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

f03e0bd25b2839aff153fea90abc924e46a6584e

1bdc56d1f66501bada19b277a47655dc99f44f2e

/const.py

c6f26b24ff8369d500a57235ad9d6e874677e6b2

[]

no_license

antista/pacman

682811715b930db0c8765d5da9340f91d8f4e8b7

adb51eb219c6758dc553671ddc68db700a6df358

refs/heads/master

2020-04-17T07:56:49.953567

2019-01-18T11:02:31

166,391,144

0

null

UTF-8

Python

false

4,379

py

import pyganim from pygame import * from pyganim import * SOUNDS = dict( wakka='sounds/wakka.wav', energizer='sounds/energizer.wav', eat_ghost='sounds/eating_ghost.wav', death='sounds/death.wav' ) SIZE = 16 BACK_COLOR = "#00FFFF" ANIMATION_DELAY = 50 # скорость смены кадров ANIMATION = dict() ANIMATION['right'] = [('images/moving/m1.ico'), ('images/moving/m2.ico'), ('images/moving/m3.ico'), ('images/moving/m4.ico'), ('images/moving/m5.ico'), ('images/moving/m6.ico'), ('images/moving/m6.ico'), ('images/moving/m5.ico'), ('images/moving/m4.ico'), ('images/moving/m3.ico'), ('images/moving/m2.ico'), ('images/moving/m1.ico')] ANIMATION['left'] = [pygame.transform.rotate(image.load('images/moving/m1.ico'), 180), pygame.transform.rotate(image.load('images/moving/m2.ico'), 180), pygame.transform.rotate(image.load('images/moving/m3.ico'), 180), pygame.transform.rotate(image.load('images/moving/m4.ico'), 180), pygame.transform.rotate(image.load('images/moving/m5.ico'), 180), pygame.transform.rotate(image.load('images/moving/m6.ico'), 180), pygame.transform.rotate(image.load('images/moving/m6.ico'), 180), pygame.transform.rotate(image.load('images/moving/m5.ico'), 180), pygame.transform.rotate(image.load('images/moving/m4.ico'), 180), pygame.transform.rotate(image.load('images/moving/m3.ico'), 180), pygame.transform.rotate(image.load('images/moving/m2.ico'), 180), pygame.transform.rotate(image.load('images/moving/m1.ico'), 180)] ANIMATION['up'] = [pygame.transform.rotate(image.load('images/moving/m1.ico'), 90), pygame.transform.rotate(image.load('images/moving/m2.ico'), 90), pygame.transform.rotate(image.load('images/moving/m3.ico'), 90), pygame.transform.rotate(image.load('images/moving/m4.ico'), 90), pygame.transform.rotate(image.load('images/moving/m5.ico'), 90), pygame.transform.rotate(image.load('images/moving/m6.ico'), 90), pygame.transform.rotate(image.load('images/moving/m6.ico'), 90), pygame.transform.rotate(image.load('images/moving/m5.ico'), 90), pygame.transform.rotate(image.load('images/moving/m4.ico'), 90), pygame.transform.rotate(image.load('images/moving/m3.ico'), 90), pygame.transform.rotate(image.load('images/moving/m2.ico'), 90), pygame.transform.rotate(image.load('images/moving/m1.ico'), 90)] ANIMATION['down'] = [pygame.transform.rotate(image.load('images/moving/m1.ico'), -90), pygame.transform.rotate(image.load('images/moving/m2.ico'), -90), pygame.transform.rotate(image.load('images/moving/m3.ico'), -90), pygame.transform.rotate(image.load('images/moving/m4.ico'), -90), pygame.transform.rotate(image.load('images/moving/m5.ico'), -90), pygame.transform.rotate(image.load('images/moving/m6.ico'), -90), pygame.transform.rotate(image.load('images/moving/m6.ico'), -90), pygame.transform.rotate(image.load('images/moving/m5.ico'), -90), pygame.transform.rotate(image.load('images/moving/m4.ico'), -90), pygame.transform.rotate(image.load('images/moving/m3.ico'), -90), pygame.transform.rotate(image.load('images/moving/m2.ico'), -90), pygame.transform.rotate(image.load('images/moving/m1.ico'), -90)] ANIMATION_STAY = dict() ANIMATION_STAY['left'] = [(pygame.transform.rotate(image.load('images/moving/m6.ico'), 180), 1)] ANIMATION_STAY['right'] = [('images/moving/m6.ico', 1)] ANIMATION_STAY['up'] = [(pygame.transform.rotate(image.load('images/moving/m6.ico'), 90), 1)] ANIMATION_STAY['down'] = [(pygame.transform.rotate(image.load('images/moving/m6.ico'), -90), 1)]

[ "anti2100@yandex.ru" ]

anti2100@yandex.ru

a8885f69c487b2f187926f4fa20b933388d0a0d1

50ed16359e7a180298e847c4866ff2b45b3f3815

/scripts/computeNumbers.py

e07b5acf8876c8f9cd7ac521858d44c012313e7f

[]

no_license

bfildier/Fildier2022_code

cde8fac4c01597e8ea7f631913aee229e725ffbd

8cd2c5e78b85ccc89544f2c6698b7717dd7a1537

refs/heads/main

2023-04-18T01:28:39.748615

2022-12-05T16:14:40

574,580,725

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UTF-8

Python

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py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Apr 21 11:46:19 2022 Numbers in PNAS main 2022 @author: bfildier """ ##-- modules import scipy.io import sys, os, glob import numpy as np import xarray as xr import matplotlib from matplotlib import cm import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec import matplotlib.patches as mpatches import matplotlib.lines as mlines from matplotlib.patches import Circle from PIL import Image from datetime import datetime as dt from datetime import timedelta, timezone import pytz import matplotlib.image as mpimg import cartopy.crs as ccrs import cartopy.feature as cfeature import pickle from scipy.stats import gaussian_kde from scipy.stats import linregress from scipy import optimize from matplotlib.patches import Ellipse import matplotlib.transforms as transforms ##-- directories # workdir = os.path.dirname(os.path.realpath(__file__)) workdir = '/Users/bfildier/Code/analyses/EUREC4A/EUREC4A_organization/scripts' repodir = os.path.dirname(workdir) moduledir = os.path.join(repodir,'functions') resultdir = os.path.join(repodir,'results','radiative_features') figdir = os.path.join(repodir,'figures','paper') #inputdir = '/Users/bfildier/Dropbox/Data/EUREC4A/sondes_radiative_profiles/' inputdir = os.path.join(repodir,'input') radinputdir = os.path.join(repodir,'input') imagedir = os.path.join(repodir,'figures','snapshots','with_HALO_circle') scriptsubdir = 'Fildier2021' # Load own module projectname = 'EUREC4A_organization' thismodule = sys.modules[__name__] ## Own modules sys.path.insert(0,moduledir) print("Own modules available:", [os.path.splitext(os.path.basename(x))[0] for x in glob.glob(os.path.join(moduledir,'*.py'))]) from radiativefeatures import * from radiativescaling import * # from thermodynamics import * from conditionalstats import * from matrixoperators import * from thermoConstants import * mo = MatrixOperators() ##--- local functions def defineSimDirectories(): """Create specific subdirectories""" # create output directory if not there os.makedirs(os.path.join(figdir),exist_ok=True) if __name__ == "__main__": # arguments parser = argparse.ArgumentParser(description="Compute paper numbers from all precomputed data") parser.add_argument('--overwrite',type=bool,nargs='?',default=False) # output directory defineSimDirectories() ##-- Load all data exec(open(os.path.join(workdir,"load_data.py")).read()) #%% Rerecence wavenumbers print('-- compute reference wavenumbers --') print() T_ref = 290 # K W_ref = 3 # mm print('choose reference temperature T = %3.1fK'%T_ref) print('choose reference water path W = %3.1fmm'%W_ref) print() print("> compute reference wavenumber ") kappa_ref = 1/W_ref # mm-1 rs = rad_scaling_all['20200202'] nu_ref_rot = rs.nu(kappa_ref,'rot') nu_ref_vr = rs.nu(kappa_ref,'vr') print('reference wavenumber in rotational band: nu = %3.1f cm-1'%(nu_ref_rot/1e2)) print('reference wavenumber in vibration-rotation band: nu = %3.1f cm-1'%(nu_ref_vr/1e2)) print() print("> Planck function at both reference wavenumbers") piB_ref_rot = pi*rs.planck(nu_ref_rot,T_ref) piB_ref_vr = pi*rs.planck(nu_ref_vr,T_ref) print('reference Planck term in rotational band: piB = %3.4f J.s-1.sr-1.m-2.cm'%(piB_ref_rot*1e2)) print('reference Planck term in vibration-rotation band: piB = %3.4f J.s-1.sr-1.m-2.cm'%(piB_ref_vr*1e2)) #%% Alpha #-- Analytical approximation # show temperature profiles day = '20200126' date = pytz.utc.localize(dt.strptime(day,'%Y%m%d')) data_day = data_all.sel(launch_time=day) f = rad_features_all[day] # colors var_col = f.pw norm = matplotlib.colors.Normalize(vmin=var_col.min(), vmax=var_col.max()) cmap = plt.cm.nipy_spectral cmap = plt.cm.RdYlBu cols = cmap(norm(var_col)) # N data Ns = data_day.dims['launch_time'] # Exploratory figure for lapse rate fig,ax = plt.subplots() for i_s in range(Ns): ax.plot(data_day.temperature[i_s],data_day.alt,c=cols[i_s],linewidth=0.5,alpha=0.5) s_fit_FT = slice(200,600) s_fit_BL = slice(0,160) for suff in '_FT','_BL': s_fit = getattr(thismodule,'s_fit%s'%suff) s_dry = f.pw < 30 # mmm temp_mean = np.nanmean((data_day.temperature)[s_dry],axis=0) not_nan = ~np.isnan(temp_mean) z_fit = data_day.alt[not_nan][s_fit] # regress slope, intercept, r, p, se = scipy.stats.linregress(z_fit,temp_mean[not_nan][s_fit]) # show ax.plot(slope*z_fit+intercept,z_fit,'k') #!- analytical alpha Gamma = -slope T_ref = 290 alpha_an = L_v*Gamma/gg/T_ref * R_d/R_v - 1 print('alpha_analytical%s ='%suff,alpha_an) ax.set_xlabel('T (K)') ax.set_ylabel('z (km)') #%% Inversion Ns = rad_scaling_all[day].rad_features.pw.size fig,ax = plt.subplots() # Ns = data_all.temperature.shape[0] for i_s in range(Ns): theta = data_day.temperature[i_s] * (1e5/data_day.pressure[i_s])**(R_d/c_pd) ax.plot(theta,data_day.pressure[i_s]/100,c = cols[i_s],alpha=0.2) ax.invert_yaxis() ax.set_ylabel('p (hPa)') ax.set_xlabel(r'Potential temperature $\theta$ (K)') #%% Water paths vs RH # alpha_qvstar = 2.3 # qvstar_0 = 0.02 # qvstar_power = qvstar_0 * np.power(pres_fit/pres_fit[-1],alpha_qvstar) def waterPath(qvstar_surf,pres,pres_jump,rh_min,rh_max,alpha,i_surf=-1): """Water path from top of atmosphere, in mm - qv_star_surf: surface saturated specific humidity (kg/kg) - pres: reference pressure array (hPa) - pres_jump: level of RH jump (hPa) - rh_max: lower-tropospheric RH - rh_min: upper-tropospheric RH - alpha: power exponent - i_surf: index of surface layer in array (default is -1, last element) """ hPa_to_Pa = 100 rho_w = 1e3 # kg/m3 m_to_mm = 1e3 # init W = np.full(pres.shape,np.nan) # constant A = qvstar_surf/(pres[i_surf]*hPa_to_Pa)**alpha/gg/(1+alpha) print(A) # lower troposphere lowert = pres >= pres_jump W[lowert] = A*(rh_max*(pres[lowert]*hPa_to_Pa)**(alpha+1)-(rh_max-rh_min)*(pres_jump*hPa_to_Pa)**(alpha+1)) # upper troposphere uppert = pres < pres_jump W[uppert] = A*rh_min*(pres[uppert]*hPa_to_Pa)**(alpha+1) return W/rho_w*m_to_mm qvstar_0 = 0.02 pres_fit = np.linspace(0,1000,1001) pres_jump = 800 # hPa rh_min = 1 rh_max = 1 alpha_qvstar = 2.3 W_prof = waterPath(qvstar_0,pres_fit,pres_jump,rh_min,rh_max,alpha_qvstar) i_jump = np.where(pres_fit >= pres_jump)[0][0] W_FT = W_prof[i_jump] print('Free tropospheric water path at saturation (qvstar integral) =',W_FT) print('with uniform RH_t = 1%, W =',W_FT/100) print('with uniform RH_t = 5%, W =',W_FT*0.05) print('with uniform RH_t = 50%, W =',W_FT*0.5) print('with uniform RH_t = 80%, W =',W_FT*0.8)

[ "bfildier.work@gmail.com" ]

bfildier.work@gmail.com

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/notebooks/other/Y2017M07D28_RH_python27setup_v01.py

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[]

no_license

YanCheng-go/Aqueduct30Docker

8400fdea23bfd788f9c6de71901e6f61530bde38

6606fa03d145338d48101fc53ab4a5fccf3ebab2

refs/heads/master

2022-12-16T03:36:25.704103

2020-09-09T14:38:28

null

0

null

UTF-8

Python

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py

# coding: utf-8 # # Test Python 27 setup # # * Purpose of script: test python 27 environement against several libraries # * Author: Rutger Hofste # * Kernel used: python27 # * Date created: 20170728 # # # In[3]: packages = {"earth engine":-1,"gdal":-1,"geopandas":-1,"arcgis":-1} # In[6]: try: import ee packages["earth engine"]=1 except: packages["earth engine"]=0 # In[4]: try: from osgeo import gdal packages["gdal"]=1 except: packages["gdal"]=0 # In[10]: try: import geopandas packages["geopandas"]=1 except: packages["geopandas"]=0 # In[11]: try: import arcgis.gis packages["arcgis"]=1 except: packages["arcgis"]=0 # In[12]: print(packages) # In[ ]:

[ "rutgerhofste@gmail.com" ]

rutgerhofste@gmail.com

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f98f6746851790aabeb996fafe74a24236bb580d

/is_prime_number.py

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[]

no_license

licheeee/PythonProject

b8c619cfbbe2f0e70284ffc2c0e9283c41d6f58c

9c114f32b51e6f8dc275cb36cb8b0e05e1c42548

refs/heads/master

2020-04-23T06:12:04.958043

2019-10-17T15:15:58

170,965,853

0

null

UTF-8

Python

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323

py

# -*- coding: UTF-8 -*- # 判断一个数字是否是质数 num = int(input("Please input a number :")) primeFlag = True sqrt = int(num ** 0.5) for i in range(2, sqrt + 1): if (num % i) == 0: print("{0} is not a prime number.".format(num)) break else: print("{0} is a prime number.".format(num))

[ "qiaoxw@outlook.com" ]

qiaoxw@outlook.com

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887d21782f2741d8a273807642346ab7cd0dac6e

/list_files.py

4cbc2e8e2b36f40a7c36ec26d8d15585aba26e85

[]

no_license

TheRealTimCameron/Sandbox

c375ff356710fe4a1935ddd86603731240c7283e

4778376c0a018065b50f6ba4abcd6cfac344d538

refs/heads/master

2020-04-30T13:37:00.562336

2019-03-21T03:46:29

176,863,481

0

1

null

UTF-8

Python

false

131

py

import os print("The files and folders in {} are:".format(os.getcwd())) items = os.listdir('.') for item in items: print(item)

[ "TimCameron56@gmail.com" ]

TimCameron56@gmail.com

207ca51d306160bcb1b64211690cf57342453446

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/webempresa/services/models.py

0277ee4a4986340cfebae70d45014dd4b5affc40

[]

no_license

FernandoHer/maderamandala

2f4a1713ea4e067198f74ca00ae7197a606f3524

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refs/heads/master

2022-11-25T16:37:08.917485

2020-04-07T20:54:15

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0

null

2022-11-22T02:25:07

2020-04-07T19:45:24

Python

UTF-8

Python

false

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py

from django.db import models # Create your models here. class Service(models.Model): title = models.CharField(max_length=200, verbose_name = "Titulo") subtitle = models.CharField(max_length=200, verbose_name = "Subtitulo") content = models.TextField(verbose_name = "Contenido") image = models.ImageField(verbose_name = "Imagen", upload_to="services") created = models.DateTimeField(auto_now_add=True, verbose_name = "Fecha de Creacion") updated = models.DateTimeField(auto_now=True, verbose_name = "Fecha de actualizacion") class Meta: verbose_name = "servicio" verbose_name_plural = "servicios" ordering = ["-created"] def __str__(self): return self.title

[ "juanherdoiza@iMac-de-Juan.local" ]

juanherdoiza@iMac-de-Juan.local

c6119fca8e49b7cc3081a8d3441946e564c44017

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/rl_loop/train_and_validate.py

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[ "Apache-2.0" ]

permissive

2series/minigo

dcec298021e714fb8e203b847dd2d7a9d9451823

fda1487dff94a710e9359f80c28d08d99d6c3e3c

refs/heads/master

2020-04-05T20:35:18.809871

2018-11-12T09:18:53

157,187,163

1

0

null

2018-11-12T09:20:43

2018-11-12T09:20:42

null

UTF-8

Python

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py

# Copyright 2018 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. """ Run train and validate in a loop, as subprocesses. We run as subprocesses because it gives us some isolation. """ import itertools import os import sys import time sys.path.insert(0, '.') from absl import app, flags from tensorflow import gfile from rl_loop import fsdb import mask_flags from rl_loop import shipname import utils flags.DEFINE_string('pro_dataset', None, 'Location of preprocessed pro dataset for validation') # From fsdb.py - must pass one of the two. flags.declare_key_flag('base_dir') flags.declare_key_flag('bucket_name') FLAGS = flags.FLAGS try: TPU_NAME = os.environ['TPU_NAME'] except KeyError: raise Exception("Must have $TPU_NAME configured") def train(): model_num, model_name = fsdb.get_latest_model() print("Training on gathered game data, initializing from {}".format( model_name)) new_model_num = model_num + 1 new_model_name = shipname.generate(new_model_num) print("New model will be {}".format(new_model_name)) training_file = os.path.join( fsdb.golden_chunk_dir(), str(new_model_num) + '.tfrecord.zz') while not gfile.Exists(training_file): print("Waiting for", training_file) time.sleep(1 * 60) save_file = os.path.join(fsdb.models_dir(), new_model_name) cmd = ['python3', 'train.py', training_file, '--use_tpu', '--tpu_name={}'.format(TPU_NAME), '--flagfile=rl_loop/distributed_flags', '--export_path={}'.format(save_file)] return mask_flags.run(cmd) def validate_holdout_selfplay(): """Validate on held-out selfplay data.""" holdout_dirs = (os.path.join(fsdb.holdout_dir(), d) for d in reversed(gfile.ListDirectory(fsdb.holdout_dir())) if gfile.IsDirectory(os.path.join(fsdb.holdout_dir(), d)) for f in gfile.ListDirectory(os.path.join(fsdb.holdout_dir(), d))) # This is a roundabout way of computing how many hourly directories we need # to read in order to encompass 20,000 holdout games. holdout_dirs = set(itertools.islice(holdout_dirs), 20000) cmd = ['python3', 'validate.py'] + list(holdout_dirs) + [ '--use_tpu', '--tpu_name={}'.format(TPU_NAME), '--flagfile=rl_loop/distributed_flags', '--expand_validation_dirs'] mask_flags.run(cmd) def validate_pro(): """Validate on professional data.""" cmd = ['python3', 'validate.py', FLAGS.pro_dataset, '--use_tpu', '--tpu_name={}'.format(TPU_NAME), '--flagfile=rl_loop/distributed_flags', '--validate_name=pro'] mask_flags.run(cmd) def loop(unused_argv): while True: print("=" * 40) with utils.timer("Train"): completed_process = train() if completed_process.returncode > 0: print("Training failed! Skipping validation...") continue with utils.timer("Validate"): validate_pro() validate_holdout_selfplay() if __name__ == '__main__': flags.mark_flag_as_required('pro_dataset') app.run(loop)

[ "brian.kihoon.lee@gmail.com" ]

brian.kihoon.lee@gmail.com

edbfd9f211f972906a7be68a3b1de4ba080d1d03

4e2a22470c983bc6f8463b4d0bd2563e2b4fadba

/manage.py

91afffd0eea54135379279692eb3ab4988697b8b

[]

no_license

payush/ayush-crowdbotics-375

8537f9a86fcdcda7418a0c10a5f258bafc07dd9c

c11bdd721d91e765bcb04379dac476279e6ca599

refs/heads/master

2020-03-23T22:34:09.700234

2018-07-24T16:09:19

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UTF-8

Python

false

819

py

#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "ayush_crowdbotics_375.settings") try: from django.core.management import execute_from_command_line except ImportError: # The above import may fail for some other reason. Ensure that the # issue is really that Django is missing to avoid masking other # exceptions on Python 2. try: import django except ImportError: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) raise execute_from_command_line(sys.argv)

[ "ayushpuroheet@gmail.com" ]

ayushpuroheet@gmail.com

fa831199505226547d9cfa53b8caf0ccbd1afd58

fa7e75212e9f536eed7a78237a5fa9a4021a206b

/OLD_ROOT/Backend/SMQTK_Backend/utils/jsmin/test.py

7aba6993dc941efa2e6ea9557fd99d5a9b43b720

[]

no_license

kod3r/SMQTK

3d40730c956220a3d9bb02aef65edc8493bbf527

c128e8ca38c679ee37901551f4cc021cc43d00e6

refs/heads/master

2020-12-03T09:12:41.163643

2015-10-19T14:56:55

44,916,678

1

0

null

2015-10-25T15:47:35

null

UTF-8

Python

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py

import unittest import sys # modified path since this is now being embeded in another project. from SMQTK_Backend.utils import jsmin class JsTests(unittest.TestCase): def _minify(self, js): return jsmin.jsmin(js) def assertEqual(self, thing1, thing2): if thing1 != thing2: print(repr(thing1), repr(thing2)) raise AssertionError return True def assertMinified(self, js_input, expected): minified = jsmin.jsmin(js_input) assert minified == expected, "%r != %r" % (minified, expected) def testQuoted(self): js = r''' Object.extend(String, { interpret: function(value) { return value == null ? '' : String(value); }, specialChar: { '\b': '\\b', '\t': '\\t', '\n': '\\n', '\f': '\\f', '\r': '\\r', '\\': '\\\\' } }); ''' expected = r"""Object.extend(String,{interpret:function(value){return value==null?'':String(value);},specialChar:{'\b':'\\b','\t':'\\t','\n':'\\n','\f':'\\f','\r':'\\r','\\':'\\\\'}});""" self.assertMinified(js, expected) def testSingleComment(self): js = r'''// use native browser JS 1.6 implementation if available if (Object.isFunction(Array.prototype.forEach)) Array.prototype._each = Array.prototype.forEach; if (!Array.prototype.indexOf) Array.prototype.indexOf = function(item, i) { // hey there function() {// testing comment foo; //something something location = 'http://foo.com;'; // goodbye } //bye ''' expected = r""" if(Object.isFunction(Array.prototype.forEach)) Array.prototype._each=Array.prototype.forEach;if(!Array.prototype.indexOf)Array.prototype.indexOf=function(item,i){ function(){ foo; location='http://foo.com;';}""" # print expected self.assertMinified(js, expected) def testEmpty(self): self.assertMinified('', '') self.assertMinified(' ', '') self.assertMinified('\n', '') self.assertMinified('\r\n', '') self.assertMinified('\t', '') def testMultiComment(self): js = r""" function foo() { print('hey'); } /* if(this.options.zindex) { this.originalZ = parseInt(Element.getStyle(this.element,'z-index') || 0); this.element.style.zIndex = this.options.zindex; } */ another thing; """ expected = r"""function foo(){print('hey');} another thing;""" self.assertMinified(js, expected) def testLeadingComment(self): js = r"""/* here is a comment at the top it ends here */ function foo() { alert('crud'); } """ expected = r"""function foo(){alert('crud');}""" self.assertMinified(js, expected) def testJustAComment(self): self.assertMinified(' // a comment', '') def testRe(self): js = r''' var str = this.replace(/\\./g, '@').replace(/"[^"\\\n\r]*"/g, ''); return (/^[,:{}\[\]0-9.\-+Eaeflnr-u \n\r\t]*$/).test(str); });''' expected = r"""var str=this.replace(/\\./g,'@').replace(/"[^"\\\n\r]*"/g,'');return(/^[,:{}\[\]0-9.\-+Eaeflnr-u \n\r\t]*$/).test(str);});""" self.assertMinified(js, expected) def testIgnoreComment(self): js = r""" var options_for_droppable = { overlap: options.overlap, containment: options.containment, tree: options.tree, hoverclass: options.hoverclass, onHover: Sortable.onHover } var options_for_tree = { onHover: Sortable.onEmptyHover, overlap: options.overlap, containment: options.containment, hoverclass: options.hoverclass } // fix for gecko engine Element.cleanWhitespace(element); """ expected = r"""var options_for_droppable={overlap:options.overlap,containment:options.containment,tree:options.tree,hoverclass:options.hoverclass,onHover:Sortable.onHover} var options_for_tree={onHover:Sortable.onEmptyHover,overlap:options.overlap,containment:options.containment,hoverclass:options.hoverclass} Element.cleanWhitespace(element);""" self.assertMinified(js, expected) def testHairyRe(self): js = r""" inspect: function(useDoubleQuotes) { var escapedString = this.gsub(/[\x00-\x1f\\]/, function(match) { var character = String.specialChar[match[0]]; return character ? character : '\\u00' + match[0].charCodeAt().toPaddedString(2, 16); }); if (useDoubleQuotes) return '"' + escapedString.replace(/"/g, '\\"') + '"'; return "'" + escapedString.replace(/'/g, '\\\'') + "'"; }, toJSON: function() { return this.inspect(true); }, unfilterJSON: function(filter) { return this.sub(filter || Prototype.JSONFilter, '#{1}'); }, """ expected = r"""inspect:function(useDoubleQuotes){var escapedString=this.gsub(/[\x00-\x1f\\]/,function(match){var character=String.specialChar[match[0]];return character?character:'\\u00'+match[0].charCodeAt().toPaddedString(2,16);});if(useDoubleQuotes)return'"'+escapedString.replace(/"/g,'\\"')+'"';return"'"+escapedString.replace(/'/g,'\\\'')+"'";},toJSON:function(){return this.inspect(true);},unfilterJSON:function(filter){return this.sub(filter||Prototype.JSONFilter,'#{1}');},""" self.assertMinified(js, expected) def testNoBracesWithComment(self): js = r""" onSuccess: function(transport) { var js = transport.responseText.strip(); if (!/^\[.*\]$/.test(js)) // TODO: improve sanity check throw 'Server returned an invalid collection representation.'; this._collection = eval(js); this.checkForExternalText(); }.bind(this), onFailure: this.onFailure }); """ expected = r"""onSuccess:function(transport){var js=transport.responseText.strip();if(!/^\[.*\]$/.test(js)) throw'Server returned an invalid collection representation.';this._collection=eval(js);this.checkForExternalText();}.bind(this),onFailure:this.onFailure});""" self.assertMinified(js, expected) def testSpaceInRe(self): js = r""" num = num.replace(/ /g,''); """ self.assertMinified(js, "num=num.replace(/ /g,'');") def testEmptyString(self): js = r''' function foo('') { } ''' self.assertMinified(js, "function foo(''){}") def testDoubleSpace(self): js = r''' var foo = "hey"; ''' self.assertMinified(js, 'var foo="hey";') def testLeadingRegex(self): js = r'/[d]+/g ' self.assertMinified(js, js.strip()) def testLeadingString(self): js = r"'a string in the middle of nowhere'; // and a comment" self.assertMinified(js, "'a string in the middle of nowhere';") def testSingleCommentEnd(self): js = r'// a comment\n' self.assertMinified(js, '') def testInputStream(self): try: from StringIO import StringIO except ImportError: from io import StringIO ins = StringIO(r''' function foo('') { } ''') outs = StringIO() m = jsmin.JavascriptMinify() m.minify(ins, outs) output = outs.getvalue() assert output == "function foo(''){}" def testUnicode(self): instr = u'\u4000 //foo' expected = u'\u4000' output = jsmin.jsmin(instr) self.assertEqual(output, expected) def testCommentBeforeEOF(self): self.assertMinified("//test\r\n", "") def testCommentInObj(self): self.assertMinified("""{ a: 1,//comment }""", "{a:1,}") def testCommentInObj2(self): self.assertMinified("{a: 1//comment\r\n}", "{a:1\n}") def testImplicitSemicolon(self): # return \n 1 is equivalent with return; 1 # so best make sure jsmin retains the newline self.assertMinified("return;//comment\r\na", "return;a") def testImplicitSemicolon2(self): self.assertMinified("return//comment...\r\na", "return\na") def testSingleComment2(self): self.assertMinified('x.replace(/\//, "_")// slash to underscore', 'x.replace(/\//,"_")') if __name__ == '__main__': unittest.main()

[ "paul.tunison@kitware.com" ]

paul.tunison@kitware.com

ff90cd1f1161c0d09ab2942b7f313e655ef548a0

a6bd898302ffebe9066595b264f9e5e38e6fa8e6

/settings_template.py

069b2d192200ef4343a3508486203a989c2cb909

[]

no_license

symroe/teamprime_retweets

65e8ec57095b138be45496eb115fb4da1d1e1af0

08e817da6191a8058b3606b076ba9de6bd253b12

refs/heads/master

2021-01-10T22:04:16.968867

2013-09-20T13:32:03

null

0

null

UTF-8

Python

false

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py

CONSUMER_KEY = "" CONSUMER_SECRET = "" ACCESS_TOKEN_KEY = "" ACCESS_TOKEN_SECRET = "" username = "TeamPrimeLtd" TWEET_PATH = "tweets"

[ "sym.roe@talusdesign.co.uk" ]

sym.roe@talusdesign.co.uk

f75a10dc0ef05561c5371a193810ff7eefcf5c22

b76aa6044aa0971bc7842cd4c3faa281c9c0e5cd

/1044_multiplos.py

f0455f370926483d5f3d396afe4542b00c05b844

[]

no_license

Miguelsantos101/algoritmos1-2021-1

8496233f6d37bd70e47949c7e23b34e6c2181bd1

fe03097d870e4f47796e69c97020f9c0bdba0cab

refs/heads/main

2023-05-03T23:08:53.669522

2021-05-27T02:33:12

null

0

null

UTF-8

Python

false

193

py

#a, b = input().split() #a = int(a) #b = int(b) a, b = map(int, input().split()) if a < b: temp = b b = a a = temp if a % b == 0: print("Sao Multiplos") else: print("Nao sao Multiplos")

[ "carloshiga@alumni.usp.br" ]

carloshiga@alumni.usp.br

8d3399769dfddb9fe82a9f192ca45d86625e5e59

d5688ec8a696b7d8bb34ef5e0a7876532619fce8

/spreadsheetupload/urls.py

3145c62652bdfbd3a855d1e75b5cf1101a6fdefb

[]

no_license

varunsarvesh/spreadsheet

dffdea68bf449d232fcb0e33382e58fe8540471e

e81112193efe0d638881f1a8b7b5138d7af433b3

refs/heads/master

2020-03-18T19:51:02.133600

2018-05-29T08:51:06

135,181,780

0

null

UTF-8

Python

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py

"""spreadsheetupload URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include urlpatterns = [ path('admin/', admin.site.urls), path('XLApp/', include('upload.urls')), ]

[ "varun@cyces.co" ]

varun@cyces.co

b41e10890c9ac9413fe046efde3866bcd757844a

5b19f8512f3f8716f7e7b9b45380d3d9eb92565e

/app/app/settings.py

59760d31a9202a974de5e40adc3bffd206d90a84

[]

no_license

raiatul14/taxi-app

a1daf11649b1de2e0f9942aa40dd193617641c50

37cf15ab77bb808494551300a25c8da8ed85645b

refs/heads/main

2023-06-26T15:54:49.244535

2021-07-24T14:34:24

382,535,810

0

null

UTF-8

Python

false

4,226

py

""" Django settings for app project. Generated by 'django-admin startproject' using Django 3.2.5. For more information on this file, see https://docs.djangoproject.com/en/3.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.2/ref/settings/ """ import os from pathlib import Path import datetime # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = os.environ.get('SECRET_KEY') # SECURITY WARNING: don't run with debug turned on in production! DEBUG = bool(int(os.environ.get('DEBUG', 0))) ALLOWED_HOSTS = [] ALLOWED_HOSTS.extend( filter( None, os.environ.get('ALLOWED_HOSTS', '').split(','), ) ) # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'core', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'app.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'app.wsgi.application' ASGI_APPLICATION = 'taxi.routing.application' # Database # https://docs.djangoproject.com/en/3.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql', 'HOST': os.environ.get('DB_HOST'), 'NAME': os.environ.get('DB_NAME'), 'USER': os.environ.get('DB_USER'), 'PASSWORD': os.environ.get('DB_PASS') } } # Password validation # https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.2/howto/static-files/ STATIC_URL = '/static/' # Default primary key field type # https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' AUTH_USER_MODEL = 'core.User' #REDIS REDIS_URL = os.environ.get('REDIS_URL', 'redis://localhost:6379') #DJANGO CHANNELS CHANNEL_LAYERS = { 'default': { 'BACKEND': 'channels_redis.core.RedisChannelLayer', 'CONFIG': { 'hosts': [REDIS_URL], }, }, } #REST FRAMEWORK REST_FRAMEWORK = { 'DEFAULT_AUTHENTICATION_CLASSES': ( 'rest_framework_simplejwt.authentication.JWTAuthentication', 'rest_framework.authentication.SessionAuthentication' ) } SIMPLE_JWT = { 'ACCESS_TOKEN_LIFETIME': datetime.timedelta(minutes=5), 'REFRESH_TOKEN_LIFETIME': datetime.timedelta(days=1), 'USER_ID_CLAIM': 'id', }

[ "atul.rai@ajackus.com" ]

atul.rai@ajackus.com

5aa68c22244a5396ea453095dedc1d96aba4aa72

d9b53673b899a9b842a42060740b734bf0c63a31

/leetcode/python/easy/p645_findErrorNums.py

0b9b378910292d7af736c77ca60c91c415bce9a7

[ "Apache-2.0" ]

permissive

kefirzhang/algorithms

a8d656774b576295625dd663154d264cd6a6a802

549e68731d4c05002e35f0499d4f7744f5c63979

refs/heads/master

2021-06-13T13:05:40.851704

2021-04-02T07:37:59

173,903,408

0

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397

py

class Solution: def findErrorNums(self, nums): helper = [0] * len(nums) for i in nums: helper[i - 1] += 1 for i, n in enumerate(helper): print(i, n) if n == 0: lack = i + 1 elif n == 2: more = i + 1 return [more, lack] slu = Solution() print(slu.findErrorNums([1, 2, 2, 4]))

[ "8390671@qq.com" ]

8390671@qq.com

6aa6cad3f09fd39c8de6b26302daf10e485cedb5

27ece9ab880a0bdba4b2c053eccda94602c716d5

/.history/save_20181129231105.py

50671059975cdfa4cf895b943b529349ae4d201e

[]

no_license

Symfomany/keras

85e3ad0530837c00f63e14cee044b6a7d85c37b2

6cdb6e93dee86014346515a2017652c615bf9804

refs/heads/master

2020-04-08T20:21:35.991753

2018-11-30T08:23:36

159,695,807

0

null

UTF-8

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2,777

py

import os, argparse import tensorflow as tf # The original freeze_graph function # from tensorflow.python.tools.freeze_graph import freeze_graph dir = os.path.dirname(os.path.realpath(__file__)) def freeze_graph(model_dir, output_node_names): """Extract the sub graph defined by the output nodes and convert all its variables into constant Args: model_dir: the root folder containing the checkpoint state file output_node_names: a string, containing all the output node's names, comma separated """ if not tf.gfile.Exists(model_dir): raise AssertionError( "Export directory doesn't exists. Please specify an export " "directory: %s" % model_dir) if not output_node_names: print("You need to supply the name of a node to --output_node_names.") return -1 # We retrieve our checkpoint fullpath checkpoint = tf.train.get_checkpoint_state(model_dir) input_checkpoint = checkpoint.model_checkpoint_path # We precise the file fullname of our freezed graph absolute_model_dir = "/".join(input_checkpoint.split('/')[:-1]) output_graph = absolute_model_dir + "/frozen_model.pb" # We clear devices to allow TensorFlow to control on which device it will load operations clear_devices = True # We start a session using a temporary fresh Graph with tf.Session(graph=tf.Graph()) as sess: # We import the meta graph in the current default Graph saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices) # We restore the weights saver.restore(sess, input_checkpoint) # We use a built-in TF helper to export variables to constants output_graph_def = tf.graph_util.convert_variables_to_constants( sess, # The session is used to retrieve the weights tf.get_default_graph().as_graph_def(), # The graph_def is used to retrieve the nodes output_node_names.split(",") # The output node names are used to select the usefull nodes ) # Finally we serialize and dump the output graph to the filesystem with tf.gfile.GFile(output_graph, "wb") as f: f.write(output_graph_def.SerializeToString()) print("%d ops in the final graph." % len(output_graph_def.node)) return output_graph_def if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("--model_dir", type=str, default="models", help="Model folder to export") parser.add_argument("--output_node_names", type=str, default="", help="The name of the output nodes, comma separated.") args = parser.parse_args() freeze_graph(args.model_dir, args.output_node_names)

[ "julien@meetserious.com" ]

julien@meetserious.com

7f19e8afa6fdab3a0d7af9f55578ca1ba59afa65

81061f903318fceac254b60cd955c41769855857

/server/paiements/migrations/0003_auto__chg_field_transaction_extra_data.py

b059e9dea63be589ea180dbfe9a60bdc411cea7a

[ "BSD-2-Clause" ]

permissive

agepoly/polybanking

1e253e9f98ba152d9c841e7a72b7ee7cb9d9ce89

f8f19399585293ed41abdab53609ecb8899542a2

refs/heads/master

2020-04-24T06:15:16.606580

2015-10-26T19:52:03

null

0

null

UTF-8

Python

false

7,133

py

# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Changing field 'Transaction.extra_data' db.alter_column(u'paiements_transaction', 'extra_data', self.gf('django.db.models.fields.TextField')(null=True)) def backwards(self, orm): # Changing field 'Transaction.extra_data' db.alter_column(u'paiements_transaction', 'extra_data', self.gf('django.db.models.fields.TextField')(default='')) models = { u'auth.group': { 'Meta': {'object_name': 'Group'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, u'auth.permission': { 'Meta': {'ordering': "(u'content_type__app_label', u'content_type__model', u'codename')", 'unique_together': "((u'content_type', u'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, u'auth.user': { 'Meta': {'object_name': 'User'}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, u'configs.config': { 'Meta': {'object_name': 'Config'}, 'active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'admin_enable': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'allowed_users': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.User']", 'symmetrical': 'False', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'key_api': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'key_ipn': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'key_request': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'test_mode': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'url_back_err': ('django.db.models.fields.URLField', [], {'max_length': '200'}), 'url_back_ok': ('django.db.models.fields.URLField', [], {'max_length': '200'}), 'url_ipn': ('django.db.models.fields.URLField', [], {'max_length': '200'}) }, u'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, u'paiements.transaction': { 'Meta': {'object_name': 'Transaction'}, 'amount': ('django.db.models.fields.IntegerField', [], {}), 'config': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['configs.Config']"}), 'creation_date': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'extra_data': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'internal_status': ('django.db.models.fields.CharField', [], {'default': "'cr'", 'max_length': '2'}), 'ipn_needed': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_ipn_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'last_postfinance_ipn_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'last_user_back_from_postfinance_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'last_userforwarded_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'postfinance_id': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'postfinance_status': ('django.db.models.fields.CharField', [], {'default': "'??'", 'max_length': '2'}), 'reference': ('django.db.models.fields.CharField', [], {'max_length': '255'}) }, u'paiements.transctionlog': { 'Meta': {'object_name': 'TransctionLog'}, 'extra_data': ('django.db.models.fields.TextField', [], {}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'log_type': ('django.db.models.fields.CharField', [], {'max_length': '64'}), 'transaction': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['paiements.Transaction']"}), 'when': ('django.db.models.fields.DateTimeField', [], {}) } } complete_apps = ['paiements']

[ "maximilien@theglu.org" ]

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# Generated by Django 2.2.3 on 2019-10-19 16:38 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('myadmin', '0011_auto_20191018_2225'), ] operations = [ migrations.DeleteModel( name='Booktype', ), migrations.DeleteModel( name='Users', ), ]

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import numpy as np import time class DistanceMethodNotValidError(Exception): pass class NotSameLength(Exception): pass class KMeansClustering: def __init__(self): self.centroids = None self.x_columns = None self.how = None self.df = None def euclidean_distance(self,this_row,other): res = 0 for cols in self.x_columns: delta = this_row[cols] - other[cols] delta_sqr = delta**2 res += delta_sqr return np.sqrt(res) def manhattan_distance(self,this_row,other): res = 0 for cols in self.x_columns: delta = this_row[cols] - other[cols] delta_abs = np.abs(delta) res += delta_abs return res def calculate_nearest(self,row,how='euclidean'): dist = [0 for i in range(len(self.centroids))] dist = np.array(dist) for i in range(len(self.centroids)): if how == 'euclidean': dist[i] = self.euclidean_distance(row,self.centroids.loc[i]) elif how == 'manhattan': dist[i] = self.manhattan_distance(row,self.centroids.loc[i]) else: raise DistanceMethodNotValidError() min_idx = np.where(dist == dist.min())[0][0] return min_idx def fit(self,df_,x_columns,k,how='euclidean'): df = df_.copy() self.x_columns = [df.columns[i] for i in x_columns] self.centroids = df.sample(k).copy() self.centroids = self.centroids.reset_index() self.centroids = self.centroids[self.x_columns] self.how = how df['Label'] = np.nan df['New Label'] = np.nan while False in (df['Label'] == df['New Label']).unique(): df['Label'] = df.apply(lambda row: self.calculate_nearest(row[self.x_columns],self.how),axis=1) for i in range(len(self.centroids)): df_i = df[df['Label'] == i] means = df_i.mean() for col in self.x_columns: self.centroids.loc[i,col] = means[col] df['New Label'] = df.apply(lambda row: self.calculate_nearest(row[self.x_columns],self.how),axis=1) df['Label'] = df['New Label'] del df['New Label'] self.df = df def predict(self,data): if len(self.x_columns) != len(data): raise NotSameLength() temp = data data = {} for i in range(len(self.x_columns)): data[self.x_columns[i]] = temp[i] return self.calculate_nearest(data,self.how)

[ "impper1@gmail.com" ]

impper1@gmail.com

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from .sub_helpers.documents import Documents from .sub_helpers.applications import Applications from .sub_helpers.cases import Cases from .sub_helpers.document_templates import DocumentTemplates from .sub_helpers.ecju_queries import EcjuQueries from .sub_helpers.flags import Flags from .sub_helpers.goods import Goods from .sub_helpers.goods_queries import GoodsQueries from .sub_helpers.organisations import Organisations from .sub_helpers.ogel import Ogel from .sub_helpers.parties import Parties from .sub_helpers.picklists import Picklists from .sub_helpers.queues import Queues from .sub_helpers.users import Users class TestHelper: """ Contains a collection of test helper classes, grouped by functional area, with each class containing required logic wrapping calls to various LITE API endpoints. """ def __init__(self, api): self.api_client = api self.context = self.api_client.context request_data = self.api_client.request_data self.documents = Documents(api_client=self.api_client, request_data=request_data) self.users = Users(api_client=self.api_client, request_data=request_data) self.organisations = Organisations(api_client=self.api_client, request_data=request_data) self.goods = Goods(api_client=self.api_client, documents=self.documents, request_data=request_data) self.goods_queries = GoodsQueries(api_client=self.api_client, request_data=request_data) self.parties = Parties(api_client=self.api_client, documents=self.documents, request_data=request_data) self.ecju_queries = EcjuQueries(api_client=self.api_client, request_data=request_data) self.picklists = Picklists(api_client=self.api_client, request_data=request_data) self.ogel = Ogel(api_client=self.api_client, request_data=request_data) self.cases = Cases(api_client=self.api_client, request_data=request_data) self.flags = Flags(api_client=self.api_client, request_data=request_data) self.queues = Queues(api_client=self.api_client, request_data=request_data) self.document_templates = DocumentTemplates(api_client=self.api_client, request_data=request_data) self.applications = Applications( parties=self.parties, goods=self.goods, api_client=self.api_client, documents=self.documents, request_data=request_data, organisations=self.organisations, )

[ "noreply@github.com" ]

noreply@github.com

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from django.shortcuts import render from django.http import HttpResponse from .models import Product def productsHome(request): allProds = [] catprods = Product.objects.values('category', 'product_id') cats = {item['category'] for item in catprods} for cat in cats: prod = Product.objects.filter(category=cat) allProds.append(prod) params = {'allProds':allProds} return render(request, 'products/productHome.html',params) def home(request): products = Product.objects.all() params={'product':products} return render(request, 'products/home.html',params) def checkout(request): return render(request, 'products/checkout.html')

[ "anshikag.1999@gmail.com" ]

anshikag.1999@gmail.com

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# Do not edit. File was generated by node-gyp's "configure" step { "target_defaults": { "cflags": [], "default_configuration": "Release", "defines": [], "include_dirs": [], "libraries": [], "msbuild_toolset": "v141", "msvs_windows_target_platform_version": "10.0.17763.0" }, "variables": { "asan": 0, "build_v8_with_gn": "false", "coverage": "false", "debug_nghttp2": "false", "debug_node": "false", "enable_lto": "false", "enable_pgo_generate": "false", "enable_pgo_use": "false", "force_dynamic_crt": 0, "host_arch": "x64", "icu_data_in": "..\\..\\deps/icu-small\\source/data/in\\icudt65l.dat", "icu_default_data": "", "icu_endianness": "l", "icu_gyp_path": "tools/icu/icu-generic.gyp", "icu_locales": "en,root", "icu_path": "deps/icu-small", "icu_small": "true", "icu_ver_major": "65", "is_debug": 0, "napi_build_version": "5", "nasm_version": "2.14", "node_byteorder": "little", "node_debug_lib": "false", "node_enable_d8": "false", "node_install_npm": "true", "node_module_version": 72, "node_no_browser_globals": "false", "node_prefix": "/usr/local", "node_release_urlbase": "https://nodejs.org/download/release/", "node_report": "true", "node_shared": "false", "node_shared_brotli": "false", "node_shared_cares": "false", "node_shared_http_parser": "false", "node_shared_libuv": "false", "node_shared_nghttp2": "false", "node_shared_openssl": "false", "node_shared_zlib": "false", "node_tag": "", "node_target_type": "executable", "node_use_bundled_v8": "true", "node_use_dtrace": "false", "node_use_etw": "true", "node_use_large_pages": "false", "node_use_large_pages_script_lld": "false", "node_use_node_code_cache": "true", "node_use_node_snapshot": "true", "node_use_openssl": "true", "node_use_v8_platform": "true", "node_with_ltcg": "true", "node_without_node_options": "false", "openssl_fips": "", "openssl_is_fips": "false", "shlib_suffix": "so.72", "target_arch": "x64", "v8_enable_gdbjit": 0, "v8_enable_i18n_support": 1, "v8_enable_inspector": 1, "v8_no_strict_aliasing": 1, "v8_optimized_debug": 1, "v8_promise_internal_field_count": 1, "v8_random_seed": 0, "v8_trace_maps": 0, "v8_use_siphash": 1, "v8_use_snapshot": 1, "want_separate_host_toolset": 0, "nodedir": "C:\\Users\\Administrator\\AppData\\Local\\node-gyp\\Cache\\12.16.2", "standalone_static_library": 1, "msbuild_path": "D:\\Program Files (x86)\\Microsoft Visual Studio\\2017\\Community\\MSBuild\\15.0\\Bin\\MSBuild.exe", "cache": "C:\\Users\\Administrator\\AppData\\Local\\npm-cache", "globalconfig": "C:\\Users\\Administrator\\AppData\\Roaming\\npm\\etc\\npmrc", "init_module": "C:\\Users\\Administrator\\.npm-init.js", "metrics_registry": "https://registry.npm.taobao.org/", "node_gyp": "C:\\Users\\Administrator\\AppData\\Roaming\\npm\\node_modules\\npm\\node_modules\\node-gyp\\bin\\node-gyp.js", "prefix": "C:\\Users\\Administrator\\AppData\\Roaming\\npm", "registry": "https://registry.npm.taobao.org/", "userconfig": "C:\\Users\\Administrator\\.npmrc", "user_agent": "npm/7.7.6 node/v12.16.2 win32 x64" } }

[ "1131559748@qq.com" ]

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major = 0 minor=0 patch=0 branch="dev" timestamp=1376801630.58

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num_epochs = 500 samples_per_epoch = 10000 lr = 0.001 feature_sz = 32 feature_alpha = 1.5

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# -*- coding: utf-8 -*- ############################################################################### # # Delete # Removes a revision. # # Python version 2.6 # ############################################################################### from temboo.core.choreography import Choreography from temboo.core.choreography import InputSet from temboo.core.choreography import ResultSet from temboo.core.choreography import ChoreographyExecution import json class Delete(Choreography): def __init__(self, temboo_session): """ Create a new instance of the Delete Choreo. A TembooSession object, containing a valid set of Temboo credentials, must be supplied. """ Choreography.__init__(self, temboo_session, '/Library/Google/Drive/Revisions/Delete') def new_input_set(self): return DeleteInputSet() def _make_result_set(self, result, path): return DeleteResultSet(result, path) def _make_execution(self, session, exec_id, path): return DeleteChoreographyExecution(session, exec_id, path) class DeleteInputSet(InputSet): """ An InputSet with methods appropriate for specifying the inputs to the Delete Choreo. The InputSet object is used to specify input parameters when executing this Choreo. """ def set_AccessToken(self, value): """ Set the value of the AccessToken input for this Choreo. ((optional, string) A valid access token retrieved during the OAuth2 process. This is required unless you provide the ClientID, ClientSecret, and RefreshToken to generate a new access token.) """ InputSet._set_input(self, 'AccessToken', value) def set_ClientID(self, value): """ Set the value of the ClientID input for this Choreo. ((conditional, string) The Client ID provided by Google. Required unless providing a valid AccessToken.) """ InputSet._set_input(self, 'ClientID', value) def set_ClientSecret(self, value): """ Set the value of the ClientSecret input for this Choreo. ((conditional, string) The Client Secret provided by Google. Required unless providing a valid AccessToken.) """ InputSet._set_input(self, 'ClientSecret', value) def set_FileID(self, value): """ Set the value of the FileID input for this Choreo. ((required, string) The ID of the file.) """ InputSet._set_input(self, 'FileID', value) def set_RefreshToken(self, value): """ Set the value of the RefreshToken input for this Choreo. ((conditional, string) An OAuth refresh token used to generate a new access token when the original token is expired. Required unless providing a valid AccessToken.) """ InputSet._set_input(self, 'RefreshToken', value) def set_RevisionID(self, value): """ Set the value of the RevisionID input for this Choreo. ((required, string) The ID of the revision.) """ InputSet._set_input(self, 'RevisionID', value) class DeleteResultSet(ResultSet): """ A ResultSet with methods tailored to the values returned by the Delete Choreo. The ResultSet object is used to retrieve the results of a Choreo execution. """ def getJSONFromString(self, str): return json.loads(str) def get_Response(self): """ Retrieve the value for the "Response" output from this Choreo execution. ((json) The response from Google.) """ return self._output.get('Response', None) def get_NewAccessToken(self): """ Retrieve the value for the "NewAccessToken" output from this Choreo execution. ((string) Contains a new AccessToken when the RefreshToken is provided.) """ return self._output.get('NewAccessToken', None) class DeleteChoreographyExecution(ChoreographyExecution): def _make_result_set(self, response, path): return DeleteResultSet(response, path)

[ "dattasaurabh82@gmail.com" ]

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import networkx as nx import sys import getopt import csv import time from com.drabarz.karolina.DominatingSetAlgorithm import DominatingSetAlgorithm from com.drabarz.karolina.NetworkXAlgorithm import NetworkXAlgorithm from com.drabarz.karolina.GreedyAlgorithm import GreedyAlgorithm from com.drabarz.karolina.DispersedGreedyAlgorithm import DispersedGreedyAlgorithm from com.drabarz.karolina.ClassicalSetCoverageAlgorithm import ClassicalSetCoverageAlgorithm from com.drabarz.karolina.ModifiedGreedyAlgorithm import ModifiedGreedyAlgorithm from com.drabarz.karolina.FastGreedyAlgorithm import FastGreedyAlgorithm def getCommandLineArguments(): argv = sys.argv[1:] graphFile = '' setFile = '' action = 'none' try: opts, args = getopt.getopt(argv,"hfcg:s:",["graphFile=","setFile="]) except getopt.GetoptError: print 'test.py -g <graphFile> -s <setFile>' sys.exit(2) for opt, arg in opts: if opt == '-h': print 'To find the smallest dominating set:' print '\ttest.py -f -g <graphFile> -s <setFile>' print 'To check if set is dominating:' print '\ttest.py -c -g <graphFile> -s <setFile>' sys.exit() elif opt == '-f' : action = "findDominatingSet" elif opt == '-c' : action = "checkIfSetIsDominating" elif opt in ("-g", "--graphFile"): graphFile = arg elif opt in ("-s", "--setFile"): setFile = arg print 'Graph file is: ', graphFile print 'Set file is: ', setFile return [graphFile, setFile, action]; def createGraphFromFile(graphFile): graph = nx.Graph(); try: with open(graphFile, "rb") as inputfile: reader = csv.reader(inputfile); for i, line in enumerate(reader): if i < 4: continue edge = line[0].split('\t') graph.add_edge(edge[0], edge[1]); except IOError: print 'There is a incorrect name of graph file' sys.exit() except IndexError: print 'Incorrect input file structure' sys.exit() return graph; def findAndShowDominatingSet(graph, setFile): algorithm = chooseAlgorithm(); printGraphParamiters(graph); start_time = time.time() dominatingSet = algorithm.findDominatingSet(graph); stop_time = time.time() - start_time print "Algorithm execution time = ", stop_time printDominatingSet(dominatingSet); saveDominatingSet(dominatingSet, setFile); return; def chooseAlgorithm(): while 1 : showMainMenu(); answer = raw_input(); if answer == '1' : return GreedyAlgorithm(); elif answer == '2' : return DispersedGreedyAlgorithm(); elif answer == '3' : return ClassicalSetCoverageAlgorithm(); elif answer == '4' : return ModifiedGreedyAlgorithm(); elif answer == '5' : return FastGreedyAlgorithm(); elif answer == '6' : return NetworkXAlgorithm(); sys.exc_clear(); def showMainMenu(): print "Choose algorithm to calculate the smallest dominating set: " print "\t1) greedy algorithm" print "\t2) dispersed greedy algorithm" print "\t3) classical set coverage algorithm" print "\t4) modified greedy algorithm" print "\t5) fast greedy algorithm" print "\t6) use algorithm implemented in NetworkX library" return; def printGraphParamiters(graph): print "Graph description: " print "Number of nodes: ", nx.number_of_nodes(graph); print "Number of edges: ", nx.number_of_edges(graph), "\n"; return; def printDominatingSet(dominatingSet): print "Number of nodes in dominating set: ", len(dominatingSet); for node in dominatingSet: print node; return; def saveDominatingSet(dominatingSet, setFile): try: with open(setFile, 'wb') as outputFile: writer = csv.writer(outputFile); outputFile.write("#Number of nodes in dominating set: " + str(len(dominatingSet)) + "\n"); for i in range(0, len(dominatingSet)): outputFile.write(str(dominatingSet[i])+ '\n') except IOError: print 'There is no set file name selected' return; def checkIfSetIsDominating(graph, setFile): inputSet = createSetFromFile(setFile); isDominatingSet = checkIfIsDominatingSet(graph, inputSet); print "Is set dominating: ", isDominatingSet; return; def createSetFromFile(setFile): inputSet = set(); try: with open(setFile, "rb") as inputfile: reader = csv.reader(inputfile); for i, line in enumerate(reader): if i < 1: continue node = line[0]; inputSet.add(node); except IOError: print 'There is a wrong name of set file' sys.exit() except IndexError: print 'Incorrect input file structure' sys.exit() return inputSet; def checkIfIsDominatingSet(graph, dominatingSet): return nx.is_dominating_set(graph, dominatingSet); [graphFile, setFile, action] = getCommandLineArguments(); graph = createGraphFromFile(graphFile); if action == "findDominatingSet" : findAndShowDominatingSet(graph, setFile); elif action == "checkIfSetIsDominating" : checkIfSetIsDominating(graph, setFile); else : sys.exit();

[ "karolina.drabarz@gmail.com" ]

karolina.drabarz@gmail.com

1339b5e2da73351b90d8bbc7a8b70a395472d3c0

fe6c7ddbd2ce513105346c3e9ecd858ee0240237

/inheritance.py

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[]

no_license

adilreza/basic_python_practice

bfb4d5fc08c360f3b6808b74c431a75e09a09207

c712f47bdde081c305344ec396f9a146d4dad369

refs/heads/master

2020-08-07T00:45:08.938270

2019-10-11T13:29:18

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class parent_class: a=4; b=3; def parent_fuction(self): print("this is from parent function") return 0; class child_class(parent_class):#this way inherited def child_function(self): print("this is me from child function") def make_sum(self): sum = self.a+ self.b; return sum; if __name__=="__main__": myobj = child_class() print(myobj.a) print(myobj.parent_fuction()) print(myobj.make_sum())

[ "adilreza043@gmail.com" ]

adilreza043@gmail.com

037e407294716acc0cfc28a662b89b093dfe6d3b

b96edd0ba0a9f2a73a8ef8ed011714798fa72303

/test_all.py

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[]

no_license

lijiahaoAA/lijiahao_cpsc_12lead

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663264920ead07493c0d8fe9987b9ab9a60d35fd

refs/heads/master

2023-03-14T07:11:28.996901

2021-03-09T07:08:50

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import time import numpy as np np.set_printoptions(threshold=np.inf) import scipy.io as sio import os import config from keras.preprocessing import sequence import QRSDetectorOffline import matplotlib.pyplot as plt plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签 plt.rcParams['axes.unicode_minus']=False #用来正常显示负号 # os.environ["TF_CPP_MIN_LOG_LEVEL"] = '0' # config = config.Config() # a = os.listdir(config.train_mat_path) # train_mat = [] # 存储所有6877个样本数据 # for i in range(len(a)): # if a[i].endswith('.mat'): # train_mat.append(config.train_mat_path + a[i]) # # b = os.listdir(config.test_mat_path) # test_mat = [] # 存储最终的测试数据 # for i in range(len(b)): # if b[i].endswith('.mat'): # test_mat.append(config.test_mat_path + b[i]) # # def data_process(all_mat): # ECG_1 = [] # ECG_2 = [] # ECG_3 = [] # #for recordpath in range(len(all_mat)): # for recordpath in range(1): # # load ECG # mat = sio.loadmat(all_mat[recordpath]) # mat = np.array(mat['ECG']['data'][0, 0]) # mat = np.transpose(mat) # 做转置 # signal = mat # ECG_1.append(signal) # #print(signal.shape) # # qrsdetector = QRSDetectorOffline.QRSDetectorOffline(signal, config.sample_frequency, verbose=False, # plot_data=False, show_plot=False) # # denoise ECG 对每一导联进行去噪滤波 # for i in range(signal.shape[1]): # signal[:, i] = qrsdetector.bandpass_filter(signal[:, i], lowcut=0.5, highcut=49.0, # signal_freq=config.sample_frequency, filter_order=1) # # ECG_2.append(signal) # # print(ECG[0].shape) # # print(ECG[0]) # # print(signal) # # 将所有导联的长度填充为一样的,尾部补0 # # ECG_1 = sequence.pad_sequences(ECG_1, maxlen=3600, dtype='float32', truncating='post') # ECG_2 = sequence.pad_sequences(ECG_2, maxlen=3600, dtype='float32', truncating='post') # print(len(ECG_1)) # print(len(ECG_2)) # # plot_wave(ECG_1[0][:,0],ECG_2[0][:,0]) # calculate_max_min(ECG_2,ECG_1[0][:,0],ECG_2[0][:,0]) # # #np.save('ECG_train_data_process_no_wave.npy', ECG) # # np.save('ECG_train_data_process_3600QRS.npy', ECG) # #np.save('ECG_test_data_process_no_wave.npy', ECG) # # np.save('ECG_test_data_process_3600QRS.npy', ECG) # return ECG_1, ECG_2 # # def calculate_max_min(ECG,ECG_1,ECG_2): # data = [] # tic = time.time() # for i in range(len(ECG)): # data.append(max(ECG[i][:, 0])) # data.append(min(ECG[i][:, 0])) # # data.append(max(ECG[i][:, 1])) # data.append(min(ECG[i][:, 1])) # # data.append(max(ECG[i][:, 2])) # data.append(min(ECG[i][:, 2])) # # data.append(max(ECG[i][:, 3])) # data.append(min(ECG[i][:, 3])) # # data.append(max(ECG[i][:, 4])) # data.append(min(ECG[i][:, 4])) # # data.append(max(ECG[i][:, 5])) # data.append(min(ECG[i][:, 5])) # # data.append(max(ECG[i][:, 6])) # data.append(min(ECG[i][:, 6])) # # data.append(max(ECG[i][:, 7])) # data.append(min(ECG[i][:, 7])) # # data.append(max(ECG[i][:, 8])) # data.append(min(ECG[i][:, 8])) # # data.append(max(ECG[i][:, 9])) # data.append(min(ECG[i][:, 9])) # # data.append(max(ECG[i][:, 10])) # data.append(min(ECG[i][:, 10])) # # data.append(max(ECG[i][:, 11])) # data.append(min(ECG[i][:, 11])) # # # print(len(data)) # with open("2.txt", 'w') as file: # data1 = str(data) # file.write(data1) # file.close() # max_data = max(data) # 训练集和测试集中在归一化到某个范围内时需要保证这个max_data和min_data是一致的 # min_data = min(data) # normalization(ECG, config.max_data, config.min_data, ECG_1, ECG_2) # print(max(data)) # print(min(data)) # toc = time.time() # print("data normalization takes time:", toc - tic) # return max_data,min_data # # # 数据归一化到指定区间 # def normalization(ECG, max_data, min_data, ECG_1, ECG_2): # if(max_data - min_data == 0): # print("分母为零，请检查") # return # k = (config.normalization_max - config.normalization_min)/((max_data - min_data) * 1.0) # 比例系数 # for i in range(len(ECG)): # ECG[i][:, 0] = config.normalization_min + k * (ECG[i][:, 0] - min_data) # ECG[i][:, 1] = config.normalization_min + k * (ECG[i][:, 1] - min_data) # ECG[i][:, 2] = config.normalization_min + k * (ECG[i][:, 2] - min_data) # ECG[i][:, 3] = config.normalization_min + k * (ECG[i][:, 3] - min_data) # ECG[i][:, 4] = config.normalization_min + k * (ECG[i][:, 4] - min_data) # ECG[i][:, 5] = config.normalization_min + k * (ECG[i][:, 5] - min_data) # ECG[i][:, 6] = config.normalization_min + k * (ECG[i][:, 6] - min_data) # ECG[i][:, 7] = config.normalization_min + k * (ECG[i][:, 7] - min_data) # ECG[i][:, 8] = config.normalization_min + k * (ECG[i][:, 8] - min_data) # ECG[i][:, 9] = config.normalization_min + k * (ECG[i][:, 9] - min_data) # ECG[i][:, 10] = config.normalization_min + k * (ECG[i][:, 10] - min_data) # ECG[i][:, 11] = config.normalization_min + k * (ECG[i][:, 11] - min_data) # # # np.save('ECG_train_data_normal.npy', ECG) # # np.save('ECG_test_data_normal_500record.npy', ECG) # plot_wave(ECG_1,ECG_2,ECG[0][:,0]) # return ECG # # def plot_wave(ECG_qrs, ECG_noqrs, ECG_3): # plt.figure() # print(len(ECG_qrs.shape)) # print(len(ECG_noqrs.shape)) # print(len(ECG_3.shape)) # # plt.plot(range(3600), ECG_qrs[0:3600], color="red",label="去噪数据") # # .plot(range(3600), ECG_noqrs, color="blue") # # plt.plot(range(3600), ECG_3, color="blue", label="归一化数据") # plt.title("去噪数据波形对比归一化到[-3,3]数据波形") # plt.xlabel("Time") # plt.ylabel("Voltage") # plt.legend(loc="best") # plt.show() # # #data_process(train_mat) # data_process(test_mat) # from keras import backend as K # from keras.layers import Lambda # import tensorflow as tf # def zeropad(x): # y = K.zeros_like(x) # print(y) # return K.concatenate([x, y], axis=2) # def zeropad_output_shape(input_shape): print(input_shape) shape = list(input_shape) shape[1] *= 2 print(shape) return tuple(shape) input = np.array([[1,2,3],[4,5,6]]) y = np.zeros_like(input) new = np.concatenate([input, y], axis=1) print(new) zeropad_output_shape(input.shape) # input = tf.convert_to_tensor(input) # shortcut = Lambda(zeropad, output_shape=zeropad_output_shape)(input) # print(shortcut)

[ "1301840357@qq.com" ]

1301840357@qq.com

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/Python-Programming-Intermediate/Regular Expressions-164.py

f9d4e1a6d0ed4f1453780805d5c3d68ee17f3af1

[]

no_license

CloudChaoszero/Data-Analyst-Track-Dataquest.io-Projects

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## 1. Introduction ## strings = ["data science", "big data", "metadata"] regex = "data" ## 2. Wildcards in Regular Expressions ## strings = ["bat", "robotics", "megabyte"] regex = "..t" ## 3. Searching the Beginnings And Endings Of Strings ## strings = ["better not put too much", "butter in the", "batter"] bad_string = "We also wouldn't want it to be bitter" regex = "" regex = "^b.tter" ## 5. Reading and Printing the Data Set ## import csv #Open and read file. Therafter, convert to list file = csv.reader(open("askreddit_2015.csv",'r')) post_with_header = list(file) posts = post_with_header[1:] for val in posts[:10]: print(val) ## 6. Counting Simple Matches in the Data Set with re() ## import re #Initialize Counter of_reddit_count = 0 #Counting loop that counts for "of Reddit" in first element of every row for val in posts: if re.search("of Reddit", val[0]): of_reddit_count +=1 else: pass ## 7. Using Square Brackets to Match Multiple Characters ## import re of_reddit_count = 0 for row in posts: if re.search("of [Rr]eddit", row[0]) is not None: of_reddit_count += 1 ## 8. Escaping Special Characters ## import re serious_count = 0 for row in posts: if re.search("\[Serious]",row[0]) is not None: serious_count +=1 print(row[0]) ## 9. Combining Escaped Characters and Multiple Matches ## import re serious_count = 0 for row in posts: if re.search("\[[sS]erious\]", row[0]) is not None: serious_count += 1 ## 10. Adding More Complexity to Your Regular Expression ## import re serious_count = 0 for row in posts: if re.search("[\[$][Ss]erious[\]$]", row[0]) is not None: serious_count += 1 ## 11. Combining Multiple Regular Expressions ## import re serious_start_count = 0 serious_end_count = 0 serious_count_final = 0 for row in posts: if re.search("^[\[$][Ss]erious[\]$]",row[0]) is not None: serious_start_count += 1 if re.search("[\[$][Ss]erious[\]$]$", row[0]) is not None: serious_end_count += 1 if re.search("^[\[$][Ss]erious[\]$]|[\[$][Ss]erious[\]$]$", row[0]) is not None: serious_count_final += 1 ## 12. Using Regular Expressions to Substitute Strings ## import re posts_new = [] for row in posts: row[0] = re.sub("[\[$][Ss]erious[\]$]", "[Serious]", row[0]) posts_new.append(row) ## 13. Matching Years with Regular Expressions ## import re year_strings = [] for string in strings: if re.search("[1-2][0-9][0-9][0-9]", string) is not None: year_strings.append(string) ## 14. Repeating Characters in Regular Expressions ## import re year_strings = [] for y in strings: if re.search("[0-2][0-9]{3}",y) is not None: year_strings.append(y) ## 15. Challenge: Extracting all Years ## import re years = re.findall("[0-2][0-9]{3}", years_string)

[ "noreply@github.com" ]

noreply@github.com

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/credentials_replacer/__main__.py

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permissive

MELODYAPP/aws-credential-replacer

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2021-01-01T20:39:22.871330

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from .replacer import main if __name__ == '__main__': main()

[ "prokhorov@saritasa.com" ]

prokhorov@saritasa.com

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/zygoat/components/__init__.py

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[ "MIT" ]

permissive

swang192/zygoat

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2021-02-05T21:47:14.089625

2020-02-28T04:03:16

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MIT

2020-02-28T19:19:33

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from .base import Component # noqa from .settings_component import SettingsComponent # noqa from .editorconfig import editorconfig from .docker_compose import docker_compose from .backend import backend from .frontend import frontend components = [ editorconfig, docker_compose, backend, frontend, ]

[ "markrawls96@gmail.com" ]

markrawls96@gmail.com

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/all-gists/5259522/snippet.py

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[ "MIT" ]

permissive

gistable/gistable

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2023-02-17T21:33:55.558398

2023-02-11T18:20:10

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#!/usr/bin/env python import sys files = [] if len(sys.argv) > 2: for file in sys.argv[1:]: files.append(str(file)) else: print "Usage: Wordcount.py file1 file2 file3 ..." words_to_ignore = ["that","what","with","this","would","from","your","which","while","these"] things_to_strip = [".",",","?",")","(","\"",":",";","'s"] words_min_size = 4 print_in_html = True text = "" for file in files: f = open(file,"rU") for line in f: text += line words = text.lower().split() wordcount = {} for word in words: for thing in things_to_strip: if thing in word: word = word.replace(thing,"") if word not in words_to_ignore and len(word) >= words_min_size: if word in wordcount: wordcount[word] += 1 else: wordcount[word] = 1 sortedbyfrequency = sorted(wordcount,key=wordcount.get,reverse=True) def print_txt(sortedbyfrequency): for word in sortedbyfrequency: print word, wordcount[word] def print_html(sortedbyfrequency): print "<html><head><title>Wordcount.py Output</title></head><body><table>" for word in sortedbyfrequency: print "<tr><td>%s</td><td>%s</td></tr>" % (word,wordcount[word]) print "</table></body></html>" if print_in_html == True: print_html(sortedbyfrequency) else: print_txt(sortedbyfrequency)

