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#!/usr/bin/env python2 # -*- encoding: utf-8 -*- # Gimp Markup Builder # author: duangsuse # date: Thu May 02 2019 CST from os import linesep from Util import stream_join class MarkupBuilder: ''' Gimp Markup SGML builder ''' def __init__(self, indent = -1, nl = linesep, buffer = str): self.marks = buffer() self.tag_stack = list() self.nl = nl self.indent = indent self.last_spaces = 0 self.revert_last_indent_size = 0 self.last_is_text = False ''' Indent rules: when starting new tag, write last spaces, last spaces += indent if new tag is not text tag start (inner is just text), write newline when leaving tag, last spaces -= indent ''' def useindent(self): return self.indent != -1 indented = property(useindent) def wnewline(self): ''' see use_indent''' self.marks += self.nl def windent(self): ''' see use_indent''' wrote = 0 for _ in range(0, self.last_spaces): self.marks += ' ' wrote += 1 # dummy? return wrote def cancel_indent(self): ''' cancel last indent ''' if self.indented: self.marks = self.marks[:-self.revert_last_indent_size] def do_indent(self, entering = True): ''' Write indent, increase last_spaces, saving wrote spaces and newline to revert_last_indent_size ''' def do(): self.wnewline() if (entering): self.last_spaces += self.indent else: self.last_spaces -= self.indent self.revert_last_indent_size = self.windent() +1 if self.indented: do() def do_last_indent(self, *args, **kwargs): ''' write indenting for last block ''' self.last_spaces -= self.indent self.do_indent(*args, **kwargs) self.last_spaces += self.indent def begin(self, tag, attrs = {}): ''' Make a tag with name and attributes Attribute name, value and tag name is escaped ''' self.last_is_text = False attrst = str() tagscape = self.escape(tag) ary = list(stream_join(attrs.keys(), attrs.values())) if attrs.__class__ is dict else list(attrs) if len(attrs) != 0: for n in range(0, len(ary), 2): attrst += self.escape(str(ary[n])) attrst += '=' #print(ary) #print(n) attrst += "\"%s\"" % self.escape(str(ary[n+1])) self.marks += '<' + tagscape if len(attrs) != 0: self.marks += ' ' self.marks += attrst + '>' # always write indents for next line # makes its possible to drop last indent (text tag) self.do_indent() self.tag_stack.append(tagscape) return self def make(self): return self.marks def tag(self, *args, **kwargs): r''' EDSL using __close__ with syntax create nodes like: with xml.tag('span', {color: '#66ccff'}): xml % 'Q \w\ Q' ''' self.last_is_text = False class TagBuilder: def __init__(self, xml): self.xml = xml def __enter__(self): self.xml.begin(*args, attrs = kwargs) def __exit__(self, *lveinfo): self.xml.end() return TagBuilder(self) def text(self, content): ''' append text content ''' self.last_is_text = True if self.indented: self.cancel_indent() self.marks += self.escape(content) return self #@staticmethod #def test(): # m = MarkupBuilder() # m > 'html' # m > 'head' # m > 'title' # m < 'Hello World' # m <= 2 # m > 'body' # m > 'text' # with m.tag("b"): # m < 'String' # m >= ['a', {'id': 'str'}] # m < '|sg.' # m <= 4 # return m def end(self): ''' delimites last tag ''' if not self.last_is_text: # cancel indentation #print(self.indent, self.tag_stack) self.cancel_indent() self.do_indent(False) self.marks += '</' + self.tag_stack.pop() + '>' self.do_indent(False) self.last_is_text = False # Not cared by Markup indent emitter def raw(self, raw): ''' write raw text (unescaped) ''' self.marks += raw return self def rawtag(self, rawtext): ''' append unescaped raw <> text ''' self.marks += '<' self.marks += rawtext self.marks += '>' def _escape(self, xml): ''' Escape XML string ' is replaced with &apos; " is replaced with &quot; & is replaced with &amp; < is replaced with &lt; > is replaced with &gt; ''' escapes = frozenset("'\"&<>") replacement = { '\'': 'apos', '"': 'quot', '&': 'amp', '<': 'lt', '>': 'gt' } if len(xml) < 1: return output = str() for i in range(0, len(xml)): char = xml[i] if (char in escapes): output += '&' output += replacement[char] output += ';' else: output += char return output escape = classmethod(_escape) def __str__(self): ''' M(marks)..[tag stack] ''' return 'M(' + self.marks + ')..' + str(self.tag_stack) __lt__ = text # chain __gt__ = begin # chain __add__ = raw # chain def __contains__(self, tag): ''' is tag inside enclosing tags ? ''' return tag in self.tag_stack def __ge__(self, tag_attr): ''' xml >= ['markup', {'name': 'abcs'}] ''' mark = tag_attr[0] attr = tag_attr[1] self.begin(mark, attr) def __le__(self, n = 1): ''' Leave (close) N tags ''' while n > 0: self.end() n -= 1
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from pkg_resources import DistributionNotFound, get_distribution, parse_version try: import psycopg2 # noqa: F401 except ImportError: raise ImportError( 'No module named psycopg2. Please install either ' 'psycopg2 or psycopg2-binary package for CPython ' 'or psycopg2cffi for Pypy.' ) for package in ['psycopg2', 'psycopg2-binary', 'psycopg2cffi']: try: if get_distribution(package).parsed_version < parse_version('2.5'): raise ImportError('Minimum required version for psycopg2 is 2.5') break except DistributionNotFound: pass __version__ = get_distribution('hs-sqlalchemy-redshift').version from sqlalchemy.dialects import registry registry.register("redshift", "sqlalchemy_redshift.dialect", "RedshiftDialect") registry.register( "redshift.psycopg2", "sqlalchemy_redshift.dialect", "RedshiftDialect" )
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# -*- coding: utf-8 -*- def message(age: int = 0, name: str = 'stranger') -> str: return f'Hello {name}, you are {age} years old'
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""" This module contains the top level API for managing the project/file templates. """ import json import logging import os import re from binaryornot.check import is_binary from hackedit.app import settings def create(template, dest_dir, answers): """ Creates a file/project from the specified template, at the specified directory. :param template: Template data. :param dest_dir: Destination directory where to create the file/project :param answers: Dict of answers for substitution variables """ def get_paths(root, path, src_dir, dest_dir): src_path = os.path.join(root, path) rel_path = os.path.relpath(src_path, src_dir) dst_path = os.path.join(dest_dir, rel_path) return src_path, dst_path def get_file_encoding(path): if is_binary(path): return 'binary' try: encodings = template['encodings'] except KeyError: encodings = ['utf-8', 'cp1252'] for encoding in encodings: try: with open(path, encoding=encoding) as f: f.read() except UnicodeDecodeError: continue else: return encoding def open_file(path, encoding, to_write=None): if encoding == 'binary': if to_write is None: mode = 'rb' else: mode = 'wb' encoding = None else: if to_write is None: mode = 'r' else: mode = 'w' content = None with open(path, mode, encoding=encoding) as f: if to_write is not None: f.write(to_write) else: content = f.read() return content def subsitute_vars(string): for var, value in answers.items(): string = re.sub('@%s@' % var, value, string) return string ret_val = [] if not os.path.exists(dest_dir): os.makedirs(dest_dir) src_dir = template['path'] for root, dirs, files in os.walk(src_dir): for file in files: if file == 'template.json' or file.endswith('.pyc'): continue src, dst = get_paths(root, file, src_dir, dest_dir) dst = subsitute_vars(dst) encoding = get_file_encoding(src) try: content = open_file(src, encoding) except OSError: _logger().exception('failed to open file: %r', src) if encoding != 'binary': content = subsitute_vars(content) if file == 'btpad_btn_img_0.png': print(len(content), encoding) try: open_file(dst, encoding, to_write=content) except PermissionError: _logger().exception('failed to write file: %r', dst) else: ret_val.append(dst) assert open_file(dst, encoding) == content for directory in dirs: src, dst = get_paths(root, directory, src_dir, dest_dir) dst = subsitute_vars(dst) try: os.mkdir(dst) except PermissionError: _logger().exception('failed to create directory: %r', dst) return ret_val def get_sources(): """ Returns the template sources (directory associated with a label). """ s = settings.load() tmpl_sources = s.value('_templates/sources', '[]') tmpl_sources = json.loads(tmpl_sources) return sorted(tmpl_sources, key=lambda x: x['label']) def add_source(label, path): """ Adds a template source :param label: Name of the template source. :param path: Path of the template source. """ tmpl_sources = get_sources() tmpl_sources.append({'label': label, 'path': path}) s = settings.load() s.setValue('_templates/sources', json.dumps(tmpl_sources)) def rm_source(label): """ Removes the specified template source. :param label: Name of the template source to remove. """ tmpl_sources = get_sources() for src in tmpl_sources: if src['label'] == label: tmpl_sources.remove(src) s = settings.load() s.setValue('_templates/sources', json.dumps(tmpl_sources)) def clear_sources(): """ Clear template sources. """ s = settings.load() s.setValue('_templates/sources', json.dumps([])) def get_template(source, template): """ Returns the specified template data. """ for t in get_templates(source_filter=source): if t['name'] == template: return t return None def _logger(): return logging.getLogger(__name__) if __name__ == '__main__': clear_sources() add_source('COBOL', '/home/colin/Documents/hackedit-cobol/hackedit_cobol/templates') add_source('Python', '/home/colin/Documents/hackedit-python/hackedit_python/templates') for tmpl in get_templates(): print(json.dumps(tmpl, indent=4, sort_keys=True))
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""" Convert video format x to MP4/H.264. """ import os import sys import logging from .videometainfo import VideoMetaInfo from .utils import sizeof_fmt, time_fmt, find_files, check_dependencies, call, ffmpeg logger = logging.getLogger(__name__) class VideoToMP4: """To Mp4""" SUPPORTED_EXTENSIONS = ".wmv, .avi, .mkv, .mov, .flv" RULES = { ".wmv": "-c:v libx264 -crf 19 ", ".avi": "-vf yadif=1 -c:v h264_nvenc -preset slow -tune film -crf 17", ".mkv": "-c copy", ".mov": "-vcodec h264 -acodec aac -strict -2 -crf 19 ", ".flv": " -r 20 ", } def process(self, video_file: str): """Convert video files to MP4 container format.""" name = os.path.splitext(video_file)[0] ext = os.path.splitext(video_file)[1] new_name = f"{name}.mp4" if os.path.exists(new_name): logger.info(f"Skipping file {new_name} already exists!") elif ext not in VideoToMP4.RULES: logger.error(f"Skipping unsupported type {ext}!") else: print(f'Convert {ext} to MP4 {new_name} ... ') meta_info = VideoMetaInfo(video_file) rule = VideoToMP4.RULES[ext] flags = "-movflags +faststart -pix_fmt yuv420p" ffmpeg( f'-i "{video_file}" {flags} {rule} -metadata date="{meta_info.original_date}" "{new_name}"' ) def file(self, filename: str) -> None: logger.debug(f"converting file {filename}") self.process(filename) def directory(self, path: str, extension: str) -> int: files = find_files(path, extension) if len(files) < 1: print("No matching files found in directory!", file=sys.stderr) else: for f in files: self.file(f)
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2.045455
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from setuptools import setup setup( name='parasol', dependency_links=[ ], install_requires=[ ] )
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2.4375
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# Generated by Django 3.2.8 on 2021-11-02 12:46 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('app', '0006_auto_20211102_1928'), ] operations = [ migrations.RemoveField( model_name='profile', name='skill', ), migrations.AddField( model_name='profile', name='tags', field=models.ManyToManyField(blank=True, to='app.Tag'), ), migrations.DeleteModel( name='Skill', ), ]
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2.057971
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import csv import io import json import logging import os import warnings from collections import defaultdict from ..utils.exceptions import OasisException from ..utils.log import oasis_log from .files import GENERAL_SETTINGS_FILE, GUL_SUMMARIES_FILE, IL_SUMMARIES_FILE, MODEL_SETTINGS_FILE def _get_summaries(summary_file): """ Get a list representation of a summary file. """ summaries_dict = defaultdict(lambda: {'leccalc': {}}) with io.open(summary_file, 'r', encoding='utf-8') as csvfile: reader = csv.reader(csvfile) for row in reader: id = int(row[0]) if row[1].startswith('leccalc'): summaries_dict[id]['leccalc'][row[1]] = row[2].lower() == 'true' else: summaries_dict[id][row[1]] = row[2].lower() == 'true' summaries = list() for id in sorted(summaries_dict): summaries_dict[id]['id'] = id summaries.append(summaries_dict[id]) return summaries def read_analysis_settings(analysis_settings_fp, il_files_exist=False, ri_files_exist=False): """Read the analysis settings file""" # Load analysis_settings file try: # Load as a json with io.open(analysis_settings_fp, 'r', encoding='utf-8') as f: analysis_settings = json.load(f) # Extract the analysis_settings part within the json if analysis_settings.get('analysis_settings'): analysis_settings = analysis_settings['analysis_settings'] except (IOError, TypeError, ValueError): raise OasisException('Invalid analysis settings file or file path: {}.'.format( analysis_settings_fp)) # Reset il_output if the files are not there if not il_files_exist or 'il_output' not in analysis_settings: # No insured loss output analysis_settings['il_output'] = False analysis_settings['il_summaries'] = [] # Same for ri_output if not ri_files_exist or 'ri_output' not in analysis_settings: # No reinsured loss output analysis_settings['ri_output'] = False analysis_settings['ri_summaries'] = [] # If we want ri_output, we will need il_output, which needs il_files if analysis_settings['ri_output'] and not analysis_settings['il_output']: if not il_files_exist: warnings.warn("ri_output selected, but il files not found") analysis_settings['ri_output'] = False analysis_settings['ri_summaries'] = [] else: analysis_settings['il_output'] = True # guard - Check if at least one output type is selected if not any([ analysis_settings['gul_output'] if 'gul_output' in analysis_settings else False, analysis_settings['il_output'] if 'il_output' in analysis_settings else False, analysis_settings['ri_output'] if 'ri_output' in analysis_settings else False, ]): raise OasisException( 'No valid output settings in: {}'.format(analysis_settings_fp)) return analysis_settings
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2.533773
1,214
