Apache-2.0" ]

null

import argparse import os import shutil import time, math, datetime, re from collections import OrderedDict import torch import torch.nn as nn import torch.nn.parallel import torch.nn.functional as F import torch.backends.cudnn as cudnn import torch.optim import torch.utils.data import torchvision.transforms as transforms import torchvision.datasets as datasets import torchvision.models as models import numpy as np from torch.autograd.variable import Variable from shared.BaseModel import BaseModel from shared.resnet_9x9 import resnet18 from shared import dataset_tools ''' Vizualization version of the final model. '''

31.859649

135

0.56663

3e38f387d0ad96aa627dd060a7aa1188e154c4a3

10,017

py

Python

graphingVisHullTwoD.py

cm-1/2D-External-Visual-Hulls

579e7d18d048d403b636d326840e5cb2a4e3a3e8

[ "MIT" ]

1

2022-02-10T07:07:35.000Z

graphingVisHullTwoD.py

cm-1/2D-External-Visual-Hulls

579e7d18d048d403b636d326840e5cb2a4e3a3e8

[ "MIT" ]

null

graphingVisHullTwoD.py

cm-1/2D-External-Visual-Hulls

579e7d18d048d403b636d326840e5cb2a4e3a3e8

[ "MIT" ]

null

import matplotlib.pyplot as plt import numpy as np from visHullTwoD import Scene, SegmentType #%% #%% world0 = Scene() world1 = Scene() world2 = Scene() world3 = Scene() world4 = Scene() world5 = Scene() world6 = Scene() world7 = Scene() world8 = Scene() world9 = Scene() world10 = Scene() world11 = Scene() world12 = Scene() # These are the tris from Petitjean's diagram polygon1 = [(0, 0), (2.25, 0.5), (1.25, 2.3)] # [(0,3),(1,1),(3,0),(4,0),(3,4)] polygon2 = [(1.15, 3.15), (4, 4), (0.9, 5.25)] # [(1,4),(2,5),(2,1),(1,3)] polygon3 = [(3, 0.7), (4.85, 1.75), (4.85, 3.4)] world0.addPolygon(polygon1) world0.addPolygon(polygon2) world0.addPolygon(polygon3) #world0.addPolygon(polygon4) polygon1 = [(0, 0), (5, 0), (5, 5), (4, 5), (4, 3), (1, 3), (1, 5), (0, 5)] world1.addPolygon(polygon1) polygon1 = [(0, 0), (5, 0), (5, 3), (4, 3), (4, 5), (1, 5), (1, 3), (0, 3)] polygon2 = [(1, 7), (3, 7), (5, 9), (4, 11), (4, 9), (1, 8), (2, 10), (0, 10)] world2.addPolygon(polygon1) world2.addPolygon(polygon2) polygon1 = [(0, 2), (1,1), (2,2), (1,0)] polygon2 = [(3,3), (4,2), (5,3)] # polygon2 = [(p[0] - 3, p[1]) for p in polygon2] # Horizontal flip for testing purposes. polygon1 = [(-p[0], p[1]) for p in polygon1] polygon2 = [(-p[0], p[1]) for p in polygon2] world3.addPolygon(polygon1) world3.addPolygon(polygon2) polygon1 = [(0, 7), (2.25, 5), (1.25, 4), (5, 5)] # [(0, 0), (2.25, 0.5), (1.25, 2.3)] # [(0,3),(1,1),(3,0),(4,0),(3,4)] polygon2 = [(1.15, -3.15), (4, -4), (2, -7), (0.9, -5.25)] #[(1.15, 3.15), (4, 4), (0.9, 5.25)] # [(1,4),(2,5),(2,1),(1,3)] polygon3 = [(3, 1), (3, 0.0), (4.85, 0.75), (4.85, 2.4), (5,4)] #[(3, 0.7), (4.85, 1.75), (4.85, 3.4)] polygon4 = [(-0.5, -1), (-0.5, 1.0), (0.5, 1), (0.5, -1)] #[(3, 0.7), (4.85, 1.75), (4.85, 3.4)] world4.addPolygon(polygon1) world4.addPolygon(polygon2) world4.addPolygon(polygon3) world4.addPolygon(polygon4) polygon1 = [(0, 0.6), (1.5, 0), (2.5, 1.25), (1.25, 0.75), (1.125, 1.8)] polygon2 = [(1.3, 2.25), (2.8, 2.8), (1.65, 3.125)] polygon3 = [(2.8, 1.25), (4.125, 0.25), (3.5, 2.0)] world5.addPolygon(polygon1) world5.addPolygon(polygon2) world5.addPolygon(polygon3) polygon1 = [(0,0), (2.5, 0), (0, 1.5)] polygon2 = [(0, 3.25), (5, 4.25), (0, 4.25)] polygon3 = [(3.5, 0), (5, 0), (5, 2.75), (3.5, 2.75)] world6.addPolygon(polygon1) world6.addPolygon(polygon2) world6.addPolygon(polygon3) polygon1 = [(-1, 1), (-2, 1), (-2, -1), (-1, -1), (0, 0), (1, -1), (2, -1), (2, 1), (1, 1), (0, 2)] world7.addPolygon(polygon1) polygon1 = [(-1, 1), (-2, 1), (-2, -1), (-1, -1)] polygon2 = [(-1, -1), (0, 0), (1, -1), (1, 1), (0, 2), (-1, 1)] polygon3 = [(1, -1), (2, -1), (2, 1), (1, 1)] # polygon1 = [(p[0], 0.9*p[1]) for p in polygon1] # polygon3 = [(p[0], 0.9*p[1]) for p in polygon3] world8.addPolygon(polygon1) world8.addPolygon(polygon2) world8.addPolygon(polygon3) # 0.9999995231628418 polygon1 = [(-1, -1), (1, -1), (1, 1), (-1, 1)] polygon2 = [(1, 1), (2, -1), (3, 0), (2, 1)] world9.addPolygon(polygon1) world9.addPolygon(polygon2) polygon1 = [(0.734870970249176, 0.26040399074554443), (-0.045375000685453415, 0.8651400208473206), (-0.8234530091285706, 0.4177840054035187), (-0.14182999730110168, 0.21450699865818024)] polygon2 = [(-1.0, 1.0108875036239624), (1.0, 1.010890007019043), (1.0, 1.3735400438308716), (-1.0, 1.373543620109558)] world10.addPolygon(polygon2) world10.addPolygon(polygon1) polygon0 = [(0.734870970249176, -1.1526894569396973), (-0.045375000685453415, 1.1651400327682495), (-0.8234530091285706, -0.9953095316886902), (-0.14182999730110168, -1.1985864639282227)] polygon1 = [(2.1045942306518555, -2.0704498291015625), (2.1045916080474854, 1.9576737880706787), (1.7419415712356567, 1.9576740264892578), (1.7419381141662598, -2.0704498291015625)] polygon2 = [(-1.7419382333755493, -2.0704498291015625), (-1.741940975189209, 1.9576740264892578), (-2.10459041595459, 1.9576740264892578), (-2.1045944690704346, -2.0704495906829834)] world11.addPolygon(polygon0) world11.addPolygon(polygon1) world11.addPolygon(polygon2) polygon0 = [(0.7000000476837158, -1.2000000476837158), (-0.10000000149011612, 1.2000000476837158), (-0.800000011920929, -1.0), (-0.10000000149011612, -1.25)] polygon1 = [(2.0999999046325684, -2.0999999046325684), (2.0999999046325684, 1.899999976158142), (1.7000000476837158, 1.899999976158142), (1.7000000476837158, -2.0999999046325684)] polygon2 = [(-1.7000000476837158, -2.0999999046325684), (-1.7000000476837158, 1.899999976158142), (-2.1000001430511475, 1.899999976158142), (-2.1000001430511475, -2.0999999046325684)] world12.addPolygon(polygon0) world12.addPolygon(polygon1) world12.addPolygon(polygon2) #world.addLine((0, 2.5), (3, 2.5)) worlds = [world0, world1, world2, world3, world4, world5, world6, world7, world8, world9, world10] worldIndex = 0 for w in worlds: print("\nWorld:", worldIndex) worldIndex += 1 w.calcFreeLines() drawScene(w) faceList = w.partitionMesh.faces for k in faceList: doubleFaceTest(faceList[k]) checkEventEquality(world12, world11) #%% reminders = [ "Is there a better way, using cos(), to handle parallelism in isLineInsideEdgeAngle()?", "Pruning of lines that intersect obj at CONTACT verts. (I sort of forget what this self-reminder meant...)", "Pruning of segments outside convex hull.", "Right now, swapDir() side effect in findIntersections(). Should this be changed?", "Just generally take a second look at how floating-point precision problems are handled.\nEspecially for the y-intercept of MyLine, since a very small difference in coordinates can lead to a larger difference in y-intercepts.\nSo instead of comparing y-intercepts, something else should maybe be compared!" ] for reminder in reminders: sep = "===========" print("\n" + sep + "\n" + reminder + "\n" + sep + "\n")

38.526923

311

0.605471

3e3c50b123745c81d1f91068db3b602d8d3f128d

5,966

py

Python

dynamo/preprocessing/dynast.py

xing-lab-pitt/dynamo-release

76c1f2a270dd6722b88f4700aac1a1a725a0c261

[ "BSD-3-Clause" ]

236

2019-07-09T22:06:21.000Z

2022-03-31T17:56:07.000Z

dynamo/preprocessing/dynast.py

xing-lab-pitt/dynamo-release

76c1f2a270dd6722b88f4700aac1a1a725a0c261

[ "BSD-3-Clause" ]

115

2019-07-12T19:06:21.000Z

2022-03-31T17:34:18.000Z

dynamo/preprocessing/dynast.py

xing-lab-pitt/dynamo-release

76c1f2a270dd6722b88f4700aac1a1a725a0c261

[ "BSD-3-Clause" ]

34

2019-07-10T03:34:04.000Z

2022-03-22T12:44:22.000Z

import numpy as np from scipy.sparse import issparse from sklearn.utils import sparsefuncs import anndata from typing import Union from ..dynamo_logger import LoggerManager, main_tqdm from ..utils import copy_adata def lambda_correction( adata: anndata.AnnData, lambda_key: str = "lambda", inplace: bool = True, copy: bool = False, ) -> Union[anndata.AnnData, None]: """Use lambda (cell-wise detection rate) to estimate the labelled RNA. Parameters ---------- adata: adata object generated from dynast. lambda_key: The key to the cell-wise detection rate. inplace: Whether to inplace update the layers. If False, new layers that append '_corrected" to the existing will be used to store the updated data. copy: Whether to copy the adata object or update adata object inplace. Returns ------- adata: :class:`~anndata.AnnData` An new or updated anndata object, based on copy parameter, that are updated with Size_Factor, normalized expression values, X and reduced dimensions, etc. """ logger = LoggerManager.gen_logger("dynamo-lambda_correction") logger.log_time() adata = copy_adata(adata) if copy else adata logger.info("apply detection rate correction to adata...", indent_level=1) if lambda_key not in adata.obs.keys(): raise ValueError( f"the lambda_key {lambda_key} is not included in adata.obs! Please ensure you have calculated " "per-cell detection rate!" ) logger.info("retrieving the cell-wise detection rate..", indent_level=1) detection_rate = adata.obs[lambda_key].values[:, None] logger.info("identify the data type..", indent_level=1) all_layers = adata.layers.keys() has_ul = np.any([i.contains("ul_") for i in all_layers]) has_un = np.any([i.contains("un_") for i in all_layers]) has_sl = np.any([i.contains("sl_") for i in all_layers]) has_sn = np.any([i.contains("sn_") for i in all_layers]) has_l = np.any([i.contains("_l_") for i in all_layers]) has_n = np.any([i.contains("_n_") for i in all_layers]) if sum(has_ul + has_un + has_sl + has_sn) == 4: datatype = "splicing_labeling" elif sum(has_l + has_n): datatype = "labeling" logger.info(f"the data type identified is {datatype}", indent_level=2) logger.info("retrieve relevant layers for detection rate correction", indent_level=1) if datatype == "splicing_labeling": layers, match_tot_layer = [], [] for layer in all_layers: if "ul_" in layer: layers += layer match_tot_layer += "unspliced" elif "un_" in layer: layers += layer match_tot_layer += "unspliced" elif "sl_" in layer: layers += layer match_tot_layer += "spliced" elif "sn_" in layer: layers += layer match_tot_layer += "spliced" elif "spliced" in layer: layers += layer elif "unspliced" in layer: layers += layer if len(layers) != 6: raise ValueError( "the adata object has to include ul, un, sl, sn, unspliced, spliced, " "six relevant layers for splicing and labeling quantified datasets." ) elif datatype == "labeling": layers, match_tot_layer = [], [] for layer in all_layers: if "_l_" in layer: layers += layer match_tot_layer += ["total"] elif "_n_" in layer: layers += layer match_tot_layer += ["total"] elif "total" in layer: layers += layer if len(layers) != 3: raise ValueError( "the adata object has to include labeled, unlabeled, three relevant layers for labeling quantified " "datasets." ) logger.info("detection rate correction starts", indent_level=1) for i, layer in enumerate(main_tqdm(layers, desc="iterating all relevant layers")): if i < len(match_tot_layer): cur_layer = adata.layers[layer] if inplace else adata.layers[layer].copy() cur_total = adata.layers[match_tot_layer[i]] # even layers is labeled RNA and odd unlabeled RNA if i % 2 == 0: # formula: min(L / lambda, (L + U)) from scNT-seq if issparse(cur_layer): sparsefuncs.inplace_row_scale(cur_layer, 1 / detection_rate) else: cur_layer /= detection_rate if inplace: adata.layers[layer] = sparse_mimmax(cur_layer, cur_total) else: adata.layers[layer + "_corrected"] = sparse_mimmax(cur_layer, cur_total) else: if inplace: adata.layers[layer] = cur_total - adata.layers[layer[i - 1]] else: adata.layers[layer + "_corrected"] = cur_total - adata.layers[layer[i - 1]] logger.finish_progress(progress_name="lambda_correction") if copy: return adata return None def sparse_mimmax(A, B, type="mim"): """Return the element-wise mimimum/maximum of sparse matrices `A` and `B`. Parameters ---------- A: The first sparse matrix B: The second sparse matrix type: The type of calculation, either mimimum or maximum. Returns ------- M: A sparse matrix that contain the element-wise maximal or mimimal of two sparse matrices. """ AgtB = (A < B).astype(int) if type == "min" else (A > B).astype(int) M = AgtB.multiply(A - B) + B return M

35.301775

120

0.578947

3e3cee6ba011350960f8e52993ae0b2666144798

4,095

py

Python

tests/fullscale/poroelasticity/cryer/TestCryer.py

cehanagan/pylith

cf5c1c34040460a82f79b6eb54df894ed1b1ee93

[ "MIT" ]

93

2015-01-08T16:41:22.000Z

2022-02-25T13:40:02.000Z

tests/fullscale/poroelasticity/cryer/TestCryer.py

sloppyjuicy/pylith

ac2c1587f87e45c948638b19560813d4d5b6a9e3

[ "MIT" ]

277

2015-02-20T16:27:35.000Z

2022-03-30T21:13:09.000Z

tests/fullscale/poroelasticity/cryer/TestCryer.py

sloppyjuicy/pylith

ac2c1587f87e45c948638b19560813d4d5b6a9e3

[ "MIT" ]

71

2015-03-24T12:11:08.000Z

2022-03-03T04:26:02.000Z

#!/usr/bin/env nemesis # # ---------------------------------------------------------------------- # # Brad T. Aagaard, U.S. Geological Survey # Charles A. Williams, GNS Science # Matthew G. Knepley, University at Buffalo # # This code was developed as part of the Computational Infrastructure # for Geodynamics (http://geodynamics.org). # # Copyright (c) 2010-2021 University of California, Davis # # See LICENSE.md for license information. # # ---------------------------------------------------------------------- # # @file tests/fullscale/poroelasticity/cryer/TestCryer.py # # @brief Test suite for testing pylith with Cryer's problem. import unittest from pylith.testing.FullTestApp import (FullTestCase, Check, check_data) import meshes import cryer_soln # We do not include trace_strain in the test of the solution fields, because of the # poor convergence of the series solution. SOLUTION_FIELDS = ["displacement", "pressure"] SOLUTION_TOLERANCE = 0.5 # ------------------------------------------------------------------------------------------------- # ------------------------------------------------------------------------------------------------- # ------------------------------------------------------------------------------------------------- # ------------------------------------------------------------------------------------------------- def test_cases(): return [ TestHex, TestTet, ] # ------------------------------------------------------------------------------------------------- if __name__ == '__main__': FullTestCase.parse_args() suite = unittest.TestSuite() for test in test_cases(): suite.addTest(unittest.makeSuite(test)) unittest.TextTestRunner(verbosity=2).run(suite) # End of file

31.022727

99

0.477656

3e3e8c87814094936e4351a80831e5bb8fce82f9

3,551

py

Python

util/data.py

pinaryazgan/GDN

469e63fa8c2dce596c6f7e99f2620ac6eec7dadf

[ "MIT" ]

156

2021-03-01T12:49:25.000Z

2022-03-28T08:27:33.000Z

util/data.py

pinaryazgan/GDN

469e63fa8c2dce596c6f7e99f2620ac6eec7dadf

[ "MIT" ]

24

2021-04-19T10:08:35.000Z

2022-03-28T11:42:54.000Z

util/data.py

pinaryazgan/GDN

469e63fa8c2dce596c6f7e99f2620ac6eec7dadf

[ "MIT" ]

54

2021-04-16T17:26:30.000Z

2022-03-28T06:08:43.000Z

# util functions about data from scipy.stats import rankdata, iqr, trim_mean from sklearn.metrics import f1_score, mean_squared_error import numpy as np from numpy import percentile # calculate F1 scores

28.18254

101

0.664602

3e41a3d23f1cd5e224926d0f23ef2a864d4c94cb

5,654

py

Python

rrl-sysadmin/sysadmin.py

HyeokjuJang/sr-drl

01fa8264c7b36f34f721303f455f37545dbce1fe

[ "MIT" ]

14

2020-10-02T17:14:04.000Z

2022-02-26T19:26:58.000Z

rrl-sysadmin/sysadmin.py

HyeokjuJang/sr-drl

01fa8264c7b36f34f721303f455f37545dbce1fe

[ "MIT" ]

1

2022-02-26T08:23:13.000Z

rrl-sysadmin/sysadmin.py

jaromiru/sr-drl

01fa8264c7b36f34f721303f455f37545dbce1fe

[ "MIT" ]

6

2021-05-04T13:24:12.000Z

2021-12-06T12:51:30.000Z

import gym, random, copy, string, uuid import numpy as np rddl_template = string.Template(''' non-fluents nf_sysadmin_inst_$uid { domain = sysadmin_mdp; objects { computer : {$objects}; }; non-fluents { REBOOT-PROB = $reboot_prob; $connections }; } instance sysadmin_inst_$uid { domain = sysadmin_mdp; non-fluents = nf_sysadmin_inst_$uid; init-state { $running }; max-nondef-actions = $maxactions; horizon = $horizon; discount = $discount; } ''') # ---------------------------------------------------------- # ---------------------------------------------------------- import networkx as nx import matplotlib.pyplot as plt COLOR_RUNNING = "#cad5fa" COLOR_DOWN = "#e33c30" COLOR_SELECTED_R = "#1b3eb5" COLOR_SELECTED_D = "#701812" # ---------------------------------------------------------- if __name__ == '__main__': NODES = 5 env = SysAdminEnv(env_num_obj=NODES, env_max_steps=10) s = env.reset() gvis = GraphVisualization(env) a = -1 while(True): # a = np.random.randint(env.num_obj) a = np.random.choice(NODES, np.random.randint(0, NODES), replace=False) probs = np.random.rand(NODES) print(a) print(probs) gvis.update_state(env, a, probs) gvis.plot().show() s, r, d, i = env.step(a) print(a, r) if d: gvis = GraphVisualization(env)

25.241071

194

0.662363

3e43d8b9a039af747051e4f38665ccd61353394f

3,974

py

Python

core/language_modelling.py

lkwate/e-greedy-lm

02e81fee93ee93faca0c1eb339b3c5ad55b4a639

[ "MIT" ]

1

2021-11-09T19:18:00.000Z

core/language_modelling.py

lkwate/e-greedy-lm

02e81fee93ee93faca0c1eb339b3c5ad55b4a639

[ "MIT" ]

null

core/language_modelling.py

lkwate/e-greedy-lm

02e81fee93ee93faca0c1eb339b3c5ad55b4a639

[ "MIT" ]

null

import torch import torch.optim as optim from transformers import AutoTokenizer from .utils import epsilon_greedy_transform_label, uid_variance_fn, OPTIMIZER_DIC import pytorch_lightning as pl

32.842975

87

0.638903

3e46f25a0298cc777cd9c283c93eaadaceb537e7

324

py

Python

tests/test_client.py

yakhinvadim/enterprise-search-python

a2010e8773a6250cb81ea48f760088bb23466bb1

[ "Apache-2.0" ]

null

tests/test_client.py

yakhinvadim/enterprise-search-python

a2010e8773a6250cb81ea48f760088bb23466bb1

[ "Apache-2.0" ]

null

tests/test_client.py

yakhinvadim/enterprise-search-python

a2010e8773a6250cb81ea48f760088bb23466bb1

[ "Apache-2.0" ]

null

from unittest import TestCase from elastic_workplace_search.client import Client

23.142857

53

0.756173

3e49611f7036088bee4b0176d2681701d3c8a29d

4,437

py

Python

test/hash_url.py

neotext/neotext-django-server

7cfe98cd541ade9b26a1877f627e45a986b011e8

[ "MIT" ]

null

test/hash_url.py

neotext/neotext-django-server

7cfe98cd541ade9b26a1877f627e45a986b011e8

[ "MIT" ]

7

2015-11-30T02:59:23.000Z

2016-10-06T15:52:52.000Z

test/hash_url.py

neotext/neotext-django-server

7cfe98cd541ade9b26a1877f627e45a986b011e8

[ "MIT" ]

null

from neotext.lib.neotext_quote_context.quote import Quote t0 = Quote( citing_quote="""I am sick and tired of watching folks like Boris Johnson, Marine Le Pen, Donald Trump and others appeal to the worst racial instincts of our species, only to be shushed by folks telling me that it’s not really racism driving their popularity. It’s economic angst. It’s regular folks tired of being spurned by out-of-touch elites. It’s a natural anxiety over rapid cultural change. Maybe it’s all those things. But at its core, it’s the last stand of old people who have been frightened to death by cynical right-wing media empires and the demagogues who enable themall of whom have based their appeals on racism as overt as anything we’ve seen in decades. It’s loathsome beyond belief, and not something I thought I’d ever see in my lifetime. But that’s where we are.""", citing_url='http://www.neotext.net/www.interfluidity.com/v2/6602.html', cited_url='http://www.motherjones.com/kevin-drum/2016/06/brexit-wins' ) t0.hashkey() t0.hash() from neotext.lib.neotext_quote_context.quote import Quote t2 = Quote( citing_quote=""" I am sick and tired of watching folks like Boris Johnson, Marine Le Pen, Donald Trump and others appeal to the worst racial instincts of our species, only to be shushed by folks telling me that it’s not really racism driving their popularity. It’s economic angst. It’s regular folks tired of being spurned by out-of-touch elites. It’s a natural anxiety over rapid cultural change. Maybe it’s all those things. But at its core, it’s the last stand of old people who have been frightened to death by cynical right-wing media empires and the demagogues who enable themall of whom have based their appeals on racism as overt as anything we’ve seen in decades. It’s loathsome beyond belief, and not something I thought I’d ever see in my lifetime. But that’s where we are. """, citing_url='http://www.neotext.net/www.interfluidity.com/v2/6602.html', cited_url='http://www.motherjones.com/kevin-drum/2016/06/brexit-wins' ) t2.hashkey() t2.hash() t1 = Quote ( citing_quote="one does not live by bread alone, " "but by every word that comes from the mouth of the Lord", citing_url='http://www.neotext.net/demo/', cited_url='https://www.biblegateway.com/passage/?search=Deuteronomy+8&version=NRSV' ) t1.hash() = '32b19d9333fff69d16d5bf89bc1eb76f6b39eb58' t1.data()['citing_context_before'] = 'ted texts on biblegateway.com; and the Al Gore example referenced an article on the washingtonpost.com. Using Neotext allows the reader to more easily make the intertextual connections between the two verses, without having to leave the current page. How the Neotext Quote-Context Service Works The example Ive given you is made possible through a WordPress Plugin that operates on the html <blockquote> tag: <blockquotecite=https://www.biblegateway.com/passage/?search=Deuteronomy+8&version=NRSV>' t1.data()['citing_context_after'] = '</blockquote> As part of the wordpress saving process, the WordPress Plugin submits the url of the post to the Neotext Web Service, which looks up the surrounding context of each quote and creates json files for each citation. Each quotes citation file is uploaded to Amazon S3 for later retreival by the client. On the client side, the Neotext custom jQuery library: uses the url from each blockquote cite attribute and the quote hashes the url and quote text looks up the previously generated json from the hash: http://read.neotext.net/quote/sha1/0.02/32/32b19d9333fff69d16d5bf89bc1eb76f6b39eb58.json injects the content from the json fields into hidden divs, which are made visible when the user clicks the arrows or link: Code Example: The code for displaying the looked-up information is part of a free open source jQuery plugin called neotext-quote-context.js, available as a wordpress or jQuery plugin. Get simplified html source of this page Download neotext-sample.html (view online) Sav' t2 = Quote( citing_quote="I took the initiative in creating the Internet.", citing_url="http://www.neotext.net/demo/" cited_url="https://www.washingtonpost.com/news/fact-checker/wp/2013/11/04/a-cautionary-tale-for-politicians-al-gore-and-the-invention-of-the-internet/" )

90.55102

1,038

0.771918

3e49ee4375c4fdbca12777a89f48b0e9f1e01d7a

3,590

py

Python

tests/imperative_vs_reactive/test_get_daily_average.py

BastiTee/bastis-python-toolbox

c313cf12607a973a1a8b8a9fbd73b2c8a47a82d8

[ "Apache-2.0" ]

1

2016-04-06T14:09:43.000Z

tests/imperative_vs_reactive/test_get_daily_average.py

BastiTee/bastis-python-toolbox

c313cf12607a973a1a8b8a9fbd73b2c8a47a82d8

[ "Apache-2.0" ]

null

tests/imperative_vs_reactive/test_get_daily_average.py

BastiTee/bastis-python-toolbox

c313cf12607a973a1a8b8a9fbd73b2c8a47a82d8

[ "Apache-2.0" ]

1

2022-03-19T04:21:40.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """Test suite for the daily average Toggl API process.""" from random import random from tempfile import NamedTemporaryFile from time import sleep, time from unittest import TestCase from recipes.imperative_vs_reactive.get_daily_average_imp import \ get_avg_daily_working_hours as imp from recipes.imperative_vs_reactive.get_daily_average_rx import \ get_avg_daily_working_hours as rx if __name__ == '__main__': TestSuite().test_integration()

39.450549

79

0.620334

3e4a37d31db8b27c20ff44c3b6b28b18b2dd20b1

4,077

py

Python

pox/stats_monitor.py

nachtkatze/sdn-diagnosis

22b187d276bf302ef5811abc946b1af125dd17bc

[ "Apache-2.0" ]

null

pox/stats_monitor.py

nachtkatze/sdn-diagnosis

22b187d276bf302ef5811abc946b1af125dd17bc

[ "Apache-2.0" ]

null

pox/stats_monitor.py

nachtkatze/sdn-diagnosis

22b187d276bf302ef5811abc946b1af125dd17bc

[ "Apache-2.0" ]

null

# Copyright 2013 Oscar Araque # # 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. """ A skeleton POX component You can customize this to do whatever you like. Don't forget to adjust the Copyright above, and to delete the Apache license if you don't want to release under Apache (but consider doing so!). Rename this file to whatever you like, .e.g., mycomponent.py. You can then invoke it with "./pox.py mycomponent" if you leave it in the ext/ directory. Implement a launch() function (as shown below) which accepts commandline arguments and starts off your component (e.g., by listening to events). Edit this docstring and your launch function's docstring. These will show up when used with the help component ("./pox.py help --mycomponent"). """ # Import some POX stuff from pox.core import core # Main POX object import pox.openflow.libopenflow_01 as of # OpenFlow 1.0 library import pox.lib.packet as pkt # Packet parsing/construction from pox.lib.addresses import EthAddr, IPAddr # Address types import pox.lib.util as poxutil # Various util functions import pox.lib.revent as revent # Event library import pox.lib.recoco as recoco # Multitasking library from pox.openflow.of_json import * import multiprocessing import json # Create a logger for this component log = core.getLogger("Monitor")

36.72973

81

0.709345

3e4a39484ed02c469223ab4065ec6d989a83a302

7,623

py

Python

tests/app_example.py

omarryhan/flask-stateless-auth

c6acefc55050d1a53235ead20cb7d5e9eb4bbf9a

[ "MIT" ]

3

2018-09-13T19:55:47.000Z

2018-09-15T18:31:22.000Z

tests/app_example.py

omarryhan/flask-stateless-auth

c6acefc55050d1a53235ead20cb7d5e9eb4bbf9a

[ "MIT" ]

null

tests/app_example.py

omarryhan/flask-stateless-auth

c6acefc55050d1a53235ead20cb7d5e9eb4bbf9a

[ "MIT" ]

null

import os import datetime import secrets import json from flask import Flask, abort, request, jsonify from flask_sqlalchemy import SQLAlchemy from sqlalchemy.orm.exc import NoResultFound, MultipleResultsFound from werkzeug.security import safe_str_cmp from flask_stateless_auth import ( StatelessAuthError, StatelessAuthManager, current_stateless_user, UserMixin, TokenMixin, token_required, ) db = SQLAlchemy() stateless_auth_manager = StatelessAuthManager() app = Flask(__name__.split(".")[0]) if __name__ == "__main__": app.config.from_object(Config()) db.init_app(app) with app.app_context(): db.create_all() stateless_auth_manager.init_app(app) app.run()

30.987805

88

0.674406

3e4e3e3f65d730e416b620ade003178d96c61532

920

py

Python

stereo/stereo.py

whaleygeek/microbit_python

1fa8e0f34cfa2a92d7c5c32fc5ee5287c5d5b105

[ "MIT" ]

8

2016-11-15T23:04:25.000Z

2021-05-17T17:42:47.000Z

stereo/stereo.py

whaleygeek/microbit_python

1fa8e0f34cfa2a92d7c5c32fc5ee5287c5d5b105

[ "MIT" ]

null

stereo/stereo.py

whaleygeek/microbit_python

1fa8e0f34cfa2a92d7c5c32fc5ee5287c5d5b105

[ "MIT" ]

null

from microbit import * import music A = False B = False PITCH = 440 # PIN2 read_analog() ACTION_VALUE = 50 VOLUMEUP_VALUE = 150 VOLUMEDOWN_VALUE = 350 #nothing: 944 prev_l = False prev_r = False l = False r = False while True: v = pin2.read_analog() if v < ACTION_VALUE: l,r = True, True elif v < VOLUMEUP_VALUE: l,r = False, True elif v < VOLUMEDOWN_VALUE: l,r = True, False else: l,r = False, False if l != prev_l: prev_l = l if l: music.pitch(PITCH, pin=pin0) display.set_pixel(0,2,9) else: display.set_pixel(0,2,0) music.stop(pin0) if r != prev_r: prev_r = r if r: display.set_pixel(4,2,9) music.pitch(PITCH, pin=pin1) else: display.set_pixel(4,2,0) music.stop(pin1)

18.77551

40

0.519565

3e50073943f2d59f2a64f9e25a36110605822852

1,062

py

Python

comments/migrations/0004_auto_20170531_1011.py

salazarpardo/redinnovacion

3f7c13af0af1887112a0492aea7782871fba0129

[ "CC-BY-3.0" ]

null

comments/migrations/0004_auto_20170531_1011.py

salazarpardo/redinnovacion

3f7c13af0af1887112a0492aea7782871fba0129

[ "CC-BY-3.0" ]

null

comments/migrations/0004_auto_20170531_1011.py

salazarpardo/redinnovacion

3f7c13af0af1887112a0492aea7782871fba0129

[ "CC-BY-3.0" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models from django.conf import settings

34.258065

114

0.615819

3e50929d0bf53c378eb006069dd354ec7a7241ac

1,096

py

Python

hospital/migrations/0011_auto_20210502_1057.py

Shreyashm16/Hospital-Appointment-and-Information-System

929b7eb22cc6a0399e6fff3c7012d1c65d7c47cb

[ "MIT" ]

7

2021-07-15T08:59:58.000Z

2021-12-29T20:21:36.000Z

hospital/migrations/0011_auto_20210502_1057.py

siddharth25pandey/Hospital-Information-Appointment-System

1df5edd1f0dc2f0f385e7195db221027b4f64efb

[ "MIT" ]

null

hospital/migrations/0011_auto_20210502_1057.py

siddharth25pandey/Hospital-Information-Appointment-System

1df5edd1f0dc2f0f385e7195db221027b4f64efb

[ "MIT" ]

4

2021-05-11T08:36:02.000Z

2021-08-08T11:45:11.000Z

# Generated by Django 3.1.5 on 2021-05-02 05:27 from django.db import migrations, models import django.db.models.deletion

34.25

148

0.583942

3e509827f57ba47184b958f8189726f8a1765c22

87

py

Python

division.py

ReverseScale/PyDemo

9cc6f3cbb8482f6e403bf65419537b0163798e61

[ "MIT" ]

null

division.py

ReverseScale/PyDemo

9cc6f3cbb8482f6e403bf65419537b0163798e61

[ "MIT" ]

null

division.py

ReverseScale/PyDemo

9cc6f3cbb8482f6e403bf65419537b0163798e61

[ "MIT" ]

null

try: print(5/0) except ZeroDivisionError: print('!') else: print('')

14.5

25

0.62069

3e522957a432795bf32198db1cc68b1e2615e3f9

1,924

py

Python

Script/calculate_RMSD.py

dhruvsangamwar/Protein-structure-prediction

99364bfd62f8293ddbe8e2c9a86ca7850b270d44

[ "MIT" ]

1

2022-01-30T08:20:08.000Z

Script/calculate_RMSD.py

dhruvsangamwar/ECS_129_Protein-structure-prediction

99364bfd62f8293ddbe8e2c9a86ca7850b270d44

[ "MIT" ]

null

Script/calculate_RMSD.py

dhruvsangamwar/ECS_129_Protein-structure-prediction

99364bfd62f8293ddbe8e2c9a86ca7850b270d44

[ "MIT" ]

null

import pdbCleanup as pc import fxndefinitions as f import numpy as np from numpy.linalg import eig pc.takeInput1() DataFrame1 = [] pc.CsvToDataframe(DataFrame1) pc.takeInput2() DataFrame2 = [] pc.CsvToDataframe(DataFrame2) xtil = [0, 0, 0] ytil = [0, 0, 0] x = np.array(DataFrame1) y = np.array(DataFrame2) # This finds the number of CA atoms in both of the proteins N1 = np.size(xtil, 0) N2 = np.size(ytil, 0) # finding the average of the x coords in protein 1 and 2 (arr1 & 2) # these two functions calculate the barycenter # Here we will be finding Xtil && Ytil = X && Y - G Gx = f.findG(x, N1) Gy = f.findG(y, N2) xtil = np.subtract(x, Gx) ytil = np.subtract(x, Gy) # we now have the ~x_k Coords and the ~y_k Coords respectively # this function will calculate all the 9 R values R11 = R12 = R13 = R21 = R22 = R23 = R31 = R32 = R33 = 0 for i in range(0, N1): R11 += xtil[i][0] * ytil[i][0] R12 += xtil[i][0] * ytil[i][1] R13 += xtil[i][0] * ytil[i][2] R21 += xtil[i][1] * ytil[i][0] R22 += xtil[i][1] * ytil[i][1] R23 += xtil[i][1] * ytil[i][2] R31 += xtil[i][2] * ytil[i][0] R32 += xtil[i][2] * ytil[i][1] R33 += xtil[i][2] * ytil[i][2] # matrix given by equation 10 from the paper Matrix = np.array([[R11+R22+R33, R23-R32, R31-R13, R12-R21], [R23-R32, R11-R22-R33, R12+R21, R13+R31], [R31-R13, R12+R21, -R11+R22-R33, R23+R32], [R12-R21, R13+R31, R23+R32, -R11-R22+R33]]) # Here we calculate the maxEigenvalue for the final calucaltion w, v = eig(Matrix) maxEig = np.amax(w) # Now we will find the best fit RMSD using the steps below temp = [0, 0, 0] for i in range(0, N1): temp += np.add((np.square(xtil[i])), np.square(ytil[i])) n = temp[0] + temp[1] + temp[2] var = np.subtract(n, 2*maxEig) temp2 = np.true_divide(var, np.size(xtil, 0)) RMSD = np.sqrt(abs(temp2)) RMSD = round(RMSD, 2) print(RMSD)

24.666667

67

0.613825

3e52b2bc37f4905d4ab47d9e200507510863fee4

626

py

Python

Python/Data Structure and Algorithm/Krish Naik/Amazon Que.py

omkarsutar1255/Python-Data

169d0c54b23d9dd5a7f1aea41ab385121c3b3c63

[ "CC-BY-3.0" ]

null

Python/Data Structure and Algorithm/Krish Naik/Amazon Que.py

omkarsutar1255/Python-Data

169d0c54b23d9dd5a7f1aea41ab385121c3b3c63

[ "CC-BY-3.0" ]

null

Python/Data Structure and Algorithm/Krish Naik/Amazon Que.py

omkarsutar1255/Python-Data

169d0c54b23d9dd5a7f1aea41ab385121c3b3c63

[ "CC-BY-3.0" ]

null

# l1 = [1095, 1094, 1095] # del l1[:] # l1.extend([1005, 1094, 1095]) # print(l1) l1 = [8676, 4444, 3333, 2222, 1111] for i, n in enumerate(l1): print(i, n) if int(n / 1000) == 1: l1[i] = n + 8000 elif int(n / 1000) == 2: l1[i] = n + 6000 elif int(n / 1000) == 3: l1[i] = n + 4000 elif int(n / 1000) == 4: l1[i] = n + 2000 elif int(n / 1000) == 6: l1[i] = n - 2000 elif int(n / 1000) == 7: l1[i] = n + 4000 elif int(n / 1000) == 8: l1[i] = n - 6000 elif int(n / 1000) == 9: l1[i] = n - 8000 else: pass print(l1)

21.586207

35

0.4377

3e572d40ef88a1ec3058d9cc94eb6dce557f2d6d

4,728

py

Python

src/voicemaker/voicemaker.py

IAL32/voicemaker

66c9dd25749743d94bb9c3aac8ba2c858f327723

[ "MIT" ]

null

src/voicemaker/voicemaker.py

IAL32/voicemaker

66c9dd25749743d94bb9c3aac8ba2c858f327723

[ "MIT" ]

1

2022-03-04T14:52:16.000Z

2022-03-08T08:00:59.000Z

src/voicemaker/voicemaker.py

IAL32/voicemaker

66c9dd25749743d94bb9c3aac8ba2c858f327723

[ "MIT" ]

null

import requests LANGUAGES_LIST = [ 'en-US', 'en-GB', 'en-AU', 'en-HK', 'en-NZ', 'en-SG', 'en-ZA', 'de-DE', 'ar-XA', 'ar-SA', 'bn-IN', 'bg-BG', 'ca-ES', 'cmn-CN', 'zh-HK', 'cmn-TW', 'cy-GB', 'cs-CZ', 'da-DK', 'de-CH', 'es-AR', 'es-CO', 'es-US', 'ga-IE', 'gu-IN', 'hr-HR', 'mr-IN', 'ms-MY', 'mt-MT', 'nl-NL', 'nl-BE', 'en-CA', 'en-IN', 'en-IE', 'et-EE', 'en-PH', 'fil-PH', 'fi-FI', 'fr-BE', 'fr-FR', 'fr-CA', 'fr-CH', 'el-GR', 'he-IL', 'hi-IN', 'hu-HU', 'id-ID', 'it-IT', 'ja-JP', 'lv-LV', 'lt-LT', 'ko-KR', 'nb-NO', 'pl-PL', 'pt-PT', 'pt-BR', 'ro-RO', 'ru-RU', 'sk-SK', 'sw-KE', 'es-ES', 'es-MX', 'es-LA', 'es-US', 'sl-SI', 'sv-SE', 'tr-TR', 'ta-IN', 'te-IN', 'th-TH', 'uk-UA', 'ur-PK', 'vi-VN' ]

39.4

164

0.597716

3e5810f45ee6abfb855c478735026a678b651dd9

1,365

py

Python

Lecture/Kapitel 9 - Seite 235 - Implementierung des Gradientenverfahrens.py

PhilippMatthes/tensorflow-playground

b5fee6e5f5044dc5cbcd54529d559388a3df7813

[ "MIT" ]

null

Lecture/Kapitel 9 - Seite 235 - Implementierung des Gradientenverfahrens.py

PhilippMatthes/tensorflow-playground

b5fee6e5f5044dc5cbcd54529d559388a3df7813

[ "MIT" ]

null

Lecture/Kapitel 9 - Seite 235 - Implementierung des Gradientenverfahrens.py

PhilippMatthes/tensorflow-playground

b5fee6e5f5044dc5cbcd54529d559388a3df7813

[ "MIT" ]

null

import tensorflow as tf import numpy as np from sklearn.datasets import fetch_california_housing housing = fetch_california_housing() m, n = housing.data.shape housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data] X = tf.constant(housing_data_plus_bias, dtype=tf.float32, name="X") y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name="y") n_epochs = 1000 learning_rate = 0.01 theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0), name="theta") y_pred = tf.matmul(X, theta, name="predictions") error = y_pred - y mse = tf.reduce_mean(tf.square(error), name="mse") gradients = 2 / m * tf.matmul(tf.transpose(X), error) training_op = tf.assign(theta, theta - learning_rate * gradients) run() gradients = tf.gradients(mse, [theta])[0] run() optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) training_op = optimizer.minimize(mse) run() optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9) training_op = optimizer.minimize(mse) run()