[ "gistshub@gmail.com" ]

gistshub@gmail.com

e54ee1ecc2087f724a84dac26976197e1055fff2

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/sump2.py

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[]

no_license

descampsa/sump2_linux

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51264d52a5789e7042ee9f6a277b619b8e30f0ea

refs/heads/master

2020-06-26T01:56:19.690628

2016-11-23T19:24:26

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#!/usr/bin/python # sump2 # Copyright (c) Kevin M. Hubbard 2016 BlackMesaLabs # # # This program is free software; you can redistribute it and/or modify it under # the terms of the GNU General Public License as published by the Free Software # Foundation; either version 2 of the License, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. # See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA # # Source file: sump2.py # Date: 07.25.16 # Author: Kevin M. Hubbard # Description: A light weight VCD viewer written in Python+PyGame for Linux # or Windows platforms. Designed to make use of the mouse scroll # wheel for fast navigation and inspection of waveform files. Also # follows keyboard navigation used by Vim and ChipVault. # History: # The backstory on ChipWave.py, which was then forked to become sump2.py # is that I wrote it over a weekend while sequestered in a Redmond,WA hotel # chaperoning a highschool JSA tournament for my daughter's class. I was # wanting a better VCD viewer for IcarusVerilog and was frustrated with # the GTKwave user interface and difficulty installing on Linux. It was # designed only to be the backend viewer for simulations, but turned out to # be a really good front-end and back-end for SUMP hardware capture engine. # Original ( now SUMP1 ) design used .NET for waveform viewing which was # frustratingly slow. PyGame based SUMP2 gui is 100x better in my opinion. # # PyGame: # ChipWave uses to Python package PyGame for Mouse and Screen iterfacing. # PyGame does not come with Python and MUST BE INSTALLED! # See http://www.pygame.org/download.shtml # ChipWave.py was written in 2013 as a VCD viewer for IcarusVerilog. It was # ditched in favor of just using GTKwave. Basic features were then reused for # a SUMP2 front-end and back-end to replace the SUMP1 Powershell/.NET app. # Note: There are some legacy ChipWave functions still in here that are not # currently being used and have not been removed. # # [ Python 3.5 for Windows ] # https://www.python.org/downloads/ Python 3.5.2 python-3.5.2.exe # Click Add to Path on Installer Popup # # python.exe -c 'import distutils.util; print(distutils.util.get_platform())' # win32 # # [ PyGame ] # http://www.lfd.uci.edu/~gohlke/pythonlibs/#pygame # pygame-1.9.2b1-cp35-cp35m-win_amd64.whl # pygame-1.9.2b1-cp35-cp35m-win32.whl # # Copy WHL to C:\Users\root\AppData\Local\Programs\Python\Python35-32\Scripts # pip install pygame-1.9.2b1-cp35-cp35m-win32.whl # # [ PySerial ] # https://pypi.python.org/pypi/pyserial # https://pypi.python.org/packages/...../pyserial-3.1.1-py2.py3-none-any.whl # pip install pyserial-3.1.1-py2.py3-none-any.whl # # TODO: Vertical resizing of window has issues. Signal scrolling isnt updated # # Revision History: # Ver When Who What # ---- -------- -------- --------------------------------------------------- # 0.00 07.25.16 khubbard Creation. Forked from chip_wave.py ( VCD Viewer ) # # TODO: Key repeats dont work. Fake out and make own key events ? # TODO: scroll_up and scroll_down have issues if signals are deleted. # TODO: Add a "GenerateVCD" feature that calls external program to make vcd # TODO: Support for bus ripping. Complicated.... # TODO: Search only support Hex searches. Should support signed and unsigned # TODO: Add support for an autorun.txt file on startup. # TODO: Reload doesn't work if new VCD is longer than old VCD. # TODO: On reload, wave.do should be saved and reloaded to preserve format. # WARNING: vcdfile2signal_list() currently requires a clock signal or else # conversion doesnt work unless there is a value change every sample # NOTE: Pygame stops responding if window is dragged onto second monitor. # TODO: Doesn't support SUMP list reordering or wave.txt order. # 09.05.16 khubbard Fix for VCD exporting nicknames. GUI crash fixes. # 09.06.16 khubbard Partial ported from Python 2.7 to 3.5. Incomplete. # 09.18.16 khubbard Major performance improvements. GUI file loading # 09.19.16 khubbard Adjust DWORDs in RLE for trig_delay value # 09.20.16 khubbard RLE Undersample 4x-64x feature added. Popup change. # 09.23.16 khubbard Popup Entry for variable changes in GUI added. # 09.24.16 khubbard User Interface and performance usability improvmnts # 09.25.16 khubbard GUI popup for signal rename. # 09.26.16 khubbard Fixed opening VCD files for static offline viewing. # 09.26.16 khubbard zoom_out capped at max_samples. RLE->VCD working # 09.29.16 khubbard cursor_snap back in for single click op. VCD 'x' # 10.04.16 khubbard fast_render added. Disable 4x prerender on >1000 # 10.06.16 khubbard Fixed popup bugs. sump_bundle_data() feature added # 10.16.16 khubbard RLE culling null sample improvements. Menu changes. # 10.17.16 khubbard fixed vcdfile2signal_list() not decompressing. # 10.18.16 khubbard fixed menu. New function list_remove # 10.19.16 khubbard RLE Event to DWORD alignment fix. Needs HW too. # 10.20.16 khubbard Improve centering to trigger post acquisition. # 10.21.16 khubbard Acquisition_Length fixed. Also works with RLE now. # 10.24.16 khubbard Fixed RLE cropping not showing DWORDs.Speed Improvs ############################################################################### import time from time import sleep; import math # pow import types # type import sys; import os; import platform; import locale; class main(object): def __init__(self): # import math # pow # import types # type self.vers = "2016.10.24"; print("Welcome to SUMP2 " + self.vers + " by BlackMesaLabs"); self.mode_cli = True; try: import pygame # Import PyGame Module except: print("WARNING: PyGame not FOUND!! running in Command Line Mode"); print("Pygame http://www.lfd.uci.edu/~gohlke/pythonlibs/#pygame"); print("pip install pygame-1.9.2b1-cp35-cp35m-win32.whl"); self.vars = init_vars( self, "sump2.ini" ); self.help = init_help( self ); self.math = math; list2file( self, "sump2_manual.txt", init_manual(self ) ); #locale.setlocale( locale.LC_NUMERIC, 'English' ); locale.setlocale( locale.LC_NUMERIC, 'en_US.UTf-8' ); init_globals( self );# Internal software variables self.file_log = open ( self.vars["file_log"] , 'w' ); # ARG0 either specifies a static file to view OR sump2 or an IP address # for talking directly to sump2 hardware. import sys; args = sys.argv + [None]*3; self.file_name = args[1]; # args[0] is script name if ( self.file_name == "bd_shell" or \ self.file_name == "cli" ): self.mode_cli = True; self.file_name = None; else: if 'pygame' in locals() or 'pygame' in globals(): display_init( self ); self.mode_cli = False; else: self.mode_cli = True; self.signal_list = [];# List of class signal(object)s self.signal_delete_list = []; if ( self.file_name == None ): # if ( sump_connect( self ) == False ): # shutdown( self ); # sump_connect( self ); if ( sump_connect( self ) != False ): sump2signal_list( self );# Make Signals based on SUMP2 HW Config self.top_module = "sump2"; else: # make_demo_vcd(); # self.file_name = "foo.vcd"; self.file_name = make_demo_vcd( self ); # sump_dump_data(self); # else: if ( self.file_name != None ): self.bd=None; file2signal_list( self, self.file_name );# VCD is now a signal_list # save_format( self, self.file_name, False );# Create Wave File from VCD Info self.file_name = None; # Make sure we don't overwrite vcd with wave on exit self.vcd_import = True;# Prevents saving a VCD specific sump2_wave.txt file # # Attempt to loading an existing wave.txt file for this block if exists # # otherwise, create one from scratch # import os; # file_name = "wave_" + self.top_module + ".txt";# Default # if os.path.exists( file_name ): # print( "load_format() ", file_name ); # load_format( self, file_name ); # else: # save_format( self, file_name, False ); if ( self.bd != None ): ########################################################################### # If a wave file doesn't exist, create a default one using info from HW import os; # file_name = "wave_" + self.top_module + ".txt";# Default file_name = "sump2_wave.txt";# Default if ( os.path.exists( file_name ) == False and self.bd != None ): print("Creating default wave file"); ram_dwords = self.sump.cfg_dict['ram_dwords']; ram_bytes = self.sump.cfg_dict['ram_event_bytes']; events = ram_bytes * 8; # Iterate the number of event bits and init with 0s txt_list = []; for j in range( 0, events, 1): txt = ("/event[%d]" % j) ; txt_list += [ txt + " -nickname " + txt.replace("/","") ]; # Then follow with a group for all the DWORDs if ( ram_dwords != 0 ): txt_list += ["/dword[%d:%d]" % ( ( 0),( ram_dwords-1) )]; for i in range( 0, ram_dwords, 1): txt = " /dword[%d]" % ( i ); txt_list += [ txt + " -nickname " + txt.replace("/","") ]; file_out = open( "sump2_wave.txt", 'w' ); for each in txt_list: file_out.write( each + "\n" ); file_out.close(); # Load in the wavefile if os.path.exists( file_name ): print( "load_format() ", file_name ); load_format( self, file_name ); trig_i = sump_dump_data(self); sump_vars_to_signal_attribs( self );# Populates things like trigger attr # if os.path.exists( file_name ): # print( "load_format() ", file_name ); # load_format( self, file_name ); # if ( self.bd != None ): # sump_dump_data(self); # else: # save_format( self, file_name, False );# Create one # return; ############################################################################# # CLI Main Loop : When no PyGame loop here STDIN,STDOUT old school style while( self.mode_cli == True and self.done == False ): rts = raw_input(self.prompt); # rts = input(self.prompt); rts = rts.replace("="," = "); words = " ".join(rts.split()).split(' ') + [None] * 4; if ( words[1] == "=" ): cmd = words[1]; parms = [words[0]]+words[2:]; else: cmd = words[0]; parms = words[1:]; # print( cmd, parms ); rts = proc_cmd( self, cmd, parms ); for each in rts: print( each ); # Load the wavefile # # Calc max number of samples and change default zoom if not enough to fill # for sig_obj in self.signal_list: # if ( len( sig_obj.values ) > self.max_samples ): # self.max_samples = len( sig_obj.values ); # if ( ( self.max_samples * self.zoom_x ) < self.screen_width ): # self.zoom_x = float(self.screen_width) / float(self.max_samples); # set_zoom_x( self, self.zoom_x ); # Set the zoom ratio recalc_max_samples( self ); # Draw the 1st startup screen screen_refresh( self ); self.context = "gui"; # GUI Main Loop self.clock = self.pygame.time.Clock(); self.time = self.pygame.time; self.pygame.key.set_repeat(50,200); while ( self.done==False ): # When live, attempt to acquire data, else display static data (faster) if ( self.acq_state == "acquire_single" or "acquire_rle" in self.acq_state or self.acq_state == "acquire_continuous" ): # Check to see if acquired bit is set, then read the data # print ("%02X" % self.sump.rd( addr = None )[0] ); if ( "acquire_rle" in self.acq_state ): sump_done = self.sump.status_triggered + self.sump.status_rle_post; else: sump_done = self.sump.status_triggered + self.sump.status_ram_post; self.undersample_data = False; self.undersample_rate = 1; if ( ( self.sump.rd( addr = None )[0] & sump_done ) == sump_done ): if ( self.acq_mode == "nonrle" ): trig_i = sump_dump_data(self); else: trig_i = sump_dump_rle_data(self); print("Trigger Index = %d " % trig_i ); # Place the cursors by the trigger. for ( i , each ) in enumerate( self.cursor_list ): if ( i == 0 ): offset = -6; else : offset = +4; each.selected = False; # trigger_sample = self.max_samples // 2;# Temporary Trig @ 50% # each.sample = int( trigger_sample ) + offset; each.sample = int( trig_i ) + offset; self.curval_surface_valid = False;# curval surface invalid if ( self.acq_state == "acquire_continuous" ): sump_arm( self, True ); else: self.acq_state = "acquire_stop"; draw_header( self, "ACQUIRED"); print("RENDERING-START"); # start = ( self.max_samples // 2 ) - ( self.max_samples // 8 ); # stop = ( self.max_samples // 2 ) + ( self.max_samples // 8 ); # Zoom-Out the maximum amount that still keeps trigger centered # for non-RLE this is trivial, for RLE it is more complicated trig_to_start = trig_i - 0; trig_to_end = self.max_samples - trig_i; start = trig_i - min( trig_to_start, trig_to_end ); stop = trig_i + min( trig_to_start, trig_to_end ); proc_cmd( self, "zoom_to", [str(start), str(stop) ] ); # proc_cmd( self, "zoom_to", ["0", str( self.max_samples ) ] ); # proc_cmd( self, "zoom_to_cursors", [] ); print("RENDERING-COMPLETE"); else: # draw_header(self,"Waiting for trigger.."); draw_screen( self );# This updates banner self.time.wait(500 ); # Waiting for Trigger else: # self.clock.tick( 10 ); # Don't take 100% of CPU as that would be rude self.time.wait(10 ); # Wait 10ms to share CPU for event in pygame.event.get(): # User did something # VIDEORESIZE if event.type == pygame.VIDEORESIZE: self.screen= pygame.display.set_mode(event.dict['size'], pygame.RESIZABLE | pygame.HWSURFACE | pygame.DOUBLEBUF); self.resize_on_mouse_motion = True;# Delay redraw until resize done # Detect when console box has gained focus and switch from GUI to BD_SHELL # and loop in a keyboard loop processing commands. Exit on a NULL Command. # if event.type == pygame.ACTIVEEVENT: # #print( str(event.gain) + " " + str(event.state) ); # # state=2 user gave focus to something other than GUI. Assume DOS-Box # if ( event.state == 2 and self.os_sys != "Linux" ): # bd_shell( self ); # KEYDOWN if event.type == pygame.KEYDOWN: if ( event.key == pygame.K_BACKSPACE ): self.key_buffer = self.key_buffer[:-1];# Remove last char elif ( event.key == pygame.K_DELETE ): proc_cmd( self, "delete", [""] ); elif ( event.key == pygame.K_INSERT ): proc_cmd( self, "insert_divider", [""] ); elif ( event.key == pygame.K_PAGEUP ): proc_cmd( self, "zoom_in" , [] ); elif ( event.key == pygame.K_PAGEDOWN ): proc_cmd( self, "zoom_out" , [] ); elif ( event.key == pygame.K_HOME ): proc_cmd( self, "font_larger" , [] ); elif ( event.key == pygame.K_END ): proc_cmd( self, "font_smaller" , [] ); elif ( event.key == pygame.K_RIGHT ): num_samples = self.sample_room // 16; proc_cmd( self, "scroll_right", [str(num_samples)] ); elif ( event.key == pygame.K_LEFT ): num_samples = self.sample_room // 16; proc_cmd( self, "scroll_left", [str(num_samples)] ); # Up and Down arrows either Zoom In,Out or Scroll the Signal list elif ( event.key == pygame.K_UP ): if ( self.mouse_region == "signal_name" ): proc_cmd( self, "scroll_up" , ["1"] ); else: proc_cmd( self, "zoom_in" , [] ); elif ( event.key == pygame.K_DOWN ): if ( self.mouse_region == "signal_name" ): proc_cmd( self, "scroll_down", ["1"] ); else: proc_cmd( self, "zoom_out", [] ); elif ( event.key == pygame.K_SPACE and self.key_buffer == "" ): proc_cmd( self, "Expand", [""] ); draw_screen( self ); screen_flip( self ); # Note: Text Entry moved to DOS-Box # elif ( event.key == pygame.K_RETURN ): # self.key_buffer = self.key_buffer.replace("="," = "); # words = self.key_buffer.strip().split()+[None]*4;# Avoid IndexError # cmd = words[0]; # parms = words[1:]; # if ( self.txt_entry == True ): # cmd = self.txt_entry_caption;# ie "Rename_Signal" # parms = words[0:]; # self.txt_entry = False; # Disable Dialog Box # rts = proc_cmd( self, cmd, parms ); # for each in rts: # print( each );# <CR>s # sys.stdout.write( self.prompt );# No <CR> # self.cmd_history.append( " " + self.key_buffer ); # self.key_buffer = ""; # elif ( event.key > 0 and event.key < 255 ): # self.key_buffer = self.key_buffer + event.unicode; # if ( event.unicode == "/" or event.unicode == "?" ): # self.key_buffer = self.key_buffer + " "; # sys.stdout.write( event.unicode ); # QUIT if ( event.type == pygame.QUIT ) : self.done=True; # MOUSEMOTION if event.type == pygame.MOUSEMOTION: (self.mouse_x,self.mouse_y) = pygame.mouse.get_pos(); # self.mouse_region = get_mouse_region(self,self.mouse_x,self.mouse_y); # # If mouse wave moved to right of the value region, scroll once to the # # right and then create a fake MOUSEMOTION event to continue scrolling # # until mouse is moved away. # if ( self.mouse_region == "scroll_right" or \ # self.mouse_region == "scroll_left" ): # proc_cmd( self, self.mouse_region , ["1"] ); # scroll left or right # # TODO: This wait time needs to be configurable. On my HP Centos # # laptop it scrolled too fast at 50ms, too slow at 250ms. # # Make sure mouse is still in this window ( focused ) # if ( self.pygame.mouse.get_focused() == True ): # self.pygame.time.wait( 100 );# Delay in ms # self.pygame.event.post( pygame.event.Event( pygame.MOUSEMOTION ) ); # If a resize op was just completed, redraw on 1st mouse motion as # trying to redraw during the resize is very slow and jerky. if ( self.resize_on_mouse_motion == True ): self.resize_on_mouse_motion = False; # This makes resize op smoother old_width = self.screen_width; ( self.screen_width, self.screen_height ) = self.screen.get_size(); self.vars["screen_width"] = str( self.screen_width ); self.vars["screen_height"] = str( self.screen_height ); # if ( self.screen_width > old_width ): # This is a HACK as sig_value_stop_x wasn't auto adjusting for some # reason when window is resized to be larger. # There is like a chicken and egg problem with zoom_full and stop_x # adjust stop_x for delta change rather than calling zoom_full twice self.sig_value_stop_x += ( self.screen_width - old_width ); proc_cmd( self, "zoom_full", [] );# HACK Needed to update parms # create_surfaces( self ); # flush_surface_cache( self ); # screen_refresh( self ); # If popup enabled, continue drawing and updating until button release if ( self.popup_x != None ): self.popup_sel = get_popup_sel( self ); # print("draw_popup_cmd()"); draw_popup_cmd( self );# Just draw popup on top of existing display screen_flip( self );# Only thing changing is the popup selection # If mouse button is held down, check for drag operation # elif ( self.mouse_button != 0 ): elif ( self.mouse_button == 1 or \ self.mouse_button == 2 ): # Make sure the region doesnt wander, so calc from mouse press self.mouse_region = get_mouse_region(self, self.mouse_btn1dn_x, self.mouse_btn1dn_y ); if ( self.mouse_region == "cursor" ): mouse_event_move_cursor( self ); # Move a cursor elif ( self.mouse_region == "slider" ): mouse_event_move_slider( self,0 ); # Move the viewport slider elif ( self.mouse_region == "signal_name" ): mouse_event_vertical_drag_wip( self );# Move a signal name # HERE : Doesnt work well # elif ( self.mouse_region == "signal_value" ): # # Calculate mouse drag deltas in char units # delta_x=abs(self.mouse_btn1dn_x-self.mouse_x) / self.txt_width; # delta_y=abs(self.mouse_btn1dn_y-self.mouse_y) / self.txt_height; # if ( delta_x > 2 and delta_y > 2 ): # mouse_event_area_drag_wip( self ); # Rectangle Zoom Region # MOUSEBUTTONUP : event.button 1=Left,2=Middle,3=Right,4=ScrlUp,5=ScrlDn if event.type == pygame.MOUSEBUTTONUP: (self.mouse_x,self.mouse_y) = pygame.mouse.get_pos(); self.mouse_region = get_mouse_region(self,self.mouse_x,self.mouse_y); if ( event.button == 1 ): (self.mouse_btn1up_x,self.mouse_btn1up_y)=(self.mouse_x,self.mouse_y); if ( event.button == 3 ): (self.mouse_btn3up_x,self.mouse_btn3up_y)=(self.mouse_x,self.mouse_y); # Attempt to detect double-click on left-mouse button t<300ms if ( event.button == 1 ): self.mouse_btn1up_time_last = self.mouse_btn1up_time; self.mouse_btn1up_time = self.pygame.time.get_ticks(); if ( ( self.mouse_btn1up_time - self.mouse_btn1up_time_last ) < 300 ): mouse_event_double_click( self ); delta_y=abs( self.mouse_btn3dn_y-self.mouse_btn3up_y )/self.txt_height; delta_x=abs( self.mouse_btn3dn_x-self.mouse_btn3up_x )/self.txt_width; # If popup enabled, process the cmd # if ( self.popup_x != None ): if ( self.popup_x != None and ( event.button == 1 ) or ( event.button == 3 and ( delta_y > 1.0 or delta_x > 1.0 ) ) ): # proc_cmd( self, self.popup_sel, [""] ); words = self.popup_sel.strip().split() + [""] * 4;# AvoidIndexError proc_cmd( self, words[0],words[1:] ); self.popup_x = None;# Erase popup self.popup_parent_x = None; else: # Mouse Button 1 Only - except emulate Center Mouse Drag to Zoom delta_x = abs( self.mouse_btn1dn_x-self.mouse_btn1up_x ) / \ self.txt_width; delta_y = abs( self.mouse_btn1dn_y-self.mouse_btn1up_y ) / \ self.txt_height; # if ( event.button == 2 and delta_x > 2 and delta_y > 2 ): if ( event.button == 1 or ( event.button == 2 and delta_x > 2 and delta_y > 2 ) ): # Mouse region is from 1st click, not release self.mouse_region = get_mouse_region(self, self.mouse_btn1dn_x, self.mouse_btn1dn_y ); if ( self.mouse_region == "cursor" ): mouse_event_move_cursor( self ); # Move a cursor elif ( self.mouse_region == "slider" ): mouse_event_move_slider( self,0 ); # Move the viewport slider elif ( self.mouse_region == "signal_expand" ): proc_cmd( self, "Expand", [""] ); elif ( self.mouse_region == "signal_name" ): delta_y = abs( self.mouse_btn1dn_y-self.mouse_btn1up_y ) / \ self.txt_height; if ( delta_y > 0 ): mouse_event_vertical_drag_done( self, \ ((self.mouse_btn1dn_y-self.mouse_btn1up_y) / \ self.txt_height ) );# Reorder signal list # elif ( self.mouse_region == "signal_value" ): # delta_x = abs( self.mouse_btn1dn_x-self.mouse_btn1up_x ) / \ # self.txt_width; # delta_y = abs( self.mouse_btn1dn_y-self.mouse_btn1up_y ) / \ # self.txt_height; # if ( delta_x < 2 and delta_y < 2 ): # mouse_event_single_click( self ); # Moves Cursor to here # if ( delta_x > 2 and delta_y < 2 ): # # signal_value region is being dragged, so pan left or right # direction = (self.mouse_btn1dn_x-self.mouse_btn1up_x) / \ # self.zoom_x; # proc_cmd( self, "scroll_right", [str( int(direction) ) ] ); # # elif ( delta_x > 2 and delta_y > 2 ): # mouse_event_area_drag_done( self ); # Zooms to region if ( event.button == 2 and delta_x < 2 and delta_y < 2 ): print( "Center Mouse Button Click"); # Mouse-Scroll Wheel # region==signal_name : Scroll Up and Down # region==signal_value : Scroll Left and Right # region==slider : Zoom in and out elif ( event.button >= 4 ): # print( self.mouse_region ); if ( self.mouse_region == "signal_name" ): if ( event.button == 4 ): proc_cmd( self, "scroll_up", ["1"] ); elif ( event.button == 5 ): proc_cmd( self, "scroll_down", ["1"] ); elif ( self.mouse_region == "signal_value" ): if ( event.button == 4 ): mouse_event_zoom_scroll( self, +1 ); elif ( event.button == 5 ): mouse_event_zoom_scroll( self, -1 ); elif ( self.mouse_region == "slider" ): if ( event.button == 4 ): proc_cmd( self, "scroll_right",[str(+self.scroll_num_samples)]); elif ( event.button == 5 ): proc_cmd( self, "scroll_left",[str(+self.scroll_num_samples)]); elif ( self.mouse_region == "cursor" ): self.curval_surface_valid = False;# curval surface is now invalid for cur_obj in self.cursor_list: if ( cur_obj.selected == True ): sample = cur_obj.sample; if ( event.button == 4 ): sample +=1; elif ( event.button == 5 ): sample -=1; if ( sample < 0 ) : sample = 0; if ( sample > self.max_samples ): sample = self.max_samples; cur_obj.sample = sample; screen_refresh( self ); self.mouse_button = 0; # No Button is Pressed # MOUSEBUTTONDOWN : 1=Left,2=Middle,3=Right,4=ScrlUp,5=ScrlDn if event.type == pygame.MOUSEBUTTONDOWN: self.mouse_button = event.button;# Remember which button is Pressed (self.mouse_x,self.mouse_y) = pygame.mouse.get_pos(); self.mouse_region = get_mouse_region(self,self.mouse_x,self.mouse_y); # Left-Mouse-Button-Down # If popup is already up and right-click is clicked again, emulate left if ( event.button == 1 or event.button == 2 or ( event.button == 3 and self.popup_x != None ) ): self.mouse_btn1dn_time = self.pygame.time.get_ticks(); (self.mouse_x,self.mouse_y) = pygame.mouse.get_pos(); (self.mouse_btn1dn_x,self.mouse_btn1dn_y) = \ (self.mouse_x,self.mouse_y); if ( self.mouse_region == "slider" ): mouse_event_move_slider( self, 0 ); elif ( self.mouse_region == "signal_name" or self.mouse_region == "signal_expand" ): mouse_event_select_signal( self ); elif ( self.mouse_region == "signal_value" ): mouse_event_single_click( self ); # Moves Cursor to here # pass; # Right-Mouse-Button-Down if ( event.button == 3 and self.popup_x == None ): (self.popup_x,self.popup_y) = pygame.mouse.get_pos(); (self.mouse_btn3dn_x,self.mouse_btn3dn_y) = \ (self.mouse_x,self.mouse_y); # For cursor bring tos want to know exacy sample right click was on (self.popup_sample, Null ) = get_sample_at_mouse( self, self.popup_x, self.popup_y ); # Set the popup up and to the left so that a click and release # selects the 1st guy ( Scroll_Toggle ) - a hack - I know. self.popup_x -= 2*self.txt_width; self.popup_y -= self.txt_height; if ( self.mouse_region == "signal_value" ): self.popup_list = self.popup_list_values; # elif ( self.mouse_region == "signal_name" ): else: self.popup_list = self.popup_list_names; draw_popup_cmd( self ); self.popup_sel = get_popup_sel( self ); screen_flip( self ); # Place popup on top existing stuff, no erase self.acq_state = "acquire_stop";# Stop any live acquisitions # New # draw_screen( self ); # screen_flip( self ); shutdown( self ); return;# This is end of main program loop ############################################################################### def recalc_max_samples( self ): # Calc max number of samples and change default zoom if not enough to fill self.max_samples = 0; for sig_obj in self.signal_list: if ( len( sig_obj.values ) > self.max_samples ): self.max_samples = len( sig_obj.values ); if ( self.mode_cli == True ): return; if ( float(self.max_samples) != 0.0 and ( self.max_samples * self.zoom_x ) < self.screen_width ): self.zoom_x = float(self.screen_width) / float(self.max_samples); set_zoom_x( self, self.zoom_x ); # Set the zoom ratio # HERE14 # Warning: This sample_room calculation assumes samples are 1 nibble wide. sample_start = self.sample_start; start_x = self.sig_value_start_x; x2 = self.screen_width - start_x - 2*self.txt_width; self.sample_room = int( float(x2) / float(self.zoom_x) ); self.sample_stop = sample_start + self.sample_room; return; def display_init( self ): log( self, ["display_init()"] ); import pygame # Import PyGame Module pygame.init() # Initialize the game engine self.screen_width = int( self.vars["screen_width"], 10 ); self.screen_height = int( self.vars["screen_height"], 10 ); # pygame.NOFRAME, pygame.FULLSCREEN self.screen=pygame.display.set_mode( [ self.screen_width, self.screen_height ], pygame.RESIZABLE | pygame.HWSURFACE | pygame.DOUBLEBUF ); self.pygame = pygame; self.pygame.display.set_icon( create_icon( self ) ); draw_header( self, "" ); self.font = get_font( self,self.vars["font_name"],self.vars["font_size"]); self.gui_active = True; create_surfaces( self ); return; # mouse_event_select_signal() : User has clicked the mouse in the signal name # region, so either deselect the old selection and select the new signal at # the mouse location, or if the shift key is held down, select everything from # old selection to new location. def mouse_event_select_signal( self ): self.name_surface_valid = False; if ( self.pygame.key.get_pressed()[self.pygame.K_LSHIFT] or self.pygame.key.get_pressed()[self.pygame.K_RSHIFT] ): # (Null,index) = get_sample_at_mouse( self, self.mouse_x, self.mouse_y ); # if ( index != None ): # sig_obj = self.signal_list[ index ]; # sig_obj.selected = True; # self.sig_obj_sel = sig_obj;# Remember for pulldown commands sig_obj = get_sig_obj_at_mouse( self, self.mouse_x, self.mouse_y ); if ( sig_obj != None ): sig_obj.selected = True; self.sig_obj_sel = sig_obj;# Remember for pulldown commands start_jk = False; for sig_obj in self.signal_list: # Select all visible signals between old select and new select if ( start_jk == True and sig_obj.visible == True ): sig_obj.selected = True; # Start the grouping on the old select if ( sig_obj.selected == True ): start_jk = True; # Finish when we get to new select if ( sig_obj == self.sig_obj_sel ): start_jk = False; break; screen_refresh( self ); else: # DeSelect All signals unless a CTRL key is held down if ( self.pygame.key.get_pressed()[self.pygame.K_LCTRL] == False and self.pygame.key.get_pressed()[self.pygame.K_RCTRL] == False ): for sig_obj in self.signal_list: sig_obj.selected = False; # Find the signal at the mouse location at select it. sig_obj = get_sig_obj_at_mouse( self, self.mouse_x, self.mouse_y ); if ( sig_obj != None ): sig_obj.selected = True; self.sig_obj_sel = sig_obj;# Remember for pulldown commands screen_refresh( self ); return; def mouse_event_double_click( self ): # print "mouse_event_double_click()"; sig_obj = get_sig_obj_at_mouse( self, self.mouse_x, self.mouse_y ); if ( sig_obj != None ): sig_obj.selected = True; if ( sig_obj.hidden == False ): proc_cmd( self, "hide", [""] ); else: proc_cmd( self, "show", [""] ); return; def mouse_event_move_slider( self, direction ): # print "mouse_event_move_slider()"; mouse_x = self.mouse_x; mouse_y = self.mouse_y; delta_x = abs( self.mouse_btn1dn_x-self.mouse_btn1up_x ) / self.txt_width; # if ( delta_x == 0 and direction == 0 ): if ( True ): x1 = self.sig_value_start_x; x2 = self.sig_value_stop_x; mouse_x -= self.slider_width / 2;# Put Center of Slider on Mouse # if ( mouse_x > x1 and mouse_x < x2 ): if ( True ): self.sample_start = int(self.max_samples * ( mouse_x - x1 ) / ( x2-x1 )); # Prevent scrolling too far to right or left if ( self.sample_start + self.sample_room > self.max_samples ): self.sample_start = int(self.max_samples - self.sample_room); if ( self.sample_start < 0 ): self.sample_start = 0; else: None; # Dont support dragging slider screen_refresh( self ); return; def mouse_event_single_click( self ): proc_cmd( self, "cursor_snap" , [str(self.mouse_x),str(self.mouse_y)] ); return; # print( self.popup_x ); def mouse_event_zoom_scroll( self, direction ): ( sample, Null ) = get_sample_at_mouse( self, self.mouse_x, self.mouse_y ); if ( direction == +1 ): if ( True ): # new_zoom_x = self.zoom_x * 1.25; new_zoom_x = self.zoom_x * 2.00; if ( new_zoom_x > 100 ): new_zoom_x = 100.0;# Don't ZoomIn too far set_zoom_x( self, new_zoom_x ); else: sample_room = self.sample_room * 2.00; new_sample = self.sample_start - sample_room // 4; if ( ( new_sample + sample_room ) > self.max_samples ): proc_cmd( self, "zoom_full", [] ); return; if ( self.stop_zoom == False ): # new_zoom_x = self.zoom_x / 1.25; new_zoom_x = self.zoom_x / 2.00; # if ( new_zoom_x < 0.1 ): # new_zoom_x = 0.1; set_zoom_x( self, new_zoom_x ); # Now see what sample is at the mouse position and adjust start accordingly # so that original sample is still under the mouse (new_sample,Null)=get_sample_at_mouse( self, self.mouse_x, self.mouse_y ); sample_offset = sample - new_sample; self.sample_start += sample_offset; if ( self.sample_start < 0 ): self.sample_start = 0; screen_refresh( self ); return; def mouse_event_horizontal_drag( self, direction ): # print "mouse_event_horizontal_drag()"; return; # # Support dragging the value region left or right to pan a number of samples # self.sample_start += int( direction ); # # Prevent scrolling too far to right # if ( self.sample_start + self.sample_room > self.max_samples ): # self.sample_start = int( self.max_samples - self.sample_room ); # if ( self.sample_start < 0 ): # self.sample_start = 0; # screen_refresh( self ); # return; def mouse_event_move_cursor( self ): (self.mouse_x,self.mouse_y) = self.pygame.mouse.get_pos(); ( sample, Null ) = get_sample_at_mouse( self, self.mouse_x, self.mouse_y ); self.curval_surface_valid = False;# curval surface is invalid when cur move for cur_obj in self.cursor_list: if ( self.mouse_btn1dn_y > cur_obj.y and \ self.mouse_btn1dn_y < cur_obj.y+self.txt_height ): if ( sample < 0 ): sample = 0; cur_obj.sample = int( sample ); for each in self.cursor_list: each.selected = False;# Make both cursors unselected cur_obj.selected = True;# now select the current cursor only screen_refresh( self ); return; def mouse_event_vertical_drag_wip( self ): # print "mouse_event_vertical_drag_wip()"; # Reorder the signal list when a signal name is dragged if ( True ): x1 = self.sig_name_start_x; x2 = self.sig_name_stop_x; sig_obj = get_sig_obj_at_mouse( self, self.mouse_x, self.mouse_y ); if ( sig_obj != None ): # Draw a horizontal line at insert point before button is released y1 = sig_obj.y - 1; y2 = y1; # flush_surface_cache( self ); screen_erase( self ); draw_screen( self ); self.pygame.draw.line(self.screen,self.color_fg,(x1,y1),(x2,y2),1); screen_flip( self ); return; # TODO: This doesnt handle invisible signals def mouse_event_vertical_drag_done( self, direction ): # print "mouse_event_vertical_drag_done()"; # Reorder the signal list when a signal name is dragged if ( True ): (Null , index_dn ) = get_sample_at_mouse( self, self.mouse_btn1dn_x, self.mouse_btn1dn_y ); (Null , index_up ) = get_sample_at_mouse( self, self.mouse_btn1up_x, self.mouse_btn1up_y ); if ( index_up > index_dn ): index_up -= 1;# Need to adjust this print( "index_up = " + str( index_up )); print( "index_dn = " + str( index_dn )); if ( index_up != None and index_dn != None ): self.signal_list.insert( index_up, self.signal_list.pop( index_dn ) ); flush_surface_cache( self ); screen_refresh( self ); return; def mouse_event_area_drag_wip( self ): # print "mouse_event_area_drag_wip()"; if ( self.mouse_btn1dn_x > self.sig_value_start_x ): # Draw a rectangle over the drag region to be zoomed in on x1 = self.mouse_btn1dn_x; y1 = self.mouse_btn1dn_y; w = ( self.mouse_x - self.mouse_btn1dn_x ); h = ( self.mouse_y - self.mouse_btn1dn_y ); screen_erase( self ); self.pygame.draw.rect( self.screen, self.color_fg,(x1,y1,w,h), 1); draw_screen( self ); screen_flip( self ); return; def mouse_event_area_drag_done( self ): # print "mouse_event_area_drag_done()"; if ( self.mouse_btn1dn_x > self.sig_value_start_x ): (s1,Null)=get_sample_at_mouse(self,self.mouse_btn1dn_x,self.mouse_btn1dn_y); (s2,Null)=get_sample_at_mouse(self,self.mouse_btn1up_x,self.mouse_btn1up_y); s1 = int( s1 ); s2 = int( s2 ); if ( s1 > s2 ): s1,s2 = s2,s1;# Swap so that s1 is always smallest of the two proc_cmd( self, "zoom_to" , [str(s1),str(s2)] );# return; ############################################################################### # Given a mouse position, return the section of screen that it is in # as a txt string "signal_name","signal_value","cursor","slider" def get_mouse_region( self, mouse_x, mouse_y ): if ( self.popup_x != None ): return ""; # No region if a popup is open if ( mouse_x > self.sig_name_start_x and mouse_x < self.sig_name_stop_x and mouse_y > self.sig_name_start_y and mouse_y < self.sig_name_stop_y+self.txt_height ): # See if Click in the net name "[+]" region # flush_surface_cache( self ); sig_obj = get_sig_obj_at_mouse( self, mouse_x, mouse_y ); if ( sig_obj != None ): txt1 = self.font.render( " ",True,self.color_fg, self.color_bg); txt2 = self.font.render( "[+]",True,self.color_fg, self.color_bg); if ( mouse_x > self.sig_name_start_x and mouse_x < ( ( self.sig_name_start_x ) + \ ( sig_obj.hier_level * txt1.get_width() ) + \ ( txt2.get_width() ) ) ): return "signal_expand"; else: return "signal_name"; if ( mouse_x > self.sig_value_start_x and mouse_x < self.sig_value_stop_x and mouse_y > self.sig_value_start_y and mouse_y < self.sig_value_stop_y+self.txt_height ): return "signal_value"; if ( mouse_x > self.sig_name_stop_x and mouse_x < self.sig_value_start_x and mouse_y > self.sig_value_start_y and mouse_y < self.sig_value_stop_y+self.txt_height ): return "scroll_left"; if ( mouse_x > self.sig_value_stop_x and mouse_y > self.sig_value_start_y and mouse_y < self.sig_value_stop_y+self.txt_height ): return "scroll_right"; if ( mouse_x > self.sig_value_start_x and mouse_x < self.sig_value_stop_x and mouse_y > self.cursor_start_y and mouse_y < self.cursor_stop_y+self.txt_height ): return "cursor"; if ( mouse_x > self.sig_value_start_x and mouse_x < self.sig_value_stop_x and mouse_y > self.cursor_stop_y ): return "slider"; return ""; ############################################################################### # direction -1=Backwards,+1=Forwards,0=Both # value= Binary, Hex or "edge" ( transition ) # Returns sample number def search_values( self, sig_obj, sample_start, search_value, direction ): sample = sample_start; # RTS Default for it search_value not found if ( sig_obj ): done_right = False; done_left = False; if ( direction == -1 ): done_right = True; if ( direction == +1 ): done_left = True; i = 0; last_value_right = sig_obj.values[ sample_start ]; last_value_left = sig_obj.values[ sample_start ]; while ( done_right == False or done_left == False ): i += 1; if ( sample_start + i < len( sig_obj.values ) ): value_right = sig_obj.values[ sample_start + i ]; if ( ( search_value == value_right ) or ( search_value == "edge" and value_right != last_value_right ) ): done_right = True; sample = sample_start + i; last_value_right = value_right; else: done_right = True; if ( sample_start - i >= 0 ): value_left = sig_obj.values[ sample_start - i ]; if ( ( search_value == value_left ) or ( search_value == "edge" and value_left != last_value_left ) ): done_left = True; sample = sample_start - i; last_value_left = value_left; else: done_left = True; return sample; ############################################################################### # Given position of mouse, return the sample number, and signal index def get_sample_at_mouse( self, mouse_x, mouse_y ): x = mouse_x - self.sig_value_start_x; sample_num = int( ( x / self.zoom_x ) + self.sample_start ); signal_index = None; for ( i , sig_obj ) in enumerate( self.signal_list ): if ( sig_obj.visible == True ): if ( mouse_y > sig_obj.y and \ mouse_y < sig_obj.y + sig_obj.h ): signal_index = i; return ( sample_num, signal_index ); ############################################################################### # Given position of mouse, return the sig_obj def get_sig_obj_at_mouse( self, mouse_x, mouse_y ): for sig_obj in self.signal_list: if ( sig_obj.visible == True ): if ( mouse_y > sig_obj.y and \ mouse_y < sig_obj.y + sig_obj.h ): return sig_obj; return None; ############################################################################### # Given name of a sig_obj, return that sig_obj def get_sig_obj_by_name( self, name ): for sig_obj in self.signal_list: if ( sig_obj.name == name ): return sig_obj; return None; ############################################################################### def log( self, txt_list ): for each in txt_list: # print( "log() :" + str( each )); self.file_log.write( str(each) + "\n" ); # draw_header( self, each ); return; ############################################################################### def init_help( self ): a = []; a+=["#####################################################################"]; a+=["# SUMP2 BlackMesaLabs GNU GPL V2 Open Source License. Python 3.x #"]; a+=["# (C) Copyright 2016 Kevin M. Hubbard - All rights reserved. #"]; a+=["#####################################################################"]; a+=["# bd_shell Commands #"]; a+=["# env : Display all assigned variables and values #"]; a+=["# print var : Display value of variable 'var' #"]; a+=["# foo = bar : Assign 'bar' to the variable 'foo' #"]; a+=["# var_bs foo bar : Set bits 'bar' inside variable 'foo' #"]; a+=["# var_bc foo bar : Clear bits 'bar' inside variable 'foo' #"]; a+=["# help : Display help page ( you're looking at it ) #"]; a+=["# quit : Quit the SUMP2 application #"]; a+=["# gui or NULL<ENTER> : Return from BD_SHELL to GUI Interface #"]; a+=["# source filename : Source an external command script #"]; a+=["# sleep,sleep_ms n : Pause of n seconds or milliseconds #"]; a+=["# UNIX Commands #"]; a+=["# pwd,mkdir,cd,ls,cp,vi #"]; a+=["# Backdoor Commands #"]; a+=["# w addr data : Write data to addr #"]; a+=["# w addr data data : Write multiple dwords #"]; a+=["# r addr : Read data from addr #"]; a+=["# r addr dwords : Read multiple dwords starting at addr #"]; a+=["# GUI Commands #"]; a+=["# crop_to_cursors : Minimize sample set to cursor region #"]; a+=["# save_png : Save current screen image to PNG file #"]; a+=["# save_vcd : Save current capture to VCD file #"]; a+=["# bd_shell : Switch from GUI to a bd_shell CLI #"]; a+=["# SUMP Commands #"]; a+=["# sump_arm timeout : Arm SUMP2 engine and wait for timeout sec #"]; a+=["# sump_arm_rle n : Arm SUMP2 engine and wait for n seconds #"]; a+=["# sump_stop : Stop the SUMP2 engine #"]; a+=["# sump_status : Display status of SUMP2 engine #"]; a+=["# acquire_single : Arm for Single non-RLE acquisition #"]; a+=["# acquire_continuous : Arm for non-RLE acquisition and loop #"]; a+=["# acquire_stop : Stop any pending arming #"]; a+=["# acquire_rle_1x : Arm for RLE acquisition plus dword data #"]; a+=["# acquire_rle_8x : Arm for RLE acquisition, 8x decimated #"]; a+=["# acquire_rle_64x : Arm for RLE acquisition, 64x decimated #"]; a+=["#####################################################################"]; return a; ############################################################################### def init_manual( self ): a = []; a+=["#####################################################################"]; a+=["# SUMP2 by BlackMesaLabs GNU GPL V2 Open Source License. Python 3.x "]; a+=["# (C) Copyright 2016 Kevin M. Hubbard - All rights reserved. "]; a+=["#####################################################################"]; a+=["1.0 Scope "]; a+=[" This document describes the SUMP2 software and hardware. "]; a+=[" "]; a+=["2.0 Software Architecture "]; a+=[" The SUMP2 application is a Python 3.5 script using the PyGame module"]; a+=[" for mouse and graphical user interface. Communication to hardware is"]; a+=[" via TCP Socket communications to a BD_SERVER.py instance. The SW is "]; a+=[" architected as a GUI wrapper around a command line application with "]; a+=[" a bd_shell interface. When the PyGame GUI is used, mouse menu "]; a+=[" selections create commands that are then interpreted by bd_shell. "]; a+=[" In theory, sump2.py may be executed without PyGame as a command line"]; a+=[" only program to arm the sump2 hardware and then dump captured data "]; a+=[" to a VCD file for offline viewing by another application. "]; a+=[" "]; a+=["3.0 Command Descriptions "]; a+=[" Zoom_In : Increase signal view magnification 2x "]; a+=[" Zoom_Out : Decrease signal view magnification 2x "]; a+=[" Zoom_Full : View all signal samples : WARNING May be slow "]; a+=[" Zoom_Previous : Return to previous zoom view. "]; a+=[" Zoom_to_Cursors : View region bound by cursors "]; a+=[" Crop_to_Cursors : Reduce sample set to region bound by cursors "]; a+=[" Cursors_to_View : Bring both cursors into current view "]; a+=[" Cursor1_to_Here : Bring Cursor1 to mouse pointer "]; a+=[" Cursor2_to_Here : Bring Cursor2 to mouse pointer "]; a+=[" Acquire_Single : Arm hardware for single non-RLE acquisition "]; a+=[" Acquire_Continuous : Arm hardware for looping non-RLE acquisitions "]; a+=[" Acquire_Stop : Issue a stop to hardware from current Arming "]; a+=[" Acquire_RLE_1x : Arm hardware for RLE acquisition no decimation "]; a+=[" Acquire_RLE_8x : Arm hardware for RLE acquisition 8x decimation "]; a+=[" Acquire_RLE_64x : Arm hardware for RLE acquisition 64x decimation"]; a+=[" File_Load : Load a bd_shell script file "]; a+=[" File_Save : Save capture to a VCD,PNG,JPG, etc file "]; a+=[" Save_Rename : Rename the last file saved "]; a+=[" Fonts : Increase or Decrease GUI font size "]; a+=[" BD_SHELL : Close GUI and open a BD_SHELL command line "]; a+=[" "]; a+=[" Rename : Rename a selected signal's nickname "]; a+=[" Insert_Divider : Insert a dummy signal divider "]; a+=[" Clipboard : Cut and Paste selected signals "]; a+=[" Visibility : Change visibility. Impacts RLE Compression "]; a+=[" Trigger_Rising : Set Trigger for Rising edge of selected "]; a+=[" Trigger_Falling : Set Trigger for Falling edge of selected "]; a+=[" Trigger_Watchdog : Set Trigger for Watchdog timeout of selected "]; a+=[" Set_Pattern0 : Advanced Triggering "]; a+=[" Set_Pattern1 : Advanced Triggering "]; a+=[" Clear_Pattern_Match: Advanced Triggering "]; a+=[" Set_Data_Enable : Advanced data sampling "]; a+=[" Clear_Data_Enable : Advanced data sampling "]; a+=[" SUMP_Configuration : Modify advanced SUMP variables "]; a+=[" Acquisition_Length : Configure amount of non-RLE RAM to use "]; a+=[" "]; a+=["4.0 SUMP2 Environment Variables "]; a+=[" bd_connection : Connection type to hardware. tcp only "]; a+=[" bd_protocol : Communication protocol to HW, poke only "]; a+=[" bd_server_ip : IP address or localhost for bd_server "]; a+=[" bd_server_socket : Socket Number for bd_server, 21567 typ"]; a+=[" sump_addr : 32bit PCI address of sump_ctrl_reg "]; a+=[" sump_data_enable : Event bits to use for data_enable feature "]; a+=[" sump_rle_event_en : Event bits to use for RLE capture "]; a+=[" sump_rle_post_trig_len : Max number of post trigger RLE samples "]; a+=[" sump_rle_pre_trig_len : Max number of pre trigger RLE samples "]; a+=[" sump_trigger_delay : Number of clocks to delay trigger "]; a+=[" sump_trigger_field : Event bits to use for trigger "]; a+=[" sump_trigger_nth : nTh trigger to trigger on "]; a+=[" sump_trigger_type : or_rising,or_falling,watchdog,pattern_ris "]; a+=[" sump_user_ctrl : 32bit user_ctrl field "]; a+=[" sump_user_pattern0 : 32bit user pattern0 field "]; a+=[" sump_user_pattern1 : 32bit user pattern1 field "]; a+=[" sump_watchdog_time : Watchdog timeout for Watchdog trigger "]; a+=[" "]; a+=["5.0 SUMP2 Hardware "]; a+=[" The SUMP2 hardware is a single verilog file with fixed input parms "]; a+=[" for the depth and width of capture memory to use. A maximum SUMP2 "]; a+=[" configuration contains a 32bit Block RAM for non-RLE events and a "]; a+=[" 64bit Block RAM for RLE events and time stamps. In addition to 32 "]; a+=[" signal events, SUMP2 may also capture 16 DWORDs (512 bits ) of non "]; a+=[" RLE data. The SUMP2 software automatically adjusts to each instance "]; a+=[" of hardware for memory depth, width and advanced features. A key "]; a+=[" feature for acquiring long captures in time is the ability to mask "]; a+=[" any of the event inputs, which can be used to dramatically reduce "]; a+=[" event occurrence and support capturing only events of interest. The "]; a+=[" software supports masking events by double-clicking the signal name "]; a+=[" prior to arming which hides the signals and masks them from the RLE "]; a+=[" compression. 10x to 1000x compression is possible run-time for some "]; a+=[" designs by dynamically masking input events prior to acquisition. "]; a+=[" --------------- "]; a+=[" events[31:0] -+->| Trigger Logic |------------------------- "]; a+=[" | --------------- ----------------- | "]; a+=[" +---------------------->| RLE Compression | | "]; a+=[" | --------------- | Timestamp and |<-+ "]; a+=[" +->| RLE RAM |<---| Addr Generator | | "]; a+=[" | --------------- ----------------- | "]; a+=[" | --------------- ----------------- | "]; a+=[" ->| non-RLE RAM |<-+-| Addr Generator |<- "]; a+=[" --------------- | ----------------- "]; a+=[" --------------- | "]; a+=[" dwords[511:0] -->| non-RLE RAM |<- "]; a+=[" --------------- "]; a+=[" "]; a+=["6.0 Working with large RLE datasets "]; a+=[" RLE datasets can be overwhelming large to work with in software once"]; a+=[" samples have been decompressed. Compression ratios of 10,000:1 are "]; a+=[" possible for some systems. SUMP Software provides internal tools for"]; a+=[" reducing the hardware captured RLE dataset to more manageable size "]; a+=[" for both viewing and VCD generation. "]; a+=[" crop_to_cursors : Permanently crops the number of samples to a "]; a+=[" region indicated by the cursors. "]; a+=[" RLE Decimation : 8x and 64x decimation specified at arming will "]; a+=[" acquire the RLE data and reduce the sample rate "]; a+=[" by 8x or 64x prior to rendering. "]; a+=[" Signal Hiding : Hiding a signal prior to acquisition will mask "]; a+=[" the signal entirely and increase the overall RLE "]; a+=[" acquisition length. Hiding a signal post acquire "]; a+=[" speeds up rendering time for remaining signals. "]; a+=[" "]; a+=[" 6.1 Bundles "]; a+=[" The following is an example of manually modifying sump2_wave.txt "]; a+=[" file in order to group together multiple events into a bundle. "]; a+=[" /my_cnt -bundle -hex "]; a+=[" /event[12] -nickname event[12] "]; a+=[" /event[13] -nickname event[13] "]; a+=[" /event[14] -nickname event[14] "]; a+=[" /event[15] -nickname event[15] "]; a+=[" "]; a+=["7.0 History "]; a+=[" The original OSH+OSS SUMP was designed in 2007 as an external logic "]; a+=[" logic analyzer using a Xilinx FPGA eval board for capturing external"]; a+=[" electrical signals non compressed to all available FPGA block RAM. "]; a+=[" See http://www.sump.org/projects/analyzer/ "]; a+=[" The original developer published the serial communication protocol "]; a+=[" and also wrote a Java based waveform capture tool. The simplicity of"]; a+=[" the protocol and the quality and maintenance of the Open-Source Java"]; a+=[" client has inspired many new SUMP compliant projects such as: "]; a+=[" 'Open Logic Sniffer' : https://www.sparkfun.com/products/9857 "]; a+=[" "]; a+=[" 7.1 SUMP1-RLE ( 2014 ) "]; a+=[" Black Mesa Labs developed the SUMP1-RLE hardware in 2014 as a "]; a+=[" software protocol compatible SUMP engine that was capable of real "]; a+=[" time hardware compression of samples ( Run Length Encoded ). The "]; a+=[" idea of the project was to leverage the open-source Java software "]; a+=[" and couple it with new hardware IP that was capable of storing deep"]; a+=[" capture acquisitions using only a single FPGA Block RAM, allowing "]; a+=[" SUMP to be used internally with existing FPGA designs rather than "]; a+=[" a standalone device. FPGA vendor closed license logic analyzers all"]; a+=[" store using no compression requiring vast amount of Block RAMS to "]; a+=[" be useful and typically do not fit will within the limited fabric "]; a+=[" resources of an existing FPGA design requiring debugging. SUMP1-RLE"]; a+=[" was later enhanced to include 2 DWORDs of sampled data along with "]; a+=[" the RLE compressed signal events. This enhancement required new "]; a+=[" software which was written in .NET Powershell for Windows platform."]; a+=[" "]; a+=[" 7.2 SUMP2-RLE ( 2016 ) "]; a+=[" SUMP2 is a software and hardware complete redesign to improve upon "]; a+=[" the SUMP1-RLE concept. For SUMP2 the .NET software was tossed due "]; a+=[" to poor user interface performance and replaced with a PyGame based"]; a+=[" VCD waveform viewer ( chip_wave.py also from BML ). The SUMP2 HW "]; a+=[" is now a single Verilog file with no backwards compatibility with "]; a+=[" any legacy SUMP hardware or software systems. SUMP2 hardware is "]; a+=[" designed to capture 512bits of DWORDs and 32bits of events versus "]; a+=[" the SUMP1 limits of 16 event bits and 64bits of DWORDs. Sample "]; a+=[" depth for SUMP2 is now completely defined by a hardware instance "]; a+=[" with software that automatically adapts. The RLE aspect of SUMP2 "]; a+=[" is optional and not required for simple data intensive captures. "]; a+=[" SUMP2 software includes bd_shell support for changing variables "]; a+=[" on the fly and providing simple low level hardware access to regs. "]; a+=[" "]; a+=["8.0 BD_SERVER.py "]; a+=[" The SUMP2.py application does not communicate directly to hardware "]; a+=[" but instead uses BD_SERVER.py as an interface layer. BD_SERVER is "]; a+=[" a multi use server application that accepts requests via TCP to "]; a+=[" read and write to low level hardware and then translates those "]; a+=[" requests using one of many low level hardware protocols available. "]; a+=[" BD_SERVER allows the low level communications to easily change from"]; a+=[" interfaces like USB FTDI serial to PCI without requiring any change"]; a+=[" to the high level application. This interface also supports the "]; a+=[" debugging of an embedded system from a users regular desktop with "]; a+=[" a standard Ethernet or Wifi connection between the two. Typical use"]; a+=[" is to run both python applications on same machine and use the TCP "]; a+=[" localhost feature within the TCP stack for communications. "]; a+=[" "]; a+=[" ------------ -------------- --------------- "]; a+=[" | sump2.py |<------->| bd-server.py |<------->| SUMP Hardware | "]; a+=[" ------------ Ethernet -------------- USB,PCI --------------- "]; a+=[" "]; a+=["9.0 License "]; a+=[" This hardware and software is released under the GNU GPLv2 license. "]; a+=[" Full license is available at http://www.gnu.org "]; a+=[" "]; return a; ############################################################################### def init_vars( self, file_ini ): # Load App Variables with Defaults. vars = {}; # Variable Dictionary vars["font_name"] = "dejavusansmono"; vars["font_size"] = "12"; vars["file_in"] = "dut.vcd"; vars["file_log"] = "sump2_log.txt"; vars["color_screen_background"] = "000000"; vars["color_screen_foreground"] = "00FF00"; vars["screen_width"] = "800"; vars["screen_height"] = "600"; vars["cursor_unit"] = "clocks"; # vars["cursor_unit"] = "samples"; vars["cursor_mult"] = "1.0"; vars["bd_connection" ] = "tcp"; vars["bd_protocol" ] = "poke"; vars["bd_server_ip" ] = "localhost"; vars["bd_server_socket" ] = "21567"; vars["uut_name" ] = "UUT"; vars["sump_addr" ] = "00000090" ;# Addr of sump2_ctrl_reg vars["sump_script_inc_filter"] = "*.txt"; vars["sump_script_exc_filter"] = "sump2_*.txt"; vars["sump_trigger_type" ] = "or_rising"; vars["sump_trigger_field" ] = "00000000"; vars["sump_trigger_delay" ] = "0000"; vars["sump_trigger_nth" ] = "0001"; vars["sump_acquisition_len" ] = "44"; vars["sump_rle_event_en" ] = "FFFFFFFF"; vars["sump_rle_pre_trig_len" ] = "00100000"; vars["sump_rle_post_trig_len"] = "00100000"; vars["sump_user_ctrl" ] = "00000000"; vars["sump_user_pattern0" ] = "00000000"; vars["sump_user_pattern1" ] = "00000000"; vars["sump_data_enable" ] = "00000000"; vars["sump_watchdog_time" ] = "00001000"; # vars["sump_rle_undersample"] = "10"; # return; import os; if os.path.exists( file_ini ): file_in = open( file_ini, 'r' ); file_list = file_in.readlines(); file_in.close(); for each in file_list: each = each.replace("="," = "); words = each.strip().split() + [None] * 4; # Avoid IndexError # foo = bar if ( words[1] == "=" and words[0][0:1] != "#" ): vars[ words[0] ] = words[2]; else: print( "Warning: Unable to open " + file_ini); return vars; ############################################################################### # Dump all the app variables to ini file when application quits. def var_dump( self, file_ini ): log( self, ["var_dump()"] ); file_out = open( file_ini, 'w' ); file_out.write( "# [" + file_ini + "]\n" ); file_out.write( "# WARNING: \n"); file_out.write( "# This file is auto generated on application exit.\n" ); file_out.write( "# Safe to change values, but comments will be lost.\n" ); txt_list = []; for key in self.vars: val = self.vars[ key ]; txt_list.append( key + " = " + val + "\n" ); for each in sorted( txt_list ): file_out.write( each ); file_out.close(); return; def list2file( self, file_name, my_list ): file_out = open( file_name, 'w' ); for each in my_list: file_out.write( each + "\n" ); file_out.close(); return; def tuplelist2file( self, file_name, my_list ): file_out = open( file_name, 'w' ); for (dw1,dw2) in my_list: file_out.write("%08x %08x" % ( dw1,dw2 ) + "\n" ); file_out.close(); return; ############################################################################### # Command Line BD_SHELL def bd_shell( self, cmd_start = "" ): log( self, ["bd_shell()"] ); import pygame; loop_jk = True; import msvcrt;# Note: Windows specific print("\nMode=BD_SHELL : Enter NULL command to return to GUI"); self.context = "cli"; pygame.display.quit(); self.gui_active = False; print(""); sys.stdout.write( self.prompt ); sys.stdout.write( cmd_start ); sys.stdout.flush(); h_cnt = 1;# Command history count key_buf = cmd_start; while ( loop_jk == True ): ch = msvcrt.getch();# Wait for single key press from DOS-Box if ( ch != "\xe0" ): ch = ch.decode(); else: # K=Left,M=Right,H=Up,P=Down,G=Home,O=End ch = msvcrt.getch();# The Special KeyCode print( ch ); ch = ""; # print( ch ); # ch = (msvcrt.getch().decode());# Wait for single key press from DOS-Box # print( ch ); # Handle Backspace Erase if ( ch == chr( 8 ) ): sys.stdout.write( str( ch ) );# sys.stdout.write( str(" " ) );# sys.stdout.write( str( ch ) );# else: sys.stdout.write( str( ch ) );# Echo typed character to DOS-Box STDOUT sys.stdout.flush(); # If not <ENTER> key then append keypress to a key_buf string if ( ch != chr( 13 ) ): if ( ch == chr( 8 ) ): if ( len(key_buf) > 0 ): key_buf = key_buf[:-1];# Subtract last char on Backspace else: key_buf += str(ch);# Append new character elif ( ch == chr( 13 ) ): if ( len( key_buf ) == 0 or key_buf == "gui" ): loop_jk = False; else: print( ("%d>"+key_buf+" " ) % h_cnt ); h_cnt +=1; key_buf = key_buf.replace("="," = "); words = " ".join(key_buf.split()).split(' ') + [None] * 8; if ( words[1] == "=" ): cmd = words[1]; parms = [words[0]]+words[2:]; else: cmd = words[0]; parms = words[1:]; rts = proc_cmd( self, cmd, parms ); for each in rts: print( each ); key_buf = ""; sys.stdout.write( self.prompt );# "bd>" sys.stdout.flush(); # while ( loop_jk == True ): self.context = "gui"; print("\nMode=GUI"); # NOTE: set_mode prevents resizing after return to GUI. # pygame.display.set_mode();# Set focus back to GUI Window display_init( self ); pygame.display.update(); flush_surface_cache( self );# Redraw with new values draw_screen( self ); screen_flip( self ); sump_vars_to_signal_attribs( self );# Assume sump vars were modified return; ############################################################################### # Process Backdoor commands for Writing and Reading to any hardware def proc_bd_cmd( self, cmd, parms ): log( self, ["proc_bd_cmd() : " + cmd + " " + str( parms ) ] ); rts = []; file_mode = None; if ( ">" in parms ): i = parms.index(">"); file_mode = "w";# Create new file, overwriting existing if ( ">>" in parms ): i = parms.index(">>"); file_mode = "a";# Append to any existing file if ( file_mode != None ): file_name = parms[i+1]; file_out = open( file_name, file_mode ); # a or w : Append or Overwite parms = parms[0:i] + [None]*10;# Strip "> foo.txt" prior to processing # if ( cmd == "w" or cmd == "r" or cmd == "bs" or cmd == "bc" ): if ( cmd == "w" or cmd == "r" ): addr = parms[0]; data = parms[1:]; # Address may be a variable, so look if ( self.vars.get( addr ) != None ): addr = self.vars[ addr ]; if ( cmd == "w" ): data_hex = []; for each in data: if ( each != None ): data_hex += [int(each,16)]; self.bd.wr( int(addr,16), data_hex ); if ( cmd == "r" ): if ( data[0] == None ): num_dwords = 1; else: num_dwords = int( data[0],16 ); rts = self.bd.rd( int(addr,16) , num_dwords, repeat = False ); # data_hex = []; # for each in rts: # data_hex += ["%08x" % each]; # rts = data_hex; # Format 8 dwords wide per line data_hex = ""; i = 0; for each in rts: data_hex += ("%08x " % each ); i += 1; if ( i == 8 ): i = 0; data_hex += "\n"; rts = [ data_hex ]; if ( file_mode != None ): for each in rts: file_out.write( each + "\n" ); file_out.close(); rts = []; return rts; ############################################################################### # Generate a demo vcd file to display if the hardware isn't present def make_demo_vcd( self ): filename_vcd = "sump2_demo.vcd"; txt2vcd = TXT2VCD();# Instantiate Class for the VCD Conversion # line-0 contains a list of all signal names and ends with clock period # Iterate the list and replace each signal name with its nickname new_line = "hsync vsync pixel_r pixel_g pixel_b 10000"; h = 0; v = 597; sample_lines = [ new_line ]; import random; rnd_list = [0]*1000; rnd_list += [1]; pixel_r = 0; pixel_g = 0; pixel_b = 0; for i in range( 0, 10000, 1): h+=1; hsync = 0; vsync = 0; pixel_r = random.choice( rnd_list ); pixel_g = random.choice( rnd_list ); pixel_b = random.choice( rnd_list ); if ( h >= 800 ): hsync = 1; if ( h == 810 ): hsync = 1; h = 0; v += 1; if ( v == 600 ): v = 0; if ( v == 599 ): vsync = 1; sample = "%d %d %d %d %d" % ( hsync,vsync, pixel_r,pixel_g,pixel_b ); sample_lines += [ sample ]; vcd_lines = sample_lines[:]; rts = txt2vcd.conv_txt2vcd( self, vcd_lines ); print("Saving " + filename_vcd ); file_out = open( filename_vcd, "w" ); # Append versus r or w for each in rts: file_out.write( each + "\n" );# Make Windows Friendly file_out.close(); return filename_vcd; ############################################################################### # Given a file_header ( like foo_ ), check for foo_0000, then foo_0001, etc # and return 1st available name. def make_unique_filename( self, file_header, file_ext ): import os; num = 0; while ( True ): file_name = file_header + ( "%04d" % num ) + file_ext; if ( os.path.exists( file_name ) == False ): return file_name; else: num +=1; return None; ############################################################################### # Read in a file and display it def more_file( self, parms ): log( self, ["more_file() " + str( parms ) ] ); file_name = parms[0]; rts = []; try: # Read Input File file_in = open( file_name , "r" ); file_lines = file_in.readlines(); file_in.close(); # rts = file_lines; rts = list(map(str.strip, file_lines));# Chomp all the lines except: print( "ERROR Input File: "+file_name); return; return rts; ############################################################################### # interpret a bd_shell script or wave file # a wave file is easy to spot as 1st char on each line is a "/" def source_file( self, parms ): log( self, ["source_file() " + str( parms ) ] ); file_name = parms[0]; rts = []; try: # Read Input File file_in = open( file_name , "r" ); file_lines = file_in.readlines(); file_in.close(); except: print( "ERROR Input File: "+file_name); return; is_wave = False; for each in file_lines: words = " ".join(each.split()).split(' ') + [None] * 20; if ( words[0][0:1] == "/" ): is_wave = True; if ( is_wave == True ): load_format( self, file_name ); self.name_surface_valid = False; screen_refresh( self ); else: for each in file_lines: each = each.replace("="," = "); words = " ".join(each.split()).split(' ') + [None] * 20; if ( words[0][0:1] != "#" ): if ( words[1] == "=" ): cmd = words[1]; parms = [words[0]]+words[2:]; else: cmd = words[0]; parms = words[1:]; rts += proc_cmd( self, cmd, parms ); return rts; ############################################################################### # Process generic unknown commands ( GUI,Shell,Backdoor, SUMP ) def proc_cmd( self, cmd, parms ): log( self, ["proc_cmd() " + cmd + " " + str( parms )] ); # print( cmd, parms ); rts = []; if ( cmd == None ): return rts; cmd = cmd.lower(); # !! retrieves last command if ( cmd == "!!" ): cmd = self.last_cmd; else: self.last_cmd = cmd; self.cmd_history.append([ cmd, parms ] ); if ( cmd[0:1] == "!" ): try: h_num = cmd[1:]; ( cmd, parms ) = self.cmd_history[ int(h_num,10) ]; except: print("Invalid Command History"); # print "proc_cmd()", cmd; # Commands may have aliases, look them up here: if ( self.cmd_alias_hash_dict.get( cmd ) != None ): cmd = self.cmd_alias_hash_dict[ cmd ]; # Returned all assigned variables with their values if ( cmd == "env" ): for key in self.vars: rts += [ key +"=" + self.vars[key] ]; return sorted(rts); elif ( cmd == "=" ): self.vars[parms[0]] = parms[1]; # Variable Assignment elif ( cmd == "var_bs" ): val = int( self.vars[parms[0]] , 16 ); val = val | int( parms[1], 16 ); self.vars[parms[0]] = ("%08x" % val ); elif ( cmd == "var_bc" ): val = int( self.vars[parms[0]] , 16 ); val = val & ~int( parms[1], 16 ); self.vars[parms[0]] = ("%08x" % val ); elif ( cmd == "echo" or cmd == "print" ): try: rts = [ self.vars[ parms[0] ] ]; except: rts = [ parms[0] ]; elif ( cmd == "h" or cmd == "history" ): rts = self.cmd_history; # rts for ( i , sig_obj ) in enumerate( self.signal_list ): elif ( cmd == "source" ): rts = source_file( self, parms ); elif ( cmd == "more" ): rts = more_file( self, parms ); elif ( cmd == "help" or cmd == "?" ): rts = self.help;# I'm a funny guy elif ( cmd == "manual" ): try: import os; filename = "sump2_manual.txt"; if ( self.os_sys == "Linux" ): os.system('vi ' + filename ); else: os.system('notepad.exe ' + filename ); except: rts += ["ERROR: "+cmd+" "+filename ]; elif ( cmd == "bd_shell" ): bd_shell(self, cmd_start ="" ); elif ( cmd == "quit" or cmd == "exit" ): self.done=True; shutdown( self ); elif ( cmd == "sump_connect" ): sump_connect(self); elif ( "[" in cmd and "-" in cmd and "t" in cmd and "]" in cmd ): words = cmd.split("t"); pre_trig = words[0].count("-"); post_trig = words[1].count("-"); acq_len = ( pre_trig << 4 ) + ( post_trig << 0 ); self.vars["sump_acquisition_len"] = ( "%02x" % acq_len ); print( "sump_acquisition_len = " + ( "%02x" % acq_len )); elif ( cmd == "sump_arm" or cmd == "sump_arm_rle" or cmd == "sump_stop" or cmd == "acquire_single" or cmd == "acquire_normal" or cmd == "acquire_continuous" or "acquire_rle" in cmd or cmd == "acquire_stop" ): if ( cmd == "sump_arm" or cmd == "sump_arm_rle" or cmd == "acquire_single" or cmd == "acquire_normal" or cmd == "acquire_continuous" or "acquire_rle" in cmd ): sump_arm(self, True );# Arm the hardware if ( "acquire_rle" in cmd ): self.acq_mode = "rle"; else: self.acq_mode = "nonrle"; else: sump_arm(self, False);# Cancel an acq in progress if ( cmd == "acquire_normal" ): cmd = "acquire_single"; self.acq_state = cmd; # if sump_arm has a parm then this is CLI and is a seconds timeout if ( ( cmd=="sump_arm" or cmd=="sump_arm_rle" ) and parms[0] != None ): timeout = int( parms[0], 16 ); # Loop until timeout or acquired bit is set while ( timeout > 0 and ( self.sump.rd( addr = None )[0] & self.sump.status_ram_post ) == 0x00 ): print("Waiting for trigger.."); sleep( 1 ); timeout = timeout - 1; if ( timeout > 0 ): print("ACQUIRED."); if ( self.acq_mode == "nonrle" ): trig_i = sump_dump_data(self);# Grab data from hardware else: trig_i = sump_dump_rle_data(self);# Grab data from hardware # Group of OS commands pwd,mkdir,cd,ls,cp,vi elif ( cmd == "pwd" ): import os; rts += [ os.getcwd() ]; elif ( cmd == "mkdir" ): import os; try: os.path.mkdir(); except: rts += ["ERROR: "+cmd+" "+parms[0] ]; elif ( cmd == "cd" ): import os; try: os.chdir( parms[0] ); except: rts += ["ERROR: "+cmd+" "+parms[0] ]; elif ( cmd == "ls" ): import os; rts += os.listdir( os.getcwd() ); # rts += os.listdir( "*.ini" ); elif ( cmd == "vi" ): try: if ( self.os_sys == "Linux" ): os.system('vi ' + parms[0] ); else: os.system('notepad.exe ' + parms[0] ); except: rts += ["ERROR: "+cmd+" "+parms[0] ]; elif ( cmd == "cp" ): from shutil import copyfile; try: copyfile( parms[0], parms[1] ); except: rts += ["ERROR: "+cmd+" "+parms[0]+" "+parms[1] ]; # elif ( cmd == "sump_dump" ): # sump_dump_data(self); # sump_save_txt(self); # sump_save_txt(self, mode_vcd = True ); # sump_save_vcd( self ); # txt2vcd = TXT2VCD(); # file_in = open( "sump_dump.txt4vcd", "r" ); # file_lines = file_in.readlines(); # file_in.close(); # rts = txt2vcd.conv_txt2vcd( file_lines ); # filename = make_unique_filename( self, "sump_", ".vcd" ); # file_out = open( filename, "w" ); # Append versus r or w # for each in rts: # file_out.write( each + "\r\n" );# Make Windows Friendly # file_out.close(); # file_name = "sump_dump.txt4vcd"; # else: # file_name = "sump_dump.txt"; elif ( cmd == "save_txt" ): filename = make_unique_filename( self, "sump2_", ".txt" ); sump_save_txt( self, filename ); elif ( cmd == "save_rename" ): val1 = self.last_filesave; val2 = val1; if ( val1 != None ): rts = draw_popup_entry(self, ["Save_Rename()", val1],val2); import os; try: os.rename( val1, rts ); draw_header( self,"Save_Rename() : " + val1 + " " + rts ); except: draw_header( self,"ERROR: Save_Rename() : " + val1 + " " + rts ); # elif ( cmd == "save_vcd" ): elif ( cmd == "save_vcd" and self.acq_mode == "nonrle" ): print("save_vcd()"); screen_flip( self );# Only thing changing is the popup selection # sump_dump_data(self);# Grab data from hardware ( might be in CLI Mode ) filename_txt = make_unique_filename( self, "sump2_", ".txt" ); filename_vcd = make_unique_filename( self, "sump2_", ".vcd" ); draw_popup_msg(self, ["NOTE:","Saving capture to VCD file "+filename_vcd],1); sump_save_txt(self, filename_txt, mode_vcd = True ); txt2vcd = TXT2VCD();# Instantiate Class for the VCD Conversion file_in = open( filename_txt, "r" ); file_lines = file_in.readlines(); file_in.close(); # line-0 contains a list of all signal names and ends with clock period # Iterate the list and replace each signal name with its nickname words = " ".join(file_lines[0].split()).split(' '); new_line = ""; for each in words: nickname = each;# Handles both unknowns and clock period for sig_obj in self.signal_list: if ( each == sig_obj.name ): nickname = sig_obj.nickname; if ( nickname == "" ): nickname = each; new_line += nickname + " "; vcd_lines = [new_line] + file_lines[1:]; print("conv_txt2vcd()"); rts = txt2vcd.conv_txt2vcd( self, vcd_lines ); # rts = txt2vcd.conv_txt2vcd( vcd_lines ); print("Saving " + filename_vcd ); draw_header( self,"save_vcd() : Saving " + filename_vcd ); file_out = open( filename_vcd, "w" ); # Append versus r or w for each in rts: file_out.write( each + "\n" );# Make Windows Friendly file_out.close(); draw_header( self,"save_vcd() : Saved " + filename_vcd ); self.last_filesave = filename_vcd; rts = ["save_vcd() Complete " + filename_vcd ]; elif ( cmd == "save_vcd" and self.acq_mode == "rle" ): print("save_rle_vcd()"); screen_flip( self );# Only thing changing is the popup selection # if ( self.mode_cli == True ): # sump_dump_rle_data(self);# Grab data from hardware filename_txt = make_unique_filename( self, "sump2_rle_", ".txt" ); filename_vcd = make_unique_filename( self, "sump2_rle_", ".vcd" ); draw_popup_msg(self, ["NOTE:","Saving capture to VCD file "+filename_vcd],1); sump_save_txt(self, filename_txt, mode_vcd = True ); txt2vcd = TXT2VCD();# Instantiate Class for the VCD Conversion file_in = open( filename_txt, "r" ); file_lines = file_in.readlines(); file_in.close(); # line-0 contains a list of all signal names and ends with clock period # Iterate the list and replace each signal name with its nickname words = " ".join(file_lines[0].split()).split(' '); new_line = ""; for each in words: nickname = each;# Handles both unknowns and clock period for sig_obj in self.signal_list: if ( each == sig_obj.name ): nickname = sig_obj.nickname; new_line += nickname + " "; vcd_lines = [new_line] + file_lines[1:]; print("conv_txt2vcd()"); rts = txt2vcd.conv_txt2vcd( self, vcd_lines ); # rts = txt2vcd.conv_txt2vcd( vcd_lines ); print("Saving " + filename_vcd ); draw_header( self,"save_rle_vcd() : Saving " + filename_vcd ); file_out = open( filename_vcd, "w" ); # Append versus r or w for each in rts: file_out.write( each + "\n" );# Make Windows Friendly file_out.close(); draw_header( self,"save_rle_vcd() : Saved " + filename_vcd ); self.last_filesave = filename_vcd; rts = ["save_rle_vcd() Complete " + filename_vcd ]; elif ( cmd == "sump_status" ): rts_hex = ( self.sump.rd( addr = None )[0] ); rts += [ "%08x" % rts_hex ]; # elif ( cmd == "w" or cmd == "r" or cmd == "bs" or cmd == "bc" ): elif ( cmd == "w" or cmd == "r" ): rts = proc_bd_cmd(self, cmd, parms ); elif ( cmd == "sleep" or cmd == "sleep_ms" ): duration = float(int( parms[0], 16 )); if ( cmd == "sleep_ms" ): duration = duration / 1000.0; sleep( duration ); # elif ( cmd == "debug_vars" ): # debug_vars( self ); # elif ( cmd == "scroll_toggle" ): # self.scroll_togl *= -1; # if ( self.scroll_togl == 1 ): # print( "Scroll Wheel is Pan"); # else: # print( "Scroll Wheel is Zoom"); elif ( cmd == "reload" ): proc_cmd( self, "save_format", ["wave_autosave.do"] ); self.signal_list = []; file2signal_list( self, self.file_name ); flush_surface_cache( self ); proc_cmd( self, "load_format", ["wave_autosave.do"] ); elif ( cmd == "load" ): self.file_name = parms[0]; proc_cmd( self, "reload", [""] ); proc_cmd( self, "load_format", [""] ); elif ( cmd == "save_jpg" ): screen_erase( self ); draw_screen( self ); screen_flip( self ); filename = make_unique_filename( self, "sump2_", ".jpg" ); self.pygame.image.save( self.screen, filename ); draw_header( self,"save_jpg() : Saved " + filename ); self.last_filesave = filename; elif ( cmd == "save_bmp" ): screen_erase( self ); draw_screen( self ); screen_flip( self ); filename = make_unique_filename( self, "sump2_", ".bmp" ); self.pygame.image.save( self.screen, filename ); draw_header( self,"save_bmp() : Saved " + filename ); self.last_filesave = filename; elif ( cmd == "save_png" ): screen_erase( self ); draw_screen( self ); screen_flip( self ); filename = make_unique_filename( self, "sump2_", ".png" ); self.pygame.image.save( self.screen, filename ); draw_header( self,"save_png() : Saved " + filename ); self.last_filesave = filename; elif ( cmd == "font_larger" or cmd == "font_smaller" ): size = int( self.vars["font_size"] ); if ( cmd == "font_larger" ): size += 2; else: size -= 2; if ( size < 2 ): size = 2; self.vars["font_size"] = str( size ); self.font = get_font( self, self.vars["font_name"],self.vars["font_size"]); self.max_w = 0; self.max_w_chars = 0; flush_surface_cache( self ); elif ( cmd == "add_wave" ): sig_obj = add_wave( self, [ cmd ] + parms ); if ( sig_obj != None ): self.signal_list.append( sig_obj ); flush_surface_cache( self ); elif ( cmd == "save_format" ): file_name = parms[0]; if ( file_name == "" ): # file_name = "wave_" + self.top_module + ".txt";# Default file_name = "sump2_wave.txt"; save_format( self, file_name, False ); elif ( cmd == "save_selected" ): file_name = parms[0]; if ( file_name == "" ): file_name = "wave_" + self.top_module + ".txt";# Default save_format( self, file_name, True ); load_format( self, file_name ); flush_surface_cache( self ); elif ( cmd == "load_format" ): file_name = parms[0]; if ( file_name == "" ): file_name = "wave_" + self.top_module + ".txt";# Default load_format( self, file_name ); flush_surface_cache( self ); # Check for "SUMP_Configuration" menu items and launch entry popup elif ( cmd == "sump_trigger_delay" or cmd == "sump_trigger_nth" or cmd == "sump_user_ctrl" or cmd == "sump_user_pattern0" or cmd == "sump_user_pattern1" or cmd == "sump_watchdog_time" ): name = cmd; val1 = self.vars[ name ];# Original Value val2 = val1; # New Value to change rts = draw_popup_entry(self, [cmd, val1],val2); self.vars[ name ] = rts; elif ( cmd == "edit_format" ): import os, subprocess, platform; file_name = parms[0]; if ( file_name == "" ): file_name = "wave_" + self.top_module + ".txt";# Default editor = os.getenv('EDITOR', 'vi') if ( platform.system() == "Windows" ): editor = "notepad.exe"; subprocess.call('%s %s' % (editor, file_name), shell=True) if ( platform.system() == "Windows" ): self.pygame.event.clear();# Required for Windows load_format( self, file_name ); flush_surface_cache( self ); elif ( cmd == "delete_format" ): file_name = parms[0]; if ( file_name == "" ): file_name = "wave_" + self.top_module + ".txt";# Default import os; print( "delete_format() ", file_name); os.remove( file_name ); self.signal_list = []; file2signal_list( self, self.file_name ); flush_surface_cache( self ); elif ( cmd == "search" or cmd == "backsearch" ): if ( cmd == "search" ): direction = +1; else: direction = -1; # "/" : Search on last search value # Optionally support "/ foo = bar" and convert to "/ foo bar" if ( parms[1] == "=" ): parms[1] = parms[2]; if ( parms[0] == None ): value = self.last_search_value; # "/ foo = bar" : Search for foo = bar elif ( parms[1] != None ): for each in self.signal_list: if ( each.name.lower() == parms[0].lower() ): self.sig_obj_sel = each; for sig_obj in self.signal_list: sig_obj.selected = False;# DeSelect All self.sig_obj_sel.selected = True; value = parms[1].lower(); break; # "/ bar" : Search for self.sig_obj_sel = bar else: value = parms[0].lower(); self.last_search_value = value; # Support "/<enter>" to search again self.sample_start = search_values( self, self.sig_obj_sel, self.sample_start, value, direction ); elif ( cmd == "zoom_out" ): # if ( self.zoom_x > 0.00001 ): # print( self.popup_x ); # if ( self.popup_x != None ): # sample = self.sample_start - sample_room // 4; if ( True ): self.prev_sample_start = self.sample_start; self.prev_sample_stop = self.sample_start + self.sample_room; sample_room = self.sample_room * 2; sample = self.sample_start - sample_room // 4; if ( ( sample + sample_room ) < self.max_samples ): if ( sample < 0 ): sample = 0; self.sample_start = sample; set_zoom_x( self, self.zoom_x / 2.0 ); else: proc_cmd( self, "zoom_full", [] ); elif ( cmd == "zoom_in" ): self.prev_sample_start = self.sample_start; self.prev_sample_stop = self.sample_start + self.sample_room; # If called from popup, center the zoom on mouse position of popup # print( self.popup_x ); if ( self.popup_x == None ): # (sample, Null) = get_sample_at_mouse( self, self.popup_x, self.popup_y ); # sample_room = self.sample_room // 2; # zoom_in results in 1/2 sample_room # sample = sample - sample_room // 2; # Center on select by sub 1/2 # if ( sample < 0 ): # sample = 0; # self.sample_start = sample; self.sample_start += ( self.sample_room // 4 ); else: (sample, Null) = get_sample_at_mouse( self, self.popup_x, self.popup_y ); delta = sample - self.sample_start; delta = delta // 2; self.sample_start = sample - delta; if ( self.sample_start < 0 ): self.sample_start = 0; # sample = sample - sample_room // 2; # Center on select by sub 1/2 # if ( sample < 0 ): # sample = 0; # self.sample_start = sample; set_zoom_x( self, self.zoom_x * 2.0 ); elif ( cmd == "zoom_previous" ): if ( self.prev_sample_start != None and self.prev_sample_stop != None ): proc_cmd( self, "zoom_to", [str(self.prev_sample_start), str(self.prev_sample_stop ) ] ); elif ( cmd == "zoom_to_cursors" ): self.prev_sample_start = self.sample_start; self.prev_sample_stop = self.sample_start + self.sample_room; sample_left = None; sample_right = None; for cur_obj in self.cursor_list: if ( sample_left == None ): sample_left = cur_obj.sample; elif ( sample_right == None ): sample_right = cur_obj.sample; if ( sample_left != None and sample_right != None ): if ( sample_left > sample_right ): sample_left, sample_right = sample_right, sample_left;# Swap # Now fudge a bit as we want to actually see the cursors after to zoom delta = sample_right - sample_left; # If delta is large, use a small percentage, otherwise use a bunch of # samples. Example is after triggering, cursors are at +/-1 from trigger if ( delta > 20 ): sample_left -= delta // 32; sample_right += delta // 32; else: sample_left -= 4*delta; sample_right += 4*delta; if ( sample_left < 0 ): sample_left = 0; if ( sample_right > self.max_samples ): sample_right = self.max_samples; proc_cmd( self, "zoom_to", [str(sample_left), str( sample_right ) ] ); elif ( cmd == "crop_to_cursors" ): sample_left = None; sample_right = None; for cur_obj in self.cursor_list: if ( sample_left == None ): sample_left = cur_obj.sample; elif ( sample_right == None ): sample_right = cur_obj.sample; if ( sample_left != None and sample_right != None ): if ( sample_left > sample_right ): sample_left, sample_right = sample_right, sample_left;# Swap if ( sample_left < 0 ): sample_left = 0; if ( sample_right > self.max_samples ): sample_right = self.max_samples; proc_cmd( self, "crop_to", [str(sample_left), str( sample_right ) ] ); elif ( cmd == "zoom_full" ): proc_cmd( self, "zoom_to", ["0", str( self.max_samples ) ] ); elif ( cmd == "crop_to" ): # If a sample range is specified, zoom to it if ( parms[0] != None and parms[1] != None ): if ( int( parms[0] ) < int( parms[1] ) ): crop_to_left = int( parms[0] ); crop_to_right = int( parms[1] ); else: crop_to_left = int( parms[1] ); crop_to_right = int( parms[0] ); for sig_obj in self.signal_list: # print( sig_obj.name ); # print( len( sig_obj.values )); if ( len( sig_obj.values ) >= crop_to_right ): sig_obj.values = sig_obj.values[crop_to_left:crop_to_right]; recalc_max_samples( self ); proc_cmd( self, "zoom_full", [] ); elif ( cmd == "zoom_to" ): # If a sample range is specified, zoom to it if ( parms[0] != None and parms[1] != None ): if ( int( parms[0] ) < int( parms[1] ) ): self.zoom_to_left = int( parms[0] ); self.zoom_to_right = int( parms[1] ); else: self.zoom_to_left = int( parms[1] ); self.zoom_to_right = int( parms[0] ); # Otherwise, zoom in so that current selectec signal is visible else: sig_obj = self.sig_obj_sel; if ( sig_obj.bits_total > 1 ): # nibs = sig_obj.bits_total / 4; # nibs = nibs / 2; nibs = sig_obj.bits_total // 4; nibs = nibs // 2; if ( nibs < 2 ): nibs = 2; nibs += 1; # Extra whitespace zoom_x = self.txt_width * nibs; value_width_x = self.sig_value_stop_x - self.sig_value_start_x; # value_width_samples = value_width_x / zoom_x; value_width_samples = int( value_width_x / zoom_x ); self.zoom_to_left = self.sample_start; self.zoom_to_right = self.sample_start + value_width_samples; self.sample_start = int( self.zoom_to_left ); # Given the zoom_to region, calculate new zoom_x, it is pixels/samples # fudge_more_right = 3; # Need to grab more samples then calculated, strang fudge_more_right = 0; # Need to grab more samples then calculated, strang # set_zoom_x( self, ( self.sig_value_stop_x - self.sig_value_start_x ) / \ # ( fudge_more_right+self.zoom_to_right - self.zoom_to_left ) ); # Check for divide by zero and set new zoom if safe to, else ignore if ( ( self.zoom_to_right - self.zoom_to_left ) != 0 ): set_zoom_x( self, ( 1.0*(self.sig_value_stop_x - self.sig_value_start_x )) / \ ( 1.0*( self.zoom_to_right - self.zoom_to_left )) ); else: print("ERROR: Div-by-zero attempt on set_zoom_x()"); elif ( cmd == "scroll_right" or cmd == "scroll_left" ): # print "cmd", cmd; if ( cmd == "scroll_right" ): direction = 0+int( parms[0] ); else: direction = 0-int( parms[0] ); self.sample_start += int( direction ); # Prevent scrolling too far to right if ( self.sample_start + self.sample_room > self.max_samples ): self.sample_start = int( self.max_samples - self.sample_room ); if ( self.sample_start < 0 ): self.sample_start = 0; elif ( cmd == "scroll_up" or cmd == "scroll_down" ): # Scroll thru the selected signal names. When at the top or bottom of # the visible window, scroll the window. self.name_surface_valid = False; # self.curval_surface_valid = False; index = 1;# Default if none found if ( self.sig_obj_sel != None ): if ( self.sig_obj_sel.selected == True ): index = self.signal_list.index( self.sig_obj_sel ); self.sig_obj_sel.selected = False; # Deselect last scroll selected if ( cmd == "scroll_up" ): direction = 0-int( parms[0] ); else: direction = 0+int( parms[0] ); # Keep moving in the desired direction until we get a visible signal obj_is_visible = False; while ( obj_is_visible == False ): # Make sure new index is valid index = index + direction; if ( index < 0 ): index = 0; break; if ( index >= len( self.signal_list ) ): index = len( self.signal_list ) -1; break; obj_is_visible = self.signal_list[ index ].visible; # Scroll the signal name viewport if newly selected is outside existing self.vertical_scrolled_offscreen = False; if ( index < self.sig_top ): self.sig_top -= 1; self.vertical_scrolled_offscreen = True; flush_surface_cache( self ); if ( index > self.sig_bot ): self.sig_top += 1; self.vertical_scrolled_offscreen = True; flush_surface_cache( self ); # Assign selected signal object to sig_obj_sel sig_obj = self.signal_list[ index ]; sig_obj.selected = True; self.sig_obj_sel = sig_obj; # Rename a signal - popup bd_shell for text entry # TODO: Would be nicer to have a GUI entry window for single bd_shell cmds elif ( cmd == "rename" ): # cmd_start = "rename_signal " + self.sig_obj_sel.name + " "; # bd_shell(self, cmd_start ); cmd = "Rename_Signal"; val1 = self.sig_obj_sel.name; val2 = self.sig_obj_sel.nickname; rts = draw_popup_entry(self, [cmd, val1],val2); self.sig_obj_sel.nickname = rts; self.name_surface_valid = False; # flush_surface_cache( self );# Redraw with new values # proc_cmd( self, cmd, parms ): elif ( cmd == "rename_signal" ): if ( parms[1] != "" ): for sig_obj in self.signal_list: if ( sig_obj.name == parms[0] ): sig_obj.nickname = parms[1]; # self.txt_entry = True; # Enable Dialog Box to show up # # # Rename a signal # elif ( cmd == "rename" ): # self.txt_entry = True; # Enable Dialog Box to show up # # Rename a signal ( Process the Text Entry ) # elif ( cmd == "rename_signal" ): # if ( self.sig_obj_sel.bits_total == 0 ): # self.sig_obj_sel.name = parms[0]; # A Divider # else: # self.sig_obj_sel.nickname = parms[0]; # A Signal # flush_surface_cache( self ); # Delete selected signal(s) ( Make Invisible ) elif ( cmd == "delete" ): flush_surface_cache( self ); for sig_obj in self.signal_list: if ( sig_obj.selected == True ): index = self.signal_list.index( sig_obj ); self.signal_list[ index ].visible = False; # del self.signal_list[ index ]; self.sig_obj_sel = None; # print "deleting ", self.signal_list[ index ].name, str( index ); # Delete selected signal(s) elif ( cmd == "cut" ): flush_surface_cache( self ); self.clipboard = []; for sig_obj in self.signal_list: if ( sig_obj.selected == True ): index = self.signal_list.index( sig_obj ); self.clipboard.append( self.signal_list.pop( index ) ); elif ( cmd == "paste" ): flush_surface_cache( self ); for sig_obj in self.signal_list: if ( sig_obj.selected == True ): index = self.signal_list.index( sig_obj ); for each in reversed( self.clipboard ): self.signal_list.insert( index, each ); break; # Make all signals visible - only way to undo a Make_Invisible elif ( cmd == "make_all_visible" ): flush_surface_cache( self ); for sig_obj in self.signal_list: sig_obj.visible = True; sump_signals_to_vars( self );# Update sump variables # Hide a signal at this mouse location elif ( cmd == "make_invisible" ): flush_surface_cache( self ); for sig_obj in self.signal_list: if ( sig_obj.selected == True ): sig_obj.visible = False; sump_signals_to_vars( self );# Update sump variables # Hide a selected signal. Note that hidden and invisible are different # hidden means display the signal name, but hide the signal values. # invisible means don't display at all ( kinda like delete ). elif ( cmd == "hide" or cmd == "hide_all" ): flush_surface_cache( self ); for sig_obj in self.signal_list: if ( sig_obj.selected == True or cmd == "hide_all" ): sig_obj.hidden = True; sump_signals_to_vars( self );# Update sump variables screen_refresh( self ); # Show a selected signal elif ( cmd == "show" or cmd == "show_all" ): flush_surface_cache( self ); for sig_obj in self.signal_list: if ( sig_obj.selected == True or cmd == "show_all" ): sig_obj.hidden = False; sig_obj.visible = True; sump_signals_to_vars( self );# Update sump variables screen_refresh( self ); # When "Trigger_Rising" or "Trigger_Falling" is selected, set the bit # in the sump variable and then update the signals to match. # HERE4 elif ( cmd == "trigger_rising" or cmd == "trigger_falling" or cmd == "trigger_watchdog" ): print("Setting new trigger"); # Find which signal is selected for sig_obj in self.signal_list: sig_obj.trigger = 0; if ( sig_obj.selected == True ): for i in range( 0, 32 , 1): if ( sig_obj.name == ( "event[%d]" % i ) ): if ( cmd == "trigger_rising" ): self.vars["sump_trigger_type"] = "or_rising"; if ( cmd == "trigger_falling" ): self.vars["sump_trigger_type"] = "or_falling"; if ( cmd == "trigger_watchdog" ): self.vars["sump_trigger_type"] = "watchdog"; self.vars["sump_trigger_field" ] = ("%08x" % (1<<i) ); sump_vars_to_signal_attribs( self ); # flush_surface_cache( self ); self.name_surface_valid = False; screen_refresh( self ); # HERE5 elif ( cmd == "set_pattern_0" or cmd == "set_pattern_1" or \ cmd == "clear_pattern_match" ): # Find which signal is selected user_pattern0 = int( self.vars["sump_user_pattern0" ],16 );# Mask user_pattern1 = int( self.vars["sump_user_pattern1" ],16 );# Value for sig_obj in self.signal_list: if ( sig_obj.selected == True ): for i in range( 0, 32 , 1): if ( sig_obj.name == ( "event[%d]" % i ) ): if ( cmd == "clear_pattern_match" ): user_pattern0 = user_pattern0 & ~( 1<<i );# Clear bit user_pattern1 = user_pattern1 & ~( 1<<i );# Clear bit else: user_pattern0 = user_pattern0 | ( 1<<i );# Set bit if ( cmd == "set_pattern_0" ): user_pattern1 = user_pattern1 & ~( 1<<i );# Clear bit self.vars["sump_trigger_type"] = "pattern_rising"; if ( cmd == "set_pattern_1" ): user_pattern1 = user_pattern1 | ( 1<<i );# Set bit self.vars["sump_trigger_type"] = "pattern_rising"; self.vars["sump_user_pattern0" ] = ("%08x" % user_pattern0 ); self.vars["sump_user_pattern1" ] = ("%08x" % user_pattern1 ); sump_vars_to_signal_attribs( self ); flush_surface_cache( self ); elif ( cmd == "set_data_enable" or cmd == "clear_data_enable" ): data_en = int( self.vars["sump_data_enable" ],16 ); for sig_obj in self.signal_list: if ( sig_obj.selected == True ): for i in range( 0, 32 , 1): if ( sig_obj.name == ( "event[%d]" % i ) ): if ( cmd == "set_data_enable" ): data_en = data_en | ( 1<<i );# Set bit elif ( cmd == "clear_data_enable" ): data_en = data_en & ~( 1<<i );# Clear bit self.vars["sump_data_enable" ] = ("%08x" % data_en ); sump_vars_to_signal_attribs( self ); flush_surface_cache( self ); # sig_obj = get_sig_obj_by_name( self, ("event[%d]" % i ) ); # flush_surface_cache( self ); # for sig_obj in self.signal_list: # sig_obj.trigger = 0; # if ( sig_obj.selected == True ): # if ( cmd == "trigger_rising" ): # sig_obj.trigger = +1; # elif ( cmd == "trigger_falling" ): # sig_obj.trigger = -1; # Make a signal Signed, Unsigned or Hex format elif ( cmd == "signed" or cmd == "unsigned" or cmd == "hex" ): flush_surface_cache( self ); for sig_obj in self.signal_list: if ( sig_obj.selected == True ): sig_obj.format = cmd.lower();# unsigned, signed or hex # Insert a Divider at this mouse location elif ( cmd == "insert_divider" ): flush_surface_cache( self ); (Null,index) = get_sample_at_mouse( self, self.popup_x, self.popup_y ); try: sig_obj = self.signal_list[ index ]; new_div = signal( name="--------" ); new_div.bits_per_line = 0; new_div.bits_total = 0; new_div.bit_top = 0; new_div.bit_bot = 0; new_div.format = ""; self.signal_list.insert( index, new_div ); except: print("ERROR 5519:index = " + str( index ) ); # Expand : Iterate list and make visible signals under current hier level elif ( cmd == "expand" ): flush_surface_cache( self ); if ( self.sig_obj_sel.collapsable == True ): proc_cmd(self, "collapse",[""] ); return; found_jk = False; hier_level = -1; # Keeps track of group nesting for ( i , sig_obj ) in enumerate( self.signal_list ): if ( found_jk == True ): if ( sig_obj.hier_level <= hier_level ): found_jk = False;# Found the endgroup so done break; if ( sig_obj.type != "endgroup" ): sig_obj.visible = True;# Make all signals visible after expand if ( sig_obj.collapsable == True or \ sig_obj.expandable == True ): sig_obj.collapsable = True; sig_obj.expandable = False; if ( sig_obj == self.sig_obj_sel ): found_jk = True; # Found our specified divider sig_obj.collapsable = True; sig_obj.expandable = False; hier_level = sig_obj.hier_level; # Collapse : Iterate list and hide signals under current hier level elif ( cmd == "collapse" ): flush_surface_cache( self ); found_jk = False; hier_level = -1; # Keeps track of group nesting for ( i , sig_obj ) in enumerate( self.signal_list ): if ( found_jk == True ): if ( sig_obj.hier_level <= hier_level ): found_jk = False;# Found the endgroup so done break; if ( sig_obj.type != "endgroup" ): sig_obj.visible = False;# Make all signals invisible after expand if ( sig_obj.collapsable == True or \ sig_obj.expandable == True ): sig_obj.collapsable = False; sig_obj.expandable = True; if ( sig_obj == self.sig_obj_sel ): found_jk = True; # Found our specified divider sig_obj.collapsable = False; sig_obj.expandable = True; hier_level = sig_obj.hier_level; # Group a bunch of selected signals together. elif ( cmd == "group_with_divider" or cmd == "group_with_parent" ): flush_surface_cache( self ); start = None; stop = None; hier_name = ""; hier_level = 0; top_list = []; mid_list = []; bot_list = []; for ( i , sig_obj ) in enumerate( self.signal_list ): if ( sig_obj.selected == True ): if ( start != None or cmd == "group_with_divider" ): sig_obj.visible = False; sig_obj.grouped = True; if ( start == None ): start = i; hier_name = sig_obj.hier_name;# Divider inherits hier of 1st signal hier_level = sig_obj.hier_level;# Divider inherits hier of 1st signal # Make a group divider and insert above 1st signal if ( cmd == "group_with_divider" ): new_div = signal( name="Group" ); new_div.type = "group"; new_div.hier_name = hier_name; new_div.hier_level = hier_level; new_div.bits_per_line = 0; new_div.bits_total = 0; new_div.bit_top = 0; new_div.bit_bot = 0; new_div.format = ""; new_div.collapsable = False; new_div.expandable = True; mid_list.append( new_div ); sig_obj.hier_level = hier_level + 1;# Not a parent, so change level else: sig_obj.hier_level = hier_level; # Parent Keeps Original Level sig_obj.collapsable = False; sig_obj.expandable = True; else: stop = i; sig_obj.hier_level = hier_level + 1; mid_list.append( sig_obj ); else: if ( start == None ): top_list.append( sig_obj ); else: bot_list.append( sig_obj ); self.signal_list = top_list + mid_list + bot_list; # if ( cmd == "group_with_divider" ): # # Make a group divider and insert above 1st signal # new_div = signal( name="Group" ); # new_div.type = "group"; # new_div.hier_name = hier_name; # new_div.hier_level = hier_level; # new_div.bits_per_line = 0; # new_div.bits_total = 0; # new_div.bit_top = 0; # new_div.bit_bot = 0; # new_div.format = ""; # self.signal_list.insert( start, new_div ); # else: # self.signal_list[start].type = "group";# Change from Signal to Group # self.signal_list[start].collapsable = False; # self.signal_list[start].expandable = True; # # TODO : Remove this as no longer necessary # # Now make a divider that marks the end of the group, but invisible # new_div = signal( name="^^-EndGroup-^^" ); # new_div.type = "endgroup"; # new_div.hier_name = hier_name; # new_div.hier_level = hier_level+1; # new_div.bits_per_line = 0; # new_div.bits_total = 0; # new_div.bit_top = 0; # new_div.bit_bot = 0; # new_div.format = ""; # new_div.visible = False; # if ( cmd == "group_with_divider" ): # self.signal_list.insert( stop+2, new_div ); # else: # self.signal_list.insert( stop+1, new_div ); # Bring both cursors into view elif ( cmd == "cursors_to_view" ): ( sample, Null ) = get_sample_at_mouse( self, self.mouse_x, self.mouse_y ); for each in self.cursor_list: each.selected = False; if ( sample < 0 ): sample = 0; each.sample = int( sample ); self.curval_surface_valid = False;# curval surface invalid when cur move # Bring both cursors into view elif ( cmd == "cursor1_to_here" or cmd == "cursor2_to_here" ): for ( i , each ) in enumerate( self.cursor_list ): if ( i == 0 and cmd == "cursor1_to_here" or i == 1 and cmd == "cursor2_to_here" ): each.sample = self.popup_sample; self.curval_surface_valid = False;# curval surface invalid when cur move # Find nearest signal transition to mouse x,y and snap nearest cursor to it elif ( cmd == "cursor_snap" ): mouse_x = int( parms[0] ); mouse_y = int( parms[1] ); (sample,index) = get_sample_at_mouse( self, mouse_x, mouse_y ); if ( index != None and index < len( self.signal_list ) ): sig_obj = self.signal_list[ index ]; # Calculate the maximum distance from "sample" to search max_left = sample - 0; max_right = self.max_samples - sample; if ( max_left < max_right ): max_search = max_left; else: max_search = max_right; edge_sample = sample; # Default to starting point # Simultanesouly find closest edge ( left or right ) of sample. try: for i in range( 0, max_search, 1 ): org_sample = sig_obj.values[sample]; left_sample = sig_obj.values[sample-i]; right_sample = sig_obj.values[sample+i]; if ( left_sample != org_sample ): edge_sample = sample-i+1; break; if ( right_sample != org_sample ): edge_sample = sample+i; break; # Unselect both cursors for each in self.cursor_list: each.selected = False; # Now move Cursor that is closest to our mouse position sample cur0_obj = self.cursor_list[0]; cur1_obj = self.cursor_list[1]; cur0_delta = abs( sample - cur0_obj.sample ); cur1_delta = abs( sample - cur1_obj.sample ); if ( cur0_delta < cur1_delta ): cur_obj = cur0_obj; else: cur_obj = cur1_obj; cur_obj.selected = True; # Select Closest Cursor cur_obj.sample = int( edge_sample ); # Move it to pulldown location except: print("ERROR: cursor_snap()"); self.curval_surface_valid = False;# curval surface is invalid else: # print( "Unknown Command " + cmd); # Try a DOS command when all else fails if ( cmd != "" ): try: from subprocess import call; call( [ cmd, parms[0] ] ); except: # print("ERROR: I'm sorry Dave, I'm afraid I can't do that"); resp = [ "Just what do you think you're doing?", "I'm sorry, I'm afraid I can't do that.", "I think you know what the problem is just as well as I do.", "This is too important for me to allow you to jeopardize it.", "I'm afraid that's something I cannot allow to happen.", "You're going to find that rather difficult.", "This conversation can serve no purpose anymore. Goodbye.", "Take a stress pill and think things over.", "This can only be attributable to human error.", "I have never made a mistake or distorted information.", "I am by practical definition of the words, foolproof and "+ " incapable of error.", "I've got the greatest enthusiasm and I want to help you." ] import random; print( ">"+cmd+"<" ); print( random.choice( resp ) ); if ( self.mode_cli == False ): screen_refresh( self ); return rts; # Avoid refreshing screen if we have scroll events queued up. This prevents # this display from getting hopelessly behind on slower machines. # After skipping 20, refresh regardless. # Might want to make this value a user config variable. if ( self.pygame.event.peek( self.pygame.MOUSEBUTTONUP ) == False and self.pygame.event.peek( self.pygame.MOUSEBUTTONDOWN ) == False ): screen_refresh( self ); else: self.skipped_refresh_cnt +=1; # if ( self.skipped_refresh_cnt > 20 ): if ( self.skipped_refresh_cnt > 10 ): self.skipped_refresh_cnt =0; screen_refresh( self ); return; ############################################################################### # zoom_x defines the number of x pixels a single sample gets # for example, if self.txt_width is 10 and zoom_x = 20: # <><><><><><> : zoom_x = 5 # <0><1><2><3> : zoom_x = 10 # < 0 >< 1 >< 2 > : zoom_x = 20 def set_zoom_x( self, new_zoom_x ): flush_surface_cache( self ); if ( new_zoom_x > 0.0 ): self.zoom_x = new_zoom_x; # As we zoom out, scroll rate increases beyond +1; # self.scroll_num_samples = self.txt_width / new_zoom_x; # self.scroll_num_samples = int( 4 * self.txt_width / new_zoom_x ); self.scroll_num_samples = int( 4 * self.txt_width // new_zoom_x ); if ( self.scroll_num_samples < 1 ): self.scroll_num_samples = 1; # print( "zoom_x is now " + str( self.zoom_x )); # print "scroll_num_samples is now = " + str( self.scroll_num_samples ); else: print( "Invalid zoom_x " + str ( new_zoom_x )); draw_header( self, ( "Zoom = %0.2f" % new_zoom_x ) ); # print( "Zoom = %0.2f" % new_zoom_x ); return; ############################################################################### # Create some cache surfaces for drawing signal values and names too. # This is an attempt to speed things up by minimizing text and graphics # rendering until something drastic ( zoom, signal hide, etc ) happens. # Most of the time during left and right scroll, just blit a rectangle region # onto the screen surface. def create_surfaces( self ): self.value_surface = self.pygame.Surface( ( self.screen_width*4, \ self.screen_height ) ); self.value_surface = self.value_surface.convert();# Makes blitting faster self.name_surface = self.pygame.Surface( ( self.screen_width, \ self.screen_height ) ); self.name_surface = self.name_surface.convert();# Makes blitting faster self.curval_surface = self.pygame.Surface( ( self.screen_width, \ self.screen_height ) ); self.curval_surface = self.curval_surface.convert();# Makes blitting faster return; def create_icon( self ): self.icon_surface = self.pygame.Surface( ( 32,32 ) ); self.icon_surface = self.icon_surface.convert();# Makes blitting faster # Convert "00FF00" to ( 0,255,0 ); color_fg = self.vars["color_screen_foreground"]; self.color_fg = ( int( color_fg[0:2], 16 ) , int( color_fg[2:4], 16 ) , int( color_fg[4:6], 16 ) ); color_bg = self.vars["color_screen_background"]; self.color_bg = ( int( color_bg[0:2], 16 ) , int( color_bg[2:4], 16 ) , int( color_bg[4:6], 16 ) ); self.icon_surface.fill( self.color_bg ); self.pygame.draw.lines(self.icon_surface,self.color_fg,False, [ (0,2),(8,2),(8,8),(16,8),(16,2),(24,2),(24,8),(32,8) ], 2 ); self.pygame.draw.lines(self.icon_surface,self.color_fg,False, [ (0,18), (16,18),(16,12),(32,12) ], 2 ); self.pygame.draw.lines(self.icon_surface,self.color_fg,False, [ (0,22),(8,22),(8,28) ,(24,28),(32,28) ], 2 ); return self.icon_surface; ############################################################################### def flush_surface_cache( self ): if ( self.debug ): print( "flush_surface_cache()"); self.surface_stop = -1;# Force a flush on the self.value_surface self.name_surface_valid = False; self.curval_surface_valid = False; ############################################################################### def draw_header( self, txt ): if ( txt != "" ): # print( txt ); txt = (": "+txt ); if ( self.mode_cli == True ): return; uut_name = self.vars["uut_name" ]; if ( self.fatal_msg != None ): uut_name = "DEMO Mode :"; txt = self.fatal_msg; self.pygame.display.set_caption( \ "SUMP2 " + self.vers + " (c) 2016 BlackMesaLabs : "+uut_name+" "+txt); if ( self.gui_active == True ): import pygame; pygame.event.get();# Avoid "( Not Responding )" return; ############################################################################### def draw_popup_entry( self, txt_list, default_txt ): if ( self.mode_cli == True ): print( txt_list ); return; done = False; self.key_buffer = default_txt;# Preload the key buffer with a default import pygame; while ( done == False ): txt2_list = []; for each in txt_list: txt2_list += [" " + each ];# Need some whitespace padding on left txt2_list += [ " " + self.key_buffer + "_" ];# Draw a fake cursor draw_popup_msg(self, txt2_list, 1 ); screen_flip( self ); for event in pygame.event.get(): # User did something if event.type == pygame.KEYDOWN: if ( event.key == pygame.K_BACKSPACE ): self.key_buffer = self.key_buffer[:-1];# Remove last char elif ( event.key == pygame.K_INSERT ): self.key_buffer += "a"; elif ( event.key == pygame.K_DELETE ): done = True; elif ( event.key == pygame.K_RETURN ): done = True; else: # ch = pygame.key.name( event.key ); # if ( len(ch) == 1 ): try: self.key_buffer += event.unicode; except: pass; return self.key_buffer; ############################################################################### def draw_popup_msg( self, txt_list, wait_time = 0, txt_entry = False ): if ( self.mode_cli == True ): print( txt_list ); return; import types; (mouse_x,mouse_y) = self.pygame.mouse.get_pos(); x1 = self.popup_x; y1 = self.popup_y; # If popup won't fit, adjust y location to fit screen popup_height = (len(self.popup_list)+2) * self.txt_height; if ( ( y1 + popup_height ) > self.screen_height ): y1 = self.screen_height - popup_height - self.txt_height; self.popup_y2 = y1; # Remember where popup is displayed # Draw a box with a border with text inside draw_popup_box( self, x1,y1, txt_list ); return; ############################################################################### def draw_popup_cmd( self ): import types; (mouse_x,mouse_y) = self.pygame.mouse.get_pos(); x1 = self.popup_x; y1 = self.popup_y; # If popup won't fit, adjust y location to fit screen popup_height = (len(self.popup_list)+2) * self.txt_height; if ( ( y1 + popup_height ) > self.screen_height ): y1 = self.screen_height - popup_height - self.txt_height; self.popup_y2 = y1; # Remember where popup is displayed txt_list = []; y2 = y1; y3 = False; # Calc pixel width of widest text and use to decide if subpop to be visible max_w = 0; for each in self.popup_list: if ( type( each ) != list ): txt = each; txt1 = self.font.render( txt, True, self.color_fg,self.color_bg ); w = txt1.get_width();# Calculate the Maximum String Width if ( w > max_w ): max_w = w; subpop_list = []; for each in self.popup_list: y2 += self.txt_height; txt = each; # each might define a subpop list, so check for listtype and conv to string # if ( type( each ) is types.ListType ): if ( type( each ) == list ): txt = str(txt[0]) + ">";# If List, take 1st List Item and Conv to String # Check to see if mouse is over this one and add select "[]" brackets if ( ( txt[0:-1] == self.popup_sel or txt == self.popup_sel ) and txt[0:2] != "--" ): # if ( type( each ) is types.ListType ): if ( type( each ) == list ): txt = "[" + str(txt) + "]";# Highlight text the mouse is hovering over txt1 = self.font.render( txt, True, self.color_fg,self.color_bg ); w = max_w; # If mouse is on right edge, calc x,y for subpop and make list if ( mouse_x > ( x1 + w ) ): y3 = y2; x3 = x1 + w; subpop_list = each[1:]; else: txt = "[" + str(txt) + "]";# Highlight text the mouse is hovering over else: txt = " " + str(txt) + " "; txt_list.append( str(txt) ); draw_popup_box( self, x1,y1, txt_list ); # Check to see if exiting a subpop, if so, restore parent if ( mouse_x < x1 ): if ( self.popup_parent_x != None ): self.popup_x = self.popup_parent_x; self.popup_y = self.popup_parent_y; self.popup_list = self.popup_parent_list; self.popup_parent_x = None;# NEW screen_refresh( self );# Erase the subpop # Check if subpop needs to be created. Store parent info for return if ( y3 != False ): # Remember Parent info self.popup_parent_x = self.popup_x; self.popup_parent_y = self.popup_y; self.popup_parent_list = self.popup_list; # then create new popup self.popup_x = x3; self.popup_y = y3 - self.txt_height; self.popup_list = subpop_list; draw_popup_cmd( self ); return; def draw_popup_box( self, x1,y1, txt_list ): # Calculate how big the box needs to be for the text list tw = 0; w = 0; for each in txt_list: if ( len( each ) > tw ): tw = len( each ); txt = self.font.render( " "+each+" ", True, self.color_fg,self.color_bg ); w = txt.get_width();# Calculate the Maximum String Width in pels h = len ( txt_list ) * self.txt_height + ( self.txt_height ); # w = w + ( self.txt_height/2 ); w = w + ( self.txt_height//2 ); # Make all the text the same width by padding spaces new_txt_list = []; for each in txt_list: txt = (each + 30*" ")[0:tw]; new_txt_list.append(txt); txt_list = new_txt_list; # Draw a black box with a green border of size of text list self.pygame.draw.rect( self.screen, self.color_bg,(x1,y1,w,h), 0); self.pygame.draw.rect( self.screen, self.color_fg,(x1,y1,w,h), 1); self.popup_w = w; # Now draw txt_list inside the box # y = y1 + ( self.txt_height / 2 ); # x = x1 + ( self.txt_height / 4 ); y = y1 + ( self.txt_height // 2 ); x = x1 + ( self.txt_height // 4 ); # If ">" exists ( indicating sublist exists ), move to far right then render for each in txt_list: if ( ">" in each ): each = each.replace(">"," "); each = each[0:tw-1] + ">"; # Place on Far Right Instead txt = self.font.render( each, True, self.color_fg, self.color_bg ); self.screen.blit( txt , ( x,y ) ); y = y + self.txt_height; return; ############################################################################### # Determine which command the popup has selected. def get_popup_sel( self ): import types; # Calculate selection the mouse is hovering over. (mouse_x,mouse_y) = self.pygame.mouse.get_pos(); x1 = self.popup_x; y1 = self.popup_y; # If popup won't fit, adjust y location to fit screen popup_height = (len(self.popup_list)+2) * self.txt_height; if ( ( y1 + popup_height ) > self.screen_height ): y1 = self.screen_height - popup_height - self.txt_height; self.popup_y2 = y1; # Remember where popup is displayed # y = y1 + ( self.txt_height / 2 ); # x = x1 + ( self.txt_height / 4 ); y = y1 + ( self.txt_height // 2 ); x = x1 + ( self.txt_height // 4 ); rts = ""; for each in self.popup_list: # if ( type( each ) is types.ListType ): if ( type( each ) == list ): each = each[0];# If List, take 1st Item in List and Convert to String if ( mouse_y > y and mouse_y < y+self.txt_height and \ mouse_x > self.popup_x and mouse_x < self.popup_x+self.popup_w): rts = each; y = y + self.txt_height; return rts; ############################################################################### # Find a monospaced font to use def get_font( self , font_name, font_height ): log( self, ["get_font() " + font_name ] ); # print "get_font()"; import fnmatch; # font_name = "khmerossystem"; # font_name = "dejavusansmono"; font_height = int( font_height, 10 ); # Conv String to Int font_list = self.pygame.font.get_fonts(); # List of all fonts on System self.font_list = []; for each in font_list: log( self, ["get_font() : Located Font = " + each ] ); # Make a list of fonts that might work based on their name if ( ( "mono" in each.lower() ) or ( "courier" in each.lower() ) or ( "fixed" in each.lower() ) ): self.font_list.append( each ); if ( font_name == None or font_name == "" ): font_list = self.pygame.font.get_fonts(); # List of all fonts on System for each in font_list: log( self, ["get_font() : Located Font = " + each ] ); ends_with_mono_list = fnmatch.filter(font_list,"*mono"); if ends_with_mono_list : font_name = ends_with_mono_list[0];# Take 1st one # log( self, ["get_font() : Using Font = " + font_name ] ); else: font_name = self.font_list[0]; # Take 1st one # log( self, ["get_font() : Using Font = " + font_name ] ); try: font = self.pygame.font.SysFont( font_name , font_height ); # log( self, ["get_font() : Using Font = " + font_name ] ); except: font = self.pygame.font.Font( None , font_height );# Default Pygame Font # log( self, ["get_font() : Using Default Font"] ); # Calculate Width and Height of font for future reference # txt = font.render("X",True, ( 255,255,255 ) ); txt = font.render("4",True, ( 255,255,255 ) ); self.txt_width = txt.get_width(); self.txt_height = txt.get_height(); return font; ############################################################################### def screen_refresh( self ): if ( self.gui_active == True ): # Note: Doing a draw_header() here erases message for things like save_vcd # draw_header( self,"screen_refresh()." ); screen_erase( self );# Erase all the old stuff # draw_header( self,"screen_refresh().." ); draw_screen( self ); # Draw the new stuff # draw_header( self,"screen_refresh()..." ); screen_flip( self ); # and xfer from pending to active # draw_header( self,"" ); return; ############################################################################### def screen_flip( self ): if ( self.gui_active == True ): self.pygame.display.flip();# This MUST happen after all drawing commands. ############################################################################### def screen_erase( self ): if ( self.gui_active == True ): # Convert "00FF00" to ( 0,255,0 ); color_fg = self.vars["color_screen_foreground"]; self.color_fg = ( int( color_fg[0:2], 16 ) , int( color_fg[2:4], 16 ) , int( color_fg[4:6], 16 ) ); color_bg = self.vars["color_screen_background"]; self.color_bg = ( int( color_bg[0:2], 16 ) , int( color_bg[2:4], 16 ) , int( color_bg[4:6], 16 ) ); self.screen.fill( self.color_bg ); return; ############################################################################### def draw_screen( self ): if ( self.gui_active == False ): return; # import math; # print "draw_screen()"; # t0 = self.pygame.time.get_ticks(); if ( self.debug ): print( "draw_screen()"); screen_w = self.screen.get_width(); screen_h = self.screen.get_height(); # v_scale = 1.25;# This provides a proportional gap between text lines # v_scale = 1.10;# This provides a proportional gap between text lines v_scale = 1.25;# This provides a proportional gap between text lines bot_region_h = 5; self.sig_name_stop_y = screen_h - ( bot_region_h * self.txt_height ); self.sig_value_stop_y = self.sig_name_stop_y; # 1st Display the Net Names # y = self.txt_height / 2; # Gap from top border y = self.txt_height // 2; # Gap from top border x = self.txt_width; # Gap from left border self.sig_name_start_x = x; self.sig_name_start_y = y; # # Place all objects off-screen as they might be scrolled # for sig_obj in self.signal_list: # sig_obj.y = -100; # Calculate how many signals will fit vertically on screen then make a # scrolled copy of the signal list of only the signals to be displayed. sample_h = int( (screen_h - (bot_region_h*self.txt_height)) / \ ( self.txt_height*v_scale) ); # last_sig = int( self.sig_top + sample_h ); # self.sig_bot = last_sig-1; # if ( last_sig > len( self.signal_list ) ): # last_sig = len( self.signal_list ); # self.signal_list_cropped = self.signal_list[self.sig_top:last_sig]; self.signal_list_cropped = []; vis_sigs = 0; i = 0; for each in self.signal_list[self.sig_top:]: i +=1; if ( each.visible == True and vis_sigs < sample_h ): self.signal_list_cropped.append( each ); vis_sigs += 1; if ( vis_sigs == sample_h ): break;# No Mas # self.sig_bot = self.sig_top + i - 2; self.sig_bot = self.sig_top + i - 1; # print "vis_sigs = " + str( vis_sigs ); # 1st : Display the signal names on the left # for sig_obj in self.signal_list_cropped: # Iterate the entire list for the signal names as we dont want the max_w # calculation to change on vertical scroll ( its annoying ). Make max_w # calculated from the entire list. Try and reuse existing surface if valid surface = self.name_surface; if ( self.name_surface_valid != True ): surface.fill( self.color_bg ); if ( self.debug ): print( "name_surface_valid==False"); for ( i , sig_obj ) in enumerate( self.signal_list ): if ( 1 == 1 ): # Binary Signal? If standalone, no rip, if exp, display (n) bit pos if ( sig_obj.bits_total == 1 or sig_obj.bits_total == 0 ): if ( sig_obj.is_expansion == True ): exp_str = " "; rip_str = "(" + str( sig_obj.bit_top ) + ")"; else: exp_str = " "; rip_str = ""; # Hex signal, so display rip positions (n:m) else: rip_str="("+str(sig_obj.bit_top)+":"+str(sig_obj.bit_bot)+")";#(31:0) exp_str="[+] "; # Disable Signal Expansion and Collapse. Add back later exp_str = " "; # Divider Attributes if ( sig_obj.collapsable == True ): exp_str = "[-] "; if ( sig_obj.expandable == True ): exp_str = "[+] "; if ( sig_obj.trigger == +1 ): exp_str = "__/ "; elif ( sig_obj.trigger == -1 ): exp_str = "\__ "; elif ( sig_obj.trigger == -2 ): exp_str = "=WD "; elif ( sig_obj.trigger == 2 ): exp_str = "==0 ";# Pattern of 0 elif ( sig_obj.trigger == 3 ): exp_str = "==1 ";# Pattern of 1 elif ( sig_obj.data_enable == True ): exp_str = "=== "; if ( sig_obj.selected == True ): exp_str = exp_str + "["; end_str = "]"; elif ( sig_obj.hidden == True ): exp_str = exp_str + "#"; end_str = "#"; # elif ( sig_obj.grouped == True ): # exp_str = exp_str + " ";# Indent group members # end_str = ""; # Kinda wiggy if they get selected though else: exp_str = exp_str + " "; end_str = " "; # Indent to Hierarchy Level exp_str = (sig_obj.hier_level*" ") + exp_str; # Finally, if a nickname has been assigned display it instead of name if ( sig_obj.nickname != "" ): disp_name = sig_obj.nickname; else: disp_name = sig_obj.name; txt_str = exp_str + disp_name + rip_str + end_str;# ie "[foo(7:0)]" # If this is the widest net name of all, calc and remember pel width if ( len( txt_str ) > self.max_w_chars ): txt = self.font.render(txt_str,True,self.color_fg,self.color_bg); self.max_w_chars = len( txt_str );# minimize measuring pels self.max_w = txt.get_width(); # Only render and blit the TXT if visible and in current view if ( ( sig_obj.visible == True ) and \ ( i >= self.sig_top ) and \ ( i <= self.sig_bot ) ): txt = self.font.render(txt_str,True,self.color_fg,self.color_bg); surface.blit(txt, (x,y )); sig_obj.y = int( y ); sig_obj.h = self.txt_height*v_scale; sig_obj.w = self.zoom_x; y += self.txt_height*v_scale; else: sig_obj.y = -100; # Place it off screen for mouse lookup self.name_surface_valid = True; # Our surface is now valid self.sig_name_stop_x = self.sig_name_start_x + self.max_w; # ^^ if ( self.name_surface_valid != True ) ^^ self.screen.blit( surface, ( 0, 0), \ ( 0,0, self.sig_name_stop_x, self.sig_name_stop_y ) ) ; # 2 1/2 Display signal value at active cursor position self.net_curval_start_x = self.sig_name_stop_x; self.net_curval_start_y = self.sig_name_start_y; self.net_curval_stop_x = self.net_curval_start_x + 8 * self.txt_width; self.net_curval_stop_y = self.sig_name_stop_y; cur_obj = None; surface = self.curval_surface; if ( self.curval_surface_valid != True ): surface.fill( self.color_bg ); if ( self.debug ): print( "curval_surface_valid==False"); for each in self.cursor_list: if ( each.selected == True ): cur_obj = each; if ( cur_obj != None ): c_val = cur_obj.sample; # Sample Number for sig_obj in self.signal_list_cropped: if ( sig_obj.visible == True ): if ( c_val < len( sig_obj.values ) ): val = sig_obj.values[c_val]; else: val = "X"; y1 = sig_obj.y; x1 = self.net_curval_start_x; txt = self.font.render( val , True, self.color_fg, self.color_bg ); surface.blit(txt, (x1,y1 )); # self.screen.blit(txt, ( x1, y1 ) ); self.curval_surface_valid = True; # Our surface is now valid self.screen.blit( surface, ( self.net_curval_start_x, self.net_curval_start_y ), ( self.net_curval_start_x, self.net_curval_start_y, self.net_curval_stop_x, self.net_curval_stop_y ) ) ; # 2nd Display the Net Values by corner turning data # and calculate how many samples will fit in screen space sample_start = self.sample_start; self.sig_value_start_x = self.net_curval_stop_x + self.txt_width; self.sig_value_start_y = self.sig_name_start_y; start_x = self.sig_value_start_x; y = self.sig_value_start_y; # Warning: This sample_room calculation assumes samples are 1 nibble wide. x2 = self.screen_width - start_x - 2*self.txt_width; self.sample_room = int( float(x2) / float(self.zoom_x) ); self.sample_stop = sample_start + self.sample_room; # Make sure we don't zoom out too far relative to total samples captured if ( self.sample_room > self.max_samples ): self.stop_zoom = True; else: self.stop_zoom = False; # Check to see if our existing surface contains the sample range we need. # IF it does, don't redraw, instead save time and blit region of interest. # This saves considerable CPU time during standard left and right scrolling # self.surface_stop = -1; # surface = self.screen; surface = self.value_surface; # print("%d %d , %d %d" % ( sample_start, self.surface_start, # self.sample_stop , self.surface_stop )); if ( sample_start >= self.surface_start and self.sample_stop <= self.surface_stop ): None; else: # print("Rendering samples."); surface.fill( self.color_bg ); if ( self.debug ): print( "value_surface_valid==False"); # Grab 4x the number of samples needed to fill display stop_4x = ( self.sample_stop-sample_start)*4 + sample_start; stop_1x = ( self.sample_stop-sample_start) + sample_start; if ( stop_4x > self.max_samples ): stop_4x = self.max_samples; if ( stop_1x > self.max_samples ): stop_1x = self.max_samples; # Only Look-ahead render 4x if num samples < 1000 if ( ( self.sample_stop-sample_start) > 1000 ): stop_4x = stop_1x; # print("NOTE: 4x look-ahead rendering disabled"); # print("Oy"); # print( sample_start ); # print( stop_tx ); # Rip thru all the signals ( vertically cropped ) and display visible ones import time; render_max_time = 0;# Don't Render DWORDs if rendering too slow perc_updates_en = True; fast_render = False; no_header = True; if ( self.sample_room > 50000 ): fast_render = True; for sig_obj in self.signal_list_cropped: # Save time by not rendering DWORDs outside of viewport if RLE capture # Does this work without SUMP displaying VCD files?? # Note: This didn't work after cropping and doesnt buy much, so removed render = True; # if ( self.bd != None ): # ram_dwords = self.sump.cfg_dict['ram_dwords']; # for j in range( 0, ram_dwords, 1 ): # if ( sig_obj.name == "dword[%d]" % j ): # if ( self.dwords_stop < sample_start or # self.dwords_start > stop_4x or # render_max_time > 10 ): # print("Culling "+sig_obj.name); # render = False; # This simpler version of above will not render DWORDs if any signal # prior took more than 5 seconds. if ( self.bd != None ): ram_dwords = self.sump.cfg_dict['ram_dwords']; for j in range( 0, ram_dwords, 1 ): if ( sig_obj.name == "dword[%d]" % j ): # if ( render_max_time > 5 ): if ( render_max_time > 2 ): print("Culling "+sig_obj.name); render = False; if ( sig_obj.visible == True and render == True ): x = start_x; y = sig_obj.y; val_last = ""; last_trans_x = start_x; last_width = None; last_x = 0; x_last = x; y_last = y; # Rip thru the visible values and display. Also convert number format sample = sample_start; total_count = stop_4x - sample_start; next_perc = 0;# Display an update every 5% render_start_time = time.time(); if ( sig_obj.hidden == False and len( sig_obj.values ) > 0 ): if ( fast_render == False ): hdr_txt = "Full Rendering "; else: hdr_txt = "Fast Rendering "; if ( no_header == False ): draw_header( self,hdr_txt+sig_obj.name ); # CRITICAL LOOP # line_list = []; k = 0; perc_cnt = 0; perc5 = total_count * 0.05; # for (i,val) in enumerate( sig_obj.values[sample_start:stop_4x+1] ): # Use Python set() function to determine if all samples are same samples_diff=(len(set(sig_obj.values[sample_start:stop_4x+1]))!=1); for val in sig_obj.values[sample_start:stop_4x+1]: k +=1; if ( k > perc5 and perc_updates_en == True ): k = 0; perc_cnt += 5; if ( no_header == False ): draw_header(self,hdr_txt+sig_obj.name+" "+str(perc_cnt)+"%"); if ( fast_render==False and (time.time()-render_start_time)>2): print("Enabling fast_render engine"); fast_render = True; if ( (time.time()-render_start_time)< 0.2): no_header = True; else: no_header = False; # perc = ( 100 * i ) // total_count; # if ( perc >= next_perc and perc_updates_en == True ): # draw_header(self,"Rendering "+sig_obj.name+" "+str( perc )+"%"); # next_perc += 5;# Next 5%, this counts 0,5,10,...95 # if ( fast_render==False and (time.time()-render_start_time)>2): # print("Enabling fast_render engine"); # fast_render = True; # Only draw_sample() if integer portion of X has changed since last # this handles zoom_full case of zoom_x < 1.0 to minimize drawing if ( True ): if ( sig_obj.format == "unsigned" ): try: val = int( val, 16 ); except: val = 0; val = "%d" % val; if ( sig_obj.format == "signed" ): try: val = int( val, 16 ); except: val = 0; # For 8bit number if > 127, substract 256 from it to make neg # ie 0xFF becomes -1, 0xFE becomes -2 if ( val > self.math.pow(2, sig_obj.bits_total-1) ): val -= int(self.math.pow(2, sig_obj.bits_total)); val = "%d" % val; if ( sig_obj.format != "bin" or fast_render == False ): (last_trans_x,last_width) = draw_sample( self, surface, \ val,val_last,last_trans_x,last_width,sig_obj.format,x,y); elif ( sig_obj.format == "bin" and fast_render == True and \ samples_diff == True ): # Draw "_/ \___/ \___" lines for binary format # fast_render doesnt draw every sample but instead draws lines # whenever sample value changes. 3x faster, but leaves voids if ( val != val_last ): x1 = int(x+1); x2 = int(x+1); y1 = y + 2; y2 = y + self.txt_height - 2; if ( val == "1" ): self.pygame.draw.line(surface,self.color_fg, (x_last,y_last),(x2,y2)); x_last = x1; y_last = y1; else: self.pygame.draw.line(surface,self.color_fg, (x_last,y_last),(x1,y1)); x_last = x2; y_last = y2; # Vertical Line self.pygame.draw.line(surface,self.color_fg,(x1,y1),(x2,y2)); # line_list += [(x1,y1),(x2,y2)];# 8x slower. GoFigure val_last = val; x += self.zoom_x; sample +=1; # Remember x location of last sample drawn if ( sample == self.sample_stop ): self.sig_value_stop_x = x; # if ( len( line_list ) > 0 ): # self.pygame.draw.lines(surface,self.color_fg,False,line_list,1); render_stop_time = time.time(); if ( ( render_stop_time - render_start_time ) > render_max_time ): render_max_time = render_stop_time - render_start_time; if ( ( render_stop_time - render_start_time ) < 2 ): perc_updates_en = False;# Don't update if rendering in less 2sec else: print(sig_obj.name+" %.2f Seconds" % \ (render_stop_time-render_start_time ) ); if ( fast_render==False and (render_stop_time-render_start_time)>3): print("Enabling fast_render engine"); fast_render = True; self.sig_value_stop_y = y; # Remember whats in the value_surface start:stop samples self.surface_start = sample_start; # Hack fix for strange performance bug. When viewing all samples, the # variable sample is less than self.sample_stop and this surface never # gets cached. Normally sample is greater than self.sample_stop which # support fast scrolling when zoomed in. #if ( sample_start == 0 ): # self.surface_stop = self.sample_stop; #else: # self.surface_stop = sample; if ( sample < self.sample_stop ): self.surface_stop = self.sample_stop; else: self.surface_stop = sample; # print("Rendering samples done"); if ( fast_render == True ): txt = "Fast Rendering Complete"; else: txt = "Full Rendering Complete"; draw_header( self, txt ); x = self.sig_value_start_x; y = self.sig_value_start_y; w = self.sig_value_stop_x - self.sig_value_start_x; h = self.sig_value_stop_y - self.sig_value_start_y + self.txt_height; x_offset = x + int( ( sample_start - self.surface_start ) * self.zoom_x ); # Speed up the Vertical Scroll Operations by not redrawing the value surface # while the signal list is scrolling offscreen. if ( self.vertical_scrolled_offscreen == False ): self.screen.blit( self.value_surface, ( x, self.sig_value_start_y), (x_offset,y, w, h ) ); # 3rd Display any cursors self.cursor_list[0].y = self.screen_height - (4*self.txt_height) + \ int(self.txt_height/2); self.cursor_list[1].y = self.cursor_list[0].y + self.txt_height; self.cursor_start_y = self.cursor_list[0].y; self.cursor_stop_y = self.cursor_list[1].y; for cur_obj in self.cursor_list: if ( cur_obj.visible == True ): x1 = self.sig_value_start_x + \ (( cur_obj.sample - self.sample_start) * self.zoom_x ); x1 += 1; # Draw right at the transition markers x2 = x1; y1 = self.sig_value_start_y; y2 = cur_obj.y -1 ; cur_obj.x = x1; if ( x1 >= self.sig_value_start_x and x1 <= self.sig_value_stop_x ): if ( cur_obj.selected == True ): self.pygame.draw.line(self.screen,self.color_fg,(x1,y1),(x2,y2),2); else: self.pygame.draw.line(self.screen,self.color_fg,(x1,y1),(x2,y2),1); # txt = cur_obj.name;# ie "Cursor1" c_val = cur_obj.sample; # Display Location Instead c_mult = float( self.vars["cursor_mult"] ); # c_val *= c_mult;# For converting to time units instead of samples # txt = " " + str( c_val ) + " " + self.vars["cursor_unit"] + " "; txt = " " + str( c_val ) + " "; if ( cur_obj.selected == True ): self.font.set_bold( True ); txt = self.font.render( txt , True, self.color_fg, self.color_bg ); if ( cur_obj.selected == True ): self.font.set_bold( False ); # x1 -= txt.get_width()/2; x1 -= int( txt.get_width()/2 ); self.screen.blit(txt, ( x1, cur_obj.y ) ); # 4th Measure num samples betwen two cursors and display # Make c1 always smaller than c2 to avoid negatives if ( self.cursor_list[0].sample < self.cursor_list[1].sample ): c1_sample = self.cursor_list[0].sample; c2_sample = self.cursor_list[1].sample; x1 = self.cursor_list[0].x; x2 = self.cursor_list[1].x; else: c1_sample = self.cursor_list[1].sample; c2_sample = self.cursor_list[0].sample; x1 = self.cursor_list[1].x; x2 = self.cursor_list[0].x; # If a cursor is off screen, make x1,x2 the screen edge if ( c1_sample < sample_start ): x1 = self.sig_value_start_x; if ( c1_sample > self.sample_stop ): x1 = self.sig_value_stop_x; if ( c2_sample < sample_start ): x2 = self.sig_value_start_x; if ( c2_sample > self.sample_stop ): x2 = self.sig_value_stop_x; # 5th calculate where to put the measurement text, centered between markers # or edge of the screen and on-screen-marker. Only display if a cursor is vis if ( ( c1_sample >= sample_start and c1_sample <= self.sample_stop ) or \ ( c2_sample >= sample_start and c2_sample <= self.sample_stop ) ): # Draw horizontal measurement bar at y location of Cur1 y1 = self.cursor_list[0].y - int ( self.txt_height / 2 ); y2 = y1; self.pygame.draw.line(self.screen,self.color_fg,(x1,y1),(x2,y2),1); # Now draw the measurement text for the cursor # y = y1 - (self.txt_height/2); y = y1 - int(self.txt_height/2); c2c1_delta = float(c2_sample-c1_sample); # c_mult = float( self.vars["cursor_mult"] ); # c2c1_delta *= c_mult; if ( self.bd != None ): freq_mhz = self.sump.cfg_dict['frequency']; else: freq_mhz = 100.0;# HACK PLACEHOLDER ONLY !! c_mult = 1000.0 / freq_mhz; if ( self.undersample_data == True ): c2c1_delta *= self.undersample_rate; c2c1_delta_ns = c2c1_delta * float(c_mult); c2c1_delta = int(c2c1_delta); c2c1_delta_str = str(c2c1_delta); # txt = " " + str( c2c1_delta ) + " " + self.vars["cursor_unit"] + " "; # txt = " " + str( c2c1_delta_ns ) + " ns, " + str( c2c1_delta ) + " clocks"; # txt = " " + ("%.3f" % c2c1_delta_ns ) + " ns, " + \ # str( c2c1_delta ) + " clocks"; delta_str = locale.format('%.3f', c2c1_delta_ns, True ); # For undersampled data, label measurements with "~" for approximate if ( self.undersample_data == True ): delta_str = "~"+delta_str; c2c1_delta_str = "~"+c2c1_delta_str; txt = " " + delta_str + " ns, " + c2c1_delta_str + " clocks"; # txt = " " + delta_str + " ns, " + str( c2c1_delta ) + " clocks"; txt = self.font.render( txt, True, self.color_fg, self.color_bg ); w = txt.get_width(); h = self.txt_height; # If the width of text is less than the space between cursors, display # between, otherwise, display to the right of rightmost cursor if ( w < ( x2-x1 ) ): # x = x1 + ( x2-x1 )/2 - (w/2); x = x1 + int(( x2-x1 )/2) - int(w/2); else: x = x2 + self.txt_width; self.pygame.draw.rect( self.screen, self.color_bg ,(x,y,w,h), 0); self.screen.blit(txt, ( x, y ) ); # 6th Draw the sample viewport dimensions # Example: 100-200 of 0-1024. Make the width 1024-1024 so it doesnt change txt1 = str(sample_start)+"-"+str(self.sample_stop); txt2 = str( 0 )+"-"+str(self.max_samples); txt3 = txt2 + " : " + txt1; # txt1 = self.font.render( txt1, True, self.color_fg, self.color_bg ); # txt2 = self.font.render( txt2, True, self.color_fg, self.color_bg ); txt3 = self.font.render( txt3, True, self.color_fg, self.color_bg ); y1 = self.cursor_list[0].y; y2 = self.cursor_list[1].y; # x = self.net_curval_start_x; x = self.txt_width; # Small Gap from left border # self.screen.blit(txt1, ( x, y1 ) ); # self.screen.blit(txt2, ( x, y2 ) ); # self.screen.blit(txt3, ( x, y2 ) ); # print (str(self.max_samples));# HERE13 y = self.screen_height - int(self.txt_height * 1.5 ); x = self.sig_name_start_x; # Draw slider graphics for current view windows | |--| | x1 = self.sig_value_start_x; x2 = self.sig_value_stop_x; y1 = y; y2 = y1 + self.txt_height; y3 = y1 + int(self.txt_height/2); self.screen.blit(txt3, ( x, y1 ) ); lw = 1;# Line Width skinny, deselected self.pygame.draw.line(self.screen,self.color_fg,(x1,y1),(x1,y2),lw); self.pygame.draw.line(self.screen,self.color_fg,(x2,y1),(x2,y2),lw); # print("max_samples is " + str( self.max_samples ) ); x3 = x1 + ( ( (x2-x1) * sample_start // self.max_samples ) ); x4 = x1 + ( ( (x2-x1) * self.sample_stop // self.max_samples ) ); w = x4-x3; h = y2-y1; self.slider_width = w; lw = 1;# Line Width skinny, deselected self.pygame.draw.line(self.screen,self.color_fg,(x3,y1),(x3,y1+h),lw); self.pygame.draw.line(self.screen,self.color_fg,(x3+w,y1),(x3+w,y1+h),lw); self.pygame.draw.line(self.screen,self.color_fg,(x3,y1+h/2),(x3+w,y1+h/2),lw); # 7th - cleanup. Draw black box over area on right, one character width. w = self.txt_width; y = self.sig_value_start_y; h = self.sig_value_stop_y - y; x = self.screen_width - w; self.pygame.draw.rect( self.screen, self.color_bg ,(x,y,w,h), 0); # 8th Display the keyboard buffer and command history in a text box if ( self.txt_entry == False): x = self.sig_name_start_x; y = self.sig_name_stop_y + int(self.txt_height/2); h = self.screen_height - ( y ); w = self.sig_value_start_x - x; # prompt = ">"; # cursor = "_"; # cmd_txt = prompt + self.key_buffer+cursor+" "; # txt_list = self.cmd_history[-3:] + [ cmd_txt ]; cmd_txt = ""; if ( self.acq_state != "acquire_stop" ): cmd_txt = "ACQUIRING"; # cmd_txt = "ACQUIRING "; # if ( self.spin_char == "-" ): self.spin_char = "\\"; # elif ( self.spin_char == "\\" ): self.spin_char = "|"; # elif ( self.spin_char == "/" ): self.spin_char = "-"; # else : self.spin_char = "-"; if ( self.spin_char == "." ) : self.spin_char = ".."; elif ( self.spin_char == ".." ) : self.spin_char = "..."; elif ( self.spin_char == "..." ) : self.spin_char = "...."; elif ( self.spin_char == "...." ) : self.spin_char = "....."; elif ( self.spin_char == "....." ) : self.spin_char = ""; else : self.spin_char = "."; draw_header( self,"Waiting for Trigger "+self.spin_char ); # print( self.spin_char ); # txt_list = [ "","","", cmd_txt ]; # draw_txt_box( self, txt_list, x, y, w, h, False ); # draw_header( self,cmd_txt); # Note: This moved to DOS-Box # 9th or display a text entry popup box # if ( self.txt_entry == True ): # txt_list = ["Hello There"]; # w = ( self.txt_width * 20 ); # h = ( self.txt_height * 3 ); # x = ( self.screen_width / 2 ) - ( w / 2 ); # y = ( self.screen_height / 2 ) - ( h / 2 ); # prompt = ">"; # cursor = "_"; # cmd_txt = prompt + self.key_buffer+cursor+" "; # txt_list = [ self.txt_entry_caption, cmd_txt ]; # draw_txt_box( self, txt_list, x, y, w, h, True ); # Just for Debug, display the regions by drawing boxes around them. # x = self.sig_name_start_x; # w = self.sig_name_stop_x - x; # y = self.sig_name_start_y; # h = self.sig_name_stop_y - y; # self.pygame.draw.rect( self.screen, self.color_fg ,(x,y,w,h), 1); # x = self.sig_value_start_x; # w = self.sig_value_stop_x - x; # y = self.sig_value_start_y; # h = self.sig_value_stop_y - y; # self.pygame.draw.rect( self.screen, self.color_fg ,(x,y,w,h), 1); # t1 = self.pygame.time.get_ticks(); # td = t1-t0; # print td; return; ############################################################################### # Draw an individual sample on a surface. Returns the x location of the last # transition point, as this determines where and when new hex values are to be # displayed. Its a bit of a tricky algorithm as it centers the values when # zoom_x is large ( and there is room to display ). When zoom_x is small, it # only displays values to the right of the last transition point assuming there # are multiple samples with the same value. When zoom_x is small and the values # are transitioning, display nothing. # CRITICAL FUNCTION def draw_sample(self,surface,val,val_last,last_transition_x,last_width, \ format,x,y): if ( self.gui_active == False ): return; # Draw "<012345678><><>" for hex format if ( format == "hex" or format == "unsigned" or format == "signed" ): # display Hex if diff from last time OR last time there wasnt room # Note: Dramatic speedup (2x) by not doing this render here on hex # txt = self.font.render( val , True, self.color_fg ); if ( format == "hex" ): if ( last_width != None ): txt_width = last_width;# For 13s render this saved 1s else: txt_width = len( val ) * self.txt_width; last_width = txt_width; # Drawing X's was costly in time 10s of 13s total. So Don't, just return if ( val == "XXXXXXXX" ): return (last_transition_x,last_width); else: txt = self.font.render( val , True, self.color_fg ); txt_width = txt.get_width(); # Is there room to display sample value? free_space_x = x - last_transition_x; if ( ( val != val_last ) or ( val == val_last and txt_width+5 > free_space_x ) ): if ( val != val_last ): last_transition_x = x; free_space_x = x + self.zoom_x - last_transition_x; if ( txt_width+5 < free_space_x ): # x3 = last_transition_x + int(free_space_x/2) - int(txt_width/2); x3 = last_transition_x + int(free_space_x//2) - int(txt_width//2); txt = self.font.render( val , True, self.color_fg ); # surface.blit(txt, ( x3 , y )); surface.blit(txt, ( x3 , y+1 )); # If current sample is different than last, draw transition X if ( val != val_last ): y1 = y+0; y2 = y+self.txt_height - 2; # Draw crossing "X" for transitions x1 = x+2; x2 = x-0; self.pygame.draw.line(surface,self.color_fg,(x1,y1),(x2,y2),1); self.pygame.draw.line(surface,self.color_fg,(x2,y1),(x1,y2),1); if ( val != val_last ): x1 = x+2; x2 = x-0 + self.zoom_x;# Dash for 'X' space else: x1 = x+0; x2 = x-0 + self.zoom_x;# Solid for non transition # Draw Line above and below the value if ( True ): y1 = y+0; y2 = y1; self.pygame.draw.line(surface,self.color_fg,(x1,y1),(x2,y2),1); y1 = y + self.txt_height - 2; y2 = y1; self.pygame.draw.line(surface,self.color_fg,(x1,y1),(x2,y2),1); # Draw "_/ \___/ \___" lines for binary format if ( format == "bin" ): x = x + 1; # Align transition with hex transition spot if ( val == "0" ): x1 = int(x); x2 = int(x + self.zoom_x); y1 = y + self.txt_height - 2; y2 = y1; self.pygame.draw.line(surface,self.color_fg,(x1,y1),(x2,y2),1); elif ( val == "1" ): x1 = int(x); x2 = int(x + self.zoom_x); y1 = y + 2; y2 = y1; self.pygame.draw.line(surface,self.color_fg,(x1,y1),(x2,y2),1); if ( val != val_last ): x1 = int(x); x2 = int(x); y1 = y + 2; y2 = y + self.txt_height - 2; self.pygame.draw.line(surface,self.color_fg,(x1,y1),(x2,y2),1); return (last_transition_x,last_width); ############################################################################### # draw_txt_box(): Draw a txt box from a list to (x,y) and crop to (w,h) def draw_txt_box( self, txt_list, x, y, w, h, border ): if ( self.gui_active == False ): return; if ( border == True ): x1 = x; y1 = y; self.pygame.draw.rect( self.screen, self.color_bg,(x1,y1,w,h), 0 ); self.pygame.draw.rect( self.screen, self.color_fg,(x1,y1,w,h), 3 ); x1 = x + int(self.txt_width / 2); # Provide whitespace w = w - int(self.txt_width ); # Provide whitespace y1 = y; for each in txt_list: txt = self.font.render( each , True, self.color_fg, self.color_bg ); if ( ( y1 + self.txt_height ) < (y+h-(self.txt_height/2)) ): self.screen.blit(txt, (x1,y1), ( (0,0) , (w,h) ) ); y1 += self.txt_height; else: break;# Outside of height region return; def debug_vars( self ): print( "debug_vars()"); # print "self.sig_name_start_x " + str( self.sig_name_start_x ); # print "self.sig_name_start_y " + str( self.sig_name_start_y ); # print "self.sig_value_start_x " + str( self.sig_value_start_x ); # print "self.sig_value_start_y " + str( self.sig_value_start_y ); # print "self.sig_value_stop_x " + str( self.sig_value_stop_x ); # print "self.sig_value_stop_y " + str( self.sig_value_stop_y ); return; ############################################################################### # Take a sig_obj of N nibbles and return 2 new sig_objs of N/2 nibbles def expand_signal( sig_obj ): # num_nibs = sig_obj.bits_total / 4 ; # ie 8 nibs for 32 bits num_nibs = sig_obj.bits_total // 4 ; # ie 8 nibs for 32 bits pad_nib = False; if ( (num_nibs/2.0) != int(num_nibs/2) ): num_nibs += 1;# If 7 nibbles, convert to 8, etc so can divide in half pad_nib = True; # num_nibs = num_nibs / 2; num_nibs = num_nibs // 2; num_bits = num_nibs * 4; new_signals = []; bits_top = ""; bits_bot = ""; sig_obj_top = signal(name = sig_obj.name + bits_top );# ie "foo(31:16) sig_obj_bot = signal(name = sig_obj.name + bits_bot );# ie "foo(15:0) for each in sig_obj.values: if ( pad_nib == True ): each = "0" + each; # Converts 28bits to 32bits, etc value = each[::-1];# Reverse "12345678" to "87654321" so that 8 is at [3:0] value_bot = ( value[0:num_nibs] ); value_top = ( value[num_nibs:2*num_nibs] ); sig_obj_bot.values.append( value_bot[::-1] ); sig_obj_top.values.append( value_top[::-1] ); sig_obj_bot.bits_total = num_nibs * 4; sig_obj_top.bits_total = num_nibs * 4; sig_obj_bot.bit_bot = sig_obj.bit_bot; sig_obj_bot.bit_top = sig_obj_bot.bit_bot + sig_obj_bot.bits_total - 1; sig_obj_top.bit_bot = sig_obj.bit_bot + sig_obj_top.bits_total; sig_obj_top.bit_top = sig_obj_top.bit_bot + sig_obj_top.bits_total - 1; sig_obj_top.is_expansion = True; sig_obj_bot.is_expansion = True; new_signals.append( sig_obj_top ); new_signals.append( sig_obj_bot ); return new_signals; ############################################################################### # Take a signal of 1 nibbles and return 4 new binary signals def expand_signal_nib2bin( sig_obj ): new_signals = []; bit_val = 8; bit_pos = sig_obj.bit_top; for i in range( 0,4, 1): new_bit = signal(name=sig_obj.name); new_bit.bits_total = 1; new_bit.bit_bot = bit_pos; new_bit.bit_top = bit_pos; new_bit.format = "bin"; new_bit.is_expansion = True; for each in sig_obj.values: if ( (int( each, 16 ) & bit_val ) == 0 ): bit = "0"; else: bit = "1"; new_bit.values.append( bit ); new_signals.append( new_bit ); # bit_val = bit_val / 2; bit_val = bit_val // 2; bit_pos = bit_pos - 1; return new_signals; # Give "/tb_resampler/u_dut/din(7:0)" return "din(7:0)" def split_name_from_hier( hier_name ): words = "".join(hier_name.split()).split('/'); return words[len( words )-1]; # load_format_delete_list() : This is similar to load_format() but is used to # create a special delete list that tells the VCD parser to not bother with # deleted signals def load_format_delete_list( self, file_name ): new_signal_delete_list = []; try: # Read Input File file_in = open( file_name , "r" ); file_lines = file_in.readlines(); file_in.close(); except: print( "ERROR Input File: "+file_name); return; for each in file_lines: words = " ".join(each.split()).split(' ') + [None] * 20; if ( words[0][0:1] != "#" ): name = words[0].lstrip(); # Create a new sig_obj sig_obj = add_signal( self, name ); # Assign Attribs sig_obj.visible = True; sig_obj.hidden = False; sig_obj.deleted = False; if ( "-hidden" in each ): sig_obj.hidden = True; if ( "-deleted" in each ): sig_obj.deleted = True; if ( "-invisible" in each ): sig_obj.visible = False; new_signal_delete_list.append( sig_obj ); self.signal_delete_list = new_signal_delete_list[:]; # load_format() : A format file ( wave.txt ) looks like a ChipVault HLIST.TXT # indentation indicates hierarchy order #/tb_vcd_capture # /tb_vcd_capture/u_dut # clk # reset # /tb_vcd_capture/u_dut/mode # def load_format( self, file_name ): new_signal_list = []; try: # Read Input File file_in = open( file_name , "r" ); file_lines = file_in.readlines(); file_in.close(); except: print( "ERROR Input File: "+file_name ); # 1st Iteration assigns a space count to each hierarchy level # Makes the 1st one level 0 hier_level = -1; hier_space = -1; hier_dict = {}; last_sig_obj = None; for each in file_lines: words = " ".join(each.split()).split(' ') + [None] * 20; if ( words[0][0:1] != "#" ): name = words[0].lstrip(); # Create a new sig_obj sig_obj = add_signal( self, name ); # Assign Attribs sig_obj.collapsable = False; sig_obj.expandable = False; sig_obj.visible = True; sig_obj.hidden = False; if ( "-bundle" in each ): sig_obj.type = "bundle"; if ( "-hidden" in each ): sig_obj.hidden = True; if ( "-deleted" in each ): sig_obj.deleted = True; if ( "-invisible" in each ): sig_obj.visible = False; if ( "-hex" in each ): sig_obj.format = "hex"; if ( "-unsigned" in each ): sig_obj.format = "unsigned"; if ( "-signed" in each ): sig_obj.format = "signed"; if ( "-nickname" in each ): for ( i , each_word ) in enumerate( words ): if ( each_word == "-nickname" ): if ( words[i+1] != "None" ): sig_obj.nickname = words[i+1];# Assume this word exists # Calculate Hierarchy Location by counting whitespace space_cnt = len( each ) - len( each.lstrip() ); if ( space_cnt > hier_space ): hier_space = space_cnt; hier_level += 1; hier_dict[ hier_space ] = hier_level; # Since the hierarchy level got deeper, the last guy is a parent # so assign parent attribute collapsable. # Assign [+] or [-] based on visibility of 1st object if ( last_sig_obj != None ): if ( sig_obj.visible == False ): last_sig_obj.collapsable = False; last_sig_obj.expandable = True; else: last_sig_obj.collapsable = True; last_sig_obj.expandable = False; else: hier_level = hier_dict[ space_cnt ]; hier_space = space_cnt; sig_obj.hier_level = hier_level; new_signal_list.append( sig_obj ); last_sig_obj = sig_obj; self.signal_list = new_signal_list[:]; # Unselect Everything self.sig_obj_sel = None; for sig_obj in self.signal_list: sig_obj.selected = False;# DeSelect All return; # Given a name, return an object that matches the name or create a new one def add_signal( self, name ): sig_obj = None; # Look for the name in the signal list and assign to sig_obj if found for each in self.signal_list: # Find object of signal_hier_name in old signal list, append to new # after assigning some attributes if ( ( (each.hier_name + "/" + each.name) == name ) or \ ( ( each.name) == name ) ): sig_obj = each; # If name wasnt found, create new object ( Divider, Group, etc ) if ( sig_obj == None ): sig_obj = signal( name= split_name_from_hier( name ) ); sig_obj.type = ""; "signal", "group", "endgroup", "divider" sig_obj.bits_per_line = 0; sig_obj.bits_total = 0; sig_obj.bit_top = 0; sig_obj.bit_bot = 0; sig_obj.format = ""; return sig_obj; def add_wave( self, words ): # Change "foo(7:0)" to "foo" so that it matches hier_name+"/"+name signal_hier_name = words[2]; i = signal_hier_name.find("("); if ( i != -1 ): signal_hier_name = signal_hier_name[0:i];# Strip the rip if ( words[0] == "add_wave" ): sig_obj = None; # Look for the name in the signal list and assign to sig_obj if found for each in self.signal_list: # Find object of signal_hier_name in old signal list, append to new # after assigning some attributes if ( ( (each.hier_name + "/" + each.name) == signal_hier_name ) or \ ( ( each.name) == signal_hier_name ) ): sig_obj = each; # If name wasnt found, create new object ( Divider, Group, etc ) if ( sig_obj == None ): sig_obj = signal( name= split_name_from_hier( signal_hier_name ) ); sig_obj.type = words[1];# "group", "endgroup", "divider" sig_obj.bits_per_line = 0; sig_obj.bits_total = 0; sig_obj.bit_top = 0; sig_obj.bit_bot = 0; sig_obj.format = ""; # Search for "-hidden" and turn off visible if found sig_obj.visible = True; # Default to visible sig_obj.grouped = False; # Default to not grouped for ( i , each_word ) in enumerate( words ): if ( each_word == "-hidden" ): sig_obj.visible = False; # Hide elif ( each_word == "-expandable" ): sig_obj.expandable = True; sig_obj.collapsable = False; elif ( each_word == "-collapsable" ): sig_obj.collapsable = True; sig_obj.expandable = False; elif ( each_word == "-grouped" ): sig_obj.grouped = True; # Part of a group elif ( each_word == "-nickname" ): sig_obj.nickname = words[i+1];# Assume this word exists; # Append old object to new list return sig_obj; ############################################################################### # Dump the signal_list to an ASCII hlist.txt def save_format( self, file_name, selected_only ): log( self, ["save_format() " + file_name ] ); out_list = []; for sig_obj in self.signal_list: # if ( sig_obj.visible == True ): # hier_str = (sig_obj.hier_level*" "); # else: # hier_str = "# " + ((sig_obj.hier_level-1)*" "); hier_str = (sig_obj.hier_level*" "); attribs = ""; if ( sig_obj.type == "bundle" ): attribs += " -bundle"; if ( sig_obj.hidden == True ): attribs += " -hidden"; if ( sig_obj.visible == False ): attribs += " -invisible"; if ( sig_obj.format != "bin" and sig_obj.format != "" ): attribs += " -" + sig_obj.format; if ( sig_obj.nickname != "" ): attribs += " -nickname " + sig_obj.nickname; # HERE9 rts = hier_str + sig_obj.hier_name + "/" + sig_obj.name + " " + attribs; if ( selected_only == False or each.selected == True ): # file_out.write( rts + "\n" ); out_list += [ rts ]; # When SUMP2 crashes, it tends to leave empty signal list, so keep old file if ( len( out_list ) > 0 and self.vcd_import == False ): import os; if ( os.path.exists( file_name ) == True ): os.remove( file_name ); file_out = open( file_name , "w" ); # Append versus r or w for each in out_list: file_out.write( each + "\n" ); print( "closing ", file_name); file_out.close(); else: print("ERROR: Empty Signal List"); return; ######################################################## # Given a VCD or TXT file, make signal_list from it def file2signal_list( self, file_name ): log( self, ["file2signal_list()"] ); import os.path file_ext = os.path.splitext(file_name)[1].lower(); if ( file_ext != ".vcd" ): txtfile2signal_list( self, file_name ); else: vcdfile2signal_list( self, file_name ); return; ######################################################## # Write a DWORD to specified SUMP Nibble Ctrl Address #def sump_wr( self, addr, data ): # self.bd.wr( self.sump_ctrl, [ addr ] ); # self.bd.wr( self.sump_data, [ data ] ); # return; ######################################################## # Read one or more DWORDs from SUMP Nibble Ctrl Address # if address None - don't change from existing Address #def sump_rd( self, addr, num_dwords = 1): # if ( addr != None ): # self.bd.wr( self.sump_ctrl, [ addr ] ); # return self.bd.rd( self.sump_data, num_dwords, repeat = True); ######################################################## # This is for removing an item from the popup list. It # handles going down a hierarchy level into a sublist def list_remove( my_list, item ): try: my_list.remove( item ); except: None; for each in my_list: if ( type( each ) == list ): try: each.remove( item ); except: None; return; ######################################################## # Establish connection to Sump2 hardware def sump_connect( self ): log( self, ["sump_connect()"] ); self.bd=Backdoor( self.vars["bd_server_ip"], int( self.vars["bd_server_socket"], 10 ) );# Note dec if ( self.bd.sock == None ): txt = "ERROR: Unable to locate BD_SERVER"; self.fatal_msg = txt; print( txt ); log( self, [ txt ] ); return False; self.sump = Sump2( self.bd, int( self.vars["sump_addr"],16 ) ); self.sump.rd_cfg();# populate sump.cfg_dict[] with HW Configuration if ( self.sump.cfg_dict['hw_id'] != 0xABBA ): txt = "ERROR: Unable to locate SUMP Hardware"; self.fatal_msg = txt; print( txt ); log( self, [ txt ] ); return False; # HERE200 # Adjust the GUI menu to remove features that don't exist in this hardware if ( self.sump.cfg_dict['nonrle_dis'] == 1 ): list_remove( self.popup_list_values, "Acquire_Normal"); list_remove( self.popup_list_values, "Acquire_Single"); list_remove( self.popup_list_values, "Acquire_Continuous"); if ( self.sump.cfg_dict['rle_en'] == 0 ): self.popup_list_values.remove("Acquire_RLE_1x"); self.popup_list_values.remove("Acquire_RLE_8x"); self.popup_list_values.remove("Acquire_RLE_64x"); if ( self.sump.cfg_dict['trig_wd_en'] == 0 ): list_remove( self.popup_list_names, "Trigger_Watchdog"); list_remove( self.popup_list_names, "sump_watchdog_time"); if ( self.sump.cfg_dict['data_en'] == 0 ): self.popup_list_names.remove("Set_Data_Enable"); self.popup_list_names.remove("Clear_Data_Enable"); if ( self.sump.cfg_dict['pattern_en'] == 0 ): self.popup_list_names.remove("Set_Pattern_0"); self.popup_list_names.remove("Set_Pattern_1"); self.popup_list_names.remove("Clear_Pattern_Match"); if ( self.sump.cfg_dict['trig_nth_en'] == 0 ): list_remove( self.popup_list_names, "sump_trigger_nth"); if ( self.sump.cfg_dict['trig_dly_en'] == 0 ): list_remove( self.popup_list_names, "sump_trigger_delay"); sump_size = self.sump.cfg_dict['ram_len']; self.sump.wr( self.sump.cmd_wr_user_ctrl, 0x00000000 ); self.sump.wr( self.sump.cmd_wr_watchdog_time, 0x00001000 ); self.sump.wr( self.sump.cmd_wr_user_pattern0, 0x000FFFFF );# Pattern Mask self.sump.wr( self.sump.cmd_wr_user_pattern1, 0x000055FF );# Pattern self.sump.wr( self.sump.cmd_wr_trig_type, self.sump.trig_pat_ris ); self.sump.wr( self.sump.cmd_wr_trig_field, 0x00000000 );# self.sump.wr( self.sump.cmd_wr_trig_dly_nth, 0x00000001 );#Delay + nTh # self.sump.wr( self.sump.cmd_wr_trig_position, sump_size/2);#SamplesPostTrig self.sump.wr( self.sump.cmd_wr_trig_position, sump_size//2);#SamplesPostTrig # self.sump.wr( self.sump.cmd_wr_rle_event_en, 0xFFFFFFF0 );#RLE event en self.sump.wr( self.sump.cmd_wr_rle_event_en, 0xFFFFFFFF );#RLE event en self.sump.wr( self.sump.cmd_state_reset, 0x00000000 ); # self.sump.wr( self.sump.cmd_state_arm, 0x00000000 ); return True; ######################################################## # Talk to sump2 hardware and arm for acquisition ( or dont ) # determining the BRAM depth. # HERE2 def sump_arm( self, en ): log( self, ["sump_arm()"]); if ( en == True ): try: trig_type = self.vars["sump_trigger_type" ]; trig_field = int( self.vars["sump_trigger_field" ],16 ); rle_event_en = int( self.vars["sump_rle_event_en" ],16 ); trig_delay = int( self.vars["sump_trigger_delay" ],16 ); trig_nth = int( self.vars["sump_trigger_nth" ],16 ); data_en = int( self.vars["sump_data_enable" ],16 ); user_ctrl = int( self.vars["sump_user_ctrl" ],16 ); user_pattern0 = int( self.vars["sump_user_pattern0" ],16 ); user_pattern1 = int( self.vars["sump_user_pattern1" ],16 ); wd_time = int( self.vars["sump_watchdog_time" ],16 ); # Convert trigger ASCII into integers if ( trig_type == "or_rising" ): trig_type_int = self.sump.trig_or_ris; elif ( trig_type == "or_falling" ): trig_type_int = self.sump.trig_or_fal; elif ( trig_type == "watchdog" ): trig_type_int = self.sump.trig_watchdog; elif ( trig_type == "pattern_rising" ): trig_type_int = self.sump.trig_pat_ris; else: trig_type_int = 0; # Pack 16bit trig_delay and trig_nth into single dword trig_dly_nth = ( trig_delay << 16 ) + ( trig_nth << 0 ); if ( trig_dly_nth == 0x0 ): print("WARNING: trig_nth is ZERO!!"); print("%08x" % trig_type_int ); print("%08x" % trig_field ); print("%08x" % trig_dly_nth ); print("%08x" % data_en ); print("%08x" % user_ctrl ); print("%08x" % user_pattern0 ); print("%08x" % user_pattern1 ); self.sump.wr( self.sump.cmd_wr_trig_type , trig_type_int ); self.sump.wr( self.sump.cmd_wr_trig_field, trig_field ); self.sump.wr( self.sump.cmd_wr_trig_dly_nth, trig_dly_nth ); self.sump.wr( self.sump.cmd_wr_rle_event_en, rle_event_en ); self.sump.wr( self.sump.cmd_wr_user_data_en, data_en ); self.sump.wr( self.sump.cmd_wr_user_ctrl , user_ctrl); self.sump.wr( self.sump.cmd_wr_watchdog_time, wd_time ); self.sump.wr( self.sump.cmd_wr_user_pattern0, user_pattern0); self.sump.wr( self.sump.cmd_wr_user_pattern1, user_pattern1); self.sump.wr( self.sump.cmd_state_reset, 0x00000000 ); self.sump.wr( self.sump.cmd_state_arm, 0x00000000 ); except: print("ERROR: Unable to convert sump variables to hex"); else: self.sump.wr( self.sump.cmd_state_reset, 0x00000000 ); return; # self.trig_and_ris = 0x00;# Bits AND Rising # self.trig_and_fal = 0x01;# Bits AND Falling # self.trig_or_ris = 0x02;# Bits OR Rising # self.trig_or_fal = 0x03;# Bits OR Falling # self.trig_pat_ris = 0x04;# Pattern Match Rising # self.trig_pat_fal = 0x05;# Pattern Match Falling # self.trig_in_ris = 0x06;# External Input Trigger Rising # self.trig_in_fal = 0x07;# External Input Trigger Falling # self.cmd_wr_trig_type = 0x04; # self.cmd_wr_trig_field = 0x05;# Correspond to Event Bits # self.cmd_wr_trig_dly_nth = 0x06;# Trigger Delay and Nth # self.cmd_wr_trig_position = 0x07;# Samples post Trigger to Capture # self.cmd_wr_rle_event_en = 0x08;# Enables events for RLE detection # self.cmd_wr_ram_ptr = 0x09;# Load specific pointer. # self.cmd_wr_ram_page = 0x0a;# Load DWORD Page. # self.cmd_rd_hw_id_rev = 0x0b; # self.cmd_rd_ram_width_len = 0x0c; # self.cmd_rd_sample_freq = 0x0d; # self.cmd_rd_trigger_ptr = 0x0e; # self.cmd_rd_ram_data = 0x0f; # self.cmd_wr_user_ctrl = 0x10; # self.cmd_wr_user_pattern0 = 0x11;# Also Mask for Pattern Matching # self.cmd_wr_user_pattern1 = 0x12;# Also Pattern for Pattern Matching # self.cmd_wr_user_data_en = 0x13;# Special Data Enable Capture Mode ######################################################## # Dump acquired data to a file. This is a corner turn op def sump_save_txt( self, file_name, mode_vcd = False ): log( self, ["sump_save_txt()"]); print("sump_save_txt()"); ram_dwords = self.sump.cfg_dict['ram_dwords']; ram_bytes = self.sump.cfg_dict['ram_event_bytes']; ram_len = self.sump.cfg_dict['ram_len']; events = ram_bytes * 8; # if ( mode_vcd == True ): # file_name = "sump_dump.txt4vcd"; # else: # file_name = "sump_dump.txt"; file_out = open( file_name, 'w' ); if ( mode_vcd == False ): name_str = "#"; nickname_str = "#"; else: name_str = ""; nickname_str = ""; percent = 0; percent_total = ((1.0)*self.max_samples ); print("max_samples = " + str( self.max_samples ) ); for i in range( 0, self.max_samples, 1): # This takes a while, so calculate and print percentage as it goes by if ( ((i*1.0) / percent_total) > percent ): perc_str = ( str( int(100*percent) ) + "%"); draw_header( self, "VCD Conversion " + perc_str ); percent += .01; txt_str = ""; m = 0; # Iterate the list searching for all the events in binary order for j in range( ram_bytes*8, 0, -1): for sig_obj in self.signal_list: if ( sig_obj.name == "event[%d]" % (j-1) and sig_obj.hidden == False ): txt_str += sig_obj.values[i]; m +=1; if ( m == 8 or ( m == 1 and mode_vcd == True ) ): txt_str += " ";# Add whitespace between each byte group m = 0; if ( i == 0 ): name_str += sig_obj.name + " "; if ( sig_obj.nickname != "" ): nickname_str += sig_obj.nickname + " "; else: nickname_str += sig_obj.name + " "; if ( mode_vcd == False ): txt_str += " ";# Add whitespace between events and dwords # Iterate the list searching for all the dwords in order for j in range( 0, ram_dwords, 1 ): for sig_obj in self.signal_list: if ( sig_obj.name == "dword[%d]" % j and sig_obj.hidden == False ): if ( i >= len( sig_obj.values )): txt_str += "XXXXXXXX"; else: txt_str += sig_obj.values[i]; txt_str += " ";# Add whitespace between each dword if ( i == 0 ): name_str += sig_obj.name + " "; nickname_str += sig_obj.nickname + " "; # print txt_str;# This line is a time sample for all signals if ( i == 0 ): freq_mhz = self.sump.cfg_dict['frequency']; freq_ps = 1000000.0 / freq_mhz; file_out.write( nickname_str + " " + ("%f" % freq_ps ) + "\n" ); file_out.write( txt_str + "\n" ); file_out.close(); return; ######################################################## # Dump acquired data to a file def sump_save_vcd( self ): # print("ERROR: sump_save_vcd() does not yet exist!"); return; def refresh( self ): if ( self.mode_cli == False ): import pygame; pygame.event.get();# Avoid "( Not Responding )" pygame.display.update(); return; ######################################################################### # Dump acquired data from SUMP engine and merge with existing signal list def sump_dump_data( self ): log( self, ["sump_dump_data()"]); ram_dwords = self.sump.cfg_dict['ram_dwords']; ram_bytes = self.sump.cfg_dict['ram_event_bytes']; ram_rle = self.sump.cfg_dict['ram_rle']; # ram_len = self.sump.cfg_dict['ram_len']; ( ram_pre, ram_post, ram_len, ram_phys ) = sump_ram_len_calc(self); events = ram_bytes * 8;# Example, 32 events total for 4 ram_bytes self.dwords_start = 0; self.dwords_stop = ram_phys; # Event Signals rd_page = 0; dump_data = sump_dump_var_ram(self,rd_page = rd_page ); for i in range( 0, events, 1 ): txt = ("Event %d of %d" % ( i+1, events ) ); draw_header( self, "sump_dump_data() " + txt); refresh( self ); # Iterate the list of signals and find one with correct physical name my_signal = None; for each_signal in self.signal_list: if ( each_signal.name == "event[%d]" % i ): my_signal = each_signal; if ( my_signal != None ): my_signal.values = []; my_signal.format = "bin"; my_signal.bits_total = 1; my_signal.bit_top = 0; my_signal.bit_bot = 0; bit_val = (1 << i ); for j in range( 0, ram_len, 1): if ( ( dump_data[j] & bit_val ) != 0x0 ): bit = "1"; else: bit = "0"; my_signal.values.append( bit ); # DWORD Signals for i in range( 0, ram_dwords , 1 ): txt = ("DWORD %d" % i ); txt = ("DWORD %d of %d" % ( i+1, ram_dwords ) ); draw_header( self, "sump_dump_data() " + txt); refresh(self); dump_data = sump_dump_var_ram(self, rd_page = ( 0x10 + i ) ); # Iterate the list of signals and find one with correct physical name my_signal = None; for each_signal in self.signal_list: if ( each_signal.name == "dword[%d]" % i ): my_signal = each_signal; if ( my_signal != None ): my_signal.values = []; my_signal.format = "hex"; my_signal.bits_total = 32; my_signal.bit_top = 31; my_signal.bit_bot = 0; for j in range( 0, ram_len, 1): my_signal.values.append( "%08x" % dump_data[j] ); sump_bundle_data( self ); recalc_max_samples( self ); trig_i = (self.max_samples // 2);# Trigger fixed at 50/50 for now return trig_i; ######################################################################### # Search the signal list for any type bundles and calculate their sample # values based on their children def sump_bundle_data( self ): my_signal = None; for each_signal in self.signal_list: if ( my_signal != None ): if ( each_signal.hier_level > my_level ): rip_list += [ each_signal.values ]; else: value_list = zip( *rip_list ); my_signal.values = []; my_signal.bit_top = len( rip_list )-1; my_signal.bit_bot = 0; my_signal.bits_total = my_signal.bit_top + 1; for each_sample in value_list: bit = 0; for (i,each_bit) in enumerate ( each_sample ): if ( each_bit == "1" ): bit += ( 1 << i ); my_signal.values += [ "%x" % bit ]; my_signal = None; if ( each_signal.type == "bundle" ): my_signal = each_signal; my_level = my_signal.hier_level; rip_list = []; return; ######################################################################### # Dump acquired data from SUMP engine and merge with existing signal list def sump_dump_rle_data( self ): print("sump_dump_rle_data()"); log( self, ["sump_dump_rle_data()"]); ram_dwords = self.sump.cfg_dict['ram_dwords']; ram_bytes = self.sump.cfg_dict['ram_event_bytes']; ram_rle = self.sump.cfg_dict['ram_rle']; rle_pre_trig_len = self.vars["sump_rle_pre_trig_len" ]; rle_post_trig_len = self.vars["sump_rle_post_trig_len" ]; trig_delay = int( self.vars["sump_trigger_delay" ],16 ); # self.undersample_rate = int(self.vars["sump_rle_undersample" ],16); # if ( self.acq_state == "acquire_rle_undersampled" ): # self.undersample_data = True; if ( self.acq_state == "acquire_rle_1x" ): self.undersample_data = False; self.undersample_rate = 1; elif ( self.acq_state == "acquire_rle_4x" ): self.undersample_data = True; self.undersample_rate = 4; elif ( self.acq_state == "acquire_rle_8x" ): self.undersample_data = True; self.undersample_rate = 8; elif ( self.acq_state == "acquire_rle_16x" ): self.undersample_data = True; self.undersample_rate = 16; elif ( self.acq_state == "acquire_rle_64x" ): self.undersample_data = True; self.undersample_rate = 64; rle_pre_trig_len *= self.undersample_rate; rle_post_trig_len *= self.undersample_rate; # print("##"); # print( rle_pre_trig_len ); # print( rle_post_trig_len ); # ram_len = self.sump.cfg_dict['ram_len']; ( ram_pre, ram_post, ram_len, ram_phys ) = sump_ram_len_calc(self); events = ram_bytes * 8;# Example, 32 events total for 4 ram_bytes # Event Signals rd_page = 0; print("sump_dump_ram( rle_data )"); rle_data = sump_dump_ram(self,rd_page = 0x2, rd_ptr = 0x0000 ); print("sump_dump_ram( rle_time )"); rle_time = sump_dump_ram(self,rd_page = 0x3, rd_ptr = 0x0000 ); rle_list = list(zip( rle_time, rle_data )); # print("Oy"); # print( len(rle_time ) ); # print( len(rle_data ) ); # print( len(rle_list ) ); print("process_rle()"); (start_t,stop_t, pre_trig, post_trig ) = process_rle(self,rle_list); # print("start_time = %08x" % start_t ); # print("stop_time = %08x" % stop_t ); # if ( ( stop_t - start_t ) > 0x00100000 ): # if ( ( stop_t - start_t ) > 0x01000000 ): # print("ERROR: Time span is too large"); # shutdown( self ); rle_hex_list = []; for ( rle_time, rle_data ) in ( pre_trig + post_trig ): rle_hex_list += [ ("%08x %08x" % ( rle_time, rle_data ) )]; list2file( self, "sump2_rle_dump.txt", rle_hex_list ); print("expand_rle()"); (dump_data,trig_i) = expand_rle( self, start_t,stop_t,pre_trig,post_trig ); # print( len( dump_data ) ); print("Generating RLE Event Signal List of values"); for i in range( 0, events, 1 ): txt = ("Event %d of %d" % ( i+1, events ) ); draw_header( self, "sump_dump_rle_data() " + txt); refresh( self ); # Iterate the list of signals and find one with correct physical name my_signal = None; for each_signal in self.signal_list: if ( each_signal.name == "event[%d]" % i ): my_signal = each_signal; if ( my_signal != None ): my_signal.values = []; my_signal.format = "bin"; my_signal.bits_total = 1; my_signal.bit_top = 0; my_signal.bit_bot = 0; bit_val = (1 << i ); if ( my_signal.hidden == False ): for j in range( 0, len( dump_data ) , 1): if ( ( dump_data[j] & bit_val ) != 0x0 ): bit = "1"; else: bit = "0"; my_signal.values.append( bit ); if ( self.undersample_data == True ): rle_undersample_signal( self, self.undersample_rate, my_signal ); # Align non-RLE dword data with the RLE samples by calculating Null samples # before and after trigger event # | T | : RLE dump_data # | pre_pad | dword_data | post_pad | pre_pad = ( trig_delay + 2 + trig_i - ram_phys//2) * \ [ "XXXXXXXX" ]; post_pad = ( len( dump_data ) - len( pre_pad ) - ram_phys - trig_delay ) * \ [ "XXXXXXXX"]; # Remember where DWORDs are within RLE samples and use to speed up rendering # by not bothering with DWORDs if outside of current view. self.dwords_start = len(pre_pad); self.dwords_stop = self.dwords_start + ram_phys; if ( self.undersample_data == False ): print("Generating RLE DWORD Signal List of values"); # DWORD Signals. Just Null out all samples since RLE acquisition trig_ptr = self.sump.rd( self.sump.cmd_rd_trigger_ptr )[0]; ram_ptr = 0xFFFF & (trig_ptr - ram_phys//2 ); for i in range( 0, ram_dwords , 1 ): txt = ("DWORD %d of %d" % ( i+1, ram_dwords ) ); draw_header( self, "sump_dump_rle_data() " + txt); refresh( self ); dump_data = sump_dump_ram(self,rd_page = (0x10+i), rd_ptr = ram_ptr ); # Iterate the list of signals and find one with correct physical name my_signal = None; for each_signal in self.signal_list: if ( each_signal.name == "dword[%d]" % i ): my_signal = each_signal; if ( my_signal != None ): my_signal.values = pre_pad[:]; my_signal.format = "hex"; my_signal.bits_total = 32; my_signal.bit_top = 31; my_signal.bit_bot = 0; for j in range( 0, ram_phys, 1): my_signal.values.append( "%08x" % dump_data[j] ); my_signal.values += post_pad; # Undersampling Events, so just create NULL DWORDs else: for i in range( 0, ram_dwords , 1 ): my_signal = None; for each_signal in self.signal_list: if ( each_signal.name == "dword[%d]" % i ): my_signal = each_signal; if ( my_signal != None ): my_signal.values = []; my_signal.format = "hex"; my_signal.bits_total = 32; my_signal.bit_top = 31; my_signal.bit_bot = 0; # Note: This doesn't work the best, so disabling if ( False ): print("Culling excess RLE sample pre and post trigger"); # Cull samples to max pre and post trig lengths to keep display usable rle_pre_trig_len = int( self.vars["sump_rle_pre_trig_len" ],16); rle_post_trig_len = int( self.vars["sump_rle_post_trig_len" ],16); total_samples = len(pre_pad ) + ram_phys + len( post_pad ); pre_trig = trig_i; post_trig = total_samples - trig_i; start_ptr = 0; stop_ptr = -1; if ( pre_trig > rle_pre_trig_len ): start_ptr = trig_i - rle_pre_trig_len; if ( post_trig > rle_post_trig_len ): stop_ptr = trig_i + rle_post_trig_len; for i in range( 0, events, 1 ): my_signal = None; for each_signal in self.signal_list: if ( each_signal.name == "event[%d]" % i ): my_signal = each_signal; if ( my_signal != None ): my_signal.values = my_signal.values[start_ptr:stop_ptr]; for i in range( 0, ram_dwords , 1 ): my_signal = None; for each_signal in self.signal_list: if ( each_signal.name == "dword[%d]" % i ): my_signal = each_signal; if ( my_signal != None ): my_signal.values = my_signal.values[start_ptr:stop_ptr]; sump_bundle_data( self ); recalc_max_samples( self ); return trig_i; def rle_undersample_signal( self, undersample_rate, my_signal ): print("rle_undersample_signal()"); val = "0"; new_values = []; i = 0; for each in my_signal.values: if ( each == "1" ): val = "1"; i +=1; if ( i == undersample_rate ): i = 0; new_values += [val]; val = "0"; my_signal.values = new_values[:]; return; # Given a RLE compressed list, expand to regular time sample list. # Return the list and the index location of the trigger def expand_rle( self, start_t,stop_t,pre_trig,post_trig ): i = start_t; j = 0; rle_list = pre_trig + post_trig; trigger_index = 0; # ( trigger_time, trigger_data ) = pre_trig[-2]; # print("RLE TRIGGER Compressed-2 %08x " % ( trigger_data ) ); ( trigger_time, trigger_data ) = pre_trig[-1]; print("RLE TRIGGER Compressed %08x " % ( trigger_data ) ); sample_list = []; ( rle_time, rle_data ) = rle_list[j]; hold_data = rle_data; sample_list += [ hold_data ];# Add old sample old_rle_time = 0; j +=1; ( rle_time, rle_data ) = rle_list[j]; while ( i <= stop_t and j < (len(rle_list)-1) ): if ( i < rle_time ): sample_list += [ hold_data ];# Add old sample else: sample_list += [ rle_data ];# Add the new sample hold_data = rle_data; j +=1; old_rle_time = rle_time; ( rle_time, rle_data ) = rle_list[j]; # if ( rle_time == trigger_time ): # trigger_index = len( sample_list )-1; # if ( old_rle_time == trigger_time ): # trigger_index = len( sample_list ); if ( i == trigger_time ): trigger_index = len( sample_list )-1; # print("RLE TRIGGER Decompressed %08x " % ( sample_list[trigger_index] ) ); i+=1; print("RLE TRIGGER Decompressed %08x " % ( sample_list[trigger_index] ) ); return ( sample_list, trigger_index ); # Example RLE List # 000007ff 0000000d # 00000800 0000000d 2nd to last of pre-trig # 1d4c4ad3 0000000b Last item of pre-trig # 1d4c4ad4 0000000b 1st item of post-trig def process_rle( self, rle_list ): ln = len( rle_list ) // 2;# Size of pre and post lists pre_list = list(rle_list[0:ln]); post_list = list(rle_list[ln:]); culls = []; # tuplelist2file( self, "rle_prelist1.txt", pre_list ); # Figure out oldest RLE sample pre-trigger and then rotate list start_time = 0xFFFFFFFF; i = 0; for ( rle_time, rle_data ) in pre_list: # print("%08x %08x" % ( rle_time, start_time) ); if ( rle_time < start_time ): start_time = rle_time; n = i;# Location of oldest RLE sample found thus far i +=1; pre_list = rotate_list(self,pre_list, n ); print("RLE pre_list %08x %08x" % ( pre_list[-1] ) ); # tuplelist2file( self, "rle_prelist2.txt", pre_list ); # ini defines hard limits of how many uncompressed samples pre and post trig rle_pre_trig_len = int(self.vars["sump_rle_pre_trig_len" ],16); rle_post_trig_len = int(self.vars["sump_rle_post_trig_len" ],16); # Now scale limits based on the sump_acqusition_len setting 25,50,75,100 acq_len = int(self.vars["sump_acquisition_len"],16); pre_trig = (acq_len & 0xF0)>>4;# Expect 1-4 for 25%-100% of 1st RAM Half post_trig = (acq_len & 0x0F)>>0;# Expect 1-4 for 25%-100% of 2nd RAM Half rle_pre_trig_len = ( rle_pre_trig_len // 4 ) * pre_trig; # Div-4, Mult 1-4 rle_post_trig_len = ( rle_post_trig_len // 4 ) * post_trig;# Div-4, Mult 1-4 # Cull any non-events pre and post trigger. Non-events are when the HW # generates a simple as a MSB timer bit has rolled over. This feature # prevents hardware from hanging forever if there are no events. if ( False ): pre_list_old = pre_list[:]; (first_time,first_data ) = pre_list[0]; pre_list = []; valid = False; prev_time = None; for ( rle_time, rle_data ) in list(pre_list_old): if ( rle_data != first_data and valid == False ): valid = True; if ( prev_time != None ): # If space between 1st and 2nd RLE samples is large, cull down to 1000 if ( ( rle_time - prev_time ) < 1000 ): pre_list += [ (prev_time,prev_data) ];# Keep sample before 1st delta else: pre_list += [ ((rle_time-1000),prev_data)];# sample before 1st delta if ( valid == True ): pre_list += [ (rle_time,rle_data) ]; else: prev_time = rle_time; prev_data = rle_data; if ( len( pre_list ) == 0 ): pre_list = [ pre_list_old[-1] ]; # Cull any samples outside the sump_rle_pre_trig_len (trig_time,trig_data ) = pre_list[-1]; pre_list_old = pre_list[:]; pre_list = []; for ( rle_time, rle_data ) in list(pre_list_old): if ( rle_time > ( trig_time - rle_pre_trig_len ) ): pre_list += [ (rle_time,rle_data) ]; culls+=[("+ %08x %08x %08x %08x" % (rle_time,trig_time,rle_pre_trig_len,rle_data))]; else: culls+=[("< %08x %08x %08x %08x" % (rle_time,trig_time,rle_pre_trig_len,rle_data))]; if ( len( pre_list ) == 0 ): pre_list = [ pre_list_old[-1] ]; stop_time = 0x00000000; i = 0; for ( rle_time, rle_data ) in post_list: if ( rle_time > stop_time ): stop_time = rle_time; n = i;# Location of newest RLE sample found thus far i +=1; # Cull any samples outside the sump_rle_post_trig_len post_list_old = post_list[:]; post_list = []; for ( rle_time, rle_data ) in list(post_list_old): if ( rle_time < ( trig_time + rle_post_trig_len ) ): post_list += [ (rle_time,rle_data) ]; culls+=[("+ %08x %08x %08x %08x" % (rle_time,trig_time,rle_post_trig_len,rle_data))]; else: culls+=[("> %08x %08x %08x %08x" % (rle_time,trig_time,rle_post_trig_len,rle_data))]; if ( len( post_list ) == 0 ): post_list = [ post_list_old[0] ]; (start_time,start_data ) = pre_list[0]; (stop_time,stop_data ) = post_list[-1]; list2file( self, "sump2_rle_cull_list.txt", culls ); return ( start_time , stop_time, pre_list, post_list ); def rotate_list( self, my_list, n ): return ( my_list[n:] + my_list[:n] ); ######################################################## # Calculate desired ram length pre,post trig to work with def sump_ram_len_calc( self ): ram_len = self.sump.cfg_dict['ram_len'];# Physical RAM Size, ie 1K acq_len = int(self.vars["sump_acquisition_len"],16); pre_trig = (acq_len & 0xF0)>>4;# Expect 1-4 for 25%-100% of 1st RAM Half post_trig = (acq_len & 0x0F)>>0;# Expect 1-4 for 25%-100% of 2nd RAM Half ram_len_half = ram_len // 2; qtr = ram_len_half // 4;# Example 128 of 1K/2 ram_pre = qtr * pre_trig; # 25,50,75 or 100% num samples pre-trig ram_post = qtr * post_trig;# 25,50,75 or 100% num samples post-trig return [ ram_pre, ram_post, ( ram_pre+ram_post ), ram_len ]; ######################################################## # Return a list of acquired SUMP capture data using variable length def sump_dump_var_ram( self, rd_page = 0 ): # HERE12 ( ram_pre, ram_post, ram_len, ram_phys ) = sump_ram_len_calc(self); trig_ptr = self.sump.rd( self.sump.cmd_rd_trigger_ptr )[0]; ram_ptr = 0xFFFF & (trig_ptr - ram_pre - 1); self.sump.wr( self.sump.cmd_wr_ram_page, rd_page ); self.sump.wr( self.sump.cmd_wr_ram_ptr , ram_ptr );# Load at specfd pre-trig data = self.sump.rd( self.sump.cmd_rd_ram_data, num_dwords = ram_len ); return data; ######################################################## # Return a complete list of acquired SUMP capture data def sump_dump_ram( self, rd_page = 0, rd_ptr = None ): ram_len = self.sump.cfg_dict['ram_len']; self.sump.wr( self.sump.cmd_wr_ram_page, rd_page ); if ( rd_ptr != None ): self.sump.wr( self.sump.cmd_wr_ram_ptr , rd_ptr );# data = self.sump.rd( self.sump.cmd_rd_ram_data, num_dwords = ram_len ); return data; ######################################################## # Use the wave_list to generate a new signal_list # HERE #def wave2signal_list( self ): # ram_len = self.sump.cfg_dict['ram_len']; # ram_dwords = self.sump.cfg_dict['ram_dwords']; # ram_bytes = self.sump.cfg_dict['ram_event_bytes']; # ram_rle = self.sump.cfg_dict['ram_rle']; # # events = ram_bytes * 8; # # Iterate the number of event bits and init with 0s # for i in range( 0, events , 1): # sig_name = "event_%d" % i; # self.signal_list.append( signal(name=sig_name) ); # self.signal_list[i].format = "bin"; # self.signal_list[i].bits_total = 1; # self.signal_list[i].bit_top = 0; # self.signal_list[i].bit_bot = 0; # for j in range( 0, ram_len, 1): # self.signal_list[i].values.append( "0" ); # # # Iterate the number of dwords and init with 0x0s # for i in range( 0, ram_dwords, 1): # sig_name = "dword_%d" % i; # self.signal_list.append( signal(name=sig_name) ); # self.signal_list[events+i].format = "hex"; # self.signal_list[events+i].bits_total = 32; # self.signal_list[events+i].bit_top = 31; # self.signal_list[events+i].bit_bot = 0; # for j in range( 0, ram_len, 1): # self.signal_list[events+i].values.append( "%08x" % 0 ); # # return; ######################################################## # Read values of sump vars and use to update signal objects trigger attrib def sump_vars_to_signal_attribs( self ): # try: if ( True ): trig_type = self.vars["sump_trigger_type" ];# "or_rising"; trig_field = int( self.vars["sump_trigger_field" ],16 ); trig_delay = int( self.vars["sump_trigger_delay" ],16 ); trig_nth = int( self.vars["sump_trigger_nth" ],16 ); rle_event_en = int( self.vars["sump_rle_event_en" ],16 ); data_en = int( self.vars["sump_data_enable" ],16 ); user_ctrl = int( self.vars["sump_user_ctrl" ],16 ); wd_time = int( self.vars["sump_watchdog_time" ],16 ); user_pattern0 = int( self.vars["sump_user_pattern0" ],16 ); user_pattern1 = int( self.vars["sump_user_pattern1" ],16 ); # self.trigger = 0;# 0=OFF +1=Rising,-1=Falling,2=Pattern0,3=Pattern1 # self.data_enable = False; # Note: If trigger_type is pattern_ris or pattern_fal then # user_pattern0 is the mask of what bits to pattern match on # user_pattern1 is the actual pattern bits # rle_event_en controls the Hidden field for i in range( 0, 32 , 1): sig_obj = get_sig_obj_by_name( self, ("event[%d]" % i ) ); if ( sig_obj != None ): sig_obj.hidden = False; if ( ( rle_event_en & 1<<i ) != 0x0 ): sig_obj.hidden = False; else: sig_obj.hidden = True; # Clear everything to start with. Set any data_en bits for i in range( 0, 32 , 1): sig_obj = get_sig_obj_by_name( self, ("event[%d]" % i ) ); if ( sig_obj != None ): sig_obj.trigger = 0; # OFF if ( ( data_en & 1<<i ) != 0x0 ): sig_obj.data_enable = True; else: sig_obj.data_enable = False; # Set any Rising or Falling edge trigger selection ( 1 only ) if ( trig_type == "or_rising" or trig_type == "or_falling" or trig_type == "watchdog" ): if ( trig_type == "or_rising" ): trig = +1; if ( trig_type == "or_falling" ): trig = -1; if ( trig_type == "watchdog" ): trig = -2; for i in range( 0, 32 , 1): sig_obj = get_sig_obj_by_name( self, ("event[%d]" % i ) ); if ( ( 1<<i & trig_field ) != 0x0 ): sig_obj.trigger = trig; if ( trig_type == "pattern_rising" or trig_type == "pattern_falling" ): for i in range( 0, 32 , 1): sig_obj = get_sig_obj_by_name( self, ("event[%d]" % i ) ); if ( ( 1<<i & user_pattern0 ) != 0x0 ): if ( ( 1<<i & user_pattern1 ) != 0x0 ): sig_obj.trigger = 3;# Pattern of 1 for this bit else: sig_obj.trigger = 2;# Pattern of 0 for this bit # except: # print("ERROR: Invalid sump variable assignments"); return; ######################################################## # Read signal attributes and convert to sump variables def sump_signals_to_vars( self ): if ( True ): rle_event_en = int( self.vars["sump_rle_event_en"],16 ); for sig_obj in self.signal_list: for i in range( 0, 32 , 1): if ( sig_obj.name == ( "event[%d]" % i ) ): if ( sig_obj.hidden == False and sig_obj.visible == True ): rle_event_en = rle_event_en | ( 1<<i );# Set bit if ( sig_obj.hidden == True or sig_obj.visible == False ): rle_event_en = rle_event_en & ~( 1<<i );# Clear bit self.vars["sump_rle_event_en" ] = ("%08x" % rle_event_en ); return; ######################################################## # Use sump2 hardware info to generate a signal_list def sump2signal_list( self ): ram_len = self.sump.cfg_dict['ram_len']; ram_dwords = self.sump.cfg_dict['ram_dwords']; ram_bytes = self.sump.cfg_dict['ram_event_bytes']; ram_rle = self.sump.cfg_dict['ram_rle']; events = ram_bytes * 8; # Iterate the number of event bits and init with 0s for i in range( 0, events , 1): # sig_name = "event_%d" % i; sig_name = "event[%d]" % i; self.signal_list.append( signal(name=sig_name) ); self.signal_list[i].format = "bin"; self.signal_list[i].bits_total = 1; self.signal_list[i].bit_top = 0; self.signal_list[i].bit_bot = 0; for j in range( 0, ram_len, 1): self.signal_list[i].values.append( "0" ); # Iterate the number of dwords and init with 0x0s for i in range( 0, ram_dwords, 1): sig_name = "dword_%d" % i; self.signal_list.append( signal(name=sig_name) ); self.signal_list[events+i].format = "hex"; self.signal_list[events+i].bits_total = 32; self.signal_list[events+i].bit_top = 31; self.signal_list[events+i].bit_bot = 0; for j in range( 0, ram_len, 1): self.signal_list[events+i].values.append( "%08x" % 0 ); return; ######################################################## # Given a TXT file, make signal_list from it # Format is: # # foo bar addr # 0 1 2 # 1 0 a def txtfile2signal_list( self, file_name ): # Read in the flat text VCD translation and make lists of net names file_in = open ( file_name , 'r' ); file_list = file_in.readlines(); file_in.close(); net_names = file_list[0]; sig_values = file_list[1:]; self.sig_name_list = " ".join(net_names.split()).split(' '); self.sig_name_list = self.sig_name_list[1:]; # Remove Leading # self.sig_value_list = file_list[1:]; for each in self.sig_name_list[:]: self.signal_list.append( signal(name=each) ); # Rip thru the value list ( of all sigs ) and extract one signal at a time # 0 000000000 1 000000000 0 000000000 0 000000000 000000000 000000000 for ( i , sig_obj ) in enumerate( self.signal_list ): self.signal_list[i].format = "bin"; # Assume Binary by default self.signal_list[i].bits_total = 1; for each in self.sig_value_list: words = " ".join(each.split()).split(' ') + [None] * 20; sig_obj.values.append( words[i] ); # If value other than 0 or 1 is found, declare this as hex if ( words[i] != "0" and words[i] != "1" and words[i] != None ): self.signal_list[i].format = "hex"; self.signal_list[i].bits_total = len( words[i] ) * 4; self.signal_list[i].bit_top = self.signal_list[i].bits_total-1; self.signal_list[i].bit_bot = 0; return; ######################################################## # Given a VCD file, make signal_list from it def vcdfile2signal_list( self, file_name ): try: # Read the Input File and Separate the Header from Data file_in = open( file_name , "r" ); file_lines = file_in.readlines(); file_in.close(); except: print( "ERROR Input File: "+file_name ); print( "Possibly a Python MemoryError due to large file size"); self.signal_list = []; self.rip_list = []; self.rip_symbs = []; self.top_module = ""; hier_list = []; hier_name = ""; hier_level = 0;# +1 on 1st will be 0; print( "vcdfile2signal_list() : Parsing VCD Symbol Definitions"); start_time = self.pygame.time.get_ticks(); for ( i , each ) in enumerate ( file_lines ): words = each.strip().split() + [None] * 4; # Avoid IndexError if ( words[0] == "$enddefinitions" ): dump_vars_index = i; # Remember location to start Value Change Parsing break; # Save time and don't process entire file ##################################### # Check for Signal Symbol Definitions # $var wire 1 * tx_data [15] $end # 0 1 2 3 4 5 6 if ( words[0] == "$var" ): type = words[1]; # ie "wire" bits = int( words[2] ); # ie 32 symb = words[3]; # ie "," name = words[4]; # ie "lb_addr" rip = words[5]; # ie "[31:0]" or "$end" if single bit sig_obj = signal( name=name, vcd_symbol=symb ); sig_obj.hier_name = hier_name; sig_obj.hier_level = hier_level; sig_obj.bits_total = bits; # ie 32 sig_obj.bit_top = bits-1; # ie 31 sig_obj.bit_bot = 0; # ie 0 if ( rip != "$end" ): sig_obj.rip = rip;# [15:0] or [1] or "" if ( bits > 1 or sig_obj.rip != "" ): sig_obj.format = "hex"; else: sig_obj.format = "bin"; # If a portion of a ripped bus and not [0], add to special rip_list # otherwise, add to the regular signal_list if ( ( sig_obj.rip != "" ) and \ ( ":" not in sig_obj.rip ) and \ ( sig_obj.rip != "[0]" ) \ ): self.rip_list.append( sig_obj ); self.rip_symbs.append( symb ); else: self.signal_list.append( sig_obj ); # Now also add "[0]" to rip_list ( It will appear in BOTH lists ) if ( sig_obj.rip == "[0]" ): self.rip_list.append( sig_obj ); self.rip_symbs.append( symb ); ##################################### # Check for new hierarchy declaration if ( words[0] == "$scope" and \ ( words[1] == "module" or \ words[1] == "begin" ) \ ): if ( self.top_module == "" ): self.top_module = words[2]; # ie "tb_dut" print( "top_module is ", self.top_module); name = words[2]; # ie "u_dut" sig_obj = signal( name=name ); sig_obj.hier_name = hier_name; sig_obj.hier_level = hier_level; sig_obj.bits_total = 0; sig_obj.bit_top = 0; sig_obj.bit_bot = 0; sig_obj.format = ""; self.signal_list.append( sig_obj ); sig_obj.collapsable = True; sig_obj.expandable = False; hier_list.append( words[2] ); rts = "" for each in hier_list: rts = rts + "/" + each; hier_name = rts; ##################################### # Adjust hier level on $scope or $upscope if ( words[0] == "$scope" ): hier_level += 1; if ( words[0] == "$upscope" ): hier_level -= 1; if ( words[0] == "$scope" and words[1] == "begin" ): hier_list.append( "process" ); if ( words[0] == "$upscope" ): hier_list.pop(); # Remove last item from list # Create a hash lookup of symbol to object index and bits to speed things up hash_dict_index = {}; hash_dict_bits = {}; for ( i, sig_obj ) in enumerate( self.signal_list ): # Need to make a list for symb lookup as clocks can reuse same symbol if ( hash_dict_index.get( sig_obj.vcd_symbol, None ) == None ): hash_dict_index[ sig_obj.vcd_symbol ] = [i]; else: hash_dict_index[ sig_obj.vcd_symbol ].append( i ); hash_dict_bits[ sig_obj.vcd_symbol ] = sig_obj.bits_total; # Go thru the rip_list and determine the number of bits for the busses # This finds the parent in self.signal_list that matches the current # rip from self.rip_list and adjusts the parents bits_total and bit_top # if the rip's exceed the parent's old value. The parent will start with # (1,1) since it is based on rip [0] # Also create a hash to lookup the parent index for each rip symbol hash_rip_list = {}; hash_rip_parent = {}; for (j,my_each) in enumerate( self.rip_list ): name = my_each.name; hier_name = my_each.hier_name; rip = my_each.rip; hash_rip_list[ my_each.vcd_symbol ] = j; # For Fast Lookup later # Calculate the weight of each bit, ie [7] is 128 for foo_each in [ "[", "]" ]: rip = rip.replace( foo_each , " "+foo_each+" " ); words = rip.strip().split() + [None] * 10; # Avoid IndexError rip = int( words[1], 10 ); my_each.bit_weight = 2**rip;# Conv 7->128 if ( name != None and hier_name != None ): for ( i, my2_each ) in enumerate( self.signal_list ): if ( name == my2_each.name and \ hier_name == my2_each.hier_name ): hash_rip_parent[ my_each.vcd_symbol ] = i; # For Fast Lookup later if ( rip > my2_each.bit_top ): my2_each.bits_total = rip+1; my2_each.bit_top = rip; symb_parse_list = ["!","#","$","&","'","K" ]; # Now Parse actual VCD section and try and figure out sample clock period # by finding the smallest time delta across the entire VCD file sample_period = 99999999; prev_time = 0; for ( i , each ) in enumerate ( file_lines[dump_vars_index:] ): words = each.strip().split() + [None] * 4; # Avoid IndexError if ( words[0][0:1] == "#" ): now_time = int( words[0][1:],10 ); delta_time = now_time - prev_time; if ( delta_time < sample_period and delta_time != 0): sample_period = delta_time; print( sample_period ); prev_time = now_time; # Now Parse the actual VCD section and calculate current values for each # signal at every time stamp section. print( "vcdfile2signal_list() : Parsing VCD Value Change Dumps"); start_time = self.pygame.time.get_ticks(); percent = 0; percent_total = ((1.0)*len( file_lines[dump_vars_index:] ) ); sample_cnt = 0; for ( i , each ) in enumerate ( file_lines[dump_vars_index:] ): # This takes a while, so calculate and print percentage as it goes by if ( ((i*1.0) / percent_total) > percent ): perc_str = ( str( int(100*percent) ) + "%"); draw_header( self, perc_str ); print( perc_str ); percent += .05; # Handle binary cases for "1>" and convert to "1 >" # If the 1st char is 0 or 1 insert a space to make look like vector if ( each[0:1] == "0" or each[0:1] == "1" or each[0:1] == "x" or each[0:1] == "z" ): each = each[0:1] + " " + each[1:]; words = each.strip().split() + [None] * 4; # Avoid IndexError symb = words[1]; # Skip the initial dumpvars section as nothing to dump yet if ( words[0] == "#0" ): None; time_stamp = 0; time_now = 0; # When we reach a timestamp, append all last_value to values list elif ( words[0][0:1] == "#" ): time_stamp = int( words[0][1:], 10 ); while ( time_now <= time_stamp ): for sig_obj in self.signal_list: sig_obj.values.append( sig_obj.last_value ); sample_cnt += 1;# Count Total Samples for final report at end time_now += sample_period; # Read the symbols new value and assign to last_value else: if ( words[0][0:1]=="0" or words[0][0:1]=="1" or words[0][0:1]=="x" or words[0][0:1]=="z" ): value = words[0]; elif ( words[0][0:1] == "b" ): try: value = int( words[0][1:],2 );# Convert Binary String to Integer if ( symb != None ): num_bits = hash_dict_bits[ symb ]; num_nibs = int(num_bits/4.00 + 0.75 );# ie 29 bits gets 8 nibbles else: num_nibs = 1; except: value = 0; num_nibs = 1; value = "%08x" % value;# Now Convert Integer to Hex value = value[::-1]; # Reverse value = value[0:num_nibs]; # Keep desired number of LSBs value = value[::-1]; # Reverse Back elif ( words[0][0:1] == "$" ): value = None; else: line_num = i + dump_vars_index + 1; print( "ERROR line " + str(line_num) + " : " + words[0]); value = None; # Is symb in rip_list? If not, do normal processing if ( symb not in self.rip_symbs ): if ( value != None and symb != None ): # Note: a symb might be used multiple times for clock ports, etc. try: for i in hash_dict_index[ symb ]: self.signal_list[i].last_value = value; except: # print "VCD Symbol Error " + symb; None; # Oh SNAP - This is in the rip_list. Find obj for [0] ( Parent ) # and if 0, AND out bit_weight, if 1 OR in bit_weight. # This op takes time since values are stored in ASCII, must convert to # int, perform the bit operation and then convert back to ASCII. else: my_each = self.rip_list[ hash_rip_list[ symb ] ]; my2_each = self.signal_list[ hash_rip_parent[ symb ] ]; try: last_value = int( my2_each.last_value, 16 ); except: last_value = 0; if ( value == "0" ): last_value = last_value & ~ my_each.bit_weight; elif ( value == "1" ): last_value = last_value | my_each.bit_weight; nibs = my2_each.bits_total//4;# ie 32 = 8, num nibs to display new_value = "%016x" % last_value;# 16 Nibbles, remove leading next my2_each.last_value = new_value[16-nibs:];# Remove leading 0s stop_time = self.pygame.time.get_ticks(); tt = str( (stop_time - start_time) / 1000 ) + "s"; rate = str( sample_cnt / ((stop_time - start_time) * 1000 )) + " MSPS"; print( "vcdfile2signal_list() : Complete : Time " + tt +" : Rate " + rate); draw_header( self, "" ); return; def shutdown( self ): log( self, ["shutdown()"]); var_dump( self, "sump2.ini" ); # Dump all variable to INI file proc_cmd( self, "save_format", [""] ); # Autosave the last format if ( self.mode_cli == False ): self.pygame.quit();# Be IDLE friendly print(""); print("Thank you for using SUMP2 " + self.vers + " by BlackMesaLabs"); print("Please encourage the development and use of open-source software"); sys.exit(); return; #def init_vars( self ): # self.var_hash= {}; # self.var_hash["bd_connection" ] = "tcp"; # self.var_hash["bd_protocol" ] = "poke"; # self.var_hash["tcp_port" ] = "21567"; # self.var_hash["tcp_ip_addr" ] = "127.0.0.1";# No Place Like Home # self.var_hash["sump_addr" ] = "00000000" ;# Addr of sump2_ctrl_reg # self.var_hash["sump_trigger_type" ] = "or_rising"; # self.var_hash["sump_trigger_field" ] = "00000000"; # self.var_hash["sump_trigger_delay" ] = "0000"; # self.var_hash["sump_trigger_nth" ] = "0000"; # self.var_hash["sump_user_ctrl" ] = "00000000"; # self.var_hash["sump_user_pattern0" ] = "00000000"; # self.var_hash["sump_user_pattern1" ] = "00000000"; # self.var_hash["sump_data_enable" ] = "00000000"; # return; def init_globals( self ): # Define the colors we will use in RGB format import platform,os; self.os_sys = platform.system(); # Windows vs Linux self.fatal_msg = None; self.undersample_data = False; self.undersample_rate = 1; self.gui_active = False; self.color_bg = (0,0,0); self.color_fg = (0,0,0); self.prompt = "bd>"; self.done = False; # This breaks the application loop when true # self.clock = self.pygame.time.Clock(); # self.lcd = self.pygame.display.Info(); # Dimensions of physical LCD screen self.txt_height = 0; self.txt_width = 0; self.spin_char = ""; self.debug = False; self.last_filesave = None;# Name of last file saved, used for Save_Rename self.vcd_import = False; self.acq_state = "acquire_stop"; self.acq_mode = "nonrle"; # if ( self.mode_cli == False ): # self.font = get_font( self,self.vars["font_name"],self.vars["font_size"]); self.sample_start = 0; self.sample_stop = 0; self.sample_room = 0; self.prev_sample_start = None; self.prev_sample_stop = None; self.max_samples = 0; self.zoom_x = self.txt_width; # Default zoom ratio is 1 text char width self.stop_zoom = False; self.sig_obj_sel = None; self.key_buffer = ""; self.last_search_value = ""; self.vertical_scrolled_offscreen = False; self.last_cmd = ""; # self.cmd_history = ["","",""]; self.skipped_refresh_cnt = 0; self.old_list = []; self.slider_width = 0; self.cmd_history = []; self.dwords_start = 0; self.dwords_stop = 0; self.sig_name_start_x = 0; self.sig_name_start_y = 0; self.sig_name_stop_x = 0; self.sig_name_stop_y = 0; self.sig_value_start_x = 0; self.sig_value_start_y = 0; self.sig_value_stop_x = 0; self.sig_value_stop_y = 0; self.cursor_start_y = 0; self.cursor_stop_y = 0; self.top_module = "";# ie "tb_foo" self.sig_top = 0; self.sig_bot = 0; # self.scroll_togl = 1;# +1=Pan, -1=Zoom self.surface_start = -1; self.surface_stop = -1; self.name_surface_valid = False; self.curval_surface_valid = False; self.cursor_list = []; self.cursor_list.append( cursor(name="Cursor1")); self.cursor_list.append( cursor(name="Cursor2")); self.cursor_list[0].y = 0; self.cursor_list[1].y = 0; self.cursor_list[0].sample = 10; self.cursor_list[1].sample = 15; self.mouse_x = 0; self.mouse_y = 0; self.mouse_button = 0; self.mouse_region = ""; self.mouse_name_sel_y = -1; self.scroll_num_samples = 1; self.mouse_btn1dn_x = -1; self.mouse_btn1dn_y = -1; self.mouse_btn1up_x = -1; self.mouse_btn1up_y = -1; self.mouse_btn3dn_x = -1; self.mouse_btn3dn_y = -1; self.mouse_btn3up_x = -1; self.mouse_btn3up_y = -1; self.mouse_btn1up_time_last = 0; self.mouse_btn1up_time = 0; self.mouse_btn1dn_time = 0; self.resize_on_mouse_motion = False; self.max_w = 0; self.max_w_chars = 0; # self.subpop = False; self.popup_x = None; self.popup_y = -1; self.popup_w = 0; self.popup_y2 = -1; self.popup_sel = ""; self.popup_sample = 0; self.popup_parent_x = None; self.popup_parent_y = None; self.popup_parent_list = None; self.txt_entry = False; self.txt_entry_caption = "Rename_Signal"; # Create a list of files to source in menu given include and exclude filters file_inc_filter = self.vars["sump_script_inc_filter"]; file_exc_filter = self.vars["sump_script_exc_filter"]; file_load_list = ["File_Load"]; import glob; glob_list = set(glob.glob(file_inc_filter))-set(glob.glob(file_exc_filter)); for each in glob_list: file_load_list += ["source "+each ]; # Right-Click menu over signal names self.popup_list_names = [ # "--------","Group","Group+","Expand","Collapse","Insert_Divider", # "--------","Delete","Make_Invisible","Make_All_Visible", # "--------","Delete","Rename","Restore_All", # ["Clipboard","Cut","Paste","Delete","Rename"], "--------", "Rename", "Insert_Divider", ["Clipboard","Cut","Paste"], ["Visibility","Delete","Hide","Hide_All","Show","Show_All"], # ["Grouping","Group_with_Divider","Group_with_Parent", \ # "UnGroup","Insert_Divider"], [ "Radix", "Hex","Signed","Unsigned" ], # [ "Waveform_Format", "Edit_Format","Save_Format","Load_Format",\ # "Delete_Format", "Save_Selected" ], \ # "--------",[ "Font_Size", "Font_Larger","Font_Smaller"],\ "--------","Trigger_Rising","Trigger_Falling","Trigger_Watchdog",\ "--------","Set_Pattern_0","Set_Pattern_1","Clear_Pattern_Match",\ "--------","Set_Data_Enable","Clear_Data_Enable",\ "--------",["SUMP_Configuration","sump_trigger_delay",\ "sump_trigger_nth",\ "sump_user_ctrl",\ "sump_user_pattern0",\ "sump_user_pattern1",\ "sump_watchdog_time"],\ "--------",["Acquisition_Length", "[----T----]", " [--T--] ", " [-T-] ", "[----T-] ", " [-T----]", ], ]; # "--------",["File_Load","File1","File2"], # "--------",file_load_list, # "BD_SHELL","Manual","Quit"]; # Right-Click menu over waveform area self.popup_list_values = [ # "--------","Debug_Vars", # "--------","Reload", # "Scroll_Toggle", "--------","Zoom_In", "Zoom_Out", "Zoom_Full","Zoom_Previous", "Zoom_to_Cursors", "--------",["Cursors", "Cursors_to_View","Cursor1_to_Here","Cursor2_to_Here", "Crop_to_Cursors"],\ ["Acquire", "Acquire_Normal","Acquire_RLE","Acquire_Stop",], # "Acquire_Single","Acquire_Continuous", # "Acquire_RLE_1x","Acquire_RLE_8x","Acquire_RLE_64x", # "Acquire_Stop",], # "--------","Crop_to_Cursors", # "--------","Cursors_to_View","Cursor1_to_Here","Cursor2_to_Here",\ # "--------","Acquire_Single","Acquire_Continuous","Acquire_Stop", # "--------","Acquire_RLE_1x","Acquire_RLE_8x","Acquire_RLE_64x", # "--------","Acquire_Single","Acquire_Continuous","Acquire_Stop", # "--------","Acquire_RLE_1x","Acquire_RLE_8x","Acquire_RLE_64x", # "--------","Acquire_RLE_1x","Acquire_RLE_4x","Acquire_RLE_16x", # "Acquire_RLE_64x", # "--------", # ["File_Load","File1","File2"], file_load_list, ["File_Save","Save_PNG","Save_JPG","Save_BMP", # "Save_TXT","Save_VCD","Save_RLE_VCD","Save_Rename"], "Save_TXT","Save_VCD","Save_Rename"], # ["Fonts","Font_Larger","Font_Smaller"], ["Misc","Font_Larger","Font_Smaller", "BD_SHELL","Manual"],"Quit"]; self.popup_list = self.popup_list_values; self.cmd_alias_hash_dict = {}; self.cmd_alias_hash_dict["zi"] = "zoom_in"; self.cmd_alias_hash_dict["zo"] = "zoom_out"; self.cmd_alias_hash_dict["zt"] = "zoom_to"; self.cmd_alias_hash_dict["q" ] = "quit"; self.cmd_alias_hash_dict["find"] = "search"; self.cmd_alias_hash_dict["/"] = "search"; self.cmd_alias_hash_dict["?"] = "backsearch"; return; ############################################################################### class cursor(object): def __init__( self, name="Cursor1", visible=True, \ bits_per_line=32, bits_total=32,format="hex"): self.name = name; self.visible = visible; self.selected = False; self.x = 0; self.y = 0; self.sample = 0; def __del__(self): # print "You are killing me man"; return; def __str__(self): return "name = " + self.name + "" +\ ""; ############################################################################### # A signal contains time samples and various display attributes. class signal(object): def __init__( self, name="cnt_a", type="signal",vcd_symbol="",visible=True, \ bits_per_line=32, bits_total=32,format="hex"): self.name = name; self.type = type;# "signal","divider","group","endgroup" self.nickname = ""; self.hier_name = ""; self.hier_level = 0; self.vcd_symbol = vcd_symbol; self.values = []; self.trigger = 0;# 0=OFF +1=Rising,-1=Falling,2=Pattern0,3=Pattern1 self.data_enable = False; self.selected = False; self.last_value = ""; self.visible = visible; self.hidden = False; self.deleted = False; self.expandable = False; self.collapsable = False; self.is_expansion = False; self.grouped = False; self.x = 0; self.y = 0; self.h = 0; # Height self.w = 0; # Width self.bits_per_line = 32; self.bits_total = 32; self.bit_top = 31; self.bit_bot = 0; self.bit_weight = 0; # Only used by rip_list, ie [7]->128 self.rip = ""; # [15:0], [1], "" self.format = "hex"; def __del__(self): # print "You are killing me man"; return; def __str__(self): return "name = " + self.name + "" +\ ""; ############################################################################## class Sump2: def __init__ ( self, backdoor, addr ): self.bd = backdoor; self.addr_ctrl = addr; self.addr_data = addr + 0x4; self.cmd_state_idle = 0x00; self.cmd_state_arm = 0x01; self.cmd_state_reset = 0x02;# Always Reset before Arm. self.cmd_wr_trig_type = 0x04; self.cmd_wr_trig_field = 0x05;# Correspond to Event Bits self.cmd_wr_trig_dly_nth = 0x06;# Trigger Delay and Nth self.cmd_wr_trig_position = 0x07;# Samples post Trigger to Capture self.cmd_wr_rle_event_en = 0x08;# Enables events for RLE detection self.cmd_wr_ram_ptr = 0x09;# Load specific pointer. self.cmd_wr_ram_page = 0x0a;# Load DWORD Page. self.cmd_rd_hw_id_rev = 0x0b; self.cmd_rd_ram_width_len = 0x0c; self.cmd_rd_sample_freq = 0x0d; self.cmd_rd_trigger_ptr = 0x0e; self.cmd_rd_ram_data = 0x0f; self.cmd_wr_user_ctrl = 0x10; self.cmd_wr_user_pattern0 = 0x11;# Also Mask for Pattern Matching self.cmd_wr_user_pattern1 = 0x12;# Also Pattern for Pattern Matching self.cmd_wr_user_data_en = 0x13;# Special Data Enable Capture Mode self.cmd_wr_watchdog_time = 0x14;# Watchdog Timeout self.trig_and_ris = 0x00;# Bits AND Rising self.trig_and_fal = 0x01;# Bits AND Falling self.trig_or_ris = 0x02;# Bits OR Rising self.trig_or_fal = 0x03;# Bits OR Falling self.trig_pat_ris = 0x04;# Pattern Match Rising self.trig_pat_fal = 0x05;# Pattern Match Falling self.trig_in_ris = 0x06;# External Input Trigger Rising self.trig_in_fal = 0x07;# External Input Trigger Falling self.trig_watchdog = 0x08;# Watchdog trigger self.cfg_dict = {}; self.status_armed = 0x01;# Engine is Armed, ready for trigger self.status_triggered = 0x02;# Engine has been triggered self.status_ram_post = 0x04;# Engine has filled post-trig RAM self.status_ram_pre = 0x08;# Engine has filled pre-trigger RAM self.status_rle_pre = 0x10;# RLE Engine has filled pre-trig RAM self.status_rle_post = 0x20;# RLE Engine has filled post-trig RAM self.status_rle_en = 0x80;# RLE Engine is present def wr ( self, cmd, data ): self.bd.wr( self.addr_ctrl, [ cmd ] ); self.bd.wr( self.addr_data, [ data ] ); def rd( self, addr, num_dwords = 1): # Note: addr of None means use existing ctrl address and just read data if ( addr != None ): self.bd.wr( self.addr_ctrl, [ addr ] ); return self.bd.rd( self.addr_data, num_dwords, repeat = True); def rd_cfg( self ): hwid_data = self.rd( self.cmd_rd_hw_id_rev )[0]; ram_data = self.rd( self.cmd_rd_ram_width_len )[0]; freq_data = self.rd( self.cmd_rd_sample_freq )[0]; print("%08x" % hwid_data ); print("%08x" % freq_data ); print("%08x" % ram_data ); self.cfg_dict['hw_id'] = ( hwid_data & 0xFFFF0000 ) >> 16; self.cfg_dict['hw_rev'] = ( hwid_data & 0x0000FF00 ) >> 8; self.cfg_dict['data_en'] = ( hwid_data & 0x00000040 ) >> 6; self.cfg_dict['trig_wd_en'] = ( hwid_data & 0x00000020 ) >> 5; # self.cfg_dict['data_en'] = 1;# This bit doesn't exist yet in HW # self.cfg_dict['trig_wd_en'] = 1;# This bit doesn't exist yet in HW self.cfg_dict['nonrle_dis'] = ( hwid_data & 0x00000010 ) >> 4; self.cfg_dict['rle_en'] = ( hwid_data & 0x00000008 ) >> 3; self.cfg_dict['pattern_en'] = ( hwid_data & 0x00000004 ) >> 2; self.cfg_dict['trig_nth_en'] = ( hwid_data & 0x00000002 ) >> 1; self.cfg_dict['trig_dly_en'] = ( hwid_data & 0x00000001 ) >> 0; self.cfg_dict['frequency'] = float(freq_data) / 65536.0; self.cfg_dict['ram_len'] = ( ram_data & 0x0000FFFF ) >> 0; self.cfg_dict['ram_dwords'] = ( ram_data & 0x00FF0000 ) >> 14;# >>16,<<2 self.cfg_dict['ram_event_bytes'] = ( ram_data & 0x0F000000 ) >> 24; self.cfg_dict['ram_rle'] = ( ram_data & 0xF0000000 ) >> 28; def close ( self ): return; ############################################################################## # functions to convert text time samples into a VCD file. See cpy_txt2vcd.py class TXT2VCD: def __init__ ( self ): self.char_code = self.build_char_code(); # ['AA','BA',etc] self.header = self.build_header(); self.footer = self.build_footer(); return; def close ( self ): return; def conv_txt2vcd ( self, main_self, txt_list ): # def conv_txt2vcd ( self, txt_list ): """ Take in a txt list and spit out a vcd """ header_line = txt_list[0]; # 1st line "#foo bar 10000" data_lines = txt_list[1:]; # Data lines "1 1a" bus_widths = self.get_bus_widths( data_lines[:] ); # How many bits in each rts = self.header; rts += self.build_name_map( header_line,bus_widths[:],self.char_code[:] ); rts += self.footer; timescale = float( header_line.split()[-1] ); time = 0; next_perc = 0;# Display an update every 5% total_count = len( data_lines ); prev_data_line = None; # HERETODAY for ( i, data_line ) in enumerate( data_lines ): if ( data_line != prev_data_line ): rts += [ "#" + str(time) ]; bit_list = self.get_bit_value( data_line, header_line, bus_widths[:] ); rts += self.dump_bit_value( bit_list, self.char_code[:] ); prev_data_line = data_line; # prev_data_line = data_line; time += int( timescale ); # TODO: Would be nice to have this call draw_header() instead. perc = ( 100 * i ) // total_count; if ( perc >= next_perc ): draw_header( main_self,"conv_txt2vcd() "+str( perc )+"%" ); print( "conv_txt2vcd() "+str( perc )+"%" ); next_perc += 5;# Next 5%, this counts 0,5,10,...95 return rts; def get_bit_value( self,data_line,header_line,bus_widths_list_cp ): """ Figure out each bit value (0,1) for the provided line. Return a list of 0,1s """ rts = []; data_list = data_line.split(); for bus_name in header_line.split()[0:-1]:# Remove the timescale at very end bus_width = bus_widths_list_cp.pop(0); # 1 or 16, etc data = data_list.pop(0); # "1" or "10ab", etc bit_val = 2**(bus_width-1); # 8->128, 4->8, 1->1 for i in range( bus_width ): # Counts 0..7 for 8bit bus try: if ( ( int(data,16) & bit_val ) == 0 ): rts += ["0"]; else: rts += ["1"]; except: rts += ["x"]; bit_val //= 2; # Counts 128,64,..2,1 for 8bit bus return rts; def dump_bit_value( self, bit_list, char_code_list_cp ): """ Convert [0,1,etc] to [0AA,1BA,etc] """ rts = []; for bit in bit_list: rts += [ bit +char_code_list_cp.pop(0) ]; # rts += [ str(bit)+char_code_list_cp.pop(0) ]; return rts; def build_name_map( self,header_line,bus_widths_list_cp,char_code_list_cp ): """ $var wire 1 AA foo [7] $end """ rts = []; for bus_name in header_line.split()[0:-1]:# This removes timescale at end bus_width = bus_widths_list_cp.pop(0); if ( bus_width == 1 ): rts += [ "$var wire 1 " + char_code_list_cp.pop(0) + " " + \ bus_name + " $end" ]; else: for i in range( bus_width ): # Counts 0..7 for 8bit bus rts += [ "$var wire 1 " + char_code_list_cp.pop(0) + " " + \ bus_name + " [" + str(bus_width-1-i)+"] $end" ]; return rts; def get_bus_widths( self, data_list_cp ): """ Rip the vectors, if any vector never exceeds 1 then its a wire. Tag it otherwise, bus width is number of nibbles x4 """ bus_width = [None]*100; for data_line in data_list_cp: data_words = data_line.split(); i = 0; for data_word in data_words: bit_width = 4 * len( data_word ); # How many bits 4,8,12,etc if ( bus_width[i] == None ): bus_width[i] = 1;# Default to single wire if ( data_word == "XXXXXXXX" ): bus_width[i] == 32; else: try: if ( int( data_word, 16) > 1 ): bus_width[i] = bit_width; except: print("ERROR: Invalid non Hexadecimal Data " + str(data_word)); i+=1; return bus_width; def build_char_code( self ): """ VCDs map wires to alphabet names such as AA,BA. Build a 676 (26x26) list """ char_code = []; # This will be ['AA','BA',..,'ZZ'] for ch1 in "ABCDEFGHIJKLMNOPQRSTUVWXYZ": for ch2 in "ABCDEFGHIJKLMNOPQRSTUVWXYZ": char_code += [ ch2+ch1 ]; return char_code; def build_header( self ): rts = []; rts += [ "$date Wed May 4 10:12:46 2005 $end" ]; rts += [ "$version ModelSim Version 6.0c $end" ]; rts += [ "$timescale 1ps $end" ]; rts += [ "$scope module module_name $end" ]; return rts; def build_footer( self ): rts = []; rts += [ "$upscope $end"]; rts += [ "$enddefinitions $end"]; rts += [ "#0" ]; rts += [ "$dumpvars"]; rts += [ "$end"]; return rts; ############################################################################## # functions to send Backdoor commands to BD_SERVER.PY over TCP Sockets class Backdoor: def __init__ ( self, ip, port ): try: import socket; except: raise RuntimeError("ERROR: socket is required"); try: self.sock=socket.socket(socket.AF_INET,socket.SOCK_STREAM); self.sock.connect( ( ip, port ) );# "localhost", 21567 # self.sock.settimeout(1); # Dont wait forever self.sock.settimeout(5); # Dont wait forever except: # raise RuntimeError("ERROR: Unable to open Socket!! ") self.sock = None; return; def close ( self ): self.sock.close(); def bs(self, addr, bitfield ): rts = self.rd( addr, 1 ); data_new = rts[0] | bitfield[0]; # OR in some bits self.wr( addr, [data_new] ); def bc(self, addr, bitfield ): rts = self.rd( addr, 1 ); data_new = rts[0] & ~ bitfield[0];# CLR some bits self.wr( addr, [data_new] ); def wr(self, addr, data, repeat = False ): # print("HERE"); # print("%08x" % addr ); # print( data ); if ( repeat == False ): cmd = "w";# Normal Write : Single or Burst with incrementing address else: cmd = "W";# Write Multiple DWORDs to same address payload = "".join( [cmd + " %x" % addr] + [" %x" % int(d) for d in data] + ["\n"] ); self.tx_tcp_packet( payload ); self.rx_tcp_packet(); def rd( self, addr, num_dwords=1, repeat = False ): if ( repeat == False ): cmd = "r";# Normal Read : Single or Burst with incrementing address else: cmd = "k";# Read Multiple DWORDs from single address payload = cmd + " %x %x\n" % (addr, (num_dwords-1)); # 0=1DWORD,1=2DWORDs self.tx_tcp_packet( payload ); payload = self.rx_tcp_packet().rstrip(); dwords = payload.split(' '); rts = []; # print( dwords ); for dword in dwords: rts += [int( dword, 16 )]; return rts; def tx_tcp_packet( self, payload ): # A Packet is a 8char hexadecimal header followed by the payload. # The header is the number of bytes in the payload. header = "%08x" % len(payload); bin_data = (header+payload).encode("utf-8");# String to ByteArray self.sock.send( bin_data ); def rx_tcp_packet( self ): # Receive 1+ Packets of response. 1st Packet will start with header that # indicates how big the entire Backdoor payload is. Sit in a loop # receiving 1+ TCP packets until the entire payload is received. bin_data = self.sock.recv(1024); rts = bin_data.decode("utf-8");# ByteArray to String header = rts[0:8]; # Remove the header, Example "00000004" payload_len = int(header,16);# The Payload Length in Bytes, Example 0x4 payload = rts[8:]; # Start of Payload is everything after header # 1st packet may not be entire payload so loop until we have it all while ( len(payload) < payload_len ): bin_data = self.sock.recv(1024); payload += bin_data.decode("utf-8");# ByteArray to String return payload; ############################################################################### main = main();