#!/usr/bin/env python #-------------------------------------------------------------------------------- #Changes the sky coordinates (x,y,z) to the disk coordinates (x_d,y_d,z_d) #The x axis is the rotation axis def FUN_rotation(x,y,z): x_d = x y_d = y*np.cos(inc) - z*np.sin(inc) z_d = y*np.sin(inc) + z*np.cos(inc) return x_d,y_d,z_d #-------------------------------------------------------------------------------- #Radiative transfer equation def FUN_intensity(I,z,x,y,optde): x_d,y_d,z_d = FUN_rotation(x,y,z) density = EQ_density(x_d,y_d,z_d) amax = EQ_amax(x_d,y_d,z_d) opa = function_ext(amax) S = funcion_S([z_d,y_d,x_d]) # print ('x,y,z', x,y,z) # print (S, x_d, y_d, z_d) # print (optde(z)) dIdz = -S*opa*density*np.exp(-optde(z)) #z es la variable de integracion (debe ser evaluada en cualquier punto) return dIdz #-------------------------------------------------------------------------------- #Optical depth def FUN_tau(tt,z,x,y): x_d,y_d,z_d = FUN_rotation(x,y,z) density = EQ_density(x_d,y_d,z_d) amax = EQ_amax(x_d,y_d,z_d) opa = function_ext(amax) dtau = -opa*density return dtau #-------------------------------------------------------------------------------- def FUN_tau_zaxis(tt,z,x,y): x_d,y_d,z_d = x,y,z density = EQ_density(x_d,y_d,z_d) amax = EQ_amax(x_d,y_d,z_d) opa = function_ext(amax) dtau = -opa*density return dtau #-------------------------------------------------------------------------------- #Black body radiation def FUN_BB(nu,T): # B = 2.*hP*nu**3/clight**2/( np.exp(hP*nu/kB/T) - 1.) B = 1./( np.exp(hP*nu/kB/T) - 1.) return B #-------------------------------------------------------------------------------- def FUN_limits_mult(xx,yy): Hout = EQ_Height(Rout) lim_z = Rout*np.sin(inc) + 2.*Hout*np.cos(inc) #Based on the geometry of the disk lim_y = Rout*np.cos(inc) + 2.*Hout*np.sin(inc) #Based on the geometry of the disk z_arr = np.linspace(1.1*lim_z, -1.1*lim_z, 200) z_crit = [] if ((np.abs(xx) <=Rout) and (np.abs(yy) <= lim_y)): xd,yd,zd = FUN_rotation(xx,yy,z_arr) crit = np.zeros((len(z_arr))) ############################################################################### #Funciona pero podria ser optimizado ############################################################################### for ii in range(len(z_arr)): #Crea un vector de densidad en la linea de vision if (EQ_density(xd,yd[ii],zd[ii]) == 0.): crit[ii] = 0 else: crit[ii] = 1 for ii in range(len(z_arr)): #Ve los indices donde cambia de 0 a algun valor, o de algun valor a 0 (fronteras) if ( (ii != 0) and (crit[ii] - crit[ii-1] != 0 )): z_crit.append(z_arr[ii]) elif(ii == 0 and crit[0] == 1): z_crit.append(z_arr[0]) ############################################################################### return z_crit #-------------------------------------------------------------------------------- def FUN_creates_source_function(x_array,y_array): #Arrays and limits Hout = EQ_Height(Rout) z_array = np.linspace(-2.*Hout, 2.*Hout, 200) Sfunction = np.zeros((len(z_array),len(y_array),len(x_array))) Temfunction = np.zeros((len(z_array),len(y_array),len(x_array))) op_depth_p = np.zeros((len(y_array),len(x_array))) #Computes the optical depth (perpendicular to the disk midplane) for j in range(len(y_array)): for i in range(len(x_array)): if(x_array[i] == 0. and y_array[j] == 0.): Sfunction[:,j,i] = 0. Temfunction[:,j,i] = 0. else: rad = np.sqrt(x_array[i]**2 + y_array[j]**2) Hscale = EQ_Height(rad) z_integ = np.linspace(2.*Hscale,-2.*Hscale,200) sol = odeint(FUN_tau_zaxis,0.,z_integ,args=(x_array[i],y_array[j])).T[0] op_depth_p[j][i] = sol[len(z_integ)-1] inter_opt = interpolate.interp1d(z_integ,sol,kind='linear', bounds_error=False,fill_value=0.) for k in range(len(z_array)): amax = EQ_amax(x_array[i],y_array[j],z_array[k]) albedo = function_alb(amax) ##########Temperature########## Omega2 = Ggrav*Mstar/(rad*AU)**3 Teff4 = 3.*Mdot*Omega2/8./np.pi/sigmaB Tacc4 = 3./4.*(7.*inter_opt(abs(z_array[k])) + 2./3.)*Teff4 Tirr4 = Tstar**4./4.*(Rstar/rad/AU)**2*np.exp(-7.*inter_opt(abs(z_array[k]))/phi_angle) Temfunction[k,j,i] = (Tacc4 + Tirr4)**(0.25) #Temfunction[k,j,i] = EQ_temperature(x_array[i],y_array[j],z_array[k]) ############################### Sfunction[k,j,i] = FUN_BB(nu,Temfunction[k,j,i])*(1.+ albedo*FUN_f(inter_opt(z_array[k]),op_depth_p[j][i],albedo)) #Crea funcion fuente y temperatura en 3D funcion_S = RegularGridInterpolator((z_array, y_array, x_array), Sfunction,bounds_error=False,fill_value=None) funcion_T = RegularGridInterpolator((z_array, y_array, x_array), Temfunction,bounds_error=False,fill_value=None) return funcion_S, funcion_T #-------------------------------------------------------------------------------- def FUN_f(t,tau,alb): eps = np.sqrt(1.-alb) fff = np.exp(-np.sqrt(3.)*eps*t) + np.exp(np.sqrt(3.)*eps*(t-tau)) fff = fff/( np.exp(-np.sqrt(3.)*eps*tau)*(eps-1.) - (eps+1.) ) return fff #-------------------------------------------------------------------------------- #Lee las tablas de opacidad DSHARP #Load opacities with np.load('default_opacities_smooth.npz') as d: a_w = d['a'] gsca_w = d['g'] lam_w = d['lam'] k_abs_w = d['k_abs'] k_sca_w = d['k_sca'] lam_avgs = wl # We split the opacities within the range of frequency to make the calculations faster k_abs_w = k_abs_w[(0.9*lam_avgs<lam_w) & (1.1*lam_avgs>lam_w),:] k_sca_w = k_sca_w[(0.9*lam_avgs<lam_w) & (1.1*lam_avgs>lam_w),:] k_sca_w = k_sca_w*(1. - gsca_w[(0.9*lam_avgs<lam_w) & (1.1*lam_avgs>lam_w),:]) lam_w = lam_w[(0.9*lam_avgs<lam_w) & (1.1*lam_avgs>lam_w)] opac_grid = opacity.size_average_opacity(lam_avgs, a_w, lam_w, k_abs_w.T, k_sca_w.T, q=3.5, plot=True) function_ext = interpolate.interp1d(a_w, opac_grid['ka'][:]+opac_grid['ks'][:],kind='cubic') function_alb = interpolate.interp1d(a_w, opac_grid['ks'][:]/(opac_grid['ka'][:]+opac_grid['ks'][:]),kind='cubic') if not scattering: function_alb = interpolate.interp1d(a_w, np.zeros((np.shape(opac_grid['ks'][:]))),kind='cubic')
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220, 1303, 15001, 319, 262, 22939, 286, 262, 11898, 628, 220, 220, 220, 1976, 62, 3258, 796, 45941, 13, 21602, 10223, 7, 16, 13, 16, 9, 2475, 62, 89, 11, 532, 16, 13, 16, 9, 2475, 62, 89, 11, 939, 8, 628, 220, 220, 220, 1976, 62, 22213, 796, 17635, 628, 220, 220, 220, 611, 14808, 37659, 13, 8937, 7, 5324, 8, 19841, 49, 448, 8, 290, 357, 37659, 13, 8937, 7, 22556, 8, 19841, 1761, 62, 88, 8, 2599, 198, 220, 220, 220, 220, 220, 220, 220, 2124, 67, 11, 5173, 11, 89, 67, 796, 29397, 62, 10599, 341, 7, 5324, 11, 22556, 11, 89, 62, 3258, 8, 198, 220, 220, 220, 220, 220, 220, 220, 1955, 796, 45941, 13, 9107, 418, 19510, 11925, 7, 89, 62, 3258, 22305, 198, 220, 220, 220, 220, 220, 220, 220, 1303, 29113, 29113, 7804, 4242, 2235, 198, 220, 220, 220, 220, 220, 220, 220, 1303, 37, 19524, 32792, 583, 78, 24573, 7496, 1055, 6436, 528, 4533, 198, 220, 220, 220, 220, 220, 220, 220, 1303, 29113, 29113, 7804, 4242, 2235, 220, 220, 220, 220, 220, 220, 220, 220, 198, 220, 220, 220, 220, 220, 220, 220, 329, 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# -*- coding: utf-8 -*- """ Created on Wed Jun 16 18:06:05 2021 @author: jhask """ import csv import pandas as pd import numpy as np import re import scipy.io as sio import os # Map MCM names to TUV labels j_vals_dict= dict({ 'O3 -> O2 + O(1D)':'J1', 'O3 -> O2 + O(3P)':'J2', 'H2O2 -> 2 OH':'J3', 'NO2 -> NO + O(3P)':'J4', 'NO3 -> NO + O2':'J5', 'NO3 -> NO2 + O(3P)':'J6', 'HNO2 -> OH + NO':'J7', 'HNO3 -> OH + NO2':'J8', 'CH2O -> H + HCO':'J11', 'CH2O -> H2 + CO':'J12', 'CH3CHO -> CH3 + HCO':'J13', 'C2H5CHO -> C2H5 + HCO':'J14', 'CH2=C(CH3)CHO -> Products':'J18', 'CH3COCH3 -> CH3CO + CH3':'J21', 'CH3COCH2CH3 -> CH3CO + CH2CH3':'J22', 'CH3COCH=CH2 -> Products':'J23', 'CHOCHO -> H2 + 2CO':'J31', 'CHOCHO -> CH2O + CO':'J32', 'CHOCHO -> HCO + HCO':'J33', 'CH3COCHO -> CH3CO + HCO':'J34', 'CH3COCOCH3 -> Products':'J35', 'CH3OOH -> CH3O + OH':'J41', 'CH3ONO2 -> CH3O + NO2':'J51', 'C2H5ONO2 -> C2H5O + NO2':'J52', 'n-C3H7ONO2 -> C3H7O + NO2':'J53', 'CH3CHONO2CH3 -> CH3CHOCH3 + NO2':'J54', 'C(CH3)3(ONO2) -> C(CH3)3(O.) + NO2':'J55', 'CH3COCH2(ONO2) -> CH3COCH2(O.) + NO2':'J56', 'CH2(OH)COCH3 -> CH3CO + CH2(OH)':'Jn10', 'CH2=CHCHO -> Products':'Jn11', 'CH3CO(OONO2) -> CH3CO(OO) + NO2':'Jn14', 'CH3CO(OONO2) -> CH3CO(O) + NO3':'Jn15', 'CH3(OONO2) -> CH3(OO) + NO2':'Jn16', 'CH3(OONO2) -> CH3(OO) + NO2':'Jn17', 'N2O5 -> NO3 + NO2':'Jn19', 'N2O5 -> NO3 + NO + O(3P)':'Jn20', 'HNO4 -> HO2 + NO2':'Jn21'}) #TUV output file. file= 'C:/Users/jhask/OneDrive/Documents/MATLAB/F0AM/Setups/SOAS_RCIM/foam_6_29_out.txt' with open(file, "r",errors="ignore") as f: # read line by line. reader = csv.reader(f, delimiter="\t") # Initialize vars we fill in reading the file. ln_num = 0; map_cols=dict({}) in_species_list=False; pass_go=False for row in reader: line = " ".join(row) # read line by line. hdrs= [key for key in list(j_vals_dict.keys()) if key in line] if len(hdrs) > 0 : headers= re.search(r"[\d]*[\=\w]", line) print(line, hdrs, j_vals_dict[ hdrs[:][0]]) if headers: map_cols[headers.group()]=j_vals_dict[ hdrs[:][0]] if (pass_go is True) and ('------' not in line ): # Append the j-values to the dataframe at this point in time. splt= [float(item) for item in line.split(" ") if item !=''] df.loc[len(df)]=np.array(splt) if 'time, hrs. sza, deg.' in line: pass_go=True df=pd.DataFrame(columns= ['time', 'sza']+ list(map_cols.values())) to_mat={name: col.values for name, col in df.items()} filename= os.path.join('C:/Users/jhask/OneDrive/Documents/MATLAB/F0AM/Setups/SOAS_RCIM/'+'F0AM_tuv.mat') sio.savemat(filename, to_mat) print(filename)
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''' Imports ''' from config import * from newspaper import Article import sys as sys import pandas as pd import csv from collections import defaultdict import re ''' URL Extract ''' columns = defaultdict(list) with open('SecurityIDRBT.csv') as f: reader = csv.DictReader(f) # read rows into a dictionary format for row in reader: # read a row as {column1: value1, column2: value2,...} for (k,v) in row.items(): # go over each column name and value columns[k].append(v) # append the value into the appropriate list url_list = [] # based on column name k for element in range(len(columns['Body'])): urls = re.findall('https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+', columns['Body'][element]) for url in urls: url_list.append(url) ''' Find Unique URLs and filter with semantic search results ''' url_unique = [] for element in url_list: if element not in url_unique: if element not in common_urls_http: if element not in common_urls_https: url_unique.append(element) ''' Write it in a new CSV ''' with open('url.csv', 'w',newline='') as myfile: wr = csv.writer(myfile, quoting=csv.QUOTE_ALL) for word in url_unique: wr.writerow([word])
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# -*- coding: utf-8 -*- # Copyright 2018, IBM. # # This source code is licensed under the Apache License, Version 2.0 found in # the LICENSE.txt file in the root directory of this source tree. # pylint: disable=missing-docstring from qiskit.mapper import _coupling from .common import QiskitTestCase class CouplingTest(QiskitTestCase): def test_coupling_dict2list(self): input_dict = {0: [1, 2], 1: [2]} result = _coupling.coupling_dict2list(input_dict) expected = [[0, 1], [0, 2], [1, 2]] self.assertEqual(expected, result) def test_coupling_dict2list_empty_dict(self): self.assertIsNone(_coupling.coupling_dict2list({})) def test_coupling_list2dict(self): input_list = [[0, 1], [0, 2], [1, 2]] result = _coupling.coupling_list2dict(input_list) expected = {0: [1, 2], 1: [2]} self.assertEqual(expected, result) def test_coupling_list2dict_empty_list(self): self.assertIsNone(_coupling.coupling_list2dict([])) def test_empty_coupling_class(self): coupling = _coupling.Coupling() self.assertEqual(0, coupling.size()) self.assertEqual([], coupling.get_qubits()) self.assertEqual([], coupling.get_edges()) self.assertFalse(coupling.connected()) self.assertEqual("", str(coupling)) def test_coupling_str(self): coupling_dict = {0: [1, 2], 1: [2]} coupling = _coupling.Coupling(coupling_dict) expected = ("qubits: q[0] @ 1, q[1] @ 2, q[2] @ 3\n" "edges: q[0]-q[1], q[0]-q[2], q[1]-q[2]") self.assertEqual(expected, str(coupling)) def test_coupling_compute_distance(self): coupling_dict = {0: [1, 2], 1: [2]} coupling = _coupling.Coupling(coupling_dict) self.assertTrue(coupling.connected()) coupling.compute_distance() qubits = coupling.get_qubits() result = coupling.distance(qubits[0], qubits[1]) self.assertEqual(1, result) def test_coupling_compute_distance_coupling_error(self): coupling = _coupling.Coupling() self.assertRaises(_coupling.CouplingError, coupling.compute_distance) def test_add_qubit(self): coupling = _coupling.Coupling() self.assertEqual("", str(coupling)) coupling.add_qubit(('q', 0)) self.assertEqual("qubits: q[0] @ 1", str(coupling)) def test_add_qubit_not_tuple(self): coupling = _coupling.Coupling() self.assertRaises(_coupling.CouplingError, coupling.add_qubit, 'q0') def test_add_qubit_tuple_incorrect_form(self): coupling = _coupling.Coupling() self.assertRaises(_coupling.CouplingError, coupling.add_qubit, ('q', '0')) def test_add_edge(self): coupling = _coupling.Coupling() self.assertEqual("", str(coupling)) coupling.add_edge(("q", 0), ('q', 1)) expected = ("qubits: q[0] @ 1, q[1] @ 2\n" "edges: q[0]-q[1]") self.assertEqual(expected, str(coupling)) def test_distance_error(self): """Test distance method validation.""" graph = _coupling.Coupling({0: [1, 2], 1: [2]}) self.assertRaises(_coupling.CouplingError, graph.distance, ('q0', 0), ('q1', 1))
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import os import re import k3d import types import random import pytest import numbers import tempfile import itertools import numpy as np import discretisedfield as df import matplotlib.pyplot as plt from .test_mesh import TestMesh def check_field(field): assert isinstance(field.mesh, df.Mesh) assert isinstance(field.dim, int) assert field.dim > 0 assert isinstance(field.array, np.ndarray) assert field.array.shape == (*field.mesh.n, field.dim) average = field.average assert isinstance(average, (tuple, numbers.Real)) rstr = repr(field) assert isinstance(rstr, str) pattern = (r'^Field\(mesh=Mesh\(region=Region\(p1=\(.+\), ' r'p2=\(.+\)\), .+\), dim=\d+\)$') assert re.search(pattern, rstr) assert isinstance(field.__iter__(), types.GeneratorType) assert len(list(field)) == len(field.mesh) line = field.line(p1=field.mesh.region.pmin, p2=field.mesh.region.pmax, n=5) assert isinstance(line, df.Line) assert line.n == 5 plane = field.plane('z', n=(2, 2)) assert isinstance(plane, df.Field) assert len(plane.mesh) == 4 assert plane.mesh.n == (2, 2, 1) project = field.project('z') assert isinstance(project, df.Field) assert project.mesh.n[2] == 1 assert isinstance(field(field.mesh.region.centre), (tuple, numbers.Real)) assert isinstance(field(field.mesh.region.random_point()), (tuple, numbers.Real)) assert field == field assert not field != field assert +field == field assert -(-field) == field assert field + field == 2*field assert field - (-field) == field + field assert 1*field == field assert -1*field == -field if field.dim == 1: grad = field.grad assert isinstance(grad, df.Field) assert grad.dim == 3 assert all(i not in dir(field) for i in 'xyz') assert isinstance((field * df.dx).integral(), numbers.Real) assert isinstance((field * df.dy).integral(), numbers.Real) assert isinstance((field * df.dz).integral(), numbers.Real) assert isinstance((field * df.dV).integral(), numbers.Real) assert isinstance((field.plane('z') * df.dS).integral(), tuple) assert isinstance((field.plane('z') * abs(df.dS)).integral(), numbers.Real) if field.dim == 3: norm = field.norm assert isinstance(norm, df.Field) assert norm == abs(field) assert norm.dim == 1 assert isinstance(field.x, df.Field) assert field.x.dim == 1 assert isinstance(field.y, df.Field) assert field.y.dim == 1 assert isinstance(field.z, df.Field) assert field.z.dim == 1 div = field.div assert isinstance(div, df.Field) assert div.dim == 1 curl = field.curl assert isinstance(curl, df.Field) assert curl.dim == 3 field_plane = field.plane('z') assert isinstance((field * df.dx).integral(), tuple) assert isinstance((field * df.dy).integral(), tuple) assert isinstance((field * df.dz).integral(), tuple) assert isinstance((field * df.dV).integral(), tuple) assert isinstance((field.plane('z') @ df.dS).integral(), numbers.Real) assert isinstance((field.plane('z') * abs(df.dS)).integral(), tuple) orientation = field.orientation assert isinstance(orientation, df.Field) assert orientation.dim == 3 assert all(i in dir(field) for i in 'xyz') class TestField: def setup(self): # Get meshes using valid arguments from TestMesh. tm = TestMesh() tm.setup() self.meshes = [] for p1, p2, n, cell in tm.valid_args: region = df.Region(p1=p1, p2=p2) mesh = df.Mesh(region=region, n=n, cell=cell) self.meshes.append(mesh) # Create lists of field values. self.consts = [0, -5., np.pi, 1e-15, 1.2e12, random.random()] self.iters = [(0, 0, 1), (0, -5.1, np.pi), [70, 1e15, 2*np.pi], [5, random.random(), np.pi], np.array([4, -1, 3.7]), np.array([2.1, 0.0, -5*random.random()])] self.sfuncs = [lambda c: 1, lambda c: -2.4, lambda c: -6.4e-15, lambda c: c[0] + c[1] + c[2] + 1, lambda c: (c[0]-1)**2 - c[1]+7 + c[2]*0.1, lambda c: np.sin(c[0]) + np.cos(c[1]) - np.sin(2*c[2])] self.vfuncs = [lambda c: (1, 2, 0), lambda c: (-2.4, 1e-3, 9), lambda c: (c[0], c[1], c[2] + 100), lambda c: (c[0]+c[2]+10, c[1], c[2]+1), lambda c: (c[0]-1, c[1]+70, c[2]*0.1), lambda c: (np.sin(c[0]), np.cos(c[1]), -np.sin(2*c[2]))] # Create a field for plotting tests mesh = df.Mesh(p1=(-5e-9, -5e-9, -5e-9), p2=(5e-9, 5e-9, 5e-9), n=(5, 5, 5)) def norm_fun(point): x, y, z = point if x**2 + y**2 <= (5e-9)**2: return 1e5 else: return 0 def value_fun(point): x, y, z = point if x <= 0: return (0, 0, 1) else: return (0, 0, -1) self.pf = df.Field(mesh, dim=3, value=value_fun, norm=norm_fun) def test_init_valid_args(self): for mesh in self.meshes: for value in self.consts + self.sfuncs: f = df.Field(mesh, dim=1, value=value) check_field(f) for value in self.iters + self.vfuncs: f = df.Field(mesh, dim=3, value=value) check_field(f) def test_init_invalid_args(self): with pytest.raises(TypeError): mesh = 'meaningless_mesh_string' f = df.Field(mesh, dim=1) for mesh in self.meshes: for dim in [0, -1, 'dim', (2, 3)]: with pytest.raises((ValueError, TypeError)): f = df.Field(mesh, dim=dim) def test_set_with_ndarray(self): for mesh in self.meshes: f = df.Field(mesh, dim=3) f.value = np.ones((*f.mesh.n, f.dim,)) check_field(f) assert isinstance(f.value, np.ndarray) assert f.average == (1, 1, 1) with pytest.raises(ValueError): f.value = np.ones((2, 2)) def test_set_with_callable(self): for mesh in self.meshes: for func in self.sfuncs: f = df.Field(mesh, dim=1, value=func) check_field(f) rp = f.mesh.region.random_point() # Make sure to be at the centre of the cell rp = f.mesh.index2point(f.mesh.point2index(rp)) assert f(rp) == func(rp) for mesh in self.meshes: for func in self.vfuncs: f = df.Field(mesh, dim=3, value=func) check_field(f) rp = f.mesh.region.random_point() rp = f.mesh.index2point(f.mesh.point2index(rp)) assert np.all(f(rp) == func(rp)) def test_set_with_dict(self): p1 = (0, 0, 0) p2 = (10e-9, 10e-9, 10e-9) n = (5, 5, 5) subregions = {'r1': df.Region(p1=(0, 0, 0), p2=(4e-9, 10e-9, 10e-9)), 'r2': df.Region(p1=(4e-9, 0, 0), p2=(10e-9, 10e-9, 10e-9))} mesh = df.Mesh(p1=p1, p2=p2, n=n, subregions=subregions) field = df.Field(mesh, dim=3, value={'r1': (0, 0, 1), 'r2': (0, 0, 2), 'r1:r2': (0, 0, 5)}) assert np.all(field((3e-9, 