26.764706

81

0.69304

3e582f1280b1545b27d8bb65ef57684f484bd7bc

1,634

py

Python

python/Fluoroseq/obsolete/scripts/intrinsic_pr_bounds.py

erisyon/whatprot

176cd7e6ee99ea3f91794dcf1ec14f3578b7ee3c

[ "MIT" ]

null

python/Fluoroseq/obsolete/scripts/intrinsic_pr_bounds.py

erisyon/whatprot

176cd7e6ee99ea3f91794dcf1ec14f3578b7ee3c

[ "MIT" ]

1

2021-06-12T00:50:08.000Z

2021-06-15T17:59:12.000Z

python/Fluoroseq/obsolete/scripts/intrinsic_pr_bounds.py

erisyon/whatprot

176cd7e6ee99ea3f91794dcf1ec14f3578b7ee3c

[ "MIT" ]

1

2021-06-11T19:34:43.000Z

# -*- coding: utf-8 -*- """ @author: Matthew Beauregard Smith (UT Austin) """ from common.peptide import Peptide from plotting.plot_pr_curve import plot_pr_curve from numpy import load from simulate.label_peptides import label_peptides TRUE_Y_FILE = 'C:/Users/Matthew/ICES/MarcotteLab/data/classification/control_15_proteins/true_pep_i.npy' NUM_PEPTIDES = 705 NUM_CHANNELS = 3 LABEL_SET = ['DE','Y','C'] PEPTIDE_FILE = 'C:/Users/Matthew/ICES/MarcotteLab/data/classification/control_15_proteins/peps.csv' true_y = load(TRUE_Y_FILE) ground_truth = [0] * len(true_y) for i in range(0, len(true_y)): ground_truth[i] = GroundTruth(true_y[i]) f = open(PEPTIDE_FILE, 'r') f.readline() # header f.readline() # Zack's null line line = f.readline() peptides = [0] * NUM_PEPTIDES i = 0 while line != '\n' and line != '': items = line.split(",") pep_id = items[0] pep_str = items[-1] peptides[i] = Peptide(pep_str, pep_id=pep_id) line = f.readline() i += 1 f.close() dye_seqs = label_peptides(peptides, LABEL_SET) id_to_prediction = {} for dye_seq in dye_seqs: for peptide in dye_seq.src_peptides: id_to_prediction[int(peptide.pep_id)] = ( int(dye_seq.src_peptides[0].pep_id), 1 / len(dye_seq.src_peptides)) predictions = [0] * len(ground_truth) for i in range(len(ground_truth)): predictions[i] = id_to_prediction[ground_truth[i].value] plot_pr_curve(predictions, ground_truth)

30.259259

105

0.676255

3e5961792d37ca4a7091e59c1c74180b0cb0ef47

337,498

py

Python

fitparse/profile.py

vlcvboyer/python-fitparse

bef76231a1c8dddfafc23070b43684e7d0c6e916

[ "MIT" ]

null

fitparse/profile.py

vlcvboyer/python-fitparse

bef76231a1c8dddfafc23070b43684e7d0c6e916

[ "MIT" ]

1

2019-01-03T08:56:11.000Z

2019-06-05T09:24:13.000Z

fitparse/profile.py

vlcvboyer/python-fitparse

bef76231a1c8dddfafc23070b43684e7d0c6e916

[ "MIT" ]