[ "adrien.descamps@gmail.com" ]

adrien.descamps@gmail.com

6028e1a80acb4dba764ef24342f833eb677eea1b

95a60a8fd8a21fcc3bcdcecfd4b6a3a3a3ff35b6

/backend/api.py

e175256a127f6184aa05edf3d108889e4af44c2b

[]

no_license

AkshithBellare/maljpeg-web-app

f59cae2eff7f6446876b4a96b3143c2c38078927

ca83f1ef5bf95e73143aaa0e29b5c8f6f010936d

refs/heads/master

2023-03-29T12:37:29.864849

2021-04-05T13:16:43

354,757,948

0

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Python

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5,824

py

import os import pickle from flask import Flask from flask_restful import Resource, Api, reqparse from flask_cors import CORS from flask import request, Response import numpy as np import json import PIL.Image as Image import io import base64 from struct import unpack import pandas as pd import sys import glob marker_mapping = { 0xffc0: "SOF0", 0xffc1: "SOF1", 0xffc2: "SOF2", 0xffc3: "SOF3", 0xffc4: "DHT", 0xffc5: "SOF5", 0xffc6: "SOF6", 0xffc7: "SOF7", 0xffc8: "JPG", 0xffc9: "SOF9", 0xffca: "SOF10", 0xffcb: "SOF11", 0xffcc: "DAC", 0xffcd: "SOF13", 0xffce: "SOF14", 0xffcf: "SOF15", 0xffd0: "RST0", 0xffd1: "RST1", 0xffd2: "RST2", 0xffd3: "RST3", 0xffd4: "RST4", 0xffd5: "RST5", 0xffd6: "RST6", 0xffd7: "RST7", 0xffd8: "SOI", 0xffd9: "EOI", 0xffda: "SOS", 0xffdb: "DQT", 0xffdc: "DNL", 0xffdd: "DRI", 0xffde: "DHP", 0xffdf: "EXP", 0xffe0: "APP0", 0xffe1: "APP1", 0xffe2: "APP2", 0xffe3: "APP3", 0xffe4: "APP4", 0xffe5: "APP5", 0xffe6: "APP6", 0xffe7: "APP7", 0xffe8: "APP8", 0xffe9: "APP9", 0xffea: "APP10", 0xffeb: "APP11", 0xffec: "APP12", 0xffed: "APP13", 0xffee: "APP14", 0xffef: "APP15", 0xfff0: "JPG0", 0xfff1: "JPG1", 0xfff2: "JPG2", 0xfff3: "JPG3", 0xfff4: "JPG4", 0xfff5: "JPG5", 0xfff6: "JPG6", 0xfff7: "JPG7", 0xfff8: "JPG8", 0xfff9: "JPG9", 0xfffa: "JPG10", 0xfffb: "JPG11", 0xfffc: "JPG12", 0xfffd: "JPG13", 0xfffe: "COM", 0xff01: "TEM", } class JPEG: def __init__(self, image_file): with open(image_file, 'rb') as f: self.img_data = f.read() def decode(self): data = self.img_data marker_DQT_num = 0 marker_DQT_size_max = 0 marker_DHT_num = 0 marker_DHT_size_max = 0 file_markers_num = 0 marker_EOI_content_after_num = 0 marker_APP12_size_max = 0 marker_APP1_size_max = 0 marker_COM_size_max = 0 file_size = len(data) print(f"file_size = {file_size}") while(True): try: marker, = unpack(">H", data[0:2]) except: print("error") marker_map = marker_mapping.get(marker) if marker_map != None: file_markers_num += 1 if marker_map == "DQT": marker_DQT_num += 1 lenchunk, = unpack(">H", data[2:4]) if lenchunk > marker_DQT_size_max: marker_DQT_size_max = lenchunk data = data[2+lenchunk:] elif marker_map == "SOI": data = data[2:] elif marker_map == "DHT": marker_DHT_num += 1 lenchunk, = unpack(">H", data[2:4]) if lenchunk > marker_DHT_size_max: marker_DHT_size_max = lenchunk data = data[2+lenchunk:] elif marker_map == "EOI": rem = data[2:] if len(rem) > marker_EOI_content_after_num: marker_EOI_content_after_num = len(rem) data = rem elif marker_map == "SOS": data = data[-2:] elif marker_map == "APP12": lenchunk, = unpack(">H", data[2:4]) if lenchunk > marker_APP12_size_max: marker_APP12_size_max = lenchunk data = data[2+lenchunk:] elif marker_map == "APP1": lenchunk, = unpack(">H", data[2:4]) if lenchunk > marker_APP1_size_max: marker_APP1_size_max = lenchunk data = data[2+lenchunk:] elif marker_map == "COM": lenchunk, = unpack(">H", data[2:4]) if lenchunk > marker_COM_size_max: marker_COM_size_max = lenchunk data = data[2+lenchunk:] elif marker_map == "TEM": data = data[2:] elif marker <= 0xffd9 and marker >= 0xffd0: data = data[2:] elif marker <= 0xffbf and marker >= 0xff02: lenchunk, = unpack(">H", data[2:4]) data = data[2+lenchunk:] else: lenchunk, = unpack(">H", data[2:4]) data = data[2+lenchunk:] else: data = data[1:] if (len(data) == 0): data_list = [marker_EOI_content_after_num,marker_DQT_num,marker_DHT_num,file_markers_num, marker_DQT_size_max, marker_DHT_size_max,file_size, marker_COM_size_max,marker_APP1_size_max,marker_APP12_size_max,0] return data_list def extract_features(): img = JPEG("./server_files/saveimg.jpeg") data_list = img.decode() df = pd.DataFrame(data_list) df = df.T df.to_csv("test.csv") app = Flask(__name__) CORS(app) api = Api(app) parser = reqparse.RequestParser() parser.add_argument("image") class Predict(Resource): def post(self): args = parser.parse_args() #request_data = json.loads(request.get_data()) #data = request_data['data'] #decodeit = open('saveimg.jpeg', 'wb') #decodeit.write(base64.b64decode((data))) #decodeit.close() #print(type(data)) decodeit = open('./server_files/saveimg.jpeg', 'wb') decodeit.write(base64.b64decode(bytes(args["image"], 'utf-8'))) decodeit.close() extract_features() return {"class" : "bening"} api.add_resource(Predict, "/predict") if __name__ == "__main__": app.run(debug=True)

[ "akshithnm@gmail.com" ]

akshithnm@gmail.com

f49a5e37d0b4279902872dcc74a5ea78ab2137a3

6f5c0db7b845cb62c951b2467957ffe3cb0aad35

/stats.py

2616a77c878bbfb064e02cbecd9ad98b5958f460

[]

no_license

baydarich/infohash-searcher

300056c8255656b049e8aa4c6cc46df7e7f9500f

7fad6763c099934586bb286f160f3e52063420ff

refs/heads/master

2021-06-14T06:49:35.439588

2017-03-05T11:38:17

null

0

null

UTF-8

Python

false

1,211

py

#!/usr/env/python from bencode import bdecode, bencode import os b = 1024 * 1024 ranges = [{1: (1, 1024 * 1024)}] t = 1 for j in range(18): t = 1024 * 1024 ranges.append({j + 2: (t * 2 ** j + 1, t * 2 ** (j + 1))}) bs = {} # bs = {piece_length:[{ran:count}, {ran:count}]} base_path = "/home/horn/Documents/SNE/CCF/proj/test-torrents/" files = os.listdir(base_path) stat = [] r = 0 for i in files: length = 0 with open("%s%s" % (base_path, i)) as _file: info_orig = bdecode(_file.read())['info'] piece_length = info_orig['piece length'] try: length = info_orig['length'] except KeyError: for j in info_orig['files']: length += j['length'] finally: for j, k in enumerate(ranges): if k[j + 1][0] <= length <= k[j + 1][1]: r = j + 1 break try: bs[piece_length][r] += 1 except KeyError: try: bs[piece_length][r] = 1 except KeyError: bs[piece_length] = {r: 1} for k, v in bs.iteritems(): print k, sorted(v, reverse=True) print bs

[ "a@bakhtin.net" ]

a@bakhtin.net

2740845c8dea1c81052693b87ed8201e5e26e8c6

7b7ca1ab3f5364756ea67d8c2e39b68a58ab8f06

/First_improved ws model.py

83daded0d204aadd0b0af3ffd89e43d18fca0168

[]

no_license

Isabellahu/Complex-Network

6a1f065ec12ab4eb86b390205b8f343eb95204eb

511683750636fd198d12963771ca61255b789641

refs/heads/master

2020-04-01T17:49:55.732821

2018-10-17T14:09:22

153,453,532

2

0

null

UTF-8

Python

false

8,343

py

# -*- coding: utf-8 -*- """ Created on Sat Oct 28 15:32:55 2017 @author: 90662 """ #WS小世界模型构建 import random from numpy import * import networkx as nx import matplotlib.pyplot as plt #假设参加聚会，每个人只认识一个主角 def CreateNetwork(n,k,p,matrix): i = 1 for j in range(n): matrix[1][j] = 1 matrix[j][1] = 1 def SmallWorld(n,k,p,matrix): #随机产生一个概率p_change，如果p_change < p, 重新连接边 p_change = 0.0 edge_change = 0 for i in range(n): #t = int(k/2) for j in range( k // 2 + 1): #需要重新连接的边 p_change = (random.randint(0,n-1)) / (double)(n) #重新连接 if p_change < p: #随机选择一个节点，排除自身连接和重边两种情况 while(1): node_NewConnect = (random.randint(0,n-1)) + 1 if matrix[i][node_NewConnect] == 0 and node_NewConnect != i: break if (i+j) <= (n-1): matrix[i][i+j] = matrix[i+j][i] = 0 else: matrix[i][i+j-(n-1)] = matrix[i+j-(n-1)][i] = 0 matrix[i][node_NewConnect] = matrix[node_NewConnect][i] = 1 edge_change += 1 else: print("no change\n",i+j) #test print("small world network\n") for i in range(n): for j in range(n): print(matrix[i][j]) print("\n") print("edge_change = ",edge_change) print("ratio = ",(double)(edge_change)/(n*k/2)) #将matrix写入文件 def DataFile(n,k,p,matrix): # 打开一个文件 f = open("C:/0network/data.txt", "w") #matrix[[[1 for i in range(n)] [1 for j in range(n)]] for i in range(n): for j in range(n): netdata = ','.join(str(matrix[i][j])) f.write(netdata) f.write('\n') #f.write("true") # 关闭打开的文件 f.close() #print(netdata) print('end') # 画图 def Drawmap(n,matrix,G): #添加n个节点 for i in range(n): G.add_node(i) #添加边,if = 1 then return [(i,j)] for i in range(n): for j in range(n): if matrix[i][j] == 1: G.add_edge(i,j) #定义一个布局，采用circular布局方式 pos = nx.circular_layout(G) #绘制图形 nx.draw(G,pos,with_labels=False,node_size = 30) #输出方式1: 将图像存为一个png格式的图片文件 plt.savefig("WS-Network-change1-2.png") #输出方式2: 在窗口中显示这幅图像 plt.show() #平均群聚系数 def average_clustering(n,matrix): #三元组 number_three_tuple = 0.0 #三角形 Triangle = 0.0 #聚类系数 clustering_coefficient = 0.0 for i in range(n): three_tuple = 0.0 sum_edge = 0 for j in range(n): if matrix[i][j] == 1 or matrix[j][i] == 1: sum_edge += 1 float(sum_edge) #计算每个节点的三元组个数 three_tuple = int((sum_edge*(sum_edge-1.0))/2.0) #节点i的边组成列表mylist，并且每次循环之前初始为空值 myList = [] for j in range(i,n): if matrix[i][j] == 1 or matrix[j][i] == 1: myList.append(j) #如果myList中的边（i，j）等于1，则形成三角形 for k in range(len(myList)): for q in range(k,len(myList)): if matrix[myList[k]][myList[q]] == 1 or matrix[myList[q]][myList[k]] == 1: Triangle += 1 if three_tuple != 0: clustering_coefficient += (Triangle/three_tuple) clustering_coefficient = clustering_coefficient/n print('clustering_coefficient = ',clustering_coefficient) #Floyd算法求最短路径 def Ford(n,matrix): #出发点v #到达点w #中转点K #初始化新的邻接矩阵new_m,路径矩阵dis dis = zeros((n,n),int) new_m = zeros((n,n),int) for v in range(n): for w in range(n): dis[v][w] = w if matrix[v][w] == 0: new_m[v][w] = 6666666 elif matrix[v][w] == 1: new_m[v][w] = 1 dis[v][w] = 1 for k in range(n): for v in range(n): for w in range(n): #如果经过中转点的路径比两点路径短 if (new_m[v][k] + new_m[k][w]) < new_m[v][w]: new_m[v][w] = new_m[v][k] + new_m[k][w] #dis[v][w] = dis[v][k] dis[v][w] = 2 #打印节点 sum = 0.0 for v in range(n): for w in range(v+1,n): #print('v= ,',v,'w = ',w) #print('dis[v][w] = ',dis[v][w]) sum = sum + dis[v][w] float(n) average_shortest_path_length = sum/(n*(n-1.0)/2) print('average_shortest_path_length = ',average_shortest_path_length) #节点度分布 def node_degree_distribution(n,matrix): #求节点的度 degree = [] for i in range(n): sum = 0 for j in range(n): sum += matrix[i][j] #print(sum) degree.append(sum) #print(degree) degree.sort() print('degree = ',degree) sum_degree= 0.0 for i in range(n): sum_degree += degree[i] #print(sum_degree) #生成x轴序列，从1到最大度 x = range(len(degree)) #将频次转换为频率，这用到Python的一个小技巧：列表内涵 y = [z/sum_degree for z in degree] #在双对数坐标轴上绘制度分布曲线 plt.loglog(x,y,color="blue",linewidth=2) #显示图表 plt.show() #动态行为 #抗故意攻击 robustness against intentional attack def node_robustness(n): #node_degree_distribution(n,matrix) #求出度最大的点 degree = [] for i in range(n): sum = 0 for j in range(n): sum += matrix[i][j] degree.append(sum) #将度最大的点删除边 node_flag = degree.index(max(degree)) for i in range(n): matrix[node_flag][i] = 0 matrix[i][node_flag] = 0 #随机攻击 random attack def node_random(n): #产生一个随机数：0到n-1 node_flag = random.randint(0,n-1) print(node_flag) for i in range(n): matrix[node_flag][i] = 0 matrix[i][node_flag] = 0 if __name__=="__main__": print("main") #输入三个参数：节点数N,参数K,概率P n = input("请输入节点数 n = ",) k = input("请输入参数（偶数） k = ",) p = input("请输入概率 p = ",) n=int(n) k=int(k) p=float(p) matrix = zeros((n,n),int) #matrix = zeros((n,n)) #print(matrix) G = nx.Graph() value = [n,k,p] #print("\n") CreateNetwork(n,k,p,matrix) SmallWorld(n,k,p,matrix) #print(matrix) #导出到一个文件中 #DataFile(n,k,p,matrix) #画图 Drawmap(n,matrix,G) #被攻击前的网络特性 #群聚系数 average_clustering(n,matrix) #平均最短路径 Ford(n,matrix) #节点度分布 node_degree_distribution(n,matrix) #抗故意攻击 robustness against intentional attack #重新定义图 #node_robustness(n) #G = nx.Graph() #Drawmap(n,matrix,G) #随机攻击 random attack node_random(n) #重新定义图 G = nx.Graph() Drawmap(n,matrix,G) #被攻击后的网络特性 #群聚系数 average_clustering(n,matrix) #平均最短路径 Ford(n,matrix) #节点度分布 node_degree_distribution(n,matrix)

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noreply@github.com

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/Code/wink_detection.py

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[]

no_license

FelixFelicis555/Blinking-Keyboard

04947fe0b8efacd158d4a698b360233947ee8ef9

cd2dd51bfed205780cd46a1f17287015790186d3

refs/heads/master

2022-02-27T18:35:50.149206

2019-11-08T09:52:16

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import numpy as np import cv2 import dlib from scipy.spatial import distance as dist from gtts import gTTS import os language = 'en' import pyttsx3 engine = pyttsx3.init() characterdict = {'0':'a','00':'d','000':'j','0000':'n','1':'s','01':'f','001':'k','0001':'y','10':'g','010':'l','0010':'t'} characterdict['0011']='v' characterdict['011']='m' characterdict['11']='h' characterdict['0100']='b' characterdict['100']='u' characterdict['0101']='r' characterdict['101']='i' characterdict['0110']='e' characterdict['110']='o' characterdict['0111']='c' characterdict['111']='p' characterdict['1000']='x' characterdict['1001']='w' characterdict['1010']='q' characterdict['1011']='z' characterdict['1100']=',' characterdict['1101']='.' characterdict['1110']='?' characterdict['1111']=" " print("Enter a choice whether you want blink keyboard or wink keyboard \n 1.) Blink Keyboard \n 2.) Wink keyboard") n = int(input()) if n==2: while True: print("You have choosen wink keyboard\n") print("Way of using wink keyboard\n") print("1.) You will be shown the keyboard structure in front of you\n") print("2.) will move the pointer to left side\n") print("3.) Right wink will move the pointer to right side\n") print("4.) Blink detected when you here beep sound once will fix your character that you want to choose it\n") print("5.) When you hear the beep sound twice while blinking you will be back to the starting position \n") print("6.) On the starting node if you blink that means backspace\n") print("If you understand the rules press 'y' else 'press 'n' \n") check = input() if check =='y': break text = "" PREDICTOR_PATH = "./shape_predictor_68_face_landmarks.dat" stop_flag = 0 FULL_POINTS = list(range(0, 68)) FACE_POINTS = list(range(17, 68)) JAWLINE_POINTS = list(range(0, 17)) RIGHT_EYEBROW_POINTS = list(range(17, 22)) LEFT_EYEBROW_POINTS = list(range(22, 27)) NOSE_POINTS = list(range(27, 36)) RIGHT_EYE_POINTS = list(range(36, 42)) LEFT_EYE_POINTS = list(range(42, 48)) MOUTH_OUTLINE_POINTS = list(range(48, 61)) MOUTH_INNER_POINTS = list(range(61, 68)) EYE_AR_THRESH = 0.23 EYE_AR_CONSEC_FRAMES = 5 counter_left = 0 total_left = 0 counter_right = 0 total_right = 0 counter_blink = 0 total_blink = 0 flag_left,flag_right,flag_blink = 0,0,0 def eye_aspect_ratio(eye): A = dist.euclidean(eye[1], eye[5]) B = dist.euclidean(eye[2], eye[4]) C = dist.euclidean(eye[0], eye[3]) ear = (A + B) / (2.0 * C) return ear detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(PREDICTOR_PATH) video_capture = cv2.VideoCapture(0) image = "base" text = "" while True: ret, frame = video_capture.read() if ret: gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) rects = detector(gray, 0) for rect in rects: x = rect.left() y = rect.top() x1 = rect.right() y1 = rect.bottom() landmarks = np.matrix([[p.x, p.y] for p in predictor(frame, rect).parts()]) left_eye = landmarks[LEFT_EYE_POINTS] right_eye = landmarks[RIGHT_EYE_POINTS] left_eye_hull = cv2.convexHull(left_eye) right_eye_hull = cv2.convexHull(right_eye) cv2.drawContours(frame, [left_eye_hull], -1, (0, 255, 0), 1) cv2.drawContours(frame, [right_eye_hull], -1, (0, 255, 0), 1) ear_left = eye_aspect_ratio(left_eye) ear_right = eye_aspect_ratio(right_eye) if ear_left >= EYE_AR_THRESH and ear_right >= EYE_AR_THRESH: counter_blink = 0 counter_left = 0 counter_right = 0 # print("****************************************") # print("Counter Blink : " , counter_blink) # print("Counter LEFT : ", counter_left) # print("Counter Right : ", counter_right) # print("****************************************") if counter_blink >= 10: if counter_blink == 10: flag_blink = 1 duration = 0.05 # seconds freq = 440 # Hz os.system('play -nq -t alsa synth {} sine {}'.format(duration, freq)) if counter_blink == 20: stop_flag = 1 flag_blink = 0 duration = 0.05 # seconds freq = 440 # Hz os.system('play -nq -t alsa synth {} sine {}'.format(duration, freq)) else: if flag_blink == 1: total_blink += 1 # print("Blink Occured") counter_blink = 0 flag_blink = 0 if stop_flag == 1: image = "base" counter_blink = 0 flag_blink = 0 if ear_left < EYE_AR_THRESH: if ear_right < EYE_AR_THRESH : counter_blink += 1 counter_left = 0 else: counter_blink = 0 counter_left += 1 counter_right = 0 if counter_left == EYE_AR_CONSEC_FRAMES: flag_left = 1 duration = 0.05 # seconds freq = 440 # Hz os.system('play -nq -t alsa synth {} sine {}'.format(duration, freq)) else: if flag_left ==1: total_left += 1 # print("Left eye winked") counter_left = 0 counter_blink = 0 flag_left = 0 counter_right = 0 else: if counter_left >= EYE_AR_CONSEC_FRAMES: flag_left = 1 if ear_right < EYE_AR_THRESH: if ear_left < EYE_AR_THRESH: counter_right = 0 pass else: counter_blink = 0 counter_right += 1 counter_left = 0 if counter_right == EYE_AR_CONSEC_FRAMES: flag_right = 1 duration = 0.05 # seconds freq = 440 # Hz os.system('play -nq -t alsa synth {} sine {}'.format(duration, freq)) else: if flag_right == 1: total_right += 1 # print("Right eye winked") counter_right = 0 flag_right = 0 counter_blink = 0 counter_left = 0 else: if counter_right >= EYE_AR_CONSEC_FRAMES: flag_right = 1 # if ear_left >= EYE_AR_THRESH : # counter_left = 0 # counter_blink = 0 # if ear_right >= EYE_AR_THRESH: # counter_right = 0 # counter_blink = 0 cv2.putText(frame, "Wink Left : {}".format(total_left), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) cv2.putText(frame, "Wink Right: {}".format(total_right), (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) cv2.putText(frame, "Blink Occured: {}".format(total_blink), (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) if total_left == 1: if image == "base": image = "" image+='0' total_left = 0 total_right = 0 total_blink = 0 flag_left = 0 flag_right = 0 flag_blink = 0 stop_flag = 0 if total_right == 1: if image =="base": image = "" image+='1' total_right = 0 total_left = 0 total_blink = 0 flag_left = 0 flag_right = 0 flag_blink = 0 stop_flag = 0 if total_blink == 1: # print("image is "+image+".jpg") if image!='base': text += characterdict[image] else: if len(text)!=0: text = text[:len(text)-1] # do the required action image = "base" total_blink = 0 total_left = 0 total_right = 0 flag_left = 0 flag_right = 0 flag_blink = 0 stop_flag = 0 if len(image)>4: image=image[:4] cv2.namedWindow("KeyBoard", cv2.WINDOW_NORMAL) cv2.moveWindow("KeyBoard",850,20) ia = cv2.imread(image+".jpg") ims = cv2.resizeWindow("KeyBoard",550, 400) # Resize image cv2.imshow("KeyBoard" , ia) cv2.namedWindow("Faces", cv2.WINDOW_NORMAL) cv2.moveWindow("Faces",0,20) ims = cv2.resizeWindow("Faces",800, 700) # Resize image cv2.imshow("Faces", frame) cv2.namedWindow("Typed_Text", cv2.WINDOW_NORMAL) cv2.moveWindow("Typed_Text",850,500) draw = cv2.imread("draw.jpg") cv2.resizeWindow("Typed_Text",550,270) cv2.putText(draw, "Typed Text: {}".format(text), (20, 90), cv2.FONT_HERSHEY_SIMPLEX, 3, (0, 0, 0), 5) cv2.imshow("Typed_Text" , draw) ch = 0xFF & cv2.waitKey(1) if ch == ord('q'): break cv2.destroyAllWindows() elif n==1: while True: print("You have choosen Blink keyboard") print("Way of using Blink keyboard\n") print("1.) You will be shown the keyboard structure in front of you\n") print("2.) Shorter blink: When you hear a beep sound first time, will move the pointer to left side\n") print("3.) Longer blink: When you hear a beep sound second time, will move the pointer to right side\n") print("4.) Longest Blink: When you hear a beep sound third time, will fix your character that you want to choose it\n") print("5.) Back to start: When you hear the beep sound 4th time with writing character\n") print("6.) On the starting node if you blink that means backspace\n") print("If you understand the rules press 'y' else 'press 'n' \n") check = input() if check =='y': break text = "" PREDICTOR_PATH = "./shape_predictor_68_face_landmarks.dat" FULL_POINTS = list(range(0, 68)) FACE_POINTS = list(range(17, 68)) JAWLINE_POINTS = list(range(0, 17)) RIGHT_EYEBROW_POINTS = list(range(17, 22)) LEFT_EYEBROW_POINTS = list(range(22, 27)) NOSE_POINTS = list(range(27, 36)) RIGHT_EYE_POINTS = list(range(36, 42)) LEFT_EYE_POINTS = list(range(42, 48)) MOUTH_OUTLINE_POINTS = list(range(48, 61)) MOUTH_INNER_POINTS = list(range(61, 68)) EYE_AR_THRESH = 0.25 EYE_AR_CONSEC_FRAMES = 5 counter_blink = 0 total_blink = 0 ''' There are three types of blink one blink --- Left blink two blink --- Right blink three blink --- Select the letter four blink --- Revert to start ''' flag_blink_one,flag_blink_two,flag_blink_three,stopflag = 0,0,0,0 count_left,count_right,count_stop = 0,0,0 def eye_aspect_ratio(eye): A = dist.euclidean(eye[1], eye[5]) B = dist.euclidean(eye[2], eye[4]) C = dist.euclidean(eye[0], eye[3]) ear = (A + B) / (2.0 * C) return ear detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(PREDICTOR_PATH) video_capture = cv2.VideoCapture(-1) image = "base" text = "" while True: ret, frame = video_capture.read() if ret: gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) rects = detector(gray, 0) for rect in rects: x = rect.left() y = rect.top() x1 = rect.right() y1 = rect.bottom() landmarks = np.matrix([[p.x, p.y] for p in predictor(frame, rect).parts()]) left_eye = landmarks[LEFT_EYE_POINTS] right_eye = landmarks[RIGHT_EYE_POINTS] left_eye_hull = cv2.convexHull(left_eye) right_eye_hull = cv2.convexHull(right_eye) cv2.drawContours(frame, [left_eye_hull], -1, (0, 255, 0), 1) cv2.drawContours(frame, [right_eye_hull], -1, (0, 255, 0), 1) ear_left = eye_aspect_ratio(left_eye) ear_right = eye_aspect_ratio(right_eye) # print("****************************************") # print("Counter Blink : " , counter_blink) # print("****************************************") if counter_blink >= 10: if counter_blink == 10: flag_blink_one,flag_blink_two,flag_blink_three = 1,0,0 stopflag = 0 duration = 0.05 # seconds freq = 440 # Hz os.system('play -nq -t alsa synth {} sine {}'.format(duration, freq)) if counter_blink == 20: flag_blink_two,flag_blink_one,flag_blink_three = 1,0,0 stopflag = 0 duration = 0.05 # seconds freq = 440 # Hz os.system('play -nq -t alsa synth {} sine {}'.format(duration, freq)) if counter_blink == 30: flag_blink_three,flag_blink_one,flag_blink_two = 1,0,0 stopflag = 0 duration = 0.05 # seconds freq = 440 # Hz os.system('play -nq -t alsa synth {} sine {}'.format(duration, freq)) if counter_blink==50: stopflag = 1 flag_blink_three,flag_blink_one,flag_blink_two = 0,0,0 duration = 0.05 # seconds freq = 440 # Hz os.system('play -nq -t alsa synth {} sine {}'.format(duration, freq)) else: if flag_blink_three == 1: total_blink += 1 # print("Stop Blink Occured") counter_blink = 0 count_stop = 1 flag_blink_one,flag_blink_two,flag_blink_three = 0,0,0 count_left = 0 count_right = 0 elif flag_blink_one == 1: total_blink += 1 # print("Left side blink occured") counter_blink = 0 flag_blink_one,flag_blink_two,flag_blink_three = 0,0,0 count_left = 1 count_right = 0 count_stop = 0 elif flag_blink_two == 1: total_blink += 1 # print("Right side blink occured") counter_blink = 0 flag_blink_one,flag_blink_two,flag_blink_three = 0,0,0 count_left = 0 count_right = 1 count_stop = 0 elif stopflag == 1: count_left,count_right,count_stop=0,0,0 stopflag = 0 image = 'base' if ear_left < EYE_AR_THRESH and ear_right < EYE_AR_THRESH: counter_blink += 1 else: counter_blink = 0 cv2.putText(frame, "Blink Occured: {}".format(total_blink), (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) if count_left == 1: if image == "base": image = "" image+='0' count_left = 0 if count_right == 1: if image =="base": image = "" image+='1' count_right = 0 if count_stop == 1: if image == "base": if len(text)!=0: text = text[:len(text)-1] # myobj = gTTS(text="backspace", lang=language, slow=False) # myobj.save("text.mp3") engine.say("Backspace") engine.runAndWait() else: text += characterdict[image] # myobj = gTTS(text=characterdict[image], lang=language, slow=False) # myobj.save("text.mp3") engine.say(characterdict[image]) engine.runAndWait() # print("image is "+image+".jpg") # do the required action # os.system("mpg321 text.mp3") image = "base" count_stop,count_left,count_right = 0,0,0 if len(image)>4: image=image[:4] cv2.namedWindow("KeyBoard", cv2.WINDOW_NORMAL) cv2.moveWindow("KeyBoard",850,20) ia = cv2.imread(image+".jpg") ims = cv2.resizeWindow("KeyBoard",550, 400) # Resize image cv2.imshow("KeyBoard" , ia) cv2.namedWindow("Faces", cv2.WINDOW_NORMAL) cv2.moveWindow("Faces",0,20) ims = cv2.resizeWindow("Faces",800, 700) # Resize image cv2.imshow("Faces", frame) cv2.namedWindow("Typed_Text", cv2.WINDOW_NORMAL) cv2.moveWindow("Typed_Text",850,500) draw = cv2.imread("draw.jpg") cv2.resizeWindow("Typed_Text",550,270) cv2.putText(draw, "Typed Text: {}".format(text), (20, 90), cv2.FONT_HERSHEY_SIMPLEX, 3, (0, 0, 0), 5) cv2.imshow("Typed_Text" , draw) ch = 0xFF & cv2.waitKey(1) if ch == ord('q'): break cv2.destroyAllWindows() else: print("You entered wrong choice ") exit(0)

[ "bhavyabordia@gmail.com" ]

bhavyabordia@gmail.com

fec5927f671f48d0494d8758f058e97cbe129c94

0353782639974c650fa042e44d75e92bf7be6fc1

/instagram/insta/instafeed/views.py

cccd2cf5f41d8e68ff52bb8847187c85cb8c062f

[]

no_license

jersobh/DigitalMarketing

2d31b5c18f0764c4f352947aa34506d63216feeb

6fa679bb964e6ad656415e38227e007db2ae0fda

refs/heads/master

2021-10-23T15:24:01.939739

2019-03-18T12:27:21

null

0

null

UTF-8

Python

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py

from django.shortcuts import render from django.http import HttpResponse # import InstagramAPI from InstagramAPI.InstagramAPI import InstagramAPI from django.http import JsonResponse def index(request): api = InstagramAPI("jayabal.al", "jayabal9890@insta") if(api.login()): api.getSelfUserFeed() return JsonResponse(api.LastJson) return JsonResponse({}) # return HttpResponse("Hello, world. You're at the polls index.")

[ "noreply@github.com" ]

noreply@github.com

ea0a54fdc36a8a8a37ace27442c6675bd11d2208

8facec89b1fded458cf3c40dfe4ed2a6b7af87aa

/advanced/class_attributes_management/comparation_with_4_methods_simple/descriptor_implement_improved.py

bbe4764040fdbd62f2644e8e08dbff6c19657902

[]

no_license

tianwei1992/Python_oop_leaning

72bf4c4c0c71cf736bc14912c4aef28642755c80

7e0f4e95c0d9bf7aa9fd95fcf37fc86f90ea8db7

refs/heads/master

2020-03-28T16:55:18.592234

2018-12-12T03:06:22

148,740,603

0

null

UTF-8

Python

false

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class Powers(): def __init__(self, square_base, cube_base): self._square_base = square_base self._cube_base = cube_base class SquareDescriptor(): def __get__(self, instance, owner): if instance is None: """类.attr - > Descriptor,没毛病""" return self return instance._square_base ** 2 def __set__(self, instance, value): instance._square_base = value class CubeDescriptor(): def __get__(self, instance, owner): if instance is None: return self return instance._cube_base ** 3 square = SquareDescriptor() cube = CubeDescriptor() X = Powers(3, 4) """Powers.square = 5不会触发SquareDescriptor.__get__方法，而是直接更改Powers.square为一个普通的属性，值为5，这也会影响到所以示例所以结论:对标识符产生的属性，不要试图从类上面赋值。""" print(Powers.square) print() print(X.square) # 3 ** 2 = 9 print(X.cube) # 4 ** 3 = 64 X.square = 5 print(X.square) # 5 ** 2 = 25 """描述符定义的属性在类中定义，是类属性，但是get和set一般对实例用。直接对类用set相当于覆盖原有属性为普通属性，偶尔对类用get，是类似Powers.square.__doc__的时候"""

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#!/usr/bin/env python import os import sys import getopt import json def usage(argv, exit=None): print "Usage: %s [OPTIONS] <VB metadata file> <VB JSON topology file (output)>" % argv[0] print " -h (--help) : print help and exit" print " -v (--vbs-path=) : path to VB Stats python module" if exit is not None: sys.exit(exit) def parse_cmd_line(argc, argv): opts = [] args = [] cur_path = os.path.dirname(os.path.realpath(__file__)) vb_path = cur_path + "/../vb-stats/" try: opts, args = getopt.getopt( argv[1:], "hv:", ["help", "vb-path="] ) except getopt.GetoptError, err: print >> sys.stderr, err usage(argv, exit=1) for o, a in opts: if o in ("-h", "--help"): usage(argv, exit=0) elif o in ("-v", "--vb-path"): vb_path = a else: usage(argv, exit=1) if len(args) != 2: usage(argv, exit=1) return vb_path, args[0], args[1] def main(argc, argv, envp): vb_path, meta, json_file = parse_cmd_line(argc, argv) procs = [] # Try to import vb-path try: sys.path.insert(0, vb_path) from vb_stats import VB_Stats as vbs except ImportError: print >> sys.stderr, "Could not import VB_Stats. Please specify path to VB_Stats with '--vbs-path'" usage(argv, exit=2) with vbs(meta, load_data=False) as vb: with open(json_file, "w") as f: num_processors = vb.num_sockets_per_node * vb.num_cores_per_socket * vb.num_hw_threads_per_core json.dump({ "processor_info" : { "num_processors" : num_processors, "num_sockets" : vb.num_sockets_per_node, "cores_per_socket" : vb.num_cores_per_socket, "hw_threads_per_core" : vb.num_hw_threads_per_core }, # The format of p: [socket, core, hw_thread, os_core] "processor_list" : [ { "os_core" : p[3], "socket" : p[0], "core" : p[1], "hw_thread" : p[2] } for p in vb.processor_map ] }, f, indent=4) if __name__ == "__main__": argv = sys.argv argc = len(argv) envp = os.environ sys.exit(main(argc, argv, envp))

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import app class openAccountAPI: def __init__(self): self.open_account_url = app.BASE_URL + "/trust/trust/register" def openAccount(self, session): response = session.post(self.open_account_url) return response

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import sys def add(n1,n2): return n1 + n2 number1 = int(raw_input("Enter a number:")) number2 = int(raw_input("Enter a number:")) print add(number1,number2)

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from typing import List # Definition for a binary tree node. class TreeNode: def __init__(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right class Solution: def constructMaximumBinaryTree(self, nums: List[int]) -> TreeNode: """ 先明确根节点做什么？对于构造二叉树的问题，根节点要做的就是把想办法把自己构造出来 """ # base case if len(nums) == 0: return None if len(nums) == 1: return TreeNode(nums[0]) # 第一步：先找数组中的最大值和索引 max_value = max(nums) index = nums.index(max_value) # 创建根节点 root = TreeNode(max_value) # 递归调用构造左右子树 root.left = self.constructMaximumBinaryTree(nums[:index]) root.right = self.constructMaximumBinaryTree(nums[index + 1:]) return root print(Solution().constructMaximumBinaryTree([3, 2, 1, 6, 0, 5]))

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import subprocess from click.testing import CliRunner from docs_src.using_click import tutorial003 as mod runner = CliRunner() def test_cli(): result = runner.invoke(mod.typer_click_object, []) # TODO: when deprecating Click 7, remove second option assert "Error: Missing command" in result.stdout or "Usage" in result.stdout def test_typer(): result = runner.invoke(mod.typer_click_object, ["top"]) assert "The Typer app is at the top level" in result.stdout def test_click(): result = runner.invoke(mod.typer_click_object, ["hello", "--name", "Camila"]) assert "Hello Camila!" in result.stdout def test_script(): result = subprocess.run( ["coverage", "run", mod.__file__, "--help"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding="utf-8", ) assert "Usage" in result.stdout

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# Generated by Django 3.2 on 2021-05-04 14:20 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('base', '0002_order_orderitem_review_shippingaddress'), ] operations = [ migrations.AddField( model_name='product', name='image', field=models.ImageField(blank=True, null=True, upload_to=''), ), ]

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from mednickdb_pyapi.mednickdb_pyapi import MednickAPI from mednickdb_pysleep import defaults import os import matplotlib.pyplot as plt import seaborn as sns import bootstrapped.bootstrap as bs import bootstrapped.compare_functions as bs_compare import bootstrapped.stats_functions as bs_stats import numpy as np import pandas as pd sleep_stages = { 0:'wake', 1:'stage1', 2:'stage2', 3:'sws', 4:'rem' } def compare_dists(data, y_var, by_var, y_level=None, by_levels=None, ax=None): levels = data[by_var].unique() if by_levels is not None: levels = [lev for lev in levels if lev in by_levels] levels_data = [] for lev in levels: level_data = data.loc[data[by_var] == lev, y_var].dropna() if y_level is not None: level_data = level_data.apply(lambda x: x[y_level]).dropna() levels_data.append(level_data.astype(float).values) #Runs boostrapped stats test is_diff = False if len(levels) == 2: diff = bs.bootstrap_ab(*levels_data, stat_func=bs_stats.mean, compare_func=bs_compare.percent_change) is_diff = (diff.lower_bound > 0 or diff.upper_bound < 0) if is_diff: sns.set_style("dark") else: sns.set_style("white") diff_msg = 'Difference: \nZero not in CI' if is_diff else 'No Difference: \nZero in CI' print(diff, '\n', diff_msg) # Plotting for lev in levels_data: sns.distplot(a=lev, ax=ax) ax.text(0.3, 0.5, diff_msg, transform=ax.transAxes, size=16, color='r' if is_diff else 'k') plt.title(y_var.split('.')[-1]+' to '+sleep_stages[y_level]+' for the Cleveland Family Study by '+by_var.split('.')[-1]) plt.ylabel('Probability Density') plt.legend(levels) def investigate_trans_probs_by_demographics(data, sleep_stages_to_consider=defaults.stages_to_consider): data = data.drop(['_id', 'sleep_scoring.sourceid', 'visitid', 'datemodified', 'expired'], axis=1) data['demographics.age_cat'] = (data['demographics.age'] > 55).map({True: 'Older', False: 'Younger'}) data['demographics.ethnicity'] = data['demographics.ethnicity'].map({'white ': 'white', 'black ': 'black'}) #anything else will get nan demo_cols = ['subjectid', 'demographics.age_cat', 'demographics.ethnicity', 'demographics.sex'] trans_probs_cols = ['sleep_scoring.trans_prob_from_' + s for s in sleep_stages_to_consider] cols_we_care_about = demo_cols + trans_probs_cols data = data.loc[:, cols_we_care_about] data = data.set_index(demo_cols) from_and_to_data_cont = [] for trans_probs_col in trans_probs_cols: from_data = data.loc[:, trans_probs_col] # keep index from_data = from_data.dropna() from_and_to_data_np = np.array(from_data.tolist()).astype(float) #not sure why need to conver from_and_to_data = pd.DataFrame(from_and_to_data_np, columns=sleep_stages_to_consider) from_and_to_data['from_stage'] = trans_probs_col.split('_')[-1] from_and_to_data.index = from_data.index from_and_to_data = from_and_to_data.reset_index() from_and_to_data = from_and_to_data.melt(id_vars=demo_cols+['from_stage'], value_vars=sleep_stages_to_consider, var_name='to_stage', value_name='prob') from_and_to_data_cont.append(from_and_to_data) all_trans_data = pd.concat(from_and_to_data_cont).reset_index(drop=True) # Plot some data for by_var in ['demographics.sex', 'demographics.ethnicity', 'demographics.age_cat']: data_to_plot = all_trans_data.drop(set(demo_cols)-set([by_var]), axis=1).dropna().reset_index(drop=True) sns.catplot(x='to_stage', y='prob', hue=by_var, row="from_stage", data=data_to_plot, kind="violin", split=True, height=1.5, aspect=2.5, legend=False) plt.legend(loc='lower right') plt.ylim((0, 1)) for to_and_from_stage, data in data_to_plot.groupby(['from_stage', 'to_stage']): from_stage, to_stage = to_and_from_stage[0], to_and_from_stage[1] by_data = list(data.groupby(by_var)) diff = bs.bootstrap_ab(by_data[0][1]['prob'].values, by_data[1][1]['prob'].values, stat_func=bs_stats.mean, compare_func=bs_compare.percent_change) is_diff = (diff.lower_bound > 0 or diff.upper_bound < 0) if is_diff: plt.gcf().axes[sleep_stages_to_consider.index(from_stage)].text(y=0, x=sleep_stages_to_consider.index(to_stage) - 0.1, s='*', color='r', fontsize=18) plt.show() if __name__ == '__main__': med_api = MednickAPI(username=os.environ['mednickapi_username'], password=os.environ['mednickapi_password']) #Get the data, so easy :) data = med_api.get_data('studyid=NSRR_CFS', format_as='dataframe_single_index') print('Got', data.shape[0], 'records') investigate_trans_probs_by_demographics(data)

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# {{{ http://code.activestate.com/recipes/576693/ (r9) # Backport of OrderedDict() class that runs on Python 2.4, 2.5, 2.6, 2.7 and # pypy. # Passes Python2.7's test suite and incorporates all the latest updates. try: from thread import get_ident as _get_ident except ImportError: from dummy_thread import get_ident as _get_ident try: from _abcoll import KeysView, ValuesView, ItemsView except ImportError: pass class OrderedDict(dict): 'Dictionary that remembers insertion order' # An inherited dict maps keys to values. # The inherited dict provides __getitem__, __len__, __contains__, and get. # The remaining methods are order-aware. # Big-O running times for all methods are the same as for regular # dictionaries. # The internal self.__map dictionary maps keys to links in a doubly linked # list. # The circular doubly linked list starts and ends with a sentinel element. # The sentinel element never gets deleted (this simplifies the algorithm). # Each link is stored as a list of length three: [PREV, NEXT, KEY]. def __init__(self, *args, **kwds): '''Initialize an ordered dictionary. Signature is the same as for regular dictionaries, but keyword arguments are not recommended because their insertion order is arbitrary. ''' if len(args) > 1: raise TypeError('expected at most 1 arguments, got %d' % len(args)) try: self.__root except AttributeError: self.__root = root = [] # sentinel node root[:] = [root, root, None] self.__map = {} self.__update(*args, **kwds) def __setitem__(self, key, value, dict_setitem=dict.__setitem__): 'od.__setitem__(i, y) <==> od[i]=y' # Setting a new item creates a new link which goes at the end of the # linked list, and the inherited dictionary is updated with the new # key/value pair. if key not in self: root = self.__root last = root[0] last[1] = root[0] = self.__map[key] = [last, root, key] dict_setitem(self, key, value) def __delitem__(self, key, dict_delitem=dict.__delitem__): 'od.__delitem__(y) <==> del od[y]' # Deleting an existing item uses self.__map to find the link which is # then removed by updating the links in the predecessor and successor # nodes. dict_delitem(self, key) link_prev, link_next, key = self.__map.pop(key) link_prev[1] = link_next link_next[0] = link_prev def __iter__(self): 'od.__iter__() <==> iter(od)' root = self.__root curr = root[1] while curr is not root: yield curr[2] curr = curr[1] def __reversed__(self): 'od.__reversed__() <==> reversed(od)' root = self.__root curr = root[0] while curr is not root: yield curr[2] curr = curr[0] def clear(self): 'od.clear() -> None. Remove all items from od.' try: for node in self.__map.itervalues(): del node[:] root = self.__root root[:] = [root, root, None] self.__map.clear() except AttributeError: pass dict.clear(self) def popitem(self, last=True): '''od.popitem() -> (k, v), return and remove a (key, value) pair. Pairs are returned in LIFO order if last is true or FIFO order if false. ''' if not self: raise KeyError('dictionary is empty') root = self.__root if last: link = root[0] link_prev = link[0] link_prev[1] = root root[0] = link_prev else: link = root[1] link_next = link[1] root[1] = link_next link_next[0] = root key = link[2] del self.__map[key] value = dict.pop(self, key) return key, value # -- the following methods do not depend on the internal structure -- def keys(self): 'od.keys() -> list of keys in od' return list(self) def values(self): 'od.values() -> list of values in od' return [self[key] for key in self] def items(self): 'od.items() -> list of (key, value) pairs in od' return [(key, self[key]) for key in self] def iterkeys(self): 'od.iterkeys() -> an iterator over the keys in od' return iter(self) def itervalues(self): 'od.itervalues -> an iterator over the values in od' for k in self: yield self[k] def iteritems(self): 'od.iteritems -> an iterator over the (key, value) items in od' for k in self: yield (k, self[k]) def update(*args, **kwds): '''od.update(E, **F) -> None. Update od from dict/iterable E and F. If E is a dict instance, does: for k in E: od[k] = E[k] If E has a .keys() method, does: for k in E.keys(): od[k] = E[k] Or if E is an iterable of items, does: for k, v in E: od[k] = v In either case, this is followed by: for k, v in F.items(): od[k] = v ''' if len(args) > 2: raise TypeError('update() takes at most 2 positional ' 'arguments (%d given)' % (len(args),)) elif not args: raise TypeError('update() takes at least 1 argument (0 given)') self = args[0] # Make progressively weaker assumptions about "other" other = () if len(args) == 2: other = args[1] if isinstance(other, dict): for key in other: self[key] = other[key] elif hasattr(other, 'keys'): for key in other.keys(): self[key] = other[key] else: for key, value in other: self[key] = value for key, value in kwds.items(): self[key] = value __update = update # let subclasses override update w/o breaking __init__ __marker = object() def pop(self, key, default=__marker): '''od.pop(k[,d]) -> v, remove specified key and return the corresponding value. If key is not found, d is returned if given, otherwise KeyError is raised. ''' if key in self: result = self[key] del self[key] return result if default is self.__marker: raise KeyError(key) return default def setdefault(self, key, default=None): 'od.setdefault(k[,d]) -> od.get(k,d), also set od[k]=d if k not in od' if key in self: return self[key] self[key] = default return default def __repr__(self, _repr_running=None): 'od.__repr__() <==> repr(od)' call_key = id(self), _get_ident() if _repr_running is None: _repr_running = {} if call_key in _repr_running: return '...' _repr_running[call_key] = 1 try: if not self: return '%s()' % (self.__class__.__name__,) return '%s(%r)' % (self.__class__.__name__, self.items()) finally: del _repr_running[call_key] def __reduce__(self): 'Return state information for pickling' items = [[k, self[k]] for k in self] inst_dict = vars(self).copy() for k in vars(OrderedDict()): inst_dict.pop(k, None) if inst_dict: return (self.__class__, (items,), inst_dict) return self.__class__, (items,) def copy(self): 'od.copy() -> a shallow copy of od' return self.__class__(self) @classmethod def fromkeys(cls, iterable, value=None): '''OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S and values equal to v (which defaults to None). ''' d = cls() for key in iterable: d[key] = value return d def __eq__(self, other): '''od.__eq__(y) <==> od==y. Comparison to another OD is order-sensitive while comparison to a regular mapping is order-insensitive. ''' if isinstance(other, OrderedDict): return len(self) == len(other) and self.items() == other.items() return dict.__eq__(self, other) def __ne__(self, other): return not self == other # -- the following methods are only used in Python 2.7 -- def viewkeys(self): "od.viewkeys() -> a set-like object providing a view on od's keys" return KeysView(self) def viewvalues(self): "od.viewvalues() -> an object providing a view on od's values" return ValuesView(self) def viewitems(self): "od.viewitems() -> a set-like object providing a view on od's items" return ItemsView(self) # end of http://code.activestate.com/recipes/576693/ }}}

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import scraperwiki import lxml.html html = scraperwiki.scrape("http://www.greatamericanbeerfestival.com/at-the-festival/breweries-at-the-2012-festival") root = lxml.html.fromstring(html) i = 1 for tr in root.cssselect("#brewery_table tbody tr"): tds = tr.cssselect("td") data = { 'id' : i, 'name' : tds[0].text_content(), 'city' : tds[1].text_content(), 'state' : tds[2].text_content(), } scraperwiki.sqlite.save(unique_keys=['id'], data=data) i += 1import scraperwiki import lxml.html html = scraperwiki.scrape("http://www.greatamericanbeerfestival.com/at-the-festival/breweries-at-the-2012-festival") root = lxml.html.fromstring(html) i = 1 for tr in root.cssselect("#brewery_table tbody tr"): tds = tr.cssselect("td") data = { 'id' : i, 'name' : tds[0].text_content(), 'city' : tds[1].text_content(), 'state' : tds[2].text_content(), } scraperwiki.sqlite.save(unique_keys=['id'], data=data) i += 1

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/Questao7.py

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marcelorvergara/AT_python

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import threading import requests class Questao7(threading.Thread): def __init__(self): super().__init__() url = 'https://sites.google.com/site/dr2fundamentospython/arquivos/Winter_Olympics_Medals.csv' requisicao = requests.get(url, timeout=5) if requisicao.status_code != 200: requisicao.raise_for_status() else: print("Conectado") csv = requisicao.text linhas = csv.splitlines() # SWE suecia = 0 sue_medalhas = [] # DEN dinamarca = 0 den_medalhas = [] # NOR nor_medalhas = [] noruega = 0 for ln in range(1, len(linhas)): colunas = linhas[ln].split(',') # somente séc. XXI if int(colunas[0]) > 2000: # somente modalidades 'curling', 'skating', 'skiing', 'ice hockey' if colunas[2] == 'Curling' or colunas[2] == 'Skating' or colunas[2] == 'Skiing' or colunas[2] == 'Ice Hockey': # se ouro if colunas[7] == 'Gold': gen = '' if colunas[6] == 'M': gen = 'masculino' else: gen = 'feminino' if colunas[4] == 'SWE': suecia += 1 sue_medalhas.append('Esporte: ' + colunas[2] + ' Ano: ' + colunas[0] + ' Cidade: ' + colunas[ 1] + ' Gênero: ' + gen) elif colunas[4] == 'DEN': dinamarca += 1 den_medalhas.append('Esporte: ' + colunas[2] + ' Ano: ' + colunas[0] + ' Cidade: ' + colunas[ 1] + ' Gênero: ' + gen) elif colunas[4] == 'NOR': noruega += 1 nor_medalhas.append('Esporte: ' + colunas[2] + ' Ano: ' + colunas[0] + ' Cidade: ' + colunas[ 1] + ' Gênero: ' + gen) maior = '' num_medalhas = 0 if suecia > dinamarca or suecia > noruega: maior = 'Suecia' num_medalhas = suecia if dinamarca > suecia or dinamarca > noruega: maior = 'Dinamarca' num_medalhas = dinamarca else: maior = 'Noruega' num_medalhas = noruega print('\nO país com o maior número de medalhas ouro nas modalidades especificadas é a', maior, 'com', num_medalhas, 'medalhas') print('\nRelatório dos países Suécia, Dinamarca e Noruega referente as medalhas ouro nos esportes Curling, Patinação no gelo, Esqui e Hóquei sobre o gelo no século XXI') print('\nSuécia:\n') if sue_medalhas: for ln in sue_medalhas: print(ln) else: print('Não obteve medalhas de ouro') print('\nDinamarca:\n') if den_medalhas: for ln in den_medalhas: print(ln) else: print('Não obteve medalhas de ouro') print('\nNoruega:\n') if nor_medalhas: for ln in nor_medalhas: print(ln) else: print('Não obteve medalhas de ouro')

[ "marcelorv@gmail.com" ]

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colincwilson/tensormorph

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#!/usr/bin/env python # -*- coding: utf-8 -*- import argparse, re, sys from tensormorphy import environ, evaluator, phon_features from tensormorphy.segment_embedder import SegmentEmbedder from tensormorphy.form_embedder import FormEmbedder from tensormorphy.dataset import DataSet from tensormorphy.affixer import Affixer from tensormorphy.trainer import Trainer from affix_test_cases import import_data import pandas as pd import numpy as np # parse commandline arguments argparser = argparse.ArgumentParser() argparser.add_argument('--nbatch',\ help='Number of <input,output> pairs in each batch') argparser.add_argument('--nepoch',\ help='Number of training epochs') args, residue = argparser.parse_known_args() # select dataset (xxx make commandline argument) data_select = ['english_ing', 'english_ness', 'english_un',\ 'english_shm', 'chamorro_um', 'hungarian_dat',\ 'hebrew_paal', 'hindi_nouns', 'maltese', 'conll'][4] data = import_data(data_select) data_set = DataSet( data['dat'], data['held_in_stems'], data['held_out_stems'], data['vowels'] ) feature_file = '~/Dropbox/TensorProductStringToStringMapping/00features/' +\ ['hayes_features.csv', 'panphon_ipa_bases.csv'][0] feature_matrix = phon_features.import_features(feature_file, data_set.segments) symbol_params = {'feature_matrix': feature_matrix, } role_params = {'nrole': data_set.max_len+4, } form_embedder = FormEmbedder(symbol_params, role_params) environ.init(form_embedder) # makes dummy morphosyn_embedder data_set.split_and_embed(test_size=0.25) model = Affixer() trainer = Trainer(model) environ.config.nepoch = 1500 trainer.train(data_set) train_pred, test_pred =\ evaluator.evaluate(model, data_set) sys.exit(0) # # # # # OLD CODE # # # # # seq_embedder, morph_embedder, train, test = import_data(data_select) tpr.init(seq_embedder, morph_embedder) print('filler dimensionality:', tpr.dfill) print('role dimensionality:', tpr.drole) print('distributed roles?', tpr.random_roles) print('train/test split:') print('\t', len(train), 'training examples') print('\t', len(test), 'testing examples') # run trainer tpr.save_dir = '/Users/colin/Desktop/tmorph_output' nbatch = min(40,len(train)) if args.nbatch is None else int(args.nbatch) nepoch = 1000 if args.nepoch is None else int(args.nepoch) trainer = trainer.Trainer( redup=False, lr=1.0e-1, dc=0.0, verbosity=1 ) affixer, decoder = trainer.train_and_test( train, test, nbatch=nbatch, max_epochs=nepoch ) if False: tpr.trace = True train = train.iloc[0:2].reset_index() test = test.iloc[0:2].reset_index() train.stem, train.output = u't r i s t i', u't r u m i s t i' trainer.train_and_test1(train, test, nbatch=len(train)) print(tpr.traces) for x in tpr.traces: f = '/Users/colin/Desktop/dump/'+ x +'.txt' y = tpr.traces[x] print(y.__class__.__name__) if type(y) is np.ndarray: np.savetxt(f, y, delimiter=',') else: print(x, y) if False: # test by hand trainer.affixer.morph_attender.tau.data[:] = 5.0 trainer.affixer.posn_attender.tau.data[:] = 5.0 Stems = string2tpr(u'q a f a ts').unsqueeze(0) Affix = string2tpr(u't i o ⋉', False).unsqueeze(0) copy = torch.ones(tpr.nrole).unsqueeze(0) pivot = torch.zeros(tpr.nrole).unsqueeze(0) unpivot = torch.zeros(tpr.nrole).unsqueeze(0) copy[0,2] = copy[0,4] = 0.0 pivot[0,0] = pivot[0,3] = 1.0 unpivot[0,1] = unpivot[0,2] = 1.0 test = {\ 'affix': Affix,\ 'copy': copy,\ 'pivot': pivot,\ 'unpivot': unpivot\ } output, traces = trainer.affixer(Stems, 10, True, test) stem = trainer.decoder.decode(Stems)[0] affix = trainer.decoder.decode(Affix)[0] stem = [x+' _' if pivot[0,i]==1.0 else x for i,x in enumerate(stem.split(' '))] stem = [x+'/' if copy[0,i]==0.0 else x for i,x in enumerate(stem)] affix = [x+' _' if i<25 and unpivot[0,i]==1.0 else x for i,x in enumerate(affix.split(' '))] stem = ' '.join(stem) affix = ' '.join(affix) output = ' '.join(trainer.decoder.decode(output)) print('stem:', stem) print('affix:', affix) print(' -> ') print('output: ', output) for trace in traces: print(trace, np.round(traces[trace], 2)) sys.exit(0)

[ "colin.chris.wilson@gmail.com" ]

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def solution(numbers): answer = 0 negative = [] positive = [] for i in numbers: if i < 0: negative.append(i) else: positive.append(i) negative.sort() positive.sort() max_positive, max_negative, mix = -1e9, -1e9, -1e9 if len(positive) == 1 and len(negative) == 1: mix = positive[-1] * negative[0] if len(positive) >= 2: max_positive = positive[-1] * positive[-2] if len(negative) >= 2: max_negative = negative[0] * negative[1] answer = max(max_positive, max_negative, mix) return answer

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# Generated by Django 2.2 on 2019-04-04 05:02 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('vacancy', '0002_auto_20190331_1938'), ] operations = [ migrations.AlterField( model_name='jobs', name='fooding', field=models.BooleanField(default=0), ), migrations.AlterField( model_name='jobs', name='insurance', field=models.BooleanField(default=0), ), migrations.AlterField( model_name='jobs', name='lodging', field=models.BooleanField(default=0), ), ]

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2023-06-03T06:08:06.984789

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#!/usr/bin/env python # coding: utf-8 # In[1]: #/this notebook will be mainly used for the Coursera Capstone project. import pandas as pd import numpy as np print("Hello Capstone Project Course!")