7e-9, 9e-9)) == (0, 0, 1)) assert np.all(field((8e-9, 2e-9, 9e-9)) == (0, 0, 2)) def test_set_exception(self): for mesh in self.meshes: with pytest.raises(ValueError): f = df.Field(mesh, dim=3, value='meaningless_string') with pytest.raises(ValueError): f = df.Field(mesh, dim=3, value=5+5j) def test_value(self): p1 = (0, 0, 0) p2 = (10e-9, 10e-9, 10e-9) n = (5, 5, 5) mesh = df.Mesh(p1=p1, p2=p2, n=n) f = df.Field(mesh, dim=3) f.value = (1, 1, 1) assert f.value == (1, 1, 1) f.array[0, 0, 0, 0] = 3 assert isinstance(f.value, np.ndarray) def test_norm(self): mesh = df.Mesh(p1=(0, 0, 0), p2=(10, 10, 10), cell=(5, 5, 5)) f = df.Field(mesh, dim=3, value=(2, 2, 2)) assert np.all(f.norm.value == 2*np.sqrt(3)) assert np.all(f.norm.array == 2*np.sqrt(3)) assert np.all(f.array == 2) f.norm = 1 assert np.all(f.norm.value == 1) assert np.all(f.norm.array == 1) assert np.all(f.array == 1/np.sqrt(3)) f.array[0, 0, 0, 0] = 3 assert isinstance(f.norm.value, np.ndarray) assert not np.all(f.norm.value == 1) for mesh in self.meshes: for value in self.iters + self.vfuncs: for norm_value in [1, 2.1, 50, 1e-3, np.pi]: f = df.Field(mesh, dim=3, value=value, norm=norm_value) # Compute norm. norm = f.array[..., 0]**2 norm += f.array[..., 1]**2 norm += f.array[..., 2]**2 norm = np.sqrt(norm) assert norm.shape == f.mesh.n assert f.norm.array.shape == (*f.mesh.n, 1) assert np.all(abs(norm - norm_value) < 1e-12) # Exception mesh = df.Mesh(p1=(0, 0, 0), p2=(10, 10, 10), cell=(1, 1, 1)) f = df.Field(mesh, dim=1, value=-5) with pytest.raises(ValueError): f.norm = 5 def test_norm_is_not_preserved(self): p1 = (0, 0, 0) p2 = (10e-9, 10e-9, 10e-9) n = (5, 5, 5) mesh = df.Mesh(p1=p1, p2=p2, n=n) f = df.Field(mesh, dim=3) f.value = (0, 3, 0) f.norm = 1 assert np.all(f.norm.array == 1) f.value = (0, 2, 0) assert np.all(f.norm.value != 1) assert np.all(f.norm.array == 2) def test_norm_zero_field_exception(self): p1 = (0, 0, 0) p2 = (10e-9, 10e-9, 10e-9) n = (5, 5, 5) mesh = df.Mesh(p1=p1, p2=p2, n=n) f = df.Field(mesh, dim=3, value=(0, 0, 0)) with pytest.raises(ValueError): f.norm = 1 def test_zero(self): p1 = (0, 0, 0) p2 = (10e-9, 10e-9, 10e-9) n = (5, 5, 5) mesh = df.Mesh(p1=p1, p2=p2, n=n) f = df.Field(mesh, dim=1, value=1e-6) zf = f.zero assert f.mesh == zf.mesh assert f.dim == zf.dim assert not np.any(zf.array) f = df.Field(mesh, dim=3, value=(5, -7, 1e3)) zf = f.zero assert f.mesh == zf.mesh assert f.dim == zf.dim assert not np.any(zf.array) def test_orientation(self): p1 = (-5e-9, -5e-9, -5e-9) p2 = (5e-9, 5e-9, 5e-9) cell = (1e-9, 1e-9, 1e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # No zero-norm cells f = df.Field(mesh, dim=3, value=(2, 0, 0)) assert f.orientation.average == (1, 0, 0) # With zero-norm cells def value_fun(point): x, y, z = point if x <= 0: return (0, 0, 0) else: return (3, 0, 4) f = df.Field(mesh, dim=3, value=value_fun) assert f.orientation((-1.5e-9, 3e-9, 0)) == (0, 0, 0) assert f.orientation((1.5e-9, 3e-9, 0)) == (0.6, 0, 0.8) f = df.Field(mesh, dim=1, value=0) with pytest.raises(ValueError): of = f.orientation def test_average(self): value = -1e-3 + np.pi tol = 1e-12 p1 = (-5e-9, -5e-9, -5e-9) p2 = (5e-9, 5e-9, 5e-9) cell = (1e-9, 1e-9, 1e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=1, value=2) assert abs(f.average - 2) < tol f = df.Field(mesh, dim=3, value=(0, 1, 2)) assert np.allclose(f.average, (0, 1, 2)) def test_field_component(self): for mesh in self.meshes: f = df.Field(mesh, dim=3, value=(1, 2, 3)) assert all(isinstance(getattr(f, i), df.Field) for i in 'xyz') assert all(getattr(f, i).dim == 1 for i in 'xyz') f = df.Field(mesh, dim=2, value=(1, 2)) assert all(isinstance(getattr(f, i), df.Field) for i in 'xy') assert all(getattr(f, i).dim == 1 for i in 'xy') # Exception. f = df.Field(mesh, dim=1, value=1) with pytest.raises(AttributeError): fx = f.x.dim def test_get_attribute_exception(self): for mesh in self.meshes: f = df.Field(mesh, dim=3) with pytest.raises(AttributeError) as excinfo: f.__getattr__('nonexisting_attribute') assert 'has no attribute' in str(excinfo.value) def test_dir(self): for mesh in self.meshes: f = df.Field(mesh, dim=3, value=(5, 6, -9)) assert all(attr in dir(f) for attr in ['x', 'y', 'z', 'div']) assert 'grad' not in dir(f) f = df.Field(mesh, dim=1, value=1) assert all(attr not in dir(f) for attr in ['x', 'y', 'z', 'div']) assert 'grad' in dir(f) def test_eq(self): p1 = (-5e-9, -5e-9, -5e-9) p2 = (15e-9, 5e-9, 5e-9) cell = (5e-9, 1e-9, 2.5e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f1 = df.Field(mesh, dim=1, value=0.2) f2 = df.Field(mesh, dim=1, value=0.2) f3 = df.Field(mesh, dim=1, value=3.1) f4 = df.Field(mesh, dim=3, value=(1, -6, 0)) f5 = df.Field(mesh, dim=3, value=(1, -6, 0)) assert f1 == f2 assert not f1 != f2 assert not f1 == f3 assert f1 != f3 assert not f2 == f4 assert f2 != f4 assert f4 == f5 assert not f4 != f5 assert not f1 == 0.2 assert f1 != 0.2 def test_allclose(self): p1 = (-5e-9, -5e-9, -5e-9) p2 = (15e-9, 5e-9, 5e-9) cell = (5e-9, 1e-9, 2.5e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f1 = df.Field(mesh, dim=1, value=0.2) f2 = df.Field(mesh, dim=1, value=0.2+1e-9) f3 = df.Field(mesh, dim=1, value=0.21) f4 = df.Field(mesh, dim=3, value=(1, -6, 0)) f5 = df.Field(mesh, dim=3, value=(1, -6+1e-8, 0)) f6 = df.Field(mesh, dim=3, value=(1, -6.01, 0)) assert f1.allclose(f2) assert not f1.allclose(f3) assert not f1.allclose(f5) assert f4.allclose(f5) assert not f4.allclose(f6) with pytest.raises(TypeError): f1.allclose(2) def test_point_neg(self): p1 = (-5e-9, -5e-9, -5e-9) p2 = (5e-9, 5e-9, 5e-9) cell = (1e-9, 1e-9, 1e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Scalar field f = df.Field(mesh, dim=1, value=3) res = -f check_field(res) assert res.average == -3 assert f == +f assert f == -(-f) assert f == +(-(-f)) # Vector field f = df.Field(mesh, dim=3, value=(1, 2, -3)) res = -f check_field(res) assert res.average == (-1, -2, 3) assert f == +f assert f == -(-f) assert f == +(-(-f)) def test_pow(self): p1 = (0, 0, 0) p2 = (15e-9, 6e-9, 6e-9) cell = (3e-9, 3e-9, 3e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Scalar field f = df.Field(mesh, dim=1, value=2) res = f**2 assert res.average == 4 res = f**(-1) assert res.average == 0.5 # Attempt vector field f = df.Field(mesh, dim=3, value=(1, 2, -2)) with pytest.raises(ValueError): res = f**2 # Attempt to raise to non numbers.Real f = df.Field(mesh, dim=1, value=2) with pytest.raises(TypeError): res = f**'a' with pytest.raises(TypeError): res = f**f def test_add_subtract(self): p1 = (0, 0, 0) p2 = (5e-9, 10e-9, -5e-9) n = (2, 2, 1) mesh = df.Mesh(p1=p1, p2=p2, n=n) # Scalar fields f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=1, value=-0.2) res = f1 + f2 assert res.average == 1 res = f1 - f2 assert res.average == 1.4 f1 += f2 assert f1.average == 1 f1 -= f2 assert f1.average == 1.2 # Vector fields f1 = df.Field(mesh, dim=3, value=(1, 2, 3)) f2 = df.Field(mesh, dim=3, value=(-1, -3, -5)) res = f1 + f2 assert res.average == (0, -1, -2) res = f1 - f2 assert res.average == (2, 5, 8) f1 += f2 assert f1.average == (0, -1, -2) f1 -= f2 assert f1.average == (1, 2, 3) # Artithmetic checks assert f1 + f2 + (1, 1, 1) == (1, 1, 1) + f2 + f1 assert f1 - f2 - (0, 0, 0) == (0, 0, 0) - (f2 - f1) assert f1 + (f1 + f2) == (f1 + f1) + f2 assert f1 - (f1 + f2) == f1 - f1 - f2 assert f1 + f2 - f1 == f2 + (0, 0, 0) # Constants f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=3, value=(-1, -3, -5)) res = f1 + 2 assert res.average == 3.2 res = f1 - 1.2 assert res.average == 0 f1 += 2.5 assert f1.average == 3.7 f1 -= 3.7 assert f1.average == 0 res = f2 + (1, 3, 5) assert res.average == (0, 0, 0) res = f2 - (1, 2, 3) assert res.average == (-2, -5, -8) f2 += (1, 1, 1) assert f2.average == (0, -2, -4) f2 -= (-1, -2, 3) assert f2.average == (1, 0, -7) # Exceptions with pytest.raises(TypeError): res = f1 + '2' # Fields with different dimensions with pytest.raises(ValueError): res = f1 + f2 # Fields defined on different meshes mesh1 = df.Mesh(p1=(0, 0, 0), p2=(5, 5, 5), n=(1, 1, 1)) mesh2 = df.Mesh(p1=(0, 0, 0), p2=(3, 3, 3), n=(1, 1, 1)) f1 = df.Field(mesh1, dim=1, value=1.2) f2 = df.Field(mesh2, dim=1, value=1) with pytest.raises(ValueError): res = f1 + f2 with pytest.raises(ValueError): f1 += f2 with pytest.raises(ValueError): f1 -= f2 def test_mul_truediv(self): p1 = (0, 0, 0) p2 = (5e-9, 5e-9, 5e-9) cell = (1e-9, 5e-9, 1e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Scalar fields f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=1, value=-2) res = f1 * f2 assert res.average == -2.4 res = f1 / f2 assert res.average == -0.6 f1 *= f2 assert f1.average == -2.4 f1 /= f2 assert f1.average == 1.2 # Scalar field with a constant f = df.Field(mesh, dim=1, value=5) res = f * 2 assert res.average == 10 res = 3 * f assert res.average == 15 res = f * (1, 2, 3) assert res.average == (5, 10, 15) res = (1, 2, 3) * f assert res.average == (5, 10, 15) res = f / 2 assert res.average == 2.5 res = 10 / f assert res.average == 2 res = (5, 10, 15) / f assert res.average == (1, 2, 3) f *= 10 assert f.average == 50 f /= 10 assert f.average == 5 # Scalar field with a vector field f1 = df.Field(mesh, dim=1, value=2) f2 = df.Field(mesh, dim=3, value=(-1, -3, 5)) res = f1 * f2 # __mul__ assert res.average == (-2, -6, 10) res = f2 * f1 # __rmul__ assert res.average == (-2, -6, 10) res = f2 / f1 # __truediv__ assert res.average == (-0.5, -1.5, 2.5) f2 *= f1 # __imul__ assert f2.average == (-2, -6, 10) f2 /= f1 # __truediv__ assert f2.average == (-1, -3, 5) with pytest.raises(ValueError): res = f1 / f2 # __rtruediv__ # Vector field with a scalar f = df.Field(mesh, dim=3, value=(1, 2, 0)) res = f * 2 assert res.average == (2, 4, 0) res = 5 * f assert res.average == (5, 10, 0) res = f / 2 assert res.average == (0.5, 1, 0) f *= 2 assert f.average == (2, 4, 0) f /= 2 assert f.average == (1, 2, 0) with pytest.raises(ValueError): res = 10 / f # Further checks f1 = df.Field(mesh, dim=1, value=2) f2 = df.Field(mesh, dim=3, value=(-1, -3, -5)) assert f1 * f2 == f2 * f1 assert 1.3 * f2 == f2 * 1.3 assert -5 * f2 == f2 * (-5) assert (1, 2.2, -1) * f1 == f1 * (1, 2.2, -1) assert f1 * (f1 * f2) == (f1 * f1) * f2 assert f1 * f2 / f1 == f2 # Exceptions f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=3, value=(-1, -3, -5)) with pytest.raises(TypeError): res = f2 * 'a' with pytest.raises(TypeError): res = 'a' / f1 with pytest.raises(ValueError): res = f2 * f2 with pytest.raises(ValueError): res = f2 / f2 with pytest.raises(ValueError): res = 1 / f2 with pytest.raises(ValueError): res = f1 / f2 with pytest.raises(TypeError): f2 *= 'a' with pytest.raises(TypeError): f2 /= 'a' with pytest.raises(ValueError): f1 /= f2 # Fields defined on different meshes mesh1 = df.Mesh(p1=(0, 0, 0), p2=(5, 5, 5), n=(1, 1, 1)) mesh2 = df.Mesh(p1=(0, 0, 0), p2=(3, 3, 3), n=(1, 1, 1)) f1 = df.Field(mesh1, dim=1, value=1.2) f2 = df.Field(mesh2, dim=1, value=1) with pytest.raises(ValueError): res = f1 * f2 with pytest.raises(ValueError): res = f1 / f2 with pytest.raises(ValueError): f1 *= f2 with pytest.raises(ValueError): f1 /= f2 def test_dot(self): p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (2, 2, 2) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Zero vectors f1 = df.Field(mesh, dim=3, value=(0, 0, 0)) res = f1@f1 assert res.dim == 1 assert res.average == 0 # Orthogonal vectors f1 = df.Field(mesh, dim=3, value=(1, 0, 0)) f2 = df.Field(mesh, dim=3, value=(0, 1, 0)) f3 = df.Field(mesh, dim=3, value=(0, 0, 1)) assert (f1 @ f2).average == 0 assert (f1 @ f3).average == 0 assert (f2 @ f3).average == 0 assert (f1 @ f1).average == 1 assert (f2 @ f2).average == 1 assert (f3 @ f3).average == 1 # Check if commutative assert f1 @ f2 == f2 @ f1 assert f1 @ (-1, 3, 2.2) == (-1, 3, 2.2) @ f1 # Vector field with a constant f = df.Field(mesh, dim=3, value=(1, 2, 3)) res = (1, 1, 1) @ f assert res.average == 6 res = f @ [1, 1, 1] assert res.average == 6 # Spatially varying vectors def value_fun1(point): x, y, z = point return (x, y, z) def value_fun2(point): x, y, z = point return (z, x, y) f1 = df.Field(mesh, dim=3, value=value_fun1) f2 = df.Field(mesh, dim=3, value=value_fun2) # Check if commutative assert f1 @ f2 == f2 @ f1 # The dot product should be x*z + y*x + z*y assert (f1 @ f2)((1, 1, 1)) == 3 assert (f1 @ f2)((3, 1, 1)) == 7 assert (f1 @ f2)((5, 7, 1)) == 47 # Check norm computed using dot product assert f1.norm == (f1 @ f1)**(0.5) # Exceptions f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=3, value=(-1, -3, -5)) with pytest.raises(ValueError): res = f1 @ f2 with pytest.raises(ValueError): res = f1 @ f2 with pytest.raises(TypeError): res = f1 @ 3 # Fields defined on different meshes mesh1 = df.Mesh(p1=(0, 0, 0), p2=(5, 5, 5), n=(1, 1, 1)) mesh2 = df.Mesh(p1=(0, 0, 0), p2=(3, 3, 3), n=(1, 1, 1)) f1 = df.Field(mesh1, dim=3, value=(1, 2, 3)) f2 = df.Field(mesh2, dim=3, value=(3, 2, 1)) with pytest.raises(ValueError): res = f1 @ f2 def test_cross(self): p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (2, 2, 2) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Zero vectors f1 = df.Field(mesh, dim=3, value=(0, 0, 0)) res = f1 & f1 assert res.dim == 3 assert res.average == (0, 0, 0) # Orthogonal vectors f1 = df.Field(mesh, dim=3, value=(1, 0, 0)) f2 = df.Field(mesh, dim=3, value=(0, 1, 0)) f3 = df.Field(mesh, dim=3, value=(0, 0, 1)) assert (f1 & f2).average == (0, 0, 1) assert (f1 & f3).average == (0, -1, 0) assert (f2 & f3).average == (1, 0, 0) assert (f1 & f1).average == (0, 0, 0) assert (f2 & f2).average == (0, 0, 0) assert (f3 & f3).average == (0, 0, 0) # Constants assert (f1 & (0, 1, 0)).average == (0, 0, 1) assert ((0, 1, 0) & f1).average == (0, 0, 1) # Check if not comutative assert f1 & f2 == -(f2 & f1) assert f1 & f3 == -(f3 & f1) assert f2 & f3 == -(f3 & f2) f1 = df.Field(mesh, dim=3, value=lambda point: (point[0], point[1], point[2])) f2 = df.Field(mesh, dim=3, value=lambda point: (point[2], point[0], point[1])) # The cross product should be # (y**2-x*z, z**2-x*y, x**2-y*z) assert (f1 & f2)((1, 1, 1)) == (0, 0, 0) assert (f1 & f2)((3, 1, 1)) == (-2, -2, 8) assert (f2 & f1)((3, 1, 1)) == (2, 2, -8) assert (f1 & f2)((5, 7, 1)) == (44, -34, 18) # Exceptions f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=3, value=(-1, -3, -5)) with pytest.raises(TypeError): res = f1 & 2 with pytest.raises(ValueError): res = f1 & f2 # Fields defined on different meshes mesh1 = df.Mesh(p1=(0, 0, 0), p2=(5, 5, 5), n=(1, 1, 1)) mesh2 = df.Mesh(p1=(0, 0, 0), p2=(3, 3, 3), n=(1, 1, 1)) f1 = df.Field(mesh1, dim=3, value=(1, 2, 3)) f2 = df.Field(mesh2, dim=3, value=(3, 2, 1)) with pytest.raises(ValueError): res = f1 & f2 def test_lshift(self): p1 = (0, 0, 0) p2 = (10e6, 10e6, 10e6) cell = (5e6, 5e6, 5e6) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f1 = df.Field(mesh, dim=1, value=1) f2 = df.Field(mesh, dim=1, value=-3) f3 = df.Field(mesh, dim=1, value=5) res = f1 << f2 << f3 assert res.dim == 3 assert res.average == (1, -3, 5) # Different dimensions f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=2, value=(-1, -3)) res = f1 << f2 assert res.average == (1.2, -1, -3) res = f2 << f1 assert res.average == (-1, -3, 1.2) # Constants f1 = df.Field(mesh, dim=1, value=1.2) res = f1 << 2 assert res.average == (1.2, 2) res = f1 << (1, -1) assert res.average == (1.2, 1, -1) res = 3 << f1 assert res.average == (3, 1.2) res = (1.2, 3) << f1 << 3 assert res.average == (1.2, 3, 1.2, 3) # Exceptions with pytest.raises(TypeError): res = 'a' << f1 with pytest.raises(TypeError): res = f1 << 'a' # Fields defined on different meshes mesh1 = df.Mesh(p1=(0, 0, 0), p2=(5, 5, 5), n=(1, 1, 1)) mesh2 = df.Mesh(p1=(0, 0, 0), p2=(3, 3, 3), n=(1, 1, 1)) f1 = df.Field(mesh1, dim=1, value=1.2) f2 = df.Field(mesh2, dim=1, value=1) with pytest.raises(ValueError): res = f1 << f2 def test_all_operators(self): p1 = (0, 0, 0) p2 = (5e-9, 5e-9, 10e-9) n = (2, 2, 1) mesh = df.Mesh(p1=p1, p2=p2, n=n) f1 = df.Field(mesh, dim=1, value=2) f2 = df.Field(mesh, dim=3, value=(-4, 0, 1)) res = ((+f1/2 + f2.x)**2 - 2*f1*3)/(-f2.z) - 2*f2.y + 1/f2.z**2 + f2@f2 assert np.all(res.array == 21) res = 1 + f1 + 0*f2.x - 3*f2.y/3 assert res.average == 3 def test_pad(self): p1 = (0, 0, 0) p2 = (10, 8, 2) cell = (1, 1, 1) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) field = df.Field(mesh, dim=1, value=1) pf = field.pad({'x': (1, 1)}, mode='constant') # zeros padded assert pf.array.shape == (12, 8, 2, 1) def test_derivative(self): p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (2, 2, 2) # f(x, y, z) = 0 -> grad(f) = (0, 0, 0) # No BC mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=1, value=0) check_field(f.derivative('x')) assert f.derivative('x', n=1).average == 0 assert f.derivative('y', n=1).average == 0 assert f.derivative('z', n=1).average == 0 assert f.derivative('x', n=2).average == 0 assert f.derivative('y', n=2).average == 0 assert f.derivative('z', n=2).average == 0 # f(x, y, z) = x + y + z -> grad(f) = (1, 1, 1) # No BC mesh = df.Mesh(p1=p1, p2=p2, cell=cell) def value_fun(point): x, y, z = point return x + y + z f = df.Field(mesh, dim=1, value=value_fun) assert