1

2018-08-30T15:06:12.000Z

################# BEGIN AUTOMATICALLY GENERATED FIT PROFILE ################## ########################### DO NOT EDIT THIS FILE ############################ ####### EXPORTED PROFILE FROM SDK VERSION 20.33 AT 2017-05-17 22:36:12 ####### ########## PARSED 118 TYPES (1699 VALUES), 76 MESSAGES (950 FIELDS) ########## from fitparse.records import ( ComponentField, Field, FieldType, MessageType, ReferenceField, SubField, BASE_TYPES, ) FIELD_TYPES = { 'activity': FieldType( name='activity', base_type=BASE_TYPES[0x00], # enum values={ 0: 'manual', 1: 'auto_multi_sport', }, ), 'activity_class': FieldType( name='activity_class', base_type=BASE_TYPES[0x00], # enum values={ 100: 'level_max', 0x7F: 'level', # 0 to 100 0x80: 'athlete', }, ), 'activity_level': FieldType( name='activity_level', base_type=BASE_TYPES[0x00], # enum values={ 0: 'low', 1: 'medium', 2: 'high', }, ), 'activity_subtype': FieldType( name='activity_subtype', base_type=BASE_TYPES[0x00], # enum values={ 0: 'generic', 1: 'treadmill', # Run 2: 'street', # Run 3: 'trail', # Run 4: 'track', # Run 5: 'spin', # Cycling 6: 'indoor_cycling', # Cycling 7: 'road', # Cycling 8: 'mountain', # Cycling 9: 'downhill', # Cycling 10: 'recumbent', # Cycling 11: 'cyclocross', # Cycling 12: 'hand_cycling', # Cycling 13: 'track_cycling', # Cycling 14: 'indoor_rowing', # Fitness Equipment 15: 'elliptical', # Fitness Equipment 16: 'stair_climbing', # Fitness Equipment 17: 'lap_swimming', # Swimming 18: 'open_water', # Swimming 254: 'all', }, ), 'activity_type': FieldType( name='activity_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'generic', 1: 'running', 2: 'cycling', 3: 'transition', # Mulitsport transition 4: 'fitness_equipment', 5: 'swimming', 6: 'walking', 8: 'sedentary', 254: 'all', # All is for goals only to include all sports. }, ), 'analog_watchface_layout': FieldType( name='analog_watchface_layout', base_type=BASE_TYPES[0x00], # enum values={ 0: 'minimal', 1: 'traditional', 2: 'modern', }, ), 'ant_network': FieldType( name='ant_network', base_type=BASE_TYPES[0x00], # enum values={ 0: 'public', 1: 'antplus', 2: 'antfs', 3: 'private', }, ), 'antplus_device_type': FieldType( name='antplus_device_type', base_type=BASE_TYPES[0x02], # uint8 values={ 1: 'antfs', 11: 'bike_power', 12: 'environment_sensor_legacy', 15: 'multi_sport_speed_distance', 16: 'control', 17: 'fitness_equipment', 18: 'blood_pressure', 19: 'geocache_node', 20: 'light_electric_vehicle', 25: 'env_sensor', 26: 'racquet', 27: 'control_hub', 31: 'muscle_oxygen', 35: 'bike_light_main', 36: 'bike_light_shared', 38: 'exd', 40: 'bike_radar', 119: 'weight_scale', 120: 'heart_rate', 121: 'bike_speed_cadence', 122: 'bike_cadence', 123: 'bike_speed', 124: 'stride_speed_distance', }, ), 'attitude_stage': FieldType( name='attitude_stage', base_type=BASE_TYPES[0x00], # enum values={ 0: 'failed', 1: 'aligning', 2: 'degraded', 3: 'valid', }, ), 'attitude_validity': FieldType( name='attitude_validity', base_type=BASE_TYPES[0x84], # uint16 values={ 0x0001: 'track_angle_heading_valid', 0x0002: 'pitch_valid', 0x0004: 'roll_valid', 0x0008: 'lateral_body_accel_valid', 0x0010: 'normal_body_accel_valid', 0x0020: 'turn_rate_valid', 0x0040: 'hw_fail', 0x0080: 'mag_invalid', 0x0100: 'no_gps', 0x0200: 'gps_invalid', 0x0400: 'solution_coasting', 0x0800: 'true_track_angle', 0x1000: 'magnetic_heading', }, ), 'auto_activity_detect': FieldType( name='auto_activity_detect', base_type=BASE_TYPES[0x86], # uint32 values={ 0x00000000: 'none', 0x00000001: 'running', 0x00000002: 'cycling', 0x00000004: 'swimming', 0x00000008: 'walking', 0x00000020: 'elliptical', 0x00000400: 'sedentary', }, ), 'auto_sync_frequency': FieldType( name='auto_sync_frequency', base_type=BASE_TYPES[0x00], # enum values={ 0: 'never', 1: 'occasionally', 2: 'frequent', 3: 'once_a_day', 4: 'remote', }, ), 'autolap_trigger': FieldType( name='autolap_trigger', base_type=BASE_TYPES[0x00], # enum values={ 0: 'time', 1: 'distance', 2: 'position_start', 3: 'position_lap', 4: 'position_waypoint', 5: 'position_marked', 6: 'off', }, ), 'autoscroll': FieldType( name='autoscroll', base_type=BASE_TYPES[0x00], # enum values={ 0: 'none', 1: 'slow', 2: 'medium', 3: 'fast', }, ), 'backlight_mode': FieldType( name='backlight_mode', base_type=BASE_TYPES[0x00], # enum values={ 0: 'off', 1: 'manual', 2: 'key_and_messages', 3: 'auto_brightness', 4: 'smart_notifications', 5: 'key_and_messages_night', 6: 'key_and_messages_and_smart_notifications', }, ), 'battery_status': FieldType( name='battery_status', base_type=BASE_TYPES[0x02], # uint8 values={ 1: 'new', 2: 'good', 3: 'ok', 4: 'low', 5: 'critical', 6: 'charging', 7: 'unknown', }, ), 'bike_light_beam_angle_mode': FieldType( name='bike_light_beam_angle_mode', base_type=BASE_TYPES[0x02], # uint8 values={ 0: 'manual', 1: 'auto', }, ), 'bike_light_network_config_type': FieldType( name='bike_light_network_config_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'auto', 4: 'individual', 5: 'high_visibility', 6: 'trail', }, ), 'body_location': FieldType( name='body_location', base_type=BASE_TYPES[0x00], # enum values={ 0: 'left_leg', 1: 'left_calf', 2: 'left_shin', 3: 'left_hamstring', 4: 'left_quad', 5: 'left_glute', 6: 'right_leg', 7: 'right_calf', 8: 'right_shin', 9: 'right_hamstring', 10: 'right_quad', 11: 'right_glute', 12: 'torso_back', 13: 'left_lower_back', 14: 'left_upper_back', 15: 'right_lower_back', 16: 'right_upper_back', 17: 'torso_front', 18: 'left_abdomen', 19: 'left_chest', 20: 'right_abdomen', 21: 'right_chest', 22: 'left_arm', 23: 'left_shoulder', 24: 'left_bicep', 25: 'left_tricep', 26: 'left_brachioradialis', # Left anterior forearm 27: 'left_forearm_extensors', # Left posterior forearm 28: 'right_arm', 29: 'right_shoulder', 30: 'right_bicep', 31: 'right_tricep', 32: 'right_brachioradialis', # Right anterior forearm 33: 'right_forearm_extensors', # Right posterior forearm 34: 'neck', 35: 'throat', 36: 'waist_mid_back', 37: 'waist_front', 38: 'waist_left', 39: 'waist_right', }, ), 'bool': FieldType( name='bool', base_type=BASE_TYPES[0x00], # enum ), 'bp_status': FieldType( name='bp_status', base_type=BASE_TYPES[0x00], # enum values={ 0: 'no_error', 1: 'error_incomplete_data', 2: 'error_no_measurement', 3: 'error_data_out_of_range', 4: 'error_irregular_heart_rate', }, ), 'camera_event_type': FieldType( name='camera_event_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'video_start', # Start of video recording 1: 'video_split', # Mark of video file split (end of one file, beginning of the other) 2: 'video_end', # End of video recording 3: 'photo_taken', # Still photo taken 4: 'video_second_stream_start', 5: 'video_second_stream_split', 6: 'video_second_stream_end', 7: 'video_split_start', # Mark of video file split start 8: 'video_second_stream_split_start', 11: 'video_pause', # Mark when a video recording has been paused 12: 'video_second_stream_pause', 13: 'video_resume', # Mark when a video recording has been resumed 14: 'video_second_stream_resume', }, ), 'camera_orientation_type': FieldType( name='camera_orientation_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'camera_orientation_0', 1: 'camera_orientation_90', 2: 'camera_orientation_180', 3: 'camera_orientation_270', }, ), 'checksum': FieldType( name='checksum', base_type=BASE_TYPES[0x02], # uint8 values={ 0: 'clear', # Allows clear of checksum for flash memory where can only write 1 to 0 without erasing sector. 1: 'ok', # Set to mark checksum as valid if computes to invalid values 0 or 0xFF. Checksum can also be set to ok to save encoding computation time. }, ), 'comm_timeout_type': FieldType( name='comm_timeout_type', base_type=BASE_TYPES[0x84], # uint16 values={ 0: 'wildcard_pairing_timeout', # Timeout pairing to any device 1: 'pairing_timeout', # Timeout pairing to previously paired device 2: 'connection_lost', # Temporary loss of communications 3: 'connection_timeout', # Connection closed due to extended bad communications }, ), 'connectivity_capabilities': FieldType( name='connectivity_capabilities', base_type=BASE_TYPES[0x8C], # uint32z values={ 0x00000001: 'bluetooth', 0x00000002: 'bluetooth_le', 0x00000004: 'ant', 0x00000008: 'activity_upload', 0x00000010: 'course_download', 0x00000020: 'workout_download', 0x00000040: 'live_track', 0x00000080: 'weather_conditions', 0x00000100: 'weather_alerts', 0x00000200: 'gps_ephemeris_download', 0x00000400: 'explicit_archive', 0x00000800: 'setup_incomplete', 0x00001000: 'continue_sync_after_software_update', 0x00002000: 'connect_iq_app_download', 0x00004000: 'golf_course_download', 0x00008000: 'device_initiates_sync', # Indicates device is in control of initiating all syncs 0x00010000: 'connect_iq_watch_app_download', 0x00020000: 'connect_iq_widget_download', 0x00040000: 'connect_iq_watch_face_download', 0x00080000: 'connect_iq_data_field_download', 0x00100000: 'connect_iq_app_managment', # Device supports delete and reorder of apps via GCM 0x00200000: 'swing_sensor', 0x00400000: 'swing_sensor_remote', 0x00800000: 'incident_detection', # Device supports incident detection 0x01000000: 'audio_prompts', 0x02000000: 'wifi_verification', # Device supports reporting wifi verification via GCM 0x04000000: 'true_up', # Device supports True Up 0x08000000: 'find_my_watch', # Device supports Find My Watch 0x10000000: 'remote_manual_sync', 0x20000000: 'live_track_auto_start', # Device supports LiveTrack auto start 0x40000000: 'live_track_messaging', # Device supports LiveTrack Messaging 0x80000000: 'instant_input', # Device supports instant input feature }, ), 'course_capabilities': FieldType( name='course_capabilities', base_type=BASE_TYPES[0x8C], # uint32z values={ 0x00000001: 'processed', 0x00000002: 'valid', 0x00000004: 'time', 0x00000008: 'distance', 0x00000010: 'position', 0x00000020: 'heart_rate', 0x00000040: 'power', 0x00000080: 'cadence', 0x00000100: 'training', 0x00000200: 'navigation', 0x00000400: 'bikeway', }, ), 'course_point': FieldType( name='course_point', base_type=BASE_TYPES[0x00], # enum values={ 0: 'generic', 1: 'summit', 2: 'valley', 3: 'water', 4: 'food', 5: 'danger', 6: 'left', 7: 'right', 8: 'straight', 9: 'first_aid', 10: 'fourth_category', 11: 'third_category', 12: 'second_category', 13: 'first_category', 14: 'hors_category', 15: 'sprint', 16: 'left_fork', 17: 'right_fork', 18: 'middle_fork', 19: 'slight_left', 20: 'sharp_left', 21: 'slight_right', 22: 'sharp_right', 23: 'u_turn', 24: 'segment_start', 25: 'segment_end', }, ), 'date_mode': FieldType( name='date_mode', base_type=BASE_TYPES[0x00], # enum values={ 0: 'day_month', 1: 'month_day', }, ), 'date_time': FieldType( # seconds since UTC 00:00 Dec 31 1989 name='date_time', base_type=BASE_TYPES[0x86], # uint32 ), 'day_of_week': FieldType( name='day_of_week', base_type=BASE_TYPES[0x00], # enum values={ 0: 'sunday', 1: 'monday', 2: 'tuesday', 3: 'wednesday', 4: 'thursday', 5: 'friday', 6: 'saturday', }, ), 'device_index': FieldType( name='device_index', base_type=BASE_TYPES[0x02], # uint8 values={ 0: 'creator', # Creator of the file is always device index 0. }, ), 'digital_watchface_layout': FieldType( name='digital_watchface_layout', base_type=BASE_TYPES[0x00], # enum values={ 0: 'traditional', 1: 'modern', 2: 'bold', }, ), 'display_heart': FieldType( name='display_heart', base_type=BASE_TYPES[0x00], # enum values={ 0: 'bpm', 1: 'max', 2: 'reserve', }, ), 'display_measure': FieldType( name='display_measure', base_type=BASE_TYPES[0x00], # enum values={ 0: 'metric', 1: 'statute', 2: 'nautical', }, ), 'display_orientation': FieldType( name='display_orientation', base_type=BASE_TYPES[0x00], # enum values={ 0: 'auto', # automatic if the device supports it 1: 'portrait', 2: 'landscape', 3: 'portrait_flipped', # portrait mode but rotated 180 degrees 4: 'landscape_flipped', # landscape mode but rotated 180 degrees }, ), 'display_position': FieldType( name='display_position', base_type=BASE_TYPES[0x00], # enum values={ 0: 'degree', # dd.dddddd 1: 'degree_minute', # dddmm.mmm 2: 'degree_minute_second', # dddmmss 3: 'austrian_grid', # Austrian Grid (BMN) 4: 'british_grid', # British National Grid 5: 'dutch_grid', # Dutch grid system 6: 'hungarian_grid', # Hungarian grid system 7: 'finnish_grid', # Finnish grid system Zone3 KKJ27 8: 'german_grid', # Gausss Krueger (German) 9: 'icelandic_grid', # Icelandic Grid 10: 'indonesian_equatorial', # Indonesian Equatorial LCO 11: 'indonesian_irian', # Indonesian Irian LCO 12: 'indonesian_southern', # Indonesian Southern LCO 13: 'india_zone_0', # India zone 0 14: 'india_zone_IA', # India zone IA 15: 'india_zone_IB', # India zone IB 16: 'india_zone_IIA', # India zone IIA 17: 'india_zone_IIB', # India zone IIB 18: 'india_zone_IIIA', # India zone IIIA 19: 'india_zone_IIIB', # India zone IIIB 20: 'india_zone_IVA', # India zone IVA 21: 'india_zone_IVB', # India zone IVB 22: 'irish_transverse', # Irish Transverse Mercator 23: 'irish_grid', # Irish Grid 24: 'loran', # Loran TD 25: 'maidenhead_grid', # Maidenhead grid system 26: 'mgrs_grid', # MGRS grid system 27: 'new_zealand_grid', # New Zealand grid system 28: 'new_zealand_transverse', # New Zealand Transverse Mercator 29: 'qatar_grid', # Qatar National Grid 30: 'modified_swedish_grid', # Modified RT-90 (Sweden) 31: 'swedish_grid', # RT-90 (Sweden) 32: 'south_african_grid', # South African Grid 33: 'swiss_grid', # Swiss CH-1903 grid 34: 'taiwan_grid', # Taiwan Grid 35: 'united_states_grid', # United States National Grid 36: 'utm_ups_grid', # UTM/UPS grid system 37: 'west_malayan', # West Malayan RSO 38: 'borneo_rso', # Borneo RSO 39: 'estonian_grid', # Estonian grid system 40: 'latvian_grid', # Latvian Transverse Mercator 41: 'swedish_ref_99_grid', # Reference Grid 99 TM (Swedish) }, ), 'display_power': FieldType( name='display_power', base_type=BASE_TYPES[0x00], # enum values={ 0: 'watts', 1: 'percent_ftp', }, ), 'event': FieldType( name='event', base_type=BASE_TYPES[0x00], # enum values={ 0: 'timer', # Group 0. Start / stop_all 3: 'workout', # start / stop 4: 'workout_step', # Start at beginning of workout. Stop at end of each step. 5: 'power_down', # stop_all group 0 6: 'power_up', # stop_all group 0 7: 'off_course', # start / stop group 0 8: 'session', # Stop at end of each session. 9: 'lap', # Stop at end of each lap. 10: 'course_point', # marker 11: 'battery', # marker 12: 'virtual_partner_pace', # Group 1. Start at beginning of activity if VP enabled, when VP pace is changed during activity or VP enabled mid activity. stop_disable when VP disabled. 13: 'hr_high_alert', # Group 0. Start / stop when in alert condition. 14: 'hr_low_alert', # Group 0. Start / stop when in alert condition. 15: 'speed_high_alert', # Group 0. Start / stop when in alert condition. 16: 'speed_low_alert', # Group 0. Start / stop when in alert condition. 17: 'cad_high_alert', # Group 0. Start / stop when in alert condition. 18: 'cad_low_alert', # Group 0. Start / stop when in alert condition. 19: 'power_high_alert', # Group 0. Start / stop when in alert condition. 20: 'power_low_alert', # Group 0. Start / stop when in alert condition. 21: 'recovery_hr', # marker 22: 'battery_low', # marker 23: 'time_duration_alert', # Group 1. Start if enabled mid activity (not required at start of activity). Stop when duration is reached. stop_disable if disabled. 24: 'distance_duration_alert', # Group 1. Start if enabled mid activity (not required at start of activity). Stop when duration is reached. stop_disable if disabled. 25: 'calorie_duration_alert', # Group 1. Start if enabled mid activity (not required at start of activity). Stop when duration is reached. stop_disable if disabled. 26: 'activity', # Group 1.. Stop at end of activity. 27: 'fitness_equipment', # marker 28: 'length', # Stop at end of each length. 32: 'user_marker', # marker 33: 'sport_point', # marker 36: 'calibration', # start/stop/marker 42: 'front_gear_change', # marker 43: 'rear_gear_change', # marker 44: 'rider_position_change', # marker 45: 'elev_high_alert', # Group 0. Start / stop when in alert condition. 46: 'elev_low_alert', # Group 0. Start / stop when in alert condition. 47: 'comm_timeout', # marker }, ), 'event_type': FieldType( name='event_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'start', 1: 'stop', 2: 'consecutive_depreciated', 3: 'marker', 4: 'stop_all', 5: 'begin_depreciated', 6: 'end_depreciated', 7: 'end_all_depreciated', 8: 'stop_disable', 9: 'stop_disable_all', }, ), 'exd_data_units': FieldType( name='exd_data_units', base_type=BASE_TYPES[0x00], # enum values={ 0: 'no_units', 1: 'laps', 2: 'miles_per_hour', 3: 'kilometers_per_hour', 4: 'feet_per_hour', 5: 'meters_per_hour', 6: 'degrees_celsius', 7: 'degrees_farenheit', 8: 'zone', 9: 'gear', 10: 'rpm', 11: 'bpm', 12: 'degrees', 13: 'millimeters', 14: 'meters', 15: 'kilometers', 16: 'feet', 17: 'yards', 18: 'kilofeet', 19: 'miles', 20: 'time', 21: 'enum_turn_type', 22: 'percent', 23: 'watts', 24: 'watts_per_kilogram', 25: 'enum_battery_status', 26: 'enum_bike_light_beam_angle_mode', 27: 'enum_bike_light_battery_status', 28: 'enum_bike_light_network_config_type', 29: 'lights', 30: 'seconds', 31: 'minutes', 32: 'hours', 33: 'calories', 34: 'kilojoules', 35: 'milliseconds', 36: 'second_per_mile', 37: 'second_per_kilometer', 38: 'centimeter', 39: 'enum_course_point', 40: 'bradians', 41: 'enum_sport', 42: 'inches_hg', 43: 'mm_hg', 44: 'mbars', 45: 'hecto_pascals', 46: 'feet_per_min', 47: 'meters_per_min', 48: 'meters_per_sec', 49: 'eight_cardinal', }, ), 'exd_descriptors': FieldType( name='exd_descriptors', base_type=BASE_TYPES[0x00], # enum values={ 0: 'bike_light_battery_status', 1: 'beam_angle_status', 2: 'batery_level', 3: 'light_network_mode', 4: 'number_lights_connected', 5: 'cadence', 6: 'distance', 7: 'estimated_time_of_arrival', 8: 'heading', 9: 'time', 10: 'battery_level', 11: 'trainer_resistance', 12: 'trainer_target_power', 13: 'time_seated', 14: 'time_standing', 15: 'elevation', 16: 'grade', 17: 'ascent', 18: 'descent', 19: 'vertical_speed', 20: 'di2_battery_level', 21: 'front_gear', 22: 'rear_gear', 23: 'gear_ratio', 24: 'heart_rate', 25: 'heart_rate_zone', 26: 'time_in_heart_rate_zone', 27: 'heart_rate_reserve', 28: 'calories', 29: 'gps_accuracy', 30: 'gps_signal_strength', 31: 'temperature', 32: 'time_of_day', 33: 'balance', 34: 'pedal_smoothness', 35: 'power', 36: 'functional_threshold_power', 37: 'intensity_factor', 38: 'work', 39: 'power_ratio', 40: 'normalized_power', 41: 'training_stress_Score', 42: 'time_on_zone', 43: 'speed', 44: 'laps', 45: 'reps', 46: 'workout_step', 47: 'course_distance', 48: 'navigation_distance', 49: 'course_estimated_time_of_arrival', 50: 'navigation_estimated_time_of_arrival', 51: 'course_time', 52: 'navigation_time', 53: 'course_heading', 54: 'navigation_heading', 55: 'power_zone', 56: 'torque_effectiveness', 57: 'timer_time', 58: 'power_weight_ratio', 59: 'left_platform_center_offset', 60: 'right_platform_center_offset', 61: 'left_power_phase_start_angle', 62: 'right_power_phase_start_angle', 63: 'left_power_phase_finish_angle', 64: 'right_power_phase_finish_angle', 65: 'gears', # Combined gear information 66: 'pace', 67: 'training_effect', 68: 'vertical_oscillation', 69: 'vertical_ratio', 70: 'ground_contact_time', 71: 'left_ground_contact_time_balance', 72: 'right_ground_contact_time_balance', 73: 'stride_length', 74: 'running_cadence', 75: 'performance_condition', 76: 'course_type', 77: 'time_in_power_zone', 78: 'navigation_turn', 79: 'course_location', 80: 'navigation_location', 81: 'compass', 82: 'gear_combo', 83: 'muscle_oxygen', 84: 'icon', 85: 'compass_heading', 86: 'gps_heading', 87: 'gps_elevation', 88: 'anaerobic_training_effect', 89: 'course', 90: 'off_course', 91: 'glide_ratio', 92: 'vertical_distance', 93: 'vmg', 94: 'ambient_pressure', 95: 'pressure', }, ), 'exd_display_type': FieldType( name='exd_display_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'numerical', 1: 'simple', 2: 'graph', 3: 'bar', 4: 'circle_graph', 5: 'virtual_partner', 6: 'balance', 7: 'string_list', 8: 'string', 9: 'simple_dynamic_icon', 10: 'gauge', }, ), 'exd_layout': FieldType( name='exd_layout', base_type=BASE_TYPES[0x00], # enum values={ 0: 'full_screen', 1: 'half_vertical', 2: 'half_horizontal', 3: 'half_vertical_right_split', 4: 'half_horizontal_bottom_split', 5: 'full_quarter_split', 6: 'half_vertical_left_split', 7: 'half_horizontal_top_split', }, ), 'exd_qualifiers': FieldType( name='exd_qualifiers', base_type=BASE_TYPES[0x00], # enum values={ 0: 'no_qualifier', 1: 'instantaneous', 2: 'average', 3: 'lap', 4: 'maximum', 5: 'maximum_average', 6: 'maximum_lap', 7: 'last_lap', 8: 'average_lap', 9: 'to_destination', 10: 'to_go', 11: 'to_next', 12: 'next_course_point', 13: 'total', 14: 'three_second_average', 15: 'ten_second_average', 16: 'thirty_second_average', 17: 'percent_maximum', 18: 'percent_maximum_average', 19: 'lap_percent_maximum', 20: 'elapsed', 21: 'sunrise', 22: 'sunset', 23: 'compared_to_virtual_partner', 24: 'maximum_24h', 25: 'minimum_24h', 26: 'minimum', 27: 'first', 28: 'second', 29: 'third', 30: 'shifter', 31: 'last_sport', 32: 'moving', 33: 'stopped', 242: 'zone_9', 243: 'zone_8', 244: 'zone_7', 245: 'zone_6', 246: 'zone_5', 247: 'zone_4', 248: 'zone_3', 249: 'zone_2', 250: 'zone_1', }, ), 'file': FieldType( name='file', base_type=BASE_TYPES[0x00], # enum values={ 1: 'device', # Read only, single file. Must be in root directory. 2: 'settings', # Read/write, single file. Directory=Settings 3: 'sport', # Read/write, multiple files, file number = sport type. Directory=Sports 4: 'activity', # Read/erase, multiple files. Directory=Activities 5: 'workout', # Read/write/erase, multiple files. Directory=Workouts 6: 'course', # Read/write/erase, multiple files. Directory=Courses 7: 'schedules', # Read/write, single file. Directory=Schedules 9: 'weight', # Read only, single file. Circular buffer. All message definitions at start of file. Directory=Weight 10: 'totals', # Read only, single file. Directory=Totals 11: 'goals', # Read/write, single file. Directory=Goals 14: 'blood_pressure', # Read only. Directory=Blood Pressure 15: 'monitoring_a', # Read only. Directory=Monitoring. File number=sub type. 20: 'activity_summary', # Read/erase, multiple files. Directory=Activities 28: 'monitoring_daily', 32: 'monitoring_b', # Read only. Directory=Monitoring. File number=identifier 34: 'segment', # Read/write/erase. Multiple Files. Directory=Segments 35: 'segment_list', # Read/write/erase. Single File. Directory=Segments 40: 'exd_configuration', # Read/write/erase. Single File. Directory=Settings 0xF7: 'mfg_range_min', # 0xF7 - 0xFE reserved for manufacturer specific file types 0xFE: 'mfg_range_max', # 0xF7 - 0xFE reserved for manufacturer specific file types }, ), 'file_flags': FieldType( name='file_flags', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x02: 'read', 0x04: 'write', 0x08: 'erase', }, ), 'fit_base_type': FieldType( name='fit_base_type', base_type=BASE_TYPES[0x02], # uint8 values={ 0: 'enum', 1: 'sint8', 2: 'uint8', 7: 'string', 10: 'uint8z', 13: 'byte', 131: 'sint16', 132: 'uint16', 133: 'sint32', 134: 'uint32', 136: 'float32', 137: 'float64', 139: 'uint16z', 140: 'uint32z', 142: 'sint64', 143: 'uint64', 144: 'uint64z', }, ), 'fit_base_unit': FieldType( name='fit_base_unit', base_type=BASE_TYPES[0x84], # uint16 values={ 0: 'other', 1: 'kilogram', 2: 'pound', }, ), 'fitness_equipment_state': FieldType( # fitness equipment event data name='fitness_equipment_state', base_type=BASE_TYPES[0x00], # enum values={ 0: 'ready', 1: 'in_use', 2: 'paused', 3: 'unknown', # lost connection to fitness equipment }, ), 'garmin_product': FieldType( name='garmin_product', base_type=BASE_TYPES[0x84], # uint16 values={ 1: 'hrm1', 2: 'axh01', # AXH01 HRM chipset 3: 'axb01', 4: 'axb02', 5: 'hrm2ss', 6: 'dsi_alf02', 7: 'hrm3ss', 8: 'hrm_run_single_byte_product_id', # hrm_run model for HRM ANT+ messaging 9: 'bsm', # BSM model for ANT+ messaging 10: 'bcm', # BCM model for ANT+ messaging 11: 'axs01', # AXS01 HRM Bike Chipset model for ANT+ messaging 12: 'hrm_tri_single_byte_product_id', # hrm_tri model for HRM ANT+ messaging 14: 'fr225_single_byte_product_id', # fr225 model for HRM ANT+ messaging 473: 'fr301_china', 474: 'fr301_japan', 475: 'fr301_korea', 494: 'fr301_taiwan', 717: 'fr405', # Forerunner 405 782: 'fr50', # Forerunner 50 987: 'fr405_japan', 988: 'fr60', # Forerunner 60 1011: 'dsi_alf01', 1018: 'fr310xt', # Forerunner 310 1036: 'edge500', 1124: 'fr110', # Forerunner 110 1169: 'edge800', 1199: 'edge500_taiwan', 1213: 'edge500_japan', 1253: 'chirp', 1274: 'fr110_japan', 1325: 'edge200', 1328: 'fr910xt', 1333: 'edge800_taiwan', 1334: 'edge800_japan', 1341: 'alf04', 1345: 'fr610', 1360: 'fr210_japan', 1380: 'vector_ss', 1381: 'vector_cp', 1386: 'edge800_china', 1387: 'edge500_china', 1410: 'fr610_japan', 1422: 'edge500_korea', 1436: 'fr70', 1446: 'fr310xt_4t', 1461: 'amx', 1482: 'fr10', 1497: 'edge800_korea', 1499: 'swim', 1537: 'fr910xt_china', 1551: 'fenix', 1555: 'edge200_taiwan', 1561: 'edge510', 1567: 'edge810', 1570: 'tempe', 1600: 'fr910xt_japan', 1623: 'fr620', 1632: 'fr220', 1664: 'fr910xt_korea', 1688: 'fr10_japan', 1721: 'edge810_japan', 1735: 'virb_elite', 1736: 'edge_touring', # Also Edge Touring Plus 1742: 'edge510_japan', 1743: 'hrm_tri', 1752: 'hrm_run', 1765: 'fr920xt', 1821: 'edge510_asia', 1822: 'edge810_china', 1823: 'edge810_taiwan', 1836: 'edge1000', 1837: 'vivo_fit', 1853: 'virb_remote', 1885: 'vivo_ki', 1903: 'fr15', 1907: 'vivo_active', 1918: 'edge510_korea', 1928: 'fr620_japan', 1929: 'fr620_china', 1930: 'fr220_japan', 1931: 'fr220_china', 1936: 'approach_s6', 1956: 'vivo_smart', 1967: 'fenix2', 1988: 'epix', 2050: 'fenix3', 2052: 'edge1000_taiwan', 2053: 'edge1000_japan', 2061: 'fr15_japan', 2067: 'edge520', 2070: 'edge1000_china', 2072: 'fr620_russia', 2073: 'fr220_russia', 2079: 'vector_s', 2100: 'edge1000_korea', 2130: 'fr920xt_taiwan', 2131: 'fr920xt_china', 2132: 'fr920xt_japan', 2134: 'virbx', 2135: 'vivo_smart_apac', 2140: 'etrex_touch', 2147: 'edge25', 2148: 'fr25', 2150: 'vivo_fit2', 2153: 'fr225', 2156: 'fr630', 2157: 'fr230', 2160: 'vivo_active_apac', 2161: 'vector_2', 2162: 'vector_2s', 2172: 'virbxe', 2173: 'fr620_taiwan', 2174: 'fr220_taiwan', 2175: 'truswing', 2188: 'fenix3_china', 2189: 'fenix3_twn', 2192: 'varia_headlight', 2193: 'varia_taillight_old', 2204: 'edge_explore_1000', 2219: 'fr225_asia', 2225: 'varia_radar_taillight', 2226: 'varia_radar_display', 2238: 'edge20', 2262: 'd2_bravo', 2266: 'approach_s20', 2276: 'varia_remote', 2327: 'hrm4_run', 2337: 'vivo_active_hr', 2347: 'vivo_smart_gps_hr', 2348: 'vivo_smart_hr', 2368: 'vivo_move', 2398: 'varia_vision', 2406: 'vivo_fit3', 2413: 'fenix3_hr', 2417: 'virb_ultra_30', 2429: 'index_smart_scale', 2431: 'fr235', 2432: 'fenix3_chronos', 2441: 'oregon7xx', 2444: 'rino7xx', 2496: 'nautix', 2530: 'edge_820', 2531: 'edge_explore_820', 2544: 'fenix5s', 2547: 'd2_bravo_titanium', 2593: 'running_dynamics_pod', 2604: 'fenix5x', 2606: 'vivo_fit_jr', 2691: 'fr935', 2697: 'fenix5', 10007: 'sdm4', # SDM4 footpod 10014: 'edge_remote', 20119: 'training_center', 65531: 'connectiq_simulator', 65532: 'android_antplus_plugin', 65534: 'connect', # Garmin Connect website }, ), 'gender': FieldType( name='gender', base_type=BASE_TYPES[0x00], # enum values={ 0: 'female', 1: 'male', }, ), 'goal': FieldType( name='goal', base_type=BASE_TYPES[0x00], # enum values={ 0: 'time', 1: 'distance', 2: 'calories', 3: 'frequency', 4: 'steps', 5: 'ascent', 6: 'active_minutes', }, ), 'goal_recurrence': FieldType( name='goal_recurrence', base_type=BASE_TYPES[0x00], # enum values={ 0: 'off', 1: 'daily', 2: 'weekly', 3: 'monthly', 4: 'yearly', 5: 'custom', }, ), 'goal_source': FieldType( name='goal_source', base_type=BASE_TYPES[0x00], # enum values={ 0: 'auto', # Device generated 1: 'community', # Social network sourced goal 2: 'user', # Manually generated }, ), 'hr_type': FieldType( name='hr_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'normal', 1: 'irregular', }, ), 'hr_zone_calc': FieldType( name='hr_zone_calc', base_type=BASE_TYPES[0x00], # enum values={ 0: 'custom', 1: 'percent_max_hr', 2: 'percent_hrr', }, ), 'intensity': FieldType( name='intensity', base_type=BASE_TYPES[0x00], # enum values={ 0: 'active', 1: 'rest', 2: 'warmup', 3: 'cooldown', }, ), 'language': FieldType( name='language', base_type=BASE_TYPES[0x00], # enum values={ 0: 'english', 1: 'french', 2: 'italian', 3: 'german', 4: 'spanish', 5: 'croatian', 6: 'czech', 7: 'danish', 8: 'dutch', 9: 'finnish', 10: 'greek', 11: 'hungarian', 12: 'norwegian', 13: 'polish', 14: 'portuguese', 15: 'slovakian', 16: 'slovenian', 17: 'swedish', 18: 'russian', 19: 'turkish', 20: 'latvian', 21: 'ukrainian', 22: 'arabic', 23: 'farsi', 24: 'bulgarian', 25: 'romanian', 26: 'chinese', 27: 'japanese', 28: 'korean', 29: 'taiwanese', 30: 'thai', 31: 'hebrew', 32: 'brazilian_portuguese', 33: 'indonesian', 34: 'malaysian', 35: 'vietnamese', 36: 'burmese', 37: 'mongolian', 254: 'custom', }, ), 'language_bits_0': FieldType( # Bit field corresponding to language enum type (1 << language). name='language_bits_0', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x01: 'english', 0x02: 'french', 0x04: 'italian', 0x08: 'german', 0x10: 'spanish', 0x20: 'croatian', 0x40: 'czech', 0x80: 'danish', }, ), 'language_bits_1': FieldType( name='language_bits_1', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x01: 'dutch', 0x02: 'finnish', 0x04: 'greek', 0x08: 'hungarian', 0x10: 'norwegian', 0x20: 'polish', 0x40: 'portuguese', 0x80: 'slovakian', }, ), 'language_bits_2': FieldType( name='language_bits_2', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x01: 'slovenian', 0x02: 'swedish', 0x04: 'russian', 0x08: 'turkish', 0x10: 'latvian', 0x20: 'ukrainian', 0x40: 'arabic', 0x80: 'farsi', }, ), 'language_bits_3': FieldType( name='language_bits_3', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x01: 'bulgarian', 0x02: 'romanian', 0x04: 'chinese', 0x08: 'japanese', 0x10: 'korean', 0x20: 'taiwanese', 0x40: 'thai', 0x80: 'hebrew', }, ), 'language_bits_4': FieldType( name='language_bits_4', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x01: 'brazilian_portuguese', 0x02: 'indonesian', 0x04: 'malaysian', 0x08: 'vietnamese', 0x10: 'burmese', 0x20: 'mongolian', }, ), 'lap_trigger': FieldType( name='lap_trigger', base_type=BASE_TYPES[0x00], # enum values={ 0: 'manual', 1: 'time', 2: 'distance', 3: 'position_start', 4: 'position_lap', 5: 'position_waypoint', 6: 'position_marked', 7: 'session_end', 8: 'fitness_equipment', }, ), 'left_right_balance': FieldType( name='left_right_balance', base_type=BASE_TYPES[0x02], # uint8 values={ 0x7F: 'mask', # % contribution 0x80: 'right', # data corresponds to right if set, otherwise unknown }, ), 'left_right_balance_100': FieldType( name='left_right_balance_100', base_type=BASE_TYPES[0x84], # uint16 values={ 0x3FFF: 'mask', # % contribution scaled by 100 0x8000: 'right', # data corresponds to right if set, otherwise unknown }, ), 'length_type': FieldType( name='length_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'idle', # Rest period. Length with no strokes 1: 'active', # Length with strokes. }, ), 'local_date_time': FieldType( # seconds since 00:00 Dec 31 1989 in local time zone name='local_date_time', base_type=BASE_TYPES[0x86], # uint32 values={ 0x10000000: 'min', # if date_time is < 0x10000000 then it is system time (seconds from device power on) }, ), 'localtime_into_day': FieldType( # number of seconds into the day since local 00:00:00 name='localtime_into_day', base_type=BASE_TYPES[0x86], # uint32 ), 'manufacturer': FieldType( name='manufacturer', base_type=BASE_TYPES[0x84], # uint16 values={ 1: 'garmin', 2: 'garmin_fr405_antfs', # Do not use. Used by FR405 for ANTFS man id. 3: 'zephyr', 4: 'dayton', 5: 'idt', 6: 'srm', 7: 'quarq', 8: 'ibike', 9: 'saris', 10: 'spark_hk', 11: 'tanita', 12: 'echowell', 13: 'dynastream_oem', 14: 'nautilus', 15: 'dynastream', 16: 'timex', 17: 'metrigear', 18: 'xelic', 19: 'beurer', 20: 'cardiosport', 21: 'a_and_d', 22: 'hmm', 23: 'suunto', 24: 'thita_elektronik', 25: 'gpulse', 26: 'clean_mobile', 27: 'pedal_brain', 28: 'peaksware', 29: 'saxonar', 30: 'lemond_fitness', 31: 'dexcom', 32: 'wahoo_fitness', 33: 'octane_fitness', 34: 'archinoetics', 35: 'the_hurt_box', 36: 'citizen_systems', 37: 'magellan', 38: 'osynce', 39: 'holux', 40: 'concept2', 42: 'one_giant_leap', 43: 'ace_sensor', 44: 'brim_brothers', 45: 'xplova', 46: 'perception_digital', 47: 'bf1systems', 48: 'pioneer', 49: 'spantec', 50: 'metalogics', 51: '4iiiis', 52: 'seiko_epson', 53: 'seiko_epson_oem', 54: 'ifor_powell', 55: 'maxwell_guider', 56: 'star_trac', 57: 'breakaway', 58: 'alatech_technology_ltd', 59: 'mio_technology_europe', 60: 'rotor', 61: 'geonaute', 62: 'id_bike', 63: 'specialized', 64: 'wtek', 65: 'physical_enterprises', 66: 'north_pole_engineering', 67: 'bkool', 68: 'cateye', 69: 'stages_cycling', 70: 'sigmasport', 71: 'tomtom', 72: 'peripedal', 73: 'wattbike', 76: 'moxy', 77: 'ciclosport', 78: 'powerbahn', 79: 'acorn_projects_aps', 80: 'lifebeam', 81: 'bontrager', 82: 'wellgo', 83: 'scosche', 84: 'magura', 85: 'woodway', 86: 'elite', 87: 'nielsen_kellerman', 88: 'dk_city', 89: 'tacx', 90: 'direction_technology', 91: 'magtonic', 92: '1partcarbon', 93: 'inside_ride_technologies', 94: 'sound_of_motion', 95: 'stryd', 96: 'icg', # Indoorcycling Group 97: 'MiPulse', 98: 'bsx_athletics', 99: 'look', 100: 'campagnolo_srl', 101: 'body_bike_smart', 102: 'praxisworks', 103: 'limits_technology', # Limits Technology Ltd. 104: 'topaction_technology', # TopAction Technology Inc. 105: 'cosinuss', 106: 'fitcare', 107: 'magene', 108: 'giant_manufacturing_co', 109: 'tigrasport', # Tigrasport 110: 'salutron', 111: 'technogym', 112: 'bryton_sensors', 255: 'development', 257: 'healthandlife', 258: 'lezyne', 259: 'scribe_labs', 260: 'zwift', 261: 'watteam', 262: 'recon', 263: 'favero_electronics', 264: 'dynovelo', 265: 'strava', 266: 'precor', # Amer Sports 267: 'bryton', 268: 'sram', 269: 'navman', # MiTAC Global Corporation (Mio Technology) 270: 'cobi', # COBI GmbH 271: 'spivi', 272: 'mio_magellan', 273: 'evesports', 274: 'sensitivus_gauge', 275: 'podoon', 276: 'life_time_fitness', 277: 'falco_e_motors', # Falco eMotors Inc. 5759: 'actigraphcorp', }, ), 'mesg_count': FieldType( name='mesg_count', base_type=BASE_TYPES[0x00], # enum values={ 0: 'num_per_file', 1: 'max_per_file', 2: 'max_per_file_type', }, ), 'mesg_num': FieldType( name='mesg_num', base_type=BASE_TYPES[0x84], # uint16 values={ 0: 'file_id', 1: 'capabilities', 2: 'device_settings', 3: 'user_profile', 4: 'hrm_profile', 5: 'sdm_profile', 6: 'bike_profile', 7: 'zones_target', 8: 'hr_zone', 9: 'power_zone', 10: 'met_zone', 12: 'sport', 15: 'goal', 18: 'session', 19: 'lap', 20: 'record', 21: 'event', 23: 'device_info', 26: 'workout', 27: 'workout_step', 28: 'schedule', 30: 'weight_scale', 31: 'course', 32: 'course_point', 33: 'totals', 34: 'activity', 35: 'software', 37: 'file_capabilities', 38: 'mesg_capabilities', 39: 'field_capabilities', 49: 'file_creator', 51: 'blood_pressure', 53: 'speed_zone', 55: 'monitoring', 72: 'training_file', 78: 'hrv', 80: 'ant_rx', 81: 'ant_tx', 82: 'ant_channel_id', 101: 'length', 103: 'monitoring_info', 105: 'pad', 106: 'slave_device', 127: 'connectivity', 128: 'weather_conditions', 129: 'weather_alert', 131: 'cadence_zone', 132: 'hr', 142: 'segment_lap', 145: 'memo_glob', 148: 'segment_id', 149: 'segment_leaderboard_entry', 150: 'segment_point', 151: 'segment_file', 158: 'workout_session', 159: 'watchface_settings', 160: 'gps_metadata', 161: 'camera_event', 162: 'timestamp_correlation', 164: 'gyroscope_data', 165: 'accelerometer_data', 167: 'three_d_sensor_calibration', 169: 'video_frame', 174: 'obdii_data', 177: 'nmea_sentence', 178: 'aviation_attitude', 184: 'video', 185: 'video_title', 186: 'video_description', 187: 'video_clip', 188: 'ohr_settings', 200: 'exd_screen_configuration', 201: 'exd_data_field_configuration', 202: 'exd_data_concept_configuration', 206: 'field_description', 207: 'developer_data_id', 208: 'magnetometer_data', }, ), 'message_index': FieldType( name='message_index', base_type=BASE_TYPES[0x84], # uint16 values={ 0x0FFF: 'mask', # index 0x7000: 'reserved', # reserved (default 0) 0x8000: 'selected', # message is selected if set }, ), 'power_phase_type': FieldType( name='power_phase_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'power_phase_start_angle', 1: 'power_phase_end_angle', 2: 'power_phase_arc_length', 3: 'power_phase_center', }, ), 'pwr_zone_calc': FieldType( name='pwr_zone_calc', base_type=BASE_TYPES[0x00], # enum values={ 0: 'custom', 1: 'percent_ftp', }, ), 'rider_position_type': FieldType( name='rider_position_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'seated', 1: 'standing', 2: 'transition_to_seated', 3: 'transition_to_standing', }, ), 'schedule': FieldType( name='schedule', base_type=BASE_TYPES[0x00], # enum values={ 0: 'workout', 1: 'course', }, ), 'segment_delete_status': FieldType( name='segment_delete_status', base_type=BASE_TYPES[0x00], # enum values={ 0: 'do_not_delete', 1: 'delete_one', 2: 'delete_all', }, ), 'segment_lap_status': FieldType( name='segment_lap_status', base_type=BASE_TYPES[0x00], # enum values={ 0: 'end', 1: 'fail', }, ), 'segment_leaderboard_type': FieldType( name='segment_leaderboard_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'overall', 1: 'personal_best', 2: 'connections', 3: 'group', 4: 'challenger', 5: 'kom', 6: 'qom', 7: 'pr', 8: 'goal', 9: 'rival', 10: 'club_leader', }, ), 'segment_selection_type': FieldType( name='segment_selection_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'starred', 1: 'suggested', }, ), 'sensor_type': FieldType( name='sensor_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'accelerometer', 1: 'gyroscope', 2: 'compass', # Magnetometer }, ), 'session_trigger': FieldType( name='session_trigger', base_type=BASE_TYPES[0x00], # enum values={ 0: 'activity_end', 1: 'manual', # User changed sport. 2: 'auto_multi_sport', # Auto multi-sport feature is enabled and user pressed lap button to advance session. 3: 'fitness_equipment', # Auto sport change caused by user linking to fitness equipment. }, ), 'side': FieldType( name='side', base_type=BASE_TYPES[0x00], # enum values={ 0: 'right', 1: 'left', }, ), 'source_type': FieldType( name='source_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'ant', # External device connected with ANT 1: 'antplus', # External device connected with ANT+ 2: 'bluetooth', # External device connected with BT 3: 'bluetooth_low_energy', # External device connected with BLE 4: 'wifi', # External device connected with Wifi 5: 'local', # Onboard device }, ), 'sport': FieldType( name='sport', base_type=BASE_TYPES[0x00], # enum values={ 0: 'generic', 1: 'running', 2: 'cycling', 3: 'transition', # Mulitsport transition 4: 'fitness_equipment', 5: 'swimming', 6: 'basketball', 7: 'soccer', 8: 'tennis', 9: 'american_football', 10: 'training', 11: 'walking', 12: 'cross_country_skiing', 13: 'alpine_skiing', 14: 'snowboarding', 15: 'rowing', 16: 'mountaineering', 17: 'hiking', 18: 'multisport', 19: 'paddling', 20: 'flying', 21: 'e_biking', 22: 'motorcycling', 23: 'boating', 24: 'driving', 25: 'golf', 26: 'hang_gliding', 27: 'horseback_riding', 28: 'hunting', 29: 'fishing', 30: 'inline_skating', 31: 'rock_climbing', 32: 'sailing', 33: 'ice_skating', 34: 'sky_diving', 35: 'snowshoeing', 36: 'snowmobiling', 37: 'stand_up_paddleboarding', 38: 'surfing', 39: 'wakeboarding', 40: 'water_skiing', 41: 'kayaking', 42: 'rafting', 43: 'windsurfing', 44: 'kitesurfing', 45: 'tactical', 46: 'jumpmaster', 47: 'boxing', 48: 'floor_climbing', 254: 'all', # All is for goals only to include all sports. }, ), 'sport_bits_0': FieldType( # Bit field corresponding to sport enum type (1 << sport). name='sport_bits_0', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x01: 'generic', 0x02: 'running', 0x04: 'cycling', 0x08: 'transition', # Mulitsport transition 0x10: 'fitness_equipment', 0x20: 'swimming', 0x40: 'basketball', 0x80: 'soccer', }, ), 'sport_bits_1': FieldType( # Bit field corresponding to sport enum type (1 << (sport-8)). name='sport_bits_1', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x01: 'tennis', 0x02: 'american_football', 0x04: 'training', 0x08: 'walking', 0x10: 'cross_country_skiing', 0x20: 'alpine_skiing', 0x40: 'snowboarding', 0x80: 'rowing', }, ), 'sport_bits_2': FieldType( # Bit field corresponding to sport enum type (1 << (sport-16)). name='sport_bits_2', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x01: 'mountaineering', 0x02: 'hiking', 0x04: 'multisport', 0x08: 'paddling', 0x10: 'flying', 0x20: 'e_biking', 0x40: 'motorcycling', 0x80: 'boating', }, ), 'sport_bits_3': FieldType( # Bit field corresponding to sport enum type (1 << (sport-24)). name='sport_bits_3', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x01: 'driving', 0x02: 'golf', 0x04: 'hang_gliding', 0x08: 'horseback_riding', 0x10: 'hunting', 0x20: 'fishing', 0x40: 'inline_skating', 0x80: 'rock_climbing', }, ), 'sport_bits_4': FieldType( # Bit field corresponding to sport enum type (1 << (sport-32)). name='sport_bits_4', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x01: 'sailing', 0x02: 'ice_skating', 0x04: 'sky_diving', 0x08: 'snowshoeing', 0x10: 'snowmobiling', 0x20: 'stand_up_paddleboarding', 0x40: 'surfing', 0x80: 'wakeboarding', }, ), 'sport_bits_5': FieldType( # Bit field corresponding to sport enum type (1 << (sport-40)). name='sport_bits_5', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x01: 'water_skiing', 0x02: 'kayaking', 0x04: 'rafting', 0x08: 'windsurfing', 0x10: 'kitesurfing', 0x20: 'tactical', 0x40: 'jumpmaster', 0x80: 'boxing', }, ), 'sport_bits_6': FieldType( # Bit field corresponding to sport enum type (1 << (sport-48)). name='sport_bits_6', base_type=BASE_TYPES[0x0A], # uint8z values={ 0x01: 'floor_climbing', }, ), 'sport_event': FieldType( name='sport_event', base_type=BASE_TYPES[0x00], # enum values={ 0: 'uncategorized', 1: 'geocaching', 2: 'fitness', 3: 'recreation', 4: 'race', 5: 'special_event', 6: 'training', 7: 'transportation', 8: 'touring', }, ), 'stroke_type': FieldType( name='stroke_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'no_event', 1: 'other', # stroke was detected but cannot be identified 2: 'serve', 3: 'forehand', 4: 'backhand', 5: 'smash', }, ), 'sub_sport': FieldType( name='sub_sport', base_type=BASE_TYPES[0x00], # enum values={ 0: 'generic', 1: 'treadmill', # Run/Fitness Equipment 2: 'street', # Run 3: 'trail', # Run 4: 'track', # Run 5: 'spin', # Cycling 6: 'indoor_cycling', # Cycling/Fitness Equipment 7: 'road', # Cycling 8: 'mountain', # Cycling 9: 'downhill', # Cycling 10: 'recumbent', # Cycling 11: 'cyclocross', # Cycling 12: 'hand_cycling', # Cycling 13: 'track_cycling', # Cycling 14: 'indoor_rowing', # Fitness Equipment 15: 'elliptical', # Fitness Equipment 16: 'stair_climbing', # Fitness Equipment 17: 'lap_swimming', # Swimming 18: 'open_water', # Swimming 19: 'flexibility_training', # Training 20: 'strength_training', # Training 21: 'warm_up', # Tennis 22: 'match', # Tennis 23: 'exercise', # Tennis 24: 'challenge', # Tennis 25: 'indoor_skiing', # Fitness Equipment 26: 'cardio_training', # Training 27: 'indoor_walking', # Walking/Fitness Equipment 28: 'e_bike_fitness', # E-Biking 29: 'bmx', # Cycling 30: 'casual_walking', # Walking 31: 'speed_walking', # Walking 32: 'bike_to_run_transition', # Transition 33: 'run_to_bike_transition', # Transition 34: 'swim_to_bike_transition', # Transition 35: 'atv', # Motorcycling 36: 'motocross', # Motorcycling 37: 'backcountry', # Alpine Skiing/Snowboarding 38: 'resort', # Alpine Skiing/Snowboarding 39: 'rc_drone', # Flying 40: 'wingsuit', # Flying 41: 'whitewater', # Kayaking/Rafting 42: 'skate_skiing', # Cross Country Skiing 43: 'yoga', # Training 44: 'pilates', # Training 45: 'indoor_running', # Run 46: 'gravel_cycling', # Cycling 47: 'e_bike_mountain', # Cycling 48: 'commuting', # Cycling 49: 'mixed_surface', # Cycling 50: 'navigate', 51: 'track_me', 52: 'map', 254: 'all', }, ), 'supported_exd_screen_layouts': FieldType( name='supported_exd_screen_layouts', base_type=BASE_TYPES[0x8C], # uint32z values={ 0x00000001: 'full_screen', 0x00000002: 'half_vertical', 0x00000004: 'half_horizontal', 0x00000008: 'half_vertical_right_split', 0x00000010: 'half_horizontal_bottom_split', 0x00000020: 'full_quarter_split', 0x00000040: 'half_vertical_left_split', 0x00000080: 'half_horizontal_top_split', }, ), 'swim_stroke': FieldType( name='swim_stroke', base_type=BASE_TYPES[0x00], # enum values={ 0: 'freestyle', 1: 'backstroke', 2: 'breaststroke', 3: 'butterfly', 4: 'drill', 5: 'mixed', 6: 'im', # IM is a mixed interval containing the same number of lengths for each of: Butterfly, Backstroke, Breaststroke, Freestyle, swam in that order. }, ), 'switch': FieldType( name='switch', base_type=BASE_TYPES[0x00], # enum values={ 0: 'off', 1: 'on', 2: 'auto', }, ), 'time_into_day': FieldType( # number of seconds into the day since 00:00:00 UTC name='time_into_day', base_type=BASE_TYPES[0x86], # uint32 ), 'time_mode': FieldType( name='time_mode', base_type=BASE_TYPES[0x00], # enum values={ 0: 'hour12', 1: 'hour24', # Does not use a leading zero and has a colon 2: 'military', # Uses a leading zero and does not have a colon 3: 'hour_12_with_seconds', 4: 'hour_24_with_seconds', 5: 'utc', }, ), 'time_zone': FieldType( name='time_zone', base_type=BASE_TYPES[0x00], # enum values={ 0: 'almaty', 1: 'bangkok', 2: 'bombay', 3: 'brasilia', 4: 'cairo', 5: 'cape_verde_is', 6: 'darwin', 7: 'eniwetok', 8: 'fiji', 9: 'hong_kong', 10: 'islamabad', 11: 'kabul', 12: 'magadan', 13: 'mid_atlantic', 14: 'moscow', 15: 'muscat', 16: 'newfoundland', 17: 'samoa', 18: 'sydney', 19: 'tehran', 20: 'tokyo', 21: 'us_alaska', 22: 'us_atlantic', 23: 'us_central', 24: 'us_eastern', 25: 'us_hawaii', 26: 'us_mountain', 27: 'us_pacific', 28: 'other', 29: 'auckland', 30: 'kathmandu', 31: 'europe_western_wet', 32: 'europe_central_cet', 33: 'europe_eastern_eet', 34: 'jakarta', 35: 'perth', 36: 'adelaide', 37: 'brisbane', 38: 'tasmania', 39: 'iceland', 40: 'amsterdam', 41: 'athens', 42: 'barcelona', 43: 'berlin', 44: 'brussels', 45: 'budapest', 46: 'copenhagen', 47: 'dublin', 48: 'helsinki', 49: 'lisbon', 50: 'london', 51: 'madrid', 52: 'munich', 53: 'oslo', 54: 'paris', 55: 'prague', 56: 