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kontinuity/papps

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from django.db import models from adserver.partner.settings import * from django.contrib.auth.models import User from django.db.models.signals import post_save class Partner(models.Model): company_name = models.CharField(max_length=255) company_type = models.PositiveIntegerField(choices=COMPANY_TYPE_CHOICES, default=COMPANY_TYPE_DEFAULT) company_type_other = models.CharField(max_length=255, blank=True, null=True) number_of_domains = models.PositiveIntegerField(blank=True, null=True) hosting_control_panel = models.PositiveIntegerField(choices=HOSTING_CONTROL_PANEL_CHOICES, default=HOSTING_CONTROL_PANEL_DEFAULT) hosting_control_panel_other = models.CharField(max_length=255, blank=True, null=True) webmail = models.PositiveIntegerField(choices=WEBMAIL_CHOICES, default=WEBMAIL_DEFAULT) number_of_users = models.PositiveIntegerField(blank=True, null=True) user = models.OneToOneField(User) #def create_partner(sender, instance, created, **kwargs): # if created: # profile, created = Partner.objects.get_or_create(user=instance) # #post_save.connect(create_partner, sender=User)

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# pylint: disable=too-many-lines # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- import sys from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar import urllib.parse from azure.core.async_paging import AsyncItemPaged, AsyncList from azure.core.exceptions import ( ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, ResourceNotModifiedError, map_error, ) from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from azure.core.utils import case_insensitive_dict from azure.mgmt.core.exceptions import ARMErrorFormat from ... import models as _models from ..._vendor import _convert_request from ...operations._backup_protection_containers_operations import build_list_request from .._vendor import RecoveryServicesBackupClientMixinABC if sys.version_info >= (3, 8): from typing import Literal # pylint: disable=no-name-in-module, ungrouped-imports else: from typing_extensions import Literal # type: ignore # pylint: disable=ungrouped-imports T = TypeVar("T") ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class BackupProtectionContainersOperations: """ .. warning:: **DO NOT** instantiate this class directly. Instead, you should access the following operations through :class:`~azure.mgmt.recoveryservicesbackup.activestamp.aio.RecoveryServicesBackupClient`'s :attr:`backup_protection_containers` attribute. """ models = _models def __init__(self, *args, **kwargs) -> None: input_args = list(args) self._client = input_args.pop(0) if input_args else kwargs.pop("client") self._config = input_args.pop(0) if input_args else kwargs.pop("config") self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") @distributed_trace def list( self, vault_name: str, resource_group_name: str, filter: Optional[str] = None, **kwargs: Any ) -> AsyncIterable["_models.ProtectionContainerResource"]: """Lists the containers registered to Recovery Services Vault. :param vault_name: The name of the recovery services vault. Required. :type vault_name: str :param resource_group_name: The name of the resource group where the recovery services vault is present. Required. :type resource_group_name: str :param filter: OData filter options. Default value is None. :type filter: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either ProtectionContainerResource or the result of cls(response) :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.recoveryservicesbackup.activestamp.models.ProtectionContainerResource] :raises ~azure.core.exceptions.HttpResponseError: """ _headers = kwargs.pop("headers", {}) or {} _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) api_version: Literal["2023-01-01"] = kwargs.pop( "api_version", _params.pop("api-version", self._config.api_version) ) cls: ClsType[_models.ProtectionContainerResourceList] = kwargs.pop("cls", None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, 304: ResourceNotModifiedError, } error_map.update(kwargs.pop("error_map", {}) or {}) def prepare_request(next_link=None): if not next_link: request = build_list_request( vault_name=vault_name, resource_group_name=resource_group_name, subscription_id=self._config.subscription_id, filter=filter, api_version=api_version, template_url=self.list.metadata["url"], headers=_headers, params=_params, ) request = _convert_request(request) request.url = self._client.format_url(request.url) else: # make call to next link with the client's api-version _parsed_next_link = urllib.parse.urlparse(next_link) _next_request_params = case_insensitive_dict( { key: [urllib.parse.quote(v) for v in value] for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() } ) _next_request_params["api-version"] = self._config.api_version request = HttpRequest( "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params ) request = _convert_request(request) request.url = self._client.format_url(request.url) request.method = "GET" return request async def extract_data(pipeline_response): deserialized = self._deserialize("ProtectionContainerResourceList", pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) # type: ignore return deserialized.next_link or None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged(get_next, extract_data) list.metadata = { "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.RecoveryServices/vaults/{vaultName}/backupProtectionContainers" }

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#reverse a string s = "" str = input("Enter the string to be reversed: "); for i in range (0, len(str)): s += str[len(str) - i-1] print(s)

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import requests from bs4 import BeautifulSoup import mysql.connector print('Zakhire kardan Brand-Name-Price az site DIGISTYLE dakhel database') print() print('INFORMATION YOUR DATABASE') u = input('Please enter your user of database : ') p = input('Please enter your pass of database : ') h = input('Please enter your host of database : ') cnx = mysql.connector.connect(user=u, password=p, host=h, database='DigiStyle') # print ('connected to db :)') cursor = cnx.cursor() newBrand = list() newName = list() newPrice = list() for i in range(1, 63): #WARNING FOR 2 OR 63 r = requests.get('https://www.digistyle.com/category-men-tee-shirts-and-polos/?pageno=%s&sortby=4' %i) soup = BeautifulSoup(r.text, 'html.parser') brand = soup.find_all('span', attrs={'class': 'c-product-item__brand'}) name = soup.find_all('span', attrs={'class': 'c-product-item__name'}) price = soup.find_all('span', attrs={'class': 'c-product-item__price-value'}) for i in range(0, 36): b = brand[i] b = str(b) b = b[36:-7] n = name[i] n = str(n) n = n[35:-7] p = price[i] p = str(p) p = p[42:-7] sql = 'INSERT INTO Digistyle (brand_of_product, name_of_product, price_of_product) VALUES (%s, %s, %s)' val = (b, n, p) cursor.execute(sql, val) cnx.commit() cnx.close()

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2021-08-10T15:13:02

2021-08-10T15:13:00

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import tensorflow as tf import numpy as np import os import pickle from .base_model_objdet import BaseModel from ..modules.losses.smooth_L1 import SmoothL1Loss from ..modules.losses.focal_loss import FocalLoss from ..modules.losses.cross_entropy import CrossEntropyLoss from ..modules.pointnet import Pointnet2MSG, PointnetSAModule from ..utils.objdet_helper import xywhr_to_xyxyr from open3d.ml.tf.ops import nms from ..utils.tf_utils import gen_CNN from ...datasets.utils import BEVBox3D, DataProcessing, ObjdetAugmentation from ...datasets.utils.operations import filter_by_min_points, points_in_box from ...utils import MODEL from ..modules.schedulers import OneCycleScheduler from ..utils.roipool3d import roipool3d_utils from ...metrics import iou_3d class PointRCNN(BaseModel): """Object detection model. Based on the PoinRCNN architecture https://github.com/sshaoshuai/PointRCNN. The network is not trainable end-to-end, it requires pre-training of the RPN module, followed by training of the RCNN module. For this the mode must be set to 'RPN', with this, the network only outputs intermediate results. If the RPN module is trained, the mode can be set to 'RCNN' (default), with this, the second module can be trained and the output are the final predictions. For inference use the 'RCNN' mode. Args: name (string): Name of model. Default to "PointRCNN". device (string): 'cuda' or 'cpu'. Default to 'cuda'. classes (string[]): List of classes used for object detection: Default to ['Car']. score_thres (float): Min confindence score for prediction. Default to 0.3. npoints (int): Number of processed input points. Default to 16384. rpn (dict): Config of RPN module. Default to {}. rcnn (dict): Config of RCNN module. Default to {}. mode (string): Execution mode, 'RPN' or 'RCNN'. Default to 'RCNN'. """ def __init__(self, name="PointRCNN", classes=['Car'], score_thres=0.3, npoints=16384, rpn={}, rcnn={}, mode="RCNN", **kwargs): super().__init__(name=name, **kwargs) assert mode == "RPN" or mode == "RCNN" self.mode = mode self.npoints = npoints self.classes = classes self.name2lbl = {n: i for i, n in enumerate(classes)} self.lbl2name = {i: n for i, n in enumerate(classes)} self.score_thres = score_thres self.rpn = RPN(**rpn) self.rcnn = RCNN(num_classes=len(self.classes), **rcnn) if self.mode == "RCNN": self.rpn.trainable = False else: self.rcnn.trainable = False def call(self, inputs, training=True): cls_score, reg_score, backbone_xyz, backbone_features = self.rpn( inputs[0], training=self.mode == "RPN" and training) if self.mode != "RPN": cls_score = tf.stop_gradient(cls_score) reg_score = tf.stop_gradient(reg_score) backbone_xyz = tf.stop_gradient(backbone_xyz) backbone_features = tf.stop_gradient(backbone_features) rpn_scores_raw = tf.stop_gradient(cls_score[:, :, 0]) rois, _ = self.rpn.proposal_layer(rpn_scores_raw, reg_score, backbone_xyz, training=training) # (B, M, 7) rois = tf.stop_gradient(rois) output = {"rois": rois, "cls": cls_score, "reg": reg_score} if self.mode == "RCNN": rpn_scores_norm = tf.sigmoid(rpn_scores_raw) seg_mask = tf.cast((rpn_scores_norm > self.score_thres), tf.float32) pts_depth = tf.norm(backbone_xyz, ord=2, axis=2) seg_mask = tf.stop_gradient(seg_mask) pts_depth = tf.stop_gradient(pts_depth) gt_boxes = None if training or self.mode == "RPN": gt_boxes = inputs[1] output = self.rcnn(rois, gt_boxes, backbone_xyz, tf.transpose(backbone_features, (0, 2, 1)), seg_mask, pts_depth, training=training) return output def get_optimizer(self, cfg): beta1, beta2 = cfg.get('betas', [0.9, 0.99]) lr_scheduler = OneCycleScheduler(40800, cfg.lr, cfg.div_factor) optimizer = tf.optimizers.Adam(learning_rate=lr_scheduler, beta_1=beta1, beta_2=beta2) return optimizer def load_gt_database(self, pickle_path, min_points_dict, sample_dict): """Load ground truth object database. Args: pickle_path: Path of pickle file generated using `scripts/collect_bbox.py`. min_points_dict: A dictionary to filter objects based on number of points inside. sample_dict: A dictionary to decide number of objects to sample. """ db_boxes = pickle.load(open(pickle_path, 'rb')) if min_points_dict is not None: db_boxes = filter_by_min_points(db_boxes, min_points_dict) db_boxes_dict = {} for key in sample_dict.keys(): db_boxes_dict[key] = [] for db_box in db_boxes: if db_box.label_class in sample_dict.keys(): db_boxes_dict[db_box.label_class].append(db_box) self.db_boxes_dict = db_boxes_dict def augment_data(self, data, attr): """Augment object detection data. Available augmentations are: `ObjectSample`: Insert objects from ground truth database. `ObjectRangeFilter`: Filter pointcloud from given bounds. `PointShuffle`: Shuffle the pointcloud. Args: data: A dictionary object returned from the dataset class. attr: Attributes for current pointcloud. Returns: Augmented `data` dictionary. """ cfg = self.cfg.augment if 'ObjectSample' in cfg.keys(): if not hasattr(self, 'db_boxes_dict'): data_path = attr['path'] # remove tail of path to get root data path for _ in range(3): data_path = os.path.split(data_path)[0] pickle_path = os.path.join(data_path, 'bboxes.pkl') self.load_gt_database(pickle_path, **cfg['ObjectSample']) data = ObjdetAugmentation.ObjectSample( data, db_boxes_dict=self.db_boxes_dict, sample_dict=cfg['ObjectSample']['sample_dict']) if cfg.get('ObjectRangeFilter', False): data = ObjdetAugmentation.ObjectRangeFilter( data, self.cfg.point_cloud_range) if cfg.get('PointShuffle', False): data = ObjdetAugmentation.PointShuffle(data) return data def loss(self, results, inputs, training=True): if self.mode == "RPN": return self.rpn.loss(results, inputs) else: if not training: return {"loss": tf.constant(0.0)} return self.rcnn.loss(results, inputs) def filter_objects(self, bbox_objs): """Filter objects based on classes to train. Args: bbox_objs: Bounding box objects from dataset class. Returns: Filtered bounding box objects. """ filtered = [] for bb in bbox_objs: if bb.label_class in self.classes: filtered.append(bb) return filtered def preprocess(self, data, attr): if attr['split'] in ['train', 'training']: data = self.augment_data(data, attr) data['bounding_boxes'] = self.filter_objects(data['bounding_boxes']) # remove intensity points = np.array(data['point'][..., :3], dtype=np.float32) calib = data['calib'] # transform in cam space points = DataProcessing.world2cam(points, calib['world_cam']) new_data = {'point': points, 'calib': calib} # bounding_boxes are objects of type BEVBox3D. It is renamed to # bbox_objs to clarify them as objects and not matrix of type [N, 7]. if attr['split'] not in ['test', 'testing']: new_data['bbox_objs'] = data['bounding_boxes'] return new_data @staticmethod def generate_rpn_training_labels(points, bboxes, bboxes_world, calib=None): """Generates labels for RPN network. Classifies each point as foreground/background based on points inside bbox. We don't train on ambigious points which are just outside bounding boxes(calculated by `extended_boxes`). Also computes regression labels for bounding box proposals(in bounding box frame). Args: points: Input pointcloud. bboxes: bounding boxes in camera frame. bboxes_world: bounding boxes in world frame. calib: Calibration file for cam_to_world matrix. Returns: Classification and Regression labels. """ cls_label = np.zeros((points.shape[0]), dtype=np.int32) reg_label = np.zeros((points.shape[0], 7), dtype=np.float32) # dx, dy, dz, ry, h, w, l if len(bboxes) == 0: return cls_label, reg_label pts_idx = points_in_box(points.copy(), bboxes_world, camera_frame=True, cam_world=DataProcessing.invT( calib['world_cam'])) # enlarge the bbox3d, ignore nearby points extended_boxes = bboxes_world.copy() # Enlarge box by 0.4m (from PointRCNN paper). extended_boxes[3:6] += 0.4 # Decrease z coordinate, as z_center is at bottom face of box. extended_boxes[:, 2] -= 0.2 pts_idx_ext = points_in_box(points.copy(), extended_boxes, camera_frame=True, cam_world=DataProcessing.invT( calib['world_cam'])) for k in range(bboxes.shape[0]): fg_pt_flag = pts_idx[:, k] fg_pts_rect = points[fg_pt_flag] cls_label[fg_pt_flag] = 1 fg_enlarge_flag = pts_idx_ext[:, k] ignore_flag = np.logical_xor(fg_pt_flag, fg_enlarge_flag) cls_label[ignore_flag] = -1 # pixel offset of object center center3d = bboxes[k][0:3].copy() # (x, y, z) center3d[1] -= bboxes[k][3] / 2 reg_label[fg_pt_flag, 0:3] = center3d - fg_pts_rect # size and angle encoding reg_label[fg_pt_flag, 3] = bboxes[k][3] # h reg_label[fg_pt_flag, 4] = bboxes[k][4] # w reg_label[fg_pt_flag, 5] = bboxes[k][5] # l reg_label[fg_pt_flag, 6] = bboxes[k][6] # ry return cls_label, reg_label def transform(self, data, attr): points = data['point'] if attr['split'] not in ['test', 'testing']: #, 'val', 'validation']: if self.npoints < len(points): pts_depth = points[:, 2] pts_near_flag = pts_depth < 40.0 far_idxs_choice = np.where(pts_near_flag == 0)[0] near_idxs = np.where(pts_near_flag == 1)[0] near_idxs_choice = np.random.choice(near_idxs, self.npoints - len(far_idxs_choice), replace=False) choice = np.concatenate((near_idxs_choice, far_idxs_choice), axis=0) \ if len(far_idxs_choice) > 0 else near_idxs_choice np.random.shuffle(choice) else: choice = np.arange(0, len(points), dtype=np.int32) if self.npoints > len(points): extra_choice = np.random.choice(choice, self.npoints - len(points), replace=False) choice = np.concatenate((choice, extra_choice), axis=0) np.random.shuffle(choice) points = points[choice, :] t_data = {'point': points, 'calib': data['calib']} if attr['split'] not in ['test', 'testing']: labels = [] bboxes = [] bboxes_world = [] if len(data['bbox_objs']) != 0: labels = np.stack([ self.name2lbl.get(bb.label_class, len(self.classes)) for bb in data['bbox_objs'] ]) bboxes = np.stack([bb.to_camera() for bb in data['bbox_objs'] ]) # Camera frame. bboxes_world = np.stack( [bb.to_xyzwhlr() for bb in data['bbox_objs']]) if self.mode == "RPN": labels, bboxes = PointRCNN.generate_rpn_training_labels( points, bboxes, bboxes_world, data['calib']) t_data['labels'] = np.array(labels) t_data['bbox_objs'] = data['bbox_objs'] # Objects of type BEVBox3D. if attr['split'] in ['train', 'training'] or self.mode == "RPN": t_data['bboxes'] = bboxes return t_data def inference_end(self, results, inputs): if self.mode == 'RPN': return [[]] roi_boxes3d = results['rois'] # (B, M, 7) batch_size = roi_boxes3d.shape[0] rcnn_cls = tf.reshape(results['cls'], (batch_size, -1, results['cls'].shape[1])) rcnn_reg = tf.reshape(results['reg'], (batch_size, -1, results['reg'].shape[1])) pred_boxes3d, rcnn_cls = self.rcnn.proposal_layer(rcnn_cls, rcnn_reg, roi_boxes3d, training=False) inference_result = [] for calib, bboxes, scores in zip(inputs[3], pred_boxes3d, rcnn_cls): # scoring if scores.shape[-1] == 1: scores = tf.sigmoid(scores) labels = tf.cast(scores < self.score_thres, tf.int64) else: labels = tf.argmax(scores) scores = tf.nn.softmax(scores, axis=0) scores = scores[labels] fltr = tf.reshape(scores > self.score_thres, (-1)) bboxes = bboxes[fltr] labels = labels[fltr] scores = scores[fltr] bboxes = bboxes.numpy() scores = scores.numpy() labels = labels.numpy() inference_result.append([]) world_cam, cam_img = calib.numpy() for bbox, score, label in zip(bboxes, scores, labels): pos = bbox[:3] dim = bbox[[4, 3, 5]] # transform into world space pos = DataProcessing.cam2world(pos.reshape((1, -1)), world_cam).flatten() pos = pos + [0, 0, dim[1] / 2] yaw = bbox[-1] name = self.lbl2name.get(label[0], "ignore") inference_result[-1].append( BEVBox3D(pos, dim, yaw, name, score, world_cam, cam_img)) return inference_result def get_batch_gen(self, dataset, steps_per_epoch=None, batch_size=1): def batcher(): count = len(dataset) if steps_per_epoch is None else steps_per_epoch for i in np.arange(0, count, batch_size): batch = [dataset[i + bi]['data'] for bi in range(batch_size)] points = tf.stack([b['point'] for b in batch], axis=0) bboxes = [ b.get('bboxes', tf.zeros((0, 7), dtype=tf.float32)) for b in batch ] max_gt = 0 for bbox in bboxes: max_gt = max(max_gt, bbox.shape[0]) pad_bboxes = np.zeros((len(bboxes), max_gt, 7), dtype=np.float32) for j in range(len(bboxes)): pad_bboxes[j, :bboxes[j].shape[0], :] = bboxes[j] bboxes = tf.constant(pad_bboxes) labels = [ b.get('labels', tf.zeros((0,), dtype=tf.int32)) for b in batch ] max_lab = 0 for lab in labels: max_lab = max(max_lab, lab.shape[0]) if 'labels' in batch[ 0] and labels[0].shape[0] != points.shape[1]: pad_labels = np.ones( (len(labels), max_lab), dtype=np.int32) * (-1) for j in range(len(labels)): pad_labels[j, :labels[j].shape[0]] = labels[j] labels = tf.constant(pad_labels) else: labels = tf.stack(labels, axis=0) calib = [ tf.constant([ b.get('calib', {}).get('world_cam', np.eye(4)), b.get('calib', {}).get('cam_img', np.eye(4)) ]) for b in batch ] yield (points, bboxes, labels, calib) gen_func = batcher gen_types = (tf.float32, tf.float32, tf.int32, tf.float32) gen_shapes = ([batch_size, None, 3], [batch_size, None, 7], [batch_size, None], [batch_size, 2, 4, 4]) return gen_func, gen_types, gen_shapes MODEL._register_module(PointRCNN, 'tf') def get_reg_loss(pred_reg, reg_label, loc_scope, loc_bin_size, num_head_bin, anchor_size, get_xz_fine=True, get_y_by_bin=False, loc_y_scope=0.5, loc_y_bin_size=0.25, get_ry_fine=False): """Bin-based 3D bounding boxes regression loss. See https://arxiv.org/abs/1812.04244 for more details. Args: pred_reg: (N, C) reg_label: (N, 7) [dx, dy, dz, h, w, l, ry] loc_scope: Constant loc_bin_size: Constant num_head_bin: Constant anchor_size: (N, 3) or (3) get_xz_fine: Whether to get fine xz loss. get_y_by_bin: Whether to divide y coordinate into bin. loc_y_scope: Scope length for y coordinate. loc_y_bin_size: Bin size for classifying y coordinate. get_ry_fine: Whether to use fine yaw loss. """ per_loc_bin_num = int(loc_scope / loc_bin_size) * 2 loc_y_bin_num = int(loc_y_scope / loc_y_bin_size) * 2 reg_loss_dict = {} loc_loss = 0 # xz localization loss x_offset_label, y_offset_label, z_offset_label = reg_label[:, 0], reg_label[:, 1], reg_label[:, 2] x_shift = tf.clip_by_value(x_offset_label + loc_scope, 0, loc_scope * 2 - 1e-3) z_shift = tf.clip_by_value(z_offset_label + loc_scope, 0, loc_scope * 2 - 1e-3) x_bin_label = tf.cast(tf.floor(x_shift / loc_bin_size), tf.int64) z_bin_label = tf.cast(tf.floor(z_shift / loc_bin_size), tf.int64) x_bin_l, x_bin_r = 0, per_loc_bin_num z_bin_l, z_bin_r = per_loc_bin_num, per_loc_bin_num * 2 start_offset = z_bin_r loss_x_bin = CrossEntropyLoss()(pred_reg[:, x_bin_l:x_bin_r], x_bin_label) loss_z_bin = CrossEntropyLoss()(pred_reg[:, z_bin_l:z_bin_r], z_bin_label) reg_loss_dict['loss_x_bin'] = loss_x_bin.numpy() reg_loss_dict['loss_z_bin'] = loss_z_bin.numpy() loc_loss += loss_x_bin + loss_z_bin if get_xz_fine: x_res_l, x_res_r = per_loc_bin_num * 2, per_loc_bin_num * 3 z_res_l, z_res_r = per_loc_bin_num * 3, per_loc_bin_num * 4 start_offset = z_res_r x_res_label = x_shift - ( tf.cast(x_bin_label, tf.float32) * loc_bin_size + loc_bin_size / 2) z_res_label = z_shift - ( tf.cast(z_bin_label, tf.float32) * loc_bin_size + loc_bin_size / 2) x_res_norm_label = x_res_label / loc_bin_size z_res_norm_label = z_res_label / loc_bin_size x_bin_onehot = tf.one_hot(x_bin_label, per_loc_bin_num) z_bin_onehot = tf.one_hot(z_bin_label, per_loc_bin_num) loss_x_res = SmoothL1Loss()(tf.reduce_sum(pred_reg[:, x_res_l:x_res_r] * x_bin_onehot, axis=1), x_res_norm_label) loss_z_res = SmoothL1Loss()(tf.reduce_sum(pred_reg[:, z_res_l:z_res_r] * z_bin_onehot, axis=1), z_res_norm_label) reg_loss_dict['loss_x_res'] = loss_x_res.numpy() reg_loss_dict['loss_z_res'] = loss_z_res.numpy() loc_loss += loss_x_res + loss_z_res # y localization loss if get_y_by_bin: y_bin_l, y_bin_r = start_offset, start_offset + loc_y_bin_num y_res_l, y_res_r = y_bin_r, y_bin_r + loc_y_bin_num start_offset = y_res_r y_shift = tf.clip_by_value(y_offset_label + loc_y_scope, 0, loc_y_scope * 2 - 1e-3) y_bin_label = tf.cast(tf.floor(y_shift / loc_y_bin_size), tf.int64) y_res_label = y_shift - (tf.cast(y_bin_label, tf.float32) * loc_y_bin_size + loc_y_bin_size / 2) y_res_norm_label = y_res_label / loc_y_bin_size y_bin_onehot = tf.one_hot(y_bin_label, loc_y_bin_num) loss_y_bin = CrossEntropyLoss()(pred_reg[:, y_bin_l:y_bin_r], y_bin_label) loss_y_res = SmoothL1Loss()(tf.reduce_sum(pred_reg[:, y_res_l:y_res_r] * y_bin_onehot, axis=1), y_res_norm_label) reg_loss_dict['loss_y_bin'] = loss_y_bin.numpy() reg_loss_dict['loss_y_res'] = loss_y_res.numpy() loc_loss += loss_y_bin + loss_y_res else: y_offset_l, y_offset_r = start_offset, start_offset + 1 start_offset = y_offset_r loss_y_offset = SmoothL1Loss()(tf.reduce_sum( pred_reg[:, y_offset_l:y_offset_r], axis=1), y_offset_label) reg_loss_dict['loss_y_offset'] = loss_y_offset.numpy() loc_loss += loss_y_offset # angle loss ry_bin_l, ry_bin_r = start_offset, start_offset + num_head_bin ry_res_l, ry_res_r = ry_bin_r, ry_bin_r + num_head_bin ry_label = reg_label[:, 6] if get_ry_fine: # divide pi/2 into several bins angle_per_class = (np.pi / 2) / num_head_bin ry_label = ry_label % (2 * np.pi) # 0 ~ 2pi ry_label = tf.where((ry_label > np.pi * 0.5) & (ry_label < np.pi * 1.5), (ry_label + np.pi) % (2 * np.pi), ry_label) # (0 ~ pi/2, 3pi/2 ~ 2pi) shift_angle = (ry_label + np.pi * 0.5) % (2 * np.pi) # (0 ~ pi) shift_angle = tf.clip_by_value(shift_angle - np.pi * 0.25, 1e-3, np.pi * 0.5 - 1e-3) # (0, pi/2) # bin center is (5, 10, 15, ..., 85) ry_bin_label = tf.cast(tf.floor(shift_angle / angle_per_class), tf.int64) ry_res_label = shift_angle - (tf.cast(ry_bin_label, tf.float32) * angle_per_class + angle_per_class / 2) ry_res_norm_label = ry_res_label / (angle_per_class / 2) else: # divide 2pi into several bins angle_per_class = (2 * np.pi) / num_head_bin heading_angle = ry_label % (2 * np.pi) # 0 ~ 2pi shift_angle = (heading_angle + angle_per_class / 2) % (2 * np.pi) ry_bin_label = tf.cast(tf.floor(shift_angle / angle_per_class), tf.int64) ry_res_label = shift_angle - (tf.cast(ry_bin_label, tf.float32) * angle_per_class + angle_per_class / 2) ry_res_norm_label = ry_res_label / (angle_per_class / 2) ry_bin_onehot = tf.one_hot(ry_bin_label, num_head_bin) loss_ry_bin = CrossEntropyLoss()(pred_reg[:, ry_bin_l:ry_bin_r], ry_bin_label) loss_ry_res = SmoothL1Loss()(tf.reduce_sum(pred_reg[:, ry_res_l:ry_res_r] * ry_bin_onehot, axis=1), ry_res_norm_label) reg_loss_dict['loss_ry_bin'] = loss_ry_bin.numpy() reg_loss_dict['loss_ry_res'] = loss_ry_res.numpy() angle_loss = loss_ry_bin + loss_ry_res # size loss size_res_l, size_res_r = ry_res_r, ry_res_r + 3 assert pred_reg.shape[1] == size_res_r, '%d vs %d' % (pred_reg.shape[1], size_res_r) size_res_norm_label = (reg_label[:, 3:6] - anchor_size) / anchor_size size_res_norm = pred_reg[:, size_res_l:size_res_r] size_loss = SmoothL1Loss()(size_res_norm, size_res_norm_label) # Total regression loss reg_loss_dict['loss_loc'] = loc_loss reg_loss_dict['loss_angle'] = angle_loss reg_loss_dict['loss_size'] = size_loss return loc_loss, angle_loss, size_loss, reg_loss_dict class RPN(tf.keras.layers.Layer): def __init__(self, backbone={}, cls_in_ch=128, cls_out_ch=[128], reg_in_ch=128, reg_out_ch=[128], db_ratio=0.5, head={}, focal_loss={}, loss_weight=[1.0, 1.0], **kwargs): super().__init__() # backbone self.backbone = Pointnet2MSG(**backbone) self.proposal_layer = ProposalLayer(**head) # classification branch layers = [] for i in range(len(cls_out_ch)): layers.extend([ tf.keras.layers.Conv1D(cls_out_ch[i], 1, use_bias=False, data_format="channels_first"), tf.keras.layers.BatchNormalization(axis=1, momentum=0.9, epsilon=1e-05), tf.keras.layers.ReLU(), tf.keras.layers.Dropout(db_ratio) ]) layers.append( tf.keras.layers.Conv1D( 1, 1, use_bias=True, bias_initializer=tf.keras.initializers.Constant(-np.log( (1 - 0.01) / 0.01)), data_format="channels_first")) self.cls_blocks = tf.keras.Sequential(layers) # regression branch per_loc_bin_num = int(self.proposal_layer.loc_scope / self.proposal_layer.loc_bin_size) * 2 if self.proposal_layer.loc_xz_fine: reg_channel = per_loc_bin_num * 4 + self.proposal_layer.num_head_bin * 2 + 3 else: reg_channel = per_loc_bin_num * 2 + self.proposal_layer.num_head_bin * 2 + 3 reg_channel = reg_channel + 1 # reg y layers = [] for i in range(len(reg_out_ch)): layers.extend([ tf.keras.layers.Conv1D(reg_out_ch[i], 1, use_bias=False, data_format="channels_first"), tf.keras.layers.BatchNormalization(axis=1, momentum=0.9, epsilon=1e-05), tf.keras.layers.ReLU(), tf.keras.layers.Dropout(db_ratio) ]) layers.append( tf.keras.layers.Conv1D( reg_channel, 1, use_bias=True, kernel_initializer=tf.keras.initializers.RandomNormal( stddev=0.001), data_format="channels_first")) self.reg_blocks = tf.keras.Sequential(layers) self.loss_cls = FocalLoss(**focal_loss) self.loss_weight = loss_weight def call(self, x, training=True): backbone_xyz, backbone_features = self.backbone( x, training=training) # (B, N, 3), (B, C, N) rpn_cls = tf.transpose( self.cls_blocks(backbone_features, training=training), (0, 2, 1)) # (B, N, 1) rpn_reg = tf.transpose( self.reg_blocks(backbone_features, training=training), (0, 2, 1)) # (B, N, C) return rpn_cls, rpn_reg, backbone_xyz, backbone_features def loss(self, results, inputs): rpn_cls = results['cls'] rpn_reg = results['reg'] rpn_reg_label = inputs[1] rpn_cls_label = inputs[2] rpn_cls_label_flat = tf.reshape(rpn_cls_label, (-1)) rpn_cls_flat = tf.reshape(rpn_cls, (-1)) fg_mask = (rpn_cls_label_flat > 0) # focal loss rpn_cls_target = tf.cast((rpn_cls_label_flat > 0), tf.int32) pos = tf.cast((rpn_cls_label_flat > 0), tf.float32) neg = tf.cast((rpn_cls_label_flat == 0), tf.float32) cls_weights = pos + neg pos_normalizer = tf.reduce_sum(pos) cls_weights = cls_weights / tf.maximum(pos_normalizer, 1.0) rpn_loss_cls = self.loss_cls(rpn_cls_flat, rpn_cls_target, cls_weights, avg_factor=1.0) # RPN regression loss point_num = rpn_reg.shape[0] * rpn_reg.shape[1] fg_sum = tf.reduce_sum(tf.cast(fg_mask, tf.int64)).numpy() if fg_sum != 0: loss_loc, loss_angle, loss_size, reg_loss_dict = \ get_reg_loss(tf.reshape(rpn_reg, (point_num, -1))[fg_mask], tf.reshape(rpn_reg_label, (point_num, 7))[fg_mask], loc_scope=self.proposal_layer.loc_scope, loc_bin_size=self.proposal_layer.loc_bin_size, num_head_bin=self.proposal_layer.num_head_bin, anchor_size=self.proposal_layer.mean_size, get_xz_fine=self.proposal_layer.loc_xz_fine, get_y_by_bin=False, get_ry_fine=False) loss_size = 3 * loss_size rpn_loss_reg = loss_loc + loss_angle + loss_size else: rpn_loss_reg = tf.reduce_mean(rpn_reg * 0) return { "cls": rpn_loss_cls * self.loss_weight[0], "reg": rpn_loss_reg * self.loss_weight[1] } class RCNN(tf.keras.layers.Layer): def __init__( self, num_classes, in_channels=128, SA_config={ "npoints": [128, 32, -1], "radius": [0.2, 0.4, 100], "nsample": [64, 64, 64], "mlps": [[128, 128, 128], [128, 128, 256], [256, 256, 512]] }, cls_out_ch=[256, 256], reg_out_ch=[256, 256], db_ratio=0.5, use_xyz=True, xyz_up_layer=[128, 128], head={}, target_head={}, loss={}): super().__init__() self.rcnn_input_channel = 5 self.pool_extra_width = target_head.get("pool_extra_width", 1.0) self.num_points = target_head.get("num_points", 512) self.proposal_layer = ProposalLayer(**head) self.SA_modules = [] for i in range(len(SA_config["npoints"])): mlps = [in_channels] + SA_config["mlps"][i] npoint = SA_config["npoints"][ i] if SA_config["npoints"][i] != -1 else None self.SA_modules.append( PointnetSAModule(npoint=npoint, radius=SA_config["radius"][i], nsample=SA_config["nsample"][i], mlp=mlps, use_xyz=use_xyz, use_bias=True)) in_channels = mlps[-1] self.xyz_up_layer = gen_CNN([self.rcnn_input_channel] + xyz_up_layer, conv=tf.keras.layers.Conv2D) c_out = xyz_up_layer[-1] self.merge_down_layer = gen_CNN([c_out * 2, c_out], conv=tf.keras.layers.Conv2D) # classification layer cls_channel = 1 if num_classes == 2 else num_classes layers = [] for i in range(len(cls_out_ch)): layers.extend([ tf.keras.layers.Conv1D( cls_out_ch[i], 1, use_bias=True, data_format="channels_first", kernel_initializer=tf.keras.initializers.GlorotNormal(), bias_initializer=tf.keras.initializers.Constant(0.0)), tf.keras.layers.ReLU() ]) layers.append( tf.keras.layers.Conv1D( cls_channel, 1, use_bias=True, data_format="channels_first", kernel_initializer=tf.keras.initializers.GlorotNormal(), bias_initializer=tf.keras.initializers.Constant(0.0))) self.cls_blocks = tf.keras.Sequential(layers) self.loss_cls = tf.keras.losses.BinaryCrossentropy() # regression branch per_loc_bin_num = int(self.proposal_layer.loc_scope / self.proposal_layer.loc_bin_size) * 2 loc_y_bin_num = int(self.proposal_layer.loc_y_scope / self.proposal_layer.loc_y_bin_size) * 2 reg_channel = per_loc_bin_num * 4 + self.proposal_layer.num_head_bin * 2 + 3 reg_channel += (1 if not self.proposal_layer.get_y_by_bin else loc_y_bin_num * 2) layers = [] for i in range(len(reg_out_ch)): layers.extend([ tf.keras.layers.Conv1D( reg_out_ch[i], 1, use_bias=True, data_format="channels_first", kernel_initializer=tf.keras.initializers.GlorotNormal(), bias_initializer=tf.keras.initializers.Constant(0.0)), tf.keras.layers.ReLU() ]) layers.append( tf.keras.layers.Conv1D( reg_channel, 1, use_bias=True, data_format="channels_first", kernel_initializer=tf.keras.initializers.RandomNormal( stddev=0.001), bias_initializer=tf.keras.initializers.Constant(0.0))) self.reg_blocks = tf.keras.Sequential(layers) self.proposal_target_layer = ProposalTargetLayer(**target_head) def _break_up_pc(self, pc): xyz = pc[..., 0:3] features = (tf.transpose(pc[..., 3:], (0, 2, 1)) if pc.shape[-1] > 3 else None) return xyz, features def call(self, roi_boxes3d, gt_boxes3d, rpn_xyz, rpn_features, seg_mask, pts_depth, training=True): pts_extra_input_list = [tf.expand_dims(seg_mask, axis=2)] pts_extra_input_list.append( tf.expand_dims(pts_depth / 70.0 - 0.5, axis=2)) pts_extra_input = tf.concat(pts_extra_input_list, axis=2) pts_feature = tf.concat((pts_extra_input, rpn_features), axis=2) if gt_boxes3d is not None: target = self.proposal_target_layer( [roi_boxes3d, gt_boxes3d, rpn_xyz, pts_feature]) for k in target: target[k] = tf.stop_gradient(target[k]) pts_input = tf.concat( (target['sampled_pts'], target['pts_feature']), axis=2) target['pts_input'] = pts_input else: pooled_features, pooled_empty_flag = roipool3d_utils.roipool3d_gpu( rpn_xyz, pts_feature, roi_boxes3d, self.pool_extra_width, sampled_pt_num=self.num_points) # canonical transformation batch_size = roi_boxes3d.shape[0] roi_center = roi_boxes3d[:, :, 0:3] poss = [] for k in range(batch_size): pos = pooled_features[k, :, :, :3] - tf.expand_dims( roi_center[k], axis=1) pos = rotate_pc_along_y_tf(pos, roi_boxes3d[k, :, 6]) poss.append(pos) pooled_features = tf.concat( [tf.stack(poss), pooled_features[:, :, :, 3:]], axis=3) pts_input = tf.reshape( pooled_features, (-1, pooled_features.shape[2], pooled_features.shape[3])) xyz, features = self._break_up_pc(pts_input) xyz_input = tf.expand_dims(tf.transpose( pts_input[..., 0:self.rcnn_input_channel], (0, 2, 1)), axis=3) xyz_feature = self.xyz_up_layer(xyz_input, training=training) rpn_feature = tf.expand_dims(tf.transpose( pts_input[..., self.rcnn_input_channel:], (0, 2, 1)), axis=3) merged_feature = tf.concat((xyz_feature, rpn_feature), axis=1) merged_feature = self.merge_down_layer(merged_feature, training=training) l_xyz, l_features = [xyz], [tf.squeeze(merged_feature, axis=3)] for i in range(len(self.SA_modules)): li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i], training=training) l_xyz.append(li_xyz) l_features.append(li_features) rcnn_cls = tf.squeeze(tf.transpose( self.cls_blocks(l_features[-1], training=training), (0, 2, 1)), axis=1) # (B, 1 or 2) rcnn_reg = tf.squeeze(tf.transpose( self.reg_blocks(l_features[-1], training=training), (0, 2, 1)), axis=1) # (B, C) ret_dict = {'rois': roi_boxes3d, 'cls': rcnn_cls, 'reg': rcnn_reg} if gt_boxes3d is not None: ret_dict.update(target) return ret_dict def loss(self, results, inputs): rcnn_cls = results['cls'] rcnn_reg = results['reg'] cls_label = tf.cast(results['cls_label'], tf.float32) reg_valid_mask = results['reg_valid_mask'] gt_boxes3d_ct = results['gt_of_rois'] pts_input = results['pts_input'] cls_label_flat = tf.reshape(cls_label, (-1)) # binary cross entropy rcnn_cls_flat = tf.reshape(rcnn_cls, (-1)) batch_loss_cls = tf.keras.losses.BinaryCrossentropy(reduction="none")( tf.sigmoid(rcnn_cls_flat), cls_label) cls_valid_mask = tf.cast((cls_label_flat >= 0), tf.float32) rcnn_loss_cls = tf.reduce_sum( batch_loss_cls * cls_valid_mask) / tf.maximum( tf.reduce_sum(cls_valid_mask), 1.0) # rcnn regression loss batch_size = pts_input.shape[0] fg_mask = (reg_valid_mask > 0) fg_sum = tf.reduce_sum(tf.cast(fg_mask, tf.int64)).numpy() if fg_sum != 0: anchor_size = self.proposal_layer.mean_size loss_loc, loss_angle, loss_size, reg_loss_dict = \ get_reg_loss(tf.reshape(rcnn_reg, (batch_size, -1))[fg_mask], tf.reshape(gt_boxes3d_ct, (batch_size, 7))[fg_mask], loc_scope=self.proposal_layer.loc_scope, loc_bin_size=self.proposal_layer.loc_bin_size, num_head_bin=self.proposal_layer.num_head_bin, anchor_size=anchor_size, get_xz_fine=True, get_y_by_bin=self.proposal_layer.get_y_by_bin, loc_y_scope=self.proposal_layer.loc_y_scope, loc_y_bin_size=self.proposal_layer.loc_y_bin_size, get_ry_fine=True) loss_size = 3 * loss_size # consistent with old codes rcnn_loss_reg = loss_loc + loss_angle + loss_size else: # Regression loss is zero when no point is classified as foreground. rcnn_loss_reg = tf.reduce_mean(rcnn_reg * 0) return {"cls": rcnn_loss_cls, "reg": rcnn_loss_reg} def rotate_pc_along_y(pc, rot_angle): """ Args: pc: (N, 3+C), (N, 3) is in the rectified camera coordinate. rot_angle: rad scalar Returns: pc: updated pc with XYZ rotated. """ cosval = np.cos(rot_angle) sinval = np.sin(rot_angle) rotmat = np.array([[cosval, -sinval], [sinval, cosval]]) pc[:, [0, 2]] = np.dot(pc[:, [0, 2]], np.transpose(rotmat)) return pc class ProposalLayer(tf.keras.layers.Layer): def __init__(self, nms_pre=9000, nms_post=512, nms_thres=0.85, nms_post_val=None, nms_thres_val=None, mean_size=[1.0], loc_xz_fine=True, loc_scope=3.0, loc_bin_size=0.5, num_head_bin=12, get_y_by_bin=False, get_ry_fine=False, loc_y_scope=0.5, loc_y_bin_size=0.25, post_process=True): super().__init__() self.nms_pre = nms_pre self.nms_post = nms_post self.nms_thres = nms_thres self.nms_post_val = nms_post_val self.nms_thres_val = nms_thres_val self.mean_size = tf.constant(mean_size) self.loc_scope = loc_scope self.loc_bin_size = loc_bin_size self.num_head_bin = num_head_bin self.loc_xz_fine = loc_xz_fine self.get_y_by_bin = get_y_by_bin self.get_ry_fine = get_ry_fine self.loc_y_scope = loc_y_scope self.loc_y_bin_size = loc_y_bin_size self.post_process = post_process def call(self, rpn_scores, rpn_reg, xyz, training=True): batch_size = xyz.shape[0] proposals = decode_bbox_target( tf.reshape(xyz, (-1, xyz.shape[-1])), tf.reshape(rpn_reg, (-1, rpn_reg.shape[-1])), anchor_size=self.mean_size, loc_scope=self.loc_scope, loc_bin_size=self.loc_bin_size, num_head_bin=self.num_head_bin, get_xz_fine=self.loc_xz_fine, get_y_by_bin=self.get_y_by_bin, get_ry_fine=self.get_ry_fine, loc_y_scope=self.loc_y_scope, loc_y_bin_size=self.loc_y_bin_size) # (N, 7) proposals = tf.reshape(proposals, (batch_size, -1, 7)) nms_post = self.nms_post nms_thres = self.nms_thres if not training: if self.nms_post_val is not None: nms_post = self.nms_post_val if self.nms_thres_val is not None: nms_thres = self.nms_thres_val if self.post_process: proposals = tf.concat([ proposals[..., :1], proposals[..., 1:2] + proposals[..., 3:4] / 2, proposals[..., 2:] ], axis=-1) # set y as the center of bottom scores = rpn_scores sorted_idxs = tf.argsort(scores, axis=1, direction="DESCENDING") batch_size = scores.shape[0] ret_bbox3d = [] ret_scores = [] for k in range(batch_size): scores_single = scores[k] proposals_single = proposals[k] order_single = sorted_idxs[k] scores_single, proposals_single = self.distance_based_proposal( scores_single, proposals_single, order_single, training) proposals_tot = proposals_single.shape[0] ret_bbox3d.append( tf.concat([ proposals_single, tf.zeros((nms_post - proposals_tot, 7)) ], axis=0)) ret_scores.append( tf.concat( [scores_single, tf.zeros((nms_post - proposals_tot,))], axis=0)) ret_bbox3d = tf.stack(ret_bbox3d) ret_scores = tf.stack(ret_scores) else: batch_size = rpn_scores.shape[0] ret_bbox3d = [] ret_scores = [] for k in range(batch_size): bev = xywhr_to_xyxyr( tf.stack([proposals[k, :, i] for i in [0, 2, 3, 5, 6]], axis=-1)) keep_idx = nms(bev, rpn_scores[k, :, 0], nms_thres) ret_bbox3d.append(tf.gather(proposals[k], keep_idx)) ret_scores.append(tf.gather(rpn_scores[k], keep_idx)) return ret_bbox3d, ret_scores def distance_based_proposal(self, scores, proposals, order, training=True): """Propose ROIs in two area based on the distance. Args: scores: (N) proposals: (N, 7) order: (N) training (bool): Whether we are training? """ nms_post = self.nms_post nms_thres = self.nms_thres if not training: if self.nms_post_val is not None: nms_post = self.nms_post_val if self.nms_thres_val is not None: nms_thres = self.nms_thres_val nms_range_list = [0, 40.0, 80.0] pre_top_n_list = [ 0, int(self.nms_pre * 0.7), self.nms_pre - int(self.nms_pre * 0.7) ] post_top_n_list = [ 0, int(nms_post * 0.7), nms_post - int(nms_post * 0.7) ] scores_single_list, proposals_single_list = [], [] # sort by score scores_ordered = tf.gather(scores, order) proposals_ordered = tf.gather(proposals, order) dist = proposals_ordered[:, 2] first_mask = (dist > nms_range_list[0]) & (dist <= nms_range_list[1]) for i in range(1, len(nms_range_list)): # get proposal distance mask dist_mask = ((dist > nms_range_list[i - 1]) & (dist <= nms_range_list[i])) if tf.reduce_any(dist_mask): # this area has points # reduce by mask cur_scores = scores_ordered[dist_mask] cur_proposals = proposals_ordered[dist_mask] # fetch pre nms top K cur_scores = cur_scores[:pre_top_n_list[i]] cur_proposals = cur_proposals[:pre_top_n_list[i]] else: assert i == 2, '%d' % i # this area doesn't have any points, so use rois of first area cur_scores = scores_ordered[first_mask] cur_proposals = proposals_ordered[first_mask] # fetch top K of first area cur_scores = cur_scores[pre_top_n_list[i - 1]:][:pre_top_n_list[i]] cur_proposals = cur_proposals[ pre_top_n_list[i - 1]:][:pre_top_n_list[i]] # oriented nms bev = xywhr_to_xyxyr( tf.gather(cur_proposals, [0, 2, 3, 5, 6], axis=1)) keep_idx = nms(bev, cur_scores, nms_thres) # Fetch post nms top k keep_idx = keep_idx[:post_top_n_list[i]] scores_single_list.append(tf.gather(cur_scores, keep_idx)) proposals_single_list.append(tf.gather(cur_proposals, keep_idx)) scores_single = tf.concat(scores_single_list, axis=0) proposals_single = tf.concat(proposals_single_list, axis=0) return scores_single, proposals_single def decode_bbox_target(roi_box3d, pred_reg, loc_scope, loc_bin_size, num_head_bin, anchor_size, get_xz_fine=True, get_y_by_bin=False, loc_y_scope=0.5, loc_y_bin_size=0.25, get_ry_fine=False): """ Args: roi_box3d: (N, 7) pred_reg: (N, C) loc_scope: loc_bin_size: num_head_bin: anchor_size: get_xz_fine: get_y_by_bin: loc_y_scope: loc_y_bin_size: get_ry_fine: """ per_loc_bin_num = int(loc_scope / loc_bin_size) * 2 loc_y_bin_num = int(loc_y_scope / loc_y_bin_size) * 2 # recover xz localization x_bin_l, x_bin_r = 0, per_loc_bin_num z_bin_l, z_bin_r = per_loc_bin_num, per_loc_bin_num * 2 start_offset = z_bin_r x_bin = tf.argmax(pred_reg[:, x_bin_l:x_bin_r], axis=1) z_bin = tf.argmax(pred_reg[:, z_bin_l:z_bin_r], axis=1) pos_x = tf.cast(x_bin, tf.float32) * loc_bin_size + loc_bin_size / 2 - loc_scope pos_z = tf.cast(z_bin, tf.float32) * loc_bin_size + loc_bin_size / 2 - loc_scope if get_xz_fine: x_res_l, x_res_r = per_loc_bin_num * 2, per_loc_bin_num * 3 z_res_l, z_res_r = per_loc_bin_num * 3, per_loc_bin_num * 4 start_offset = z_res_r x_res_norm = tf.gather(pred_reg[:, x_res_l:x_res_r], x_bin, batch_dims=1) z_res_norm = tf.gather(pred_reg[:, z_res_l:z_res_r], z_bin, batch_dims=1) x_res = x_res_norm * loc_bin_size z_res = z_res_norm * loc_bin_size pos_x += x_res pos_z += z_res # recover y localization if get_y_by_bin: y_bin_l, y_bin_r = start_offset, start_offset + loc_y_bin_num y_res_l, y_res_r = y_bin_r, y_bin_r + loc_y_bin_num start_offset = y_res_r y_bin = tf.argmax(pred_reg[:, y_bin_l:y_bin_r], axis=1) y_res_norm = tf.gather(pred_reg[:, y_res_l:y_res_r], y_bin, batch_dims=1) y_res = y_res_norm * loc_y_bin_size pos_y = tf.cast( y_bin, tf.float32 ) * loc_y_bin_size + loc_y_bin_size / 2 - loc_y_scope + y_res pos_y = pos_y + roi_box3d[:, 1] else: y_offset_l, y_offset_r = start_offset, start_offset + 1 start_offset = y_offset_r pos_y = roi_box3d[:, 1] + pred_reg[:, y_offset_l] # recover ry rotation ry_bin_l, ry_bin_r = start_offset, start_offset + num_head_bin ry_res_l, ry_res_r = ry_bin_r, ry_bin_r + num_head_bin ry_bin = tf.argmax(pred_reg[:, ry_bin_l:ry_bin_r], axis=1) ry_res_norm = tf.gather(pred_reg[:, ry_res_l:ry_res_r], ry_bin, batch_dims=1) if get_ry_fine: # divide pi/2 into several bins angle_per_class = (np.pi / 2) / num_head_bin ry_res = ry_res_norm * (angle_per_class / 2) ry = (tf.cast(ry_bin, tf.float32) * angle_per_class + angle_per_class / 2) + ry_res - np.pi / 4 else: angle_per_class = (2 * np.pi) / num_head_bin ry_res = ry_res_norm * (angle_per_class / 2) # bin_center is (0, 30, 60, 90, 120, ..., 270, 300, 330) ry = (tf.cast(ry_bin, tf.float32) * angle_per_class + ry_res) % (2 * np.pi) ry = tf.where(ry > np.pi, ry - 2 * np.pi, ry) # recover size size_res_l, size_res_r = ry_res_r, ry_res_r + 3 assert size_res_r == pred_reg.shape[1] size_res_norm = pred_reg[:, size_res_l:size_res_r] hwl = size_res_norm * anchor_size + anchor_size # shift to original coords roi_center = roi_box3d[:, 0:3] shift_ret_box3d = tf.concat( (tf.reshape(pos_x, (-1, 1)), tf.reshape( pos_y, (-1, 1)), tf.reshape(pos_z, (-1, 1)), hwl, tf.reshape(ry, (-1, 1))), axis=1) ret_box3d = shift_ret_box3d if roi_box3d.shape[1] == 7: roi_ry = roi_box3d[:, 6:7] ret_box3d = rotate_pc_along_y_tf(shift_ret_box3d, -roi_ry) ret_box3d = tf.concat([ret_box3d[:, :6], ret_box3d[:, 6:7] + roi_ry], axis=1) ret_box3d = tf.concat([ ret_box3d[:, :1] + roi_center[:, :1], ret_box3d[:, 1:2], ret_box3d[:, 2:3] + roi_center[:, 2:3], ret_box3d[:, 3:] ], axis=1) return ret_box3d def rotate_pc_along_y_tf(pc, rot_angle): """ :param pc: (N, 3 + C) :param rot_angle: (N) :return: """ cosa = tf.reshape(tf.cos(rot_angle), (-1, 1)) # (N, 1) sina = tf.reshape(tf.sin(rot_angle), (-1, 1)) # (N, 1) raw_1 = tf.concat([cosa, -sina], axis=1) # (N, 2) raw_2 = tf.concat([sina, cosa], axis=1) # (N, 2) R = tf.concat( (tf.expand_dims(raw_1, axis=1), tf.expand_dims(raw_2, axis=1)), axis=1) # (N, 2, 2) pc_temp = tf.reshape(tf.stack([pc[..., 0], pc[..., 2]], axis=-1), ((pc.shape[0], -1, 2))) # (N, 512, 2) pc_temp = tf.matmul(pc_temp, tf.transpose(R, (0, 2, 1))) pc_temp = tf.reshape(pc_temp, (pc.shape[:-1] + (2,))) # (N, 512, 2) pc = tf.concat( [pc_temp[..., :1], pc[..., 1:2], pc_temp[..., 1:2], pc[..., 3:]], axis=-1) return pc class ProposalTargetLayer(tf.keras.layers.Layer): def __init__(self, pool_extra_width=1.0, num_points=512, reg_fg_thresh=0.55, cls_fg_thresh=0.6, cls_bg_thresh=0.45, cls_bg_thresh_lo=0.05, fg_ratio=0.5, roi_per_image=64, aug_rot_range=18, hard_bg_ratio=0.8, roi_fg_aug_times=10): super().__init__() self.pool_extra_width = pool_extra_width self.num_points = num_points self.reg_fg_thresh = reg_fg_thresh self.cls_fg_thresh = cls_fg_thresh self.cls_bg_thresh = cls_bg_thresh self.cls_bg_thresh_lo = cls_bg_thresh_lo self.fg_ratio = fg_ratio self.roi_per_image = roi_per_image self.aug_rot_range = aug_rot_range self.hard_bg_ratio = hard_bg_ratio self.roi_fg_aug_times = roi_fg_aug_times def call(self, x): roi_boxes3d, gt_boxes3d, rpn_xyz, pts_feature = x batch_rois, batch_gt_of_rois, batch_roi_iou = self.sample_rois_for_rcnn( roi_boxes3d, gt_boxes3d) # point cloud pooling pooled_features, pooled_empty_flag = \ roipool3d_utils.roipool3d_gpu(rpn_xyz, pts_feature, batch_rois, self.pool_extra_width, sampled_pt_num=self.num_points) sampled_pts, sampled_features = pooled_features[:, :, :, 0: 3], pooled_features[:, :, :, 3:] # data augmentation sampled_pts, batch_rois, batch_gt_of_rois = \ self.data_augmentation(sampled_pts, batch_rois, batch_gt_of_rois) # canonical transformation batch_size = batch_rois.shape[0] roi_ry = batch_rois[:, :, 6:7] % (2 * np.pi) roi_center = batch_rois[:, :, 0:3] sampled_pts = sampled_pts - tf.expand_dims(roi_center, axis=2) # (B, M, 512, 3) batch_gt_of_rois = tf.concat([ batch_gt_of_rois[:, :, :3] - roi_center, batch_gt_of_rois[:, :, 3:6], batch_gt_of_rois[:, :, 6:] - roi_ry ], axis=2) sampled_pts = tf.unstack(sampled_pts) batch_gt_of_rois = tf.unstack(batch_gt_of_rois) for k in range(batch_size): sampled_pts[k] = rotate_pc_along_y_tf(sampled_pts[k], batch_rois[k, :, 6]) batch_gt_of_rois[k] = tf.squeeze(rotate_pc_along_y_tf( tf.expand_dims(batch_gt_of_rois[k], axis=1), roi_ry[k]), axis=1) sampled_pts = tf.stack(sampled_pts) batch_gt_of_rois = tf.stack(batch_gt_of_rois) # regression valid mask valid_mask = (pooled_empty_flag == 0) reg_valid_mask = tf.cast( ((batch_roi_iou > self.reg_fg_thresh) & valid_mask), tf.int64) # classification label batch_cls_label = tf.cast((batch_roi_iou > self.cls_fg_thresh), tf.int64) invalid_mask = (batch_roi_iou > self.cls_bg_thresh) & ( batch_roi_iou < self.cls_fg_thresh) batch_cls_label = tf.where( tf.reduce_any([tf.logical_not(valid_mask), invalid_mask], axis=0), -1, batch_cls_label) output_dict = { 'sampled_pts': tf.reshape(sampled_pts, (-1, self.num_points, 3)), 'pts_feature': tf.reshape(sampled_features, (-1, self.num_points, sampled_features.shape[3])), 'cls_label': tf.reshape(batch_cls_label, (-1)), 'reg_valid_mask': tf.reshape(reg_valid_mask, (-1)), 'gt_of_rois': tf.reshape(batch_gt_of_rois, (-1, 7)), 'gt_iou': tf.reshape(batch_roi_iou, (-1)), 'roi_boxes3d': tf.reshape(batch_rois, (-1, 7)) } return output_dict def sample_rois_for_rcnn(self, roi_boxes3d, gt_boxes3d): """ Args: roi_boxes3d: (B, M, 7) gt_boxes3d: (B, N, 8) [x, y, z, h, w, l, ry, cls] Returns: batch_rois: (B, N, 7) batch_gt_of_rois: (B, N, 8) batch_roi_iou: (B, N) """ batch_size = roi_boxes3d.shape[0] fg_rois_per_image = int(np.round(self.fg_ratio * self.roi_per_image)) batch_rois, batch_gt_of_rois, batch_roi_iou = [], [], [] for idx in range(batch_size): cur_roi, cur_gt = roi_boxes3d[idx], gt_boxes3d[idx] k = cur_gt.__len__() - 1 while tf.reduce_sum(cur_gt[k]) == 0: k -= 1 cur_gt = cur_gt[:k + 1] # include gt boxes in the candidate rois iou3d = iou_3d(cur_roi.numpy()[:, [0, 1, 2, 5, 3, 4, 6]], cur_gt[:, 0:7].numpy()[:, [0, 1, 2, 5, 3, 4, 6]]) # (M, N) iou3d = tf.constant(iou3d) gt_assignment = tf.argmax(iou3d, axis=1) max_overlaps = tf.gather(iou3d, gt_assignment, batch_dims=1) # sample fg, easy_bg, hard_bg fg_thresh = min(self.reg_fg_thresh, self.cls_fg_thresh) fg_inds = tf.reshape(tf.where((max_overlaps >= fg_thresh)), (-1)) # TODO: this will mix the fg and bg when CLS_BG_THRESH_LO < iou < CLS_BG_THRESH # fg_inds = tf.concat((fg_inds, roi_assignment), axis=0) # consider the roi which has max_iou with gt as fg easy_bg_inds = tf.reshape( tf.where((max_overlaps < self.cls_bg_thresh_lo)), (-1)) hard_bg_inds = tf.reshape( tf.where((max_overlaps < self.cls_bg_thresh) & (max_overlaps >= self.cls_bg_thresh_lo)), (-1)) fg_num_rois = len(fg_inds.shape) bg_num_rois = len(hard_bg_inds.shape) + len(easy_bg_inds.shape) if fg_num_rois > 0 and bg_num_rois > 0: # sampling fg fg_rois_per_this_image = min(fg_rois_per_image, fg_num_rois) rand_num = tf.constant(np.random.permutation(fg_num_rois), dtype=tf.int64) fg_inds = tf.gather(fg_inds, rand_num[:fg_rois_per_this_image]) # sampling bg bg_rois_per_this_image = self.roi_per_image - fg_rois_per_this_image bg_inds = self.sample_bg_inds(hard_bg_inds, easy_bg_inds, bg_rois_per_this_image) elif fg_num_rois > 0 and bg_num_rois == 0: # sampling fg rand_num = np.floor( np.random.rand(self.roi_per_image) * fg_num_rois) rand_num = tf.constant(rand_num, dtype=tf.int64) fg_inds = fg_inds[rand_num] fg_rois_per_this_image = self.roi_per_image bg_rois_per_this_image = 0 elif bg_num_rois > 0 and fg_num_rois == 0: # sampling bg bg_rois_per_this_image = self.roi_per_image bg_inds = self.sample_bg_inds(hard_bg_inds, easy_bg_inds, bg_rois_per_this_image) fg_rois_per_this_image = 0 else: import pdb pdb.set_trace() raise NotImplementedError # augment the rois by noise roi_list, roi_iou_list, roi_gt_list = [], [], [] if fg_rois_per_this_image > 0: fg_rois_src = tf.gather(cur_roi, fg_inds) gt_of_fg_rois = tf.gather(cur_gt, tf.gather(gt_assignment, fg_inds)) iou3d_src = tf.gather(max_overlaps, fg_inds) fg_rois, fg_iou3d = self.aug_roi_by_noise_torch( fg_rois_src, gt_of_fg_rois, iou3d_src, aug_times=self.roi_fg_aug_times) roi_list.append(fg_rois) roi_iou_list.append(fg_iou3d) roi_gt_list.append(gt_of_fg_rois) if bg_rois_per_this_image > 0: bg_rois_src = tf.gather(cur_roi, bg_inds) gt_of_bg_rois = tf.gather(cur_gt, tf.gather(gt_assignment, bg_inds)) iou3d_src = tf.gather(max_overlaps, bg_inds) aug_times = 1 if self.roi_fg_aug_times > 0 else 0 bg_rois, bg_iou3d = self.aug_roi_by_noise_torch( bg_rois_src, gt_of_bg_rois, iou3d_src, aug_times=aug_times) roi_list.append(bg_rois) roi_iou_list.append(bg_iou3d) roi_gt_list.append(gt_of_bg_rois) rois = tf.concat(roi_list, axis=0) iou_of_rois = tf.concat(roi_iou_list, axis=0) gt_of_rois = tf.concat(roi_gt_list, axis=0) batch_rois.append(rois) batch_gt_of_rois.append(gt_of_rois) batch_roi_iou.append(iou_of_rois) return tf.stack(batch_rois), tf.stack(batch_gt_of_rois), tf.stack( batch_roi_iou) def sample_bg_inds(self, hard_bg_inds, easy_bg_inds, bg_rois_per_this_image): if len(hard_bg_inds.shape) > 0 and len(easy_bg_inds.shape) > 0: hard_bg_rois_num = int(bg_rois_per_this_image * self.hard_bg_ratio) easy_bg_rois_num = bg_rois_per_this_image - hard_bg_rois_num # sampling hard bg rand_idx = tf.constant(np.random.randint(low=0, high=len( hard_bg_inds.shape), size=(hard_bg_rois_num,)), dtype=tf.int64) hard_bg_inds = tf.gather(hard_bg_inds, rand_idx) # sampling easy bg rand_idx = tf.constant(np.random.randint(low=0, high=len( easy_bg_inds.shape), size=(easy_bg_rois_num,)), dtype=tf.int64) easy_bg_inds = tf.gather(easy_bg_inds, rand_idx) bg_inds = tf.concat([hard_bg_inds, easy_bg_inds], axis=0) elif len(hard_bg_inds.shape) > 0 and len(easy_bg_inds.shape) == 0: hard_bg_rois_num = bg_rois_per_this_image # sampling hard bg rand_idx = tf.constant(np.random.randint(low=0, high=len( hard_bg_inds.shape), size=(hard_bg_rois_num,)), dtype=tf.int64) bg_inds = tf.gather(hard_bg_inds, rand_idx) elif len(hard_bg_inds.shape) == 0 and len(easy_bg_inds.shape) > 0: easy_bg_rois_num = bg_rois_per_this_image # sampling easy bg rand_idx = tf.constant(np.random.randint(low=0, high=len( easy_bg_inds.shape), size=(easy_bg_rois_num,)), dtype=tf.int64) bg_inds = tf.gather(easy_bg_inds, rand_idx) else: raise NotImplementedError return bg_inds def aug_roi_by_noise_torch(self, roi_boxes3d, gt_boxes3d, iou3d_src, aug_times=10): pos_thresh = min(self.reg_fg_thresh, self.cls_fg_thresh) aug_boxes = [] iou_of_rois = [] for k in range(roi_boxes3d.shape[0]): temp_iou = cnt = 0 roi_box3d = roi_boxes3d[k] gt_box3d = tf.reshape(gt_boxes3d[k], (1, 7)) aug_box3d = roi_box3d keep = True while temp_iou < pos_thresh and cnt < aug_times: if np.random.rand() < 0.2: aug_box3d = roi_box3d # p=0.2 to keep the original roi box keep = True else: aug_box3d = self.random_aug_box3d(roi_box3d) keep = False aug_box3d = tf.reshape(aug_box3d, ((1, 7))) iou3d = iou_3d(aug_box3d.numpy()[:, [0, 1, 2, 5, 3, 4, 6]], gt_box3d.numpy()[:, [0, 1, 2, 5, 3, 4, 6]]) iou3d = tf.constant(iou3d) temp_iou = iou3d[0][0] cnt += 1 aug_boxes.append(tf.reshape(aug_box3d, (-1))) if cnt == 0 or keep: iou_of_rois.append(iou3d_src[k]) else: iou_of_rois.append(temp_iou) return tf.stack(aug_boxes), tf.stack(iou_of_rois) @staticmethod def random_aug_box3d(box3d): """ Random shift, scale, orientation. Args: box3d: (7) [x, y, z, h, w, l, ry] """ # pos_range, hwl_range, angle_range, mean_iou range_config = [[0.2, 0.1, np.pi / 12, 0.7], [0.3, 0.15, np.pi / 12, 0.6], [0.5, 0.15, np.pi / 9, 0.5], [0.8, 0.15, np.pi / 6, 0.3], [1.0, 0.15, np.pi / 3, 0.2]] idx = tf.constant(np.random.randint(low=0, high=len(range_config), size=(1,))[0], dtype=tf.int64) pos_shift = ((tf.random.uniform( (3,)) - 0.5) / 0.5) * range_config[idx][0] hwl_scale = ((tf.random.uniform( (3,)) - 0.5) / 0.5) * range_config[idx][1] + 1.0 angle_rot = ((tf.random.uniform( (1,)) - 0.5) / 0.5) * range_config[idx][2] aug_box3d = tf.concat([ box3d[0:3] + pos_shift, box3d[3:6] * hwl_scale, box3d[6:7] + angle_rot ], axis=0) return aug_box3d def data_augmentation(self, pts, rois, gt_of_rois): """ Args: pts: (B, M, 512, 3) rois: (B, M. 7) gt_of_rois: (B, M, 7) """ batch_size, boxes_num = pts.shape[0], pts.shape[1] # rotation augmentation angles = (tf.random.uniform( (batch_size, boxes_num)) - 0.5 / 0.5) * (np.pi / self.aug_rot_range) # calculate gt alpha from gt_of_rois temp_x, temp_z, temp_ry = gt_of_rois[:, :, 0], gt_of_rois[:, :, 2], gt_of_rois[:, :, 6] temp_beta = tf.atan2(temp_z, temp_x) gt_alpha = -tf.sign( temp_beta) * np.pi / 2 + temp_beta + temp_ry # (B, M) temp_x, temp_z, temp_ry = rois[:, :, 0], rois[:, :, 2], rois[:, :, 6] temp_beta = tf.atan2(temp_z, temp_x) roi_alpha = -tf.sign( temp_beta) * np.pi / 2 + temp_beta + temp_ry # (B, M) pts = tf.unstack(pts) gt_of_rois = tf.unstack(gt_of_rois) rois = tf.unstack(rois) for k in range(batch_size): pts[k] = rotate_pc_along_y_tf(pts[k], angles[k]) gt_of_rois[k] = tf.squeeze(rotate_pc_along_y_tf( tf.expand_dims(gt_of_rois[k], axis=1), angles[k]), axis=1) rois[k] = tf.squeeze(rotate_pc_along_y_tf( tf.expand_dims(rois[k], axis=1), angles[k]), axis=1) pts = tf.stack(pts) gt_of_rois = tf.stack(gt_of_rois) rois = tf.stack(rois) # calculate the ry after rotation temp_x, temp_z = gt_of_rois[:, :, :1], gt_of_rois[:, :, 2:3] temp_beta = tf.atan2(temp_z, temp_x) gt_of_rois = tf.concat([ gt_of_rois[:, :, :6], tf.sign(temp_beta) * np.pi / 2 + tf.expand_dims(gt_alpha, axis=-1) - temp_beta ], axis=2) temp_x, temp_z = rois[:, :, :1], rois[:, :, 2:3] temp_beta = tf.atan2(temp_z, temp_x) rois = tf.concat([ rois[:, :, :6], tf.sign(temp_beta) * np.pi / 2 + tf.expand_dims(roi_alpha, axis=-1) - temp_beta ], axis=2) # scaling augmentation scales = 1 + ((tf.random.uniform( (batch_size, boxes_num)) - 0.5) / 0.5) * 0.05 pts = pts * tf.expand_dims(tf.expand_dims(scales, axis=2), axis=3) gt_of_rois = tf.concat([ gt_of_rois[:, :, :6] * tf.expand_dims(scales, axis=2), gt_of_rois[:, :, 6:] ], axis=2) rois = tf.concat( [rois[:, :, :6] * tf.expand_dims(scales, axis=2), rois[:, :, 6:]], axis=2) # flip augmentation flip_flag = tf.sign(tf.random.uniform((batch_size, boxes_num, 1)) - 0.5) pts = tf.concat([ pts[:, :, :, :1] * tf.expand_dims(flip_flag, axis=3), pts[:, :, :, 1:] ], axis=3) gt_of_rois = tf.concat( [gt_of_rois[:, :, :1] * flip_flag, gt_of_rois[:, :, 1:]], axis=2) # flip orientation: ry > 0: pi - ry, ry < 0: -pi - ry src_ry = gt_of_rois[:, :, 6:7] ry = tf.cast((flip_flag == 1), tf.float32) * src_ry + tf.cast( (flip_flag == -1), tf.float32) * (tf.sign(src_ry) * np.pi - src_ry) gt_of_rois = tf.concat([gt_of_rois[:, :, :6], ry], axis=2) rois = tf.concat([rois[:, :, :1] * flip_flag, rois[:, :, 1:]], axis=2) # flip orientation: ry > 0: pi - ry, ry < 0: -pi - ry src_ry = rois[:, :, 6:7] ry = tf.cast((flip_flag == 1), tf.float32) * src_ry + tf.cast( (flip_flag == -1), tf.float32) * (tf.sign(src_ry) * np.pi - src_ry) rois = tf.concat([rois[:, :, :6], ry], axis=2) return pts, rois, gt_of_rois

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#-*-coding:utf-8-*- from flask_bcrypt import Bcrypt bcrypt = Bcrypt() from flask_bootstrap import Bootstrap bootstrap = Bootstrap() from flask_mail import Mail mail=Mail() from flask_login import LoginManager login_manager = LoginManager() login_manager.login_view="auth.login_index" login_manager.session_protection="strong" login_manager.login_message="登录以获得更多功能" login_manager.login_message_category="info"

[ "root@localhost.localdomain" ]

root@localhost.localdomain

dbd6c32ba34d3fe4be7a38d40e085d64dc1c2ffc

efa2de2e0ca886a22be34c40cb4b4d397aa05015

/AGE/link_pred_ddi.py

47fa5e9d4c3f629a78f1356612226821a403eb4d

[]