f.derivative('x', n=1).average == 1 assert f.derivative('y', n=1).average == 1 assert f.derivative('z', n=1).average == 1 assert f.derivative('x', n=2).average == 0 assert f.derivative('y', n=2).average == 0 assert f.derivative('z', n=2).average == 0 # f(x, y, z) = x*y + 2*y + x*y*z -> # grad(f) = (y+y*z, x+2+x*z, x*y) # No BC mesh = df.Mesh(p1=p1, p2=p2, cell=cell) def value_fun(point): x, y, z = point return x*y + 2*y + x*y*z f = df.Field(mesh, dim=1, value=value_fun) assert f.derivative('x')((7, 5, 1)) == 10 assert f.derivative('y')((7, 5, 1)) == 16 assert f.derivative('z')((7, 5, 1)) == 35 assert f.derivative('x', n=2)((1, 1, 1)) == 0 assert f.derivative('y', n=2)((1, 1, 1)) == 0 assert f.derivative('z', n=2)((1, 1, 1)) == 0 # f(x, y, z) = (0, 0, 0) # -> dfdx = (0, 0, 0) # -> dfdy = (0, 0, 0) # -> dfdz = (0, 0, 0) # No BC mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=3, value=(0, 0, 0)) check_field(f.derivative('y')) assert f.derivative('x').average == (0, 0, 0) assert f.derivative('y').average == (0, 0, 0) assert f.derivative('z').average == (0, 0, 0) # f(x, y, z) = (x, y, z) # -> dfdx = (1, 0, 0) # -> dfdy = (0, 1, 0) # -> dfdz = (0, 0, 1) def value_fun(point): x, y, z = point return (x, y, z) f = df.Field(mesh, dim=3, value=value_fun) assert f.derivative('x').average == (1, 0, 0) assert f.derivative('y').average == (0, 1, 0) assert f.derivative('z').average == (0, 0, 1) # f(x, y, z) = (x*y, y*z, x*y*z) # -> dfdx = (y, 0, y*z) # -> dfdy = (x, z, x*z) # -> dfdz = (0, y, x*y) def value_fun(point): x, y, z = point return (x*y, y*z, x*y*z) f = df.Field(mesh, dim=3, value=value_fun) assert f.derivative('x')((3, 1, 3)) == (1, 0, 3) assert f.derivative('y')((3, 1, 3)) == (3, 3, 9) assert f.derivative('z')((3, 1, 3)) == (0, 1, 3) assert f.derivative('x')((5, 3, 5)) == (3, 0, 15) assert f.derivative('y')((5, 3, 5)) == (5, 5, 25) assert f.derivative('z')((5, 3, 5)) == (0, 3, 15) # f(x, y, z) = (3+x*y, x-2*y, x*y*z) # -> dfdx = (y, 1, y*z) # -> dfdy = (x, -2, x*z) # -> dfdz = (0, 0, x*y) def value_fun(point): x, y, z = point return (3+x*y, x-2*y, x*y*z) f = df.Field(mesh, dim=3, value=value_fun) assert f.derivative('x')((7, 5, 1)) == (5, 1, 5) assert f.derivative('y')((7, 5, 1)) == (7, -2, 7) assert f.derivative('z')((7, 5, 1)) == (0, 0, 35) # f(x, y, z) = 2*x*x + 2*y*y + 3*z*z # -> grad(f) = (4, 4, 6) def value_fun(point): x, y, z = point return 2*x*x + 2*y*y + 3*z*z f = df.Field(mesh, dim=1, value=value_fun) assert f.derivative('x', n=2).average == 4 assert f.derivative('y', n=2).average == 4 assert f.derivative('z', n=2).average == 6 # f(x, y, z) = (2*x*x, 2*y*y, 3*z*z) def value_fun(point): x, y, z = point return (2*x*x, 2*y*y, 3*z*z) f = df.Field(mesh, dim=3, value=value_fun) assert f.derivative('x', n=2).average == (4, 0, 0) assert f.derivative('y', n=2).average == (0, 4, 0) assert f.derivative('z', n=2).average == (0, 0, 6) with pytest.raises(NotImplementedError): res = f.derivative('x', n=3) def test_derivative_pbc(self): p1 = (0, 0, 0) p2 = (10, 8, 6) cell = (2, 2, 2) mesh_nopbc = df.Mesh(p1=p1, p2=p2, cell=cell) mesh_pbc = df.Mesh(p1=p1, p2=p2, cell=cell, bc='xyz') # Scalar field def value_fun(point): return point[0]*point[1]*point[2] # No PBC f = df.Field(mesh_nopbc, dim=1, value=value_fun) assert f.derivative('x')((9, 1, 1)) == 1 assert f.derivative('y')((1, 7, 1)) == 1 assert f.derivative('z')((1, 1, 5)) == 1 # PBC f = df.Field(mesh_pbc, dim=1, value=value_fun) assert f.derivative('x')((9, 1, 1)) == -1.5 assert f.derivative('y')((1, 7, 1)) == -1 assert f.derivative('z')((1, 1, 5)) == -0.5 # Vector field def value_fun(point): return (point[0]*point[1]*point[2],) * 3 # No PBC f = df.Field(mesh_nopbc, dim=3, value=value_fun) assert f.derivative('x')((9, 1, 1)) == (1, 1, 1) assert f.derivative('y')((1, 7, 1)) == (1, 1, 1) assert f.derivative('z')((1, 1, 5)) == (1, 1, 1) # PBC f = df.Field(mesh_pbc, dim=3, value=value_fun) assert f.derivative('x')((9, 1, 1)) == (-1.5, -1.5, -1.5) assert f.derivative('y')((1, 7, 1)) == (-1, -1, -1) assert f.derivative('z')((1, 1, 5)) == (-0.5, -0.5, -0.5) def test_derivative_neumann(self): p1 = (0, 0, 0) p2 = (10, 8, 6) cell = (2, 2, 2) mesh_noneumann = df.Mesh(p1=p1, p2=p2, cell=cell) mesh_neumann = df.Mesh(p1=p1, p2=p2, cell=cell, bc='neumann') # Scalar field def value_fun(point): return point[0]*point[1]*point[2] # No Neumann f1 = df.Field(mesh_noneumann, dim=1, value=value_fun) assert f1.derivative('x')((9, 1, 1)) == 1 assert f1.derivative('y')((1, 7, 1)) == 1 assert f1.derivative('z')((1, 1, 5)) == 1 # Neumann f2 = df.Field(mesh_neumann, dim=1, value=value_fun) assert (f1.derivative('x')(f1.mesh.region.centre) == f2.derivative('x')(f2.mesh.region.centre)) assert (f1.derivative('x')((1, 7, 1)) != f2.derivative('x')((1, 7, 1))) def test_derivative_single_cell(self): p1 = (0, 0, 0) p2 = (10, 10, 2) cell = (2, 2, 2) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Scalar field: f(x, y, z) = x + y + z # -> grad(f) = (1, 1, 1) def value_fun(point): x, y, z = point return x + y + z f = df.Field(mesh, dim=1, value=value_fun) # only one cell in the z-direction assert f.plane('x').derivative('x').average == 0 assert f.plane('y').derivative('y').average == 0 assert f.derivative('z').average == 0 # Vector field: f(x, y, z) = (x, y, z) # -> grad(f) = (1, 1, 1) def value_fun(point): x, y, z = point return (x, y, z) f = df.Field(mesh, dim=3, value=value_fun) # only one cell in the z-direction assert f.plane('x').derivative('x').average == (0, 0, 0) assert f.plane('y').derivative('y').average == (0, 0, 0) assert f.derivative('z').average == (0, 0, 0) def test_grad(self): p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (2, 2, 2) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # f(x, y, z) = 0 -> grad(f) = (0, 0, 0) f = df.Field(mesh, dim=1, value=0) check_field(f.grad) assert f.grad.average == (0, 0, 0) # f(x, y, z) = x + y + z -> grad(f) = (1, 1, 1) def value_fun(point): x, y, z = point return x + y + z f = df.Field(mesh, dim=1, value=value_fun) assert f.grad.average == (1, 1, 1) # f(x, y, z) = x*y + y + z -> grad(f) = (y, x+1, 1) def value_fun(point): x, y, z = point return x*y + y + z f = df.Field(mesh, dim=1, value=value_fun) assert f.grad((3, 1, 3)) == (1, 4, 1) assert f.grad((5, 3, 5)) == (3, 6, 1) # f(x, y, z) = x*y + 2*y + x*y*z -> # grad(f) = (y+y*z, x+2+x*z, x*y) def value_fun(point): x, y, z = point return x*y + 2*y + x*y*z f = df.Field(mesh, dim=1, value=value_fun) assert f.grad((7, 5, 1)) == (10, 16, 35) assert f.grad.x == f.derivative('x') assert f.grad.y == f.derivative('y') assert f.grad.z == f.derivative('z') # Exception f = df.Field(mesh, dim=3, value=(1, 2, 3)) with pytest.raises(ValueError): res = f.grad def test_div_curl(self): p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (2, 2, 2) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # f(x, y, z) = (0, 0, 0) # -> div(f) = 0 # -> curl(f) = (0, 0, 0) f = df.Field(mesh, dim=3, value=(0, 0, 0)) check_field(f.div) assert f.div.dim == 1 assert f.div.average == 0 check_field(f.curl) assert f.curl.dim == 3 assert f.curl.average == (0, 0, 0) # f(x, y, z) = (x, y, z) # -> div(f) = 3 # -> curl(f) = (0, 0, 0) def value_fun(point): x, y, z = point return (x, y, z) f = df.Field(mesh, dim=3, value=value_fun) assert f.div.average == 3 assert f.curl.average == (0, 0, 0) # f(x, y, z) = (x*y, y*z, x*y*z) # -> div(f) = y + z + x*y # -> curl(f) = (x*z-y, -y*z, -x) def value_fun(point): x, y, z = point return (x*y, y*z, x*y*z) f = df.Field(mesh, dim=3, value=value_fun) assert f.div((3, 1, 3)) == 7 assert f.div((5, 3, 5)) == 23 assert f.curl((3, 1, 3)) == (8, -3, -3) assert f.curl((5, 3, 5)) == (22, -15, -5) # f(x, y, z) = (3+x*y, x-2*y, x*y*z) # -> div(f) = y - 2 + x*y # -> curl(f) = (x*z, -y*z, 1-x) def value_fun(point): x, y, z = point return (3+x*y, x-2*y, x*y*z) f = df.Field(mesh, dim=3, value=value_fun) assert f.div((7, 5, 1)) == 38 assert f.curl((7, 5, 1)) == (7, -5, -6) # Exception f = df.Field(mesh, dim=1, value=3.11) with pytest.raises(ValueError): res = f.div with pytest.raises(ValueError): res = f.curl def test_laplace(self): p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (2, 2, 2) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # f(x, y, z) = (0, 0, 0) # -> laplace(f) = 0 f = df.Field(mesh, dim=3, value=(0, 0, 0)) check_field(f.laplace) assert f.laplace.dim == 3 assert f.laplace.average == (0, 0, 0) # f(x, y, z) = x + y + z # -> laplace(f) = 0 def value_fun(point): x, y, z = point return x + y + z f = df.Field(mesh, dim=1, value=value_fun) check_field(f.laplace) assert f.laplace.average == 0 # f(x, y, z) = 2*x*x + 2*y*y + 3*z*z # -> laplace(f) = 4 + 4 + 6 = 14 def value_fun(point): x, y, z = point return 2*x*x + 2*y*y + 3*z*z f = df.Field(mesh, dim=1, value=value_fun) assert f.laplace.average == 14 # f(x, y, z) = (2*x*x, 2*y*y, 3*z*z) # -> laplace(f) = (4, 4, 6) def value_fun(point): x, y, z = point return (2*x*x, 2*y*y, 3*z*z) f = df.Field(mesh, dim=3, value=value_fun) assert f.laplace.average == (4, 4, 6) def test_integral(self): # Volume integral. p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (1, 1, 1) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=1, value=0) assert (f * df.dV).integral() == 0 assert (f * df.dx*df.dy*df.dz).integral() == 0 f = df.Field(mesh, dim=1, value=2) assert (f * df.dV).integral() == 2000 assert (f * df.dx*df.dy*df.dz).integral() == 2000 f = df.Field(mesh, dim=3, value=(-1, 0, 3)) assert (f * df.dV).integral() == (-1000, 0, 3000) assert (f * df.dx*df.dy*df.dz).integral() == (-1000, 0, 3000) def value_fun(point): x, y, z = point if x <= 5: return (-1, -2, -3) else: return (1, 2, 3) f = df.Field(mesh, dim=3, value=value_fun) assert (f * df.dV).integral() == (0, 0, 0) assert (f * df.dx*df.dy*df.dz).integral() == (0, 0, 0) # Surface integral. p1 = (0, 0, 0) p2 = (10, 5, 3) cell = (1, 1, 1) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=1, value=0) assert (f.plane('x') * abs(df.dS)).integral() == 0 assert (f.plane('x') * df.dy*df.dz).integral() == 0 f = df.Field(mesh, dim=1, value=2) assert (f.plane('x') * abs(df.dS)).integral() == 30 assert (f.plane('x') * df.dy*df.dz).integral() == 30 assert (f.plane('y') * abs(df.dS)).integral() == 60 assert (f.plane('y') * df.dx*df.dz).integral() == 60 assert (f.plane('z') * abs(df.dS)).integral() == 100 assert (f.plane('z') * df.dx*df.dy).integral() == 100 f = df.Field(mesh, dim=3, value=(-1, 0, 3)) assert (f.plane('x') * abs(df.dS)).integral() == (-15, 0, 45) assert (f.plane('y') * abs(df.dS)).integral() == (-30, 0, 90) assert (f.plane('z') * abs(df.dS)).integral() == (-50, 0, 150) f = df.Field(mesh, dim=3, value=(-1, 0, 3)) assert df.integral(f.plane('x') @ df.dS) == -15 assert df.integral(f.plane('y') @ df.dS) == 0 assert df.integral(f.plane('z') @ df.dS) == 150 # Directional integral p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (1, 1, 1) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=3, value=(1, 1, 1)) f = f.integral(direction='x') assert isinstance(f, df.Field) assert f.dim == 3 assert f.mesh.n == (1, 10, 10) assert f.average == (10, 10, 10) f = f.integral(direction='x').integral(direction='y') assert isinstance(f, df.Field) assert f.dim == 3 assert f.mesh.n == (1, 1, 10) assert f.average == (100, 100, 100) f = f.integral('x').integral('y').integral('z') assert f.dim == 3 assert f.mesh.n == (1, 1, 1) assert f.average == (1000, 1000, 1000) assert (f.integral('x').integral('y').integral('z').average == f.integral()) # Improper integral p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (1, 1, 1) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=3, value=(1, 1, 1)) f = f.integral(direction='x', improper=True) assert isinstance(f, df.Field) assert f.dim == 3 assert f.mesh.n == (10, 10, 10) assert f.average == (5.5, 5.5, 5.5) assert f((0, 0, 0)) == (1, 1, 1) assert f((10, 10, 10)) == (10, 10, 10) # Exceptions with pytest.raises(ValueError): res = f.integral(direction='xy', improper=True) def test_line(self): mesh = df.Mesh(p1=(0, 0, 0), p2=(10, 10, 10), n=(10, 10, 10)) f = df.Field(mesh, dim=3, value=(1, 2, 3)) check_field(f) line = f.line(p1=(0, 0, 0), p2=(5, 5, 5), n=20) assert isinstance(line, df.Line) assert line.n == 20 assert line.dim == 3 def test_plane(self): for mesh, direction in itertools.product(self.meshes, ['x', 'y', 'z']): f = df.Field(mesh, dim=1, value=3) check_field(f) plane = f.plane(direction, n=(3, 3)) assert isinstance(plane, df.Field) p, v = zip(*list(plane)) assert len(p) == 9 assert len(v) == 9 def test_getitem(self): p1 = (0, 0, 0) p2 = (90, 50, 10) cell = (5, 5, 5) subregions = {'r1': df.Region(p1=(0, 0, 0), p2=(30, 50, 10)), 'r2': df.Region(p1=(30, 0, 0), p2=(90, 50, 10))} mesh = df.Mesh(p1=p1, p2=p2, cell=cell, subregions=subregions) def value_fun(point): x, y, z = point if x <= 60: return (-1, -2, -3) else: return (1, 2, 3) f = df.Field(mesh, dim=3, value=value_fun) check_field(f) check_field(f['r1']) check_field(f['r2']) check_field(f[subregions['r1']]) check_field(f[subregions['r2']]) assert f['r1'].average == (-1, -2, -3) assert f['r2'].average == (0, 0, 0) assert f[subregions['r1']].average == (-1, -2, -3) assert f[subregions['r2']].average == (0, 0, 0) assert len(f['r1'].mesh) + len(f['r2'].mesh) == len(f.mesh) # Meshes are not aligned subregion = df.Region(p1=(1.1, 0, 0), p2=(9.9, 15, 5)) assert f[subregion].array.shape == (2, 3, 1, 3) def test_project(self): p1 = (-5, -5, -5) p2 = (5, 5, 5) cell = (1, 1, 1) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Constant scalar field f = df.Field(mesh, dim=1, value=5) check_field(f) assert f.project('x').array.shape == (1, 10, 10, 1) assert f.project('y').array.shape == (10, 1, 10, 1) assert f.project('z').array.shape == (10, 10, 1, 1) # Constant vector field f = df.Field(mesh, dim=3, value=(1, 2, 3)) assert f.project('x').array.shape == (1, 10, 10, 3) assert f.project('y').array.shape == (10, 1, 10, 3) assert f.project('z').array.shape == (10, 10, 1, 3) # Spatially varying scalar field def value_fun(point): x, y, z = point if z <= 0: return 1 else: return -1 f = df.Field(mesh, dim=1, value=value_fun) sf = f.project('z') assert sf.array.shape == (10, 10, 1, 1) assert sf.average == 0 # Spatially varying vector field def value_fun(point): x, y, z = point if z <= 0: return (3, 2, 1) else: return (3, 2, -1) f = df.Field(mesh, dim=3, value=value_fun) sf = f.project('z') assert sf.array.shape == (10, 10, 1, 3) assert sf.average == (3, 2, 0) def test_angle(self): p1 = (0, 0, 0) p2 = (8e-9, 2e-9, 2e-9) cell = (2e-9, 2e-9, 2e-9) mesh = df.Mesh(region=df.Region(p1=p1, p2=p2), cell=cell) def value_fun(point): x, y, z = point if x < 2e-9: return (1, 1, 1) elif 2e-9 <= x < 4e-9: return (1, -1, 0) elif 4e-9 <= x < 6e-9: return (-1, -1, 0) elif 6e-9 <= x < 8e-9: return (-1, 1, 0) f = df.Field(mesh, dim=3, value=value_fun) assert abs(f.plane('z').angle((1e-9, 2e-9, 2e-9)) - np.pi/4) < 1e-3 assert abs(f.plane('z').angle((3e-9, 2e-9, 2e-9)) - 7*np.pi/4) < 1e-3 assert abs(f.plane('z').angle((5e-9, 2e-9, 2e-9)) - 5*np.pi/4) < 1e-3 assert abs(f.plane('z').angle((7e-9, 2e-9, 2e-9)) - 3*np.pi/4) < 1e-3 # Exception with pytest.raises(ValueError): res = f.angle # the field is not sliced def test_write_read_ovf(self): representations = ['txt', 'bin4', 'bin8'] filename = 'testfile.ovf' p1 = (0, 0, 0) p2 = (8e-9, 5e-9, 3e-9) cell = (1e-9, 1e-9, 1e-9) mesh = df.Mesh(region=df.Region(p1=p1, p2=p2), cell=cell) # Write/read for dim, value in [(1, lambda point: point[0] + point[1] + point[2]), (3, lambda point: (point[0], point[1], point[2]))]: f = df.Field(mesh, dim=dim, value=value) for rep in representations: with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) f.write(tmpfilename, representation=rep) f_read = df.Field.fromfile(tmpfilename) assert f.allclose(f_read) # Extend scalar for rep in representations: f = df.Field(mesh, dim=1, value=lambda point: point[0]+point[1]+point[2]) with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) f.write(tmpfilename, extend_scalar=True) f_read = df.Field.fromfile(tmpfilename) assert f.allclose(f_read.x) # Read different OOMMF representations # (OVF1, OVF2) x (txt, bin4, bin8) filenames = ['oommf-ovf2-txt.omf', 'oommf-ovf2-bin4.omf', 'oommf-ovf2-bin8.omf', 'oommf-ovf1-txt.omf', 'oommf-ovf1-bin4.omf', 'oommf-ovf1-bin8.omf'] dirname = os.path.join(os.path.dirname(__file__), 'test_sample') for filename in filenames: omffilename = os.path.join(dirname, filename) f_read = df.Field.fromfile(omffilename) if 'ovf2' in filename: # The magnetisation is in the x-direction in OVF2 files. assert abs(f_read.orientation.x.average - 1) < 1e-2 else: # The norm of magnetisation is known. assert abs(f_read.norm.average - 1261566.2610100) < 1e-3 # Read different mumax3 bin4 files (made on linux and windows) filenames = ['mumax-bin4-linux.ovf', 'mumax-bin4-windows.ovf'] dirname = os.path.join(os.path.dirname(__file__), 'test_sample') for filename in filenames: omffilename = os.path.join(dirname, filename) f_read = df.Field.fromfile(omffilename) # We know the saved magentisation. f_saved = df.Field(f_read.mesh, dim=3, value=(1, 0.1, 0), norm=1) assert f_saved.allclose(f_read) # Exception (dim=2) f = df.Field(mesh, dim=2, value=(1, 