'reykjavik', 57: 'rome', 58: 'stockholm', 59: 'vienna', 60: 'warsaw', 61: 'zurich', 62: 'quebec', 63: 'ontario', 64: 'manitoba', 65: 'saskatchewan', 66: 'alberta', 67: 'british_columbia', 68: 'boise', 69: 'boston', 70: 'chicago', 71: 'dallas', 72: 'denver', 73: 'kansas_city', 74: 'las_vegas', 75: 'los_angeles', 76: 'miami', 77: 'minneapolis', 78: 'new_york', 79: 'new_orleans', 80: 'phoenix', 81: 'santa_fe', 82: 'seattle', 83: 'washington_dc', 84: 'us_arizona', 85: 'chita', 86: 'ekaterinburg', 87: 'irkutsk', 88: 'kaliningrad', 89: 'krasnoyarsk', 90: 'novosibirsk', 91: 'petropavlovsk_kamchatskiy', 92: 'samara', 93: 'vladivostok', 94: 'mexico_central', 95: 'mexico_mountain', 96: 'mexico_pacific', 97: 'cape_town', 98: 'winkhoek', 99: 'lagos', 100: 'riyahd', 101: 'venezuela', 102: 'australia_lh', 103: 'santiago', 253: 'manual', 254: 'automatic', }, ), 'timer_trigger': FieldType( # timer event data name='timer_trigger', base_type=BASE_TYPES[0x00], # enum values={ 0: 'manual', 1: 'auto', 2: 'fitness_equipment', }, ), 'turn_type': FieldType( name='turn_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'arriving_idx', 1: 'arriving_left_idx', 2: 'arriving_right_idx', 3: 'arriving_via_idx', 4: 'arriving_via_left_idx', 5: 'arriving_via_right_idx', 6: 'bear_keep_left_idx', 7: 'bear_keep_right_idx', 8: 'continue_idx', 9: 'exit_left_idx', 10: 'exit_right_idx', 11: 'ferry_idx', 12: 'roundabout_45_idx', 13: 'roundabout_90_idx', 14: 'roundabout_135_idx', 15: 'roundabout_180_idx', 16: 'roundabout_225_idx', 17: 'roundabout_270_idx', 18: 'roundabout_315_idx', 19: 'roundabout_360_idx', 20: 'roundabout_neg_45_idx', 21: 'roundabout_neg_90_idx', 22: 'roundabout_neg_135_idx', 23: 'roundabout_neg_180_idx', 24: 'roundabout_neg_225_idx', 25: 'roundabout_neg_270_idx', 26: 'roundabout_neg_315_idx', 27: 'roundabout_neg_360_idx', 28: 'roundabout_generic_idx', 29: 'roundabout_neg_generic_idx', 30: 'sharp_turn_left_idx', 31: 'sharp_turn_right_idx', 32: 'turn_left_idx', 33: 'turn_right_idx', 34: 'uturn_left_idx', 35: 'uturn_right_idx', 36: 'icon_inv_idx', 37: 'icon_idx_cnt', }, ), 'user_local_id': FieldType( name='user_local_id', base_type=BASE_TYPES[0x84], # uint16 values={ 0x0000: 'local_min', 0x000F: 'local_max', 0x0010: 'stationary_min', 0x00FF: 'stationary_max', 0x0100: 'portable_min', 0xFFFE: 'portable_max', }, ), 'watchface_mode': FieldType( name='watchface_mode', base_type=BASE_TYPES[0x00], # enum values={ 0: 'digital', 1: 'analog', 2: 'connect_iq', 3: 'disabled', }, ), 'weather_report': FieldType( name='weather_report', base_type=BASE_TYPES[0x00], # enum values={ 0: 'current', 1: 'forecast', # Deprecated use hourly_forecast instead 1: 'hourly_forecast', 2: 'daily_forecast', }, ), 'weather_severe_type': FieldType( name='weather_severe_type', base_type=BASE_TYPES[0x00], # enum values={ 0: 'unspecified', 1: 'tornado', 2: 'tsunami', 3: 'hurricane', 4: 'extreme_wind', 5: 'typhoon', 6: 'inland_hurricane', 7: 'hurricane_force_wind', 8: 'waterspout', 9: 'severe_thunderstorm', 10: 'wreckhouse_winds', 11: 'les_suetes_wind', 12: 'avalanche', 13: 'flash_flood', 14: 'tropical_storm', 15: 'inland_tropical_storm', 16: 'blizzard', 17: 'ice_storm', 18: 'freezing_rain', 19: 'debris_flow', 20: 'flash_freeze', 21: 'dust_storm', 22: 'high_wind', 23: 'winter_storm', 24: 'heavy_freezing_spray', 25: 'extreme_cold', 26: 'wind_chill', 27: 'cold_wave', 28: 'heavy_snow_alert', 29: 'lake_effect_blowing_snow', 30: 'snow_squall', 31: 'lake_effect_snow', 32: 'winter_weather', 33: 'sleet', 34: 'snowfall', 35: 'snow_and_blowing_snow', 36: 'blowing_snow', 37: 'snow_alert', 38: 'arctic_outflow', 39: 'freezing_drizzle', 40: 'storm', 41: 'storm_surge', 42: 'rainfall', 43: 'areal_flood', 44: 'coastal_flood', 45: 'lakeshore_flood', 46: 'excessive_heat', 47: 'heat', 48: 'weather', 49: 'high_heat_and_humidity', 50: 'humidex_and_health', 51: 'humidex', 52: 'gale', 53: 'freezing_spray', 54: 'special_marine', 55: 'squall', 56: 'strong_wind', 57: 'lake_wind', 58: 'marine_weather', 59: 'wind', 60: 'small_craft_hazardous_seas', 61: 'hazardous_seas', 62: 'small_craft', 63: 'small_craft_winds', 64: 'small_craft_rough_bar', 65: 'high_water_level', 66: 'ashfall', 67: 'freezing_fog', 68: 'dense_fog', 69: 'dense_smoke', 70: 'blowing_dust', 71: 'hard_freeze', 72: 'freeze', 73: 'frost', 74: 'fire_weather', 75: 'flood', 76: 'rip_tide', 77: 'high_surf', 78: 'smog', 79: 'air_quality', 80: 'brisk_wind', 81: 'air_stagnation', 82: 'low_water', 83: 'hydrological', 84: 'special_weather', }, ), 'weather_severity': FieldType( name='weather_severity', base_type=BASE_TYPES[0x00], # enum values={ 0: 'unknown', 1: 'warning', 2: 'watch', 3: 'advisory', 4: 'statement', }, ), 'weather_status': FieldType( name='weather_status', base_type=BASE_TYPES[0x00], # enum values={ 0: 'clear', 1: 'partly_cloudy', 2: 'mostly_cloudy', 3: 'rain', 4: 'snow', 5: 'windy', 6: 'thunderstorms', 7: 'wintry_mix', 8: 'fog', 11: 'hazy', 12: 'hail', 13: 'scattered_showers', 14: 'scattered_thunderstorms', 15: 'unknown_precipitation', 16: 'light_rain', 17: 'heavy_rain', 18: 'light_snow', 19: 'heavy_snow', 20: 'light_rain_snow', 21: 'heavy_rain_snow', 22: 'cloudy', }, ), 'weight': FieldType( name='weight', base_type=BASE_TYPES[0x84], # uint16 values={ 0xFFFE: 'calculating', }, ), 'wkt_step_duration': FieldType( name='wkt_step_duration', base_type=BASE_TYPES[0x00], # enum values={ 0: 'time', 1: 'distance', 2: 'hr_less_than', 3: 'hr_greater_than', 4: 'calories', 5: 'open', 6: 'repeat_until_steps_cmplt', 7: 'repeat_until_time', 8: 'repeat_until_distance', 9: 'repeat_until_calories', 10: 'repeat_until_hr_less_than', 11: 'repeat_until_hr_greater_than', 12: 'repeat_until_power_less_than', 13: 'repeat_until_power_greater_than', 14: 'power_less_than', 15: 'power_greater_than', 16: 'training_peaks_tss', 17: 'repeat_until_power_last_lap_less_than', 18: 'repeat_until_max_power_last_lap_less_than', 19: 'power_3s_less_than', 20: 'power_10s_less_than', 21: 'power_30s_less_than', 22: 'power_3s_greater_than', 23: 'power_10s_greater_than', 24: 'power_30s_greater_than', 25: 'power_lap_less_than', 26: 'power_lap_greater_than', 27: 'repeat_until_training_peaks_tss', 28: 'repetition_time', }, ), 'wkt_step_target': FieldType( name='wkt_step_target', base_type=BASE_TYPES[0x00], # enum values={ 0: 'speed', 1: 'heart_rate', 2: 'open', 3: 'cadence', 4: 'power', 5: 'grade', 6: 'resistance', 7: 'power_3s', 8: 'power_10s', 9: 'power_30s', 10: 'power_lap', 11: 'swim_stroke', 12: 'speed_lap', 13: 'heart_rate_lap', }, ), 'workout_capabilities': FieldType( name='workout_capabilities', base_type=BASE_TYPES[0x8C], # uint32z values={ 0x00000001: 'interval', 0x00000002: 'custom', 0x00000004: 'fitness_equipment', 0x00000008: 'firstbeat', 0x00000010: 'new_leaf', 0x00000020: 'tcx', # For backwards compatibility. Watch should add missing id fields then clear flag. 0x00000080: 'speed', # Speed source required for workout step. 0x00000100: 'heart_rate', # Heart rate source required for workout step. 0x00000200: 'distance', # Distance source required for workout step. 0x00000400: 'cadence', # Cadence source required for workout step. 0x00000800: 'power', # Power source required for workout step. 0x00001000: 'grade', # Grade source required for workout step. 0x00002000: 'resistance', # Resistance source required for workout step. 0x00004000: 'protected', }, ), 'workout_equipment': FieldType( name='workout_equipment', base_type=BASE_TYPES[0x00], # enum values={ 0: 'none', 1: 'swim_fins', 2: 'swim_kickboard', 3: 'swim_paddles', 4: 'swim_pull_buoy', 5: 'swim_snorkel', }, ), 'workout_hr': FieldType( # 0 - 100 indicates% of max hr; >100 indicates bpm (255 max) plus 100 name='workout_hr', base_type=BASE_TYPES[0x86], # uint32 values={ 100: 'bpm_offset', }, ), 'workout_power': FieldType( # 0 - 1000 indicates % of functional threshold power; >1000 indicates watts plus 1000. name='workout_power', base_type=BASE_TYPES[0x86], # uint32 values={ 1000: 'watts_offset', }, ), } FIELD_TYPE_TIMESTAMP = Field(name='timestamp', type=FIELD_TYPES['date_time'], def_num=253, units='s') MESSAGE_TYPES = { ############################ Common Messages ############################# 0: MessageType( # Must be first message in file. name='file_id', mesg_num=0, fields={ 0: Field( name='type', type=FIELD_TYPES['file'], def_num=0, ), 1: Field( name='manufacturer', type=FIELD_TYPES['manufacturer'], def_num=1, ), 2: Field( name='product', type=BASE_TYPES[0x84], # uint16 def_num=2, subfields=( SubField( name='garmin_product', def_num=2, type=FIELD_TYPES['garmin_product'], ref_fields=( ReferenceField( name='manufacturer', def_num=1, value='garmin', raw_value=1, ), ReferenceField( name='manufacturer', def_num=1, value='dynastream', raw_value=15, ), ReferenceField( name='manufacturer', def_num=1, value='dynastream_oem', raw_value=13, ), ), ), ), ), 3: Field( name='serial_number', type=BASE_TYPES[0x8C], # uint32z def_num=3, ), 4: Field( # Only set for files that are can be created/erased. name='time_created', type=FIELD_TYPES['date_time'], def_num=4, ), 5: Field( # Only set for files that are not created/erased. name='number', type=BASE_TYPES[0x84], # uint16 def_num=5, ), 8: Field( # Optional free form string to indicate the devices name or model name='product_name', type=BASE_TYPES[0x07], # string def_num=8, ), }, ), #################################### #################################### 1: MessageType( name='capabilities', mesg_num=1, fields={ 0: Field( # Use language_bits_x types where x is index of array. name='languages', type=BASE_TYPES[0x0A], # uint8z def_num=0, ), 1: Field( # Use sport_bits_x types where x is index of array. name='sports', type=FIELD_TYPES['sport_bits_0'], def_num=1, ), 21: Field( name='workouts_supported', type=FIELD_TYPES['workout_capabilities'], def_num=21, ), 23: Field( name='connectivity_supported', type=FIELD_TYPES['connectivity_capabilities'], def_num=23, ), }, ), 3: MessageType( name='user_profile', mesg_num=3, fields={ 0: Field( name='friendly_name', type=BASE_TYPES[0x07], # string def_num=0, ), 1: Field( name='gender', type=FIELD_TYPES['gender'], def_num=1, ), 2: Field( name='age', type=BASE_TYPES[0x02], # uint8 def_num=2, units='years', ), 3: Field( name='height', type=BASE_TYPES[0x02], # uint8 def_num=3, scale=100, units='m', ), 4: Field( name='weight', type=BASE_TYPES[0x84], # uint16 def_num=4, scale=10, units='kg', ), 5: Field( name='language', type=FIELD_TYPES['language'], def_num=5, ), 6: Field( name='elev_setting', type=FIELD_TYPES['display_measure'], def_num=6, ), 7: Field( name='weight_setting', type=FIELD_TYPES['display_measure'], def_num=7, ), 8: Field( name='resting_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=8, units='bpm', ), 9: Field( name='default_max_running_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=9, units='bpm', ), 10: Field( name='default_max_biking_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=10, units='bpm', ), 11: Field( name='default_max_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=11, units='bpm', ), 12: Field( name='hr_setting', type=FIELD_TYPES['display_heart'], def_num=12, ), 13: Field( name='speed_setting', type=FIELD_TYPES['display_measure'], def_num=13, ), 14: Field( name='dist_setting', type=FIELD_TYPES['display_measure'], def_num=14, ), 16: Field( name='power_setting', type=FIELD_TYPES['display_power'], def_num=16, ), 17: Field( name='activity_class', type=FIELD_TYPES['activity_class'], def_num=17, ), 18: Field( name='position_setting', type=FIELD_TYPES['display_position'], def_num=18, ), 21: Field( name='temperature_setting', type=FIELD_TYPES['display_measure'], def_num=21, ), 22: Field( name='local_id', type=FIELD_TYPES['user_local_id'], def_num=22, ), 23: Field( name='global_id', type=BASE_TYPES[0x0D], # byte def_num=23, ), 28: Field( # Typical wake time name='wake_time', type=FIELD_TYPES['localtime_into_day'], def_num=28, ), 29: Field( # Typical bed time name='sleep_time', type=FIELD_TYPES['localtime_into_day'], def_num=29, ), 30: Field( name='height_setting', type=FIELD_TYPES['display_measure'], def_num=30, ), 31: Field( # User defined running step length set to 0 for auto length name='user_running_step_length', type=BASE_TYPES[0x84], # uint16 def_num=31, scale=1000, units='m', ), 32: Field( # User defined walking step length set to 0 for auto length name='user_walking_step_length', type=BASE_TYPES[0x84], # uint16 def_num=32, scale=1000, units='m', ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 4: MessageType( name='hrm_profile', mesg_num=4, fields={ 0: Field( name='enabled', type=FIELD_TYPES['bool'], def_num=0, ), 1: Field( name='hrm_ant_id', type=BASE_TYPES[0x8B], # uint16z def_num=1, ), 2: Field( name='log_hrv', type=FIELD_TYPES['bool'], def_num=2, ), 3: Field( name='hrm_ant_id_trans_type', type=BASE_TYPES[0x0A], # uint8z def_num=3, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 5: MessageType( name='sdm_profile', mesg_num=5, fields={ 0: Field( name='enabled', type=FIELD_TYPES['bool'], def_num=0, ), 1: Field( name='sdm_ant_id', type=BASE_TYPES[0x8B], # uint16z def_num=1, ), 2: Field( name='sdm_cal_factor', type=BASE_TYPES[0x84], # uint16 def_num=2, scale=10, units='%', ), 3: Field( name='odometer', type=BASE_TYPES[0x86], # uint32 def_num=3, scale=100, units='m', ), 4: Field( # Use footpod for speed source instead of GPS name='speed_source', type=FIELD_TYPES['bool'], def_num=4, ), 5: Field( name='sdm_ant_id_trans_type', type=BASE_TYPES[0x0A], # uint8z def_num=5, ), 7: Field( # Rollover counter that can be used to extend the odometer name='odometer_rollover', type=BASE_TYPES[0x02], # uint8 def_num=7, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 6: MessageType( name='bike_profile', mesg_num=6, fields={ 0: Field( name='name', type=BASE_TYPES[0x07], # string def_num=0, ), 1: Field( name='sport', type=FIELD_TYPES['sport'], def_num=1, ), 2: Field( name='sub_sport', type=FIELD_TYPES['sub_sport'], def_num=2, ), 3: Field( name='odometer', type=BASE_TYPES[0x86], # uint32 def_num=3, scale=100, units='m', ), 4: Field( name='bike_spd_ant_id', type=BASE_TYPES[0x8B], # uint16z def_num=4, ), 5: Field( name='bike_cad_ant_id', type=BASE_TYPES[0x8B], # uint16z def_num=5, ), 6: Field( name='bike_spdcad_ant_id', type=BASE_TYPES[0x8B], # uint16z def_num=6, ), 7: Field( name='bike_power_ant_id', type=BASE_TYPES[0x8B], # uint16z def_num=7, ), 8: Field( name='custom_wheelsize', type=BASE_TYPES[0x84], # uint16 def_num=8, scale=1000, units='m', ), 9: Field( name='auto_wheelsize', type=BASE_TYPES[0x84], # uint16 def_num=9, scale=1000, units='m', ), 10: Field( name='bike_weight', type=BASE_TYPES[0x84], # uint16 def_num=10, scale=10, units='kg', ), 11: Field( name='power_cal_factor', type=BASE_TYPES[0x84], # uint16 def_num=11, scale=10, units='%', ), 12: Field( name='auto_wheel_cal', type=FIELD_TYPES['bool'], def_num=12, ), 13: Field( name='auto_power_zero', type=FIELD_TYPES['bool'], def_num=13, ), 14: Field( name='id', type=BASE_TYPES[0x02], # uint8 def_num=14, ), 15: Field( name='spd_enabled', type=FIELD_TYPES['bool'], def_num=15, ), 16: Field( name='cad_enabled', type=FIELD_TYPES['bool'], def_num=16, ), 17: Field( name='spdcad_enabled', type=FIELD_TYPES['bool'], def_num=17, ), 18: Field( name='power_enabled', type=FIELD_TYPES['bool'], def_num=18, ), 19: Field( name='crank_length', type=BASE_TYPES[0x02], # uint8 def_num=19, scale=2, offset=-110, units='mm', ), 20: Field( name='enabled', type=FIELD_TYPES['bool'], def_num=20, ), 21: Field( name='bike_spd_ant_id_trans_type', type=BASE_TYPES[0x0A], # uint8z def_num=21, ), 22: Field( name='bike_cad_ant_id_trans_type', type=BASE_TYPES[0x0A], # uint8z def_num=22, ), 23: Field( name='bike_spdcad_ant_id_trans_type', type=BASE_TYPES[0x0A], # uint8z def_num=23, ), 24: Field( name='bike_power_ant_id_trans_type', type=BASE_TYPES[0x0A], # uint8z def_num=24, ), 37: Field( # Rollover counter that can be used to extend the odometer name='odometer_rollover', type=BASE_TYPES[0x02], # uint8 def_num=37, ), 38: Field( # Number of front gears name='front_gear_num', type=BASE_TYPES[0x0A], # uint8z def_num=38, ), 39: Field( # Number of teeth on each gear 0 is innermost name='front_gear', type=BASE_TYPES[0x0A], # uint8z def_num=39, ), 40: Field( # Number of rear gears name='rear_gear_num', type=BASE_TYPES[0x0A], # uint8z def_num=40, ), 41: Field( # Number of teeth on each gear 0 is innermost name='rear_gear', type=BASE_TYPES[0x0A], # uint8z def_num=41, ), 44: Field( name='shimano_di2_enabled', type=FIELD_TYPES['bool'], def_num=44, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 8: MessageType( name='hr_zone', mesg_num=8, fields={ 1: Field( name='high_bpm', type=BASE_TYPES[0x02], # uint8 def_num=1, units='bpm', ), 2: Field( name='name', type=BASE_TYPES[0x07], # string def_num=2, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 9: MessageType( name='power_zone', mesg_num=9, fields={ 1: Field( name='high_value', type=BASE_TYPES[0x84], # uint16 def_num=1, units='watts', ), 2: Field( name='name', type=BASE_TYPES[0x07], # string def_num=2, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 10: MessageType( name='met_zone', mesg_num=10, fields={ 1: Field( name='high_bpm', type=BASE_TYPES[0x02], # uint8 def_num=1, ), 2: Field( name='calories', type=BASE_TYPES[0x84], # uint16 def_num=2, scale=10, units='kcal/min', ), 3: Field( name='fat_calories', type=BASE_TYPES[0x02], # uint8 def_num=3, scale=10, units='kcal/min', ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 12: MessageType( name='sport', mesg_num=12, fields={ 0: Field( name='sport', type=FIELD_TYPES['sport'], def_num=0, ), 1: Field( name='sub_sport', type=FIELD_TYPES['sub_sport'], def_num=1, ), 3: Field( name='name', type=BASE_TYPES[0x07], # string def_num=3, ), }, ), 18: MessageType( name='session', mesg_num=18, fields={ 0: Field( # session name='event', type=FIELD_TYPES['event'], def_num=0, ), 1: Field( # stop name='event_type', type=FIELD_TYPES['event_type'], def_num=1, ), 2: Field( name='start_time', type=FIELD_TYPES['date_time'], def_num=2, ), 3: Field( name='start_position_lat', type=BASE_TYPES[0x85], # sint32 def_num=3, units='semicircles', ), 4: Field( name='start_position_long', type=BASE_TYPES[0x85], # sint32 def_num=4, units='semicircles', ), 5: Field( name='sport', type=FIELD_TYPES['sport'], def_num=5, ), 6: Field( name='sub_sport', type=FIELD_TYPES['sub_sport'], def_num=6, ), 7: Field( # Time (includes pauses) name='total_elapsed_time', type=BASE_TYPES[0x86], # uint32 def_num=7, scale=1000, units='s', ), 8: Field( # Timer Time (excludes pauses) name='total_timer_time', type=BASE_TYPES[0x86], # uint32 def_num=8, scale=1000, units='s', ), 9: Field( name='total_distance', type=BASE_TYPES[0x86], # uint32 def_num=9, scale=100, units='m', ), 10: Field( name='total_cycles', type=BASE_TYPES[0x86], # uint32 def_num=10, units='cycles', subfields=( SubField( name='total_strides', def_num=10, type=BASE_TYPES[0x86], # uint32 units='strides', ref_fields=( ReferenceField( name='sport', def_num=5, value='running', raw_value=1, ), ReferenceField( name='sport', def_num=5, value='walking', raw_value=11, ), ), ), ), ), 11: Field( name='total_calories', type=BASE_TYPES[0x84], # uint16 def_num=11, units='kcal', ), 13: Field( # If New Leaf name='total_fat_calories', type=BASE_TYPES[0x84], # uint16 def_num=13, units='kcal', ), 14: Field( # total_distance / total_timer_time name='avg_speed', type=BASE_TYPES[0x84], # uint16 def_num=14, scale=1000, units='m/s', components=( ComponentField( name='enhanced_avg_speed', def_num=124, scale=1000, units='m/s', accumulate=False, bits=16, bit_offset=0, ), ), ), 15: Field( name='max_speed', type=BASE_TYPES[0x84], # uint16 def_num=15, scale=1000, units='m/s', components=( ComponentField( name='enhanced_max_speed', def_num=125, scale=1000, units='m/s', accumulate=False, bits=16, bit_offset=0, ), ), ), 16: Field( # average heart rate (excludes pause time) name='avg_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=16, units='bpm', ), 17: Field( name='max_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=17, units='bpm', ), 18: Field( # total_cycles / total_timer_time if non_zero_avg_cadence otherwise total_cycles / total_elapsed_time name='avg_cadence', type=BASE_TYPES[0x02], # uint8 def_num=18, units='rpm', subfields=( SubField( name='avg_running_cadence', def_num=18, type=BASE_TYPES[0x02], # uint8 units='strides/min', ref_fields=( ReferenceField( name='sport', def_num=5, value='running', raw_value=1, ), ), ), ), ), 19: Field( name='max_cadence', type=BASE_TYPES[0x02], # uint8 def_num=19, units='rpm', subfields=( SubField( name='max_running_cadence', def_num=19, type=BASE_TYPES[0x02], # uint8 units='strides/min', ref_fields=( ReferenceField( name='sport', def_num=5, value='running', raw_value=1, ), ), ), ), ), 20: Field( # total_power / total_timer_time if non_zero_avg_power otherwise total_power / total_elapsed_time name='avg_power', type=BASE_TYPES[0x84], # uint16 def_num=20, units='watts', ), 21: Field( name='max_power', type=BASE_TYPES[0x84], # uint16 def_num=21, units='watts', ), 22: Field( name='total_ascent', type=BASE_TYPES[0x84], # uint16 def_num=22, units='m', ), 23: Field( name='total_descent', type=BASE_TYPES[0x84], # uint16 def_num=23, units='m', ), 24: Field( name='total_training_effect', type=BASE_TYPES[0x02], # uint8 def_num=24, scale=10, ), 25: Field( name='first_lap_index', type=BASE_TYPES[0x84], # uint16 def_num=25, ), 26: Field( name='num_laps', type=BASE_TYPES[0x84], # uint16 def_num=26, ), 27: Field( name='event_group', type=BASE_TYPES[0x02], # uint8 def_num=27, ), 28: Field( name='trigger', type=FIELD_TYPES['session_trigger'], def_num=28, ), 29: Field( name='nec_lat', type=BASE_TYPES[0x85], # sint32 def_num=29, units='semicircles', ), 30: Field( name='nec_long', type=BASE_TYPES[0x85], # sint32 def_num=30, units='semicircles', ), 31: Field( name='swc_lat', type=BASE_TYPES[0x85], # sint32 def_num=31, units='semicircles', ), 32: Field( name='swc_long', type=BASE_TYPES[0x85], # sint32 def_num=32, units='semicircles', ), 34: Field( name='normalized_power', type=BASE_TYPES[0x84], # uint16 def_num=34, units='watts', ), 35: Field( name='training_stress_score', type=BASE_TYPES[0x84], # uint16 def_num=35, scale=10, units='tss', ), 36: Field( name='intensity_factor', type=BASE_TYPES[0x84], # uint16 def_num=36, scale=1000, units='if', ), 37: Field( name='left_right_balance', type=FIELD_TYPES['left_right_balance_100'], def_num=37, ), 41: Field( name='avg_stroke_count', type=BASE_TYPES[0x86], # uint32 def_num=41, scale=10, units='strokes/lap', ), 42: Field( name='avg_stroke_distance', type=BASE_TYPES[0x84], # uint16 def_num=42, scale=100, units='m', ), 43: Field( name='swim_stroke', type=FIELD_TYPES['swim_stroke'], def_num=43, units='swim_stroke', ), 44: Field( name='pool_length', type=BASE_TYPES[0x84], # uint16 def_num=44, scale=100, units='m', ), 45: Field( name='threshold_power', type=BASE_TYPES[0x84], # uint16 def_num=45, units='watts', ), 46: Field( name='pool_length_unit', type=FIELD_TYPES['display_measure'], def_num=46, ), 47: Field( # # of active lengths of swim pool name='num_active_lengths', type=BASE_TYPES[0x84], # uint16 def_num=47, units='lengths', ), 48: Field( name='total_work', type=BASE_TYPES[0x86], # uint32 def_num=48, units='J', ), 49: Field( name='avg_altitude', type=BASE_TYPES[0x84], # uint16 def_num=49, scale=5, offset=500, units='m', components=( ComponentField( name='enhanced_avg_altitude', def_num=126, scale=5, offset=500, units='m', accumulate=False, bits=16, bit_offset=0, ), ), ), 50: Field( name='max_altitude', type=BASE_TYPES[0x84], # uint16 def_num=50, scale=5, offset=500, units='m', components=( ComponentField( name='enhanced_max_altitude', def_num=128, scale=5, offset=500, units='m', accumulate=False, bits=16, bit_offset=0, ), ), ), 51: Field( name='gps_accuracy', type=BASE_TYPES[0x02], # uint8 def_num=51, units='m', ), 52: Field( name='avg_grade', type=BASE_TYPES[0x83], # sint16 def_num=52, scale=100, units='%', ), 53: Field( name='avg_pos_grade', type=BASE_TYPES[0x83], # sint16 def_num=53, scale=100, units='%', ), 54: Field( name='avg_neg_grade', type=BASE_TYPES[0x83], # sint16 def_num=54, scale=100, units='%', ), 55: Field( name='max_pos_grade', type=BASE_TYPES[0x83], # sint16 def_num=55, scale=100, units='%', ), 56: Field( name='max_neg_grade', type=BASE_TYPES[0x83], # sint16 def_num=56, scale=100, units='%', ), 57: Field( name='avg_temperature', type=BASE_TYPES[0x01], # sint8 def_num=57, units='C', ), 58: Field( name='max_temperature', type=BASE_TYPES[0x01], # sint8 def_num=58, units='C', ), 59: Field( name='total_moving_time', type=BASE_TYPES[0x86], # uint32 def_num=59, scale=1000, units='s', ), 60: Field( name='avg_pos_vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=60, scale=1000, units='m/s', ), 61: Field( name='avg_neg_vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=61, scale=1000, units='m/s', ), 62: Field( name='max_pos_vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=62, scale=1000, units='m/s', ), 63: Field( name='max_neg_vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=63, scale=1000, units='m/s', ), 64: Field( name='min_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=64, units='bpm', ), 65: Field( name='time_in_hr_zone', type=BASE_TYPES[0x86], # uint32 def_num=65, scale=1000, units='s', ), 66: Field( name='time_in_speed_zone', type=BASE_TYPES[0x86], # uint32 def_num=66, scale=1000, units='s', ), 67: Field( name='time_in_cadence_zone', type=BASE_TYPES[0x86], # uint32 def_num=67, scale=1000, units='s', ), 68: Field( name='time_in_power_zone', type=BASE_TYPES[0x86], # uint32 def_num=68, scale=1000, units='s', ), 69: Field( name='avg_lap_time', type=BASE_TYPES[0x86], # uint32 def_num=69, scale=1000, units='s', ), 70: Field( name='best_lap_index', type=BASE_TYPES[0x84], # uint16 def_num=70, ), 71: Field( name='min_altitude', type=BASE_TYPES[0x84], # uint16 def_num=71, scale=5, offset=500, units='m', components=( ComponentField( name='enhanced_min_altitude', def_num=127, scale=5, offset=500, units='m', accumulate=False, bits=16, bit_offset=0, ), ), ), 82: Field( name='player_score', type=BASE_TYPES[0x84], # uint16 def_num=82, ), 83: Field( name='opponent_score', type=BASE_TYPES[0x84], # uint16 def_num=83, ), 84: Field( name='opponent_name', type=BASE_TYPES[0x07], # string def_num=84, ), 85: Field( # stroke_type enum used as the index name='stroke_count', type=BASE_TYPES[0x84], # uint16 def_num=85, units='counts', ), 86: Field( # zone number used as the index name='zone_count', type=BASE_TYPES[0x84], # uint16 def_num=86, units='counts', ), 87: Field( name='max_ball_speed', type=BASE_TYPES[0x84], # uint16 def_num=87, scale=100, units='m/s', ), 88: Field( name='avg_ball_speed', type=BASE_TYPES[0x84], # uint16 def_num=88, scale=100, units='m/s', ), 89: Field( name='avg_vertical_oscillation', type=BASE_TYPES[0x84], # uint16 def_num=89, scale=10, units='mm', ), 90: Field( name='avg_stance_time_percent', type=BASE_TYPES[0x84], # uint16 def_num=90, scale=100, units='percent', ), 91: Field( name='avg_stance_time', type=BASE_TYPES[0x84], # uint16 def_num=91, scale=10, units='ms', ), 92: Field( # fractional part of the avg_cadence name='avg_fractional_cadence', type=BASE_TYPES[0x02], # uint8 def_num=92, scale=128, units='rpm', ), 93: Field( # fractional part of the max_cadence name='max_fractional_cadence', type=BASE_TYPES[0x02], # uint8 def_num=93, scale=128, units='rpm', ), 94: Field( # fractional part of the total_cycles name='total_fractional_cycles', type=BASE_TYPES[0x02], # uint8 def_num=94, scale=128, units='cycles', ), 95: Field( # Avg saturated and unsaturated hemoglobin name='avg_total_hemoglobin_conc', type=BASE_TYPES[0x84], # uint16 def_num=95, scale=100, units='g/dL', ), 96: Field( # Min saturated and unsaturated hemoglobin name='min_total_hemoglobin_conc', type=BASE_TYPES[0x84], # uint16 def_num=96, scale=100, units='g/dL', ), 97: Field( # Max saturated and unsaturated hemoglobin name='max_total_hemoglobin_conc', type=BASE_TYPES[0x84], # uint16 def_num=97, scale=100, units='g/dL', ), 98: Field( # Avg percentage of hemoglobin saturated with oxygen name='avg_saturated_hemoglobin_percent', type=BASE_TYPES[0x84], # uint16 def_num=98, scale=10, units='%', ), 99: Field( # Min percentage of hemoglobin saturated with oxygen name='min_saturated_hemoglobin_percent', type=BASE_TYPES[0x84], # uint16 def_num=99, scale=10, units='%', ), 100: Field( # Max percentage of hemoglobin saturated with oxygen name='max_saturated_hemoglobin_percent', type=BASE_TYPES[0x84], # uint16 def_num=100, scale=10, units='%', ), 101: Field( name='avg_left_torque_effectiveness', type=BASE_TYPES[0x02], # uint8 def_num=101, scale=2, units='percent', ), 102: Field( name='avg_right_torque_effectiveness', type=BASE_TYPES[0x02], # uint8 def_num=102, scale=2, units='percent', ), 103: Field( name='avg_left_pedal_smoothness', type=BASE_TYPES[0x02], # uint8 def_num=103, scale=2, units='percent', ), 104: Field( name='avg_right_pedal_smoothness', type=BASE_TYPES[0x02], # uint8 def_num=104, scale=2, units='percent', ), 105: Field( name='avg_combined_pedal_smoothness', type=BASE_TYPES[0x02], # uint8 def_num=105, scale=2, units='percent', ), 111: Field( name='sport_index', type=BASE_TYPES[0x02], # uint8 def_num=111, ), 112: Field( # Total time spend in the standing position name='time_standing', type=BASE_TYPES[0x86], # uint32 def_num=112, scale=1000, units='s', ), 113: Field( # Number of transitions to the standing state name='stand_count', type=BASE_TYPES[0x84], # uint16 def_num=113, ), 114: Field( # Average platform center offset Left name='avg_left_pco', type=BASE_TYPES[0x01], # sint8 def_num=114, units='mm', ), 115: Field( # Average platform center offset Right name='avg_right_pco', type=BASE_TYPES[0x01], # sint8 def_num=115, units='mm', ), 116: Field( # Average left power phase angles. Indexes defined by power_phase_type. name='avg_left_power_phase', type=BASE_TYPES[0x02], # uint8 def_num=116, scale=0.7111111, units='degrees', ), 117: Field( # Average left power phase peak angles. Data value indexes defined by power_phase_type. name='avg_left_power_phase_peak', type=BASE_TYPES[0x02], # uint8 def_num=117, scale=0.7111111, units='degrees', ), 118: Field( # Average right power phase angles. Data value indexes defined by power_phase_type. name='avg_right_power_phase', type=BASE_TYPES[0x02], # uint8 def_num=118, scale=0.7111111, units='degrees', ), 119: Field( # Average right power phase peak angles data value indexes defined by power_phase_type. name='avg_right_power_phase_peak', type=BASE_TYPES[0x02], # uint8 def_num=119, scale=0.7111111, units='degrees', ), 120: Field( # Average power by position. Data value indexes defined by rider_position_type. name='avg_power_position', type=BASE_TYPES[0x84], # uint16 def_num=120, units='watts', ), 121: Field( # Maximum power by position. Data value indexes defined by rider_position_type. name='max_power_position', type=BASE_TYPES[0x84], # uint16 def_num=121, units='watts', ), 122: Field( # Average cadence by position. Data value indexes defined by rider_position_type. name='avg_cadence_position', type=BASE_TYPES[0x02], # uint8 def_num=122, units='rpm', ), 123: Field( # Maximum cadence by position. Data value indexes defined by rider_position_type. name='max_cadence_position', type=BASE_TYPES[0x02], # uint8 def_num=123, units='rpm', ), 124: Field( # total_distance / total_timer_time name='enhanced_avg_speed', type=BASE_TYPES[0x86], # uint32 def_num=124, scale=1000, units='m/s', ), 125: Field( name='enhanced_max_speed', type=BASE_TYPES[0x86], # uint32 def_num=125, scale=1000, units='m/s', ), 126: Field( name='enhanced_avg_altitude', type=BASE_TYPES[0x86], # uint32 def_num=126, scale=5, offset=500, units='m', ), 127: Field( name='enhanced_min_altitude', type=BASE_TYPES[0x86], # uint32 def_num=127, scale=5, offset=500, units='m', ), 128: Field( name='enhanced_max_altitude', type=BASE_TYPES[0x86], # uint32 def_num=128, scale=5, offset=500, units='m', ), 129: Field( # lev average motor power during session name='avg_lev_motor_power', type=BASE_TYPES[0x84], # uint16 def_num=129, units='watts', ), 130: Field( # lev maximum motor power during session name='max_lev_motor_power', type=BASE_TYPES[0x84], # uint16 def_num=130, units='watts', ), 131: Field( # lev battery consumption during session name='lev_battery_consumption', type=BASE_TYPES[0x02], # uint8 def_num=131, scale=2, units='percent', ), 132: Field( name='avg_vertical_ratio', type=BASE_TYPES[0x84], # uint16 def_num=132, scale=100, units='percent', ), 133: Field( name='avg_stance_time_balance', type=BASE_TYPES[0x84], # uint16 def_num=133, scale=100, units='percent', ), 134: Field( name='avg_step_length', type=BASE_TYPES[0x84], # uint16 def_num=134, scale=10, units='mm', ), 137: Field( name='total_anaerobic_training_effect', type=BASE_TYPES[0x02], # uint8 def_num=137, scale=10, ), 139: Field( name='avg_vam', type=BASE_TYPES[0x84], # uint16 def_num=139, scale=1000, units='m/s', ), 253: FIELD_TYPE_TIMESTAMP, # Sesson end time. 254: Field( # Selected bit is set for the current session. name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 19: MessageType( name='lap', mesg_num=19, fields={ 0: Field( name='event', type=FIELD_TYPES['event'], def_num=0, ), 1: Field( name='event_type', type=FIELD_TYPES['event_type'], def_num=1, ), 2: Field( name='start_time', type=FIELD_TYPES['date_time'], def_num=2, ), 3: Field( name='start_position_lat', type=BASE_TYPES[0x85], # sint32 def_num=3, units='semicircles', ), 4: Field( name='start_position_long', type=BASE_TYPES[0x85], # sint32 def_num=4, units='semicircles', ), 5: Field( name='end_position_lat', type=BASE_TYPES[0x85], # sint32 def_num=5, units='semicircles', ), 6: Field( name='end_position_long', type=BASE_TYPES[0x85], # sint32 def_num=6, units='semicircles', ), 7: Field( # Time (includes pauses) name='total_elapsed_time', type=BASE_TYPES[0x86], # uint32 def_num=7, scale=1000, units='s', ), 8: Field( # Timer Time (excludes pauses) name='total_timer_time', type=BASE_TYPES[0x86], # uint32 def_num=8, scale=1000, units='s', ), 9: Field( name='total_distance', type=BASE_TYPES[0x86], # uint32 def_num=9, scale=100, units='m', ), 10: Field( name='total_cycles', type=BASE_TYPES[0x86], # uint32 def_num=10, units='cycles', subfields=( SubField( name='total_strides', def_num=10, type=BASE_TYPES[0x86], # uint32 units='strides', ref_fields=( ReferenceField( name='sport', def_num=25, value='running', raw_value=1, ), ReferenceField( name='sport', def_num=25, value='walking', raw_value=11, ), ), ), ), ), 11: Field( name='total_calories', type=BASE_TYPES[0x84], # uint16 def_num=11, units='kcal', ), 12: Field( # If New Leaf name='total_fat_calories', type=BASE_TYPES[0x84], # uint16 def_num=12, units='kcal', ), 13: Field( name='avg_speed', type=BASE_TYPES[0x84], # uint16 def_num=13, scale=1000, units='m/s', components=( ComponentField( name='enhanced_avg_speed', def_num=110, scale=1000, units='m/s', accumulate=False, bits=16, bit_offset=0, ), ), ), 14: Field( name='max_speed', type=BASE_TYPES[0x84], # uint16 def_num=14, scale=1000, units='m/s', components=( ComponentField( name='enhanced_max_speed', def_num=111, scale=1000, units='m/s', accumulate=False, bits=16, bit_offset=0, ), ), ), 15: Field( name='avg_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=15, units='bpm', ), 16: Field( name='max_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=16, units='bpm', ), 17: Field( # total_cycles / total_timer_time if non_zero_avg_cadence otherwise total_cycles / total_elapsed_time name='avg_cadence', type=BASE_TYPES[0x02], # uint8 def_num=17, units='rpm', subfields=( SubField( name='avg_running_cadence', def_num=17, type=BASE_TYPES[0x02], # uint8 units='strides/min', ref_fields=( ReferenceField( name='sport', def_num=25, value='running', raw_value=1, ), ), ), ), ), 18: Field( name='max_cadence', type=BASE_TYPES[0x02], # uint8 def_num=18, units='rpm', subfields=( SubField( name='max_running_cadence', def_num=18, type=BASE_TYPES[0x02], # uint8 units='strides/min', ref_fields=( ReferenceField( name='sport', def_num=25, value='running', raw_value=1, ), ), ), ), ), 19: Field( # total_power / total_timer_time if non_zero_avg_power otherwise total_power / total_elapsed_time name='avg_power', type=BASE_TYPES[0x84], # uint16 def_num=19, units='watts', ), 20: Field( name='max_power', type=BASE_TYPES[0x84], # uint16 def_num=20, units='watts', ), 21: Field( name='total_ascent', type=BASE_TYPES[0x84], # uint16 def_num=21, units='m', ), 22: Field( name='total_descent', type=BASE_TYPES[0x84], # uint16 def_num=22, units='m', ), 23: Field( name='intensity', type=FIELD_TYPES['intensity'], def_num=23, ), 24: Field( name='lap_trigger', type=FIELD_TYPES['lap_trigger'], def_num=24, ), 25: Field( name='sport', type=FIELD_TYPES['sport'], def_num=25, ), 26: Field( name='event_group', type=BASE_TYPES[0x02], # uint8 def_num=26, ), 32: Field( # # of lengths of swim pool name='num_lengths', type=BASE_TYPES[0x84], # uint16 def_num=32, units='lengths', ), 33: Field( name='normalized_power', type=BASE_TYPES[0x84], # uint16 def_num=33, units='watts', ), 34: Field( name='left_right_balance', type=FIELD_TYPES['left_right_balance_100'], def_num=34, ), 35: Field( name='first_length_index', type=BASE_TYPES[0x84], # uint16 def_num=35, ), 37: Field( name='avg_stroke_distance', type=BASE_TYPES[0x84], # uint16 def_num=37, scale=100, units='m', ), 38: Field( name='swim_stroke', type=FIELD_TYPES['swim_stroke'], def_num=38, ), 39: Field( name='sub_sport', type=FIELD_TYPES['sub_sport'], def_num=39, ), 40: Field( # # of active lengths of swim pool name='num_active_lengths', type=BASE_TYPES[0x84], # uint16 def_num=40, units='lengths', ), 41: Field( name='total_work', type=BASE_TYPES[0x86], # uint32 def_num=41, units='J', ), 42: Field( name='avg_altitude', type=BASE_TYPES[0x84], # uint16 def_num=42, scale=5, offset=500, units='m', components=( ComponentField( name='enhanced_avg_altitude', def_num=112, scale=5, offset=500, units='m', accumulate=False, bits=16, bit_offset=0, ), ), ), 43: Field( name='max_altitude', type=BASE_TYPES[0x84], # uint16 def_num=43, scale=5, offset=500, units='m', components=( ComponentField( name='enhanced_max_altitude', def_num=114, scale=5, offset=500, units='m', accumulate=False, bits=16, bit_offset=0, ), ), ), 44: Field( name='gps_accuracy', type=BASE_TYPES[0x02], # uint8 def_num=44, units='m', ), 45: Field( name='avg_grade', type=BASE_TYPES[0x83], # sint16 def_num=45, scale=100, units='%', ), 46: Field( name='avg_pos_grade', type=BASE_TYPES[0x83], # sint16 def_num=46, scale=100, units='%', ), 47: Field( name='avg_neg_grade', type=BASE_TYPES[0x83], # sint16 def_num=47, scale=100, units='%', ), 48: Field( name='max_pos_grade', type=BASE_TYPES[0x83], # sint16 def_num=48, scale=100, units='%', ), 49: Field( name='max_neg_grade', type=BASE_TYPES[0x83], # sint16 def_num=49, scale=100, units='%', ), 50: Field( name='avg_temperature', type=BASE_TYPES[0x01], # sint8 def_num=50, units='C', ), 51: Field( name='max_temperature', type=BASE_TYPES[0x01], # sint8 def_num=51, units='C', ), 52: Field( name='total_moving_time', type=BASE_TYPES[0x86], # uint32 def_num=52, scale=1000, units='s', ), 53: Field( name='avg_pos_vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=53, scale=1000, units='m/s', ), 54: Field( name='avg_neg_vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=54, scale=1000, units='m/s', ), 55: Field( name='max_pos_vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=55, scale=1000, units='m/s', ), 56: Field( name='max_neg_vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=56, scale=1000, units='m/s', ), 57: Field( name='time_in_hr_zone', type=BASE_TYPES[0x86], # uint32 def_num=57, scale=1000, units='s', ), 58: Field( name='time_in_speed_zone', type=BASE_TYPES[0x86], # uint32 def_num=58, scale=1000, units='s', ), 59: Field( name='time_in_cadence_zone', type=BASE_TYPES[0x86], # uint32 def_num=59, scale=1000, units='s', ), 60: Field( name='time_in_power_zone', type=BASE_TYPES[0x86], # uint32 def_num=60, scale=1000, units='s', ), 61: Field( name='repetition_num', type=BASE_TYPES[0x84], # uint16 def_num=61, ), 62: Field( name='min_altitude', type=BASE_TYPES[0x84], # uint16 def_num=62, scale=5, offset=500, units='m', components=( ComponentField( name='enhanced_min_altitude', def_num=113, scale=5, offset=500, units='m', accumulate=False, bits=16, bit_offset=0, ), ), ), 63: Field( name='min_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=63, units='bpm', ), 71: Field( name='wkt_step_index', type=FIELD_TYPES['message_index'], def_num=71, ), 74: Field( name='opponent_score', type=BASE_TYPES[0x84], # uint16 def_num=74, ), 75: Field( # stroke_type enum used as the index name='stroke_count', type=BASE_TYPES[0x84], # uint16 def_num=75, units='counts', ), 76: Field( # zone number used as the index name='zone_count', type=BASE_TYPES[0x84], # uint16 def_num=76, units='counts', ), 77: Field( name='avg_vertical_oscillation', type=BASE_TYPES[0x84], # uint16 def_num=77, scale=10, units='mm', ), 78: Field( name='avg_stance_time_percent', type=BASE_TYPES[0x84], # uint16 def_num=78, scale=100, units='percent', ), 79: Field( name='avg_stance_time', type=BASE_TYPES[0x84], # uint16 def_num=79, scale=10, units='ms', ), 80: Field( # fractional part of the avg_cadence name='avg_fractional_cadence', type=BASE_TYPES[0x02], # uint8 def_num=80, scale=128, units='rpm', ), 81: Field( # fractional part of the max_cadence name='max_fractional_cadence', type=BASE_TYPES[0x02], # uint8 def_num=81, scale=128, units='rpm', ), 82: Field( # fractional part of the total_cycles name='total_fractional_cycles', type=BASE_TYPES[0x02], # uint8 def_num=82, scale=128, units='cycles', ), 83: Field( name='player_score', type=BASE_TYPES[0x84], # uint16 def_num=83, ), 84: Field( # Avg saturated and unsaturated hemoglobin name='avg_total_hemoglobin_conc', type=BASE_TYPES[0x84], # uint16 def_num=84, scale=100, units='g/dL', ), 85: Field( # Min saturated and unsaturated hemoglobin name='min_total_hemoglobin_conc', type=BASE_TYPES[0x84], # uint16 def_num=85, scale=100, units='g/dL', ), 86: Field( # Max saturated and unsaturated hemoglobin name='max_total_hemoglobin_conc', type=BASE_TYPES[0x84], # uint16 def_num=86, scale=100, units='g/dL', ), 87: Field( # Avg percentage of hemoglobin saturated with oxygen name='avg_saturated_hemoglobin_percent', type=BASE_TYPES[0x84], # uint16 def_num=87, scale=10, units='%', ), 88: Field( # Min percentage of hemoglobin saturated with oxygen name='min_saturated_hemoglobin_percent', type=BASE_TYPES[0x84], # uint16 def_num=88, scale=10, units='%', ), 89: Field( # Max percentage of hemoglobin saturated with oxygen name='max_saturated_hemoglobin_percent', type=BASE_TYPES[0x84], # uint16 def_num=89, scale=10, units='%', ), 91: Field( name='avg_left_torque_effectiveness', type=BASE_TYPES[0x02], # uint8 def_num=91, scale=2, units='percent', ), 92: Field( name='avg_right_torque_effectiveness', type=BASE_TYPES[0x02], # uint8 def_num=92, scale=2, units='percent', ), 93: Field( name='avg_left_pedal_smoothness', type=BASE_TYPES[0x02], # uint8 def_num=93, scale=2, units='percent', ), 94: Field( name='avg_right_pedal_smoothness', type=BASE_TYPES[0x02], # uint8 def_num=94, scale=2, units='percent', ), 95: Field( name='avg_combined_pedal_smoothness', type=BASE_TYPES[0x02], # uint8 def_num=95, scale=2, units='percent', ), 98: Field( # Total time spent in the standing position name='time_standing', type=BASE_TYPES[0x86], # uint32 def_num=98, scale=1000, units='s', ), 99: Field( # Number of transitions to the standing state name='stand_count', type=BASE_TYPES[0x84], # uint16 def_num=99, ), 100: Field( # Average left platform center offset name='avg_left_pco', type=BASE_TYPES[0x01], # sint8 def_num=100, units='mm', ), 101: Field( # Average right platform center offset name='avg_right_pco', type=BASE_TYPES[0x01], # sint8 def_num=101, units='mm', ), 102: Field( # Average left power phase angles. Data value indexes defined by power_phase_type. name='avg_left_power_phase', type=BASE_TYPES[0x02], # uint8 def_num=102, scale=0.7111111, units='degrees', ), 103: Field( # Average left power phase peak angles. Data value indexes defined by power_phase_type. name='avg_left_power_phase_peak', type=BASE_TYPES[0x02], # uint8 def_num=103, scale=0.7111111, units='degrees', ), 104: Field( # Average right power phase angles. Data value indexes defined by power_phase_type. name='avg_right_power_phase', type=BASE_TYPES[0x02], # uint8 def_num=104, scale=0.7111111, units='degrees', ), 105: Field( # Average right power phase peak angles. Data value indexes defined by power_phase_type. name='avg_right_power_phase_peak', type=BASE_TYPES[0x02], # uint8 def_num=105, scale=0.7111111, units='degrees', ), 106: Field( # Average power by position. Data value indexes defined by rider_position_type. name='avg_power_position', type=BASE_TYPES[0x84], # uint16 def_num=106, units='watts', ), 107: Field( # Maximum power by position. Data value indexes defined by rider_position_type. name='max_power_position', type=BASE_TYPES[0x84], # uint16 def_num=107, units='watts', ), 108: Field( # Average cadence by position. Data value indexes defined by rider_position_type. name='avg_cadence_position', type=BASE_TYPES[0x02], # uint8 def_num=108, units='rpm', ), 109: Field( # Maximum cadence by position. Data value indexes defined by rider_position_type. name='max_cadence_position', type=BASE_TYPES[0x02], # uint8 def_num=109, units='rpm', ), 110: Field( name='enhanced_avg_speed', type=BASE_TYPES[0x86], # uint32 def_num=110, scale=1000, units='m/s', ), 111: Field( name='enhanced_max_speed', type=BASE_TYPES[0x86], # uint32 def_num=111, scale=1000, units='m/s', ), 112: Field( name='enhanced_avg_altitude', type=BASE_TYPES[0x86], # uint32 def_num=112, scale=5, offset=500, units='m', ), 113: Field( name='enhanced_min_altitude', type=BASE_TYPES[0x86], # uint32 def_num=113, scale=5, offset=500, units='m', ), 114: Field( name='enhanced_max_altitude', type=BASE_TYPES[0x86], # uint32 def_num=114, scale=5, offset=500, units='m', ), 115: Field( # lev average motor power during lap name='avg_lev_motor_power', type=BASE_TYPES[0x84], # uint16 def_num=115, units='watts', ), 116: Field( # lev maximum motor power during lap name='max_lev_motor_power', type=BASE_TYPES[0x84], # uint16 def_num=116, units='watts', ), 117: Field( # lev battery consumption during lap name='lev_battery_consumption', type=BASE_TYPES[0x02], # uint8 def_num=117, scale=2, units='percent', ), 118: Field( name='avg_vertical_ratio', type=BASE_TYPES[0x84], # uint16 def_num=118, scale=100, units='percent', ), 119: Field( name='avg_stance_time_balance', type=BASE_TYPES[0x84], # uint16 def_num=119, scale=100, units='percent', ), 120: Field( name='avg_step_length', type=BASE_TYPES[0x84], # uint16 def_num=120, scale=10, units='mm', ), 121: Field( name='avg_vam', type=BASE_TYPES[0x84], # uint16 def_num=121, scale=1000, units='m/s', ), 253: FIELD_TYPE_TIMESTAMP, # Lap end time. 