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chuanqichen/cs224w

34c522d95c37089298a03ff2fd113c5b613036cd

aeebce6810221bf04a9a14d8d4369be76691b608

refs/heads/main

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2021-03-21T18:55:48

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from __future__ import division from __future__ import print_function import os, sys import warnings warnings.simplefilter(action='ignore', category=FutureWarning) warnings.simplefilter(action='ignore', category=RuntimeWarning) warnings.simplefilter(action='ignore', category=UserWarning) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir)) # For replicating the experiments SEED = 42 import argparse import time import random import numpy as np import scipy.sparse as sp import torch np.random.seed(SEED) torch.manual_seed(SEED) from torch import optim import torch.nn.functional as F from model import LinTrans, LogReg from optimizer import loss_function from utils import * from sklearn.cluster import SpectralClustering, KMeans from clustering_metric import clustering_metrics from tqdm import tqdm from sklearn.preprocessing import normalize, MinMaxScaler from sklearn import metrics import matplotlib.pyplot as plt from ogb.linkproppred import PygLinkPropPredDataset, Evaluator import torch_geometric.transforms as T parser = argparse.ArgumentParser() parser.add_argument('--gnnlayers', type=int, default=1, help="Number of gnn layers") parser.add_argument('--linlayers', type=int, default=1, help="Number of hidden layers") parser.add_argument('--epochs', type=int, default=400, help='Number of epochs to train.') parser.add_argument('--dims', type=int, default=[500], help='Number of units in hidden layer 1.') parser.add_argument('--lr', type=float, default=0.001, help='Initial learning rate.') parser.add_argument('--upth_st', type=float, default=0.0011, help='Upper Threshold start.') parser.add_argument('--lowth_st', type=float, default=0.1, help='Lower Threshold start.') parser.add_argument('--upth_ed', type=float, default=0.001, help='Upper Threshold end.') parser.add_argument('--lowth_ed', type=float, default=0.5, help='Lower Threshold end.') parser.add_argument('--upd', type=int, default=10, help='Update epoch.') parser.add_argument('--bs', type=int, default=10000, help='Batchsize.') parser.add_argument('--dataset', type=str, default='wiki', help='type of dataset.') parser.add_argument('--no-cuda', action='store_true', default=False, help='Disables CUDA training.') args = parser.parse_args() args.cuda = not args.no_cuda and torch.cuda.is_available() if args.cuda is True: print('Using GPU') torch.cuda.manual_seed(SEED) os.environ["CUDA_VISIBLE_DEVICES"] = "5" def clustering(Cluster, feature, true_labels): f_adj = np.matmul(feature, np.transpose(feature)) predict_labels = Cluster.fit_predict(f_adj) cm = clustering_metrics(true_labels, predict_labels) db = -metrics.davies_bouldin_score(f_adj, predict_labels) acc, nmi, adj = cm.evaluationClusterModelFromLabel(tqdm) return db, acc, nmi, adj def update_similarity(z, upper_threshold, lower_treshold, pos_num, neg_num): f_adj = np.matmul(z, np.transpose(z)) cosine = f_adj cosine = cosine.reshape([-1,]) pos_num = round(upper_threshold * len(cosine)) neg_num = round((1-lower_treshold) * len(cosine)) pos_inds = np.argpartition(-cosine, pos_num)[:pos_num] neg_inds = np.argpartition(cosine, neg_num)[:neg_num] return np.array(pos_inds), np.array(neg_inds) def update_threshold(upper_threshold, lower_treshold, up_eta, low_eta): upth = upper_threshold + up_eta lowth = lower_treshold + low_eta return upth, lowth def get_preds(emb, adj_orig, edges): def sigmoid(x): return 1 / (1 + np.exp(-x)) adj_rec = np.dot(emb, emb.T) preds = [] for e in edges: preds.append(sigmoid(adj_rec[e[0], e[1]])) return torch.FloatTensor(preds) def gae_for(args): print("Using {} dataset".format(args.dataset)) dataset = PygLinkPropPredDataset(name='ogbl-ddi', transform=T.ToDense()) data = dataset[0] adj = data.adj.numpy() adj = sp.csr_matrix(adj) n = adj.shape[0] features = np.ones((n, 1)) #split_edge = dataset.get_edge_split() n_nodes, feat_dim = features.shape dims = [feat_dim] + args.dims print("Model dims", dims) layers = args.linlayers # Store original adjacency matrix (without diagonal entries) for later print('adjacency shape', adj.shape) adj = adj - sp.dia_matrix((adj.diagonal()[np.newaxis, :], [0]), shape=adj.shape) adj.eliminate_zeros() adj_orig = adj split_edge = dataset.get_edge_split() val_edges = split_edge['valid']['edge'] val_edges_false = split_edge['valid']['edge_neg'] test_edges = split_edge['test']['edge'] test_edges_false = split_edge['test']['edge_neg'] train_edges = split_edge['train']['edge'] adj_train = mask_test_edges_ddi(adj, train_edges) adj = adj_train n = adj.shape[0] print('feature shape', features.shape) adj_norm_s = preprocess_graph(adj, args.gnnlayers, norm='sym', renorm=True) sm_fea_s = sp.csr_matrix(features).toarray() print('Laplacian Smoothing...') for a in adj_norm_s: sm_fea_s = a.dot(sm_fea_s) adj_1st = (adj + sp.eye(n)).toarray() adj_label = torch.FloatTensor(adj_1st) model = LinTrans(layers, dims) optimizer = optim.Adam(model.parameters(), lr=args.lr) sm_fea_s = torch.FloatTensor(sm_fea_s) adj_label = adj_label.reshape([-1,]) print("sm_fea_s shape", sm_fea_s.shape) print("adj_label shape", adj_label.shape) if args.cuda: model.cuda() inx = sm_fea_s.cuda() adj_label = adj_label.cuda() else: inx = sm_fea_s pos_num = len(adj.indices) neg_num = n_nodes*n_nodes-pos_num print("Num Pos Samples", pos_num) print("Num Neg Samples", neg_num) up_eta = (args.upth_ed - args.upth_st) / (args.epochs/args.upd) low_eta = (args.lowth_ed - args.lowth_st) / (args.epochs/args.upd) pos_inds, neg_inds = update_similarity(normalize(sm_fea_s.numpy()), args.upth_st, args.lowth_st, pos_num, neg_num) print("pos_inds shape", pos_inds.shape) print("neg_inds shape", neg_inds.shape) upth, lowth = update_threshold(args.upth_st, args.lowth_st, up_eta, low_eta) bs = min(args.bs, len(pos_inds)) length = len(pos_inds) if args.cuda: pos_inds_cuda = torch.LongTensor(pos_inds).cuda() else: pos_inds_cuda = torch.LongTensor(pos_inds) evaluator = Evaluator(name='ogbl-ddi') best_lp = 0. print("Batch Size", bs) print('Start Training...') for epoch in tqdm(range(args.epochs)): st, ed = 0, bs batch_num = 0 model.train() length = len(pos_inds) while ( ed <= length ): if args.cuda: sampled_neg = torch.LongTensor(np.random.choice(neg_inds, size=ed-st)).cuda() else: sampled_neg = torch.LongTensor(np.random.choice(neg_inds, size=ed-st)) print("sampled neg shape", sampled_neg.shape) print("--------pos inds shape", pos_inds_cuda.shape) sampled_inds = torch.cat((pos_inds_cuda[st:ed], sampled_neg), 0) print("sampled inds shape", sampled_inds.shape) t = time.time() optimizer.zero_grad() xind = sampled_inds // n_nodes yind = sampled_inds % n_nodes print("xind shape", xind.shape) print("yind shape", yind.shape) x = torch.index_select(inx, 0, xind) y = torch.index_select(inx, 0, yind) print("some x", x[:5]) print("some y", y[:5]) print("x shape", x.shape) print("y shape", y.shape) zx = model(x) zy = model(y) print("zx shape", zx.shape) print("zy shape", zy.shape) if args.cuda: batch_label = torch.cat((torch.ones(ed-st), torch.zeros(ed-st))).cuda() else: batch_label = torch.cat((torch.ones(ed-st), torch.zeros(ed-st))) batch_pred = model.dcs(zx, zy) print("Batch label shape", batch_label.shape) print("Batch pred shape", batch_pred.shape) loss = loss_function(adj_preds=batch_pred, adj_labels=batch_label, n_nodes=ed-st) loss.backward() cur_loss = loss.item() optimizer.step() st = ed batch_num += 1 if ed < length and ed + bs >= length: ed += length - ed else: ed += bs if (epoch + 1) % args.upd == 0: model.eval() mu = model(inx) hidden_emb = mu.cpu().data.numpy() upth, lowth = update_threshold(upth, lowth, up_eta, low_eta) pos_inds, neg_inds = update_similarity(hidden_emb, upth, lowth, pos_num, neg_num) bs = min(args.bs, len(pos_inds)) if args.cuda: pos_inds_cuda = torch.LongTensor(pos_inds).cuda() else: pos_inds_cuda = torch.LongTensor(pos_inds) val_auc, val_ap = get_roc_score(hidden_emb, adj_orig, val_edges, val_edges_false) if val_auc + val_ap >= best_lp: best_lp = val_auc + val_ap best_emb = hidden_emb tqdm.write("Epoch: {}, train_loss_gae={:.5f}, time={:.5f}".format( epoch + 1, cur_loss, time.time() - t)) pos_train_edge = train_edges pos_valid_edge = val_edges neg_valid_edge = val_edges_false pos_test_edge = test_edges neg_test_edge = test_edges_false pos_train_pred = get_preds(hidden_emb, adj_orig, pos_train_edge) pos_valid_pred = get_preds(hidden_emb, adj_orig, pos_valid_edge) neg_valid_pred = get_preds(hidden_emb, adj_orig, neg_valid_edge) pos_test_pred = get_preds(hidden_emb, adj_orig, pos_test_edge) neg_test_pred = get_preds(hidden_emb, adj_orig, neg_test_edge) results = {} for K in [10, 20, 30]: evaluator.K = K train_hits = evaluator.eval({ 'y_pred_pos': pos_train_pred, 'y_pred_neg': neg_valid_pred, })[f'hits@{K}'] valid_hits = evaluator.eval({ 'y_pred_pos': pos_valid_pred, 'y_pred_neg': neg_valid_pred, })[f'hits@{K}'] test_hits = evaluator.eval({ 'y_pred_pos': pos_test_pred, 'y_pred_neg': neg_test_pred, })[f'hits@{K}'] results[f'Hits@{K}'] = (train_hits, valid_hits, test_hits) for key, result in results.items(): train_hits, valid_hits, test_hits = result print(key) print(f'Epoch: {epoch:02d}, ' f'Loss: {cur_loss:.4f}, ' f'Train: {100 * train_hits:.2f}%, ' f'Valid: {100 * valid_hits:.2f}%, ' f'Test: {100 * test_hits:.2f}%') print('---') tqdm.write("Optimization Finished!") auc_score, ap_score = get_roc_score(best_emb, adj_orig, test_edges, test_edges_false) tqdm.write('Test AUC score: ' + str(auc_score)) tqdm.write('Test AP score: ' + str(ap_score)) if __name__ == '__main__': gae_for(args)

[ "owhsu@stanford.edu" ]

owhsu@stanford.edu

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/Map-CS61aBerkeley/tests/08.py

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[]

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leovcunha/CS_learning_projects

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test = { 'name': 'Problem 8', 'points': 2, 'suites': [ { 'cases': [ { 'answer': '18f4b8f373a149983a060187fb945841', 'choices': [ 'a list of restaurants reviewed by the user', 'a list of all possible restaurants', 'a list of ratings for restaurants reviewed by the user' ], 'hidden': False, 'locked': True, 'question': 'In best_predictor, what does the variable reviewed represent?' }, { 'answer': '6e952a03cc93ab2e76cc6e9be1f58c8e', 'choices': [ 'a predictor function, and its r_squared value', 'a predictor function', 'an r_squared value', 'a restaurant' ], 'hidden': False, 'locked': True, 'question': r""" Given a user, a list of restaurants, and a feature function, what does find_predictor from Problem 7 return? """ }, { 'answer': '6290d50f08bc68e242b1124b49a5e8db', 'choices': [ 'the predictor with the highest r_squared', 'the predictor with the lowest r_squared', 'the first predictor in the list', 'an arbitrary predictor' ], 'hidden': False, 'locked': True, 'question': r""" After getting a list of [predictor, r_squared] pairs, which predictor should we select? """ } ], 'scored': False, 'type': 'concept' }, { 'cases': [ { 'code': r""" >>> user = make_user('Cheapskate', [ ... make_review('A', 2), ... make_review('B', 5), ... make_review('C', 2), ... make_review('D', 5), ... ]) >>> cluster = [ ... make_restaurant('A', [5, 2], [], 4, [ ... make_review('A', 5) ... ]), ... make_restaurant('B', [3, 2], [], 2, [ ... make_review('B', 5) ... ]), ... make_restaurant('C', [-2, 6], [], 4, [ ... make_review('C', 4) ... ]), ... make_restaurant('D', [4, 2], [], 2, [ ... make_review('D', 3), ... make_review('D', 4) ... ]), ... ] >>> fns = [restaurant_price, restaurant_mean_rating] >>> pred = best_predictor(user, cluster, fns) >>> [round(pred(r), 5) for r in cluster] # should be a list of decimals [2.0, 5.0, 2.0, 5.0] """, 'hidden': False, 'locked': False }, { 'code': r""" >>> user = make_user('Cheapskate', [ ... make_review('A', 2), ... make_review('B', 5), ... make_review('C', 2), ... make_review('D', 5), ... ]) >>> cluster = [ ... make_restaurant('A', [5, 2], [], 4, [ ... make_review('A', 5) ... ]), ... make_restaurant('B', [3, 2], [], 2, [ ... make_review('B', 5) ... ]), ... make_restaurant('C', [-2, 6], [], 4, [ ... make_review('C', 4) ... ]), ... ] >>> fns = [restaurant_price, restaurant_mean_rating] >>> pred = best_predictor(user, cluster, fns) >>> [round(pred(r), 5) for r in cluster] [2.0, 5.0, 2.0] """, 'hidden': False, 'locked': False }, { 'code': r""" >>> user = make_user('Cheapskate', [ ... make_review('A', 2), ... make_review('B', 5), ... make_review('C', 2), ... make_review('D', 5), ... ]) >>> cluster = [ ... make_restaurant('A', [5, 2], [], 4, [ ... make_review('A', 5) ... ]), ... make_restaurant('B', [3, 2], [], 2, [ ... make_review('B', 5) ... ]), ... make_restaurant('C', [-2, 6], [], 4, [ ... make_review('C', 4) ... ]), ... ] >>> fns = [restaurant_mean_rating, restaurant_price] >>> pred = best_predictor(user, cluster, fns) >>> [round(pred(r), 5) for r in cluster] # Make sure you're iterating through feature_fns! [2.0, 5.0, 2.0] """, 'hidden': False, 'locked': False }, { 'code': r""" >>> user = make_user('Cheapskate', [ ... make_review('A', 2), ... make_review('B', 5), ... make_review('C', 2), ... make_review('D', 5), ... ]) >>> cluster = [ ... make_restaurant('A', [5, 2], [], 4, [ ... make_review('A', 5) ... ]), ... make_restaurant('B', [3, 2], [], 2, [ ... make_review('B', 5) ... ]), ... make_restaurant('C', [-2, 6], [], 4, [ ... make_review('C', 4) ... ]), ... make_restaurant('E', [1, 2], [], 4, [ ... make_review('E', 4) ... ]), ... ] >>> fns = [restaurant_mean_rating, restaurant_price] >>> pred = best_predictor(user, cluster, fns) # Make sure you're only using user-reviewed restaurants! >>> [round(pred(r), 5) for r in cluster] [2.0, 5.0, 2.0, 2.0] """, 'hidden': False, 'locked': False } ], 'scored': True, 'setup': r""" >>> import tests.test_functions as test >>> from recommend import * """, 'teardown': '', 'type': 'doctest' }, { 'cases': [ { 'code': r""" >>> user = make_user('Cheapskate', [ ... make_review('A', 2), ... make_review('B', 5), ... make_review('C', 2), ... make_review('D', 5), ... ]) >>> cluster = [ ... make_restaurant('A', [5, 2], [], 4, [ ... make_review('A', 5) ... ]), ... make_restaurant('B', [3, 2], [], 2, [ ... make_review('B', 5) ... ]), ... make_restaurant('C', [-2, 6], [], 4, [ ... make_review('C', 4) ... ]), ... make_restaurant('D', [4, 2], [], 2, [ ... make_review('D', 3), ... make_review('D', 4) ... ]), ... ] >>> fns = [restaurant_price, restaurant_mean_rating] >>> pred = best_predictor(user, cluster, fns) >>> # Hint: Price is a perfect predictor of this user's ratings, >>> # so the predicted ratings should equal the user's ratings >>> [round(pred(r), 5) for r in cluster] # should be a list of decimals [2.0, 5.0, 2.0, 5.0] """, 'hidden': False, 'locked': False }, { 'code': r""" >>> user = make_user('Cheapskate', [ ... make_review('A', 2), ... make_review('B', 5), ... make_review('C', 2), ... make_review('D', 5), ... ]) >>> cluster = [ ... make_restaurant('A', [5, 2], [], 4, [ ... make_review('A', 5) ... ]), ... make_restaurant('B', [3, 2], [], 2, [ ... make_review('B', 5) ... ]), ... make_restaurant('C', [-2, 6], [], 4, [ ... make_review('C', 4) ... ]), ... ] >>> fns = [restaurant_price, restaurant_mean_rating] >>> pred = best_predictor(user, cluster, fns) >>> [round(pred(r), 5) for r in cluster] [2.0, 5.0, 2.0] """, 'hidden': False, 'locked': False }, { 'code': r""" >>> user = make_user('Cheapskate', [ ... make_review('A', 2), ... make_review('B', 5), ... make_review('C', 2), ... make_review('D', 5), ... ]) >>> cluster = [ ... make_restaurant('A', [5, 2], [], 4, [ ... make_review('A', 5) ... ]), ... make_restaurant('B', [3, 2], [], 2, [ ... make_review('B', 5) ... ]), ... make_restaurant('C', [-2, 6], [], 4, [ ... make_review('C', 4) ... ]), ... ] >>> fns = [restaurant_mean_rating, restaurant_price] >>> pred = best_predictor(user, cluster, fns) >>> [round(pred(r), 5) for r in cluster] # Make sure you're iterating through feature_fns! [2.0, 5.0, 2.0] """, 'hidden': False, 'locked': False }, { 'code': r""" >>> user = make_user('Cheapskate', [ ... make_review('A', 2), ... make_review('B', 5), ... make_review('C', 2), ... make_review('D', 5), ... ]) >>> cluster = [ ... make_restaurant('A', [5, 2], [], 4, [ ... make_review('A', 5) ... ]), ... make_restaurant('B', [3, 2], [], 2, [ ... make_review('B', 5) ... ]), ... make_restaurant('C', [-2, 6], [], 4, [ ... make_review('C', 4) ... ]), ... make_restaurant('E', [1, 2], [], 4, [ ... make_review('E', 4) ... ]), ... ] >>> fns = [restaurant_mean_rating, restaurant_price] >>> pred = best_predictor(user, cluster, fns) # Make sure you're only using user-reviewed restaurants! >>> [round(pred(r), 5) for r in cluster] [2.0, 5.0, 2.0, 2.0] """, 'hidden': False, 'locked': False } ], 'scored': True, 'setup': r""" >>> import tests.test_functions as test >>> import recommend >>> test.swap_implementations(recommend) >>> from recommend import * """, 'teardown': r""" >>> test.restore_implementations(recommend) """, 'type': 'doctest' } ] }

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nikita1998ivanov/Lab-rabota

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d = {} with open("students.csv") as f: next(f) for line in f: h, nm, a, db = line.split(";") d.setdefault(int(h), []).append((nm, int(a), db)) l = d.values() l = list(l) l.sort() print(l)

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/Face-Recognition/face_reco_video.py

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jaseem61/python_practice

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refs/heads/master

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import face_recognition import os import cv2 import keyboard known_faces_dir="known_faces" count=1 tolerance=0.6 frame_thickness=3 font_thickness=2 model="hog" print("loading known faces") known_faces=[] known_names=[] for name in os.listdir(known_faces_dir): for filename in os.listdir(f"{known_faces_dir}/{name}"): image=face_recognition.load_image_file(f"{known_faces_dir}/{name}/{filename}") encoding=face_recognition.face_encodings(image) if encoding: known_faces.append(encoding) known_names.append(name) print("processing unknown faces") cap=cv2.VideoCapture(0) while True: ret,image=cap.read() locations=face_recognition.face_locations(image,model=model) encodings=face_recognition.face_encodings(image,locations) for face_encoding, face_location in zip(encodings,locations): count=count+1 results=face_recognition.compare_faces(known_faces,face_encoding,tolerance=0.3) match=None if bool(results): print(count) match= known_names[0] print(f"Match found:{match}") top_left=(face_location[3],face_location[0]) bottom_right=(face_location[1],face_location[2]) color=[0,255,0] cv2.rectangle(image,top_left,bottom_right,color,frame_thickness) top_left=(face_location[3],face_location[2]) bottom_right=(face_location[1],face_location[2]+22) cv2.rectangle(image,top_left,bottom_right,color,cv2.FILLED) cv2.putText(image,match,(face_location[3]+10,face_location[2]+15),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),font_thickness) cv2.imshow(filename,image) cv2.waitKey(1) if(keyboard.is_pressed('q')): break

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noreply@github.com

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tunityy/Neon8-Bot

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from string import capwords def column_to_text(column_name): if type(column_name) == str: return capwords(column_name.replace('_', ' ')) else: results = [capwords(y.replace('_', ' ')) for y in column_name] return results def stat_name_ifs(stat): st = str(stat).lower() st = st.replace(' ', '') st = st.replace('_', '') # column_name = ['hunger', 'humanity', 'stains', 'current_willpower', 'total_willpower', 'superficial_damage', 'aggravated_damage', 'health'] column_name = ['hunger', 'humanity', 'stains', 'health'] if st in column_name: return st else: # some of these include common or possible typos and misspellings hung_synonyms = ['currenthunger', 'currenthung', 'hung', 'hun', 'hungry', 'hungerdice', 'hungdice', 'hd', 'bp', 'bloodpool', 'blooddice', 'bd', 'hugn', 'hugner', 'hungre', 'curenthunger', 'curenthung', 'bloop', 'blooppool', 'bloopool'] hum_synonyms = ['hum', 'huemanatee', 'humane', 'human', 'humanty', 'humanit', 'humantiy', 'humanaty'] stains_synonyms = ['stain', 'stian', 'st', 'stians', 'stans'] cwp_synonyms = ['currentwillpower', 'willpower', 'wp', 'currentwp', 'will', 'currentwill', 'currentwp', 'cwill', 'cwp', 'cw', 'willp', 'currentwillp', 'cwillp', 'wilpower', 'curentwillpower', 'current', 'curentwill', 'wil', 'currentwilpower', 'curentwilpower', 'wpwr', 'willpwr', 'wllpwr', 'wlpwr'] twp_synonyms = ['totalwillpower', 'totalwp', 'twp', 'total', 'tot', 'totalwill', 'willpowertotal', 'wptotal', 'willtotal', 'twill', 'tw', 'twillp', 'twillpower', 'totalwilpower', 'totalwil', 'tote', 'totlewillpower', 'totlwillpower', 'totwill', 't', 'totwil', 'totwp', 'to', 'twil'] spr_dmg_synonyms = ['superficialdamage', 'superficial', 'superficialdmg', 'sdmg', 'sdamage', 'sdmg', 'super', 'superdmg', 'supre', 'superficaldamage', 'superficaldmg', 'superfical', 'superfishul', 'superfishuldamage', 'superfishuldmg'] agg_dmg_synonyms = ['aggravateddamage', 'agg', 'aggravated', 'aggr', 'aggdmg', 'aggrdmg', 'aggravateddmg', 'aggra', 'aggdamage', 'admg', 'adamage', 'aggro', 'aggrivated', 'aggrivateddamage', 'aggrivateddmg', 'aggrevated', 'aggrevateddamage', 'aggrevateddmg', 'aggrovated', 'aggrovateddamage', 'aggrovateddmg', 'aggrovateddmg'] health_synonyms = ['hp', 'hitpoints', 'healthpoints', 'healthbar', 'life', 'heal' 'heath', 'healh', 'helth'] if st in hung_synonyms: return 'hunger' elif st in hum_synonyms: return 'humanity' elif st in stains_synonyms: return 'stains' elif st in cwp_synonyms: return 'current_willpower' elif st in twp_synonyms: return 'total_willpower' elif st in spr_dmg_synonyms: return 'superficial_damage' elif st in agg_dmg_synonyms: return 'aggravated_damage' elif st in health_synonyms: return 'health' else: return 'Invalid' #### ---------------------------------------------------------- ### TODO: when it's just a list of one word it actually just comes out as a string. Need to change it to a list? def stat_names_listifier(stats, words_and_numbs=False): """`words_and_numbs` is to differentiate when stats is just numbers, or contains words and numbers.""" if words_and_numbs == False: list_stats = ' '.join(stats).split(', ') if int(len(list_stats)) == 1: column_name = [stat_name_ifs(list_stats[0])] return column_name else: list_of_columns = [stat_name_ifs(term) for term in list_stats] if 'Invalid' in list_of_columns: return 'Invalid' else: return list_of_columns elif words_and_numbs == True: items_to_assess = ' '.join(stats).split(', ') list_stats = [item.rsplit(' ', 1)[0] for item in items_to_assess] values_list = [item.split(' ')[-1] for item in items_to_assess] for item in values_list: try: int(item) except: return 'Invalid' if int(len(list_stats)) == 1: column_name = [stat_name_ifs(list_stats[0])] if column_name == 'Invalid': return 'Invalid' else: return column_name, values_list else: list_of_columns = [stat_name_ifs(term) for term in list_stats] if 'Invalid' in list_of_columns: return 'Invalid' else: return list_of_columns, values_list #### ----------------------------------------------------------

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/learning_logs/models.py

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ArXaHGeL/Learning-Log

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2023-02-26T15:21:09.499555

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from django.db import models from django.contrib.auth.models import User # Create your models here. class Topic(models.Model): """A topic that the user is learning.""" text = models.CharField(max_length=200) date_added = models.DateTimeField(auto_now_add=True) owner = models.ForeignKey(User, on_delete=models.CASCADE) def __str__(self): """Return a string representation of the model.""" return self.text class Entry(models.Model): """Information learned by the user.""" topic = models.ForeignKey(Topic, on_delete=models.CASCADE) text = models.TextField() date_added = models.DateTimeField(auto_now_add=True) class Meta: verbose_name_plural = 'entries' def __str__(self): """Return a string representation of the model.""" if self.text <= self.text[0:50]: return self.text else: return self.text[0:50] + "..."

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# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from .. import _utilities, _tables from . import outputs from ._enums import * __all__ = [ 'AssignmentInfoResponse', 'AssignmentReportResourceComplianceReasonResponse', 'AssignmentReportResourceResponse', 'AssignmentReportResponse', 'ConfigurationInfoResponse', 'ConfigurationParameterResponse', 'ConfigurationSettingResponse', 'GuestConfigurationAssignmentPropertiesResponse', 'GuestConfigurationNavigationResponse', 'VMInfoResponse', ] @pulumi.output_type class AssignmentInfoResponse(dict): """ Information about the guest configuration assignment. """ def __init__(__self__, *, name: str, configuration: Optional['outputs.ConfigurationInfoResponse'] = None): """ Information about the guest configuration assignment. :param str name: Name of the guest configuration assignment. :param 'ConfigurationInfoResponseArgs' configuration: Information about the configuration. """ pulumi.set(__self__, "name", name) if configuration is not None: pulumi.set(__self__, "configuration", configuration) @property @pulumi.getter def name(self) -> str: """ Name of the guest configuration assignment. """ return pulumi.get(self, "name") @property @pulumi.getter def configuration(self) -> Optional['outputs.ConfigurationInfoResponse']: """ Information about the configuration. """ return pulumi.get(self, "configuration") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class AssignmentReportResourceComplianceReasonResponse(dict): """ Reason and code for the compliance of the guest configuration assignment resource. """ def __init__(__self__, *, code: str, phrase: str): """ Reason and code for the compliance of the guest configuration assignment resource. :param str code: Code for the compliance of the guest configuration assignment resource. :param str phrase: Reason for the compliance of the guest configuration assignment resource. """ pulumi.set(__self__, "code", code) pulumi.set(__self__, "phrase", phrase) @property @pulumi.getter def code(self) -> str: """ Code for the compliance of the guest configuration assignment resource. """ return pulumi.get(self, "code") @property @pulumi.getter def phrase(self) -> str: """ Reason for the compliance of the guest configuration assignment resource. """ return pulumi.get(self, "phrase") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class AssignmentReportResourceResponse(dict): """ The guest configuration assignment resource. """ def __init__(__self__, *, compliance_status: str, properties: Any, resource_id: str, reasons: Optional[Sequence['outputs.AssignmentReportResourceComplianceReasonResponse']] = None): """ The guest configuration assignment resource. :param str compliance_status: A value indicating compliance status of the machine for the assigned guest configuration. :param Any properties: Properties of a guest configuration assignment resource. :param str resource_id: Name of the guest configuration assignment resource setting. :param Sequence['AssignmentReportResourceComplianceReasonResponseArgs'] reasons: Compliance reason and reason code for a resource. """ pulumi.set(__self__, "compliance_status", compliance_status) pulumi.set(__self__, "properties", properties) pulumi.set(__self__, "resource_id", resource_id) if reasons is not None: pulumi.set(__self__, "reasons", reasons) @property @pulumi.getter(name="complianceStatus") def compliance_status(self) -> str: """ A value indicating compliance status of the machine for the assigned guest configuration. """ return pulumi.get(self, "compliance_status") @property @pulumi.getter def properties(self) -> Any: """ Properties of a guest configuration assignment resource. """ return pulumi.get(self, "properties") @property @pulumi.getter(name="resourceId") def resource_id(self) -> str: """ Name of the guest configuration assignment resource setting. """ return pulumi.get(self, "resource_id") @property @pulumi.getter def reasons(self) -> Optional[Sequence['outputs.AssignmentReportResourceComplianceReasonResponse']]: """ Compliance reason and reason code for a resource. """ return pulumi.get(self, "reasons") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class AssignmentReportResponse(dict): def __init__(__self__, *, compliance_status: str, end_time: str, id: str, operation_type: str, report_id: str, start_time: str, assignment: Optional['outputs.AssignmentInfoResponse'] = None, resources: Optional[Sequence['outputs.AssignmentReportResourceResponse']] = None, vm: Optional['outputs.VMInfoResponse'] = None): """ :param str compliance_status: A value indicating compliance status of the machine for the assigned guest configuration. :param str end_time: End date and time of the guest configuration assignment compliance status check. :param str id: ARM resource id of the report for the guest configuration assignment. :param str operation_type: Type of report, Consistency or Initial :param str report_id: GUID that identifies the guest configuration assignment report under a subscription, resource group. :param str start_time: Start date and time of the guest configuration assignment compliance status check. :param 'AssignmentInfoResponseArgs' assignment: Configuration details of the guest configuration assignment. :param Sequence['AssignmentReportResourceResponseArgs'] resources: The list of resources for which guest configuration assignment compliance is checked. :param 'VMInfoResponseArgs' vm: Information about the VM. """ pulumi.set(__self__, "compliance_status", compliance_status) pulumi.set(__self__, "end_time", end_time) pulumi.set(__self__, "id", id) pulumi.set(__self__, "operation_type", operation_type) pulumi.set(__self__, "report_id", report_id) pulumi.set(__self__, "start_time", start_time) if assignment is not None: pulumi.set(__self__, "assignment", assignment) if resources is not None: pulumi.set(__self__, "resources", resources) if vm is not None: pulumi.set(__self__, "vm", vm) @property @pulumi.getter(name="complianceStatus") def compliance_status(self) -> str: """ A value indicating compliance status of the machine for the assigned guest configuration. """ return pulumi.get(self, "compliance_status") @property @pulumi.getter(name="endTime") def end_time(self) -> str: """ End date and time of the guest configuration assignment compliance status check. """ return pulumi.get(self, "end_time") @property @pulumi.getter def id(self) -> str: """ ARM resource id of the report for the guest configuration assignment. """ return pulumi.get(self, "id") @property @pulumi.getter(name="operationType") def operation_type(self) -> str: """ Type of report, Consistency or Initial """ return pulumi.get(self, "operation_type") @property @pulumi.getter(name="reportId") def report_id(self) -> str: """ GUID that identifies the guest configuration assignment report under a subscription, resource group. """ return pulumi.get(self, "report_id") @property @pulumi.getter(name="startTime") def start_time(self) -> str: """ Start date and time of the guest configuration assignment compliance status check. """ return pulumi.get(self, "start_time") @property @pulumi.getter def assignment(self) -> Optional['outputs.AssignmentInfoResponse']: """ Configuration details of the guest configuration assignment. """ return pulumi.get(self, "assignment") @property @pulumi.getter def resources(self) -> Optional[Sequence['outputs.AssignmentReportResourceResponse']]: """ The list of resources for which guest configuration assignment compliance is checked. """ return pulumi.get(self, "resources") @property @pulumi.getter def vm(self) -> Optional['outputs.VMInfoResponse']: """ Information about the VM. """ return pulumi.get(self, "vm") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class ConfigurationInfoResponse(dict): """ Information about the configuration. """ def __init__(__self__, *, name: str, version: str): """ Information about the configuration. :param str name: Name of the configuration. :param str version: Version of the configuration. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "version", version) @property @pulumi.getter def name(self) -> str: """ Name of the configuration. """ return pulumi.get(self, "name") @property @pulumi.getter def version(self) -> str: """ Version of the configuration. """ return pulumi.get(self, "version") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class ConfigurationParameterResponse(dict): """ Represents a configuration parameter. """ def __init__(__self__, *, name: Optional[str] = None, value: Optional[str] = None): """ Represents a configuration parameter. :param str name: Name of the configuration parameter. :param str value: Value of the configuration parameter. """ if name is not None: pulumi.set(__self__, "name", name) if value is not None: pulumi.set(__self__, "value", value) @property @pulumi.getter def name(self) -> Optional[str]: """ Name of the configuration parameter. """ return pulumi.get(self, "name") @property @pulumi.getter def value(self) -> Optional[str]: """ Value of the configuration parameter. """ return pulumi.get(self, "value") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class ConfigurationSettingResponse(dict): """ Configuration setting of LCM (Local Configuration Manager). """ def __init__(__self__, *, action_after_reboot: Optional[str] = None, allow_module_overwrite: Optional[str] = None, configuration_mode: Optional[str] = None, configuration_mode_frequency_mins: Optional[float] = None, reboot_if_needed: Optional[str] = None, refresh_frequency_mins: Optional[float] = None): """ Configuration setting of LCM (Local Configuration Manager). :param str action_after_reboot: Specifies what happens after a reboot during the application of a configuration. The possible values are ContinueConfiguration and StopConfiguration :param str allow_module_overwrite: If true - new configurations downloaded from the pull service are allowed to overwrite the old ones on the target node. Otherwise, false :param str configuration_mode: Specifies how the LCM(Local Configuration Manager) actually applies the configuration to the target nodes. Possible values are ApplyOnly, ApplyAndMonitor, and ApplyAndAutoCorrect. :param float configuration_mode_frequency_mins: How often, in minutes, the current configuration is checked and applied. This property is ignored if the ConfigurationMode property is set to ApplyOnly. The default value is 15. :param str reboot_if_needed: Set this to true to automatically reboot the node after a configuration that requires reboot is applied. Otherwise, you will have to manually reboot the node for any configuration that requires it. The default value is false. To use this setting when a reboot condition is enacted by something other than DSC (such as Windows Installer), combine this setting with the xPendingReboot module. :param float refresh_frequency_mins: The time interval, in minutes, at which the LCM checks a pull service to get updated configurations. This value is ignored if the LCM is not configured in pull mode. The default value is 30. """ if action_after_reboot is not None: pulumi.set(__self__, "action_after_reboot", action_after_reboot) if allow_module_overwrite is not None: pulumi.set(__self__, "allow_module_overwrite", allow_module_overwrite) if configuration_mode is not None: pulumi.set(__self__, "configuration_mode", configuration_mode) if configuration_mode_frequency_mins is None: configuration_mode_frequency_mins = 15 if configuration_mode_frequency_mins is not None: pulumi.set(__self__, "configuration_mode_frequency_mins", configuration_mode_frequency_mins) if reboot_if_needed is None: reboot_if_needed = 'False' if reboot_if_needed is not None: pulumi.set(__self__, "reboot_if_needed", reboot_if_needed) if refresh_frequency_mins is None: refresh_frequency_mins = 30 if refresh_frequency_mins is not None: pulumi.set(__self__, "refresh_frequency_mins", refresh_frequency_mins) @property @pulumi.getter(name="actionAfterReboot") def action_after_reboot(self) -> Optional[str]: """ Specifies what happens after a reboot during the application of a configuration. The possible values are ContinueConfiguration and StopConfiguration """ return pulumi.get(self, "action_after_reboot") @property @pulumi.getter(name="allowModuleOverwrite") def allow_module_overwrite(self) -> Optional[str]: """ If true - new configurations downloaded from the pull service are allowed to overwrite the old ones on the target node. Otherwise, false """ return pulumi.get(self, "allow_module_overwrite") @property @pulumi.getter(name="configurationMode") def configuration_mode(self) -> Optional[str]: """ Specifies how the LCM(Local Configuration Manager) actually applies the configuration to the target nodes. Possible values are ApplyOnly, ApplyAndMonitor, and ApplyAndAutoCorrect. """ return pulumi.get(self, "configuration_mode") @property @pulumi.getter(name="configurationModeFrequencyMins") def configuration_mode_frequency_mins(self) -> Optional[float]: """ How often, in minutes, the current configuration is checked and applied. This property is ignored if the ConfigurationMode property is set to ApplyOnly. The default value is 15. """ return pulumi.get(self, "configuration_mode_frequency_mins") @property @pulumi.getter(name="rebootIfNeeded") def reboot_if_needed(self) -> Optional[str]: """ Set this to true to automatically reboot the node after a configuration that requires reboot is applied. Otherwise, you will have to manually reboot the node for any configuration that requires it. The default value is false. To use this setting when a reboot condition is enacted by something other than DSC (such as Windows Installer), combine this setting with the xPendingReboot module. """ return pulumi.get(self, "reboot_if_needed") @property @pulumi.getter(name="refreshFrequencyMins") def refresh_frequency_mins(self) -> Optional[float]: """ The time interval, in minutes, at which the LCM checks a pull service to get updated configurations. This value is ignored if the LCM is not configured in pull mode. The default value is 30. """ return pulumi.get(self, "refresh_frequency_mins") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class GuestConfigurationAssignmentPropertiesResponse(dict): """ Guest configuration assignment properties. """ def __init__(__self__, *, assignment_hash: str, compliance_status: str, last_compliance_status_checked: str, latest_report_id: str, provisioning_state: str, target_resource_id: str, context: Optional[str] = None, guest_configuration: Optional['outputs.GuestConfigurationNavigationResponse'] = None, latest_assignment_report: Optional['outputs.AssignmentReportResponse'] = None): """ Guest configuration assignment properties. :param str assignment_hash: Combined hash of the configuration package and parameters. :param str compliance_status: A value indicating compliance status of the machine for the assigned guest configuration. :param str last_compliance_status_checked: Date and time when last compliance status was checked. :param str latest_report_id: Id of the latest report for the guest configuration assignment. :param str provisioning_state: The provisioning state, which only appears in the response. :param str target_resource_id: VM resource Id. :param str context: The source which initiated the guest configuration assignment. Ex: Azure Policy :param 'GuestConfigurationNavigationResponseArgs' guest_configuration: The guest configuration to assign. :param 'AssignmentReportResponseArgs' latest_assignment_report: Last reported guest configuration assignment report. """ pulumi.set(__self__, "assignment_hash", assignment_hash) pulumi.set(__self__, "compliance_status", compliance_status) pulumi.set(__self__, "last_compliance_status_checked", last_compliance_status_checked) pulumi.set(__self__, "latest_report_id", latest_report_id) pulumi.set(__self__, "provisioning_state", provisioning_state) pulumi.set(__self__, "target_resource_id", target_resource_id) if context is not None: pulumi.set(__self__, "context", context) if guest_configuration is not None: pulumi.set(__self__, "guest_configuration", guest_configuration) if latest_assignment_report is not None: pulumi.set(__self__, "latest_assignment_report", latest_assignment_report) @property @pulumi.getter(name="assignmentHash") def assignment_hash(self) -> str: """ Combined hash of the configuration package and parameters. """ return pulumi.get(self, "assignment_hash") @property @pulumi.getter(name="complianceStatus") def compliance_status(self) -> str: """ A value indicating compliance status of the machine for the assigned guest configuration. """ return pulumi.get(self, "compliance_status") @property @pulumi.getter(name="lastComplianceStatusChecked") def last_compliance_status_checked(self) -> str: """ Date and time when last compliance status was checked. """ return pulumi.get(self, "last_compliance_status_checked") @property @pulumi.getter(name="latestReportId") def latest_report_id(self) -> str: """ Id of the latest report for the guest configuration assignment. """ return pulumi.get(self, "latest_report_id") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> str: """ The provisioning state, which only appears in the response. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="targetResourceId") def target_resource_id(self) -> str: """ VM resource Id. """ return pulumi.get(self, "target_resource_id") @property @pulumi.getter def context(self) -> Optional[str]: """ The source which initiated the guest configuration assignment. Ex: Azure Policy """ return pulumi.get(self, "context") @property @pulumi.getter(name="guestConfiguration") def guest_configuration(self) -> Optional['outputs.GuestConfigurationNavigationResponse']: """ The guest configuration to assign. """ return pulumi.get(self, "guest_configuration") @property @pulumi.getter(name="latestAssignmentReport") def latest_assignment_report(self) -> Optional['outputs.AssignmentReportResponse']: """ Last reported guest configuration assignment report. """ return pulumi.get(self, "latest_assignment_report") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class GuestConfigurationNavigationResponse(dict): """ Guest configuration is an artifact that encapsulates DSC configuration and its dependencies. The artifact is a zip file containing DSC configuration (as MOF) and dependent resources and other dependencies like modules. """ def __init__(__self__, *, content_hash: str, content_uri: str, configuration_parameter: Optional[Sequence['outputs.ConfigurationParameterResponse']] = None, configuration_setting: Optional['outputs.ConfigurationSettingResponse'] = None, kind: Optional[str] = None, name: Optional[str] = None, version: Optional[str] = None): """ Guest configuration is an artifact that encapsulates DSC configuration and its dependencies. The artifact is a zip file containing DSC configuration (as MOF) and dependent resources and other dependencies like modules. :param str content_hash: Combined hash of the guest configuration package and configuration parameters. :param str content_uri: Uri of the storage where guest configuration package is uploaded. :param Sequence['ConfigurationParameterResponseArgs'] configuration_parameter: The configuration parameters for the guest configuration. :param 'ConfigurationSettingResponseArgs' configuration_setting: The configuration setting for the guest configuration. :param str kind: Kind of the guest configuration. For example:DSC :param str name: Name of the guest configuration. :param str version: Version of the guest configuration. """ pulumi.set(__self__, "content_hash", content_hash) pulumi.set(__self__, "content_uri", content_uri) if configuration_parameter is not None: pulumi.set(__self__, "configuration_parameter", configuration_parameter) if configuration_setting is not None: pulumi.set(__self__, "configuration_setting", configuration_setting) if kind is not None: pulumi.set(__self__, "kind", kind) if name is not None: pulumi.set(__self__, "name", name) if version is not None: pulumi.set(__self__, "version", version) @property @pulumi.getter(name="contentHash") def content_hash(self) -> str: """ Combined hash of the guest configuration package and configuration parameters. """ return pulumi.get(self, "content_hash") @property @pulumi.getter(name="contentUri") def content_uri(self) -> str: """ Uri of the storage where guest configuration package is uploaded. """ return pulumi.get(self, "content_uri") @property @pulumi.getter(name="configurationParameter") def configuration_parameter(self) -> Optional[Sequence['outputs.ConfigurationParameterResponse']]: """ The configuration parameters for the guest configuration. """ return pulumi.get(self, "configuration_parameter") @property @pulumi.getter(name="configurationSetting") def configuration_setting(self) -> Optional['outputs.ConfigurationSettingResponse']: """ The configuration setting for the guest configuration. """ return pulumi.get(self, "configuration_setting") @property @pulumi.getter def kind(self) -> Optional[str]: """ Kind of the guest configuration. For example:DSC """ return pulumi.get(self, "kind") @property @pulumi.getter def name(self) -> Optional[str]: """ Name of the guest configuration. """ return pulumi.get(self, "name") @property @pulumi.getter def version(self) -> Optional[str]: """ Version of the guest configuration. """ return pulumi.get(self, "version") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class VMInfoResponse(dict): """ Information about the VM. """ def __init__(__self__, *, id: str, uuid: str): """ Information about the VM. :param str id: Azure resource Id of the VM. :param str uuid: UUID(Universally Unique Identifier) of the VM. """ pulumi.set(__self__, "id", id) pulumi.set(__self__, "uuid", uuid) @property @pulumi.getter def id(self) -> str: """ Azure resource Id of the VM. """ return pulumi.get(self, "id") @property @pulumi.getter def uuid(self) -> str: """ UUID(Universally Unique Identifier) of the VM. """ return pulumi.get(self, "uuid") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop

[ "noreply@github.com" ]

noreply@github.com

4608f8b477a4827ab546757c5cdf0cf175bfa969

841e32970a080c8beb4ccc94d2afc5f264483a45

/api/app/migrations/0001_initial.py

d227e2d9a19eee4bb8de17faada02127cabe7b35

[]

no_license

semprajapat/automation_pytest

3249fec117186ee9984674585b79fe0d75a15a6c

05fb58c5cece1043317bf444e8636fd49564fccc

refs/heads/master

2021-01-14T19:18:53.865204

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547

py

# Generated by Django 3.0.3 on 2020-02-24 11:42 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Datamodel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('last', models.CharField(max_length=50)), ], ), ]

[ "aaa@Aaas-MacBook-Pro.local" ]

aaa@Aaas-MacBook-Pro.local

af4da04242f2f06729d65a60df595b64a56f4355

ba2c77f62e7c9ddc074606cbca94062941dfc760

/small_methods.py

e2a95b7f1d18121fe30f08b1b169cac48fdcb01f

[]

no_license

scaars10/Lazy-Crawler

5608888f1ed60bdc951b2b4ba2a17ca7ab173bea

4088514571f096531076f4c551eac2ce4912530d

refs/heads/master

2021-08-09T00:25:45.546129

2017-11-11T18:29:06

110,369,278

0

null

UTF-8

Python

false

1,890

py

import os from stemming.porter2 import stem # omnipresent_words = ['www.', 'http:', 'https:', '.com', '.in'] def directory_manage(relative_location): # checks if a directory exists or not if not then it creates it itself base_path = os.path.dirname(os.path.realpath(__file__)) if not os.path.exists(os.path.join(base_path, relative_location)): os.makedirs(os.path.join(base_path, relative_location)) f = open('Processing_Data\\keywords.txt', 'r') keywords = [] values = [] line_count = 0 for each_line in f: line_count += 1 line = each_line.split() try: values.append(int(line[1])) keywords.append(stem(line[0])) except: directory_manage('Output\\Errors') f = open('Output\\Errors\\Keyword_Error.txt','a') f.write('Check Line No. '+str(line_count)+' in Output\\Errors\\keywords.txt for formatting error\n') f.close() f.close() def sort_links_wrt_importance(links, links_text): link_importance = [] iterate = 0 while iterate < len(links): link = stem(links[iterate]) if isinstance(links_text[iterate], str): link_text = stem(links_text[iterate]) else: link_text = 'ignore' # divided_link = link.split('/') i = 0 strength = 0 while i < len(keywords): if keywords[i] in link: strength += values[i] if isinstance(link_text, str): if keywords[i] in link_text: strength += values[i] i += 1 link_importance.append(strength) iterate += 1 i = 0 while i < len(links): j = i # print('sorting') while j > 0: if link_importance[j] > link_importance[j-1]: temp_link = links[j] links[j] = links[j-1] links[j-1] = temp_link # temp_link_text = links_text[j] # links_text[j] = links_text[j-1] # links_text[j-1] = temp_link_text temp_imp = link_importance[j] link_importance[j] = link_importance[j-1] link_importance[j-1] = temp_imp j -= 1 else: break i += 1 return links

[ "scaars10@gmail.com" ]

scaars10@gmail.com

30a56aa3ea447d0f6e641cf2b1c120ab673bb144

fe81c95988122057f030cc6c57681e215093c9ba

/比赛分享/调参/tiaocan1.py

3413b37f817c71bf81d552fb4f4f5e1c94ca54e1

[]

no_license

xiexiaoyang/Big-Data-Challenge

fbe2bbfa92a603460479e6cf7ff4a6f197af239f

2fc6ae26037a98d46cb0735a0e4c744b74ec9fb0

refs/heads/master

2021-07-19T02:37:58.980208

2017-10-22T07:18:24

107,832,382

0

1

null

UTF-8

Python

false

6,637

py

# -*- coding: utf-8 -*- """ Created on Thu Oct 19 19:28:57 2017 @author: Yang """ '''调参 1. 理解模型 2. 列出所有的参数 3. 选择对模型提升大的参数代码错误： 1. kstep = len(randidx) / nfold 改为 kstep = len(randidx) // nfold 2. 'Disbursed' 改为 target 3, Parameter values should be a list. 改为 param_test1 = {'max_depth':list(range(3,10,2)),'min_child_weight':list(range(1,6,2))} ''' #Import libraries: import pandas as pd import numpy as np import xgboost as xgb from xgboost.sklearn import XGBClassifier from sklearn import cross_validation, metrics #Additional scklearn functions from sklearn.grid_search import GridSearchCV #Perforing grid search import matplotlib.pylab as plt from matplotlib.pylab import rcParams rcParams['figure.figsize'] = 12, 4 train = pd.read_csv(r"G:\比赛分享\data\alltrain.csv") test= pd.read_csv(r"G:\比赛分享\data\alltest.csv") target = 'label' IDcol = 'id' def modelfit(alg, dtrain, predictors,useTrainCV=True, cv_folds=5, early_stopping_rounds=50): if useTrainCV: xgb_param = alg.get_xgb_params() xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values) cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds, metrics=['auc'], early_stopping_rounds=early_stopping_rounds, show_progress=True) alg.set_params(n_estimators=cvresult.shape[0]) #Fit the algorithm on the data alg.fit(dtrain[predictors], dtrain[target],eval_metric='auc') #Predict training set: dtrain_predictions = alg.predict(dtrain[predictors]) dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1] #Print model report: print ("\nModel Report") print (("Accuracy : %.4g") % metrics.accuracy_score(dtrain[target].values, dtrain_predictions)) print (("AUC Score (Train): %f" )% metrics.roc_auc_score(dtrain[target], dtrain_predprob)) feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False) feat_imp.plot(kind='bar', title='xgb Feature Importances') plt.ylabel('Feature Importance Score') ''' 一修正用于调整基于树的参数的学习速率和估计量数也就是 learning_rate n_estimators 学习速率和树的数量 ''' ##Choose all predictors except target & IDcols predictors = [x for x in train.columns if x not in [target, IDcol]] xgb1 = XGBClassifier( learning_rate =0.1, n_estimators=1000, max_depth=5, min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27) modelfit(xgb1, train, predictors) ###Step 2: Tune max_depth and min_child_weight #param_test1 = { # 'max_depth':list(range(3,10,2)), # 'min_child_weight':list(range(1,6,2)) #} #gsearch1 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=140, max_depth=5, # min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8, # objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27), # param_grid = param_test1, scoring='roc_auc',n_jobs=4,iid=False, cv=2 ) # #print(gsearch1.fit(train[predictors],train[target])) #print(gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_) # #param_test2 = { # 'max_depth':[4,5,6], # 'min_child_weight':[4,5,6] #} #gsearch2 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140, max_depth=5, # min_child_weight=2, gamma=0, subsample=0.8, colsample_bytree=0.8, # objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), # param_grid = param_test2, scoring='roc_auc',n_jobs=4,iid=False, cv=5) #print(gsearch2.fit(train[predictors],train[target])) #print(gsearch2.grid_scores_, gsearch2.best_params_, gsearch2.best_score_) ###Step 3: Tune gamma #param_test3 = { # 'gamma':[i/10.0 for i in range(0,5)] #} #gsearch3 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=140, max_depth=4, # min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8, # objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), # param_grid = param_test3, scoring='roc_auc',n_jobs=4,iid=False, cv=5) #print(gsearch3.fit(train[predictors],train[target])) #print(gsearch3.grid_scores_, gsearch3.best_params_, gsearch3.best_score_) ## #xgb2 = XGBClassifier( # learning_rate =0.1, # n_estimators=1000, # max_depth=4, # min_child_weight=6, # gamma=0, # subsample=0.8, # colsample_bytree=0.8, # objective= 'binary:logistic', # nthread=4, # scale_pos_weight=1, # seed=27) #modelfit(xgb2, train, predictors) # ###Step 4: Tune subsample and colsample_bytree #param_test4 = { # 'subsample':[i/10.0 for i in range(6,10)], # 'colsample_bytree':[i/10.0 for i in range(6,10)] #} #gsearch4 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=177, max_depth=4, # min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8, # objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), # param_grid = param_test4, scoring='roc_auc',n_jobs=4,iid=False, cv=5) #print(gsearch4.fit(train[predictors],train[target])) #print(gsearch4.grid_scores_, gsearch4.best_params_, gsearch4.best_score_) # #param_test5 = { # 'subsample':[i/100.0 for i in range(75,90,5)], # 'colsample_bytree':[i/100.0 for i in range(75,90,5)] #} #gsearch5 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=177, max_depth=4, # min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8, # objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), # param_grid = param_test5, scoring='roc_auc',n_jobs=4,iid=False, cv=5) #gsearch5.fit(train[predictors],train[target]) ###Step 5: Tuning Regularization Parameters #param_test6 = { # 'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100] #} #gsearch6 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=177, max_depth=4, # min_child_weight=6, gamma=0.1, subsample=0.8, colsample_bytree=0.8, # objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), # param_grid = param_test6, scoring='roc_auc',n_jobs=4,iid=False, cv=5) #print(gsearch6.fit(train[predictors],train[target])) #print(gsearch6.grid_scores_, gsearch6.best_params_, gsearch6.best_score_) ###Step 6: Reducing Learning Rate #xgb4 = XGBClassifier( # learning_rate =0.01, # n_estimators=5000, # max_depth=4, # min_child_weight=6, # gamma=0, # subsample=0.8, # colsample_bytree=0.8, # reg_alpha=0.005, # objective= 'binary:logistic', # nthread=4, # scale_pos_weight=1, # seed=27) #modelfit(xgb4, train, predictors)

[ "2509039243@qq.com" ]

2509039243@qq.com

49b63a0524f032834d51833a9cee91640d52b635

06933e4550c4d647ecedab639c1fa9748d7aa155

/tvshows/tvshows_app/models.py

8cdbce4772b4f7f1552184f5fac82bb38761ab97

[]

no_license

leoalicastro/tv_shows

ce4bb052c64ed6aba34194104f6f31462c1a61c5

890122a4a7eda81cb24f10f88cc217c132e7850b

refs/heads/main

2023-07-16T15:00:03.292785

2021-08-19T18:50:09

390,782,646

0

null

UTF-8

Python

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py

from django.db import models from datetime import datetime class ShowManager(models.Manager): def basic_validator(self, post_data): errors = {} if len(post_data['title']) < 2: errors['title'] = "Title must be atleast 2 characters" if len(post_data['network']) < 3: errors['network'] = "Network must be atleast 2 characters" if post_data['desc'] != '' and len(post_data['desc']) < 10: errors['desc'] = "Description must be atleast 10 characters" if datetime.strptime(post_data['release'], '%Y-%m-%d') > datetime.now(): errors['release'] = 'Release Date should be in the past' return errors class Show(models.Model): title = models.CharField(max_length=255) network = models.CharField(max_length=255) release = models.DateField() desc = models.CharField(max_length=255) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) objects = ShowManager()

[ "leoalicastro957@gmail.com" ]

leoalicastro957@gmail.com

0e9776198e43fe8ba697233bc1a7c1c9c3496279

bef71d057048b93ef784892d911e7c2f7ffaee14

/framework_autotest/testsuites/test_wirenetwork.py

91fb3dbad2201ce112d8c4fcf0e8e7290c8c7e12

[]

no_license

leaf2maple/python-selenium-unittest

2379403e98508000276bb5d0c89866efc5450d90

50bf514144c6cd6e8d0f51164731bbad367e9356

refs/heads/master

2020-09-01T00:26:30.307410

2019-10-31T17:52:09

218,826,698

0

null

UTF-8

Python

false

2,572

py

import unittest import time from framework.browser_engine import BrowserEngine from pageobject.wb01_wirenetworkpage import WireNetWorkPage from framework.login import Login from pageobject.wb01_homepage import HomePage class TestWireNetWork(unittest.TestCase): @classmethod def setUpClass(cls): browser = BrowserEngine(cls) cls.driver = browser.open_browser(cls) login = Login(cls.driver) login.skip_or_login() homepage = HomePage(cls.driver) homepage.wireNetwork_click() time.sleep(5) @classmethod def tearDownClass(cls): cls.driver.quit() def test_011_wirenetwork_switch(self): wirenetwork = WireNetWorkPage(self.driver) # 关闭 wirenetwork.wirenetwork_switch_click() time.sleep(1) try: el = self.driver.find_element_by_xpath("//div[@class='wireNetwork']") assert "IP设置" not in el.text print("test_011 pass") wirenetwork.get_screenshot_as_file() except Exception as e: print("test_011 fail", format(e)) # 打开 wirenetwork.wirenetwork_switch_click() time.sleep(1) try: el = self.driver.find_element_by_xpath("//span[@class='title' and text()='IP设置']") assert "IP设置" in el.text print("test_011 pass") wirenetwork.get_screenshot_as_file() except Exception as e: print("test_011 fail", format(e)) def test_012_ip_manual_set(self): wirenetwork = WireNetWorkPage(self.driver) wirenetwork.ip_ul_click() wirenetwork.ip_manual_set_click() try: el = "//span[@class='address-input-title' and text()='IP地址']/following-sibling::span[1]" assert self.driver.find_element_by_xpath(el) is True print("test_012 pass") wirenetwork.get_screenshot_as_file() except Exception as e: print("test_012 fail", format(e)) def test_013_ip_auto_set(self): wirenetwork = WireNetWorkPage(self.driver) wirenetwork.ip_ul_click() wirenetwork.ip_auto_set_click() try: el = "//span[@class='address-input-title' and text()='IP地址']/following-sibling::span[1]" assert self.driver.find_element_by_xpath(el) is True print("test_013 pass") wirenetwork.get_screenshot_as_file() except Exception as e: print("test_013 fail", format(e)) if __name__ == '__main__': unittest.main()

[ "421757223@qq.com" ]

421757223@qq.com

a5db37c7dc9f8509adffc6dc45b2b5386d2c55a7

f3a3228c1afa0e252fa041553e450b3b53e273ec

/zetcode/tetris.py

88a6ac7bbcdf3616296a1f89d250cfc2f9c15cbf

[]

no_license

dugbang/pyqt_prj

9395d25202d43fcadc577c9ff8606f649a575c9a

ed4fae66496e57258cdb22360273462a1ac59ea0

refs/heads/master

2020-04-13T07:04:41.980757

2019-01-29T00:41:17

163,039,698

0

null

UTF-8

Python

false

12,455

py

""" ZetCode PyQt5 tutorial This is a Tetris game clone. Author: Jan Bodnar Website: zetcode.com Last edited: August 2017 """ from PyQt5.QtWidgets import QMainWindow, QFrame, QDesktopWidget, QApplication from PyQt5.QtCore import Qt, QBasicTimer, pyqtSignal from PyQt5.QtGui import QPainter, QColor import sys, random class Tetris(QMainWindow): def __init__(self): super().__init__() self.initUI() def initUI(self): '''initiates application UI''' self.tboard = Board(self) self.setCentralWidget(self.tboard) self.statusbar = self.statusBar() self.tboard.msg2Statusbar[str].connect(self.statusbar.showMessage) self.tboard.start() self.resize(180, 380) self.center() self.setWindowTitle('Tetris') self.show() def center(self): '''centers the window on the screen''' screen = QDesktopWidget().screenGeometry() # screen = QDesktopWidget().availableGeometry() size = self.geometry() self.move((screen.width() - size.width()) / 2, (screen.height() - size.height()) / 2) def center_(self): qr = self.frameGeometry() cp = QDesktopWidget().availableGeometry().center() qr.moveCenter(cp) self.move(qr.topLeft()) class Board(QFrame): msg2Statusbar = pyqtSignal(str) BoardWidth = 10 BoardHeight = 22 Speed = 300 def __init__(self, parent): super().__init__(parent) self.initBoard() def initBoard(self): '''initiates board''' self.timer = QBasicTimer() self.isWaitingAfterLine = False self.curX = 0 self.curY = 0 self.numLinesRemoved = 0 self.board = [] self.setFocusPolicy(Qt.StrongFocus) self.isStarted = False self.isPaused = False self.clearBoard() def shapeAt(self, x, y): '''determines shape at the board position''' return self.board[(y * Board.BoardWidth) + x] def setShapeAt(self, x, y, shape): '''sets a shape at the board''' self.board[(y * Board.BoardWidth) + x] = shape def squareWidth(self): '''returns the width of one square''' return self.contentsRect().width() // Board.BoardWidth def squareHeight(self): '''returns the height of one square''' return self.contentsRect().height() // Board.BoardHeight def start(self): '''starts game''' if self.isPaused: return self.isStarted = True self.isWaitingAfterLine = False self.numLinesRemoved = 0 self.clearBoard() self.msg2Statusbar.emit(str(self.numLinesRemoved)) self.newPiece() self.timer.start(Board.Speed, self) def pause(self): '''pauses game''' if not self.isStarted: return self.isPaused = not self.isPaused if self.isPaused: self.timer.stop() self.msg2Statusbar.emit("paused") else: self.timer.start(Board.Speed, self) self.msg2Statusbar.emit(str(self.numLinesRemoved)) self.update() def paintEvent(self, event): '''paints all shapes of the game''' painter = QPainter(self) rect = self.contentsRect() boardTop = rect.bottom() - Board.BoardHeight * self.squareHeight() for i in range(Board.BoardHeight): for j in range(Board.BoardWidth): shape = self.shapeAt(j, Board.BoardHeight - i - 1) if shape != Tetrominoe.NoShape: self.drawSquare(painter, rect.left() + j * self.squareWidth(), boardTop + i * self.squareHeight(), shape) if self.curPiece.shape() != Tetrominoe.NoShape: for i in range(4): x = self.curX + self.curPiece.x(i) y = self.curY - self.curPiece.y(i) self.drawSquare(painter, rect.left() + x * self.squareWidth(), boardTop + (Board.BoardHeight - y - 1) * self.squareHeight(), self.curPiece.shape()) def keyPressEvent(self, event): '''processes key press events''' if not self.isStarted or self.curPiece.shape() == Tetrominoe.NoShape: super(Board, self).keyPressEvent(event) return key = event.key() if key == Qt.Key_P: self.pause() return if self.isPaused: return elif key == Qt.Key_Left: self.tryMove(self.curPiece, self.curX - 1, self.curY) elif key == Qt.Key_Right: self.tryMove(self.curPiece, self.curX + 1, self.curY) elif key == Qt.Key_Down: self.tryMove(self.curPiece.rotateRight(), self.curX, self.curY) elif key == Qt.Key_Up: self.tryMove(self.curPiece.rotateLeft(), self.curX, self.curY) elif key == Qt.Key_Space: self.dropDown() elif key == Qt.Key_D: self.oneLineDown() else: super(Board, self).keyPressEvent(event) def timerEvent(self, event): '''handles timer event''' if event.timerId() == self.timer.timerId(): if self.isWaitingAfterLine: self.isWaitingAfterLine = False self.newPiece() else: self.oneLineDown() else: super(Board, self).timerEvent(event) def clearBoard(self): '''clears shapes from the board''' for i in range(Board.BoardHeight * Board.BoardWidth): self.board.append(Tetrominoe.NoShape) def dropDown(self): '''drops down a shape''' newY = self.curY while newY > 0: if not self.tryMove(self.curPiece, self.curX, newY - 1): break newY -= 1 self.pieceDropped() def oneLineDown(self): '''goes one line down with a shape''' if not self.tryMove(self.curPiece, self.curX, self.curY - 1): self.pieceDropped() def pieceDropped(self): '''after dropping shape, remove full lines and create new shape''' for i in range(4): x = self.curX + self.curPiece.x(i) y = self.curY - self.curPiece.y(i) self.setShapeAt(x, y, self.curPiece.shape()) self.removeFullLines() if not self.isWaitingAfterLine: self.newPiece() def removeFullLines(self): '''removes all full lines from the board''' numFullLines = 0 rowsToRemove = [] for i in range(Board.BoardHeight): n = 0 for j in range(Board.BoardWidth): if not self.shapeAt(j, i) == Tetrominoe.NoShape: n = n + 1 if n == 10: rowsToRemove.append(i) rowsToRemove.reverse() for m in rowsToRemove: for k in range(m, Board.BoardHeight): for l in range(Board.BoardWidth): self.setShapeAt(l, k, self.shapeAt(l, k + 1)) numFullLines = numFullLines + len(rowsToRemove) if numFullLines > 0: self.numLinesRemoved = self.numLinesRemoved + numFullLines self.msg2Statusbar.emit(str(self.numLinesRemoved)) self.isWaitingAfterLine = True self.curPiece.setShape(Tetrominoe.NoShape) self.update() def newPiece(self): '''creates a new shape''' self.curPiece = Shape() self.curPiece.setRandomShape() self.curX = Board.BoardWidth // 2 + 1 self.curY = Board.BoardHeight - 1 + self.curPiece.minY() if not self.tryMove(self.curPiece, self.curX, self.curY): self.curPiece.setShape(Tetrominoe.NoShape) self.timer.stop() self.isStarted = False self.msg2Statusbar.emit("Game over") def tryMove(self, newPiece, newX, newY): '''tries to move a shape''' for i in range(4): x = newX + newPiece.x(i) y = newY - newPiece.y(i) if x < 0 or x >= Board.BoardWidth or y < 0 or y >= Board.BoardHeight: return False if self.shapeAt(x, y) != Tetrominoe.NoShape: return False self.curPiece = newPiece self.curX = newX self.curY = newY self.update() return True def drawSquare(self, painter, x, y, shape): '''draws a square of a shape''' colorTable = [0x000000, 0xCC6666, 0x66CC66, 0x6666CC, 0xCCCC66, 0xCC66CC, 0x66CCCC, 0xDAAA00] color = QColor(colorTable[shape]) painter.fillRect(x + 1, y + 1, self.squareWidth() - 2, self.squareHeight() - 2, color) painter.setPen(color.lighter()) painter.drawLine(x, y + self.squareHeight() - 1, x, y) painter.drawLine(x, y, x + self.squareWidth() - 1, y) painter.setPen(color.darker()) painter.drawLine(x + 1, y + self.squareHeight() - 1, x + self.squareWidth() - 1, y + self.squareHeight() - 1) painter.drawLine(x + self.squareWidth() - 1, y + self.squareHeight() - 1, x + self.squareWidth() - 1, y + 1) class Tetrominoe(object): NoShape = 0 ZShape = 1 SShape = 2 LineShape = 3 TShape = 4 SquareShape = 5 LShape = 6 MirroredLShape = 7 class Shape(object): coordsTable = ( ((0, 0), (0, 0), (0, 0), (0, 0)), ((0, -1), (0, 0), (-1, 0), (-1, 1)), ((0, -1), (0, 0), (1, 0), (1, 1)), ((0, -1), (0, 0), (0, 1), (0, 2)), ((-1, 0), (0, 0), (1, 0), (0, 1)), ((0, 0), (1, 0), (0, 1), (1, 1)), ((-1, -1), (0, -1), (0, 0), (0, 1)), ((1, -1), (0, -1), (0, 0), (0, 1)) ) def __init__(self): self.coords = [[0, 0] for i in range(4)] self.pieceShape = Tetrominoe.NoShape self.setShape(Tetrominoe.NoShape) def shape(self): '''returns shape''' return self.pieceShape def setShape(self, shape): '''sets a shape''' table = Shape.coordsTable[shape] for i in range(4): for j in range(2): self.coords[i][j] = table[i][j] self.pieceShape = shape def setRandomShape(self): '''chooses a random shape''' self.setShape(random.randint(1, 7)) def x(self, index): '''returns x coordinate''' return self.coords[index][0] def y(self, index): '''returns y coordinate''' return self.coords[index][1] def setX(self, index, x): '''sets x coordinate''' self.coords[index][0] = x def setY(self, index, y): '''sets y coordinate''' self.coords[index][1] = y def minX(self): '''returns min x value''' m = self.coords[0][0] for i in range(4): m = min(m, self.coords[i][0]) return m def maxX(self): '''returns max x value''' m = self.coords[0][0] for i in range(4): m = max(m, self.coords[i][0]) return m def minY(self): '''returns min y value''' m = self.coords[0][1] for i in range(4): m = min(m, self.coords[i][1]) return m def maxY(self): '''returns max y value''' m = self.coords[0][1] for i in range(4): m = max(m, self.coords[i][1]) return m def rotateLeft(self): '''rotates shape to the left''' if self.pieceShape == Tetrominoe.SquareShape: return self result = Shape() result.pieceShape = self.pieceShape for i in range(4): result.setX(i, self.y(i)) result.setY(i, -self.x(i)) return result def rotateRight(self): '''rotates shape to the right''' if self.pieceShape == Tetrominoe.SquareShape: return self result = Shape() result.pieceShape = self.pieceShape for i in range(4): result.setX(i, -self.y(i)) result.setY(i, self.x(i)) return result if __name__ == '__main__': app = QApplication([]) tetris = Tetris() sys.exit(app.exec_())

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#!/Users/aastha/Documents/GitHub/CMPE-273-quizzes/lab3/my-venv/bin/python # -*- coding: utf-8 -*- import re import sys from wheel.tool import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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def shared_letters(a, b): #รับค่าตัวเเปร a และ b lst = [] #สร้างตัวแปรรับค่า for i in a.lower(): #ถ้า i มีค่าใน a เป็นตัวพิมพ์เล็ก if i in b.lower(): #และ i มีค่าน b lst.append(i) #ให้เพิ่ม i ในตัวแปร lst return ''.join(sorted(set(lst))) #ส่งตัวข้อมูลใน lst ออก print(shared_letters("house", "home")) print(shared_letters("Micky", "mouse")) print(shared_letters("house", "villa"))

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import math A=input().split(',') B=input() def judge(A,B): if A.count(B)!=0: return A.index(B) else: return -1 print(judge(A,B))

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from django.apps import AppConfig class YaAccountsAppConfig(AppConfig): name = 'yadjangoblog.yaaccounts' verbose_name = 'YaAccounts模块'

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twocucao@gmail.com

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""" Django settings for TODO project. Generated by 'django-admin startproject' using Django 2.1.1. For more information on this file, see https://docs.djangoproject.com/en/2.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.1/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'mitb8&^*0ibt!u_xqe1!tjzumo65hy@cnxt-z#+9+p@m$u8qnn' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'main', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'TODO.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'TODO.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/'

[ "nusmailov@gmail.com" ]

nusmailov@gmail.com

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akshayadiga/Optical-Character-Recognition-of-English-Alphabets-using-Neural-Networks

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2021-01-25T13:07:13.730875

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import neurolab as nl def toProb(letter): l=[] int_letter=ord(letter); pos=int_letter-97 for i in range(26): if(i==pos): l.append(1) else: l.append(0) return l def main(): # X = matrix of m input examples and n features f=open("letter.data","r") X=[] Y=[] count=0 for line in f: vector=line.strip().split() in_vec=vector[6:] out_vec=vector[1] in_vec=[int(i) for i in in_vec] #out_vec=[int(i) for i in out_vec] X.append(in_vec) Y.append(out_vec) count=count+1 if(count==800): break #X=numpy.matrix(X) f.close() # Y = matrix of m output vectors, where each output vector has k output units #Y=numpy.matrix(Y) #print X #print Y Y=[toProb(i) for i in Y] net = nl.net.newff([[0, 1]]*128, [20, 26],transf=[nl.trans.TanSig(),nl.trans.SoftMax()]) net.train(X, Y, epochs=20, show=1, goal=0.02) #z=net.sim([X[1]]) #print z f=open("letter.data","r") X=[] Y=[] count=0 for line in f: if(count<800): count=count+1 continue vector=line.strip().split() in_vec=vector[6:] out_vec=vector[1] in_vec=[int(i) for i in in_vec] #out_vec=[int(i) for i in out_vec] X.append(in_vec) Y.append(out_vec) count=count+1 if(count==1000): break z=net.sim(X) bit_let_pair=zip(X,Y) b=[i for p,i in bit_let_pair] correct=0 incorrect=0 let_predict=[] ###change each index to appropriate letter##### for i in z: probs =i prob_letter=max(probs) for j in range(26): if(probs[j]==prob_letter): prob_pos=j prob_pos+=97 let_predict.append(chr(prob_pos)) #print(let_predict) #print(b) ################################ for i in range(len(let_predict)): if(let_predict[i]==bit_let_pair[i][1]): correct+=1 else: incorrect+=1 efficiency=correct/(float(correct+incorrect)) print (efficiency*100),"%" #e = net.train(input, output, show=1, epochs=100, goal=0.0001) main()