2)) with pytest.raises(TypeError) as excinfo: f.write(filename) def test_write_read_vtk(self): filename = 'testfile.vtk' p1 = (0, 0, 0) p2 = (1e-9, 2e-9, 1e-9) cell = (1e-9, 1e-9, 1e-9) mesh = df.Mesh(region=df.Region(p1=p1, p2=p2), cell=cell) for dim, value in [(1, -1.2), (3, (1e-3, -5e6, 5e6))]: f = df.Field(mesh, dim=dim, value=value) with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) f.write(tmpfilename) f_read = df.Field.fromfile(tmpfilename) assert np.allclose(f.array, f_read.array) assert np.allclose(f.mesh.region.pmin, f_read.mesh.region.pmin) assert np.allclose(f.mesh.region.pmax, f_read.mesh.region.pmax) assert np.allclose(f.mesh.cell, f_read.mesh.cell) assert f.mesh.n == f_read.mesh.n def test_write_read_hdf5(self): filenames = ['testfile.hdf5', 'testfile.h5'] p1 = (0, 0, 0) p2 = (10e-12, 5e-12, 5e-12) cell = (1e-12, 1e-12, 1e-12) mesh = df.Mesh(region=df.Region(p1=p1, p2=p2), cell=cell) for dim, value in [(1, -1.23), (3, (1e-3 + np.pi, -5e6, 6e6))]: f = df.Field(mesh, dim=dim, value=value) for filename in filenames: with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) f.write(tmpfilename) f_read = df.Field.fromfile(tmpfilename) assert f == f_read def test_read_write_invalid_extension(self): filename = 'testfile.jpg' p1 = (0, 0, 0) p2 = (10e-12, 5e-12, 3e-12) cell = (1e-12, 1e-12, 1e-12) mesh = df.Mesh(region=df.Region(p1=p1, p2=p2), cell=cell) f = df.Field(mesh, dim=1, value=5e-12) with pytest.raises(ValueError) as excinfo: f.write(filename) with pytest.raises(ValueError) as excinfo: f = df.Field.fromfile(filename) def test_mpl_scalar(self): # No axes self.pf.x.plane('x', n=(3, 4)).mpl_scalar() # Axes fig = plt.figure() ax = fig.add_subplot(111) self.pf.x.plane('x', n=(3, 4)).mpl_scalar(ax=ax) # All arguments self.pf.x.plane('x').mpl_scalar(figsize=(10, 10), filter_field=self.pf.norm, colorbar=True, colorbar_label='something', multiplier=1e-6, cmap='hsv', clim=(-1, 1)) # Lightness field filenames = ['skyrmion.omf', 'skyrmion-disk.omf'] for i in filenames: filename = os.path.join(os.path.dirname(__file__), 'test_sample', i) field = df.Field.fromfile(filename) field.plane('z').angle.mpl_scalar(lightness_field=field.z) field.plane('z').angle.mpl_scalar(lightness_field=-field.z, filter_field=field.norm) field.plane('z').mpl(scalar_lightness_field=-field.z) # Saving plot filename = 'testfigure.pdf' with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) self.pf.x.plane('x', n=(3, 4)).mpl_scalar(filename=tmpfilename) # Exceptions with pytest.raises(ValueError): self.pf.x.mpl_scalar() # not sliced with pytest.raises(ValueError): self.pf.plane('z').mpl_scalar() # vector field with pytest.raises(ValueError): # wrong filter field self.pf.x.plane('z').mpl_scalar(filter_field=self.pf) with pytest.raises(ValueError): # wrong filter field self.pf.x.plane('z').mpl_scalar(lightness_field=self.pf) plt.close('all') def test_mpl_vector(self): # No axes self.pf.plane('x', n=(3, 4)).mpl_vector() # Axes fig = plt.figure() ax = fig.add_subplot(111) self.pf.plane('x', n=(3, 4)).mpl_vector(ax=ax) # All arguments self.pf.plane('x').mpl_vector(figsize=(10, 10), color_field=self.pf.y, colorbar=True, colorbar_label='something', multiplier=1e-6, cmap='hsv', clim=(-1, 1)) # Saving plot filename = 'testfigure.pdf' with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) self.pf.plane('x', n=(3, 4)).mpl_vector(filename=tmpfilename) # Exceptions with pytest.raises(ValueError) as excinfo: self.pf.mpl_vector() # not sliced with pytest.raises(ValueError) as excinfo: self.pf.y.plane('z').mpl_vector() # scalar field with pytest.raises(ValueError) as excinfo: # wrong color field self.pf.plane('z').mpl_vector(color_field=self.pf) plt.close('all') def test_mpl(self): # No axes self.pf.plane('x', n=(3, 4)).mpl() # Axes fig = plt.figure() ax = fig.add_subplot(111) self.pf.x.plane('x', n=(3, 4)).mpl(ax=ax) # All arguments for a vector field self.pf.plane('x').mpl(figsize=(12, 6), scalar_field=self.pf.plane('x').angle, scalar_filter_field=self.pf.norm, scalar_colorbar_label='something', scalar_cmap='twilight', vector_field=self.pf, vector_color_field=self.pf.y, vector_color=True, vector_colorbar=True, vector_colorbar_label='vector', vector_cmap='hsv', vector_clim=(0, 1e6), multiplier=1e-12) # All arguments for a scalar field self.pf.z.plane('x').mpl(figsize=(12, 6), scalar_field=self.pf.x, scalar_filter_field=self.pf.norm, scalar_colorbar_label='something', scalar_cmap='twilight', vector_field=self.pf, vector_color_field=self.pf.y, vector_color=True, vector_colorbar=True, vector_colorbar_label='vector', vector_cmap='hsv', vector_clim=(0, 1e6), multiplier=1e-12) # Saving plot filename = 'testfigure.pdf' with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) self.pf.plane('x', n=(3, 4)).mpl(filename=tmpfilename) # Exception with pytest.raises(ValueError): self.pf.mpl() plt.close('all') def test_k3d_nonzero(self): # Default self.pf.norm.k3d_nonzero() # Color self.pf.x.k3d_nonzero(color=0xff00ff) # Multiplier self.pf.x.k3d_nonzero(color=0xff00ff, multiplier=1e-6) # Interactive field self.pf.x.plane('z').k3d_nonzero(color=0xff00ff, multiplier=1e-6, interactive_field=self.pf) # kwargs self.pf.x.plane('z').k3d_nonzero(color=0xff00ff, multiplier=1e-6, interactive_field=self.pf, wireframe=True) # Plot plot = k3d.plot() plot.display() self.pf.x.plane(z=0).k3d_nonzero(plot=plot, color=0xff00ff, multiplier=1e-6, interactive_field=self.pf) # Continuation for interactive plot testing. self.pf.x.plane(z=1e-9).k3d_nonzero(plot=plot, color=0xff00ff, multiplier=1e-6, interactive_field=self.pf) assert len(plot.objects) == 2 with pytest.raises(ValueError) as excinfo: self.pf.k3d_nonzero() def test_k3d_scalar(self): # Default self.pf.y.k3d_scalar() # Filter field self.pf.y.k3d_scalar(filter_field=self.pf.norm) # Colormap self.pf.x.k3d_scalar(filter_field=self.pf.norm, cmap='hsv', color=0xff00ff) # Multiplier self.pf.y.k3d_scalar(filter_field=self.pf.norm, color=0xff00ff, multiplier=1e-6) # Interactive field self.pf.y.k3d_scalar(filter_field=self.pf.norm, color=0xff00ff, multiplier=1e-6, interactive_field=self.pf) # kwargs self.pf.y.k3d_scalar(filter_field=self.pf.norm, color=0xff00ff, multiplier=1e-6, interactive_field=self.pf, wireframe=True) # Plot plot = k3d.plot() plot.display() self.pf.y.plane(z=0).k3d_scalar(plot=plot, filter_field=self.pf.norm, color=0xff00ff, multiplier=1e-6, interactive_field=self.pf) # Continuation for interactive plot testing. self.pf.y.plane(z=1e-9).k3d_scalar(plot=plot, filter_field=self.pf.norm, color=0xff00ff, multiplier=1e-6, interactive_field=self.pf) assert len(plot.objects) == 2 # Exceptions with pytest.raises(ValueError) as excinfo: self.pf.k3d_scalar() with pytest.raises(ValueError): self.pf.x.k3d_scalar(filter_field=self.pf) # filter field dim=3 def test_k3d_vector(self): # Default self.pf.k3d_vector() # Color field self.pf.k3d_vector(color_field=self.pf.x) # Colormap self.pf.k3d_vector(color_field=self.pf.norm, cmap='hsv') # Head size self.pf.k3d_vector(color_field=self.pf.norm, cmap='hsv', head_size=3) # Points self.pf.k3d_vector(color_field=self.pf.norm, cmap='hsv', head_size=3, points=False) # Point size self.pf.k3d_vector(color_field=self.pf.norm, cmap='hsv', head_size=3, points=False, point_size=1) # Vector multiplier self.pf.k3d_vector(color_field=self.pf.norm, cmap='hsv', head_size=3, points=False, point_size=1, vector_multiplier=1) # Multiplier self.pf.k3d_vector(color_field=self.pf.norm, cmap='hsv', head_size=3, points=False, point_size=1, vector_multiplier=1, multiplier=1e-6) # Interactive field self.pf.plane('z').k3d_vector(color_field=self.pf.norm, cmap='hsv', head_size=3, points=False, point_size=1, vector_multiplier=1, multiplier=1e-6, interactive_field=self.pf) # Plot plot = k3d.plot() plot.display() self.pf.plane(z=0).k3d_vector(plot=plot, interactive_field=self.pf) # Continuation for interactive plot testing. self.pf.plane(z=1e-9).k3d_vector(plot=plot, interactive_field=self.pf) assert len(plot.objects) == 3 # Exceptions with pytest.raises(ValueError) as excinfo: self.pf.x.k3d_vector() with pytest.raises(ValueError): self.pf.k3d_vector(color_field=self.pf) # filter field dim=3 def test_plot_large_sample(self): p1 = (0, 0, 0) p2 = (50e9, 50e9, 50e9) cell = (25e9, 25e9, 25e9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) value = (1e6, 1e6, 1e6) field = df.Field(mesh, dim=3, value=value) field.plane('z').mpl() field.norm.k3d_nonzero() field.x.k3d_scalar() field.k3d_vector()
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__all__ = ['get'] import collections def _iterable(obj): return isinstance(obj, collections.Iterable) def _string(value): try: return isinstance(value, basestring) except NameError: return isinstance(value, str) def get(input): """return a list with input values or [] if input is None""" if input is None: return [] if not _iterable(input) or _string(input): return [input] return list(input)
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#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2019/8/20 0020 16:49 # @Author : Hadrianl # @File : __init__.py from .widget import StrategyReviewer
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import xml.etree.ElementTree as ET xml_string = ''' <stuff> <users> <user x = "2"> <id>001</id> <name>Chuck</name> </user> <user x = "7"> <id>007</id> <name>Brent</name> </user> </users> </stuff> ''' root_stuff = ET.fromstring(xml_string) #don't usually refer to root element user_elements = root_stuff.findall('users/user') print ('user count:', len(user_elements)) for user in user_elements: print('name:', user.find('name').text) print('id:', user.find('id').text) print('attribute(x):', user.get('x')) #to identify attribute use 'get's
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from multiprocessing.dummy import Pool def calc_power2(num): return num * num pool = Pool(3) origin_num = [x for x in range(10)] result = pool.map(calc_power2, origin_num) print(f'计算1-10的平方分别为:{result}')
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from django.apps import AppConfig class TrafStatConfig(AppConfig): name = 'traf_stat'
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2.875
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from django.core.urlresolvers import reverse from django.views.generic.detail import DetailView from django.views.generic.list import ListView from django.views.generic.edit import CreateView, UpdateView, DeleteView from django.core.urlresolvers import reverse_lazy from django.utils.decorators import method_decorator from django.contrib.auth.decorators import login_required from .models import Eintrag from .forms import EintragForm class EintragDetailView(DetailView): model = Eintrag class EintragListView(ListView): model = Eintrag class EintragCreate(CreateView): model = Eintrag template_name_suffix = '_create' form_class = EintragForm success_url = '.' class EintragUpdate(UpdateView): model = Eintrag form_class = EintragForm template_name_suffix = '_create' class EintragDelete(DeleteView): model = Eintrag template_name = 'vocabs/confirm_delete.html' success_url = reverse_lazy('browsing:browse_entries')
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from typing import Any, Optional, Tuple, Union import torch from torch.nn.functional import mse_loss import pystiche import pystiche.loss.functional as F from pystiche import enc, loss from pystiche_papers.utils import HyperParameters from ._utils import ( extract_normalized_patches2d, hyper_parameters as _hyper_parameters, multi_layer_encoder as _multi_layer_encoder, target_transforms as _target_transforms, ) __all__ = [ "FeatureReconstructionLoss", "content_loss", "MRFLoss", "style_loss", "TotalVariationLoss", "regularization", "perceptual_loss", ] class FeatureReconstructionLoss(loss.FeatureReconstructionLoss): r"""Feature reconstruction loss from :cite:`LW2016`. Args: encoder: Encoder used to encode the input. impl_params: If ``False``, calculate the score with the squared error (SE) instead of the mean squared error (MSE). **feature_reconstruction_loss_kwargs: Additional parameters of a :class:`pystiche.loss.FeatureReconstructionLoss`. .. seealso:: :class:`pystiche.loss.FeatureReconstructionLoss` """ def __init__( self, encoder: enc.Encoder, impl_params: bool = True, **feature_reconstruction_loss_kwargs: Any, ): super().__init__(encoder, **feature_reconstruction_loss_kwargs) # https://github.com/pmeier/CNNMRF/blob/fddcf4d01e2a6ce201059d8bc38597f74a09ba3f/mylib/content.lua#L15 # nn.MSECriterion() was used as criterion to calculate the content loss, which # by default uses reduction="mean" self.loss_reduction = "mean" if impl_params else "sum" def calculate_score( self, input_repr: torch.Tensor, target_repr: torch.Tensor, ctx: Optional[torch.Tensor], ) -> torch.Tensor: return mse_loss(input_repr, target_repr, reduction=self.loss_reduction) def content_loss( impl_params: bool = True, multi_layer_encoder: Optional[enc.MultiLayerEncoder] = None, hyper_parameters: Optional[HyperParameters] = None, ) -> FeatureReconstructionLoss: r"""Content loss from :cite:`LW2016`. Args: impl_params: Switch the behavior and hyper-parameters between the reference implementation of the original authors and what is described in the paper. For details see :ref:`here <li_wand_2016-impl_params>`. multi_layer_encoder: Pretrained multi-layer encoder. If omitted, :func:`~pystiche_papers.li_wand_2016.multi_layer_encoder` is used. hyper_parameters: Hyper parameters. If omitted, :func:`~pystiche_papers.li_wand_2016.hyper_parameters` is used. .. seealso:: :class:`pystiche_papers.li_wand_2016.FeatureReconstructionLoss` """ if multi_layer_encoder is None: multi_layer_encoder = _multi_layer_encoder() if hyper_parameters is None: hyper_parameters = _hyper_parameters(impl_params=impl_params) return FeatureReconstructionLoss( multi_layer_encoder.extract_encoder(hyper_parameters.content_loss.layer), impl_params=impl_params, score_weight=hyper_parameters.content_loss.score_weight, ) class MRFLoss(loss.MRFLoss): r"""MRF loss from :cite:`LW2016`. Args: encoder: Encoder used to encode the input. patch_size: Spatial size of the neural patches. impl_params: If ``True``, normalize the gradient of the neural patches. If ``False``, use a score correction factor of 1/2. **mrf_loss_kwargs: Additional parameters of a :class:`pystiche.loss.MRFLoss`. In contrast to :class:`pystiche.loss.MRFLoss`, the score is calculated with the squared error (SE) instead of the mean squared error (MSE). .. seealso:: - :class:`pystiche.loss.MRFLoss` - :func:`pystiche_papers.li_wand_2016.extract_normalized_patches2d` """ def __init__( self, encoder: enc.Encoder, patch_size: Union[int, Tuple[int, int]], impl_params: bool = True, **mrf_loss_kwargs: Any, ): super().__init__(encoder, patch_size, **mrf_loss_kwargs) # https://github.com/pmeier/CNNMRF/blob/fddcf4d01e2a6ce201059d8bc38597f74a09ba3f/mylib/mrf.lua#L221 # https://github.com/pmeier/CNNMRF/blob/fddcf4d01e2a6ce201059d8bc38597f74a09ba3f/mylib/mrf.lua#L224 # They use normalized patches instead of the unnormalized patches described in # the paper. self.normalize_patches_grad = impl_params self.loss_reduction = "sum" # The score correction factor is not visible in the reference implementation # of the original authors, since the calculation is performed with respect to # the gradient and not the score. Roughly speaking, since the calculation # comprises a *squared* distance, we need a factor of 1/2 in the forward pass. # https://github.com/pmeier/CNNMRF/blob/fddcf4d01e2a6ce201059d8bc38597f74a09ba3f/mylib/mrf.lua#L220 self.score_correction_factor = 1.0 / 2.0 if impl_params else 1.0 def enc_to_repr(self, enc: torch.Tensor, is_guided: bool) -> torch.Tensor: if self.normalize_patches_grad: repr = extract_normalized_patches2d(enc, self.patch_size, self.stride) else: repr = pystiche.extract_patches2d(enc, self.patch_size, self.stride) if not is_guided: return repr return self._guide_repr(repr) def calculate_score( self, input_repr: torch.Tensor, target_repr: torch.Tensor, ctx: Optional[torch.Tensor], ) -> torch.Tensor: score = F.mrf_loss( input_repr, target_repr, reduction=self.loss_reduction, batched_input=True ) return score * self.score_correction_factor def style_loss( impl_params: bool = True, multi_layer_encoder: Optional[enc.MultiLayerEncoder] = None, hyper_parameters: Optional[HyperParameters] = None, ) -> loss.MultiLayerEncodingLoss: r"""Style loss from :cite:`LW2016`. Args: impl_params: Switch the behavior and hyper-parameters between the reference implementation of the original authors and what is described in the paper. For details see :ref:`here <li_wand_2016-impl_params>`. multi_layer_encoder: Pretrained multi-layer encoder. If omitted, :func:`~pystiche_papers.li_wand_2016.multi_layer_encoder` is used. hyper_parameters: Hyper parameters. If omitted, :func:`~pystiche_papers.li_wand_2016.hyper_parameters` is used. .. seealso:: - :class:`pystiche_papers.li_wand_2016.MRFLoss` """ if multi_layer_encoder is None: multi_layer_encoder = _multi_layer_encoder() if hyper_parameters is None: hyper_parameters = _hyper_parameters(impl_params=impl_params) def encoding_loss_fn(encoder: enc.Encoder, layer_weight: float) -> MRFLoss: return MRFLoss( encoder, hyper_parameters.style_loss.patch_size, # type: ignore[union-attr] impl_params=impl_params, stride=hyper_parameters.style_loss.stride, # type: ignore[union-attr] target_transforms=_target_transforms( impl_params=impl_params, hyper_parameters=hyper_parameters ), score_weight=layer_weight, ) return loss.MultiLayerEncodingLoss( multi_layer_encoder, hyper_parameters.style_loss.layers, encoding_loss_fn, layer_weights=hyper_parameters.style_loss.layer_weights, score_weight=hyper_parameters.style_loss.score_weight, ) class TotalVariationLoss(loss.TotalVariationLoss): r"""Total variation loss from :cite:`LW2016`. Args: impl_params: If ``False``, use a score correction factor of 1/2. **total_variation_loss_kwargs: Additional parameters of a :class:`pystiche.loss.TotalVariationLoss`. In contrast to :class:`pystiche.loss.TotalVariationLoss`, the the score is calculated with the squared error (SE) instead of the mean squared error (MSE). .. seealso:: - :class:`pystiche.loss.TotalVariationLoss` """ def __init__(self, impl_params: bool = True, **total_variation_loss_kwargs: Any): super().