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 20: MessageType( name='record', mesg_num=20, fields={ 0: Field( name='position_lat', type=BASE_TYPES[0x85], # sint32 def_num=0, units='semicircles', ), 1: Field( name='position_long', type=BASE_TYPES[0x85], # sint32 def_num=1, units='semicircles', ), 2: Field( name='altitude', type=BASE_TYPES[0x84], # uint16 def_num=2, scale=5, offset=500, units='m', components=( ComponentField( name='enhanced_altitude', def_num=78, scale=5, offset=500, units='m', accumulate=False, bits=16, bit_offset=0, ), ), ), 3: Field( name='heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=3, units='bpm', ), 4: Field( name='cadence', type=BASE_TYPES[0x02], # uint8 def_num=4, units='rpm', ), 5: Field( name='distance', type=BASE_TYPES[0x86], # uint32 def_num=5, scale=100, units='m', ), 6: Field( name='speed', type=BASE_TYPES[0x84], # uint16 def_num=6, scale=1000, units='m/s', components=( ComponentField( name='enhanced_speed', def_num=73, scale=1000, units='m/s', accumulate=False, bits=16, bit_offset=0, ), ), ), 7: Field( name='power', type=BASE_TYPES[0x84], # uint16 def_num=7, units='watts', ), 8: Field( name='compressed_speed_distance', type=BASE_TYPES[0x0D], # byte def_num=8, components=( ComponentField( name='speed', def_num=6, scale=100, units='m/s', accumulate=False, bits=12, bit_offset=0, ), ComponentField( name='distance', def_num=5, scale=16, units='m', accumulate=True, bits=12, bit_offset=12, ), ), ), 9: Field( name='grade', type=BASE_TYPES[0x83], # sint16 def_num=9, scale=100, units='%', ), 10: Field( # Relative. 0 is none 254 is Max. name='resistance', type=BASE_TYPES[0x02], # uint8 def_num=10, ), 11: Field( name='time_from_course', type=BASE_TYPES[0x85], # sint32 def_num=11, scale=1000, units='s', ), 12: Field( name='cycle_length', type=BASE_TYPES[0x02], # uint8 def_num=12, scale=100, units='m', ), 13: Field( name='temperature', type=BASE_TYPES[0x01], # sint8 def_num=13, units='C', ), 17: Field( # Speed at 1s intervals. Timestamp field indicates time of last array element. name='speed_1s', type=BASE_TYPES[0x02], # uint8 def_num=17, scale=16, units='m/s', ), 18: Field( name='cycles', type=BASE_TYPES[0x02], # uint8 def_num=18, components=( ComponentField( name='total_cycles', def_num=19, units='cycles', accumulate=True, bits=8, bit_offset=0, ), ), ), 19: Field( name='total_cycles', type=BASE_TYPES[0x86], # uint32 def_num=19, units='cycles', ), 28: Field( name='compressed_accumulated_power', type=BASE_TYPES[0x84], # uint16 def_num=28, components=( ComponentField( name='accumulated_power', def_num=29, units='watts', accumulate=True, bits=16, bit_offset=0, ), ), ), 29: Field( name='accumulated_power', type=BASE_TYPES[0x86], # uint32 def_num=29, units='watts', ), 30: Field( name='left_right_balance', type=FIELD_TYPES['left_right_balance'], def_num=30, ), 31: Field( name='gps_accuracy', type=BASE_TYPES[0x02], # uint8 def_num=31, units='m', ), 32: Field( name='vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=32, scale=1000, units='m/s', ), 33: Field( name='calories', type=BASE_TYPES[0x84], # uint16 def_num=33, units='kcal', ), 39: Field( name='vertical_oscillation', type=BASE_TYPES[0x84], # uint16 def_num=39, scale=10, units='mm', ), 40: Field( name='stance_time_percent', type=BASE_TYPES[0x84], # uint16 def_num=40, scale=100, units='percent', ), 41: Field( name='stance_time', type=BASE_TYPES[0x84], # uint16 def_num=41, scale=10, units='ms', ), 42: Field( name='activity_type', type=FIELD_TYPES['activity_type'], def_num=42, ), 43: Field( name='left_torque_effectiveness', type=BASE_TYPES[0x02], # uint8 def_num=43, scale=2, units='percent', ), 44: Field( name='right_torque_effectiveness', type=BASE_TYPES[0x02], # uint8 def_num=44, scale=2, units='percent', ), 45: Field( name='left_pedal_smoothness', type=BASE_TYPES[0x02], # uint8 def_num=45, scale=2, units='percent', ), 46: Field( name='right_pedal_smoothness', type=BASE_TYPES[0x02], # uint8 def_num=46, scale=2, units='percent', ), 47: Field( name='combined_pedal_smoothness', type=BASE_TYPES[0x02], # uint8 def_num=47, scale=2, units='percent', ), 48: Field( name='time128', type=BASE_TYPES[0x02], # uint8 def_num=48, scale=128, units='s', ), 49: Field( name='stroke_type', type=FIELD_TYPES['stroke_type'], def_num=49, ), 50: Field( name='zone', type=BASE_TYPES[0x02], # uint8 def_num=50, ), 51: Field( name='ball_speed', type=BASE_TYPES[0x84], # uint16 def_num=51, scale=100, units='m/s', ), 52: Field( # Log cadence and fractional cadence for backwards compatability name='cadence256', type=BASE_TYPES[0x84], # uint16 def_num=52, scale=256, units='rpm', ), 53: Field( name='fractional_cadence', type=BASE_TYPES[0x02], # uint8 def_num=53, scale=128, units='rpm', ), 54: Field( # Total saturated and unsaturated hemoglobin name='total_hemoglobin_conc', type=BASE_TYPES[0x84], # uint16 def_num=54, scale=100, units='g/dL', ), 55: Field( # Min saturated and unsaturated hemoglobin name='total_hemoglobin_conc_min', type=BASE_TYPES[0x84], # uint16 def_num=55, scale=100, units='g/dL', ), 56: Field( # Max saturated and unsaturated hemoglobin name='total_hemoglobin_conc_max', type=BASE_TYPES[0x84], # uint16 def_num=56, scale=100, units='g/dL', ), 57: Field( # Percentage of hemoglobin saturated with oxygen name='saturated_hemoglobin_percent', type=BASE_TYPES[0x84], # uint16 def_num=57, scale=10, units='%', ), 58: Field( # Min percentage of hemoglobin saturated with oxygen name='saturated_hemoglobin_percent_min', type=BASE_TYPES[0x84], # uint16 def_num=58, scale=10, units='%', ), 59: Field( # Max percentage of hemoglobin saturated with oxygen name='saturated_hemoglobin_percent_max', type=BASE_TYPES[0x84], # uint16 def_num=59, scale=10, units='%', ), 62: Field( name='device_index', type=FIELD_TYPES['device_index'], def_num=62, ), 67: Field( # Left platform center offset name='left_pco', type=BASE_TYPES[0x01], # sint8 def_num=67, units='mm', ), 68: Field( # Right platform center offset name='right_pco', type=BASE_TYPES[0x01], # sint8 def_num=68, units='mm', ), 69: Field( # Left power phase angles. Data value indexes defined by power_phase_type. name='left_power_phase', type=BASE_TYPES[0x02], # uint8 def_num=69, scale=0.7111111, units='degrees', ), 70: Field( # Left power phase peak angles. Data value indexes defined by power_phase_type. name='left_power_phase_peak', type=BASE_TYPES[0x02], # uint8 def_num=70, scale=0.7111111, units='degrees', ), 71: Field( # Right power phase angles. Data value indexes defined by power_phase_type. name='right_power_phase', type=BASE_TYPES[0x02], # uint8 def_num=71, scale=0.7111111, units='degrees', ), 72: Field( # Right power phase peak angles. Data value indexes defined by power_phase_type. name='right_power_phase_peak', type=BASE_TYPES[0x02], # uint8 def_num=72, scale=0.7111111, units='degrees', ), 73: Field( name='enhanced_speed', type=BASE_TYPES[0x86], # uint32 def_num=73, scale=1000, units='m/s', ), 78: Field( name='enhanced_altitude', type=BASE_TYPES[0x86], # uint32 def_num=78, scale=5, offset=500, units='m', ), 81: Field( # lev battery state of charge name='battery_soc', type=BASE_TYPES[0x02], # uint8 def_num=81, scale=2, units='percent', ), 82: Field( # lev motor power name='motor_power', type=BASE_TYPES[0x84], # uint16 def_num=82, units='watts', ), 83: Field( name='vertical_ratio', type=BASE_TYPES[0x84], # uint16 def_num=83, scale=100, units='percent', ), 84: Field( name='stance_time_balance', type=BASE_TYPES[0x84], # uint16 def_num=84, scale=100, units='percent', ), 85: Field( name='step_length', type=BASE_TYPES[0x84], # uint16 def_num=85, scale=10, units='mm', ), 253: FIELD_TYPE_TIMESTAMP, }, ), 21: MessageType( name='event', mesg_num=21, fields={ 0: Field( name='event', type=FIELD_TYPES['event'], def_num=0, ), 1: Field( name='event_type', type=FIELD_TYPES['event_type'], def_num=1, ), 2: Field( name='data16', type=BASE_TYPES[0x84], # uint16 def_num=2, components=( ComponentField( name='data', def_num=3, accumulate=False, bits=16, bit_offset=0, ), ), ), 3: Field( name='data', type=BASE_TYPES[0x86], # uint32 def_num=3, subfields=( SubField( name='battery_level', def_num=3, type=BASE_TYPES[0x84], # uint16 scale=1000, units='V', ref_fields=( ReferenceField( name='event', def_num=0, value='battery', raw_value=11, ), ), ), SubField( name='cad_high_alert', def_num=3, type=BASE_TYPES[0x84], # uint16 units='rpm', ref_fields=( ReferenceField( name='event', def_num=0, value='cad_high_alert', raw_value=17, ), ), ), SubField( name='cad_low_alert', def_num=3, type=BASE_TYPES[0x84], # uint16 units='rpm', ref_fields=( ReferenceField( name='event', def_num=0, value='cad_low_alert', raw_value=18, ), ), ), SubField( name='calorie_duration_alert', def_num=3, type=BASE_TYPES[0x86], # uint32 units='calories', ref_fields=( ReferenceField( name='event', def_num=0, value='calorie_duration_alert', raw_value=25, ), ), ), SubField( name='comm_timeout', def_num=3, type=FIELD_TYPES['comm_timeout_type'], ref_fields=( ReferenceField( name='event', def_num=0, value='comm_timeout', raw_value=47, ), ), ), SubField( name='course_point_index', def_num=3, type=FIELD_TYPES['message_index'], ref_fields=( ReferenceField( name='event', def_num=0, value='course_point', raw_value=10, ), ), ), SubField( name='distance_duration_alert', def_num=3, type=BASE_TYPES[0x86], # uint32 scale=100, units='m', ref_fields=( ReferenceField( name='event', def_num=0, value='distance_duration_alert', raw_value=24, ), ), ), SubField( name='fitness_equipment_state', def_num=3, type=FIELD_TYPES['fitness_equipment_state'], ref_fields=( ReferenceField( name='event', def_num=0, value='fitness_equipment', raw_value=27, ), ), ), SubField( name='gear_change_data', def_num=3, type=BASE_TYPES[0x86], # uint32 ref_fields=( ReferenceField( name='event', def_num=0, value='front_gear_change', raw_value=42, ), ReferenceField( name='event', def_num=0, value='rear_gear_change', raw_value=43, ), ), components=( ComponentField( name='rear_gear_num', def_num=11, accumulate=False, bits=8, bit_offset=0, ), ComponentField( name='rear_gear', def_num=12, accumulate=False, bits=8, bit_offset=8, ), ComponentField( name='front_gear_num', def_num=9, accumulate=False, bits=8, bit_offset=16, ), ComponentField( name='front_gear', def_num=10, accumulate=False, bits=8, bit_offset=24, ), ), ), SubField( name='hr_high_alert', def_num=3, type=BASE_TYPES[0x02], # uint8 units='bpm', ref_fields=( ReferenceField( name='event', def_num=0, value='hr_high_alert', raw_value=13, ), ), ), SubField( name='hr_low_alert', def_num=3, type=BASE_TYPES[0x02], # uint8 units='bpm', ref_fields=( ReferenceField( name='event', def_num=0, value='hr_low_alert', raw_value=14, ), ), ), SubField( name='power_high_alert', def_num=3, type=BASE_TYPES[0x84], # uint16 units='watts', ref_fields=( ReferenceField( name='event', def_num=0, value='power_high_alert', raw_value=19, ), ), ), SubField( name='power_low_alert', def_num=3, type=BASE_TYPES[0x84], # uint16 units='watts', ref_fields=( ReferenceField( name='event', def_num=0, value='power_low_alert', raw_value=20, ), ), ), SubField( # Indicates the rider position value. name='rider_position', def_num=3, type=FIELD_TYPES['rider_position_type'], ref_fields=( ReferenceField( name='event', def_num=0, value='rider_position_change', raw_value=44, ), ), ), SubField( name='speed_high_alert', def_num=3, type=BASE_TYPES[0x86], # uint32 scale=1000, units='m/s', ref_fields=( ReferenceField( name='event', def_num=0, value='speed_high_alert', raw_value=15, ), ), ), SubField( name='speed_low_alert', def_num=3, type=BASE_TYPES[0x86], # uint32 scale=1000, units='m/s', ref_fields=( ReferenceField( name='event', def_num=0, value='speed_low_alert', raw_value=16, ), ), ), SubField( name='sport_point', def_num=3, type=BASE_TYPES[0x86], # uint32 ref_fields=( ReferenceField( name='event', def_num=0, value='sport_point', raw_value=33, ), ), components=( ComponentField( name='score', def_num=7, accumulate=False, bits=16, bit_offset=0, ), ComponentField( name='opponent_score', def_num=8, accumulate=False, bits=16, bit_offset=16, ), ), ), SubField( name='time_duration_alert', def_num=3, type=BASE_TYPES[0x86], # uint32 scale=1000, units='s', ref_fields=( ReferenceField( name='event', def_num=0, value='time_duration_alert', raw_value=23, ), ), ), SubField( name='timer_trigger', def_num=3, type=FIELD_TYPES['timer_trigger'], ref_fields=( ReferenceField( name='event', def_num=0, value='timer', raw_value=0, ), ), ), SubField( name='virtual_partner_speed', def_num=3, type=BASE_TYPES[0x84], # uint16 scale=1000, units='m/s', ref_fields=( ReferenceField( name='event', def_num=0, value='virtual_partner_pace', raw_value=12, ), ), ), ), ), 4: Field( name='event_group', type=BASE_TYPES[0x02], # uint8 def_num=4, ), 7: Field( # Do not populate directly. Autogenerated by decoder for sport_point subfield components name='score', type=BASE_TYPES[0x84], # uint16 def_num=7, ), 8: Field( # Do not populate directly. Autogenerated by decoder for sport_point subfield components name='opponent_score', type=BASE_TYPES[0x84], # uint16 def_num=8, ), 9: Field( # Do not populate directly. Autogenerated by decoder for gear_change subfield components. Front gear number. 1 is innermost. name='front_gear_num', type=BASE_TYPES[0x0A], # uint8z def_num=9, ), 10: Field( # Do not populate directly. Autogenerated by decoder for gear_change subfield components. Number of front teeth. name='front_gear', type=BASE_TYPES[0x0A], # uint8z def_num=10, ), 11: Field( # Do not populate directly. Autogenerated by decoder for gear_change subfield components. Rear gear number. 1 is innermost. name='rear_gear_num', type=BASE_TYPES[0x0A], # uint8z def_num=11, ), 12: Field( # Do not populate directly. Autogenerated by decoder for gear_change subfield components. Number of rear teeth. name='rear_gear', type=BASE_TYPES[0x0A], # uint8z def_num=12, ), 13: Field( name='device_index', type=FIELD_TYPES['device_index'], def_num=13, ), 253: FIELD_TYPE_TIMESTAMP, }, ), 23: MessageType( name='device_info', mesg_num=23, fields={ 0: Field( name='device_index', type=FIELD_TYPES['device_index'], def_num=0, ), 1: Field( name='device_type', type=FIELD_TYPES['antplus_device_type'], # uint8 def_num=1, subfields=( SubField( name='ant_device_type', def_num=1, type=BASE_TYPES[0x02], # uint8 ref_fields=( ReferenceField( name='source_type', def_num=25, value='ant', raw_value=0, ), ), ), SubField( name='antplus_device_type', def_num=1, type=FIELD_TYPES['antplus_device_type'], ref_fields=( ReferenceField( name='source_type', def_num=25, value='antplus', raw_value=1, ), ), ), ), ), 2: Field( name='manufacturer', type=FIELD_TYPES['manufacturer'], def_num=2, ), 3: Field( name='serial_number', type=BASE_TYPES[0x8C], # uint32z def_num=3, ), 4: Field( name='product', type=BASE_TYPES[0x84], # uint16 def_num=4, subfields=( SubField( name='garmin_product', def_num=4, type=FIELD_TYPES['garmin_product'], ref_fields=( ReferenceField( name='manufacturer', def_num=2, value='garmin', raw_value=1, ), ReferenceField( name='manufacturer', def_num=2, value='dynastream', raw_value=15, ), ReferenceField( name='manufacturer', def_num=2, value='dynastream_oem', raw_value=13, ), ), ), ), ), 5: Field( name='software_version', type=BASE_TYPES[0x84], # uint16 def_num=5, scale=100, ), 6: Field( name='hardware_version', type=BASE_TYPES[0x02], # uint8 def_num=6, ), 7: Field( # Reset by new battery or charge. name='cum_operating_time', type=BASE_TYPES[0x86], # uint32 def_num=7, units='s', ), 10: Field( name='battery_voltage', type=BASE_TYPES[0x84], # uint16 def_num=10, scale=256, units='V', ), 11: Field( name='battery_status', type=FIELD_TYPES['battery_status'], def_num=11, ), 18: Field( # Indicates the location of the sensor name='sensor_position', type=FIELD_TYPES['body_location'], def_num=18, ), 19: Field( # Used to describe the sensor or location name='descriptor', type=BASE_TYPES[0x07], # string def_num=19, ), 20: Field( name='ant_transmission_type', type=BASE_TYPES[0x0A], # uint8z def_num=20, ), 21: Field( name='ant_device_number', type=BASE_TYPES[0x8B], # uint16z def_num=21, ), 22: Field( name='ant_network', type=FIELD_TYPES['ant_network'], def_num=22, ), 25: Field( name='source_type', type=FIELD_TYPES['source_type'], def_num=25, ), 27: Field( # Optional free form string to indicate the devices name or model name='product_name', type=BASE_TYPES[0x07], # string def_num=27, ), 253: FIELD_TYPE_TIMESTAMP, }, ), 27: MessageType( name='workout_step', mesg_num=27, fields={ 0: Field( name='wkt_step_name', type=BASE_TYPES[0x07], # string def_num=0, ), 1: Field( name='duration_type', type=FIELD_TYPES['wkt_step_duration'], def_num=1, ), 2: Field( name='duration_value', type=BASE_TYPES[0x86], # uint32 def_num=2, subfields=( SubField( name='duration_calories', def_num=2, type=BASE_TYPES[0x86], # uint32 units='calories', ref_fields=( ReferenceField( name='duration_type', def_num=1, value='calories', raw_value=4, ), ), ), SubField( name='duration_distance', def_num=2, type=BASE_TYPES[0x86], # uint32 scale=100, units='m', ref_fields=( ReferenceField( name='duration_type', def_num=1, value='distance', raw_value=1, ), ), ), SubField( name='duration_hr', def_num=2, type=FIELD_TYPES['workout_hr'], units='% or bpm', ref_fields=( ReferenceField( name='duration_type', def_num=1, value='hr_less_than', raw_value=2, ), ReferenceField( name='duration_type', def_num=1, value='hr_greater_than', raw_value=3, ), ), ), SubField( name='duration_power', def_num=2, type=FIELD_TYPES['workout_power'], units='% or watts', ref_fields=( ReferenceField( name='duration_type', def_num=1, value='power_less_than', raw_value=14, ), ReferenceField( name='duration_type', def_num=1, value='power_greater_than', raw_value=15, ), ), ), SubField( # message_index of step to loop back to. Steps are assumed to be in the order by message_index. custom_name and intensity members are undefined for this duration type. name='duration_step', def_num=2, type=BASE_TYPES[0x86], # uint32 ref_fields=( ReferenceField( name='duration_type', def_num=1, value='repeat_until_steps_cmplt', raw_value=6, ), ReferenceField( name='duration_type', def_num=1, value='repeat_until_time', raw_value=7, ), ReferenceField( name='duration_type', def_num=1, value='repeat_until_distance', raw_value=8, ), ReferenceField( name='duration_type', def_num=1, value='repeat_until_calories', raw_value=9, ), ReferenceField( name='duration_type', def_num=1, value='repeat_until_hr_less_than', raw_value=10, ), ReferenceField( name='duration_type', def_num=1, value='repeat_until_hr_greater_than', raw_value=11, ), ReferenceField( name='duration_type', def_num=1, value='repeat_until_power_less_than', raw_value=12, ), ReferenceField( name='duration_type', def_num=1, value='repeat_until_power_greater_than', raw_value=13, ), ), ), SubField( name='duration_time', def_num=2, type=BASE_TYPES[0x86], # uint32 scale=1000, units='s', ref_fields=( ReferenceField( name='duration_type', def_num=1, value='time', raw_value=0, ), ReferenceField( name='duration_type', def_num=1, value='repetition_time', raw_value=28, ), ), ), ), ), 3: Field( name='target_type', type=FIELD_TYPES['wkt_step_target'], def_num=3, ), 4: Field( name='target_value', type=BASE_TYPES[0x86], # uint32 def_num=4, subfields=( SubField( name='repeat_calories', def_num=4, type=BASE_TYPES[0x86], # uint32 units='calories', ref_fields=( ReferenceField( name='duration_type', def_num=1, value='repeat_until_calories', raw_value=9, ), ), ), SubField( name='repeat_distance', def_num=4, type=BASE_TYPES[0x86], # uint32 scale=100, units='m', ref_fields=( ReferenceField( name='duration_type', def_num=1, value='repeat_until_distance', raw_value=8, ), ), ), SubField( name='repeat_hr', def_num=4, type=FIELD_TYPES['workout_hr'], units='% or bpm', ref_fields=( ReferenceField( name='duration_type', def_num=1, value='repeat_until_hr_less_than', raw_value=10, ), ReferenceField( name='duration_type', def_num=1, value='repeat_until_hr_greater_than', raw_value=11, ), ), ), SubField( name='repeat_power', def_num=4, type=FIELD_TYPES['workout_power'], units='% or watts', ref_fields=( ReferenceField( name='duration_type', def_num=1, value='repeat_until_power_less_than', raw_value=12, ), ReferenceField( name='duration_type', def_num=1, value='repeat_until_power_greater_than', raw_value=13, ), ), ), SubField( # # of repetitions name='repeat_steps', def_num=4, type=BASE_TYPES[0x86], # uint32 ref_fields=( ReferenceField( name='duration_type', def_num=1, value='repeat_until_steps_cmplt', raw_value=6, ), ), ), SubField( name='repeat_time', def_num=4, type=BASE_TYPES[0x86], # uint32 scale=1000, units='s', ref_fields=( ReferenceField( name='duration_type', def_num=1, value='repeat_until_time', raw_value=7, ), ), ), SubField( # Zone (1-?); Custom = 0; name='target_cadence_zone', def_num=4, type=BASE_TYPES[0x86], # uint32 ref_fields=( ReferenceField( name='target_type', def_num=3, value='cadence', raw_value=3, ), ), ), SubField( # hr zone (1-5);Custom =0; name='target_hr_zone', def_num=4, type=BASE_TYPES[0x86], # uint32 ref_fields=( ReferenceField( name='target_type', def_num=3, value='heart_rate', raw_value=1, ), ), ), SubField( # Power Zone ( 1-7); Custom = 0; name='target_power_zone', def_num=4, type=BASE_TYPES[0x86], # uint32 ref_fields=( ReferenceField( name='target_type', def_num=3, value='power', raw_value=4, ), ), ), SubField( # speed zone (1-10);Custom =0; name='target_speed_zone', def_num=4, type=BASE_TYPES[0x86], # uint32 ref_fields=( ReferenceField( name='target_type', def_num=3, value='speed', raw_value=0, ), ), ), SubField( name='target_stroke_type', def_num=4, type=FIELD_TYPES['swim_stroke'], ref_fields=( ReferenceField( name='target_type', def_num=3, value='swim_stroke', raw_value=11, ), ), ), ), ), 5: Field( name='custom_target_value_low', type=BASE_TYPES[0x86], # uint32 def_num=5, subfields=( SubField( name='custom_target_cadence_low', def_num=5, type=BASE_TYPES[0x86], # uint32 units='rpm', ref_fields=( ReferenceField( name='target_type', def_num=3, value='cadence', raw_value=3, ), ), ), SubField( name='custom_target_heart_rate_low', def_num=5, type=FIELD_TYPES['workout_hr'], units='% or bpm', ref_fields=( ReferenceField( name='target_type', def_num=3, value='heart_rate', raw_value=1, ), ), ), SubField( name='custom_target_power_low', def_num=5, type=FIELD_TYPES['workout_power'], units='% or watts', ref_fields=( ReferenceField( name='target_type', def_num=3, value='power', raw_value=4, ), ), ), SubField( name='custom_target_speed_low', def_num=5, type=BASE_TYPES[0x86], # uint32 scale=1000, units='m/s', ref_fields=( ReferenceField( name='target_type', def_num=3, value='speed', raw_value=0, ), ), ), ), ), 6: Field( name='custom_target_value_high', type=BASE_TYPES[0x86], # uint32 def_num=6, subfields=( SubField( name='custom_target_cadence_high', def_num=6, type=BASE_TYPES[0x86], # uint32 units='rpm', ref_fields=( ReferenceField( name='target_type', def_num=3, value='cadence', raw_value=3, ), ), ), SubField( name='custom_target_heart_rate_high', def_num=6, type=FIELD_TYPES['workout_hr'], units='% or bpm', ref_fields=( ReferenceField( name='target_type', def_num=3, value='heart_rate', raw_value=1, ), ), ), SubField( name='custom_target_power_high', def_num=6, type=FIELD_TYPES['workout_power'], units='% or watts', ref_fields=( ReferenceField( name='target_type', def_num=3, value='power', raw_value=4, ), ), ), SubField( name='custom_target_speed_high', def_num=6, type=BASE_TYPES[0x86], # uint32 scale=1000, units='m/s', ref_fields=( ReferenceField( name='target_type', def_num=3, value='speed', raw_value=0, ), ), ), ), ), 7: Field( name='intensity', type=FIELD_TYPES['intensity'], def_num=7, ), 8: Field( name='notes', type=BASE_TYPES[0x07], # string def_num=8, ), 9: Field( name='equipment', type=FIELD_TYPES['workout_equipment'], def_num=9, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 32: MessageType( name='course_point', mesg_num=32, fields={ 1: Field( name='timestamp', type=FIELD_TYPES['date_time'], def_num=1, ), 2: Field( name='position_lat', type=BASE_TYPES[0x85], # sint32 def_num=2, units='semicircles', ), 3: Field( name='position_long', type=BASE_TYPES[0x85], # sint32 def_num=3, units='semicircles', ), 4: Field( name='distance', type=BASE_TYPES[0x86], # uint32 def_num=4, scale=100, units='m', ), 5: Field( name='type', type=FIELD_TYPES['course_point'], def_num=5, ), 6: Field( name='name', type=BASE_TYPES[0x07], # string def_num=6, ), 8: Field( name='favorite', type=FIELD_TYPES['bool'], def_num=8, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 37: MessageType( name='file_capabilities', mesg_num=37, fields={ 0: Field( name='type', type=FIELD_TYPES['file'], def_num=0, ), 1: Field( name='flags', type=FIELD_TYPES['file_flags'], def_num=1, ), 2: Field( name='directory', type=BASE_TYPES[0x07], # string def_num=2, ), 3: Field( name='max_count', type=BASE_TYPES[0x84], # uint16 def_num=3, ), 4: Field( name='max_size', type=BASE_TYPES[0x86], # uint32 def_num=4, units='bytes', ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 38: MessageType( name='mesg_capabilities', mesg_num=38, fields={ 0: Field( name='file', type=FIELD_TYPES['file'], def_num=0, ), 1: Field( name='mesg_num', type=FIELD_TYPES['mesg_num'], def_num=1, ), 2: Field( name='count_type', type=FIELD_TYPES['mesg_count'], def_num=2, ), 3: Field( name='count', type=BASE_TYPES[0x84], # uint16 def_num=3, subfields=( SubField( name='max_per_file', def_num=3, type=BASE_TYPES[0x84], # uint16 ref_fields=( ReferenceField( name='count_type', def_num=2, value='max_per_file', raw_value=1, ), ), ), SubField( name='max_per_file_type', def_num=3, type=BASE_TYPES[0x84], # uint16 ref_fields=( ReferenceField( name='count_type', def_num=2, value='max_per_file_type', raw_value=2, ), ), ), SubField( name='num_per_file', def_num=3, type=BASE_TYPES[0x84], # uint16 ref_fields=( ReferenceField( name='count_type', def_num=2, value='num_per_file', raw_value=0, ), ), ), ), ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 39: MessageType( name='field_capabilities', mesg_num=39, fields={ 0: Field( name='file', type=FIELD_TYPES['file'], def_num=0, ), 1: Field( name='mesg_num', type=FIELD_TYPES['mesg_num'], def_num=1, ), 2: Field( name='field_num', type=BASE_TYPES[0x02], # uint8 def_num=2, ), 3: Field( name='count', type=BASE_TYPES[0x84], # uint16 def_num=3, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 49: MessageType( name='file_creator', mesg_num=49, fields={ 0: Field( name='software_version', type=BASE_TYPES[0x84], # uint16 def_num=0, ), 1: Field( name='hardware_version', type=BASE_TYPES[0x02], # uint8 def_num=1, ), }, ), 53: MessageType( name='speed_zone', mesg_num=53, fields={ 0: Field( name='high_value', type=BASE_TYPES[0x84], # uint16 def_num=0, scale=1000, units='m/s', ), 1: Field( name='name', type=BASE_TYPES[0x07], # string def_num=1, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 55: MessageType( name='monitoring', mesg_num=55, fields={ 0: Field( # Associates this data to device_info message. Not required for file with single device (sensor). name='device_index', type=FIELD_TYPES['device_index'], def_num=0, ), 1: Field( # Accumulated total calories. Maintained by MonitoringReader for each activity_type. See SDK documentation name='calories', type=BASE_TYPES[0x84], # uint16 def_num=1, units='kcal', ), 2: Field( # Accumulated distance. Maintained by MonitoringReader for each activity_type. See SDK documentation. name='distance', type=BASE_TYPES[0x86], # uint32 def_num=2, scale=100, units='m', ), 3: Field( # Accumulated cycles. Maintained by MonitoringReader for each activity_type. See SDK documentation. name='cycles', type=BASE_TYPES[0x86], # uint32 def_num=3, scale=2, units='cycles', subfields=( SubField( name='steps', def_num=3, type=BASE_TYPES[0x86], # uint32 units='steps', ref_fields=( ReferenceField( name='activity_type', def_num=5, value='walking', raw_value=6, ), ReferenceField( name='activity_type', def_num=5, value='running', raw_value=1, ), ), ), SubField( name='strokes', def_num=3, type=BASE_TYPES[0x86], # uint32 scale=2, units='strokes', ref_fields=( ReferenceField( name='activity_type', def_num=5, value='cycling', raw_value=2, ), ReferenceField( name='activity_type', def_num=5, value='swimming', raw_value=5, ), ), ), ), ), 4: Field( name='active_time', type=BASE_TYPES[0x86], # uint32 def_num=4, scale=1000, units='s', ), 5: Field( name='activity_type', type=FIELD_TYPES['activity_type'], def_num=5, ), 6: Field( name='activity_subtype', type=FIELD_TYPES['activity_subtype'], def_num=6, ), 7: Field( name='activity_level', type=FIELD_TYPES['activity_level'], def_num=7, ), 8: Field( name='distance_16', type=BASE_TYPES[0x84], # uint16 def_num=8, units='100*m', ), 9: Field( name='cycles_16', type=BASE_TYPES[0x84], # uint16 def_num=9, units='2*cycles or steps', ), 10: Field( name='active_time_16', type=BASE_TYPES[0x84], # uint16 def_num=10, units='s', ), 11: Field( # Must align to logging interval, for example, time must be 00:00:00 for daily log. name='local_timestamp', type=FIELD_TYPES['local_date_time'], def_num=11, ), 12: Field( # Avg temperature during the logging interval ended at timestamp name='temperature', type=BASE_TYPES[0x83], # sint16 def_num=12, scale=100, units='C', ), 14: Field( # Min temperature during the logging interval ended at timestamp name='temperature_min', type=BASE_TYPES[0x83], # sint16 def_num=14, scale=100, units='C', ), 15: Field( # Max temperature during the logging interval ended at timestamp name='temperature_max', type=BASE_TYPES[0x83], # sint16 def_num=15, scale=100, units='C', ), 16: Field( # Indexed using minute_activity_level enum name='activity_time', type=BASE_TYPES[0x84], # uint16 def_num=16, units='minutes', ), 19: Field( name='active_calories', type=BASE_TYPES[0x84], # uint16 def_num=19, units='kcal', ), 24: Field( # Indicates single type / intensity for duration since last monitoring message. name='current_activity_type_intensity', type=BASE_TYPES[0x0D], # byte def_num=24, components=( ComponentField( name='activity_type', def_num=5, accumulate=False, bits=5, bit_offset=0, ), ComponentField( name='intensity', def_num=28, accumulate=False, bits=3, bit_offset=5, ), ), ), 25: Field( name='timestamp_min_8', type=BASE_TYPES[0x02], # uint8 def_num=25, units='min', ), 26: Field( name='timestamp_16', type=BASE_TYPES[0x84], # uint16 def_num=26, units='s', ), 27: Field( name='heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=27, units='bpm', ), 28: Field( name='intensity', type=BASE_TYPES[0x02], # uint8 def_num=28, scale=10, ), 29: Field( name='duration_min', type=BASE_TYPES[0x84], # uint16 def_num=29, units='min', ), 30: Field( name='duration', type=BASE_TYPES[0x86], # uint32 def_num=30, units='s', ), 31: Field( name='ascent', type=BASE_TYPES[0x86], # uint32 def_num=31, scale=1000, units='m', ), 32: Field( name='descent', type=BASE_TYPES[0x86], # uint32 def_num=32, scale=1000, units='m', ), 33: Field( name='moderate_activity_minutes', type=BASE_TYPES[0x84], # uint16 def_num=33, units='minutes', ), 34: Field( name='vigorous_activity_minutes', type=BASE_TYPES[0x84], # uint16 def_num=34, units='minutes', ), 253: FIELD_TYPE_TIMESTAMP, # Must align to logging interval, for example, time must be 00:00:00 for daily log. }, ), 72: MessageType( # Corresponds to file_id of workout or course. name='training_file', mesg_num=72, fields={ 0: Field( name='type', type=FIELD_TYPES['file'], def_num=0, ), 1: Field( name='manufacturer', type=FIELD_TYPES['manufacturer'], def_num=1, ), 2: Field( name='product', type=BASE_TYPES[0x84], # uint16 def_num=2, subfields=( SubField( name='garmin_product', def_num=2, type=FIELD_TYPES['garmin_product'], ref_fields=( ReferenceField( name='manufacturer', def_num=1, value='garmin', raw_value=1, ), ReferenceField( name='manufacturer', def_num=1, value='dynastream', raw_value=15, ), ReferenceField( name='manufacturer', def_num=1, value='dynastream_oem', raw_value=13, ), ), ), ), ), 3: Field( name='serial_number', type=BASE_TYPES[0x8C], # uint32z def_num=3, ), 4: Field( name='time_created', type=FIELD_TYPES['date_time'], def_num=4, ), 253: FIELD_TYPE_TIMESTAMP, }, ), 78: MessageType( # Heart rate variability name='hrv', mesg_num=78, fields={ 0: Field( # Time between beats name='time', type=BASE_TYPES[0x84], # uint16 def_num=0, scale=1000, units='s', ), }, ), 80: MessageType( name='ant_rx', mesg_num=80, fields={ 0: Field( name='fractional_timestamp', type=BASE_TYPES[0x84], # uint16 def_num=0, scale=32768, units='s', ), 1: Field( name='mesg_id', type=BASE_TYPES[0x0D], # byte def_num=1, ), 2: Field( name='mesg_data', type=BASE_TYPES[0x0D], # byte def_num=2, components=( ComponentField( name='channel_number', def_num=3, accumulate=False, bits=8, bit_offset=0, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=8, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=16, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=24, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=32, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=40, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=48, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=56, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=64, ), ), ), 3: Field( name='channel_number', type=BASE_TYPES[0x02], # uint8 def_num=3, ), 4: Field( name='data', type=BASE_TYPES[0x0D], # byte def_num=4, ), 253: FIELD_TYPE_TIMESTAMP, }, ), 81: MessageType( name='ant_tx', mesg_num=81, fields={ 0: Field( name='fractional_timestamp', type=BASE_TYPES[0x84], # uint16 def_num=0, scale=32768, units='s', ), 1: Field( name='mesg_id', type=BASE_TYPES[0x0D], # byte def_num=1, ), 2: Field( name='mesg_data', type=BASE_TYPES[0x0D], # byte def_num=2, components=( ComponentField( name='channel_number', def_num=3, accumulate=False, bits=8, bit_offset=0, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=8, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=16, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=24, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=32, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=40, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=48, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=56, ), ComponentField( name='data', def_num=4, accumulate=False, bits=8, bit_offset=64, ), ), ), 3: Field( name='channel_number', type=BASE_TYPES[0x02], # uint8 def_num=3, ), 4: Field( name='data', type=BASE_TYPES[0x0D], # byte def_num=4, ), 253: FIELD_TYPE_TIMESTAMP, }, ), 82: MessageType( name='ant_channel_id', mesg_num=82, fields={ 0: Field( name='channel_number', type=BASE_TYPES[0x02], # uint8 def_num=0, ), 1: Field( name='device_type', type=BASE_TYPES[0x0A], # uint8z def_num=1, ), 2: Field( name='device_number', type=BASE_TYPES[0x8B], # uint16z def_num=2, ), 3: Field( name='transmission_type', type=BASE_TYPES[0x0A], # uint8z def_num=3, ), 4: Field( name='device_index', type=FIELD_TYPES['device_index'], def_num=4, ), }, ), 101: MessageType( name='length', mesg_num=101, fields={ 0: Field( name='event', type=FIELD_TYPES['event'], def_num=0, ), 1: Field( name='event_type', type=FIELD_TYPES['event_type'], def_num=1, ), 2: Field( name='start_time', type=FIELD_TYPES['date_time'], def_num=2, ), 3: Field( name='total_elapsed_time', type=BASE_TYPES[0x86], # uint32 def_num=3, scale=1000, units='s', ), 4: Field( name='total_timer_time', type=BASE_TYPES[0x86], # uint32 def_num=4, scale=1000, units='s', ), 5: Field( name='total_strokes', type=BASE_TYPES[0x84], # uint16 def_num=5, units='strokes', ), 6: Field( name='avg_speed', type=BASE_TYPES[0x84], # uint16 def_num=6, scale=1000, units='m/s', ), 7: Field( name='swim_stroke', type=FIELD_TYPES['swim_stroke'], def_num=7, units='swim_stroke', ), 9: Field( name='avg_swimming_cadence', type=BASE_TYPES[0x02], # uint8 def_num=9, units='strokes/min', ), 10: Field( name='event_group', type=BASE_TYPES[0x02], # uint8 def_num=10, ), 11: Field( name='total_calories', type=BASE_TYPES[0x84], # uint16 def_num=11, units='kcal', ), 12: Field( name='length_type', type=FIELD_TYPES['length_type'], def_num=12, ), 18: Field( name='player_score', type=BASE_TYPES[0x84], # uint16 def_num=18, ), 19: Field( name='opponent_score', type=BASE_TYPES[0x84], # uint16 def_num=19, ), 20: Field( # stroke_type enum used as the index name='stroke_count', type=BASE_TYPES[0x84], # uint16 def_num=20, units='counts', ), 21: Field( # zone number used as the index name='zone_count', type=BASE_TYPES[0x84], # uint16 def_num=21, units='counts', ), 253: FIELD_TYPE_TIMESTAMP, 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 106: MessageType( name='slave_device', mesg_num=106, fields={ 0: Field( name='manufacturer', type=FIELD_TYPES['manufacturer'], def_num=0, ), 1: Field( name='product', type=BASE_TYPES[0x84], # uint16 def_num=1, subfields=( SubField( name='garmin_product', def_num=1, type=FIELD_TYPES['garmin_product'], ref_fields=( ReferenceField( name='manufacturer', def_num=0, value='garmin', raw_value=1, ), ReferenceField( name='manufacturer', def_num=0, value='dynastream', raw_value=15, ), ReferenceField( name='manufacturer', def_num=0, value='dynastream_oem', raw_value=13, ), ), ), ), ), }, ), 127: MessageType( name='connectivity', mesg_num=127, fields={ 0: Field( # Use Bluetooth for connectivity features name='bluetooth_enabled', type=FIELD_TYPES['bool'], def_num=0, ), 1: Field( # Use Bluetooth Low Energy for connectivity features name='bluetooth_le_enabled', type=FIELD_TYPES['bool'], def_num=1, ), 2: Field( # Use ANT for connectivity features name='ant_enabled', type=FIELD_TYPES['bool'], def_num=2, ), 3: Field( name='name', type=BASE_TYPES[0x07], # string def_num=3, ), 4: Field( name='live_tracking_enabled', type=FIELD_TYPES['bool'], def_num=4, ), 5: Field( name='weather_conditions_enabled', type=FIELD_TYPES['bool'], def_num=5, ), 6: Field( name='weather_alerts_enabled', type=FIELD_TYPES['bool'], def_num=6, ), 7: Field( name='auto_activity_upload_enabled', type=FIELD_TYPES['bool'], def_num=7, ), 8: Field( name='course_download_enabled', type=FIELD_TYPES['bool'], def_num=8, ), 9: Field( name='workout_download_enabled', type=FIELD_TYPES['bool'], def_num=9, ), 10: Field( name='gps_ephemeris_download_enabled', type=FIELD_TYPES['bool'], def_num=10, ), 11: Field( name='incident_detection_enabled', type=FIELD_TYPES['bool'], def_num=11, ), 12: Field( name='grouptrack_enabled', type=FIELD_TYPES['bool'], def_num=12, ), }, ), 128: MessageType( name='weather_conditions', mesg_num=128, fields={ 0: Field( # Current or forecast name='weather_report', type=FIELD_TYPES['weather_report'], def_num=0, ), 1: Field( name='temperature', type=BASE_TYPES[0x01], # sint8 def_num=1, units='C', ), 2: Field( # Corresponds to GSC Response weatherIcon field name='condition', type=FIELD_TYPES['weather_status'], def_num=2, ), 3: Field( name='wind_direction', type=BASE_TYPES[0x84], # uint16 def_num=3, units='degrees', ), 4: Field( name='wind_speed', type=BASE_TYPES[0x84], # uint16 def_num=4, scale=1000, units='m/s', ), 5: Field( # range 0-100 name='precipitation_probability', type=BASE_TYPES[0x02], # uint8 def_num=5, ), 6: Field( # Heat Index if GCS heatIdx above or equal to 90F or wind chill if GCS windChill below or equal to 32F name='temperature_feels_like', type=BASE_TYPES[0x01], # sint8 def_num=6, units='C', ), 7: Field( name='relative_humidity', type=BASE_TYPES[0x02], # uint8 def_num=7, ), 8: Field( # string corresponding to GCS response location string name='location', type=BASE_TYPES[0x07], # string def_num=8, ), 9: Field( name='observed_at_time', type=FIELD_TYPES['date_time'], def_num=9, ), 10: Field( name='observed_location_lat', type=BASE_TYPES[0x85], # sint32 def_num=10, units='semicircles', ), 11: Field( name='observed_location_long', type=BASE_TYPES[0x85], # sint32 def_num=11, units='semicircles', ), 12: Field( name='day_of_week', type=FIELD_TYPES['day_of_week'], def_num=12, ), 13: Field( name='high_temperature', type=BASE_TYPES[0x01], # sint8 def_num=13, units='C', ), 14: Field( name='low_temperature', type=BASE_TYPES[0x01], # sint8 def_num=14, units='C', ), 253: FIELD_TYPE_TIMESTAMP, # time of update for current conditions, else forecast time }, ), 129: MessageType( name='weather_alert', mesg_num=129, fields={ 0: Field( # Unique identifier from GCS report ID string, length is 12 name='report_id', type=BASE_TYPES[0x07], # string def_num=0, ), 1: Field( # Time alert was issued name='issue_time', type=FIELD_TYPES['date_time'], def_num=1, ), 2: Field( # Time alert expires name='expire_time', type=FIELD_TYPES['date_time'], def_num=2, ), 3: Field( # Warning, Watch, Advisory, Statement name='severity', type=FIELD_TYPES['weather_severity'], def_num=3, ), 4: Field( # Tornado, Severe Thunderstorm, etc. name='type', type=FIELD_TYPES['weather_severe_type'], def_num=4, ), 253: FIELD_TYPE_TIMESTAMP, }, ), 131: MessageType( name='cadence_zone', mesg_num=131, fields={ 0: Field( name='high_value', type=BASE_TYPES[0x02], # uint8 def_num=0, units='rpm', ), 1: Field( name='name', type=BASE_TYPES[0x07], # string def_num=1, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 132: MessageType( name='hr', mesg_num=132, fields={ 0: Field( name='fractional_timestamp', type=BASE_TYPES[0x84], # uint16 def_num=0, scale=32768, units='s', ), 1: Field( name='time256', type=BASE_TYPES[0x02], # uint8 def_num=1, components=( ComponentField( name='fractional_timestamp', def_num=0, scale=256, units='s', accumulate=False, bits=8, bit_offset=0, ), ), ), 6: Field( name='filtered_bpm', type=BASE_TYPES[0x02], # uint8 def_num=6, units='bpm', ), 9: Field( name='event_timestamp', type=BASE_TYPES[0x86], # uint32 def_num=9, scale=1024, units='s', ), 10: Field( name='event_timestamp_12', type=BASE_TYPES[0x0D], # byte def_num=10, components=( ComponentField( name='event_timestamp', def_num=9, scale=1024, units='s', accumulate=True, bits=12, bit_offset=0, ), ComponentField( name='event_timestamp', def_num=9, scale=1024, units='s', accumulate=True, bits=12, bit_offset=12, ), ComponentField( name='event_timestamp', def_num=9, scale=1024, units='s', accumulate=True, bits=12, bit_offset=24, ), ComponentField( name='event_timestamp', def_num=9, scale=1024, units='s', accumulate=True, bits=12, bit_offset=36, ), ComponentField( name='event_timestamp', def_num=9, scale=1024, units='s', accumulate=True, bits=12, bit_offset=48, ), ComponentField( name='event_timestamp', def_num=9, scale=1024, units='s', accumulate=True, bits=12, bit_offset=60, ), ComponentField( name='event_timestamp', def_num=9, scale=1024, units='s', accumulate=True, bits=12, bit_offset=72, ), ComponentField( name='event_timestamp', def_num=9, scale=1024, units='s', accumulate=True, bits=12, bit_offset=84, ), ComponentField( name='event_timestamp', def_num=9, scale=1024, units='s', accumulate=True, bits=12, bit_offset=96, ), ComponentField( name='event_timestamp', def_num=9, scale=1024, units='s', accumulate=True, bits=12, bit_offset=108, ), ), ), 253: FIELD_TYPE_TIMESTAMP, }, ), 142: MessageType( name='segment_lap', mesg_num=142, fields={ 0: Field( name='event', type=FIELD_TYPES['event'], def_num=0, ), 1: Field( name='event_type', type=FIELD_TYPES['event_type'], def_num=1, ), 2: Field( name='start_time', type=FIELD_TYPES['date_time'], def_num=2, ), 3: Field( name='start_position_lat', type=BASE_TYPES[0x85], # sint32 def_num=3, units='semicircles', ), 4: Field( name='start_position_long', type=BASE_TYPES[0x85], # sint32 def_num=4, units='semicircles', ), 5: Field( name='end_position_lat', type=BASE_TYPES[0x85], # sint32 def_num=5, units='semicircles', ), 6: Field( name='end_position_long', type=BASE_TYPES[0x85], # sint32 def_num=6, units='semicircles', ), 7: Field( # Time (includes pauses) name='total_elapsed_time', type=BASE_TYPES[0x86], # uint32 def_num=7, scale=1000, units='s', ), 8: Field( # Timer Time (excludes pauses) name='total_timer_time', type=BASE_TYPES[0x86], # uint32 def_num=8, scale=1000, units='s', ), 9: Field( name='total_distance', type=BASE_TYPES[0x86], # uint32 def_num=9, scale=100, units='m', ), 10: Field( name='total_cycles', type=BASE_TYPES[0x86], # uint32 def_num=10, units='cycles', subfields=( SubField( name='total_strokes', def_num=10, type=BASE_TYPES[0x86], # uint32 units='strokes', ref_fields=( ReferenceField( name='sport', def_num=23, value='cycling', raw_value=2, ), ), ), ), ), 11: Field( name='total_calories', type=BASE_TYPES[0x84], # uint16 def_num=11, units='kcal', ), 12: Field( # If New Leaf name='total_fat_calories', type=BASE_TYPES[0x84], # uint16 def_num=12, units='kcal', ), 13: Field( name='avg_speed', type=BASE_TYPES[0x84], # uint16 def_num=13, scale=1000, units='m/s', ), 14: Field( name='max_speed', type=BASE_TYPES[0x84], # uint16 def_num=14, scale=1000, units='m/s', ), 15: Field( name='avg_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=15, units='bpm', ), 16: Field( name='max_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=16, units='bpm', ), 17: Field( # total_cycles / total_timer_time if non_zero_avg_cadence otherwise total_cycles / total_elapsed_time name='avg_cadence', type=BASE_TYPES[0x02], # uint8 def_num=17, units='rpm', ), 18: Field( name='max_cadence', type=BASE_TYPES[0x02], # uint8 def_num=18, units='rpm', ), 19: Field( # total_power / total_timer_time if non_zero_avg_power otherwise total_power / total_elapsed_time name='avg_power', type=BASE_TYPES[0x84], # uint16 def_num=19, units='watts', ), 20: Field( name='max_power', type=BASE_TYPES[0x84], # uint16 def_num=20, units='watts', ), 21: Field( name='total_ascent', type=BASE_TYPES[0x84], # uint16 def_num=21, units='m', ), 22: Field( name='total_descent', type=BASE_TYPES[0x84], # uint16 def_num=22, units='m', ), 23: Field( name='sport', type=FIELD_TYPES['sport'], def_num=23, ), 24: Field( name='event_group', type=BASE_TYPES[0x02], # uint8 def_num=24, ), 25: Field( # North east corner