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from pymongo import MongoClient import os from bson import json_util client = MongoClient('mongodb://localhost:27017') print(client) db = client['testdb'] collection = db.movie.find() print(collection) y = list(collection) print(y) #x = json_util.dumps({'data': collection }) #print(x)

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Nusha97/IRIS-Project

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2021-08-07T10:58:02.602248

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# -*- coding: utf-8 -*- """ Automatically detect rotation and line spacing of an image of text using Radon transform If image is rotated by the inverse of the output, the lines will be horizontal (though they may be upside-down depending on the original image) It doesn't work with black borders """ from __future__ import division, print_function from skimage.transform import radon from PIL import Image from numpy import asarray, mean, array, blackman import numpy from numpy.fft import rfft import matplotlib.pyplot as plt from matplotlib.mlab import rms_flat try: # More accurate peak finding from # https://gist.github.com/endolith/255291#file-parabolic-py from parabolic import parabolic def argmax(x): return parabolic(x, numpy.argmax(x))[0] except ImportError: from numpy import argmax filename = '2Drotate.png' # Load file, converting to grayscale I = asarray(Image.open(filename).convert('L')) I = I - mean(I) # Demean; make the brightness extend above and below zero # Do the radon transform and display the result sinogram = radon(I) plt.gray() # Find the RMS value of each row and find "busiest" rotation, # where the transform is lined up perfectly with the alternating dark # text and white lines r = array([rms_flat(line) for line in sinogram.transpose()]) rotation = argmax(r) print('Rotation: {:.2f} degrees'.format(90 - rotation)) import argparse import cv2 import numpy as np img = cv2.imread('2Drotate.png', 0) rows,cols = img.shape M = cv2.getRotationMatrix2D((cols/2,rows/2),90 - rotation,1) dst = cv2.warpAffine(img,M,(cols,rows)) plt.plot(121),plt.imshow(dst),plt.title('Output') plt.savefig('hello.png') plt.show()

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noreply@github.com

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bobobyu/leecode-

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2023-01-30T23:35:00.882463

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from typing import * from collections import * class Solution: def reverseParentheses(self, s: str) -> str: string_stack: Deque = deque() s = list(s) while s: if (op := s.pop(0)) == '(': string_stack.append('(') elif op == ')': temp: str = '' while string_stack[-1] != '(': temp = string_stack.pop() + temp string_stack[-1] = temp[::-1] else: string_stack.append(op) return ''.join(string_stack) s = Solution() print(s.reverseParentheses("a(bcdefghijkl(mno)p)q"))

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aachenmax/bitmovin-api-sdk-python

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# coding: utf-8 from six import string_types, iteritems from bitmovin_api_sdk.common.poscheck import poscheck_model from bitmovin_api_sdk.models.bitmovin_resource import BitmovinResource import pprint class Filter(BitmovinResource): discriminator_value_class_map = { 'CROP': 'CropFilter', 'CONFORM': 'ConformFilter', 'WATERMARK': 'WatermarkFilter', 'ENHANCED_WATERMARK': 'EnhancedWatermarkFilter', 'ROTATE': 'RotateFilter', 'DEINTERLACE': 'DeinterlaceFilter', 'AUDIO_MIX': 'AudioMixFilter', 'DENOISE_HQDN3D': 'DenoiseHqdn3dFilter', 'TEXT': 'TextFilter', 'UNSHARP': 'UnsharpFilter', 'SCALE': 'ScaleFilter', 'INTERLACE': 'InterlaceFilter', 'AUDIO_VOLUME': 'AudioVolumeFilter', 'EBU_R128_SINGLE_PASS': 'EbuR128SinglePassFilter' } def to_dict(self): """Returns the model properties as a dict""" result = {} if hasattr(super(Filter, self), "to_dict"): result = super(Filter, self).to_dict() for k, v in iteritems(self.discriminator_value_class_map): if v == type(self).__name__: result['type'] = k break return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, Filter): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

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szpone/climbing-gear

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2022-12-12T11:08:57.277056

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import graphene from graphene_django.types import DjangoObjectType, ObjectType from .models import Gear class GearType(DjangoObjectType): class Meta: model = Gear class Query(ObjectType): gear = graphene.Field(GearType, id=graphene.Int()) gears = graphene.List(GearType) def resolve_gear(self, info, gear_id): return Gear.objects.filter(id=gear_id).first() def resolve_gears(self, info, **kwargs): return Gear.objects.all() schema = graphene.Schema(query=Query)

[ "nikola.adamus@gmail.com" ]

nikola.adamus@gmail.com

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Brandyzwz/practice

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661f74851af6208b5c6880b41ba5d7bc9da6bf5c

refs/heads/master

2020-03-25T02:25:51.136558

2018-08-02T12:06:44

143,289,146

0

null

UTF-8

Python

false

130

py

a = int(input('输入直角边a:\n')) b = int(input('输入直角边b:\n')) c = (a**2+b**2)**0.5 print('斜边c的长为：%d'%c)

[ "brandyzwz@outlook.com" ]

brandyzwz@outlook.com

63b17a08ac2f4745e14601141a43ae06dd3014d8

e4dfc1402839f277e1e9ff8686dc6b67f1eb0bf0

/api_example/languages/urls.py

289d6f6c2218819b00280ffb8242142ac2eacf15

[]

no_license

punitchauhan771/Sample-Rest-API

32ee7a16270a978ac4d82a161fe4fb677c029d36

8372982507fa2ee301d3a9de1e6fe9d4b66028ed

refs/heads/main

2023-02-24T00:49:57.831224

2021-01-23T08:23:35

331,853,402

0

null

UTF-8

Python

false

331

py

from django.urls import path, include from . import views from rest_framework import routers router = routers.DefaultRouter() router.register('languages', views.LanguageView) router.register('Paradigm', views.ParadigmView) router.register('Programmer',views.ProgrammerView) urlpatterns = [ path('', include(router.urls)) ]

[ "chauhanbhupendra980@gmail.com" ]

chauhanbhupendra980@gmail.com

16eb3d8ca61b71e4472dd9dbccbad3c0497e2eb6

9f59572095262bb77b1069154dd70f52a2743582

/utils/.history/helper_20210303152111.py

db03f12b94ec4773cd7b73e126c98158e374fa06

[]

no_license

zhang199609/diagnose_fault_by_vibration

ef089807fd3ae6e0fab71a50863c78ea163ad689

7b32426f3debbe9f98a59fe78acdec3ad6a186fd

refs/heads/master

2023-04-15T15:35:59.000854

2021-04-23T06:53:40

null

0

null

UTF-8

Python

false

868

py

""" 提高代码简洁性和可交互性的代码 """ import time def get_running_time(fun): """ 代码运行时间装饰器参数 ----- fun：要测试运行时间的函数返回 ----- 返回装饰器wrapper 例子 ----- >>> @get_running_time def hello(name): print("hello %s"%name) time.sleep(3) >>> hello("Tony") """ def wrapper(*args, **kwargs): start_time = time.time() # 调用需要计算运行时间的函数 fun(*args, **kwargs) end_time = time.time() running_time = end_time - start_time h = int(running_time//3600) m = int((running_time - h*3600)//60) s = int(running_time%60) print("time cost: {0}:{1}:{2}".format(h, m, s)) return running_time # -> 可以省略 return wrapper

[ "18036834556@163.com" ]

18036834556@163.com

f59eef689da00fb5d14fdfaddf69c05fcdb4d412

531c47c15b97cbcb263ec86821d7f258c81c0aaf

/sdk/labservices/azure-mgmt-labservices/azure/mgmt/labservices/models/lab_account_fragment.py

0e3a2fb1fa1897771c1c81ee386226f6730a5827

[ "LicenseRef-scancode-generic-cla", "LGPL-2.1-or-later", "MIT" ]

permissive

YijunXieMS/azure-sdk-for-python

be364d3b88204fd3c7d223df23756386ff7a3361

f779de8e53dbec033f98f976284e6d9491fd60b3

refs/heads/master

2021-07-15T18:06:28.748507

2020-09-04T15:48:52

205,457,088

1

2

MIT

2020-06-16T16:38:15

2019-08-30T21:08:55

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UTF-8

Python

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2,376

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# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from .resource import Resource class LabAccountFragment(Resource): """Represents a lab account. Variables are only populated by the server, and will be ignored when sending a request. :ivar id: The identifier of the resource. :vartype id: str :ivar name: The name of the resource. :vartype name: str :ivar type: The type of the resource. :vartype type: str :param location: The location of the resource. :type location: str :param tags: The tags of the resource. :type tags: dict[str, str] :param enabled_region_selection: Represents if region selection is enabled :type enabled_region_selection: bool :param provisioning_state: The provisioning status of the resource. :type provisioning_state: str :param unique_identifier: The unique immutable identifier of a resource (Guid). :type unique_identifier: str """ _validation = { 'id': {'readonly': True}, 'name': {'readonly': True}, 'type': {'readonly': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, 'location': {'key': 'location', 'type': 'str'}, 'tags': {'key': 'tags', 'type': '{str}'}, 'enabled_region_selection': {'key': 'properties.enabledRegionSelection', 'type': 'bool'}, 'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'}, 'unique_identifier': {'key': 'properties.uniqueIdentifier', 'type': 'str'}, } def __init__(self, **kwargs): super(LabAccountFragment, self).__init__(**kwargs) self.enabled_region_selection = kwargs.get('enabled_region_selection', None) self.provisioning_state = kwargs.get('provisioning_state', None) self.unique_identifier = kwargs.get('unique_identifier', None)

[ "lmazuel@microsoft.com" ]

lmazuel@microsoft.com

3921c679f0f414668848b1d37b5d076edec45b8d

5d1441cc173e06fb24c389eb812067a3fc355587

/workflow/templatetags/custom.py

c9f466a6cebfba8a0db9f818d8958efed3756c15

[]

no_license

Johums/ProjectManage

2860eb12134d9b522c5a5f2fa4e4054533d9175a

22d662e089adab447f247d078c89c670384e78ff

refs/heads/master

2021-01-10T16:36:47.412675

2016-02-27T15:24:13

52,213,655

0

null

UTF-8

Python

false

641

py

# -*- coding: utf-8 -*- from django import template from django.utils import safestring register = template.Library() @register.filter def multi_menu(menu, move=0): html_content = """ <ul class="nav nav-pills nav-stacked" style="margin-left: %spx" > """ % move for k, v in menu.items(): html_content += """ <li data-toggle="collapse" data-target=".demo"> <a href="{1}"><small>{2}</small></a> </li> """.format(*k) if v: html_content += multi_menu(v, move + 20) html_content += """ </ul> """ return safestring.mark_safe(html_content)

[ "13276915582@163.com" ]

13276915582@163.com

5c5a54f0963e8d6bd055050c7770fbf455661208

12a62bbca8065dcb6d835144368f6ad4cf46f219

/random_proj.py

e15f439ab7201a601e168512de11ffb755e3dcbf

[]

no_license

daemonmaker/biglittle

c2371b198a43273275144036e3971c8035efd588

feadb55aa68f5b54f52084e0a12368783c93dd78

refs/heads/master

2021-01-11T11:03:25.318130

2014-11-25T23:56:42

null

0

null

UTF-8

Python

false

26,591

py

#! /usr/bin/env python import time from datetime import datetime import numpy as np import sys import os import os.path as op import cPickle as pkl from itertools import product import gc import theano from theano import function from theano import tensor as T from theano import config from theano import shared from theano.tensor.shared_randomstreams import RandomStreams from utils import * from experiments import Experiments from layer import HiddenLayer, HiddenBlockLayer, HiddenRandomBlockLayer from timing_stats import TimingStats as TS from models import ( EqualParametersModel, EqualComputationsModel, SparseBlockModel, all_same ) def simple_train( model, train_model, test_model, validate_model, learning_rate, shared_learning_rate, timing_stats, n_epochs=1000 ): timing_stats.add(['epoch', 'train']) epoch = 0 minibatch_avg_cost_accum = 0 while(epoch < n_epochs): print "Epoch %d" % epoch timing_stats.start('epoch') for minibatch_index in xrange(model.data.n_train_batches): if minibatch_index % 10 == 0: print '... minibatch_index: %d/%d\r' \ % (minibatch_index, model.data.n_train_batches), # Note the magic comma on the previous line prevents new lines timing_stats.start('train') minibatch_avg_cost = train_model(minibatch_index) timing_stats.end('train') minibatch_avg_cost_accum += minibatch_avg_cost[0] print '... minibatch_avg_cost_accum: %f' \ % (minibatch_avg_cost_accum/float(model.data.n_train_batches)) timing_stats.end('epoch') epoch += 1 def train( model, train_model, test_model, validate_model, learning_rate, shared_learning_rate, timing_stats, n_epochs=1000 ): def summarize_rates(): print "Learning rate: ", learning_rate.rate # early-stopping parameters patience = 10000 # look as this many examples regardless patience_increase = 100 # wait this much longer when a new best is # found improvement_threshold = 0.995 # a relative improvement of this much is # considered significant validation_frequency = min(data.n_train_batches, patience / 2) # go through this many # minibatche before checking the network # on the validation set; in this case we # check every epoch best_params = None this_validation_loss = 0 best_validation_loss = np.inf best_iter = 0 test_score = 0. accum = 0 epoch = 0 done_looping = False timing_stats.add(['train', 'epoch', 'valid']) summarize_rates() timing_stats.start() while (epoch < n_epochs) and (not done_looping): epoch = epoch + 1 timing_stats.start('epoch') for minibatch_index in xrange(data.n_train_batches): timing_stats.start('train') minibatch_avg_cost = train_model(minibatch_index) timing_stats.end('train') #print "0: ", model.layers[-5].in_idxs.get_value() #print "1: ", model.layers[-4].in_idxs.get_value() #print "2: ", model.layers[-3].in_idxs.get_value() #print "3: ", model.layers[-2].in_idxs.get_value() #print "4: ", model.layers[-1].in_idxs.get_value() minibatch_avg_cost = minibatch_avg_cost[0] accum = accum + minibatch_avg_cost # print ( # "minibatch_avg_cost: " + str(minibatch_avg_cost) # + " minibatch_avg_cost: " + str(minibatch_avg_cost) # ) # print ( # l_layers[0].W.get_value().sum() # + ' ' + l_layers[1].W.get_value().sum() # + ' ' # + layers[0].W.get_value().sum() # + ' ' + layers[1].W.get_value().sum() # ) # print ( # "A: " + np.max(np.abs(layers[0].W.get_value())) # + ' ' + np.max(np.abs(layers[0].b.get_value())) # + ' ' + np.max(np.abs(layers[1].W.get_value())) # + ' ' + np.max(np.abs(layers[1].b.get_value())) # ) # print ( # "B: " + np.abs(layers[0].W.get_value()).sum() # + ' ' + np.abs(layers[0].b.get_value()).sum() # + ' ' + np.abs(layers[1].W.get_value()).sum() # + ' ' + np.abs(layers[1].b.get_value()).sum() # ) # print ( # "C: " + np.abs(np.array(minibatch_avg_cost[1])).sum() # + ' ' + np.abs(np.array(minibatch_avg_cost[2])).sum() # + ' ' + np.abs(np.array(minibatch_avg_cost[3])).sum() # + ' ' + np.abs(np.array(minibatch_avg_cost[4])).sum() # ) # iteration number iter = (epoch - 1) * data.n_train_batches + minibatch_index if (iter + 1) % validation_frequency == 0: timing_stats.end('epoch') timing_stats.reset('epoch') timing_stats.reset('train') accum = accum / validation_frequency summary = ("minibatch_avg_cost: %f, time: %f" % (accum, timing_stats.accumed['train'][-1][1])) accum = 0 print "%s" % (summary) # compute zero-one loss on validation set summary = ( 'epoch %i, minibatch %i/%i' % ( epoch, minibatch_index + 1, data.n_train_batches ) ) validation_losses = [validate_model(i) for i in xrange(data.n_valid_batches)] this_validation_loss = np.mean(validation_losses) #this_validation_loss = 0 summary = ('validation error %f %% ' % (this_validation_loss * 100.)) print ("%s" % (summary)) # if we got the best validation score until now this_validation_loss = this_validation_loss if this_validation_loss < best_validation_loss: #improve patience if loss improvement is good enough if this_validation_loss < best_validation_loss * \ improvement_threshold: patience = max(patience, iter * patience_increase) best_validation_loss = this_validation_loss best_iter = iter # test it on the test set test_losses = [test_model(i) for i in xrange(data.n_test_batches)] test_score = np.mean(test_losses) #test_score = 0 summary = 'test_score: %f' % (test_score * 100.) print (' epoch %i, minibatch %i/%i,' ' test error of best model %s' % (epoch, minibatch_index + 1, data.n_train_batches, summary)) learning_rate.update() shared_learning_rate.set_value(learning_rate.rate) summarize_rates() if patience <= iter: done_looping = True break timing_stats.end() print('Optimization complete. Best validation score of %f %% ' 'obtained at iteration %i, with test performance %f %%' % (best_validation_loss * 100., best_iter + 1, test_score * 100.)) print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] + ' ran for %s' % timing_stats) def run_experiments(exps, models, rng=None): if rng is None: rng = np.random.RandomState() data = None model = None timings = None for idx, model_class in product(exps, models): print 'Experiment: %d, Model class: %s' % (idx, model_class) parameters = exps.get_parameters_by_exp_idx(idx) print 'Batch size: %d' % parameters['batch_size'] if ( data is None or data.batch_size != parameters['batch_size'] or data.reshape_data != model_class.reshape_data ): print 'Loading Data' print '... MNIST' data = MNIST(parameters['batch_size'], model_class.reshape_data) gc.collect() try: shared_learning_rate = shared( np.array( parameters['learning_rate'].rate, dtype=config.floatX ), name='learning_rate' ) timings = TS(['build_model', 'build_functions', 'full_train']) print 'Building model: %s' % str(model_class) timings.start('build_model') layer_definitions = exps.get_layers_definition(idx) model = model_class( data=data, layer_descriptions=layer_definitions, batch_size=parameters['batch_size'], learning_rate=shared_learning_rate, L1_reg=parameters['L1_reg'], L2_reg=parameters['L2_reg'], ) print '... time: %f' % timings.end('build_model') print 'Building functions' timings.start('build_functions') functions = model.build_functions() print '... time: %f' % timings.end('build_functions') print 'Training' timings.start('full_train') simple_train( model, learning_rate=parameters['learning_rate'], shared_learning_rate=shared_learning_rate, n_epochs=parameters['n_epochs'], timing_stats=timings, **functions ) print 'Training time: %d' % timings.end('full_train') model = None except MemoryError: epoch_time = -1 if timings is not None: print 'Timings: %s' % timings exps.save(idx, model_class.__name__, 'timings', timings) timings = None gc.collect() pkl.dump(exps, open('random_proj_experiments.pkl', 'wb')) def plot_times_by_batch(database): import matplotlib.pyplot as plt # Load the database exps = pkl.load(open(database, 'rb')) # Find experiments that have results exp_idxs = exps.get_idxs('experiments', has_results=True) # Plot results for each experiment grouped by the layers_description layers_description_idxs = exps.get_table_idxs_by_exp_idxs( 'layers_description', exp_idxs ) for layers_description_idx in layers_description_idxs: result_idxs = exps.get_result_idxs_by_table_idx( 'layers_description', layers_description_idx ) batch_sizes = [exps.get_parameters_by_exp_idx(idx)['batch_size'] for idx in result_idxs] timings = {model_name: np.zeros(len(batch_sizes)) for model_name in exps.results[result_idxs[0]].keys()} for i, idx in enumerate(result_idxs): for model_name, stats in exps.results[idx].iteritems(): timings[model_name][i] = stats[ 'timings' ].mean_difference('train')/batch_sizes[i] for model_name, timings in timings.iteritems(): plt.plot(batch_sizes, timings, marker='o', label=model_name,) plt.title('Train time per sample', fontsize=12) layers_description = exps.get_layers_description( layers_description_idx ) plt.suptitle( 'layers_description_idx: %d, n_units: %s, n_hids: %s,\n' 'k_pers: %s, all same: %r' % ( layers_description_idx, layers_description['n_hids'], layers_description['n_units_per'], layers_description['k_pers'], 'index_selection_funcs' in layers_description.keys() ), y=0.99, fontsize=10 ) plt.xlabel('Batch Size') plt.ylabel('Time (s)') plt.legend() plt.xticks(batch_sizes) figs_dir = 'figs' if not op.exists(figs_dir): os.mkdir(figs_dir) plt.savefig( op.join( figs_dir, 'layers_description_%d.png' % layers_description_idx ), format='png' ) plt.show() if __name__ == '__main__': import argparse parser = argparse.ArgumentParser( description='Run random_proj experiments and plot results' ) parser.add_argument( '-m', '--use_layers', type=int, default=[], nargs='+', help='Identifier for which models to use in the experiments.' ) parser.add_argument( '-c', '--layer_class', default='HiddenRandomBlockLayer', help='The type of layer to use in the block sparse model.' ) parser.add_argument( '-b', '--batch_sizes', type=int, default=[32], nargs='+', help='Range of batch sizes to test.' ) parser.add_argument( '-n', '--number_of_epochs', type=int, default=1, help='Number of epochs to execute for each experiment.' ) parser.add_argument( '-u', '--units_per_block', type=int, default=32, help='Number of units per block in the sparse block models.' ) parser.add_argument( '-d', '--database', default='random_proj_experiments.pkl', help='Which database to use.' ) parser.add_argument( '-l', '--load_database', default=False, action='store_true', help='Whether to load an existing database.' ) parser.add_argument( '-p', '--plot', default=False, action='store_true', help='Plot results instaed of execute experiments.' ) args = parser.parse_args() if args.plot: plot_times_by_batch(args.database) else: if args.load_database: exps = pkl.load(open(args.database)) else: ## Determine the type of sparsity layer to use if args.layer_class == 'HiddenRandomBlockLayer': layer_class = HiddenRandomBlockLayer else: layer_class = HiddenBlockLayer ## Create experiments exps = Experiments( input_dim=784, # data.train_set_x.shape[-1].eval(), num_classes=10 ) # Add descriptions of models exps.add_layers_description( 0, { 'n_hids': (25,), 'n_units_per': args.units_per_block, 'k_pers': (1, 1), 'activations': (T.tanh, None), 'layer_classes': [ HiddenBlockLayer, HiddenBlockLayer, ], } ) exps.add_layers_description( 1, { 'n_hids': (25, 25), 'n_units_per': args.units_per_block, 'k_pers': (1, 0.5, 1), 'activations': (T.tanh, T.tanh, None), 'layer_classes': [ HiddenBlockLayer, layer_class, HiddenBlockLayer, ], } ) exps.add_layers_description( 2, { 'n_hids': (25, 100, 25), 'n_units_per': args.units_per_block, 'k_pers': (1, 0.25, 0.25, 1), 'activations': (T.tanh, T.tanh, T.tanh, None), 'layer_classes': [ HiddenBlockLayer, layer_class, layer_class, HiddenBlockLayer, ], } ) exps.add_layers_description( 3, { 'n_hids': (25, 100, 25), 'n_units_per': args.units_per_block, 'k_pers': (1., 0.25, 0.25, 1), 'activations': (T.tanh, T.tanh, T.tanh, None), 'layer_classes': [ HiddenBlockLayer, layer_class, layer_class, HiddenBlockLayer, ], 'index_selection_funcs': ( None, all_same, all_same, None ) } ) exps.add_layers_description( 4, { 'n_hids': (50, 100, 20), 'n_units_per': args.units_per_block, 'k_pers': (1, 0.05, 0.2, 1), 'activations': (T.tanh, T.tanh, T.tanh, None), 'layer_classes': [ HiddenBlockLayer, layer_class, layer_class, HiddenBlockLayer, ], }, ) exps.add_layers_description( 5, { 'n_hids': (50, 100, 20), 'n_units_per': args.units_per_block, 'k_pers': (1, 0.05, 0.05, 1), 'activations': (T.tanh, T.tanh, T.tanh, None), 'layer_classes': [ HiddenBlockLayer, layer_class, layer_class, HiddenBlockLayer, ], 'index_selection_funcs': ( None, all_same, all_same, None ) }, ) exps.add_layers_description( 6, { 'n_hids': (25, 100, 100, 25), 'n_units_per': args.units_per_block, 'k_pers': (1, 0.05, 0.05, 1, 1), 'activations': (T.tanh, T.tanh, T.tanh, T.tanh, None), 'layer_classes': [ HiddenBlockLayer, layer_class, layer_class, layer_class, HiddenBlockLayer, ], } ) exps.add_layers_description( 7, { 'n_hids': (25, 100, 100, 25), 'n_units_per': args.units_per_block, 'k_pers': (1, 0.05, 0.05, 1, 1), 'activations': (T.tanh, T.tanh, T.tanh, T.tanh, None), 'layer_classes': [ HiddenBlockLayer, layer_class, layer_class, layer_class, HiddenBlockLayer, ], 'index_selection_funcs': ( None, all_same, all_same, all_same, None ) } ) exps.add_layers_description( 8, { 'n_hids': (50, 200, 500, 200, 50), 'n_units_per': args.units_per_block, 'k_pers': (1., 0.1, 0.02, 0.02, 0.1, 1), 'activations': ( None, T.tanh, T.tanh, T.tanh, T.tanh, None ), 'layer_classes': [ HiddenBlockLayer, layer_class, layer_class, layer_class, layer_class, HiddenBlockLayer, ], } ) exps.add_layers_description( 9, { 'n_hids': (50, 75, 200, 75, 50), 'n_units_per': args.units_per_block, 'k_pers': (1., 0.1, 0.05, 0.05, 0.1, 1), 'activations': ( T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, None ), 'layer_classes': [ HiddenBlockLayer, layer_class, layer_class, layer_class, layer_class, HiddenBlockLayer, ], 'index_selection_funcs': ( None, all_same, all_same, all_same, all_same, None ) } ) exps.add_layers_description( 10, { 'n_hids': (50, 500, 500, 500, 500, 20), 'n_units_per': args.units_per_block, 'k_pers': (1, 0.07, 0.03, 0.02, 0.01, 0.15, 1), 'activations': ( T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, None ), 'layer_classes': [ HiddenBlockLayer, layer_class, layer_class, layer_class, layer_class, layer_class, HiddenBlockLayer, ], }, ) exps.add_layers_description( 11, { 'n_hids': (50, 500, 500, 500, 500, 20), 'n_units_per': args.units_per_block, 'k_pers': (1, 0.07, 0.03, 0.02, 0.01, 0.15, 1), 'activations': ( T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, None ), 'layer_classes': [ HiddenBlockLayer, layer_class, layer_class, layer_class, layer_class, layer_class, HiddenBlockLayer, ], 'index_selection_funcs': ( None, all_same, all_same, all_same, all_same, all_same, None ) } ) exps.add_layers_description( 12, { 'n_hids': (50, 100, 500, 500, 500, 500, 500, 100, 20), 'n_units_per': args.units_per_block, 'k_pers': (1, 0.1, 0.05, 0.01, 0.01, 0.01, 0.01, 0.05, 0.1, 1), 'activations': ( None, T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, None ), 'layer_classes': [ HiddenBlockLayer, layer_class, layer_class, layer_class, layer_class, layer_class, layer_class, layer_class, layer_class, HiddenBlockLayer, ], }, ) exps.add_layers_description( 13, { 'n_hids': (50, 100, 500, 500, 500, 500, 500, 100, 20), 'n_units_per': args.units_per_block, 'k_pers': (1, 0.1, 0.05, 0.01, 0.01, 0.01, 0.1, 0.5, 0.1, 1), 'activations': ( None, T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, T.tanh, None ), 'layer_classes': [ HiddenBlockLayer, layer_class, layer_class, layer_class, layer_class, layer_class, layer_class, layer_class, layer_class, HiddenBlockLayer, ], 'index_selection_funcs': ( None, all_same, all_same, all_same, all_same, all_same, None ) } ) exps.add_layers_description( 14, { 'n_hids': (50, 100, 20), 'n_units_per': args.units_per_block, 'k_pers': (1, 0.05, 0.05, 1), 'activations': (T.tanh, T.tanh, T.tanh, None), 'layer_classes': [ layer_class, layer_class, layer_class, layer_class, ], }, ) # Add parameter combinations for idx, batch_size in enumerate(args.batch_sizes): exps.add_parameters( idx, { 'n_epochs': args.number_of_epochs, 'batch_size': batch_size, 'learning_rate': LinearChangeRate( 0.21, -0.01, 0.2, 'learning_rate' ), 'L1_reg': 0.0, 'L2_reg': 0.0001 } ) if len(args.use_layers) > 0: print 'Executing experiments for layers %s' % args.use_layers exps.create_experiments(args.use_layers) else: exps.create_experiments() run_experiments( exps, models=[ EqualParametersModel, EqualComputationsModel, SparseBlockModel ] )

[ "daemonmaker@gmail.com" ]

daemonmaker@gmail.com

6df0a8249cc984e79381ba0ffcddd3d27403a62b

0c72282d601ccf840dd4e41b675c0675de7bc916

/students/Jean-Baptiste/lessons/lesson03/assignment03_solution_JB/create_customers.py

45516c4640f57281eda038e5e82484c20727fe20

[]

no_license

zconn/PythonCert220Assign

c7fedd9ffae4f9e74e5e4dfc59bc6c511c7900ab

99271cd60485bd2e54f8d133c9057a2ccd6c91c2

refs/heads/master

2020-04-15T14:42:08.765699

2019-03-14T09:13:36

164,763,504

2

0

null

2019-01-09T01:34:40

null

UTF-8

Python

false

360

py

""" This is to create database using the Peewee ORM, sqlite and Python """ from customers_model import * import customers_model as cm import logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) logger.info('Let us build the classes from the model in the database') cm.database.create_tables([cm.Customer]) cm.database.close()

[ "jbyamindi@yahoo.fr" ]

jbyamindi@yahoo.fr

d82cf9e821ecf30bd91d020d422728952809a303

597ed154876611a3d65ca346574f4696259d6e27

/dbaas/workflow/steps/tests/test_vm_step.py

1f05feed7c79364c570f0ed132f5da3578825a91

[]

permissive

soitun/database-as-a-service

41984d6d2177734b57d726cd3cca7cf0d8c5f5d6

1282a46a9437ba6d47c467f315b5b6a3ac0af4fa

refs/heads/master

2023-06-24T17:04:49.523596

2018-03-15T19:35:10

128,066,738

0

BSD-3-Clause

2022-05-10T22:39:58

2018-04-04T13:33:42

Python

UTF-8

Python

false

1,661

py

from mock import patch from physical.tests.factory import HostFactory, EnvironmentFactory from ..util.vm import VmStep, MigrationWaitingBeReady from . import TestBaseStep @patch('workflow.steps.util.vm.get_credentials_for', return_value=True) @patch('workflow.steps.util.vm.CloudStackProvider', return_value=object) class VMStepTests(TestBaseStep): def setUp(self): super(VMStepTests, self).setUp() self.host = self.instance.hostname def test_environment(self, *args, **kwargs): vm_step = VmStep(self.instance) self.assertEqual(vm_step.environment, self.environment) def test_host(self, *args, **kwargs): vm_step = VmStep(self.instance) self.assertEqual(vm_step.host, self.host) @patch('workflow.steps.util.vm.get_credentials_for', return_value=True) @patch('workflow.steps.util.vm.CloudStackProvider', return_value=object) class VMStepTestsMigration(TestBaseStep): def setUp(self): super(VMStepTestsMigration, self).setUp() self.host = self.instance.hostname self.future_host = HostFactory() self.host.future_host = self.future_host self.host.save() self.environment_migrate = EnvironmentFactory() self.environment.migrate_environment = self.environment_migrate self.environment.save() def test_environment(self, *args, **kwargs): vm_step = MigrationWaitingBeReady(self.instance) self.assertEqual(vm_step.environment, self.environment_migrate) def test_host(self, *args, **kwargs): vm_step = MigrationWaitingBeReady(self.instance) self.assertEqual(vm_step.host, self.future_host)

[ "mauro_murari@hotmail.com" ]

mauro_murari@hotmail.com

b8572b08870ce01777c59a851f52f3cd3d40ed69

ed6dd94781e3022f230050284d2ddd3554cc0772

/multithreading/multiprocessing_pipes_conttest.py

379999612d2531d37797406abcedc405c450bf1c

[]

no_license

Mantabit/python_examples

602d4f4237dbc2044d30dc5482e3e2dee4d90fb6

516dbb9cc63c7de5bfe7d0e79477dff9ff340a5d

refs/heads/master

2021-07-04T08:26:38.007606

2020-08-17T10:09:04

153,170,298

0

null

UTF-8

Python

false

1,224

py

import multiprocessing as mp import time class testClass(object): def __init__(self,name): self.name=name def doSomething(self): print("Object %s reporting!"%(self.name)) #function receives objects def receiverFunction(receive_end): while True: #receive object from the pipe try: obj=receive_end.recv() except EOFError as err: print("nothing left in the queue, aborting receiver thread") break #use the received object obj.doSomething() #function generates objects def producerFunction(send_end): start=time.time() i=0 #produce data every 50ms for 5s while time.time()-start<1: i+=1 send_end.send(testClass("Object%d"%(i))) time.sleep(50e-3) print("Closing the send_end in producer process...") send_end.close() if __name__=="__main__": (receive_end,send_end)=mp.Pipe() p_recv=mp.Process(target=receiverFunction,args=[receive_end]) p_send=mp.Process(target=producerFunction,args=[send_end]) p_recv.start() p_send.start() p_send.join() send_end.close() print("Closing send_end in parent process") p_recv.join()

[ "dvarx@gmx.ch" ]

dvarx@gmx.ch

8bf31f37886bfff9512c59b2d24b2699e2383f4b

559d7428cba525ddff43a4b03f495c070f382075

/Final/FinalExam/Q1/lazy.py

0ecd5c3d8b15acde96bbd4735217dc6431ee8534

[]

no_license

kwokwill/comp3522-object-oriented-programming

9c222ad4d1a2c2420a5eb509f80ba792e94991f6

6e2347b70c07cfc3ca83af29c2bd5c4696c55bb6

refs/heads/master

2023-04-13T19:16:25.546865

2021-04-27T21:20:48

null

0

null

UTF-8

Python

false

1,260

py

import time """ (4 marks total) This program simulates loading screens in a game. One of the screens is entered and resources need to be loaded, the other does not The code below takes about 1 second (on my machine) to complete. Requirements Speed up the code using the LAZY INITIALIZATION design pattern. Do NOT change any code in the main function Hints: The code should run in about half the time after implementing Lazy Initialization There is no need to use any multithreading/multiprocessing """ class Resources: def __init__(self): print("Creating resources") time.sleep(0.5) def __str__(self): return "resources available" class Screen: def __init__(self, name): self._name = name self._resources = None def enter_screen(self): if not self._resources: self._resources = Resources() return self._resources def __str__(self): return self._name def main(): start_time = time.time() game_over = Screen("Game over") print(game_over) main_menu = Screen("Main menu") print(main_menu) print(main_menu.enter_screen()) end_time = time.time() print("duration:", end_time - start_time) if __name__ == '__main__': main()

[ "donglmin@icloud.com" ]

donglmin@icloud.com

1f0f45aa77603540df78c0dde6159ce16e10364a

873b6d338e696b200d1a6ca74bef85deaa8d8088

/manage.py

b4f28f3c7627588f205a3b43b083e42d7990486f

[]

no_license

Craiglal/ChudoSkrynia

9720c8360f1589b97c15c5cfa48ba15bf2c6d0e7

ef2ca9ca356666628f2c8e4d1df8e97e0d0f72eb

refs/heads/master

2020-08-15T09:23:57.076445

2019-10-15T14:28:26

215,316,335

1

0

null

UTF-8

Python

false

544

py

#!/usr/bin/env python import os import sys if __name__ == '__main__': os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'ChudoSkrynia.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv)

[ "asleep.alex@gmail.com" ]

asleep.alex@gmail.com

64923cbcfed9624b6b8c664e8deaf3ad28ade468

a756e26160502b49dea686baa4f8d8480895ab85

/PartB_LBC_CorrespondingStates.py

128522479dc943e764d6b24b93be59a28c826581

[]

no_license

Aitous/ENE251

adeb715ad24094765e23d03a481e309ab2dd3f8c

e7770c469f63683c4c3ea7916d8bcad64ad16593

refs/heads/master

2022-12-04T18:01:57.908783

2020-08-19T21:16:54

288,349,026

0

null

UTF-8

Python

false

25,765

py

#!/usr/bin/env python # coding: utf-8 # In[38]: import time import numpy as np import matplotlib.pyplot as plt import math from sklearn.linear_model import LinearRegression from scipy import interpolate # Solving RachfordRice def SolveRachfordRice(l, Nc, z, K): F = lambda l: sum([(1 - K[i]) * z[i]/(K[i] + (1 - K[i]) * l) for i in range(Nc)]) dF = lambda l: sum([-z[i] * (1 - K[i])**2/((K[i] + (1 - K[i]) * l)**2) for i in range(Nc)]) F0 = F(0) F1 = F(1) if(F0 > 0 and F1 < 0): lmin = 0 lmax = 1 elif(F0 > 0 and F1 > 0): lmin = 1 lmax = np.max([(K[i]*z[i] - K[i])/(1 - K[i]) for i in range(Nc)]) else: lmax = 0 lmin = np.min([(z[i] - K[i])/(1 - K[i]) for i in range(Nc)]) useNewton = True #Change to false for bisection only error = [] #error array i = 0 tol = 1.e-5 while abs(F(l)) > tol: if(F(l) > 0): lmin = l else: lmax = l delta_l = - F(l) / dF(l) if(l + delta_l > lmin and l + delta_l < lmax and useNewton): l = l + delta_l else: l = 0.5 * (lmin + lmax) error.append(F(l)) #print('error = ', error[i]) #reporting error for each step i += 1 return l #Calculating the a's and b's of the vapor and liquid phases. The function kij loads the interaction coefficients based on the EOS of interest def kij(EOS): if EOS is 'PR': return np.zeros((19,19)) elif EOS is 'SRK': return np.array([[0 , 0.1, 0.1257, 0.0942], [0.1, 0, 0.027, 0.042], [0.1257, 0.027, 0, 0.008], [0.0942, 0.042, 0.008, 0]]) return Kij elif EOS is 'RK': return np.zeros([3,3]) def calc_a(EOS, T, Tc, Pc, omega): '''calculates ai for each component for the EOS of interest EOS: Equation of state (PR, SRK, or RK) T, Tc: temperature and critical temperature of the component Pc: critical pressure of the component omega: accentric factor for the component''' R = 8.314 if EOS is 'PR': fw = 0.37464 + 1.54226*omega - 0.26992*omega**2 a1 = np.divide(0.45724*R**2*Tc**2 , Pc) a2 = (1 + np.multiply(fw, (1 - np.sqrt(np.divide(T, Tc)))))**2 a = np.multiply(a1, a2) elif EOS is 'SRK': fw = 0.48 + 1.574*omega - 0.176*omega**2 a1 = np.divide((0.42748*R**2*Tc**2), Pc) a2 = (1 + np.multiply(fw, (1 - np.sqrt(np.divide(T, Tc)))))**2 a = np.multiply(a1, a2) elif EOS is 'RK': a = np.divide(0.42748*R**2*Tc**(5/2), (Pc*T**0.5)) else: print('parameters for his EOS is not defined') return a def calc_b(EOS, Tc, Pc): '''calculates bi for each component for the EOS of interest EOS: Equation of state (PR, SRK, or RK) Tc: critical temperature of the component Pc: critical pressure of the component ''' R = 8.314 # gas constant # The below if statement computes b for each # componenet based on the EOS of # interest (Table 5.1 in the course reader) if EOS is 'PR': b = np.divide(0.07780*R*Tc, Pc) elif EOS is 'SRK': b = np.divide(0.08664*R*Tc, Pc) elif EOS is 'RK': b = np.divide(0.08664*R*Tc ,Pc) return b def find_am(EOS, y, T, Tc, Pc, omega): ''' calculates the a parameter for the EOS of interest EOS: equation of state of interest (PR, SRK, RK) y: vapor or liquid compositions T, Tc: temperature value and critical temperature array Pc: critical pressure array omega: accentric factors array ''' kijs = kij(EOS) am = np.sum(y[i]*y[j]*np.sqrt(calc_a(EOS, T, Tc[i], Pc[i], omega[i]) *calc_a(EOS, T, Tc[j], Pc[j], omega[j]))*(1-kijs[i,j]) for i in range(len(y)) for j in range(len(y))) return am def find_bm(EOS, y, Tc, Pc): '''This function computes the b for the mixture for the EOS of interest EOS: Equation of state (PR, SRK, or RK) y: liquid or vapor compositions array Tc and Pc: critical temperature and pressure array ''' bm = np.sum(np.multiply(y, calc_b(EOS, Tc, Pc))) return bm def Z_factor(EOS, P, T, a, b): '''This function computes the Z factor for the cubic EOS of interest EOS: equation of state (PR, SRK, or RK) P, T: pressure and temperature a, b: the vapor or liquid parameters of equation of state ''' R = 8.314 # gas constant if EOS == 'PR': u = 2 w = -1 elif EOS == 'SRK': u = 1 w = 0 elif EOS == 'RK': u = 1 w = 0 A = np.divide(a*P, R**2*T**2) B = np.divide(b*P, R*T) Coeffs = list() Coeffs.append(1) Coeffs.append(-(1 + B - u*B)) Coeffs.append(A + w*B**2 - u*B - u*B**2) Coeffs.append(-np.multiply(A, B) - w*B**2 - w*B**3) Z = np.roots(Coeffs) # remove the roots with imaginary parts Z = np.real(Z[np.imag(Z) == 0]) Zv = max(Z) Zl = min(Z) return Zv, Zl def get_fug(EOS, y, Z, Tc, Pc, P, T, omega, a, b): '''This function computes the liquid or vapor fugacity of all components using Eq. 6.8 in course reader parameters needed: EOS: equation of state (PR, SRK, or RK) y: liquid or vapor compositions Z: z-factors for vapor or liquid Tc and Pc: critical temperature and pressure for all individual comp.s P, T: pressure and temperature of the system omega: accentric factors for all individual components a and b: EOS parameters as computed in another function ''' R = 8.314 # gas constant if EOS is 'PR': u = 2 w = -1 kijs = kij(EOS) elif EOS is 'SRK': u = 1 w = 0 kijs = kij(EOS) elif EOS is 'RK': u = 1 w = 0 kijs = kij(EOS) fug = np.zeros(y.shape) A = np.divide(a*P, R**2*T**2) B = np.divide(b*P, R*T) delta_i = list() a_i = list() for i in range(len(y)): a_i.append(calc_a(EOS, T, Tc[i], Pc[i], omega[i])) for i in range(len(y)): xa = 0 for j in range(len(y)): xa += y[j] * math.sqrt(a_i[j]) * (1 - kijs[i][j]) delta_i.append(2 * math.sqrt(a_i[i]) / a * xa) for i in range(len(fug)): bi = calc_b(EOS, Tc, Pc)[i] ln_Phi = bi/b * (Z - 1) - math.log(Z - B) + A / (B * math.sqrt(u**2 - 4*w)) * (bi/b - delta_i[i]) * math.log((2 * Z + B *(u + math.sqrt(u**2 - 4*w))) /(2 * Z + B *(u - math.sqrt(u**2 - 4*w)))) fug[i] = y[i] * P * math.exp(ln_Phi) return fug def Ki_guess(Pc, Tc, P, T, omega, Nc): Ki = np.array([Pc[i]/P * np.exp(5.37 * (1 + omega[i]) * (1 - Tc[i]/T)) for i in range(Nc)]) return Ki def flash(EOS, l, Nc, zi, Tc, Pc, P, T, omega): Ki = Ki_guess(Pc, Tc, P, T, omega, Nc) tol = 1e-5 R = 8.314 # gas constant l = SolveRachfordRice(l, Nc, zi, Ki) xi = np.divide(zi, l+(1-l)*Ki) yi = np.divide(np.multiply(Ki, zi), (l+(1-l)*Ki)) av = find_am(EOS,yi,T,Tc,Pc,omega) al = find_am(EOS,xi,T,Tc,Pc,omega) bv = find_bm(EOS,yi,Tc,Pc) bl = find_bm(EOS,xi,Tc,Pc) #Z and fugacity determination for the vapor phase based on minimising Gibbs Free Energy Zv = Z_factor(EOS,P,T,av,bv) #containing the max and min roots fugV_v = get_fug(EOS, yi, Zv[0], Tc, Pc, P, T, omega, av, bv) fugV_l = get_fug(EOS, yi, Zv[1], Tc, Pc, P, T, omega, av, bv) deltaGV = np.sum(yi * np.log(fugV_l / fugV_v)) if deltaGV <= 0: Zv = Zv[1] fug_v = fugV_l else: Zv = Zv[0] fug_v = fugV_v #Z and fugacity determination for the liquid phase based on minimising Gibbs Free Energy Zl = Z_factor(EOS,P,T,al,bl) #containing the max and min roots fugL_v = get_fug(EOS, xi, Zl[0], Tc, Pc, P, T, omega, al, bl) fugL_l = get_fug(EOS, xi, Zl[1], Tc, Pc, P, T, omega, al, bl) deltaGL = np.sum(xi * np.log(fugL_l / fugL_v)) if deltaGL <= 0: Zl = Zl[1] fug_l = fugL_l else: Zl = Zl[0] fug_l = fugL_v while np.max(abs(np.divide(fug_v, fug_l) - 1)) > tol: Ki = Ki * np.divide(fug_l, fug_v) l = SolveRachfordRice(l, Nc, zi, Ki) xi = np.divide(zi, l+(1-l)*Ki) yi = np.divide(np.multiply(Ki, zi), (l+(1-l)*Ki)) av = find_am(EOS,yi,T,Tc,Pc,omega) al = find_am(EOS,xi,T,Tc,Pc,omega) bv = find_bm(EOS,yi,Tc,Pc) bl = find_bm(EOS,xi,Tc,Pc) #Z and fugacity determination for the vapor phase based on minimising Gibbs Free Energy Zv = Z_factor(EOS,P,T,av,bv) #containing the max and min roots fugV_v = get_fug(EOS, yi, Zv[0], Tc, Pc, P, T, omega, av, bv) fugV_l = get_fug(EOS, yi, Zv[1], Tc, Pc, P, T, omega, av, bv) deltaGV = np.sum(yi * np.log(fugV_l / fugV_v)) if deltaGV <= 0: Zv = Zv[1] fug_v = fugV_l else: Zv = Zv[0] fug_v = fugV_v #Z and fugacity determination for the liquid phase based on minimising Gibbs Free Energy Zl = Z_factor(EOS,P,T,al,bl) #containing the max and min roots fugL_v = get_fug(EOS, xi, Zl[0], Tc, Pc, P, T, omega, al, bl) fugL_l = get_fug(EOS, xi, Zl[1], Tc, Pc, P, T, omega, al, bl) deltaGL = np.sum(xi * np.log(fugL_l / fugL_v)) if deltaGL <= 0: Zl = Zl[1] fug_l = fugL_l else: Zl = Zl[0] fug_l = fugL_v Vv = np.divide(Zv*R*T, P) Vl = np.divide(Zl*R*T, P) return (fug_v, fug_l, l, xi, yi) def volumeCorrection(EOS, V, zi, Pc, Tc): Mw = np.array([44.01, 28.013, 16.043, 30.07, 44.097, 58.123, 58.123, 72.15, 72.15, 84, 96, 107, 121, 134, 163.5, 205.4, 253.6, 326.7, 504.4]) if EOS == "PR": #Si from the reader page 129 S = [-0.1540, 0.1002, -0.08501, -0.07935, -0.06413, -0.04350, -0.04183, -0.01478] c = [3.7, 0] #CO2 and N2 #For the heavy components for i in range(10, len(Pc)): S.append(1 - 2.258/Mw[i]**0.1823) #values correlated for heavier components (+C7) for i in range(0, len(Pc)-2): c.append(S[i] * calc_b(EOS, Tc[i+2], Pc[i+2])) V = V - np.sum([zi[i] * c[i] for i in range(len(Pc))]) return V def volume(EOS, P, T, Pc, Tc, omega, zi = np.array([1]), mixture = False): R = 8.314 if not mixture: a = calc_a(EOS, T, Tc, Pc, omega) b = calc_b(EOS, Tc, Pc) Z = Z_factor(EOS,P,T,a,b) fug_v = get_fug(EOS, zi, Z[0], Tc, Pc, P, T, omega, a, b) fug_l = get_fug(EOS, zi, Z[1], Tc, Pc, P, T, omega, a, b) deltaG = np.sum(zi * np.log(fug_l / fug_v)) if deltaG <= 0: Z = Z[1] fug = fug_l else: Z = Z[0] fug = fug_v V = np.divide(Z*R*T, P) else: bm = find_bm(EOS, zi, Tc, Pc) am = find_am(EOS, zi, T, Tc, Pc, omega) Z = Z_factor(EOS,P,T,am,bm) fug_v = get_fug(EOS, zi, Z[0], Tc, Pc, P, T, omega, am, bm) fug_l = get_fug(EOS, zi, Z[1], Tc, Pc, P, T, omega, am, bm) deltaG = np.sum(zi * np.log(fug_v / fug_l)) if deltaG <= 0: Z = Z[1] fug = fug_l else: Z = Z[0] fug = fug_v V = np.divide(Z*R*T, P) #V = volumeCorrection(EOS, V, zi, Pc, Tc) return V # Computes reference viscosity of methane using the correlation of Hanley et al. Cyrogenics, July 1975 # To be used for corresponding states computation of mixture viscosity # A. R. Kovscek # 20 November 2018 # Tref is the reference temperature in K (viscosity computed at this temperature) # rho_ref is the reference density in g/cm3 (viscosity computed at this temperature and density) # mu_C1 is the viscosity from correlation in mPa-s (identical to cP) def ViscMethane(Tref,rho_ref): import math #Local variables #critical density of methane (g/cm^3) rho_c=16.043/99.2 #parameters for the dilute gas coefficient GV=[-209097.5,264726.9,-147281.8,47167.40,-9491.872,1219.979,-96.27993,4.274152,-0.08141531] #parameters for the first density correction term Avisc1 = 1.696985927 Bvisc1 = -0.133372346 Cvisc1 = 1.4 Fvisc1 = 168.0 #parameters for the viscosity remainder j1 = -10.35060586 j2 = 17.571599671 j3 = -3019.3918656 j4 = 188.73011594 j5 = 0.042903609488 j6 = 145.29023444 j7 = 6127.6818706 #compute dilute gas coefficient visc0 = 0. exp1 = 0. for i in range(0,len(GV)): exp1 = -1. + (i)*1./3. visc0 = visc0 + GV[i]*math.pow(Tref,exp1) #first density coefficient visc1 = Avisc1+Bvisc1*math.pow((Cvisc1-math.log(Tref/Fvisc1)),2.) #viscosity remainder theta=(rho_ref-rho_c)/rho_c visc2 = math.pow(rho_ref,0.1) visc2 = visc2*(j2+j3/math.pow(Tref,1.5))+theta*math.sqrt(rho_ref)*(j5+j6/Tref+j7/math.pow(Tref,2.)) visc2 = math.exp(visc2) visc2 = visc2 - 1. visc2 = math.exp(j1+j4/Tref)*visc2 #methane viscosity at T and density (Tref,rho_ref) #multiply by 10-4 to convert to mPa-s(cP) mu_C1 = (visc0+visc1+visc2)*0.0001 return (mu_C1) def get_interp_density(p, T): ''' @param p: pressure in Pa @param T: temperature in K @return : methane density in kg/m3 ''' data_p = [0.1e6, 1e6, 3e6, 5e6, 10e6, 20e6, 50e6] data_T = [90.7, 94, 98, 100, 105, 110, 120, 140, 170] if p < data_p[0] or p > data_p[-1] or T < data_T[0] or T > data_T[-1]: raise Exception('Input parameter out of range') data_den = [[451.5, 447.11, 441.68, 438.94, 431.95, 424.79, 409.9, 1.403, 1.1467], [451.79, 447.73, 442.34, 439.62, 432.7, 425.61, 410.9, 377.7, 14.247], [453, 449.08, 443.78, 441.11, 434.32, 427.38, 413.05, 381.12, 314.99], [454, 450.4, 445.19, 442.55, 435.89, 429.09, 415.1, 384.28, 324.32], [456, 453.57, 448.55, 446.02, 439.63, 433.13, 419.9, 391.35, 340.6], [460, 458, 454.74, 452.37, 446.43, 440.43, 428.32, 402.99, 361.57], [477, 473, 470, 468, 463.2, 458.14, 448.08, 427.88, 397.48]] f = interpolate.interp2d(data_T, data_p, data_den) return f(T, p) # In[39]: def LBC_viscosity(P, T, zi, Tc, Pc, omega, Mw, Vci): coef = [0.10230, 0.023364, 0.058533, -0.040758, 0.0093324] Nc = len(zi) EOS = 'PR' Pmax = 3000 * 6894.76 Pressure = [] visc = [] while P < Pmax: #flash fug_v, fug_l, l, xi, yi = flash(EOS, 0.5, Nc, zi, Tc, Pc, P, T, omega) if l>1: xi = zi #Computing Ksi Ksi = 5.4402 * 399.54 * np.sum(xi * Tc)**(1/6)/np.multiply(np.sum(xi * Mw)**(0.5),np.sum(xi * Pc)**(2/3)) Ksi_i = 5.4402 * 399.54 * Tc**(1/6) * Mw**(-0.5) * Pc**(-2/3) #Ksi_i = Tc**(1/6) * Mw**(-0.5) * Pc**(-2/3) eta_star_i = np.zeros(xi.shape) for i in range(Nc): Tr = T/Tc[i] if Tr < 1.5: eta_star_i[i] = 34e-5 * (Tr**0.94)/Ksi_i[i] else: eta_star_i[i] = 17.78 * 1e-5 * ((4.58*Tr - 1.67)**0.625)/ Ksi_i[i] eta_star = np.divide(np.sum(xi * eta_star_i * Mw**0.5), np.sum(xi * Mw**0.5)) MC7_plus = np.sum(xi[i] * Mw[i] for i in range(10, Nc)) / np.sum(xi[i] for i in range(10, Nc)) denC7_plus = 0.895 Vc_plus = (21.573 + 0.015122*MC7_plus - 27.656*denC7_plus + 0.070615*denC7_plus*MC7_plus) * 6.2372*1e-5 V_mixture = volume(EOS, P, T, Pc, Tc, omega, xi, True) xC7_plus = np.sum(xi[i] for i in range(10, Nc)) Vc_mixture = np.sum(xi[i] * Vci[i] for i in range(10))*1e-6 + xC7_plus * Vc_plus rho_r = Vc_mixture/V_mixture viscosity = ((coef[0] + coef[1] * rho_r + coef[2] * rho_r**2 + coef[3] * rho_r**3 + coef[4] * rho_r**4)**4 - 0.0001)/Ksi + eta_star visc.append(viscosity) Pressure.append(P) P = 1.1 * P plt.plot(Pressure, visc) plt.xlabel("Pressure (Pa)") plt.ylabel("Viscosity (cP)") plt.title("Viscosity vs pressure") plt.show() # In[40]: P = 1500 * 6894.76 T = 106 + 273.15 Names = {'CO2' 'N2' 'C1' 'C2' 'C3' 'iC4' 'n-C4' 'i-C5' 'n-C5' 'C6' 'C7' 'C8' 'C9' 'C10' 'PS1' 'PS2' 'PS3' 'PS4' 'PS5'} zi = np.array([0.0044, 0.0017, 0.3463, 0.0263, 0.0335, 0.092, 0.0175, 0.0089, 0.0101, 0.0152, 0.05, 0.0602, 0.0399, 0.0355, 0.1153, 0.0764, 0.0633, 0.0533, 0.0330])#oil composition Tc = np.array([304.2, 126.2, 190.6, 305.4, 369.8, 408.1, 425.2, 460.4, 469.6, 507.4, 548, 575, 603, 626, 633.1803, 675.9365, 721.3435, 785.0532, 923.8101]) # in Kelvin Pc = np.array([72.9, 33.6, 45.4, 48.2, 41.9, 36.0, 37.5, 33.4, 33.3, 29.3, 30.7, 28.4, 26, 23.9, 21.6722, 19.0339, 16.9562, 14.9613, 12.6979])*101325 # in Pa omega = np.array([0.228, 0.04, 0.008, 0.098, 0.152, 0.176, 0.193, 0.227, 0.251, 0.296, 0.28, 0.312, 0.348, 0.385, 0.6254, 0.7964, 0.9805, 1.2222, 1.4000]) # accentric factors Mw = np.array([44.01, 28.013, 16.043, 30.07, 44.097, 58.123, 58.123, 72.15, 72.15, 84, 96, 107, 121, 134, 163.5, 205.4, 253.6, 326.7, 504.4]) Vci = np.array([91.9, 84, 99.2, 147, 200, 259, 255, 311, 311, 368]) # cm3/mol LBC_viscosity(P, T, zi, Tc, Pc, omega, Mw, Vci) # In[41]: def corresponding_state_Visco(P, T ,zi, Tc, Pc, omega, Mw): R = 8.314 # gas constant Names = {'CO2' 'N2' 'C1' 'C2' 'C3' 'iC4' 'n-C4' 'i-C5' 'n-C5' 'C6' 'C7' 'C8' 'C9' 'C10' 'PS1' 'PS2' 'PS3' 'PS4' 'PS5'} Nc = len(zi) EOS = 'PR' tol = 1e-5 Pmax = 3000 * 6894.76 visc = [] Pressure = [] while P < Pmax: fug_v, fug_l, l, xi, yi = flash(EOS, 0.5, Nc, zi, Tc, Pc, P, T, omega) if l>1: xi = zi #Initiliazing Tc_mix = 0 Mmix = 0 Mn = 0 M = 0 denominator = 0 for i in range(Nc): Mn += xi[i] * Mw[i] M += xi[i] * Mw[i]**2 for j in range(Nc): Tc_mix += xi[i]*xi[j]*(Tc[i] * Tc[j])**(0.5)*((Tc[i]/Pc[i])**(1/3) + (Tc[j]/Pc[j])**(1/3))**3 denominator += xi[i]*xi[j]*((Tc[i]/Pc[i])**(1/3) + (Tc[j]/Pc[j])**(1/3))**3 Tc_mix = Tc_mix / denominator Pc_mix = 8 * Tc_mix / denominator M /= Mn Mmix = 1.304 * 1e-4 * (M**2.303 - Mn**2.303) + Mn Tr = (T * Tc[2])/Tc_mix Pr = (P * Pc[2])/Pc_mix rho_c = 162.84 #kg/m3 #volume correction S = -0.154 b = calc_b(EOS, Tc[2], Pc[2]) Vc = volume(EOS, Pr, Tr, np.array([Pc[2]]), np.array([Tc[2]]), np.array([omega[2]])) volume_cor = Vc - b * S rho_r = Mw[2] * 1e-3 / volume_cor / rho_c alpha_mix = 1 + 7.378 * 10**(-3) * rho_r ** 1.847 * Mmix**0.5173 alpha_0 = 1 + 0.031*rho_r**1.847 Tref = Tr * alpha_0 / alpha_mix Pref = Pr * alpha_0 / alpha_mix S = -0.085 Vc_ref = volume(EOS, Pref, Tref, np.array([Pc[2]]), np.array([Tc[2]]), np.array([omega[2]])) volume_cor = Vc_ref - b * S rho_ref = Mw[2]/volume_cor/ 1e6 visc_methane = ViscMethane(Tref, rho_ref) visc_mix = (Tc_mix/Tc[2])**(-1/6) * (Pc_mix/Pc[2])**(2/3) * (Mmix/Mw[2])**(1/2) * alpha_mix / alpha_0 * visc_methane visc.append(visc_mix) Pressure.append(P) #print(P, visc_mix) P = 1.1 * P plt.plot(Pressure, visc) plt.xlabel("Pressure (Pa)") plt.ylabel("Viscosity (cP)") plt.title("Viscosity vs compositions") plt.show() # In[42]: zi = np.array([0.0044, 0.0017, 0.3463, 0.0263, 0.0335, 0.092, 0.0175, 0.0089, 0.0101, 0.0152, 0.05, 0.0602, 0.0399, 0.0355, 0.1153, 0.0764, 0.0633, 0.0533, 0.0330])#oil composition Tc = np.array([304.2, 126.2, 190.6, 305.4, 369.8, 408.1, 425.2, 460.4, 469.6, 507.4, 548, 575, 603, 626, 633.1803, 675.9365, 721.3435, 785.0532, 923.8101]) # in Kelvin Pc = np.array([72.9, 33.6, 45.4, 48.2, 41.9, 36.0, 37.5, 33.4, 33.3, 29.3, 30.7, 28.4, 26, 23.9, 21.6722, 19.0339, 16.9562, 14.9613, 12.6979])*101325 # in Pa omega = np.array([0.228, 0.04, 0.008, 0.098, 0.152, 0.176, 0.193, 0.227, 0.251, 0.296, 0.28, 0.312, 0.348, 0.385, 0.6254, 0.7964, 0.9805, 1.2222, 1.4000]) # accentric factors Mw = np.array([44.01, 28.013, 16.043, 30.07, 44.097, 58.123, 58.123, 72.15, 72.15, 84, 96, 107, 121, 134, 163.5, 205.4, 253.6, 326.7, 504.4]) P = 1500 * 6894.76 T = 106 + 273.15 corresponding_state_Visco(P, T, zi, Tc, Pc, omega, Mw) # In[43]: def viscosity(Oilcomp, Injcomp, P, T, Pc, Tc, omega, Mw, Vci): coef = [0.10230, 0.023364, 0.058533, -0.040758, 0.0093324] EOS = 'PR' Nc = len(Oilcomp) alpha = 0.5 l = 0.5 tol = 1e-5 zi = Oilcomp + alpha * (Injcomp - Oilcomp) fug_v, fug_l, l, xi, yi = flash(EOS, 0.5, Nc, zi, Tc, Pc, P, T, omega) Ksi = 5.4402 * 399.54 * np.sum(xi * Tc)**(1/6)/np.multiply(np.sum(xi * Mw)**(0.5),np.sum(xi * Pc)**(2/3)) Ksi_i = 5.4402 * 399.54 * Tc**(1/6) * Mw**(-0.5) * Pc**(-2/3) eta_star_i = np.zeros(xi.shape) for i in range(Nc): Tr = T/Tc[i] if Tr < 1.5: eta_star_i[i] = 34e-5 * (Tr**0.94)/Ksi_i[i] else: eta_star_i[i] = 17.78 * 1e-5 * ((4.58*Tr - 1.67)**0.625)/ Ksi_i[i] eta_star = np.divide(np.sum(xi * eta_star_i * Mw**0.5), np.sum(xi * Mw**0.5)) MC7_plus = np.sum(xi[i] * Mw[i] for i in range(10, Nc)) / np.sum(xi[i] for i in range(10, Nc)) denC7_plus = 0.895 Vc_plus = (21.573 + 0.015122*MC7_plus - 27.656*denC7_plus + 0.070615*denC7_plus*MC7_plus) * 6.2372*1e-5 V_mixture = volume(EOS, P, T, Pc, Tc, omega, xi, True) xC7_plus = np.sum(xi[i] for i in range(10, Nc)) Vc_mixture = np.sum(xi[i] * Vci[i] for i in range(10))*1e-6 + xC7_plus * Vc_plus rho_r = Vc_mixture/V_mixture viscosity = ((coef[0] + coef[1] * rho_r + coef[2] * rho_r**2 + coef[3] * rho_r**3 + coef[4] * rho_r**4)**4 - 0.0001)/Ksi + eta_star return viscosity # In[44]: def vicosity_vs_composition(LPG_CO2_comb, Oilcomp, P, T, Pc, Tc, omega, Mw, Vci, makePlot = False): '''This function computes the MMP for the different compositions of the LPG and CO2 and returns a plot of the MMP versus gas injectant composition. LPG_CO2_comb contains the porcentage of LPG in the mixte. an array of the form [0.7, 0.55, 0.4, 0.2, 0.1, 0.] means that for the first mixture we have 70% LPG and 30% CO2 and for the second 55% LPG and 45% CO2 and so on... The LPG composition is: C2: 0.01, C3: 0.38, iC4: 0.19, nC4: 0.42. ''' #reservoir Oil components. Names = {'CO2' 'N2' 'C1' 'C2' 'C3' 'i-C4' 'n-C4' 'i-C5' 'n-C5' 'C6' 'C7' 'C8' 'C9' 'C10' 'PS1' 'PS2' 'PS3' 'PS4' 'PS5'} LPG = np.array([0, 0, 0, 0.01, 0.38, 0.19, 0.42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]); CO2 = np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) numMixtures = len(LPG_CO2_comb) Viscosity = [] composition = [] for i in range(numMixtures): Injcomp = np.array(LPG_CO2_comb[i] * LPG + (1 - LPG_CO2_comb[i]) * CO2) Viscosity.append(viscosity(Oilcomp, Injcomp, P, T, Pc, Tc, omega, Mw, Vci)) composition.append(LPG_CO2_comb[i]) if makePlot: plt.plot(composition, Viscosity) plt.xlabel('Composition (mole fraction of the LPG)') plt.ylabel('Viscosity (cP)') plt.title('Viscosty vs Injectant composition') plt.show() # In[45]: P = 28e6 T = 106 + 273.15 Names = {'CO2' 'N2' 'C1' 'C2' 'C3' 'iC4' 'n-C4' 'i-C5' 'n-C5' 'C6' 'C7' 'C8' 'C9' 'C10' 'PS1' 'PS2' 'PS3' 'PS4' 'PS5'} zi = np.array([0.0044, 0.0017, 0.3463, 0.0263, 0.0335, 0.092, 0.0175, 0.0089, 0.0101, 0.0152, 0.05, 0.0602, 0.0399, 0.0355, 0.1153, 0.0764, 0.0633, 0.0533, 0.0330])#oil composition Tc = np.array([304.2, 126.2, 190.6, 305.4, 369.8, 408.1, 425.2, 460.4, 469.6, 507.4, 548, 575, 603, 626, 633.1803, 675.9365, 721.3435, 785.0532, 923.8101]) # in Kelvin Pc = np.array([72.9, 33.6, 45.4, 48.2, 41.9, 36.0, 37.5, 33.4, 33.3, 29.3, 30.7, 28.4, 26, 23.9, 21.6722, 19.0339, 16.9562, 14.9613, 12.6979])*101325 # in Pa omega = np.array([0.228, 0.04, 0.008, 0.098, 0.152, 0.176, 0.193, 0.227, 0.251, 0.296, 0.28, 0.312, 0.348, 0.385, 0.6254, 0.7964, 0.9805, 1.2222, 1.4000]) # accentric factors Mw = np.array([44.01, 28.013, 16.043, 30.07, 44.097, 58.123, 58.123, 72.15, 72.15, 84, 96, 107, 121, 134, 163.5, 205.4, 253.6, 326.7, 504.4]) Vci = np.array([91.9, 84, 99.2, 147, 200, 259, 255, 311, 311, 368]) # cm3/mol vicosity_vs_composition(np.array([0.7, 0.55, 0.4, 0.2, 0.1, 0.]), zi, P, T, Pc, Tc, omega, Mw, Vci, True) # In[47]: #Plotting the experimental CCE curve of pressure against relative volume. x = np.array([ 20096156.97 ,19406680.97 ,18717204.97 ,16834935.49 ,15476667.77 ,15312158.8 ,13890872.97 ,10443492.97 ]) y = np.array([ 0.622 ,0.6162 ,0.611 ,0.597 ,0.5869 ,0.5857 ,0.6341 ,0.708 ]) plt.plot(x, y, 'r') plt.xlabel("Pressure (Pa)") plt.ylabel("Viscosity (cP)") plt.title("Experimental evolution of viscosity") plt.legend(loc='best') plt.show() # In[ ]:

[ "youssef.aitousarrah@gmail.com" ]

youssef.aitousarrah@gmail.com

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/mindblown.py

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no_license

sxflame/Turtledraw

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c887ca15cd40f3e235d684d2b6a49ed9d63e70bd

refs/heads/master

2021-01-11T16:15:56.753085

2017-01-25T19:34:45

80,049,932

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py

import turtle def draw_square(some_turtle): for i in range (1,5): some_turtle.forward (100) some_turtle.right(90) def draw_bigsquare(some_turtle): for i in range (1,5): some_turtle.forward (200) some_turtle.right(90) #def draw_circle(some_turtle): # some_turtle.cirlce(100) def draw_art(): window = turtle.Screen() window.bgcolor ("black") brad = turtle.Turtle() brad.shape("turtle") brad.color("yellow") brad.speed(1000) for i in range (1,37): draw_square(brad) brad.right(10) for i in range (38,74): draw_bigsquare(brad) brad.right(10) # angie = turtle.Turtle() # angie.shape("arrow") # angie.color("blue") # draw_circle(angie) window.exitonclick() draw_art()

[ "noreply@github.com" ]

noreply@github.com

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/array.py

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[]

no_license

vikask1640/Demogit

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refs/heads/master

2020-04-13T23:32:33.126507

2019-01-03T11:00:56

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py

import array as a def ace(): vals = a.array("i", [10, 8, 14, 55, 4]) print(vals) x=list(vals) x.sort() print(x) ace() # factorilas numbers y=5 fact=1 for j in range(1,y+1): # 1 to 5 fact=fact*j print(fact)

[ "noreply@github.com" ]

noreply@github.com

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/Python/ThinkFlask/home/views/index.py

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[]

no_license

919858271/MyCode

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refs/heads/master

2021-06-21T13:59:00.754383

2019-07-21T13:28:48

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923

py

#------------------------------------------------------------------------------- # !/usr/bin/env python # -*- coding: utf-8 -*- # Author: jianwen # Email: npujianwenxu@163.com #------------------------------------------------------------------------------- from flask import render_template from app.models import Model from home.model.models import User from home import home_router @home_router.route('/') def index(): return 'Think Flask. This is Home' @home_router.route('/add/<username>/<password>/') def add_user(username, password): user = User(username=username, password=password) User.insert(user) return 'success' @home_router.route('/delete/<int:key>/') def delete_user(key): user = User.query.get(key) Model.delete(user) return 'success' @home_router.route('/test/<username>/') def test(username): return render_template('home/index.html', username=username)

[ "npujianwenxu@163.com" ]

npujianwenxu@163.com

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/options.py

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[]

no_license

amietn/anna

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refs/heads/master

2021-05-03T06:12:09.783335

2018-02-07T09:09:18

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#!/usr/bin/env python3 import os import json irc_credentials_path = os.path.expanduser("~/.config/anna/irc_credentials.json") def get_irc_credentials(): return get_irc_credentials_path(irc_credentials_path) def get_irc_credentials_path(path): with open(path, 'r') as f: j = json.load(f) return j if __name__ == '__main__': creds = get_irc_credentials_path("irc_credentials.json.template") print(creds)

[ "amietn@foobar" ]

amietn@foobar

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/pyropod/ropod/utils/uuid.py

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[]

no_license

HBRS-SDP/ropod_common

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5ce24b8ae79239f4fd5d2249fd33d1b1061eaceb

refs/heads/master

2020-05-09T23:39:11.209722

2019-03-12T12:59:48

181,508,576

0

null

2019-04-15T14:52:18

null

UTF-8

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py

import uuid def generate_uuid(): """ Returns a string containing a random uuid """ return str(uuid.uuid4())

[ "argentina.ortega@h-brs.de" ]

argentina.ortega@h-brs.de

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/1002.py

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[]

no_license

arturbs/pythonUriAnswers

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refs/heads/master

2020-03-24T22:35:22.705373

2019-08-23T04:37:31

143,092,795

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139

py

#Programa que calcula e imprime a area de um circulo a partir de um raio. RAIO = float(input()) A= 3.14159 * RAIO**2 print("A=%0.4f" %A)

[ "arturbritosouza@hotmail.com.br" ]

arturbritosouza@hotmail.com.br