__init__(**total_variation_loss_kwargs) self.loss_reduction = "sum" # The score correction factor is not visible in the reference implementation # of the original authors, since the calculation is performed with respect to # the gradient and not the score. Roughly speaking, since the calculation # comprises a *squared* distance, we need a factor of 1/2 in the forward pass. # https://github.com/pmeier/CNNMRF/blob/fddcf4d01e2a6ce201059d8bc38597f74a09ba3f/mylib/tv.lua#L20-L30 self.score_correction_factor = 1.0 / 2.0 if impl_params else 1.0 def calculate_score(self, input_repr: torch.Tensor) -> torch.Tensor: score = F.total_variation_loss( input_repr, exponent=self.exponent, reduction=self.loss_reduction ) return score * self.score_correction_factor def regularization( impl_params: bool = True, hyper_parameters: Optional[HyperParameters] = None, ) -> TotalVariationLoss: r"""Regularization from :cite:`LW2016`. Args: impl_params: Switch the behavior and hyper-parameters between the reference implementation of the original authors and what is described in the paper. For details see :ref:`here <li_wand_2016-impl_params>`. hyper_parameters: Hyper parameters. If omitted, :func:`~pystiche_papers.li_wand_2016.hyper_parameters` is used. .. seealso:: - :class:`pystiche_papers.li_wand_2016.TotalVariationLoss` """ if hyper_parameters is None: hyper_parameters = _hyper_parameters() return TotalVariationLoss( impl_params=impl_params, score_weight=hyper_parameters.regularization.score_weight, ) def perceptual_loss( impl_params: bool = True, multi_layer_encoder: Optional[enc.MultiLayerEncoder] = None, hyper_parameters: Optional[HyperParameters] = None, ) -> loss.PerceptualLoss: r"""Perceptual loss from :cite:`LW2016`. Args: impl_params: Switch the behavior and hyper-parameters between the reference implementation of the original authors and what is described in the paper. For details see :ref:`here <li_wand_2016-impl_params>`. multi_layer_encoder: Pretrained multi-layer encoder. If omitted, :func:`~pystiche_papers.li_wand_2016.multi_layer_encoder` is used. hyper_parameters: Hyper parameters. If omitted, :func:`~pystiche_papers.li_wand_2016.hyper_parameters` is used. .. seealso:: - :func:`pystiche_papers.li_wand_2016.content_loss` - :func:`pystiche_papers.li_wand_2016.style_loss` - :func:`pystiche_papers.li_wand_2016.regularization` """ if multi_layer_encoder is None: multi_layer_encoder = _multi_layer_encoder() if hyper_parameters is None: hyper_parameters = _hyper_parameters() return loss.PerceptualLoss( content_loss( impl_params=impl_params, multi_layer_encoder=multi_layer_encoder, hyper_parameters=hyper_parameters, ), style_loss( impl_params=impl_params, multi_layer_encoder=multi_layer_encoder, hyper_parameters=hyper_parameters, ), regularization(impl_params=impl_params, hyper_parameters=hyper_parameters), )
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# -*- coding: utf-8 -*- # (c) 2017-2019, ETH Zurich, Institut fuer Theoretische Physik # Author: Dominik Gresch <[email protected]> """ Configuration file for the pytest tests. """ import os import json import pytest import numpy as np import bands_inspect as bi import parameters # pylint: disable=wrong-import-order #--------------------------FIXTURES-------------------------------------#
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import os # import tensorflow as tf import tensorrt as trt from tensorrt.parsers import uffparser import pycuda.driver as cuda # import uff import cv2 import numpy as np from tqdm import tqdm TEST_PATH = "/media/andy/Data/DevWorkSpace/Projects/imageClassifier/data/test/" LABEL = 0 ENGINE_PATH = "/home/andy/caffe/examples/mydata/slot_classifier/engine/px2_classifier.engine" NET_INPUT_SHAPE = (256, 256) NET_OUTPUT_SHAPE = 5 class_labels = ['error', 'half', 'invlb', 'invls', 'valid'] # Load Image def load_image(img_path, net_input_shape): img = cv2.resize(cv2.imread(img_path), net_input_shape) # BGR -> RGB #img = img[:,:, (2, 1, 0)] ## Method 1 # imgT = np.transpose(img, (2, 0, 1)) # c,w,h # imgF = np.asarray(imgT, dtype=np.float32) # mean = [[[88.159309]], [[97.966286]], [[103.66106]]] # Caffe image mean # imgS = np.subtract(imgF,mean) ## Method 2 imgF = np.asarray(img, dtype=np.float32) mean = [88.159309, 97.966286, 103.66106] # Caffe image mean imgSS = np.subtract(imgF, mean) imgS = np.transpose(imgSS, (2, 0, 1)) # CHW # RGB_MEAN_PIXELS = np.array([88.159309, 97.966286, 103.66106]).reshape((1,1,1,3)).astype(np.float32) return np.ascontiguousarray(imgS, dtype=np.float32) # avoid error: ndarray is not contiguous def test_Loader(TEST_PATH, net_input_shape): label_list = [] img_list = [] pair = [] folders = os.listdir(TEST_PATH) for folder in folders: folder_path = os.path.join(TEST_PATH, folder) imgs = os.listdir(folder_path) for img in tqdm(imgs): img_path = os.path.join(folder_path, img) img = load_image(img_path, net_input_shape) label = class_labels.index(folder) img_list.append(img) label_list.append(label) pair.append((img, label)) return pair, (img_list, label_list) imgTestData = test_Loader(TEST_PATH, NET_INPUT_SHAPE) # Load Engine file G_LOGGER = trt.infer.ConsoleLogger(trt.infer.LogSeverity.ERROR) engine = trt.utils.load_engine(G_LOGGER, ENGINE_PATH) context = engine.create_execution_context() runtime = trt.infer.create_infer_runtime(G_LOGGER) # output = np.empty(1, dtype = np.float32) # # Alocate device memory # d_input = cuda.mem_alloc(1 * imgTestData[0][0][0].nbytes) # d_output = cuda.mem_alloc(NET_OUTPUT_SHAPE * output.nbytes) # bindings = [int(d_input), int(d_output)] # stream = cuda.Stream() predicts = [] pair = imgTestData[0] for img, label in pair: output = np.empty(NET_OUTPUT_SHAPE, dtype = np.float32) # Alocate device memory d_input = cuda.mem_alloc(1 * img.nbytes) d_output = cuda.mem_alloc(1 * output.nbytes) bindings = [int(d_input), int(d_output)] stream = cuda.Stream() # Transfer input data to device cuda.memcpy_htod_async(d_input, img, stream) # Execute model context.enqueue(1, bindings, stream.handle, None) # Transfer predictions back cuda.memcpy_dtoh_async(output, d_output, stream) # Syncronize threads stream.synchronize() softmax = np.exp(output) / np.sum(np.exp(output)) predict = np.argmax(softmax) predicts.append(predict) print("True = ",label, ", predict = ", predict, ", softmax = ", softmax) grandTrue = np.array(imgTestData[1][1]) predicts = np.array(predicts) error = predicts[predicts!=grandTrue] print(imgTestData[1][1]) print("-------") print(predicts) print("-------") print(len(error)) print((len(imgTestData[0])-len(error))/len(imgTestData[0]))
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import filters import numpy as np import matplotlib.pyplot as plt from scipy.signal import freqz from sklearn.neural_network import MLPRegressor def filterModel(x): # [fc, bandwidth, gain] w_final = None db_final = 0 fs = 44100 for fc, BW, gain in x: b, a = filters.bandpass_peaking(fc=fc, gain=gain, BW=BW) w, h = freqz(b, a, worN=np.linspace(np.pi*2/fs*20, np.pi*2/fs*20e3, 500)) db = 20 * np.log10(abs(h)) w_final = w db_final += db # plt.semilogx(w_final * fs / (2*np.pi), db_final) return w_final*fs/(2*np.pi), db_final def genXY(n, filtersNum): total = n * filtersNum fc = np.random.uniform(20, 20e3, size=(total,1)) bw = np.random.uniform(100, 10000, size=(total,1)) gain = np.random.uniform(0, 20, size=(total,1)) Y = np.concatenate((fc,bw,gain), axis=1) Y = Y.reshape(n, filtersNum, 3) X = [] for paras in Y: f, db = filterModel(paras) X.append(db) X = np.array(X) Y = Y.reshape(n, filtersNum*3) return X, Y if __name__ == "__main__": # Create a random dataset # [fc, bandwidth, gain] n = 100 filtersNum = 1 X, Y = genXY(n=n, filtersNum=filtersNum) # Fit regression model regr = MLPRegressor(hidden_layer_sizes=(10,), max_iter=10000) regr.fit(X, Y) print('train loss', regr.loss_) # Predict X_test, Y_test = genXY(n=n, filtersNum=filtersNum) print('test loss', ((Y_test - regr.predict(X_test)) ** 2).mean()) # paras = [(1e4, 2500, 3), (300, 201, 10), (400, 600, 5), (600, 200, 8), # (2000, 3500, 13), (6000, 4000, 3), (8500, 6000, 2.75),] paras = [(1e4, 2500, 3),] f, db = filterModel(paras) plt.semilogx(f, db, label="target", color='red') y_pred = regr.predict([db]) f, db = filterModel(y_pred.reshape(filtersNum, 3)) plt.semilogx(f, db, label="NN") plt.legend() plt.show()
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import torch.distributed as dist import torch def get_world_size(): if not dist.is_available(): return 1 if not dist.is_initialized(): return 1 return dist.get_world_size() def get_rank(): if not dist.is_available(): return 0 if not dist.is_initialized(): return 0 return dist.get_rank() def is_main_process(): return get_rank() == 0 def synchronize(): """ Helper function to synchronize (barrier) among all processes when using distributed training """ if not dist.is_available(): return if not dist.is_initialized(): return world_size = dist.get_world_size() if world_size == 1: return dist.barrier() def reduce_value(value, average=True): world_size = get_world_size() if world_size < 2: return value with torch.no_grad(): dist.all_reduce(value) if average: value /= world_size return value
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import json import pymysql import datetime from dbutils.pooled_db import PooledDB import pymysql from conf.common import * class MysqlClient(object): __pool = None def __init__(self): """ :param mincached:连接池中空闲连接的初始数量 :param maxcached:连接池中空闲连接的最大数量 :param maxshared:共享连接的最大数量 :param maxconnections:创建连接池的最大数量 :param blocking:超过最大连接数量时候的表现,为True等待连接数量下降,为false直接报错处理 :param maxusage:单个连接的最大重复使用次数 :param setsession:optional list of SQL commands that may serve to prepare the session, e.g. ["set datestyle to ...", "set time zone ..."] :param reset:how connections should be reset when returned to the pool (False or None to rollback transcations started with begin(), True to always issue a rollback for safety's sake) :param host:数据库ip地址 :param port:数据库端口 :param db:库名 :param user:用户名 :param passwd:密码 :param charset:字符编码 """ mincached = 10 maxcached = 20 maxshared = 10 maxconnections = 200 blocking = True maxusage = 100 setsession = None reset = True host = MYSQL_HOST port = MYSQL_PORT db = DATABASE user = USER passwd = PASSWORD charset = 'utf8mb4' if not self.__pool: self.__class__.__pool = PooledDB(pymysql, mincached, maxcached, maxshared, maxconnections, blocking, maxusage, setsession, reset, host=host, port=port, db=db, user=user, passwd=passwd, charset=charset, cursorclass=pymysql.cursors.DictCursor ) self._conn = None self._cursor = None self.__get_conn() def __get_conn(self): self._conn = self.__pool.connection() self._cursor = self._conn.cursor() def close(self): try: self._cursor.close() self._conn.close() except Exception as e: print(e) def __execute(self, sql, param=()): count = self._cursor.execute(sql, param) print(count) return count def select_one(self, sql, param=()): """查询单个结果""" count = self.__execute(sql, param) result = self._cursor.fetchone() """:type result:dict""" result = self.__dict_datetime_obj_to_str(result) return count, result def select_many(self, sql, param=()): """ 查询多个结果 :param sql: qsl语句 :param param: sql参数 :return: 结果数量和查询结果集 """ count = self.__execute(sql, param) result = self._cursor.fetchall() """:type result:list""" [self.__dict_datetime_obj_to_str(row_dict) for row_dict in result] return count, result def execute(self, sql, param=()): count = self.__execute(sql, param) return count def begin(self): """开启事务""" self._conn.autocommit(0) def end(self, option='commit'): """结束事务""" if option == 'commit': self._conn.autocommit() else: self._conn.rollback() mysql_client = MysqlClient()
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#!/usr/bin/env python """ Script to launch a VDI session (or connect to already running session) and start a Jupyter server on the VDI A ssh tunnel from the local machine to the VDI is set up and the local webbrowser is spawned. This is a python3 script (uses unicode strings). If you don't have python3 on your local machine, try installing Miniconda3 The only external module is pexpect which may need to be installed using conda or pip. Usage: - if you use a password, the script will ask for your password when needed - if you have already set up SSH public key with Strudel, try running $ ssh-add ~/.ssh/MassiveLauncherKey to add your public key to the ssh key agent. Author: James Munroe, 2017 """ from __future__ import print_function import re import sys import time import getpass import pexpect import os import configparser # Requires future module https://pypi.org/project/future/ from builtins import input import argparse import logging logging.basicConfig(format='[%(asctime)s jupyter_vdi.py] %(message)s', datefmt='%H:%M:%S', level=logging.INFO) try: import appscript except ImportError: import webbrowser is_mac = False else: is_mac = True DEFAULTS = { 'user' : getpass.getuser(), 'JupyterPort' : '8889', 'BokehPort' : '8787', 'execHost' : 'vdi.nci.org.au' } verbose = 0 config_path = os.path.expanduser('~/cosima_cookbook.conf') parser = configparser.ConfigParser(defaults=DEFAULTS) if os.path.exists(config_path): logging.info('Using config file: {}'.format(config_path)) parser.read(config_path) else: logging.warn('No config file found. Creating default {} file.'.format(config_path)) logging.warn('*** Please edit this file as needed. ***') while DEFAULTS['user']==getpass.getuser() or DEFAULTS['user']=="": DEFAULTS['user']=input('What is your NCI username? ') parser = configparser.ConfigParser(defaults=DEFAULTS) with open(config_path, 'w') as f: parser.write(f) params = parser.defaults() def parse_args(args): parser = argparse.ArgumentParser(description="Log into the VDI, start a jupyter notebook session and ssh tunnel to local machine") parser.add_argument("-v","--verbose", help="Increase verbosity", action='count', default=0) return parser.parse_args(args) def clean_params(params): for key, value in params.items(): try: params[key] = value.decode() except AttributeError: pass def ssh(cmd, params, login_timeout=10): """ Run a remote command via SSH """ clean_params(params) cmd = ("ssh -x -l {user} {exechost} " + cmd).format(**params) if verbose > 0: logging.info(cmd) s = pexpect.spawn(cmd) # SSH pexpect logic taken from pxshh: i = s.expect(["(?i)are you sure you want to continue connecting", "(?i)(?:password)|(?:passphrase for key)", "(?i)permission denied", "(?i)connection closed by remote host", pexpect.EOF, pexpect.TIMEOUT], timeout=login_timeout) # First phase if i == 0: # New certificate -- always accept it. # This is what you get if SSH does not have the remote host's # public key stored in the 'known_hosts' cache. s.sendline("yes") i = s.expect(["(?i)are you sure you want to continue connecting", "(?i)(?:password)|(?:passphrase for key)", "(?i)permission denied", "(?i)connection closed by remote host", pexpect.EOF, pexpect.TIMEOUT], timeout=login_timeout) if i == 1: # password or passphrase if 'password' not in params: params['password'] = getpass.getpass('password: ') s.sendline(params['password']) i = s.expect(["(?i)are you sure you want to continue connecting", "(?i)(?:password)|(?:passphrase for key)", "(?i)permission denied", "(?i)connection closed by remote host", pexpect.EOF, pexpect.TIMEOUT], timeout=login_timeout) # TODO: check if ssh connection is successful return s def session(func, *args, **kwargs): """wrapper for sending session-ctl commands""" cmd = '/opt/vdi/bin/session-ctl --configver=20151620513 ' + func s = ssh(cmd, *args, **kwargs) s.close() return s def open_jupyter_url(params): # Open browser locally status = '' url = 'http://localhost:{jupyterport}/?token={token}'.format(**params) if is_mac: status = "Using appscript to open {}".format(url) safari = appscript.app("Safari") safari.make(new=appscript.k.document, with_properties={appscript.k.URL: url}) else: status = "Opening {}".format(url) webbrowser.open(url) return status tunnel_started = False tunnel = None def start_tunnel(params): # Create ssh tunnel for local access to jupyter notebook cmd = ' '.join(['-N -f -L {jupyterport}:localhost:{jupyterport}', '-L {bokehport}:localhost:{bokehport}']) # This print statement is needed as there are /r/n line endings from # the jupyter notebook output that are difficult to suppress logging.info("Starting ssh tunnel...") tunnel = ssh(cmd, params, login_timeout=2) tunnel.expect (pexpect.EOF) # Open web browser and log result logging.info(open_jupyter_url(params)) def main(args): # global verbose means it doesn't need to be passed to every routine global verbose verbose = args.verbose logging.info("Checking SSH keys to VDI are configured...") r = session('hello --partition main', params) if r.exitstatus != 0: # suggest setting up SSH keys logging.error("Error with ssh keys/password and VDI.") logging.error(" Incorrect user name in ~/cosima_cookbook.conf file?") logging.error(" Edit ~/cosima_cookbook.conf before continuing.") sys.exit(1) logging.info("SSH keys configured OK") logging.info("Determine if VDI session is already running...") r = session('list-avail --partition main', params) m = re.search('#~#id=(?P<jobid>(?P<jobidNumber>.