latitude. name='nec_lat', type=BASE_TYPES[0x85], # sint32 def_num=25, units='semicircles', ), 26: Field( # North east corner longitude. name='nec_long', type=BASE_TYPES[0x85], # sint32 def_num=26, units='semicircles', ), 27: Field( # South west corner latitude. name='swc_lat', type=BASE_TYPES[0x85], # sint32 def_num=27, units='semicircles', ), 28: Field( # South west corner latitude. name='swc_long', type=BASE_TYPES[0x85], # sint32 def_num=28, units='semicircles', ), 29: Field( name='name', type=BASE_TYPES[0x07], # string def_num=29, ), 30: Field( name='normalized_power', type=BASE_TYPES[0x84], # uint16 def_num=30, units='watts', ), 31: Field( name='left_right_balance', type=FIELD_TYPES['left_right_balance_100'], def_num=31, ), 32: Field( name='sub_sport', type=FIELD_TYPES['sub_sport'], def_num=32, ), 33: Field( name='total_work', type=BASE_TYPES[0x86], # uint32 def_num=33, units='J', ), 34: Field( name='avg_altitude', type=BASE_TYPES[0x84], # uint16 def_num=34, scale=5, offset=500, units='m', ), 35: Field( name='max_altitude', type=BASE_TYPES[0x84], # uint16 def_num=35, scale=5, offset=500, units='m', ), 36: Field( name='gps_accuracy', type=BASE_TYPES[0x02], # uint8 def_num=36, units='m', ), 37: Field( name='avg_grade', type=BASE_TYPES[0x83], # sint16 def_num=37, scale=100, units='%', ), 38: Field( name='avg_pos_grade', type=BASE_TYPES[0x83], # sint16 def_num=38, scale=100, units='%', ), 39: Field( name='avg_neg_grade', type=BASE_TYPES[0x83], # sint16 def_num=39, scale=100, units='%', ), 40: Field( name='max_pos_grade', type=BASE_TYPES[0x83], # sint16 def_num=40, scale=100, units='%', ), 41: Field( name='max_neg_grade', type=BASE_TYPES[0x83], # sint16 def_num=41, scale=100, units='%', ), 42: Field( name='avg_temperature', type=BASE_TYPES[0x01], # sint8 def_num=42, units='C', ), 43: Field( name='max_temperature', type=BASE_TYPES[0x01], # sint8 def_num=43, units='C', ), 44: Field( name='total_moving_time', type=BASE_TYPES[0x86], # uint32 def_num=44, scale=1000, units='s', ), 45: Field( name='avg_pos_vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=45, scale=1000, units='m/s', ), 46: Field( name='avg_neg_vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=46, scale=1000, units='m/s', ), 47: Field( name='max_pos_vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=47, scale=1000, units='m/s', ), 48: Field( name='max_neg_vertical_speed', type=BASE_TYPES[0x83], # sint16 def_num=48, scale=1000, units='m/s', ), 49: Field( name='time_in_hr_zone', type=BASE_TYPES[0x86], # uint32 def_num=49, scale=1000, units='s', ), 50: Field( name='time_in_speed_zone', type=BASE_TYPES[0x86], # uint32 def_num=50, scale=1000, units='s', ), 51: Field( name='time_in_cadence_zone', type=BASE_TYPES[0x86], # uint32 def_num=51, scale=1000, units='s', ), 52: Field( name='time_in_power_zone', type=BASE_TYPES[0x86], # uint32 def_num=52, scale=1000, units='s', ), 53: Field( name='repetition_num', type=BASE_TYPES[0x84], # uint16 def_num=53, ), 54: Field( name='min_altitude', type=BASE_TYPES[0x84], # uint16 def_num=54, scale=5, offset=500, units='m', ), 55: Field( name='min_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=55, units='bpm', ), 56: Field( name='active_time', type=BASE_TYPES[0x86], # uint32 def_num=56, scale=1000, units='s', ), 57: Field( name='wkt_step_index', type=FIELD_TYPES['message_index'], def_num=57, ), 58: Field( name='sport_event', type=FIELD_TYPES['sport_event'], def_num=58, ), 59: Field( name='avg_left_torque_effectiveness', type=BASE_TYPES[0x02], # uint8 def_num=59, scale=2, units='percent', ), 60: Field( name='avg_right_torque_effectiveness', type=BASE_TYPES[0x02], # uint8 def_num=60, scale=2, units='percent', ), 61: Field( name='avg_left_pedal_smoothness', type=BASE_TYPES[0x02], # uint8 def_num=61, scale=2, units='percent', ), 62: Field( name='avg_right_pedal_smoothness', type=BASE_TYPES[0x02], # uint8 def_num=62, scale=2, units='percent', ), 63: Field( name='avg_combined_pedal_smoothness', type=BASE_TYPES[0x02], # uint8 def_num=63, scale=2, units='percent', ), 64: Field( name='status', type=FIELD_TYPES['segment_lap_status'], def_num=64, ), 65: Field( name='uuid', type=BASE_TYPES[0x07], # string def_num=65, ), 66: Field( # fractional part of the avg_cadence name='avg_fractional_cadence', type=BASE_TYPES[0x02], # uint8 def_num=66, scale=128, units='rpm', ), 67: Field( # fractional part of the max_cadence name='max_fractional_cadence', type=BASE_TYPES[0x02], # uint8 def_num=67, scale=128, units='rpm', ), 68: Field( # fractional part of the total_cycles name='total_fractional_cycles', type=BASE_TYPES[0x02], # uint8 def_num=68, scale=128, units='cycles', ), 69: Field( name='front_gear_shift_count', type=BASE_TYPES[0x84], # uint16 def_num=69, ), 70: Field( name='rear_gear_shift_count', type=BASE_TYPES[0x84], # uint16 def_num=70, ), 71: Field( # Total time spent in the standing position name='time_standing', type=BASE_TYPES[0x86], # uint32 def_num=71, scale=1000, units='s', ), 72: Field( # Number of transitions to the standing state name='stand_count', type=BASE_TYPES[0x84], # uint16 def_num=72, ), 73: Field( # Average left platform center offset name='avg_left_pco', type=BASE_TYPES[0x01], # sint8 def_num=73, units='mm', ), 74: Field( # Average right platform center offset name='avg_right_pco', type=BASE_TYPES[0x01], # sint8 def_num=74, units='mm', ), 75: Field( # Average left power phase angles. Data value indexes defined by power_phase_type. name='avg_left_power_phase', type=BASE_TYPES[0x02], # uint8 def_num=75, scale=0.7111111, units='degrees', ), 76: Field( # Average left power phase peak angles. Data value indexes defined by power_phase_type. name='avg_left_power_phase_peak', type=BASE_TYPES[0x02], # uint8 def_num=76, scale=0.7111111, units='degrees', ), 77: Field( # Average right power phase angles. Data value indexes defined by power_phase_type. name='avg_right_power_phase', type=BASE_TYPES[0x02], # uint8 def_num=77, scale=0.7111111, units='degrees', ), 78: Field( # Average right power phase peak angles. Data value indexes defined by power_phase_type. name='avg_right_power_phase_peak', type=BASE_TYPES[0x02], # uint8 def_num=78, scale=0.7111111, units='degrees', ), 79: Field( # Average power by position. Data value indexes defined by rider_position_type. name='avg_power_position', type=BASE_TYPES[0x84], # uint16 def_num=79, units='watts', ), 80: Field( # Maximum power by position. Data value indexes defined by rider_position_type. name='max_power_position', type=BASE_TYPES[0x84], # uint16 def_num=80, units='watts', ), 81: Field( # Average cadence by position. Data value indexes defined by rider_position_type. name='avg_cadence_position', type=BASE_TYPES[0x02], # uint8 def_num=81, units='rpm', ), 82: Field( # Maximum cadence by position. Data value indexes defined by rider_position_type. name='max_cadence_position', type=BASE_TYPES[0x02], # uint8 def_num=82, units='rpm', ), 83: Field( # Manufacturer that produced the segment name='manufacturer', type=FIELD_TYPES['manufacturer'], def_num=83, ), 253: FIELD_TYPE_TIMESTAMP, # Lap end time. 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 149: MessageType( # Unique Identification data for an individual segment leader within a segment file name='segment_leaderboard_entry', mesg_num=149, fields={ 0: Field( # Friendly name assigned to leader name='name', type=BASE_TYPES[0x07], # string def_num=0, ), 1: Field( # Leader classification name='type', type=FIELD_TYPES['segment_leaderboard_type'], def_num=1, ), 2: Field( # Primary user ID of this leader name='group_primary_key', type=BASE_TYPES[0x86], # uint32 def_num=2, ), 3: Field( # ID of the activity associated with this leader time name='activity_id', type=BASE_TYPES[0x86], # uint32 def_num=3, ), 4: Field( # Segment Time (includes pauses) name='segment_time', type=BASE_TYPES[0x86], # uint32 def_num=4, scale=1000, units='s', ), 5: Field( # String version of the activity_id. 21 characters long, express in decimal name='activity_id_string', type=BASE_TYPES[0x07], # string def_num=5, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 150: MessageType( # Navigation and race evaluation point for a segment decribing a point along the segment path and time it took each segment leader to reach that point name='segment_point', mesg_num=150, fields={ 1: Field( name='position_lat', type=BASE_TYPES[0x85], # sint32 def_num=1, units='semicircles', ), 2: Field( name='position_long', type=BASE_TYPES[0x85], # sint32 def_num=2, units='semicircles', ), 3: Field( # Accumulated distance along the segment at the described point name='distance', type=BASE_TYPES[0x86], # uint32 def_num=3, scale=100, units='m', ), 4: Field( # Accumulated altitude along the segment at the described point name='altitude', type=BASE_TYPES[0x84], # uint16 def_num=4, scale=5, offset=500, units='m', ), 5: Field( # Accumualted time each leader board member required to reach the described point. This value is zero for all leader board members at the starting point of the segment. name='leader_time', type=BASE_TYPES[0x86], # uint32 def_num=5, scale=1000, units='s', ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 158: MessageType( name='workout_session', mesg_num=158, fields={ 0: Field( name='sport', type=FIELD_TYPES['sport'], def_num=0, ), 1: Field( name='sub_sport', type=FIELD_TYPES['sub_sport'], def_num=1, ), 2: Field( name='num_valid_steps', type=BASE_TYPES[0x84], # uint16 def_num=2, ), 3: Field( name='first_step_index', type=BASE_TYPES[0x84], # uint16 def_num=3, ), 4: Field( name='pool_length', type=BASE_TYPES[0x84], # uint16 def_num=4, scale=100, units='m', ), 5: Field( name='pool_length_unit', type=FIELD_TYPES['display_measure'], def_num=5, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 159: MessageType( name='watchface_settings', mesg_num=159, fields={ 0: Field( name='mode', type=FIELD_TYPES['watchface_mode'], def_num=0, ), 1: Field( name='layout', type=BASE_TYPES[0x0D], # byte def_num=1, subfields=( SubField( name='analog_layout', def_num=1, type=FIELD_TYPES['analog_watchface_layout'], ref_fields=( ReferenceField( name='mode', def_num=0, value='analog', raw_value=1, ), ), ), SubField( name='digital_layout', def_num=1, type=FIELD_TYPES['digital_watchface_layout'], ref_fields=( ReferenceField( name='mode', def_num=0, value='digital', raw_value=0, ), ), ), ), ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 160: MessageType( name='gps_metadata', mesg_num=160, fields={ 0: Field( # Millisecond part of the timestamp. name='timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=0, units='ms', ), 1: Field( name='position_lat', type=BASE_TYPES[0x85], # sint32 def_num=1, units='semicircles', ), 2: Field( name='position_long', type=BASE_TYPES[0x85], # sint32 def_num=2, units='semicircles', ), 3: Field( name='enhanced_altitude', type=BASE_TYPES[0x86], # uint32 def_num=3, scale=5, offset=500, units='m', ), 4: Field( name='enhanced_speed', type=BASE_TYPES[0x86], # uint32 def_num=4, scale=1000, units='m/s', ), 5: Field( name='heading', type=BASE_TYPES[0x84], # uint16 def_num=5, scale=100, units='degrees', ), 6: Field( # Used to correlate UTC to system time if the timestamp of the message is in system time. This UTC time is derived from the GPS data. name='utc_timestamp', type=FIELD_TYPES['date_time'], def_num=6, units='s', ), 7: Field( # velocity[0] is lon velocity. Velocity[1] is lat velocity. Velocity[2] is altitude velocity. name='velocity', type=BASE_TYPES[0x83], # sint16 def_num=7, scale=100, units='m/s', ), 253: FIELD_TYPE_TIMESTAMP, # Whole second part of the timestamp. }, ), 161: MessageType( name='camera_event', mesg_num=161, fields={ 0: Field( # Millisecond part of the timestamp. name='timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=0, units='ms', ), 1: Field( name='camera_event_type', type=FIELD_TYPES['camera_event_type'], def_num=1, ), 2: Field( name='camera_file_uuid', type=BASE_TYPES[0x07], # string def_num=2, ), 3: Field( name='camera_orientation', type=FIELD_TYPES['camera_orientation_type'], def_num=3, ), 253: FIELD_TYPE_TIMESTAMP, # Whole second part of the timestamp. }, ), 162: MessageType( name='timestamp_correlation', mesg_num=162, fields={ 0: Field( # Fractional part of the UTC timestamp at the time the system timestamp was recorded. name='fractional_timestamp', type=BASE_TYPES[0x84], # uint16 def_num=0, scale=32768, units='s', ), 1: Field( # Whole second part of the system timestamp name='system_timestamp', type=FIELD_TYPES['date_time'], def_num=1, units='s', ), 2: Field( # Fractional part of the system timestamp name='fractional_system_timestamp', type=BASE_TYPES[0x84], # uint16 def_num=2, scale=32768, units='s', ), 3: Field( # timestamp epoch expressed in local time used to convert timestamps to local time name='local_timestamp', type=FIELD_TYPES['local_date_time'], def_num=3, units='s', ), 4: Field( # Millisecond part of the UTC timestamp at the time the system timestamp was recorded. name='timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=4, units='ms', ), 5: Field( # Millisecond part of the system timestamp name='system_timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=5, units='ms', ), 253: FIELD_TYPE_TIMESTAMP, # Whole second part of UTC timestamp at the time the system timestamp was recorded. }, ), 164: MessageType( name='gyroscope_data', mesg_num=164, fields={ 0: Field( # Millisecond part of the timestamp. name='timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=0, units='ms', ), 1: Field( # Each time in the array describes the time at which the gyro sample with the corrosponding index was taken. Limited to 30 samples in each message. The samples may span across seconds. Array size must match the number of samples in gyro_x and gyro_y and gyro_z name='sample_time_offset', type=BASE_TYPES[0x84], # uint16 def_num=1, units='ms', ), 2: Field( # These are the raw ADC reading. Maximum number of samples is 30 in each message. The samples may span across seconds. A conversion will need to be done on this data once read. name='gyro_x', type=BASE_TYPES[0x84], # uint16 def_num=2, units='counts', ), 3: Field( # These are the raw ADC reading. Maximum number of samples is 30 in each message. The samples may span across seconds. A conversion will need to be done on this data once read. name='gyro_y', type=BASE_TYPES[0x84], # uint16 def_num=3, units='counts', ), 4: Field( # These are the raw ADC reading. Maximum number of samples is 30 in each message. The samples may span across seconds. A conversion will need to be done on this data once read. name='gyro_z', type=BASE_TYPES[0x84], # uint16 def_num=4, units='counts', ), 5: Field( # Calibrated gyro reading name='calibrated_gyro_x', type=BASE_TYPES[0x88], # float32 def_num=5, units='deg/s', ), 6: Field( # Calibrated gyro reading name='calibrated_gyro_y', type=BASE_TYPES[0x88], # float32 def_num=6, units='deg/s', ), 7: Field( # Calibrated gyro reading name='calibrated_gyro_z', type=BASE_TYPES[0x88], # float32 def_num=7, units='deg/s', ), 253: FIELD_TYPE_TIMESTAMP, # Whole second part of the timestamp }, ), 165: MessageType( name='accelerometer_data', mesg_num=165, fields={ 0: Field( # Millisecond part of the timestamp. name='timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=0, units='ms', ), 1: Field( # Each time in the array describes the time at which the accelerometer sample with the corrosponding index was taken. Limited to 30 samples in each message. The samples may span across seconds. Array size must match the number of samples in accel_x and accel_y and accel_z name='sample_time_offset', type=BASE_TYPES[0x84], # uint16 def_num=1, units='ms', ), 2: Field( # These are the raw ADC reading. Maximum number of samples is 30 in each message. The samples may span across seconds. A conversion will need to be done on this data once read. name='accel_x', type=BASE_TYPES[0x84], # uint16 def_num=2, units='counts', ), 3: Field( # These are the raw ADC reading. Maximum number of samples is 30 in each message. The samples may span across seconds. A conversion will need to be done on this data once read. name='accel_y', type=BASE_TYPES[0x84], # uint16 def_num=3, units='counts', ), 4: Field( # These are the raw ADC reading. Maximum number of samples is 30 in each message. The samples may span across seconds. A conversion will need to be done on this data once read. name='accel_z', type=BASE_TYPES[0x84], # uint16 def_num=4, units='counts', ), 5: Field( # Calibrated accel reading name='calibrated_accel_x', type=BASE_TYPES[0x88], # float32 def_num=5, units='g', ), 6: Field( # Calibrated accel reading name='calibrated_accel_y', type=BASE_TYPES[0x88], # float32 def_num=6, units='g', ), 7: Field( # Calibrated accel reading name='calibrated_accel_z', type=BASE_TYPES[0x88], # float32 def_num=7, units='g', ), 8: Field( # Calibrated accel reading name='compressed_calibrated_accel_x', type=BASE_TYPES[0x83], # sint16 def_num=8, units='mG', ), 9: Field( # Calibrated accel reading name='compressed_calibrated_accel_y', type=BASE_TYPES[0x83], # sint16 def_num=9, units='mG', ), 10: Field( # Calibrated accel reading name='compressed_calibrated_accel_z', type=BASE_TYPES[0x83], # sint16 def_num=10, units='mG', ), 253: FIELD_TYPE_TIMESTAMP, # Whole second part of the timestamp }, ), 167: MessageType( name='three_d_sensor_calibration', mesg_num=167, fields={ 0: Field( # Indicates which sensor the calibration is for name='sensor_type', type=FIELD_TYPES['sensor_type'], def_num=0, ), 1: Field( # Calibration factor used to convert from raw ADC value to degrees, g, etc. name='calibration_factor', type=BASE_TYPES[0x86], # uint32 def_num=1, subfields=( SubField( # Accelerometer calibration factor name='accel_cal_factor', def_num=1, type=BASE_TYPES[0x86], # uint32 units='g', ref_fields=( ReferenceField( name='sensor_type', def_num=0, value='accelerometer', raw_value=0, ), ), ), SubField( # Gyro calibration factor name='gyro_cal_factor', def_num=1, type=BASE_TYPES[0x86], # uint32 units='deg/s', ref_fields=( ReferenceField( name='sensor_type', def_num=0, value='gyroscope', raw_value=1, ), ), ), ), ), 2: Field( # Calibration factor divisor name='calibration_divisor', type=BASE_TYPES[0x86], # uint32 def_num=2, units='counts', ), 3: Field( # Level shift value used to shift the ADC value back into range name='level_shift', type=BASE_TYPES[0x86], # uint32 def_num=3, ), 4: Field( # Internal calibration factors, one for each: xy, yx, zx name='offset_cal', type=BASE_TYPES[0x85], # sint32 def_num=4, ), 5: Field( # 3 x 3 rotation matrix (row major) name='orientation_matrix', type=BASE_TYPES[0x85], # sint32 def_num=5, scale=65535, ), 253: FIELD_TYPE_TIMESTAMP, # Whole second part of the timestamp }, ), 169: MessageType( name='video_frame', mesg_num=169, fields={ 0: Field( # Millisecond part of the timestamp. name='timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=0, units='ms', ), 1: Field( # Number of the frame that the timestamp and timestamp_ms correlate to name='frame_number', type=BASE_TYPES[0x86], # uint32 def_num=1, ), 253: FIELD_TYPE_TIMESTAMP, # Whole second part of the timestamp }, ), 174: MessageType( name='obdii_data', mesg_num=174, fields={ 0: Field( # Fractional part of timestamp, added to timestamp name='timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=0, units='ms', ), 1: Field( # Offset of PID reading [i] from start_timestamp+start_timestamp_ms. Readings may span accross seconds. name='time_offset', type=BASE_TYPES[0x84], # uint16 def_num=1, units='ms', ), 2: Field( # Parameter ID name='pid', type=BASE_TYPES[0x0D], # byte def_num=2, ), 3: Field( # Raw parameter data name='raw_data', type=BASE_TYPES[0x0D], # byte def_num=3, ), 4: Field( # Optional, data size of PID[i]. If not specified refer to SAE J1979. name='pid_data_size', type=BASE_TYPES[0x02], # uint8 def_num=4, ), 5: Field( # System time associated with sample expressed in ms, can be used instead of time_offset. There will be a system_time value for each raw_data element. For multibyte pids the system_time is repeated. name='system_time', type=BASE_TYPES[0x86], # uint32 def_num=5, ), 6: Field( # Timestamp of first sample recorded in the message. Used with time_offset to generate time of each sample name='start_timestamp', type=FIELD_TYPES['date_time'], def_num=6, ), 7: Field( # Fractional part of start_timestamp name='start_timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=7, units='ms', ), 253: FIELD_TYPE_TIMESTAMP, # Timestamp message was output }, ), 177: MessageType( name='nmea_sentence', mesg_num=177, fields={ 0: Field( # Fractional part of timestamp, added to timestamp name='timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=0, units='ms', ), 1: Field( # NMEA sentence name='sentence', type=BASE_TYPES[0x07], # string def_num=1, ), 253: FIELD_TYPE_TIMESTAMP, # Timestamp message was output }, ), 178: MessageType( name='aviation_attitude', mesg_num=178, fields={ 0: Field( # Fractional part of timestamp, added to timestamp name='timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=0, units='ms', ), 1: Field( # System time associated with sample expressed in ms. name='system_time', type=BASE_TYPES[0x86], # uint32 def_num=1, units='ms', ), 2: Field( # Range -PI/2 to +PI/2 name='pitch', type=BASE_TYPES[0x83], # sint16 def_num=2, scale=10430.38, units='radians', ), 3: Field( # Range -PI to +PI name='roll', type=BASE_TYPES[0x83], # sint16 def_num=3, scale=10430.38, units='radians', ), 4: Field( # Range -78.4 to +78.4 (-8 Gs to 8 Gs) name='accel_lateral', type=BASE_TYPES[0x83], # sint16 def_num=4, scale=100, units='m/s^2', ), 5: Field( # Range -78.4 to +78.4 (-8 Gs to 8 Gs) name='accel_normal', type=BASE_TYPES[0x83], # sint16 def_num=5, scale=100, units='m/s^2', ), 6: Field( # Range -8.727 to +8.727 (-500 degs/sec to +500 degs/sec) name='turn_rate', type=BASE_TYPES[0x83], # sint16 def_num=6, scale=1024, units='radians/second', ), 7: Field( name='stage', type=FIELD_TYPES['attitude_stage'], def_num=7, ), 8: Field( # The percent complete of the current attitude stage. Set to 0 for attitude stages 0, 1 and 2 and to 100 for attitude stage 3 by AHRS modules that do not support it. Range - 100 name='attitude_stage_complete', type=BASE_TYPES[0x02], # uint8 def_num=8, units='%', ), 9: Field( # Track Angle/Heading Range 0 - 2pi name='track', type=BASE_TYPES[0x84], # uint16 def_num=9, scale=10430.38, units='radians', ), 10: Field( name='validity', type=FIELD_TYPES['attitude_validity'], def_num=10, ), 253: FIELD_TYPE_TIMESTAMP, # Timestamp message was output }, ), 184: MessageType( name='video', mesg_num=184, fields={ 0: Field( name='url', type=BASE_TYPES[0x07], # string def_num=0, ), 1: Field( name='hosting_provider', type=BASE_TYPES[0x07], # string def_num=1, ), 2: Field( # Playback time of video name='duration', type=BASE_TYPES[0x86], # uint32 def_num=2, units='ms', ), }, ), 185: MessageType( name='video_title', mesg_num=185, fields={ 0: Field( # Total number of title parts name='message_count', type=BASE_TYPES[0x84], # uint16 def_num=0, ), 1: Field( name='text', type=BASE_TYPES[0x07], # string def_num=1, ), 254: Field( # Long titles will be split into multiple parts name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 186: MessageType( name='video_description', mesg_num=186, fields={ 0: Field( # Total number of description parts name='message_count', type=BASE_TYPES[0x84], # uint16 def_num=0, ), 1: Field( name='text', type=BASE_TYPES[0x07], # string def_num=1, ), 254: Field( # Long descriptions will be split into multiple parts name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), 187: MessageType( name='video_clip', mesg_num=187, fields={ 0: Field( name='clip_number', type=BASE_TYPES[0x84], # uint16 def_num=0, ), 1: Field( name='start_timestamp', type=FIELD_TYPES['date_time'], def_num=1, ), 2: Field( name='start_timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=2, ), 3: Field( name='end_timestamp', type=FIELD_TYPES['date_time'], def_num=3, ), 4: Field( name='end_timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=4, ), 6: Field( # Start of clip in video time name='clip_start', type=BASE_TYPES[0x86], # uint32 def_num=6, units='ms', ), 7: Field( # End of clip in video time name='clip_end', type=BASE_TYPES[0x86], # uint32 def_num=7, units='ms', ), }, ), 188: MessageType( name='ohr_settings', mesg_num=188, fields={ 0: Field( name='enabled', type=FIELD_TYPES['switch'], def_num=0, ), }, ), 200: MessageType( name='exd_screen_configuration', mesg_num=200, fields={ 0: Field( name='screen_index', type=BASE_TYPES[0x02], # uint8 def_num=0, ), 1: Field( # number of fields in screen name='field_count', type=BASE_TYPES[0x02], # uint8 def_num=1, ), 2: Field( name='layout', type=FIELD_TYPES['exd_layout'], def_num=2, ), 3: Field( name='screen_enabled', type=FIELD_TYPES['bool'], def_num=3, ), }, ), 201: MessageType( name='exd_data_field_configuration', mesg_num=201, fields={ 0: Field( name='screen_index', type=BASE_TYPES[0x02], # uint8 def_num=0, ), 1: Field( name='concept_field', type=BASE_TYPES[0x0D], # byte def_num=1, components=( ComponentField( name='field_id', def_num=2, accumulate=False, bits=4, bit_offset=0, ), ComponentField( name='concept_count', def_num=3, accumulate=False, bits=4, bit_offset=4, ), ), ), 2: Field( name='field_id', type=BASE_TYPES[0x02], # uint8 def_num=2, ), 3: Field( name='concept_count', type=BASE_TYPES[0x02], # uint8 def_num=3, ), 4: Field( name='display_type', type=FIELD_TYPES['exd_display_type'], def_num=4, ), 5: Field( name='title', type=BASE_TYPES[0x07], # string def_num=5, ), }, ), 202: MessageType( name='exd_data_concept_configuration', mesg_num=202, fields={ 0: Field( name='screen_index', type=BASE_TYPES[0x02], # uint8 def_num=0, ), 1: Field( name='concept_field', type=BASE_TYPES[0x0D], # byte def_num=1, components=( ComponentField( name='field_id', def_num=2, accumulate=False, bits=4, bit_offset=0, ), ComponentField( name='concept_index', def_num=3, accumulate=False, bits=4, bit_offset=4, ), ), ), 2: Field( name='field_id', type=BASE_TYPES[0x02], # uint8 def_num=2, ), 3: Field( name='concept_index', type=BASE_TYPES[0x02], # uint8 def_num=3, ), 4: Field( name='data_page', type=BASE_TYPES[0x02], # uint8 def_num=4, ), 5: Field( name='concept_key', type=BASE_TYPES[0x02], # uint8 def_num=5, ), 6: Field( name='scaling', type=BASE_TYPES[0x02], # uint8 def_num=6, ), 8: Field( name='data_units', type=FIELD_TYPES['exd_data_units'], def_num=8, ), 9: Field( name='qualifier', type=FIELD_TYPES['exd_qualifiers'], def_num=9, ), 10: Field( name='descriptor', type=FIELD_TYPES['exd_descriptors'], def_num=10, ), 11: Field( name='is_signed', type=FIELD_TYPES['bool'], def_num=11, ), }, ), 206: MessageType( # Must be logged before developer field is used name='field_description', mesg_num=206, fields={ 0: Field( name='developer_data_index', type=BASE_TYPES[0x02], # uint8 def_num=0, ), 1: Field( name='field_definition_number', type=BASE_TYPES[0x02], # uint8 def_num=1, ), 2: Field( name='fit_base_type_id', type=FIELD_TYPES['fit_base_type'], def_num=2, ), 3: Field( name='field_name', type=BASE_TYPES[0x07], # string def_num=3, ), 4: Field( name='array', type=BASE_TYPES[0x02], # uint8 def_num=4, ), 5: Field( name='components', type=BASE_TYPES[0x07], # string def_num=5, ), 6: Field( name='scale', type=BASE_TYPES[0x02], # uint8 def_num=6, ), 7: Field( name='offset', type=BASE_TYPES[0x01], # sint8 def_num=7, ), 8: Field( name='units', type=BASE_TYPES[0x07], # string def_num=8, ), 9: Field( name='bits', type=BASE_TYPES[0x07], # string def_num=9, ), 10: Field( name='accumulate', type=BASE_TYPES[0x07], # string def_num=10, ), 13: Field( name='fit_base_unit_id', type=FIELD_TYPES['fit_base_unit'], def_num=13, ), 14: Field( name='native_mesg_num', type=FIELD_TYPES['mesg_num'], def_num=14, ), 15: Field( name='native_field_num', type=BASE_TYPES[0x02], # uint8 def_num=15, ), }, ), 207: MessageType( # Must be logged before field description name='developer_data_id', mesg_num=207, fields={ 0: Field( name='developer_id', type=BASE_TYPES[0x0D], # byte def_num=0, ), 1: Field( name='application_id', type=BASE_TYPES[0x0D], # byte def_num=1, ), 2: Field( name='manufacturer_id', type=FIELD_TYPES['manufacturer'], def_num=2, ), 3: Field( name='developer_data_index', type=BASE_TYPES[0x02], # uint8 def_num=3, ), 4: Field( name='application_version', type=BASE_TYPES[0x86], # uint32 def_num=4, ), }, ), 208: MessageType( name='magnetometer_data', mesg_num=208, fields={ 0: Field( # Millisecond part of the timestamp. name='timestamp_ms', type=BASE_TYPES[0x84], # uint16 def_num=0, units='ms', ), 1: Field( # Each time in the array describes the time at which the compass sample with the corrosponding index was taken. Limited to 30 samples in each message. The samples may span across seconds. Array size must match the number of samples in cmps_x and cmps_y and cmps_z name='sample_time_offset', type=BASE_TYPES[0x84], # uint16 def_num=1, units='ms', ), 2: Field( # These are the raw ADC reading. Maximum number of samples is 30 in each message. The samples may span across seconds. A conversion will need to be done on this data once read. name='mag_x', type=BASE_TYPES[0x84], # uint16 def_num=2, units='counts', ), 3: Field( # These are the raw ADC reading. Maximum number of samples is 30 in each message. The samples may span across seconds. A conversion will need to be done on this data once read. name='mag_y', type=BASE_TYPES[0x84], # uint16 def_num=3, units='counts', ), 4: Field( # These are the raw ADC reading. Maximum number of samples is 30 in each message. The samples may span across seconds. A conversion will need to be done on this data once read. name='mag_z', type=BASE_TYPES[0x84], # uint16 def_num=4, units='counts', ), 5: Field( # Calibrated Magnetometer reading name='calibrated_mag_x', type=BASE_TYPES[0x88], # float32 def_num=5, units='G', ), 6: Field( # Calibrated Magnetometer reading name='calibrated_mag_y', type=BASE_TYPES[0x88], # float32 def_num=6, units='G', ), 7: Field( # Calibrated Magnetometer reading name='calibrated_mag_z', type=BASE_TYPES[0x88], # float32 def_num=7, units='G', ), 253: FIELD_TYPE_TIMESTAMP, # Whole second part of the timestamp }, ), ######################### Activity File Messages ######################### 34: MessageType( name='activity', mesg_num=34, fields={ 0: Field( # Exclude pauses name='total_timer_time', type=BASE_TYPES[0x86], # uint32 def_num=0, scale=1000, units='s', ), 1: Field( name='num_sessions', type=BASE_TYPES[0x84], # uint16 def_num=1, ), 2: Field( name='type', type=FIELD_TYPES['activity'], def_num=2, ), 3: Field( name='event', type=FIELD_TYPES['event'], def_num=3, ), 4: Field( name='event_type', type=FIELD_TYPES['event_type'], def_num=4, ), 5: Field( # timestamp epoch expressed in local time, used to convert activity timestamps to local time name='local_timestamp', type=FIELD_TYPES['local_date_time'], def_num=5, ), 6: Field( name='event_group', type=BASE_TYPES[0x02], # uint8 def_num=6, ), 253: FIELD_TYPE_TIMESTAMP, }, ), ###################### Blood Pressure File Messages ###################### 51: MessageType( name='blood_pressure', mesg_num=51, fields={ 0: Field( name='systolic_pressure', type=BASE_TYPES[0x84], # uint16 def_num=0, units='mmHg', ), 1: Field( name='diastolic_pressure', type=BASE_TYPES[0x84], # uint16 def_num=1, units='mmHg', ), 2: Field( name='mean_arterial_pressure', type=BASE_TYPES[0x84], # uint16 def_num=2, units='mmHg', ), 3: Field( name='map_3_sample_mean', type=BASE_TYPES[0x84], # uint16 def_num=3, units='mmHg', ), 4: Field( name='map_morning_values', type=BASE_TYPES[0x84], # uint16 def_num=4, units='mmHg', ), 5: Field( name='map_evening_values', type=BASE_TYPES[0x84], # uint16 def_num=5, units='mmHg', ), 6: Field( name='heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=6, units='bpm', ), 7: Field( name='heart_rate_type', type=FIELD_TYPES['hr_type'], def_num=7, ), 8: Field( name='status', type=FIELD_TYPES['bp_status'], def_num=8, ), 9: Field( # Associates this blood pressure message to a user. This corresponds to the index of the user profile message in the blood pressure file. name='user_profile_index', type=FIELD_TYPES['message_index'], def_num=9, ), 253: FIELD_TYPE_TIMESTAMP, }, ), ########################## Course File Messages ########################## 31: MessageType( name='course', mesg_num=31, fields={ 4: Field( name='sport', type=FIELD_TYPES['sport'], def_num=4, ), 5: Field( name='name', type=BASE_TYPES[0x07], # string def_num=5, ), 6: Field( name='capabilities', type=FIELD_TYPES['course_capabilities'], def_num=6, ), 7: Field( name='sub_sport', type=FIELD_TYPES['sub_sport'], def_num=7, ), }, ), ########################## Device File Messages ########################## 35: MessageType( name='software', mesg_num=35, fields={ 3: Field( name='version', type=BASE_TYPES[0x84], # uint16 def_num=3, scale=100, ), 5: Field( name='part_number', type=BASE_TYPES[0x07], # string def_num=5, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), ########################## Goals File Messages ########################### 15: MessageType( name='goal', mesg_num=15, fields={ 0: Field( name='sport', type=FIELD_TYPES['sport'], def_num=0, ), 1: Field( name='sub_sport', type=FIELD_TYPES['sub_sport'], def_num=1, ), 2: Field( name='start_date', type=FIELD_TYPES['date_time'], def_num=2, ), 3: Field( name='end_date', type=FIELD_TYPES['date_time'], def_num=3, ), 4: Field( name='type', type=FIELD_TYPES['goal'], def_num=4, ), 5: Field( name='value', type=BASE_TYPES[0x86], # uint32 def_num=5, ), 6: Field( name='repeat', type=FIELD_TYPES['bool'], def_num=6, ), 7: Field( name='target_value', type=BASE_TYPES[0x86], # uint32 def_num=7, ), 8: Field( name='recurrence', type=FIELD_TYPES['goal_recurrence'], def_num=8, ), 9: Field( name='recurrence_value', type=BASE_TYPES[0x84], # uint16 def_num=9, ), 10: Field( name='enabled', type=FIELD_TYPES['bool'], def_num=10, ), 11: Field( name='source', type=FIELD_TYPES['goal_source'], def_num=11, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), ######################## Monitoring File Messages ######################## 103: MessageType( name='monitoring_info', mesg_num=103, fields={ 0: Field( # Use to convert activity timestamps to local time if device does not support time zone and daylight savings time correction. name='local_timestamp', type=FIELD_TYPES['local_date_time'], def_num=0, units='s', ), 1: Field( name='activity_type', type=FIELD_TYPES['activity_type'], def_num=1, ), 3: Field( # Indexed by activity_type name='cycles_to_distance', type=BASE_TYPES[0x84], # uint16 def_num=3, scale=5000, units='m/cycle', ), 4: Field( # Indexed by activity_type name='cycles_to_calories', type=BASE_TYPES[0x84], # uint16 def_num=4, scale=5000, units='kcal/cycle', ), 5: Field( name='resting_metabolic_rate', type=BASE_TYPES[0x84], # uint16 def_num=5, units='kcal/day', ), 253: FIELD_TYPE_TIMESTAMP, }, ), ############################# Other Messages ############################# 145: MessageType( name='memo_glob', mesg_num=145, fields={ 0: Field( # Block of utf8 bytes name='memo', type=BASE_TYPES[0x0D], # byte def_num=0, ), 1: Field( # Allows relating glob to another mesg If used only required for first part of each memo_glob name='message_number', type=BASE_TYPES[0x84], # uint16 def_num=1, ), 2: Field( # Index of external mesg name='message_index', type=FIELD_TYPES['message_index'], def_num=2, ), 250: Field( # Sequence number of memo blocks name='part_index', type=BASE_TYPES[0x86], # uint32 def_num=250, ), }, ), ######################### Schedule File Messages ######################### 28: MessageType( name='schedule', mesg_num=28, fields={ 0: Field( # Corresponds to file_id of scheduled workout / course. name='manufacturer', type=FIELD_TYPES['manufacturer'], def_num=0, ), 1: Field( # Corresponds to file_id of scheduled workout / course. name='product', type=BASE_TYPES[0x84], # uint16 def_num=1, subfields=( SubField( name='garmin_product', def_num=1, type=FIELD_TYPES['garmin_product'], ref_fields=( ReferenceField( name='manufacturer', def_num=0, value='garmin', raw_value=1, ), ReferenceField( name='manufacturer', def_num=0, value='dynastream', raw_value=15, ), ReferenceField( name='manufacturer', def_num=0, value='dynastream_oem', raw_value=13, ), ), ), ), ), 2: Field( # Corresponds to file_id of scheduled workout / course. name='serial_number', type=BASE_TYPES[0x8C], # uint32z def_num=2, ), 3: Field( # Corresponds to file_id of scheduled workout / course. name='time_created', type=FIELD_TYPES['date_time'], def_num=3, ), 4: Field( # TRUE if this activity has been started name='completed', type=FIELD_TYPES['bool'], def_num=4, ), 5: Field( name='type', type=FIELD_TYPES['schedule'], def_num=5, ), 6: Field( name='scheduled_time', type=FIELD_TYPES['local_date_time'], def_num=6, ), }, ), ######################### Segment File Messages ########################## 148: MessageType( # Unique Identification data for a segment file name='segment_id', mesg_num=148, fields={ 0: Field( # Friendly name assigned to segment name='name', type=BASE_TYPES[0x07], # string def_num=0, ), 1: Field( # UUID of the segment name='uuid', type=BASE_TYPES[0x07], # string def_num=1, ), 2: Field( # Sport associated with the segment name='sport', type=FIELD_TYPES['sport'], def_num=2, ), 3: Field( # Segment enabled for evaluation name='enabled', type=FIELD_TYPES['bool'], def_num=3, ), 4: Field( # Primary key of the user that created the segment name='user_profile_primary_key', type=BASE_TYPES[0x86], # uint32 def_num=4, ), 5: Field( # ID of the device that created the segment name='device_id', type=BASE_TYPES[0x86], # uint32 def_num=5, ), 6: Field( # Index for the Leader Board entry selected as the default race participant name='default_race_leader', type=BASE_TYPES[0x02], # uint8 def_num=6, ), 7: Field( # Indicates if any segments should be deleted name='delete_status', type=FIELD_TYPES['segment_delete_status'], def_num=7, ), 8: Field( # Indicates how the segment was selected to be sent to the device name='selection_type', type=FIELD_TYPES['segment_selection_type'], def_num=8, ), }, ), ####################### Segment List File Messages ####################### 151: MessageType( # Summary of the unique segment and leaderboard information associated with a segment file. This message is used to compile a segment list file describing all segment files on a device. The segment list file is used when refreshing the contents of a segment file with the latest available leaderboard information. name='segment_file', mesg_num=151, fields={ 1: Field( # UUID of the segment file name='file_uuid', type=BASE_TYPES[0x07], # string def_num=1, ), 3: Field( # Enabled state of the segment file name='enabled', type=FIELD_TYPES['bool'], def_num=3, ), 4: Field( # Primary key of the user that created the segment file name='user_profile_primary_key', type=BASE_TYPES[0x86], # uint32 def_num=4, ), 7: Field( # Leader type of each leader in the segment file name='leader_type', type=FIELD_TYPES['segment_leaderboard_type'], def_num=7, ), 8: Field( # Group primary key of each leader in the segment file name='leader_group_primary_key', type=BASE_TYPES[0x86], # uint32 def_num=8, ), 9: Field( # Activity ID of each leader in the segment file name='leader_activity_id', type=BASE_TYPES[0x86], # uint32 def_num=9, ), 10: Field( # String version of the activity ID of each leader in the segment file. 21 characters long for each ID, express in decimal name='leader_activity_id_string', type=BASE_TYPES[0x07], # string def_num=10, ), 11: Field( # Index for the Leader Board entry selected as the default race participant name='default_race_leader', type=BASE_TYPES[0x02], # uint8 def_num=11, ), 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), ######################### Settings File Messages ######################### 2: MessageType( name='device_settings', mesg_num=2, fields={ 0: Field( # Index into time zone arrays. name='active_time_zone', type=BASE_TYPES[0x02], # uint8 def_num=0, ), 1: Field( # Offset from system time. Required to convert timestamp from system time to UTC. name='utc_offset', type=BASE_TYPES[0x86], # uint32 def_num=1, ), 2: Field( # Offset from system time. name='time_offset', type=BASE_TYPES[0x86], # uint32 def_num=2, units='s', ), 4: Field( # Display mode for the time name='time_mode', type=FIELD_TYPES['time_mode'], def_num=4, ), 5: Field( # timezone offset in 1/4 hour increments name='time_zone_offset', type=BASE_TYPES[0x01], # sint8 def_num=5, scale=4, units='hr', ), 12: Field( # Mode for backlight name='backlight_mode', type=FIELD_TYPES['backlight_mode'], def_num=12, ), 36: Field( # Enabled state of the activity tracker functionality name='activity_tracker_enabled', type=FIELD_TYPES['bool'], def_num=36, ), 39: Field( # UTC timestamp used to set the devices clock and date name='clock_time', type=FIELD_TYPES['date_time'], def_num=39, ), 40: Field( # Bitfield to configure enabled screens for each supported loop name='pages_enabled', type=BASE_TYPES[0x84], # uint16 def_num=40, ), 46: Field( # Enabled state of the move alert name='move_alert_enabled', type=FIELD_TYPES['bool'], def_num=46, ), 47: Field( # Display mode for the date name='date_mode', type=FIELD_TYPES['date_mode'], def_num=47, ), 55: Field( name='display_orientation', type=FIELD_TYPES['display_orientation'], def_num=55, ), 56: Field( name='mounting_side', type=FIELD_TYPES['side'], def_num=56, ), 57: Field( # Bitfield to indicate one page as default for each supported loop name='default_page', type=BASE_TYPES[0x84], # uint16 def_num=57, ), 58: Field( # Minimum steps before an autosync can occur name='autosync_min_steps', type=BASE_TYPES[0x84], # uint16 def_num=58, units='steps', ), 59: Field( # Minimum minutes before an autosync can occur name='autosync_min_time', type=BASE_TYPES[0x84], # uint16 def_num=59, units='minutes', ), 80: Field( # Enable auto-detect setting for the lactate threshold feature. name='lactate_threshold_autodetect_enabled', type=FIELD_TYPES['bool'], def_num=80, ), 86: Field( # Automatically upload using BLE name='ble_auto_upload_enabled', type=FIELD_TYPES['bool'], def_num=86, ), 89: Field( # Helps to conserve battery by changing modes name='auto_sync_frequency', type=FIELD_TYPES['auto_sync_frequency'], def_num=89, ), 90: Field( # Allows setting specific activities auto-activity detect enabled/disabled settings name='auto_activity_detect', type=FIELD_TYPES['auto_activity_detect'], def_num=90, ), 94: Field( # Number of screens configured to display name='number_of_screens', type=BASE_TYPES[0x02], # uint8 def_num=94, ), 95: Field( # Smart Notification display orientation name='smart_notification_display_orientation', type=FIELD_TYPES['display_orientation'], def_num=95, ), }, ), ###################### Sport Settings File Messages ###################### 7: MessageType( name='zones_target', mesg_num=7, fields={ 1: Field( name='max_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=1, ), 2: Field( name='threshold_heart_rate', type=BASE_TYPES[0x02], # uint8 def_num=2, ), 3: Field( name='functional_threshold_power', type=BASE_TYPES[0x84], # uint16 def_num=3, ), 5: Field( name='hr_calc_type', type=FIELD_TYPES['hr_zone_calc'], def_num=5, ), 7: Field( name='pwr_calc_type', type=FIELD_TYPES['pwr_zone_calc'], def_num=7, ), }, ), ########################## Totals File Messages ########################## 33: MessageType( name='totals', mesg_num=33, fields={ 0: Field( # Excludes pauses name='timer_time', type=BASE_TYPES[0x86], # uint32 def_num=0, units='s', ), 1: Field( name='distance', type=BASE_TYPES[0x86], # uint32 def_num=1, units='m', ), 2: Field( name='calories', type=BASE_TYPES[0x86], # uint32 def_num=2, units='kcal', ), 3: Field( name='sport', type=FIELD_TYPES['sport'], def_num=3, ), 4: Field( # Includes pauses name='elapsed_time', type=BASE_TYPES[0x86], # uint32 def_num=4, units='s', ), 5: Field( name='sessions', type=BASE_TYPES[0x84], # uint16 def_num=5, ), 6: Field( name='active_time', type=BASE_TYPES[0x86], # uint32 def_num=6, units='s', ), 9: Field( name='sport_index', type=BASE_TYPES[0x02], # uint8 def_num=9, ), 253: FIELD_TYPE_TIMESTAMP, 254: Field( name='message_index', type=FIELD_TYPES['message_index'], def_num=254, ), }, ), ####################### Weight Scale File Messages ####################### 30: MessageType( name='weight_scale', mesg_num=30, fields={ 0: Field( name='weight', type=FIELD_TYPES['weight'], def_num=0, scale=100, units='kg', ), 1: Field( name='percent_fat', type=BASE_TYPES[0x84], # uint16 def_num=1, scale=100, units='%', ), 2: Field( name='percent_hydration', type=BASE_TYPES[0x84], # uint16 def_num=2, scale=100, units='%', ), 3: Field( name='visceral_fat_mass', type=BASE_TYPES[0x84], # uint16 def_num=3, scale=100, units='kg', ), 4: Field( name='bone_mass', type=BASE_TYPES[0x84], # uint16 def_num=4, scale=100, units='kg', ), 5: Field( name='muscle_mass', type=BASE_TYPES[0x84], # uint16 def_num=5, scale=100, units='kg', ), 7: Field( name='basal_met', type=BASE_TYPES[0x84], # uint16 def_num=7, scale=4, units='kcal/day', ), 8: Field( name='physique_rating', type=BASE_TYPES[0x02], # uint8 def_num=8, ), 9: Field( # ~4kJ per kcal, 0.25 allows max 16384 kcal name='active_met', type=BASE_TYPES[0x84], # uint16 def_num=9, scale=4, units='kcal/day', ), 10: Field( name='metabolic_age', type=BASE_TYPES[0x02], # uint8 def_num=10, units='years', ), 11: Field( name='visceral_fat_rating', type=BASE_TYPES[0x02], # uint8 def_num=11, ), 12: Field( # Associates this weight scale message to a user. This corresponds to the index of the user profile message in the weight scale file. name='user_profile_index', type=FIELD_TYPES['message_index'], def_num=12, ), 253: FIELD_TYPE_TIMESTAMP, }, ), ######################### Workout File Messages ########################## 26: MessageType( name='workout', mesg_num=26, fields={ 4: Field( name='sport', type=FIELD_TYPES['sport'], def_num=4, ), 5: Field( name='capabilities', type=FIELD_TYPES['workout_capabilities'], def_num=5, ), 6: Field( # number of valid steps name='num_valid_steps', type=BASE_TYPES[0x84], # uint16 def_num=6, ), 8: Field( name='wkt_name', type=BASE_TYPES[0x07], # string def_num=8, ), 11: Field( name='sub_sport', type=FIELD_TYPES['sub_sport'], def_num=11, ), 14: Field( name='pool_length', type=BASE_TYPES[0x84], # uint16 def_num=14, scale=100, units='m', ), 15: Field( name='pool_length_unit', type=FIELD_TYPES['display_measure'], def_num=15, ), }, ), }

33.844565

336

0.397916

3e5b857f8383e340919c32b08170a5b4cd5f70b7

820

py

Python

python-basic-project/unit08/myfinance.py

sharebook-kr/learningspoons-bootcamp-finance

0288f3f3b39f54420e4e9987f1de12892dc680ea

[ "MIT" ]

9

2020-10-25T15:13:32.000Z

2022-03-26T11:27:21.000Z

python-basic-project/unit08/myfinance.py

sharebook-kr/learningspoons-bootcamp-finance

0288f3f3b39f54420e4e9987f1de12892dc680ea

[ "MIT" ]

null

python-basic-project/unit08/myfinance.py

sharebook-kr/learningspoons-bootcamp-finance

0288f3f3b39f54420e4e9987f1de12892dc680ea

[ "MIT" ]

7

2021-03-01T11:06:45.000Z

2022-03-14T07:06:04.000Z

import requests from bs4 import BeautifulSoup if __name__ == "__main__": kospi = get_tickers(market=2) kosdaq = get_tickers(market=3) print(len(kospi)) print(len(kosdaq)) print(get_dvr("005930"))

24.117647

90

0.603659

3e5d6df95ec953ae6051ebc8540af72617f83181

7,209

py

Python

TravelWebsite/travello/admin.py

DSAnup/Django

76025d181bafbb41783912577f80ec728884549d

[ "MIT" ]

1

2020-03-15T05:22:30.000Z

TravelWebsite/travello/admin.py

DSAnup/Django

76025d181bafbb41783912577f80ec728884549d

[ "MIT" ]

null

TravelWebsite/travello/admin.py

DSAnup/Django

76025d181bafbb41783912577f80ec728884549d

[ "MIT" ]

null

from django.contrib import admin from django.utils.html import format_html from django.shortcuts import redirect from .models import * # Register your models here. admin.site.register(CategoryDes) admin.site.register(Destination, DestinationAdmin) admin.site.register(Besttrip, BesttripAdmin) admin.site.register(Testominal, TestomonialAdmin) admin.site.register(Subscibtion, SubcriptionAdmin) admin.site.register(Homeslider, HomesliderAdmin) admin.site.register(Intro, IntroAdmin) admin.site.register(FooterContact, FooterContent) admin.site.register(HomeStatic, HomeFixedContent) admin.site.register(about_us_fixed, AboutUs) admin.site.register(why_choose_us, WhyChoose) admin.site.register(team, OurTeam)

49.717241

164

0.659315

3e5e4207adc8922463d0a98148721a7ee4e6e6eb

1,428

py

Python

demos/cookie-clicker/cookie-clicker.py

Coding-Kakis/Automating-Shenanigans-in-Python

c8e00231468668fbe231e0b35e32b9e99d5bd458

[ "MIT" ]

1

2021-09-11T13:05:17.000Z

demos/cookie-clicker/cookie-clicker.py

Coding-Kakis/Automating-Shenanigans-in-Python

c8e00231468668fbe231e0b35e32b9e99d5bd458

[ "MIT" ]

null

demos/cookie-clicker/cookie-clicker.py

Coding-Kakis/Automating-Shenanigans-in-Python

c8e00231468668fbe231e0b35e32b9e99d5bd458

[ "MIT" ]

null

# Cookie clicker auto-clicker # Works for the classic version here: https://orteil.dashnet.org/experiments/cookie/ import pyautogui def locate_cookie(): """ Returns the locations of the Big Cookie Does not return until the cookie is found """ loc = None while loc == None: loc = pyautogui.locateCenterOnScreen('rsrc/bigcookie.png') return loc def click_cookie(loc, ntimes): """ Moves mouse to `loc` and clicks `ntimes` """ x,y = loc pyautogui.moveTo(x,y) for _ in range(ntimes): pyautogui.click() def round(): """ Does 1 round. Returns `Yes` if user wants to continue Returns `No` otherwise. """ loc = locate_cookie() pyautogui.alert( title = "Found cookie!", text = str(loc)) while True: number_of_times = pyautogui.prompt( title = "Continue?", text = "Click how many times?") if not number_of_times.isdigit(): pyautogui.alert( title = "Error!", text = "Input isn't an integer!") continue break number_of_times = int(number_of_times) click_cookie(loc, number_of_times) reply = pyautogui.confirm( title = "Done!", text = "Another round?", buttons = ["Yes", "No"]) return reply while True: reply = round() if reply == "No": break

19.833333

84

0.573529

3e5e941943139ba0623e31d497e78bf7beb9106d

1,485

py

Python

esupa/templatetags/esupa.py

Abando/esupa

84888ff7d7879437659fd06a8707ac033f25b8ab

[ "Apache-2.0" ]

null

esupa/templatetags/esupa.py

Abando/esupa

84888ff7d7879437659fd06a8707ac033f25b8ab

[ "Apache-2.0" ]

4

2015-11-09T02:01:15.000Z

2016-01-20T14:51:13.000Z

esupa/templatetags/esupa.py

ekevoo/esupa

84888ff7d7879437659fd06a8707ac033f25b8ab

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Copyright 2015, Ekevoo.com. # # 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 datetime import datetime from django.template import Library from django.template.defaultfilters import date from django.utils.safestring import mark_safe from django.utils.timesince import timesince, timeuntil from django.utils.translation import ugettext register = Library()

37.125

107

0.703704

3e62b645957319fa784b6eef70fbe8c8812a5575

3,305

py

Python

ivy/pages.py

swsch/ivy

4932cf7541acff13815be613b0f3335b21c86670

[ "Unlicense" ]

null

ivy/pages.py

swsch/ivy

4932cf7541acff13815be613b0f3335b21c86670

[ "Unlicense" ]

null

ivy/pages.py

swsch/ivy

4932cf7541acff13815be613b0f3335b21c86670

[ "Unlicense" ]

null

# ------------------------------------------------------------------------------ # This module renders and writes HTML pages to disk. # ------------------------------------------------------------------------------ import re import os from . import site from . import events from . import filters from . import utils from . import templates from . import hashes from typing import List from .nodes import Node # A Page instance represents a single HTML page in the rendered site.

35.537634

80

0.579728

3e64ce743607e76cfc572cc4ea2cfe77fba2b173

5,646

py

Python

mvyaml/mvyaml.py

gchiesa/mvyaml

6d4c580bc596d220b45e6a6ccf9b2c3ef582f554

[ "MIT" ]

null

mvyaml/mvyaml.py

gchiesa/mvyaml

6d4c580bc596d220b45e6a6ccf9b2c3ef582f554

[ "MIT" ]

null

mvyaml/mvyaml.py

gchiesa/mvyaml

6d4c580bc596d220b45e6a6ccf9b2c3ef582f554

[ "MIT" ]

null

"""Main module.""" from copy import deepcopy from datetime import datetime from difflib import Differ from io import StringIO from typing import IO, Iterable, AnyStr from datadiff.tools import assert_equal from ruamel.yaml import YAML from ruamel.yaml.comments import CommentedMap def as_yaml(data: Iterable) -> AnyStr: yaml = YAML() output = StringIO() yaml.dump(data, output) return output.getvalue() def override(self, data: [Iterable]): self._curr_data = CommentedMap() self._curr_data.update(data) self._commit(comment='Overridden') return self def _commit(self, *args, **kwargs): return self._commit_head(*args, **kwargs) def _commit_head(self, tag: AnyStr = None, comment: AnyStr = None): """ apply the modifications on curr_data to the underling opened version and create a new tag """ commented_map = CommentedMap() commented_map.update(self._curr_data or self.data) if tag: self._raw[tag] = commented_map self._raw['__current'] = tag else: new_tag = self._make_tag() self._raw.insert(2, new_tag, commented_map, comment=comment) self._raw['__current'] = new_tag self._curr_version = None self._curr_data = None return self def _commit_tail(self, tag: AnyStr = None, comment: AnyStr = None): """ apply the modifications on curr_data to the underling opened version and create a new tag """ commented_map = CommentedMap() commented_map.update(self._curr_data or self.data) if tag: self._raw[tag] = commented_map self._raw['__current'] = tag else: new_tag = self._make_tag() self._raw.insert(len(self._raw.keys()), new_tag, commented_map, comment=comment) self._raw['__current'] = new_tag self._curr_version = None self._curr_data = None return self def _has_changes(self): orig = self._raw[self._curr_version or self.current] current = self._curr_data or self.data try: assert_equal(orig, current) except AssertionError: return True return False

32.079545

108

0.613355

3e667e1d0cd277296f1a4241baaa4af12a192a1e

1,889

py

Python

tests/test_monkeypatch.py

marcdemers/py_vollib_vectorized

0c2519ff58e3caf2caee37ca37d878e6e5e1eefd

[ "MIT" ]

40

2020-12-17T16:36:32.000Z

2022-02-07T20:11:26.000Z

tests/test_monkeypatch.py

marcdemers/py_vollib_vectorized

0c2519ff58e3caf2caee37ca37d878e6e5e1eefd

[ "MIT" ]

8

2021-01-20T04:17:50.000Z

2022-02-18T07:02:27.000Z

tests/test_monkeypatch.py

marcdemers/py_vollib_vectorized

0c2519ff58e3caf2caee37ca37d878e6e5e1eefd

[ "MIT" ]

13

2020-12-30T21:05:10.000Z

2022-03-27T12:30:26.000Z

import unittest from unittest import TestCase

42.931818

137

0.728957

3e6846fed01d2e5081085a1f9b9ca2203cbb1dad

1,137

py

Python

b2share/modules/deposit/search.py

hjhsalo/b2share-new

2a2a961f7cc3a5353850e9a409fd7e879c715b0b

[ "MIT" ]

null

b2share/modules/deposit/search.py

hjhsalo/b2share-new

2a2a961f7cc3a5353850e9a409fd7e879c715b0b

[ "MIT" ]

null

b2share/modules/deposit/search.py

hjhsalo/b2share-new

2a2a961f7cc3a5353850e9a409fd7e879c715b0b

[ "MIT" ]

1

2020-09-29T10:56:03.000Z

from elasticsearch_dsl import Q, TermsFacet from flask import has_request_context from flask_login import current_user from invenio_search import RecordsSearch from invenio_search.api import DefaultFilter from .permissions import admin_permission_factory def deposits_filter(): """Filter list of deposits. Permit to the user to see all if: * The user is an admin (see func:`invenio_deposit.permissions:admin_permission_factory`). * It's called outside of a request. Otherwise, it filters out any deposit where user is not the owner. """ if not has_request_context() or admin_permission_factory().can(): return Q() else: return Q( 'match', **{'_deposit.owners': getattr(current_user, 'id', 0)} )

25.266667

74

0.664908

3e69d58aa5e27029fd5fb9a2126945c9c542b4c9

1,586

py

Python

code/find_nconfsources.py

fornax-navo/fornax-demo-notebooks

49525d5bed3440d0d1903c29b9a1af8e0ff7e975

[ "BSD-3-Clause" ]

1

2022-02-03T18:12:59.000Z

code/find_nconfsources.py

fornax-navo/fornax-demo-notebooks

49525d5bed3440d0d1903c29b9a1af8e0ff7e975

[ "BSD-3-Clause" ]

1

2022-03-11T21:17:35.000Z

2022-03-11T22:28:46.000Z

code/find_nconfsources.py

fornax-navo/fornax-demo-notebooks

49525d5bed3440d0d1903c29b9a1af8e0ff7e975

[ "BSD-3-Clause" ]

2

2022-02-01T00:57:35.000Z

2022-02-13T22:20:55.000Z

import numpy as np from determine_source_type import determine_source_type #function to figure out how many sources are in cutout #and set up necessary tractor input for those sources

38.682927

111

0.663934

3e6ad0d35aefd868861d6a14144cf80665b8e7ea

274

py

Python

setup.py

dalejung/earthdragon

6fc9308288361bbe54d1d0107b4a77e3f27cd9be

[ "MIT" ]

1

2019-12-02T15:10:49.000Z

setup.py

dalejung/earthdragon

6fc9308288361bbe54d1d0107b4a77e3f27cd9be

[ "MIT" ]

5

2015-08-13T16:00:04.000Z

2016-03-14T18:43:11.000Z

setup.py

dalejung/earthdragon

6fc9308288361bbe54d1d0107b4a77e3f27cd9be

[ "MIT" ]

null

from distutils.core import setup DISTNAME='earthdragon' FULLVERSION='0.1' setup( name=DISTNAME, version=FULLVERSION, packages=['earthdragon'], install_requires = [ 'asttools', 'toolz', 'typeguard', 'more_itertools', ] )

16.117647

32

0.605839

3e6ad6e1c6ce978983b60511c62b60c613bacb9a

92

py

Python

script.py

juand1809/JuanVelasquez_Ejercicio23

40b20dc3db6e3a9a884265a950dd3ccac1f7a615

[ "MIT" ]

null

script.py

juand1809/JuanVelasquez_Ejercicio23

40b20dc3db6e3a9a884265a950dd3ccac1f7a615

[ "MIT" ]

null

script.py

juand1809/JuanVelasquez_Ejercicio23

40b20dc3db6e3a9a884265a950dd3ccac1f7a615

[ "MIT" ]

null

import os a = os.system("g++ sumatoria.cpp -o sumatoria.x") a = os.system("./sumatoria.x")

18.4

49

0.652174

3e6b0a9948d6ab9ae3bf82cdb88963f7746825d0

334

py

Python

consultas/urls.py

Valarr/django-app

2faac602ce5f36dc9007d4af7a3acd38504f4f95

[ "MIT" ]

null

consultas/urls.py

Valarr/django-app

2faac602ce5f36dc9007d4af7a3acd38504f4f95

[ "MIT" ]

null

consultas/urls.py

Valarr/django-app

2faac602ce5f36dc9007d4af7a3acd38504f4f95

[ "MIT" ]

null

from django.urls import path from . import views urlpatterns = [ path('', views.index, name='index'), path('consultaticket', views.consultaticket, name='consultaticket'), path('consultadecredito', views.consultadecredito, name='consultadecredito'), path('mostrarticket', views.mostrarticket, name='mostrarticket'), ]

33.4

81

0.730539

3e6c1c6b5fbe5a4ffcca63260b56292216d80f44

1,973

py

Python

order_history.py

zylizy/DBMS_Project

d6ff25d566a362495e3b4eb68d48d8400f2f20e6

[ "MIT" ]

null

order_history.py

zylizy/DBMS_Project

d6ff25d566a362495e3b4eb68d48d8400f2f20e6

[ "MIT" ]

null

order_history.py

zylizy/DBMS_Project

d6ff25d566a362495e3b4eb68d48d8400f2f20e6

[ "MIT" ]

null

import streamlit as st from db_functions import *

51.921053

115

0.611759

3e6d175a2c46fd4c086a5aa6dbda506eabe35fd4

1,415

py

Python

cogs/commands/utility/8ball.py

teSill/temflix

31d40265fa71695966c6178145a1057cd2aeda27

[ "MIT" ]

3

2020-12-21T20:51:56.000Z

2022-01-04T11:55:45.000Z

cogs/commands/utility/8ball.py

teSill/temflix

31d40265fa71695966c6178145a1057cd2aeda27

[ "MIT" ]

null

cogs/commands/utility/8ball.py

teSill/temflix

31d40265fa71695966c6178145a1057cd2aeda27

[ "MIT" ]

null

import discord from discord.ext import commands import random

36.282051

123

0.466431

3e70d3317c13de0952315e701a55b920df03ec85

1,441

py

Python

20210607_fizzbuzz.py

sayloren/codechallenges

b31b64c176a1c03c937e915f3b60657669495681

[ "Apache-2.0" ]

null

20210607_fizzbuzz.py

sayloren/codechallenges

b31b64c176a1c03c937e915f3b60657669495681

[ "Apache-2.0" ]

null

20210607_fizzbuzz.py

sayloren/codechallenges

b31b64c176a1c03c937e915f3b60657669495681

[ "Apache-2.0" ]

null

# code golf challenge # https://code.golf/fizz-buzz#python # wren 20210607 # Print the numbers from 1 to 100 inclusive, each on their own line. # If, however, the number is a multiple of three then print Fizz instead, # and if the number is a multiple of five then print Buzz. # For numbers which are multiples of both three and five then print FizzBuzz. # iterate through reange of required numbers - plus one because range is # zero based if 3 print fizz, if 5 print buzz, and if both fizzbuzz # i feel like there should be a way to have the 15/fizzbuzz not be a # seperate condition for number in range(1,101): # if there are no remainders for the number in the range divied by 15 # condition for both 5 and 3 - 15 is lowest divisor, where both fizz and buzz if number%15==0):print('FizzBuzz') # if there are no remainders for the number in the range divied by 3 # elif condition for just 3 - fizz elif number%3 ==0:print('Fizz') # if there are no remainders for the number in the range divied by 5 # elif condition for just 5, buzz elif number%5==0:print('Buzz') # else none of the conditions, just print the number else:print(number) # alternative approach in one liner # for loop the same, but conditions where there are no remainders for the # divisability are all wrapped in a print statement for number in range(1, 101): print("Fizz"*(number%3==0)+"Buzz"*(number%5==0) or str(number))

46.483871

92

0.725191

3e730fa82d3520ad13dc948a854e1cd1df0331d4

275

py

Python

setup.py

grro/install-raspberry

f6db2d451c1277127a77fdc6b00ea55708f0bd17

[ "Apache-2.0" ]

null

setup.py

grro/install-raspberry

f6db2d451c1277127a77fdc6b00ea55708f0bd17

[ "Apache-2.0" ]

null

setup.py

grro/install-raspberry

f6db2d451c1277127a77fdc6b00ea55708f0bd17

[ "Apache-2.0" ]

null

from setuptools import setup setup( name='install-raspberry', version='', packages=[''], url='https://github.com/grro/httpstreamproxy', license='Apache Software License', author='grro', author_email='[email protected]', description='test' )

21.153846

50

0.658182

3e73f6fed18b11f2933d0b20530ca1d6b4de649e

2,701

py

Python

py_privatekonomi/tests/swedbank/test_swedbank_db.py

nilsFK/py-privatekonomi

9172dfa85e439e18558a60fdb3b69e956e70e783

[ "MIT" ]

2

2015-01-04T21:27:45.000Z

2015-01-05T13:31:52.000Z

py_privatekonomi/tests/swedbank/test_swedbank_db.py

nilsFK/py-privatekonomi

9172dfa85e439e18558a60fdb3b69e956e70e783

[ "MIT" ]

28

2015-01-04T22:13:24.000Z

2019-11-29T13:41:01.000Z

py_privatekonomi/tests/swedbank/test_swedbank_db.py

nilsFK/py-privatekonomi

9172dfa85e439e18558a60fdb3b69e956e70e783

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals import unittest import inspect from py_privatekonomi.utilities import common from py_privatekonomi.tests.test_base import TestBase from py_privatekonomi.tests.dataset.swedbank.sample1 import test_data as test_data_1 from py_privatekonomi.tests.dataset.swedbank.sample2 import test_data as test_data_2 from py_privatekonomi.tests.dataset.swedbank.sample3 import test_data as test_data_3 from py_privatekonomi.tests.dataset.swedbank.sample5 import test_data as test_data_5 if __name__ == '__main__': unittest.main()

37

84

0.652721

3e74eb605f50a2789671592734f1dea5fd163012

918

py

Python

gharchive/parse_json.py

IAMABOY/Mining-Github

cf11c94e72b11f3ce9d638b562df438c8e56d149

[ "MIT" ]

8

2019-12-08T11:57:59.000Z

2022-01-24T06:26:56.000Z

gharchive/parse_json.py

IAMABOY/Mining-Github

cf11c94e72b11f3ce9d638b562df438c8e56d149

[ "MIT" ]

null

gharchive/parse_json.py

IAMABOY/Mining-Github

cf11c94e72b11f3ce9d638b562df438c8e56d149

[ "MIT" ]

2

2019-12-17T02:38:55.000Z

2021-12-16T01:53:11.000Z

import sys import os import json import gzip if __name__ == '__main__': jsonReader('2019-09-19-10.json.gz',1)

24.157895

63

0.484749

3e753e4b76a7bccde83190218fa4e3ea302764fe

393

py

Python

iotalib/check_roof.py

WWGolay/iota

f3e67502d7f96bb836b45b7eca4ebb9fe5490e6d

[ "MIT" ]

null

iotalib/check_roof.py

WWGolay/iota

f3e67502d7f96bb836b45b7eca4ebb9fe5490e6d

[ "MIT" ]

null

iotalib/check_roof.py

WWGolay/iota

f3e67502d7f96bb836b45b7eca4ebb9fe5490e6d

[ "MIT" ]

null

#!/usr/bin/python import pycurl from io import BytesIO

21.833333

59

0.592875

3e7863d676fdd4741e30575b304165077d18541c

2,238

py

Python

egg/app.py

eanorambuena/Driver

3cb14f5d741c6bae364326305ae0ded04e10e9d4

[ "MIT" ]

null

egg/app.py

eanorambuena/Driver

3cb14f5d741c6bae364326305ae0ded04e10e9d4

[ "MIT" ]

null

egg/app.py

eanorambuena/Driver

3cb14f5d741c6bae364326305ae0ded04e10e9d4

[ "MIT" ]

null

# Imports from egg.resources.console import get, clearConsole from egg.resources.constants import * from egg.resources.modules import install, upgrade, Repo from egg.resources.help import help from egg.resources.auth import login, register """ FUNCTION eggConsole(condition: bool = True) Display the Egg Console Currently, the Egg Console commands are: $nqs Start the NQS Depeloper console $new Start the News Journalist console $login Log in Egg-cosystem *comming soon* $register Register in Egg-cosystem *comming soon* $install Install a pip package $upgrade Upgrade a pip package $pull Import a package stored on a GitHUb repository *comming soon: currently, just use github_com package* $help Get started command $clear Clear the Egg Console $end End the Egg Console WARNING: Always use $end command in every console you run *ONLY use a condition different to True as an argument of eggConsole(condition) if you know what are you doing** This is the reason why condition only allows <<bool>> as data type """

32.434783

113

0.605004

3e78c123f36641a6b522ac2d459248b01e28de60

1,204

py

Python

hello/hello_pil.py

East196/hello-py

a77c7a0c8e5e2b5e8cefaf0fda335ab0c3b1da21

[ "Apache-2.0" ]

1

2017-10-23T14:58:47.000Z

hello/hello_pil.py

East196/hello-py

a77c7a0c8e5e2b5e8cefaf0fda335ab0c3b1da21

[ "Apache-2.0" ]

null

hello/hello_pil.py

East196/hello-py

a77c7a0c8e5e2b5e8cefaf0fda335ab0c3b1da21

[ "Apache-2.0" ]

1

2018-04-06T07:49:18.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from PIL import Image, ImageDraw, ImageFont, ImageFilter import random im = Image.open('F:/workspace/python/data/backpink.jpg') im2 = im.filter(ImageFilter.BLUR) im2.save('F:/workspace/python/data/backpink_blur.png', 'png') im2.save('F:/workspace/python/data/backpink_blur.jpg', 'jpeg') # : # 1: # 2: # 240 x 60: width = 60 * 4 height = 60 image = Image.new('RGB', (width, height), (255, 255, 255)) # Font: font = ImageFont.truetype('C:/Windows/Fonts/Arial.ttf', 36) # Draw: draw = ImageDraw.Draw(image) # : for x in range(width): for y in range(height): draw.point((x, y), fill=random_color()) # : for t in range(4): draw.text((60 * t + 10, 10), random_char(), font=font, fill=random_color2()) # : image = image.filter(ImageFilter.BLUR) image.save('code.jpg', 'jpeg') print((image.format, image.size, image.mode)) # image.show()

24.571429

84

0.680233

3e7a2d3d3d5314bdb0dff02d7d69496583791bdc

382

py

Python

terra_layer/migrations/0028_layergroup_exclusive.py

Terralego/terra-layer

6564a63d389503d3ae1f63ce46e674b228d6764b

[ "MIT" ]

1

2019-08-08T15:17:32.000Z

terra_layer/migrations/0028_layergroup_exclusive.py

Terralego/terra-layer

6564a63d389503d3ae1f63ce46e674b228d6764b

[ "MIT" ]

65

2019-10-21T10:05:00.000Z

2022-03-08T14:08:27.000Z

terra_layer/migrations/0028_layergroup_exclusive.py

Terralego/terra-layer

6564a63d389503d3ae1f63ce46e674b228d6764b

[ "MIT" ]

null

# Generated by Django 2.1.9 on 2019-07-16 10:15 from django.db import migrations, models

22.470588

63

0.636126

3e7a7495c3ea96af4211a7bee33396978138c459

605

py

Python

docs/examples/best_practices/dynamic_configuration/cdk_appconfig.py

ran-isenberg/aws-lambda-handler-cookbook

adfe58dacd87315151265818869bb842c7eb4971

[ "MIT" ]

61

2022-02-07T05:21:14.000Z

2022-03-27T14:11:30.000Z

docs/examples/best_practices/dynamic_configuration/cdk_appconfig.py

ran-isenberg/aws-lambda-handler-cookbook

adfe58dacd87315151265818869bb842c7eb4971

[ "MIT" ]

17

2022-02-26T05:25:31.000Z

2022-03-16T20:02:46.000Z

docs/examples/best_practices/dynamic_configuration/cdk_appconfig.py

ran-isenberg/aws-lambda-handler-cookbook

adfe58dacd87315151265818869bb842c7eb4971

[ "MIT" ]

4

2022-02-17T16:35:27.000Z

2022-03-07T03:13:07.000Z

from aws_cdk import Stack from aws_lambda_handler_cookbook.service_stack.configuration.configuration_construct import ConfigurationStore from aws_lambda_handler_cookbook.service_stack.constants import CONFIGURATION_NAME, ENVIRONMENT, SERVICE_NAME from constructs import Construct

43.214286

124

0.809917

3e7d231b81300bc8be65b86f6758957fdbb26baa

653

py

Python

backend-project/small_eod/users/models.py

merito/small_eod

ab19b82f374cd7c4b21d8f9412657dbe7f7f03e2

[ "MIT" ]

64

2019-12-30T11:24:03.000Z

2021-06-24T01:04:56.000Z

backend-project/small_eod/users/models.py

merito/small_eod

ab19b82f374cd7c4b21d8f9412657dbe7f7f03e2

[ "MIT" ]

465

2018-06-13T21:43:43.000Z

2022-01-04T23:33:56.000Z

backend-project/small_eod/users/models.py

merito/small_eod

ab19b82f374cd7c4b21d8f9412657dbe7f7f03e2

[ "MIT" ]

72

2018-12-02T19:47:03.000Z

2022-01-04T22:54:49.000Z

from django.contrib.auth.models import AbstractUser from ..notifications.utils import TemplateKey, TemplateMailManager

29.681818

79

0.658499

3e7efc62df24d3372d57ba9f3602f16dfbfbeff6

2,689

py

Python

rtlsdr_sstv/utils.py

martinber/rtlsdr_sstv

f59ca523408e949f98c4b81b09b2d46232111f4a

[ "MIT" ]

3

2019-03-16T01:20:09.000Z

2020-12-31T12:31:17.000Z

rtlsdr_sstv/utils.py

martinber/rtlsdr_sstv

f59ca523408e949f98c4b81b09b2d46232111f4a

[ "MIT" ]

null

rtlsdr_sstv/utils.py

martinber/rtlsdr_sstv

f59ca523408e949f98c4b81b09b2d46232111f4a

[ "MIT" ]

1

2020-12-27T02:31:18.000Z

import collections import math import numpy as np def escribir_pixel(img, columna, linea, canal, valor): '''funcion encargada de escribir pixel por pixel la imagen''' if linea >= img.height: return if canal == "lum": prev = img.getpixel((columna,linea-1)) datapixel = (mapeadora(valor), prev[1], prev[2]) img.putpixel((columna,linea-1), datapixel) if canal == "cr": prev = img.getpixel((columna,linea-1)) nxt_prev = img.getpixel((columna,linea)) datapixel = (prev[0], prev[1], mapeadora(valor)) nxt_datapixel = (nxt_prev[0], nxt_prev[1], mapeadora(valor)) img.putpixel((columna,linea-1), datapixel) img.putpixel((columna,linea), nxt_datapixel) if canal == "cb": prev = img.getpixel((columna,linea-1)) nxt_prev = img.getpixel((columna,linea)) datapixel = (prev[0], mapeadora(valor), prev[2]) nxt_datapixel = (nxt_prev[0], mapeadora(valor), nxt_prev[2]) img.putpixel((columna,linea-1), datapixel) img.putpixel((columna,linea), nxt_datapixel) if canal == "nxt_lum": prev = img.getpixel((columna,linea)) datapixel = (mapeadora(valor), prev[1], prev[2]) img.putpixel((columna,linea), datapixel) def lowpass(cutout, delta_w, atten): ''' cutout y delta_w en fracciones de pi radianes por segundo. atten en decibeles positivos. ''' beta = 0 if atten > 50: beta = 0.1102 * (atten - 8.7) elif atten < 21: beta = 0 else: beta = 0.5842 * (atten - 21)**0.4 + 0.07886 * (atten - 21) length = math.ceil((atten - 8) / (2.285 * delta_w * math.pi)) + 1; if length % 2 == 0: length += 1 coeffs = np.kaiser(length, beta) # i es el indice en el vector, n es el eje con el cero centrado en el medio # del filtro for i, n in enumerate(range( int(-(length - 1) / 2), int((length - 1) / 2)+1)): if n == 0: coeffs[i] *= cutout else: coeffs[i] *= math.sin(n * math.pi * cutout) / (n * math.pi) return coeffs

29.549451

80

0.581629

3e7f9f610ed95d40e15a8580e0dd70e9219fb93d

3,653

py

Python

Pong.py

Mishkanian/pong_game

5a04b4b5fc36af2159e60fb85941034a2325996c

[ "MIT" ]

null

Pong.py

Mishkanian/pong_game

5a04b4b5fc36af2159e60fb85941034a2325996c

[ "MIT" ]

null

Pong.py

Mishkanian/pong_game

5a04b4b5fc36af2159e60fb85941034a2325996c

[ "MIT" ]

1

2021-11-15T20:21:53.000Z

""" Pong game by Michael Mishkanian """ import turtle wn = turtle.Screen() wn.title("Pong by Michael Mishkanian") wn.bgcolor("black") wn.setup(width=800, height=600) wn.tracer(0) # Paddle A paddle_a = turtle.Turtle() paddle_a.speed(0) paddle_a.shape("square") paddle_a.color("white") paddle_a.shapesize(stretch_wid=5, stretch_len=1) # make paddle a rectangle paddle_a.penup() paddle_a.goto(-350, 0) # starting location of paddle on left side of screen # Paddle B paddle_b = turtle.Turtle() paddle_b.speed(0) paddle_b.shape("square") paddle_b.color("white") paddle_b.shapesize(stretch_wid=5, stretch_len=1) paddle_b.penup() paddle_b.goto(350, 0) # starting location of paddle on right side of screen # Ball ball = turtle.Turtle() ball.speed(0) ball.shape("square") ball.color("white") ball.penup() ball.goto(0, 0) # ball starts in middle of screen ball.dx = .33 # movement speed of the ball dx ball.dy = .33 # movement speed of the ball dy # Score Display pen = turtle.Turtle() pen.speed(0) pen.color("white") pen.penup() pen.hideturtle() pen.goto(0, 260) pen.write("Player 1: 0 Player 2: 0", align="center", font=("Courier", 24, "normal")) # Start Tracking Scores score_a = 0 score_b = 0 def paddle_a_up(): """ This function takes in the current y-coordinate of paddle A and then increases the position by 20 (AKA "go up") """ y = paddle_a.ycor() y += 20 paddle_a.sety(y) def paddle_a_down(): """ This function takes in the current y-coordinate of paddle A and then decreases the position down 20 (AKA "go down") """ y = paddle_a.ycor() y -= 20 paddle_a.sety(y) def paddle_b_up(): """ This function takes in the current y-coordinate of paddle B and then increases the position by 20 (AKA "go up") """ y = paddle_b.ycor() y += 20 paddle_b.sety(y) def paddle_b_down(): """ This function takes in the current y-coordinate of paddle B and then decreases the position by 20 (AKA "go down") """ y = paddle_b.ycor() y -= 20 paddle_b.sety(y) # Key bindings wn.listen() wn.onkeypress(paddle_a_up, "w") wn.onkeypress(paddle_a_down, "s") wn.onkeypress(paddle_b_up, "Up") wn.onkeypress(paddle_b_down, "Down") # Main game loop while True: wn.update() # Ball movement ball.setx(ball.xcor() + ball.dx) ball.sety(ball.ycor() + ball.dy) # Border checks if ball.ycor() > 290: ball.sety(290) ball.dy *= -1 # reverse direction if ball is too high if ball.ycor() < -290: ball.sety(-290) ball.dy *= -1 # reverse direction if ball is too low # retart game when the ball passes a paddle if ball.xcor() > 390: ball.goto(0, 0) ball.dx *= -1 score_a += 1 pen.clear() # clear score pen.write("Player 1: {} Player 2: {}".format(score_a, score_b), align="center", font=("Courier", 24, "normal")) if ball.xcor() < -390: ball.goto(0, 0) ball.dx *= -1 score_b += 1 pen.clear() # clear score pen.write("Player 1: {} Player 2: {}".format(score_a, score_b), align="center", font=("Courier", 24, "normal")) # Collisions if (ball.xcor() > 340 and ball.xcor() < 350) and (ball.ycor() < paddle_b.ycor() + 40 and ball.ycor() > paddle_b.ycor() - 40): ball.setx(340) ball.dx *= -1 if (ball.xcor() < -340 and ball.xcor() > -350) and (ball.ycor() < paddle_a.ycor() + 40 and ball.ycor() > paddle_a.ycor() - 40): ball.setx(-340) ball.dx *= -1