*?))#~#state=(?P<state>.*?)(?:#~#time_rem=(?P<remainingWalltime>.*?))?#~#', r.before.decode()) if m is not None: params.update(m.groupdict()) w = int(params['remainingWalltime']) remainingWalltime = '{:02}:{:02}:{:02}'.format( w // 3600, w % 3600 // 60, w % 60) logging.info('Time remaining: %s', remainingWalltime) # TODO: should give user option of starting a new session if the remaining walltime is short else: logging.info('No VDI session found') logging.info("Launching a new VDI session...") r = session('launch --partition main', params) m = re.search('#~#id=(?P<jobid>(?P<jobidNumber>.*?))#~#', r.before.decode()) if m is None: logging.info('Unable to launch new VDI session:\n'+r.before.decode()) params.update(m.groupdict()) time.sleep(2) # TODO: instead of waiting, should check for confirmation # use has-started logging.info("Determine jobid for VDI session...{jobid}".format(**params)) logging.info("Get exechost for VDI session...") r = session('get-host --jobid {jobid}', params) m = re.search('#~#host=(?P<exechost>.*?)#~#', r.before.decode()) params.update(m.groupdict()) logging.info('exechost: {exechost}'.format(**params)) logging.info("Running Jupyter on VDI...") setupconda = params.get('setupconda', """module use /g/data3/hh5/public/modules && module load conda/analysis3 """.replace('\n', ' ')) jupyterapp = params.get('jupyterapp', "notebook") run_jupyter = "jupyter %s --no-browser --port {jupyterport}" % jupyterapp run_jupyter = setupconda + ' && ' + run_jupyter cmd = ' '.join(['-t', """'bash -l -c "%s"'""" % run_jupyter]) logging.info("Waiting for Jupyter to start...") # Launch jupyter on VDI s = ssh(cmd, params, login_timeout=2) ret = s.expect('http://\S*:(?P<jupyterport>\d+)/\?token=(?P<token>[a-zA-Z0-9]+)') if s.match: params.update(s.match.groupdict()) start_tunnel(params) else: logging.info("Could not find url information in jupyter output") sys.exit(1) # Grab all the output up to the incorrect URL -- uses the token twice, which is unhelpful ret = s.expect('http://.*') logging.info("Use Control-C to stop the Notebook server and shut down all kernels (twice to skip confirmation)\n\n") # give control over to user s.interact() logging.info('end of script') # optional: terminate to close the vdi session? def main_argv(): args = parse_args(sys.argv[1:]) main(args) if __name__ == "__main__": main_argv()
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# -*- coding=utf-8 -*- __all__ = [ 'tiny_imagenet', 'imagewoof2', 'imagenette2' ] import os import torch import torchvision _default_batch_size = 32 _default_num_workers = 4 def _transform(train=True): mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] if train: return torchvision.transforms.Compose([ torchvision.transforms.RandomResizedCrop(224), torchvision.transforms.RandomHorizontalFlip(), torchvision.transforms.ToTensor(), torchvision.transforms.Normalize(mean, std) ]) else: return torchvision.transforms.Compose([ torchvision.transforms.CenterCrop(224), torchvision.transforms.ToTensor(), torchvision.transforms.Normalize(mean, std) ]) def tiny_imagenet(name='train', batch_size=_default_batch_size, num_workers=_default_num_workers): dataset = torchvision.datasets.ImageFolder( os.path.join('datasets', 'tiny-imagenet-200', name), transform=_transform(name == 'train') ) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, drop_last=True, shuffle=name == 'train') return dataloader def imagewoof2(name='train', batch_size=_default_batch_size, num_workers=_default_num_workers): dataset = torchvision.datasets.ImageFolder( os.path.join('datasets', 'imagewoof2', name), transform=_transform(name == 'train') ) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, drop_last=True, shuffle=name == 'train') return dataloader def imagenette2(name='train', batch_size=_default_batch_size, num_workers=_default_num_workers): dataset = torchvision.datasets.ImageFolder( os.path.join('datasets', 'imagenette2', name), transform=_transform(name == 'train') ) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, drop_last=True, shuffle=name == 'train') return dataloader
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from django.db import models from django.contrib.auth.models import User from django.db.models import Sum from datetime import datetime class Author(models.Model): author = models.CharField(max_length=100) rating = models.IntegerField(default=0) user = models.OneToOneField(User, on_delete=models.CASCADE) def __str__(self): return self.author # .aggregate(Sum("rating")) def update_rating(self): auth = Author.objects.get(author=self.author) sum_rat_post = 0 posts = auth.post_set.all() for post in posts: sum_rat_post += post.rating_post * 3 usr = auth.one_to_one_rel sum_rat_comm = 0 comments = usr.comment_set.all() for comm in comments: sum_rat_comm += comm.rating_comm sum_rat_auth = 0 # comments_posts = auth.post_set.comment_set.all() for post in posts: comm_posts = post.comment_set.all() for comm_post in comm_posts: sum_rat_auth += comm_post.rating_comm self.rating = sum_rat_post + sum_rat_comm + sum_rat_auth self.save() class Category(models.Model): category = models.CharField(max_length=100, unique=True) class Post(models.Model): article = 'AR' new = 'NE' POSITIONS = [ (article, 'Статья'), (new, 'Новость') ] ar_or_new = models.CharField(max_length=2, choices=POSITIONS, default=article) created = models.DateTimeField(auto_now_add=True) post_name = models.CharField(max_length=250) content = models.TextField() rating = models.IntegerField(default=0) author = models.ForeignKey(Author, on_delete=models.CASCADE) category = models.ManyToManyField(Category, through='PostCategory') def like(self): self.rating += 1 self.save() def dislike(self): self.rating -= 1 if self.rating_comm < 0: self.rating_comm = 0 self.save() def preview(self): prev = self.content[:124] + '...' return prev class PostCategory(models.Model): post = models.ForeignKey(Post, on_delete=models.CASCADE) category = models.ForeignKey(Category, on_delete=models.CASCADE) class Comment(models.Model): comment = models.TextField() created = models.DateTimeField(auto_now_add=True) rating = models.IntegerField(default=0) post = models.ForeignKey(Post, on_delete=models.CASCADE) user = models.ForeignKey(User, on_delete=models.CASCADE) def like(self): self.rating += 1 self.save() def dislike(self): self.rating -= 1 if self.rating < 0: self.rating = 0 self.save()
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#!/usr/bin/env pytest import io import json from os import path from pytest import fixture, mark from sls import App import storyscript.hub.Hub as StoryHub from storyhub.sdk.AutoUpdateThread import AutoUpdateThread from tests.e2e.utils.features import parse_options from tests.e2e.utils.fixtures import find_test_files, hub, test_dir test_files = find_test_files(relative=True) # compile a story and compare its completion with the expected tree def run_test_completion(uri, source, expected, patch, options): action = options.pop("action", "complete") if action == "complete": result = App(hub=hub).complete(uri=uri, text=source, **options) else: assert action == "click" result = App(hub=hub).click(uri=uri, text=source, **options) assert result == expected # load a story from the file system and load its expected result file (.json) def run_test(story_path, patch): story_string = None with io.open(story_path, "r") as f: story_string = f.read() expected_path = path.splitext(story_path)[0] assert path.isfile( expected_path + ".json" ), f"Path: `{expected_path}.json` does not exist." expected_completion = None with io.open(expected_path + ".json", "r") as f: expected_completion = f.read() # deserialize the expected completion expected = json.loads(expected_completion) options = parse_options(story_string) return run_test_completion( story_path, story_string, expected, patch, options )
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""" Where's My Mouse? """ import tkinter def mouse_click(event): # retrieve XY coords as a tuple coords = root.winfo_pointerxy() print('coords: {}'.format(coords)) print('X: {}'.format(coords[0])) print('Y: {}'.format(coords[1])) root = tkinter.Tk() root.bind('<Button>', mouse_click) root.mainloop()
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from dataclasses import dataclass from enum import Enum from typing import Callable, Dict, Final, Optional, Type, Union from botx import Bot, Collector, Message from botx.concurrency import callable_to_coroutine from botx.middlewares.base import BaseMiddleware from botx.typing import Executor _default_transition: Final = object() class FlowError(Exception): pass class FSM: def __init__(self, states: Type[Enum]) -> None: self.transitions: Dict[Enum, Transition] = {} self.collector = Collector() self.states = states def handler( self, on_state: Enum, next_state: Optional[Union[Enum, object]] = _default_transition, on_failure: Optional[Union[Enum, object]] = _default_transition, ) -> Callable: def decorator(handler: Callable) -> Callable: self.collector.add_handler( handler, body=on_state.name, name=on_state.name, include_in_status=False, ) self.transitions[on_state] = Transition( on_success=next_state, on_failure=on_failure, ) return handler return decorator def change_state(message: Message, new_state: Optional[Enum]) -> None: message.bot.state.fsm_state[(message.user_huid, message.group_chat_id)] = new_state class FSMMiddleware(BaseMiddleware): def __init__( self, executor: Executor, bot: Bot, fsm: FSM, initial_state: Optional[Enum] = None, ) -> None: super().__init__(executor) bot.state.fsm_state = {} self.fsm = fsm self.initial_state = initial_state for state in self.fsm.states: # check that for each state there is registered handler assert state in self.fsm.transitions
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#!/usr/bin/env python ''' This program attempts to convert XPLOR Pseudocontact shift restraints in AMBER format XPLOR: assign ( resid 200 and name OO ) ( resid 200 and name Z ) ( resid 200 and name X ) (resid 200 and name Y ) ( resid 13 and name C ) 0.2400 0.2000 assign ( resid 200 and name OO ) ( resid 200 and name Z ) ( resid 200 and name X ) ( resid 200 and name Y ) ( resid 13 and name CA ) 0.4300 0.2000 assign ( resid 200 and name OO ) ( resid 200 and name Z ) ( resid 200 and name X ) ( resid 200 and name Y )( resid 13 and name CB ) 0.1000 0.2000 AMBER: &align num_datasets=2, dcut= -1.0, freezemol= .false., ndip= 10, dwt= 5*0.1, 5*0.1 gigj= 5*-3.1631,5*-3.1631, dij= 5*1.041,5*1.041, s11= -4.236,-4.236 s12= 56.860,56.860 s13= -34.696,-34.696 s22= -27.361,-27.361 s23= -12.867,-12.867 dataset=1, id(1)=20, jd(1)=19, dobsl(1)=-2.13, dobsu(1)=-2.13, id(2)=31, jd(2)=30, dobsl(2)= 1.10, dobsu(2)= 1.10, id(3)=43, jd(3)=42, dobsl(3)=-5.54, dobsu(3)=-5.54, ... ... &end ''' import sys import os import commands from optparse import OptionParser from xml_parser import * from normalize_tbl import normalize from constants import convtable def searchres(nres, lpdb): for l in lpdb: if l.strip().lower().startswith('atom'): s=l.split() if int(nres)==int(s[4]): return s[3] def searchC(outx): i=0 c=[] while i<len(outx): if outx[i].strip().startswith('XDIPO_RDC>frun'): while i<len(outx): i+=1 if i>=len(outx): break if outx[i].strip().startswith('C1='): t=[] l=outx[i].split() for x in range(1,len(l),2): t.append(l[x]) c.append(t) break i+=1 return c def convert(pdb, new, wd): if new.calculation.protocol.xrdc: xfiles=[] if len(new.calculation.protocol.xrdc)==1: xfiles.append(new.calculation.protocol.xrdc.attrib_.xrdc_file) else: for i in range(len(new.calculation.protocol.xrdc)): xfiles.append(new.calculation.protocol.xrdc[i].attrib_.xrdc_file) else: sys.exit('%s: RDC not found\n' % sys.argv[0]) try: lpdb=open(pdb, 'r').readlines() except IOError, (errno, strerror): sys.exit('%s: IOError(%s): %s %s\n' % (sys.argv[0], errno, pdb, strerror)) numMap = {} for l in lpdb: if l.strip().lower().startswith('atom'): ls=l.split() k='%s:%s' % (ls[4],ls[2]) numMap[k]=ls[1] cmd=' /opt/local_prog/xplor-nih-2.22/bin/xplor tensor.inp' outx=commands.getoutput(cmd) outx=outx.split('\n') #outx=open('xplor.outx').readlines() c=searchC(outx) out=[' &align\n'] out.append(' num_datasets=%d,\n' % len(xfiles)) out.append(' dcut=-1.0, freezemol=.false.,\n') out.append(' ndip=10,') out.append(' dcut=-1.0,dwt=92*0.1,\n') out.append(' gigj=92*-3.163,\n') out.append(' dij=92*1.01,\n') s11=' s11=' s12=' s12=' s13=' s13=' s22=' s22=' s23=' s23=' for i in range(len(c)): s11='%s%s,' % (s11, c[i][0]) s12='%s%s,' % (s12, c[i][1]) s13='%s%s,' % (s13, c[i][2]) s22='%s%s,' % (s22, c[i][3]) s23='%s%s,' % (s23, c[i][4]) out.append('%s\n' % s11) out.append('%s\n' % s12) out.append('%s\n' % s13) out.append('%s\n' % s22) out.append('%s\n' % s23) counter=0 nrdc=0 for xfile in xfiles: counter+=1 nxfile=os.path.join(wd, 'rdc_%d_web_enmr_normalized.tbl' % counter) xfile=os.path.join(wd, xfile) try: normalize(xfile, nxfile, new, wd) except: sys.exit('%s: unable to normalize %s tbl file\n' % (sys.argv[0], xfile)) try: xp=open(nxfile,'r').readlines() except IOError, (errno, strerror): sys.exit('%s: IOError(%s): %s %s\n' % (sys.argv[0], errno, nxfile, strerror)) out.append(' dataset=%d,\n' % counter) for l in xp: if l.strip().startswith('assign'): nrdc+=1 ls=l.split() res=searchres(ls[31], lpdb) kk='%s:%s' % (res, ls[34]) if convtable.has_key(kk): ls[34]=convtable[kk].split(':')[1] k='%s:%s' % (ls[31], ls[34]) natm1=numMap[k] res=searchres(ls[38], lpdb) kk='%s:%s' % (res, ls[41]) if convtable.has_key(kk): ls[41]=convtable[kk].split(':')[1] k='%s:%s' % (ls[38], ls[41]) natm2=numMap[k] out.append(' id(%s)=%s, jd(%s)=%s, dobsl(%s)=%s, dobsu(%s)=%s, \n' % (nrdc, natm1, nrdc, natm2, nrdc, ls[43], nrdc, ls[43])) out[3]=' ndip=%d,' % nrdc out.append(' &end') return out if __name__ == '__main__': usage = "usage: %prog -w working_directory -p pdb_filename -o out_filename" parser = OptionParser(usage) parser.add_option("-w", "--wdir", dest="wd", help="Working directory", metavar="WORKDIR") parser.add_option("-p", "--pdbfile", dest="pdbfile", help="PDB filename", metavar="FILE") parser.add_option("-o", "--outfile", dest="outfile", help="Output filename", metavar="FILE") (options, args) = parser.parse_args() if not options.wd: parser.error("Working directory is required") wd=os.path.abspath(options.wd)+'/' if options.pdbfile: pdbfile=os.path.join(wd, options.pdbfile) else: parser.error("PDB filename is required") if options.outfile: outfile=os.path.join(wd, options.outfile) else: parser.error("Output filename is required") xml_input=os.path.join(wd,'input.xml') doc = etree.parse(xml_input) ndoc = etree.tostring(doc) new=parse_node(etree.fromstring(ndoc)) out=convert(pdbfile, new, wd) fout=open(outfile,'w') fout.writelines(out) fout.close()
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"""Versioned body readers and writers for track message bodies. Attributes: LATEST_VERSION (int): Latest version supported by the library. """ from typing import Callable, Tuple from . import TrackInfo, codec LATEST_VERSION = 2 def _read_body_v1_2(stream: codec.Reader, version: int) -> TrackInfo: return TrackInfo( title=stream.read_utf(), author=stream.read_utf(), duration=stream.read_long() / 1000, identifier=stream.read_utf(), is_stream=stream.read_bool(), uri=stream.read_optional_utf() if version >= 2 else None, ) def read_body_v1(stream: codec.Reader) -> TrackInfo: return _read_body_v1_2(stream, 1) def read_body_v2(stream: codec.Reader) -> TrackInfo: return _read_body_v1_2(stream, 2) def _write_body_v1_2(stream: codec.Writer, track: TrackInfo, version: int) -> None: stream.write_utf(track.title) stream.write_utf(track.author) stream.write_long(int(track.duration * 1000)) stream.write_utf(track.identifier) stream.write_bool(track.is_stream) if version >= 2: stream.write_optional_utf(track.uri) def write_body_v1(stream: codec.Writer, track: TrackInfo) -> None: _write_body_v1_2(stream, track, 1) def write_body_v2(stream: codec.Writer, track: TrackInfo) -> None: _write_body_v1_2(stream, track, 2) ReaderType = Callable[[codec.Reader], TrackInfo] WriterType = Callable[[codec.Writer, TrackInfo], None] _FORMAT_VERSIONS = { 1: (read_body_v1, write_body_v1), 2: (read_body_v2, write_body_v2), } def _get_format(version: int) -> Tuple: try: return _FORMAT_VERSIONS[version] except KeyError: raise ValueError(f"Unsupported version: {version}") from None def get_reader(version: int) -> ReaderType: """Get a body reader for the given version. Raises: ValueError: If the version isn't supported. """ return _get_format(version)[0] def get_writer(version: int) -> WriterType: """Get a body writer for the given version. Raises: ValueError: If the version isn't supported. """ return _get_format(version)[1]