26.280576

120

0.611005

3e7fe9149a1b5f7c3cd431d38f69f6e9b05ff08e

108

py

Python

loops_part2/sequence_2k+1.py

MaggieIllustrations/softuni-github-programming

f5695cb14602f3d2974359f6d8734332acc650d3

[ "MIT" ]

null

loops_part2/sequence_2k+1.py

MaggieIllustrations/softuni-github-programming

f5695cb14602f3d2974359f6d8734332acc650d3

[ "MIT" ]

null

loops_part2/sequence_2k+1.py

MaggieIllustrations/softuni-github-programming

f5695cb14602f3d2974359f6d8734332acc650d3

[ "MIT" ]

1

2022-01-14T17:12:44.000Z

number = int(input()) counter = 1 while counter <= number: print(counter) counter = 2 * counter + 1

18

29

0.62963

3e82a49073596a4c986e7e70b3ddc02848ac39cb

385

py

Python

tests/fixtures.py

hdsr-mid/string_finder

64aa38afa562beddc897a0fcb84bf39e53b935fd

[ "MIT" ]

null

tests/fixtures.py

hdsr-mid/string_finder

64aa38afa562beddc897a0fcb84bf39e53b935fd

[ "MIT" ]

null

tests/fixtures.py

hdsr-mid/string_finder

64aa38afa562beddc897a0fcb84bf39e53b935fd

[ "MIT" ]

null

from pathlib import Path from string_finder.constants import TEST_DATA_DIR from typing import List import pytest

25.666667

90

0.719481

3e83c39b04f2c10f748cc83b7509198a99b52216

1,432

py

Python

clean.py

glqstrauss/oopsgenie

d1984e332b11f972db2008867f1aba0917457b9b

[ "MIT" ]

5

2020-01-02T21:15:31.000Z

2020-07-29T18:01:51.000Z

clean.py

glqstrauss/oopsgenie

d1984e332b11f972db2008867f1aba0917457b9b

[ "MIT" ]

2

2020-01-07T15:36:44.000Z

2020-01-13T20:38:45.000Z

clean.py

glqstrauss/oopsgenie

d1984e332b11f972db2008867f1aba0917457b9b

[ "MIT" ]

1

2020-07-29T17:10:32.000Z

import csv from utils import get_valid_colum_indices

34.926829

73

0.48743

3e869ea6160f40dc58804e7f852689a43590b0fc

516

py

Python

issues_list/migrations/0003_auto_20181106_1541.py

vmcggh18/bits_tracker

7c09aae321efb13979bed274d973c77319ce795e

[ "PostgreSQL" ]

null

issues_list/migrations/0003_auto_20181106_1541.py

vmcggh18/bits_tracker

7c09aae321efb13979bed274d973c77319ce795e

[ "PostgreSQL" ]

7

2020-06-05T19:50:41.000Z

2022-03-11T23:39:39.000Z

issues_list/migrations/0003_auto_20181106_1541.py

vmcggh18/bits_tracker

7c09aae321efb13979bed274d973c77319ce795e

[ "PostgreSQL" ]

1

2019-02-19T15:30:16.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.16 on 2018-11-06 15:41 from __future__ import unicode_literals from django.conf import settings from django.db import migrations

23.454545

66

0.649225

3e87d53a9a7c9e621189fb9905a20df232be3db0

6,203

py

Python

sentiment_analysis.py

bernardomelo/projeto-final

f0207144282b04e1b781604bf96d69634ca68ee8

[ "MIT" ]

null

sentiment_analysis.py

bernardomelo/projeto-final

f0207144282b04e1b781604bf96d69634ca68ee8

[ "MIT" ]

null

sentiment_analysis.py

bernardomelo/projeto-final

f0207144282b04e1b781604bf96d69634ca68ee8

[ "MIT" ]

null

############################################################################### # Univesidade Federal de Pernambuco -- UFPE (http://www.ufpe.br) # Centro de Informatica -- CIn (http://www.cin.ufpe.br) # Bacharelado em Sistemas de Informacao # IF968 -- Programacao 1 # # Autor: Bernardo Gomes de Melo # Aluno de S.I do Cin # # Email: [email protected] # # # Data: 2016-06-10 # # Descricao: Este e' um modelo de arquivo para ser utilizado para a implementacao # do projeto pratico da disciplina de Programacao 1. # A descricao do projeto encontra-se no site da disciplina e trata-se # de uma adaptacao do projeto disponivel em # /t http://nifty.stanford.edu/2016/manley-urness-movie-review-sentiment/ # O objetivo deste projeto e' implementar um sistema de analise de # sentimentos de comentarios de filmes postados no site Rotten Tomatoes. # # Licenca: The MIT License (MIT) # Copyright(c) 2016 Bernardo Gomes de Melo, Aluno do CIn # ############################################################################### import sys import re def clean_up(s): ''' Retorna uma versao da string 's' na qual todas as letras sao convertidas para minusculas e caracteres de pontuacao sao removidos de ambos os extremos. A pontuacao presente no interior da string e' mantida intacta. ''' punctuation = ''''!"',;:.-?)([]<>*#\n\t\r''' result = s.lower().strip(punctuation) return result def split_on_separators(original, separators): ''' Retorna um vetor de strings nao vazias obtido a partir da quebra da string original em qualquer dos caracteres contidos em 'separators'. 'separtors' e' uma string formada com caracteres unicos a serem usados como separadores. Por exemplo, '^$' e' uma string valida, indicando que a string original sera quebrada em '^' e '$'. ''' return filter(lambda x: x != '',re.split('[{0}]'.format(separators),original)) arq.close() for x in words.keys(): words[x] = x[2]/x[1] return words def readTestSet(fname): reviews = [] arq = open(fname,'r') for line in arq: reviews.append((int(line[0]),clean_up(line[1:-1]))) arq.close() return reviews def computeSentiment(review,words): ''' Retorna o sentimento do comentario recebido como parametro. O sentimento de um comentario e' a media dos escores de suas palavras. Se uma palavra nao estiver no conjunto de palavras do conjunto de treinamento, entao seu escore e' 2. Review e' a parte textual de um comentario. Words e' o dicionario com as palavras e seus escores medios no conjunto de treinamento. ''' score = 0.0 count = 0 s = split_on_separators(review,' ') for i in s: palavra = clean_up(i) for p in words.keys(): if p[0] == palavra: score += p[2] count += 1 else: score += 2 count += 1 return score/count def computeSumSquaredErrors(reviews,words): ''' Computa a soma dos quadrados dos erros dos comentarios recebidos como parametro. O sentimento de um comentario e' obtido com a funcao computeSentiment. Reviews e' um vetor de pares (escore,texto) Words e' um dicionario com as palavras e seus escores medios no conjunto de treinamento. ''' sse = 0 final = 0 for line in reviews: sentiment = computeSentiment(line[1],words) if line[0] != sentiment: diference = float(line[0]) - sentiment result = diference**2 final += result sse = final / len(reviews) return sse def main(): # Os arquivos sao passados como argumentos da linha de comando para o programa # Voce deve buscar mais informacoes sobre o funcionamento disso (e' parte do # projeto). # A ordem dos parametros e' a seguinte: o primeiro e' o nome do arquivo # com o conjunto de treinamento, em seguida o arquivo do conjunto de teste. if len(sys.argv) < 3: print ('Numero invalido de argumentos') print ('O programa deve ser executado como python sentiment_analysis.py <arq-treino> <arq-teste>') sys.exit(0) # Lendo conjunto de treinamento e computando escore das palavras words = readTrainingSet(sys.argv[1]) # Lendo conjunto de teste reviews = readTestSet(sys.argv[2]) # Inferindo sentimento e computando soma dos quadrados dos erros sse = computeSumSquaredErrors(reviews,words) print( 'A soma do quadrado dos erros e\': {0}'.format(sse)) if __name__ == '__main__': main()

35.855491

107

0.563759

3e8a5b0b6fc0612db9638f1736e52adef498431d

37,129

py

Python

morm/db.py

neurobin/python-morm

2b6dcedc7090a9e642331300a24dfcca41ea1afe

[ "BSD-3-Clause" ]

4

2021-03-12T16:36:24.000Z

2022-03-06T09:26:14.000Z

morm/db.py

neurobin/python-morm

2b6dcedc7090a9e642331300a24dfcca41ea1afe

[ "BSD-3-Clause" ]

null

morm/db.py

neurobin/python-morm

2b6dcedc7090a9e642331300a24dfcca41ea1afe

[ "BSD-3-Clause" ]

null

"""DB utilities. """ __author__ = 'Md Jahidul Hamid <[email protected]>' __copyright__ = 'Copyright Md Jahidul Hamid <https://github.com/neurobin/>' __license__ = '[BSD](http://www.opensource.org/licenses/bsd-license.php)' __version__ = '0.1.0' import collections import re import asyncio import nest_asyncio # type: ignore import atexit import logging import asyncpg # type: ignore from asyncpg import Record, Connection # type: ignore from typing import Optional, Dict, List, Tuple, TypeVar, Union, Any from morm import exceptions from morm.model import ModelType, Model, ModelBase, _FieldNames from morm.q import Q from morm.types import Void LOGGER_NAME = 'morm.db-' log = logging.getLogger(LOGGER_NAME) nest_asyncio.apply() def record_to_model(record: Record, model_class: ModelType) -> Model: """Convert a Record object to Model object. Args: record (Record): Record object. model_class (ModelType): Model class Returns: Model: Model instance. """ new_record = model_class() for k,v in record.items(): new_record.Meta._fromdb_.append(k) setattr(new_record, k, v) return new_record def get_update_query(self, mob: ModelBase, reset=False) -> Tuple[str, List[Any]]: """Get the update query for the changed data in the model object (mob) Args: mob (ModelBase): Model object reset (bool): If True, this method can be called just once to get the changes done on mob. Subsequent call will return empty query. Raises: AttributeError: If primary key does not exists i.e if not updatable Returns: str, args: tuple of query, args """ pkval = getattr(mob, mob.__class__._get_pk_()) #save method depends on it's AttributeError data = mob.Meta._fields_ new_data_gen = mob.__class__._get_FieldValue_data_valid_(data, up=True) colval = [] values = [] c = 0 for n,v in new_data_gen: if n == mob.__class__._get_pk_(): continue if v.value_change_count > 0: c += 1 colval.append(f'"{n}"=${c}') values.append(v.value) if reset: v.value_change_count = 0 colval_q = ', '.join(colval) if colval_q: where = f'"{mob.__class__._get_pk_()}"=${c+1}' values.append(pkval) query = f'UPDATE "{mob.__class__._get_db_table_()}" SET {colval_q} WHERE {where}' else: query = '' return query, values def get_delete_query(self, mob: ModelBase) -> Tuple[str, List[Any]]: """Get the delete query for the model object. Args: mob (ModelBase): model object. Returns: Tuple[str, List[Any]]: quey, args """ pkval = getattr(mob, mob.__class__._get_pk_()) query = f'DELETE FROM "{mob.__class__._get_db_table_()}" WHERE "{mob.__class__._get_pk_()}"=$1' return query, [pkval] def q(self, model: ModelType = None) -> 'ModelQuery': """Return a ModelQuery for model If `None` is passed, it will give a `ModelQuery` without setting `self.model` on the `ModelQuery` object. Args: model (ModelType, optional): model class. Defaults to None. Raises: TypeError: If invalid model type is passed Returns: ModelQuery: ModelQuery object """ return self(model) def __call__(self, model: ModelType = None) -> 'ModelQuery': """Return a ModelQuery for model If `None` is passed, it will give a `ModelQuery` without setting `self.model` on the `ModelQuery` object. Args: model (ModelType, optional): model class. Defaults to None. Raises: TypeError: If invalid model type is passed Returns: ModelQuery: ModelQuery object """ if isinstance(model, ModelType) or model is None: return ModelQuery(self, model) raise TypeError(f"Invalid model: {model}. model must be of type {ModelType.__name__}. Make sure you did not pass a model object by mistake.") class ModelQuery(): """Query builder for model class. Calling `db(Model)` gives you a model query handler which have several query methods to help you make queries. Use `q(query, *args)` method to make queries with positional arguments. If you want named arguments, use the uderscored version of these methods. For example, `q(query, *args)` has an underscored version `q_(query, *args, **kwargs)` that can take named arguments. You can add a long query part by part: ```python from morm.db import DB db = DB(DB_POOL) # get a db handle. qh = db(User) # get a query handle. query, args = qh.q(f'SELECT * FROM {qh.db_table}')\ .q(f'WHERE {qh.f.profession} = ${qh.c}', 'Teacher')\ .q_(f'AND {qh.f.age} = :age', age=30)\ .getq() print(query, args) # fetch: await qh.fetch() ``` The `q` family of methods (`q, qc, qu etc..`) can be used to build a query step by step. These methods can be chained together to break down the query building in multiple steps. Several properties are available to get information of the model such as: 1. `qh.db_table`: Quoted table name e.g `"my_user_table"`. 2. `qh.pk`: Quoted primary key name e.g `"id"`. 3. `qh.ordering`: ordering e.g `"price" ASC, "quantity" DESC`. 4. `qh.f.<field_name>`: quoted field names e.g`"profession"`. 5. `qh.c`: Current available position for positional argument (Instead of hardcoded `$1`, `$2`, use `f'${qh.c}'`, `f'${qh.c+1}'`). `qh.c` is a counter that gives an integer representing the last existing argument position plus 1. `reset()` can be called to reset the query to start a new. To execute a query, you need to run one of the execution methods : `fetch, fetchrow, fetchval, execute`. **Notable convenience methods:** * `qupdate(data)`: Initialize a update query for data * `qfilter()`: Initialize a filter query upto WHERE clasue. * `get(pkval)`: Get an item by primary key. Args: db (DB): DB object model_class (ModelType): model """ def reset(self) -> 'ModelQuery': """Reset the model query by returning it to its initial state. Returns: self (Enables method chaining) """ self._query_str_queue: List[str] = [] self.end_query_str = '' self.start_query_str = '' self._args: List[Any] = [] self._arg_count = 0 self._named_args: Dict[str, Any] = {} self._named_args_mapper: Dict[str, int] = {} self.__filter_initiated = False self._ordering = '' self.__update_initiated = False return self def _process_positional_args(self, *args): if args: self._args.extend(args) self._arg_count += len(args) def q(self, q: str, *args: Any) -> 'ModelQuery': """Add raw query stub without parsing to check for keyword arguments Use `$1`, `$2` etc. for arguments. Use `self.c` (instance property, use fstring) to get the current available argument position. This is an efficient way to add query that do not have any keyword arguments to handle, compared to `q_()` which checks for keyword arguments everytime it is called. Example: ```python mq = db(SomeModel) mq\ .q('SELECT * FROM "table" WHERE $1', True)\ .q('AND "price" >= $2', 33)\ .q(f'OR "price" = ${mq.c}', 0) # mq.c=3 (now)\ .q_('OR "status" = :status', status='OK')\ # :status is $4: .q('OR "active" = $5', 0)\ .q_('AND "status" = :status')\ # status='OK' from previous call .q('OR "price" = $2')\ # $2=33 from previous call #using format string and mq.c to get the argument position: .q(f'OR "price" > ${mq.c} OR "quantity" > ${mq.c+1}', 12, 3) # mq.c=6 ^ ``` Args: q (str): raw query string *args (Any): positional arguments Returns: ModelQuery: self, enables method chaining. """ self._process_positional_args(*args) self._query_str_queue.append(q) return self def q_(self, q: str, *args, **kwargs) -> 'ModelQuery': """Add a query stub having keyword params. Use the format `:field_name` for keyword parameter. `:field_name` is converted to positional parameter (`$n`). This method checks the query against all keyword arguments that has been added so far with other `q*()` methods. Args: q (str): query string (SQL) Returns: ModelQuery: returns `self` to enable method chaining """ self._process_positional_args(*args) q = self._process_keyword_args(q, **kwargs) self._query_str_queue.append(q) return self def qq(self, word: str) -> 'ModelQuery': """Quote and add a word to the query. Enable to add names with auto-quote. For example, if the name for a field value is `status`, it can be added to the query with auto-quoting, i.e for postgresql it will be added as `"status"`. Example: ```python .qq('price').q('>= $1',34) ``` Args: word (str): the word that needs to be added with quote. Returns: ModelQuery: returns `self` to enable method chaining """ if word: self._query_str_queue.append(Q(word)) return self def qc(self, word: str, rest: str, *args) -> 'ModelQuery': """Add query by quoting `word` while adding the `rest` as is. This is a shorthand for making where clause conditions. For example: `qc('price', '>=$1', 34)` is a safe way to write a where condition like: `"price" >=34`. The same can be achieved by using a combination of `qq()` and `q()` or manually quoting and using with `q()` Example: ```python .qc('price', '>= $1', 34) ``` Args: word (str): left part of query that needs to be quoted rest (str): right part of query that does not need to be quoted *args (any): args Returns: ModelQuery: returns `self` to enable method chaining """ return self.qq(word).q(rest, *args) def qc_(self, word: str, rest: str, *args, **kwargs) -> 'ModelQuery': """Add query by quoting `word` while adding the `rest` as is. Same as `qc()` except this method parses the `rest` query string for keyword params in the format: `:field_name` Args: word (str): left part of query that needs to be quoted rest (str): right part of query that does not need to be quoted *args (any): args *kwargs: keyword args Returns: ModelQuery: returns `self` to enable method chaining """ return self.qq(word).q_(rest, *args, **kwargs) def qorder(self): """Add ORDER BY Returns: ModelQuery: returns `self` to enable method chaining """ return self.q('ORDER BY') def qo(self, order: str) -> 'ModelQuery': """Convert `+/-field_name,` to proper order_by criteria and add to query. Example: `-field_name,` will become: `"field_name" DESC,` * `+` at beginning means ascending order (default) * `-` at beginning means descending order * `,` at end means you will add more order criteria Ommit the comma (`,`) when it is the last ordering criteria. Args: order (str): order criteria in the format `+/-field_name,` Returns: ModelQuery: returns `self` to enable method chaining """ direction = 'ASC' if order.startswith('-'): order = order[1:] direction = 'DESC' elif order.startswith('+'): order = order[1:] if order.endswith(','): order = order[0:-1] direction += ',' return self.qq(order).q(direction) def qu(self, data: dict) -> 'ModelQuery': """Convert data to `"column"=$n` query with args as the values and add to the main query. The counter of positional arguments increases by the number of items in `data`. Make use of `self.c` counter to add more queries after using this method. Args: data (dict): data in format: `{'column': value}` Returns: ModelQuery: returns `self` to enable method chaining """ setq = ', '.join([f'"{c}"=${i}' for i,c in enumerate(data, self.c)]) return self.q(setq, *data.values()) def qreturning(self, *column_names) -> 'ModelQuery': """Convenience to add a `RETURNING` clause. Args: column_names: column names. Returns: ModelQuery: returns `self` to enable method chaining """ q = '","'.join(column_names) if q: q = f'RETURNING "{q}"' return self.q(q) def qwhere(self) -> 'ModelQuery': """Convenience to add 'WHERE' to the main query. Make use of `qc()` method to add conditions. Returns: ModelQuery: returns `self` to enable method chaining """ return self.q('WHERE') def qfilter(self, no_ordering=False) -> 'ModelQuery': """Initiate a filter. This initiates a `SELECT` query upto `WHERE`. You can then use the `q()`, `qc()`, etc. methods to add conditions and finally execute the `fetch()` method to get all results or execute the `fetchrow()` method to get a single row. Example: ```python .qfilter().q('"price" >= $1 AND "status" = $2', 32.12, 'OK') ``` Args: no_ordering (bool): Whether to remove the default ordering SQL. Defaults to False. Returns: ModelQuery: returns self to enable method chaining """ if not self.__filter_initiated: down_fields = ','.join([Q(x) for x in self.model._get_fields_(up=False)]) #type: ignore self.reset().q(f'SELECT {down_fields} FROM "{self.model._get_db_table_()}" WHERE') #type: ignore self.__filter_initiated = True order_by = self.ordering if order_by and not no_ordering: self.end_query_str = f'ORDER BY {order_by}' else: raise ValueError(f"Filter is already initiated for this {self.__class__.__name__} query object: {self}") return self def qupdate(self, data: dict) -> 'ModelQuery': """Initiate a UPDATE query for data. This initiates an `UPDATE` query upto `WHERE` and leaves you to add conditions with other methods such as `qc` or the generic method `q()`. Finally call the `execute()` method to execute the query or call the `fetchval()` method if using `RETURNING` clause. Args: data (dict): data in key value dictionary Returns: ModelQuery: returns `self` to enable method chaining """ if not self.__update_initiated: self.reset().q(f'UPDATE {self.db_table} SET').qu(data).qwhere() self.__update_initiated = True else: raise ValueError(f"update is already initiated for this {self.__class__.__name__} query: {self}") return self def getq(self) -> Tuple[str, List[Any]]: """Return query string and arg list Returns: tuple: (str, list) : (query, args) """ query = ' '.join(self._query_str_queue) self._query_str_queue = [query] query = f'{self.start_query_str} {query} {self.end_query_str}' return query, self._args SERIALIZABLE = 'serializable' REPEATABLE_READ = 'repeatable_read' READ_COMMITTED = 'read_committed'

33.969808

267

0.577177

3e8b7eee7855784a75f5858aea2cd7099da89f3d

4,197

py

Python

gistsig/cli.py

derekmerck/check-hashes

aaa7d596281e41bbb5b73850c5d43113b7d0632b

[ "MIT" ]

1

2019-01-26T22:33:02.000Z

gistsig/cli.py

derekmerck/check-hashes

aaa7d596281e41bbb5b73850c5d43113b7d0632b

[ "MIT" ]

null

gistsig/cli.py

derekmerck/check-hashes

aaa7d596281e41bbb5b73850c5d43113b7d0632b

[ "MIT" ]

null

""" gistsig Derek Merck Winter 2019 Sign and verify Python packages using public gists. """ import logging from pprint import pformat from datetime import datetime import click from . import get_gist, update_gist from . import get_pkg_info, get_pkg_gist def find_gist_id(pkg_name): # Check the package gist_id = get_pkg_gist(pkg_name) if gist_id and \ click.confirm("No reference gist set, use package declared gist? ({})".format(gist_id)): return gist_id click.echo("No gist found for this package") return None def _cli(): cli.add_command(show) cli.add_command(pull) cli.add_command(verify) cli.add_command(push) cli(auto_envvar_prefix="GISTSIG", obj={}) if __name__ == "__main__": _cli()

27.794702

99

0.646414

3e8c2e49f52c5a966e053c091e7e268d680d58d4

2,397

py

Python

cvxpy/reductions/solvers/conic_solvers/super_scs_conif.py

mostafaelaraby/cvxpy

078e025be8b8315b5f579bd0209e8e3a1e2a2a19

[ "ECL-2.0", "Apache-2.0" ]

2

2021-09-24T12:59:45.000Z

2021-09-24T13:00:08.000Z

cvxpy/reductions/solvers/conic_solvers/super_scs_conif.py

mostafaelaraby/cvxpy

078e025be8b8315b5f579bd0209e8e3a1e2a2a19

[ "ECL-2.0", "Apache-2.0" ]

null

cvxpy/reductions/solvers/conic_solvers/super_scs_conif.py

mostafaelaraby/cvxpy

078e025be8b8315b5f579bd0209e8e3a1e2a2a19

[ "ECL-2.0", "Apache-2.0" ]

1

2020-04-12T05:17:18.000Z

""" Copyright 2018 Riley Murray 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. """ import cvxpy.settings as s from cvxpy.reductions.solvers.conic_solvers.conic_solver import ConicSolver from cvxpy.reductions.solvers.conic_solvers.scs_conif import dims_to_solver_dict, SCS

33.291667

88

0.635378

3e8fb96193b2244d64a924fa63c9c59dfafd9741

557

py

Python

Software_University/fundamentals/functions/lecture/orders.py

Ivanazzz/SoftUni-W3resource-Python

892321a290e22a91ff2ac2fef5316179a93f2f17

[ "MIT" ]

1

2022-01-26T07:38:11.000Z

Software_University/fundamentals/functions/lecture/orders.py

Ivanazzz/SoftUni-W3resource-Python

892321a290e22a91ff2ac2fef5316179a93f2f17

[ "MIT" ]

null

Software_University/fundamentals/functions/lecture/orders.py

Ivanazzz/SoftUni-W3resource-Python

892321a290e22a91ff2ac2fef5316179a93f2f17

[ "MIT" ]

null

product_type = input("Enter the product type(coffee, water, coke, snacks): ") quantity = int(input("Enter the quantity: ")) print(f"{price():.2f}")

30.944444

77

0.626571

e40f115d7100a36cb4b801ec2f9f1a7a1eb33d05

4,984

py

Python

linear_model.py

gavb222/flatpanel-localize

6504eb94379f5df268ae280f996c7dd66f063e4e

[ "MIT" ]

1

2021-02-01T18:17:11.000Z

linear_model.py

gavb222/flatpanel-localize

6504eb94379f5df268ae280f996c7dd66f063e4e

[ "MIT" ]

null

linear_model.py

gavb222/flatpanel-localize

6504eb94379f5df268ae280f996c7dd66f063e4e

[ "MIT" ]

1

2021-02-01T18:07:12.000Z

import torch import torch.nn as nn import torch.nn.functional as F import math import time import random import matlab.engine #panel_x, panel_y = panel dimensions #n_freq = n frequency bins #x, y = top left of gaussian #spread = spread of the gaussian #NB that x-spread > 0, y-spread > 0, x+spread < panel_x, y+spread < panel_y model = Conv_Net(1,16,24) model.cuda() model.train() loss_fn = nn.MSELoss criterion = torch.optim.Adam(model.parameters(), lr = .0001, betas = (.5,.999)) keep_training = True epoch_counter = 0 panel_x = 50 panel_y = 50 eng = matlab.engine.start_matlab() #make a panel driver_locations = torch.tensor((0.25, 0.25, 0.75, 0.75, 0.25, 0.75, 0.75, 0.25)).view(4,2) Lx = 0.3 Ly = 0.5 while keep_training: epoch_counter = epoch_counter + 1 time_start = time.time() gt = torch.ones(panel_x,panel_y)*-1 model.zero_grad() #random init starting conditions while gt[0,0] == -1: #returns -1 for invalid configuration gt = produce_freq_response(panel_x,panel_y,1,random.randint(1,panel_x-1),random.randint(1,panel_y-1),random.randint(3,15)) coefs = model(gt.unsqueeze(0).unsqueeze(0).cuda()) print(coefs.size()) #very possible that the interpreter doesnt like torch tensors, might have to go numpy with this response1, frequencies = eng.get_biquad_response(coefs[0,:].cpu().detach().numpy(),44100,nargout = 2) response2, temp = eng.get_biquad_response(coefs[1,:].cpu().detach().numpy(),44100,nargout = 2) response3, temp = eng.get_biquad_response(coefs[2,:].cpu().detach().numpy(),44100,nargout = 2) response4, temp = eng.get_biquad_response(coefs[3,:].cpu().detach().numpy(),44100,nargout = 2) responses = torch.stack((response1,response2,response3,response4),dim=-1) matlab_panel = eng.Clamped_Panel[driver_locations,responses,frequencies,Lx,Ly] matlab_out = eng.matlab_panel.view_total_scan(200,0) loss = loss_fn(matlab_out,gt) criterion.step() print("holy moly!")

33.006623

131

0.647673

e40f68af3b51a18af4106a68a0e2666e5541b720

4,438

py

Python

client/client.py

s-ball/remo_serv

66accbd77183db0628a9618cf258656ec2d81316

[ "MIT" ]

null

client/client.py

s-ball/remo_serv

66accbd77183db0628a9618cf258656ec2d81316

[ "MIT" ]

null

client/client.py

s-ball/remo_serv

66accbd77183db0628a9618cf258656ec2d81316

[ "MIT" ]

null

# Copyright (c) 2020 SBA- MIT License import getpass import argparse import sys import cmd import shlex from urllib.error import HTTPError from cryptography.hazmat.primitives import serialization from client.clientlib import login, Connection from client import smartcard # noinspection PyArgumentList if __name__ == '__main__': run(sys.argv[1:])

30.190476

83

0.570077

e410307635af99e3b3cc52fdda648a0910806c95

1,867

py

Python

unfollower.py

Sam-F90/unfollower

feee9815f440d3a654f77a21ec84680ac92022c1

[ "MIT" ]

null

unfollower.py

Sam-F90/unfollower

feee9815f440d3a654f77a21ec84680ac92022c1

[ "MIT" ]

null

unfollower.py

Sam-F90/unfollower

feee9815f440d3a654f77a21ec84680ac92022c1

[ "MIT" ]

null

import tweepy import datetime import os # get keys from evironment variable "TWITTER_KEYS" TWITTER_API_KEYS = (os.environ.get("TWITTER_KEYS").split(",")) consumer_key,consumer_secret,access_token_key,access_token_secret = TWITTER_API_KEYS # Authenticate to Twitter auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token_key, access_token_secret) # establish api api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True) # verify try: api.verify_credentials() except: print("Error during authentication") exit() # get my id me = api.me() # get list of friends (id) friends = api.friends_ids(me.id) # get list of followers (id) follower_ids = [] for follower in tweepy.Cursor(api.followers, me.screen_name).items(api.me().friends_count): follower_ids.append(follower.id) # get list of muted friends (id) muted_friends = api.mutes_ids() # create list of users who are muted and do not follow you to_unfollow = [] for friend in friends: if friend not in follower_ids and friend in muted_friends: to_unfollow.append(friend) # create log to record data and string to send to DM log = [datetime.datetime.now().strftime("%m-%d-%Y %H:%M:%S")] dm = [datetime.datetime.now().strftime("%m-%d-%Y %H:%M:%S")] # unfollow useres in to_unfollow[] and record them in log[] and dm[] for user in to_unfollow: # unfollowed = api.destroy_friendship(user) unfollowed = api.get_user(user) log.append('unfollowed ' + unfollowed.screen_name + " [" +str(unfollowed.friends_count) + "," + str(unfollowed.followers_count) + "]") dm.append("@" + unfollowed.screen_name) # write info to log with open("unfollow_log.txt","a") as fp: for line in log: fp.write(line + "\n") fp.write("\n") api.send_direct_message(api.me().id,"\n".join(dm)) print("finished")

27.455882

139

0.719336

e4129e9fa1ffc789238869830a16a81f822bb51c

2,113

py

Python

alpha/NN/autoencoders/charlie.py

DanielBerns/keras-effective-adventure

d9bc8c08f769f0c07379d2a3756d040ca14239f2

[ "MIT" ]

null

alpha/NN/autoencoders/charlie.py

DanielBerns/keras-effective-adventure

d9bc8c08f769f0c07379d2a3756d040ca14239f2

[ "MIT" ]

null

alpha/NN/autoencoders/charlie.py

DanielBerns/keras-effective-adventure

d9bc8c08f769f0c07379d2a3756d040ca14239f2

[ "MIT" ]

null

# https://medium.com/datadriveninvestor/deep-autoencoder-using-keras-b77cd3e8be95 from keras.datasets import mnist from keras.layers import Input, Dense from keras.models import Model import numpy as np import pandas as pd import matplotlib.pyplot as plt (X_train, _), (X_test, _) = mnist.load_data() X_train = X_train.astype('float32')/255 X_test = X_test.astype('float32')/255 X_train = X_train.reshape(len(X_train), np.prod(X_train.shape[1:])) X_test = X_test.reshape(len(X_test), np.prod(X_test.shape[1:])) print(X_train.shape) print(X_test.shape) input_img= Input(shape=(784,)) encoded = Dense(units=128, activation='relu')(input_img) encoded = Dense(units=64, activation='relu')(encoded) encoded = Dense(units=32, activation='relu')(encoded) decoded = Dense(units=64, activation='relu')(encoded) decoded = Dense(units=128, activation='relu')(decoded) decoded = Dense(units=784, activation='sigmoid')(decoded) autoencoder=Model(input_img, decoded) encoder = Model(input_img, encoded) print('autoencoder') autoencoder.summary() print('encoder') encoder.summary() autoencoder.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) autoencoder.fit(X_train, X_train, epochs=50, batch_size=256, shuffle=True, validation_data=(X_test, X_test)) encoded_imgs = encoder.predict(X_test) predicted = autoencoder.predict(X_test) plt.figure(figsize=(40, 4)) for i in range(10): # display original images ax = plt.subplot(3, 20, i + 1) plt.imshow(X_test[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) # display encoded images ax = plt.subplot(3, 20, i + 1 + 20) plt.imshow(encoded_imgs[i].reshape(8,4)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) # display reconstructed images ax = plt.subplot(3, 20, 2*20 +i+ 1) plt.imshow(predicted[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) plt.show()

27.802632

87

0.69664

e41482448ad0c9a9ce2ec0102c5edc24cd4e69ff

11,339

py

Python

tests/test_views/test_memberships.py

freelancing-solutions/GCP-Based-Database-as-a-Service

7d6a12c33db238ca2f748bf4ddea6d2cf3c16da3

[ "MIT" ]

1

2021-04-15T19:45:04.000Z

tests/test_views/test_memberships.py

freelancing-solutions/pinydesk

7d6a12c33db238ca2f748bf4ddea6d2cf3c16da3

[ "MIT" ]

516

2021-05-02T11:46:36.000Z

2022-03-29T06:09:49.000Z

tests/test_views/test_memberships.py

freelancing-solutions/pinydesk

7d6a12c33db238ca2f748bf4ddea6d2cf3c16da3

[ "MIT" ]

1

2021-09-04T22:40:14.000Z

import random import typing from datetime import datetime, timedelta from random import randint from google.cloud import ndb from data_service.config.stocks import currency_symbols from data_service.store.mixins import AmountMixin from data_service.views.memberships import MembershipsView from data_service.store.memberships import Memberships, MembershipPlans from data_service.utils.utils import create_id from .. import test_app # noinspection PyUnresolvedReferences from pytest import raises # noinspection PyUnresolvedReferences from pytest_mock import mocker membership_mock_data: dict = { "uid": create_id(), "plan_id": create_id(), "status": "unpaid", "date_created": datetime.now(), "plan_start_date": datetime.date(datetime.now() + timedelta(days=5)) } # noinspection PyShadowingNames # noinspection PyShadowingNames # noinspection PyShadowingNames # noinspection PyShadowingNames # noinspection PyShadowingNames # noinspection PyShadowingNames # noinspection PyShadowingNames # noinspection PyShadowingNames # noinspection PyShadowingNames # noinspection PyShadowingNames

45.175299

115

0.71038

e414c3ce91122f63e50497c6f5b8998f2cc88f9e

3,893

py

Python

padmini/prakarana/dvitva.py

sanskrit/padmini

8e7e8946a7d2df9c941f689ea4bc7b6ebb7ca1d0

[ "MIT" ]

1

2022-03-01T05:05:04.000Z

padmini/prakarana/dvitva.py

sanskrit/padmini

8e7e8946a7d2df9c941f689ea4bc7b6ebb7ca1d0

[ "MIT" ]

null

padmini/prakarana/dvitva.py

sanskrit/padmini

8e7e8946a7d2df9c941f689ea4bc7b6ebb7ca1d0

[ "MIT" ]

null

from padmini import filters as f from padmini import operations as op from padmini.constants import Tag as T from padmini.sounds import s from padmini.prakriya import Term, Prakriya from padmini.term_views import TermView from padmini.prakarana.utils import eka_ac

31.144

81

0.554585

e415ae5887fb3b1c6bbb5eae6bf773f7d423747e

1,152

py

Python

8kyu/a-wolf-in-sheeps-clothing/solution.py

Morioki/Code-Katas

65bffc0675d3c0f68c60706e95e38ab1dcfc8636

[ "MIT" ]

null

8kyu/a-wolf-in-sheeps-clothing/solution.py

Morioki/Code-Katas

65bffc0675d3c0f68c60706e95e38ab1dcfc8636

[ "MIT" ]

null

8kyu/a-wolf-in-sheeps-clothing/solution.py

Morioki/Code-Katas

65bffc0675d3c0f68c60706e95e38ab1dcfc8636

[ "MIT" ]

null

import unittest

57.6

175

0.62934

e4160c8bd63d807a761f9c2eb1581d092fef5ff0

449

py

Python

modules/dbnd-airflow/src/dbnd_airflow/scheduler/dags/dbnd_dropin_scheduler.py

ipattarapong/dbnd

7bd65621c46c73e078eb628f994127ad4c7dbd1a

[ "Apache-2.0" ]

null

modules/dbnd-airflow/src/dbnd_airflow/scheduler/dags/dbnd_dropin_scheduler.py

ipattarapong/dbnd

7bd65621c46c73e078eb628f994127ad4c7dbd1a

[ "Apache-2.0" ]

null

modules/dbnd-airflow/src/dbnd_airflow/scheduler/dags/dbnd_dropin_scheduler.py

ipattarapong/dbnd

7bd65621c46c73e078eb628f994127ad4c7dbd1a

[ "Apache-2.0" ]

null

import logging logger = logging.getLogger("dbnd-scheduler") try: from dbnd_airflow.scheduler.scheduler_dags_provider import get_dags # airflow will only scan files containing the text DAG or airflow. This comment performs this function dags = get_dags() if dags: for dag in dags: globals()[dag.dag_id] = dag except Exception as e: logging.exception("Failed to get dags form databand server") raise e

24.944444

106

0.710468

e41914e68f6a31dadb107fe8bb9eaf841bed6173

4,268

py

Python

tanacompendium/utils/modelmanagers.py

nkoech/tanacompendium

b4fd81b23f2c8263735806765d93eb4a70be8aba

[ "MIT" ]

null

tanacompendium/utils/modelmanagers.py

nkoech/tanacompendium

b4fd81b23f2c8263735806765d93eb4a70be8aba

[ "MIT" ]

null

tanacompendium/utils/modelmanagers.py

nkoech/tanacompendium

b4fd81b23f2c8263735806765d93eb4a70be8aba

[ "MIT" ]

null

import datetime from django.contrib.contenttypes.models import ContentType from django.db.models import FieldDoesNotExist from django.db.models.base import ObjectDoesNotExist def create_model_type(instance, model_type, key, slugify, **kwargs): """ Create object by model type :param instance: Model manager instance :param model_type: Content/model type :param key: Primary key or slug :param slugify: Boolean to indicate availability of a slug or primary key :param kwargs: Fields to be created :return: Data object :rtype: Object """ model_qs = ContentType.objects.filter(model=model_type) if model_qs.exists(): any_model = model_qs.first().model_class() if slugify: obj_qs = any_model.objects.filter(slug=key) else: obj_qs = any_model.objects.filter(pk=key) if obj_qs.exists() and obj_qs.count() == 1: field_values = { 'content_type': model_qs.first(), 'object_id': obj_qs.first().id } field_values.update(kwargs) data_instance = instance.model(**field_values) data_instance.save() return data_instance return None def model_instance_filter(call_instance, current_instance, model_manager): """ Object query based on a model instance :param call_instance: Instance of the model calling this method :param current_instance: Instance of the model manager class this method would be called from :param model_manager: The model manager class :return: Object due to instantiation of the calling model class :rtye: Object/record """ parent_obj = super(model_manager, current_instance) content_type = ContentType.objects.get_for_model(call_instance.__class__) try: qs = parent_obj.filter(content_type=content_type, object_id=call_instance.id) except parent_obj.DoesNotExist: return None return qs def model_foreign_key_qs(call_instance, current_instance, model_manager): """ Object query based on foreign key :param call_instance: Instance of the model calling this method :param current_instance: Instance of the model manager class this method would be called from :param model_manager: The model manager class :return: Object query based on foreign key otherwise return none :rtype: Object/record """ model_name = str(call_instance._meta.model_name) # Foreignkey name should be similar to related model name qs_filter = {model_name: call_instance.id} obj_qs = super(model_manager, current_instance).filter(**qs_filter) return obj_qs def model_type_filter(current_instance, obj_qs, model_manager): """ Object query based on a model class :param current_instance: Instance of the model manager class this method would be called from :param obj_qs: Initial object query :param model_manager: The model manager class :return: Object query based on the model type/class otherwise return none :rtype: Object/record """ if obj_qs.exists(): if model_field_exists(obj_qs, 'content_type'): for obj in obj_qs.iterator(): try: qs = super(model_manager, current_instance).filter(content_type=obj.content_type) and obj_qs return qs except ObjectDoesNotExist: return None return obj_qs def model_field_exists(instance, field_name): """ Check if field exists :param instance: Instance of the model manager class this method would be called from :param field_name: Field name to be checked :return: True if field exists otherwise return false :rtype: Boolean """ try: instance.model._meta.get_field(field_name) return True except FieldDoesNotExist: return False def get_year_choices(): """ Get years as model choices :return: Years """ year_choice = [] for r in range(1950, (datetime.datetime.now().year + 1)): year_choice.append((r, r)) return year_choice def get_datetime_now(): """ Get current year :return: Curreent year """ return datetime.datetime.now().year

34.419355

112

0.684161

e4193bf7c1b3cd811dde985083067c06d301bbfb

2,588

py

Python

deletion_test.py

tjake/cassandra-dtest

df49e4f16b2ed8b9c38f767fffd796ae3d9cc6f3

[ "Apache-2.0" ]

null

deletion_test.py

tjake/cassandra-dtest

df49e4f16b2ed8b9c38f767fffd796ae3d9cc6f3

[ "Apache-2.0" ]

null

deletion_test.py

tjake/cassandra-dtest

df49e4f16b2ed8b9c38f767fffd796ae3d9cc6f3

[ "Apache-2.0" ]

null

from dtest import Tester import os, sys, time from ccmlib.cluster import Cluster from tools import require, since from jmxutils import make_mbean, JolokiaAgent

34.052632

91

0.632921