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""" The Alarm Extension provides easy access to setting an application alarm to handle timing out operations. See the `Python Signal Library <https://docs.python.org/3.5/library/signal.html>`_. Requirements ------------ * No external dependencies. * Only available on Unix/Linux Configuration ------------- This extension does not honor any application configuration settings. Usage ----- .. code-block:: python import time from cement.core.foundation import CementApp from cement.core.exc import CaughtSignal class MyApp(CementApp): class Meta: label = 'myapp' exit_on_close = True extensions = ['alarm'] with MyApp() as app: try: app.run() app.alarm.set(3, "The operation timed out after 3 seconds!") # do something that takes time to operate time.sleep(5) app.alarm.stop() except CaughtSignal as e: print(e.msg) app.exit_code = 1 Looks like: .. code-block:: console $ python myapp.py ERROR: The operation timed out after 3 seconds! Caught signal 14 """ import signal from ..utils.misc import minimal_logger LOG = minimal_logger(__name__) def alarm_handler(app, signum, frame): if signum == signal.SIGALRM: app.log.error(app.alarm.msg) class AlarmManager(object): """ Lets the developer easily set and stop an alarm. If the alarm exceeds the given time it will raise ``signal.SIGALRM``. """ def __init__(self, *args, **kw): super(AlarmManager, self).__init__(*args, **kw) self.msg = None def set(self, time, msg): """ Set the application alarm to ``time`` seconds. If the time is exceeded ``signal.SIGALRM`` is raised. :param time: The time in seconds to set the alarm to. :param msg: The message to display if the alarm is triggered. """ LOG.debug('setting application alarm for %s seconds' % time) self.msg = msg signal.alarm(int(time)) def stop(self): """ Stop the application alarm. """ LOG.debug('stopping application alarm') signal.alarm(0) def load(app): app.catch_signal(signal.SIGALRM) app.extend('alarm', AlarmManager()) app.hook.register('signal', alarm_handler)
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from meross_iot.cloud.abilities import * from meross_iot.cloud.device import AbstractMerossDevice from meross_iot.logger import POWER_PLUGS_LOGGER as l from meross_iot.meross_event import DeviceSwitchStatusEvent class GenericPlug(AbstractMerossDevice): # Channels _channels = [] # Dictionary {channel->status} _state = {} def __init__(self, cloud_client, device_uuid, **kwords): super(GenericPlug, self).__init__(cloud_client, device_uuid, **kwords) def _get_consumptionx(self): return self.execute_command("GET", CONSUMPTIONX, {}) def _get_electricity(self): return self.execute_command("GET", ELECTRICITY, {}) def _toggle(self, status, callback=None): payload = {"channel": 0, "toggle": {"onoff": status}} return self.execute_command("SET", TOGGLE, payload, callback=callback) def _togglex(self, channel, status, callback=None): payload = {'togglex': {"onoff": status, "channel": channel}} return self.execute_command("SET", TOGGLEX, payload, callback=callback) def _channel_control_impl(self, channel, status, callback=None): if TOGGLE in self.get_abilities(): return self._toggle(status, callback=callback) elif TOGGLEX in self.get_abilities(): return self._togglex(channel, status, callback=callback) else: raise Exception("The current device does not support neither TOGGLE nor TOGGLEX.") def _handle_push_notification(self, namespace, payload, from_myself=False): def fire_switch_state_change(dev, channel_id, o_state, n_state, f_myself): if o_state != n_state: evt = DeviceSwitchStatusEvent(dev=dev, channel_id=channel_id, switch_state=n_state, generated_by_myself=f_myself) self.fire_event(evt) with self._state_lock: if namespace == TOGGLE: # Update the local state and fire the event only if the state actually changed channel_index = 0 old_switch_state = self._state.get(channel_index) switch_state = payload['toggle']['onoff'] == 1 self._state[channel_index] = switch_state fire_switch_state_change(self, channel_index, old_switch_state, switch_state, from_myself) elif namespace == TOGGLEX: if isinstance(payload['togglex'], list): for c in payload['togglex']: # Update the local state and fire the event only if the state actually changed channel_index = c['channel'] old_switch_state = self._state.get(channel_index) switch_state = c['onoff'] == 1 self._state[channel_index] = switch_state fire_switch_state_change(self, channel_index, old_switch_state, switch_state, from_myself) elif isinstance(payload['togglex'], dict): # Update the local state and fire the event only if the state actually changed channel_index = payload['togglex']['channel'] old_switch_state = self._state.get(channel_index) switch_state = payload['togglex']['onoff'] == 1 self._state[channel_index] = switch_state fire_switch_state_change(self, channel_index, old_switch_state, switch_state, from_myself) elif namespace == REPORT or namespace == CONSUMPTIONX: # For now, we simply ignore push notification of these kind. # In the future, we might think of handling such notification by caching them # and avoid the network round-trip when asking for power consumption (if the latest report is # recent enough) pass else: l.error("Unknown/Unsupported namespace/command: %s" % namespace) def _get_status_impl(self): res = {} data = self.get_sys_data()['all'] if 'digest' in data: for c in data['digest']['togglex']: res[c['channel']] = c['onoff'] == 1 elif 'control' in data: res[0] = data['control']['toggle']['onoff'] == 1 return res def _get_channel_id(self, channel): # Otherwise, if the passed channel looks like the channel spec, lookup its array indexindex if channel in self._channels: return self._channels.index(channel) # if a channel name is given, lookup the channel id from the name if isinstance(channel, str): for i, c in enumerate(self.get_channels()): if c['devName'] == channel: return c['channel'] # If an integer is given assume that is the channel ID elif isinstance(channel, int): return channel # In other cases return an error raise Exception("Invalid channel specified.") def get_status(self, channel=0): # In order to optimize the network traffic, we don't call the get_status() api at every request. # On the contrary, we only call it the first time. Then, the rest of the API will silently listen # for state changes and will automatically update the self._state structure listening for # messages of the device. # Such approach, however, has a side effect. If we call TOGGLE/TOGGLEX and immediately after we call # get_status(), the reported status will be still the old one. This is a race condition because the # "status" RESPONSE will be delivered some time after the TOGGLE REQUEST. It's not a big issue for now, # and synchronizing the two things would be inefficient and probably not very useful. # Just remember to wait some time before testing the status of the item after a toggle. with self._state_lock: c = self._get_channel_id(channel) if self._state == {}: self._state = self._get_status_impl() return self._state[c] def get_power_consumption(self): if CONSUMPTIONX in self.get_abilities(): return self._get_consumptionx()['consumptionx'] else: # Not supported! return None def get_electricity(self): if ELECTRICITY in self.get_abilities(): return self._get_electricity()['electricity'] else: # Not supported! return None def get_channels(self): return self._channels def get_channel_status(self, channel): c = self._get_channel_id(channel) return self.get_status(c) def turn_on_channel(self, channel, callback=None): c = self._get_channel_id(channel) return self._channel_control_impl(c, 1, callback=callback) def turn_off_channel(self, channel, callback=None): c = self._get_channel_id(channel) return self._channel_control_impl(c, 0, callback=callback) def turn_on(self, channel=0, callback=None): c = self._get_channel_id(channel) return self._channel_control_impl(c, 1, callback=callback) def turn_off(self, channel=0, callback=None): c = self._get_channel_id(channel) return self._channel_control_impl(c, 0, callback=callback) def get_usb_channel_index(self): # Look for the usb channel for i, c in enumerate(self.get_channels()): if 'type' in c and c['type'] == 'USB': return i return None def enable_usb(self, callback=None): c = self.get_usb_channel_index() if c is None: return else: return self.turn_on_channel(c, callback=callback) def disable_usb(self, callback=None): c = self.get_usb_channel_index() if c is None: return else: return self.turn_off_channel(c, callback=callback) def get_usb_status(self): c = self.get_usb_channel_index() if c is None: return else: return self.get_channel_status(c) def __str__(self): base_str = super().__str__() with self._state_lock: if not self.online: return base_str channels = "Channels: " channels += ",".join(["%d = %s" % (k, "ON" if v else "OFF") for k, v in enumerate(self._state)]) return base_str + "\n" + "\n" + channels
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2.326531
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from django.db.backends.mysql.introspection import * from django.db.backends.mysql.introspection import DatabaseIntrospection as MYSQLDatabaseIntrospection from django.utils.functional import cached_property class DatabaseIntrospection(MYSQLDatabaseIntrospection): def get_table_list(self, cursor): """ Returns a list of table and view names in the current database. """ cursor.execute("SHOW TABLES") return [TableInfo(row[0], {'BASE TABLE': 't', 'VIEW': 'v'}.get(row[1])) for row in cursor.fetchall()]
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2.654206
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import highiq import numpy as np def test_io(): my_ds = highiq.io.load_arm_netcdf(highiq.testing.TEST_FILE) assert 'acf' in my_ds.variables.keys() assert 'acf_bkg' in my_ds.variables.keys() my_ds.close() def test_spectra(): my_ds = highiq.io.load_arm_netcdf(highiq.testing.TEST_FILE) my_spectra = highiq.calc.get_psd(my_ds) assert 'power_spectral_density_interp' in my_spectra.variables.keys() assert 'power_spectral_density' in my_spectra.variables.keys() psd = my_spectra['power_spectra_normed'].sel(range=400, method='nearest') vel_bins = my_spectra['vel_bins'] dV = vel_bins[1] - vel_bins[0] np.testing.assert_almost_equal(psd.sum()*dV.values, 100) my_ds.close() my_spectra.close() def test_moments(): my_ds = highiq.io.load_arm_netcdf(highiq.testing.TEST_FILE) my_spectra = highiq.calc.get_psd(my_ds) my_moments = highiq.calc.get_lidar_moments(my_spectra) intensity = my_moments['intensity'].values velocity = my_moments['doppler_velocity'].values assert np.nanmin(intensity) > 1. assert np.nanmin(velocity) < -2. my_ds.close() my_spectra.close() def test_peaks(): my_ds = highiq.io.load_arm_netcdf(highiq.testing.TEST_FILE) my_spectra = highiq.calc.get_psd(my_ds) my_peaks = highiq.calc.calc_num_peaks(my_spectra) my_moments = highiq.calc.get_lidar_moments(my_spectra) my_peaks['npeaks'] = my_peaks['npeaks'].where(my_moments.intensity > 1.1) num_peaks = my_peaks['npeaks'].values assert np.nanmax(num_peaks) == 3 my_ds.close() my_spectra.close() my_peaks.close() my_moments.close()
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2.175766
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# Base imports import subprocess from typing import Iterable, Optional # Project imports from docker import common from docker.run import run def migrate(arguments: Iterable[str], deps: Optional[bool] = True) -> int: print(">>>>>>>>>> Running database migration <<<<<<<<<<") run(['backend', 'python3', common.MANAGE_PY, 'migrate'], deps) def make_migrations(arguments: Iterable[str], deps: Optional[bool] = True) -> int: print(">>>>>>>>>> Running database migration <<<<<<<<<<") run(['backend', 'python3', common.MANAGE_PY, 'makemigrations'], deps)
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3.081081
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import json import glob import numpy as np import os path = "data_state_space_v3/" out_path = "small_data/" files = glob.glob(path + "*.npy") # ワイルドカードが使用可能 train_data_num = 100 test_data_num = 10 train_data = {} test_data = {} for filename in files: obj = np.load(filename) if filename.find("_test.npy") >= 0: test_data[filename] = obj else: train_data[filename] = obj os.makedirs(out_path, exist_ok=True) for k, v in train_data.items(): b = os.path.basename(k) print(b, v.shape) o = v[:train_data_num] np.save(out_path + b, o) for k, v in test_data.items(): b = os.path.basename(k) print(b, v.shape) o = v[:test_data_num] np.save(out_path + b, o) fp = open(path + "pack_selected_info.json") obj = json.load(fp) obj["pid_list_train"] = obj["pid_list_train"][:train_data_num] obj["pid_list_test"] = obj["pid_list_test"][:test_data_num] fp = open(out_path + "pack_selected_info.json", "w") json.dump(obj, fp)
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2.206818
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# https://www.hackerrank.com/challenges/three-month-preparation-kit-maxsubarray/problem #!/bin/python3 import math import os import random import re import sys # # Complete the 'maxSubarray' function below. # # The function is expected to return an INTEGER_ARRAY. # The function accepts INTEGER_ARRAY arr as parameter. # def maxSubarray(arr): p = max(0,arr[0]) l = e = m = arr[0] for z in arr[1:]: e,m,l,p = max(z,e+z),max(m,max(z,e+z)),max(l,z),max(0,z)+p return m,l if(l<0) else p if __name__ == '__main__': fptr = open(os.environ['OUTPUT_PATH'], 'w') t = int(input().strip()) for t_itr in range(t): n = int(input().strip()) arr = list(map(int, input().rstrip().split())) result = maxSubarray(arr) fptr.write(' '.join(map(str, result))) fptr.write('\n') fptr.close()
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import torch from torch.nn.modules.module import Module from torch.autograd import Function import correlation_cuda class CorrelationFunction(Function): class Correlation(Module): def __init__(self, pad_size=0, kernel_size=0, max_displacement=0, stride1=1, stride2=2, corr_multiply=1): super(Correlation, self).__init__() self.pad_size = pad_size self.kernel_size = kernel_size self.max_displacement = max_displacement self.stride1 = stride1 self.stride2 = stride2 self.corr_multiply = corr_multiply self.out_channels = ((max_displacement/stride2)*2 + 1) * ((max_displacement/stride2)*2 + 1) def forward(self, input1, input2): param_dict = {'pad_size':self.pad_size, 'kernel_size':self.kernel_size, 'max_disp':self.max_displacement, 'stride1':self.stride1, 'stride2':self.stride2, 'corr_multiply':self.corr_multiply} result = CorrelationFunction.apply(input1, input2, param_dict) return result
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from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow.python.ops import data_flow_ops import tensorflow.contrib.tensorrt as trt import numpy as np import time from tensorflow.python.platform import gfile from tensorflow.python.client import timeline import argparse, sys, itertools,datetime import json tf.logging.set_verbosity(tf.logging.INFO) import os from utils import * def getGraph(filename): with gfile.FastGFile(filename, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) return graph_def def getFP32(input_file, output_prefix, output, batch_size=128, workspace_size=1<<20): trt_graph = trt.create_inference_graph(getGraph(input_file), output, max_batch_size=batch_size, max_workspace_size_bytes=workspace_size, precision_mode="FP32") # Get optimized graph with gfile.FastGFile(output_prefix+'.FP32.pb', 'wb') as f: f.write(trt_graph.SerializeToString()) return trt_graph def getFP16(input_file, output_prefix, output, batch_size=128, workspace_size=1<<20): trt_graph = trt.create_inference_graph(getGraph(input_file), output, max_batch_size=batch_size, max_workspace_size_bytes=workspace_size, precision_mode="FP16") # Get optimized graph with gfile.FastGFile(output_prefix+'.FP16.pb','wb') as f: f.write(trt_graph.SerializeToString()) return trt_graph def getINT8CalibGraph(input_file, output_prefix, output, batch_size=128, workspace_size=1<<20): trt_graph = trt.create_inference_graph(getGraph(input_file), output, max_batch_size=batch_size, max_workspace_size_bytes=workspace_size, precision_mode="INT8") # calibration with gfile.FastGFile(output_prefix+'.INT8Calib.pb','wb') as f: f.write(trt_graph.SerializeToString()) return trt_graph def getINT8InferenceGraph(output_prefix, calibGraph): trt_graph=trt.calib_graph_to_infer_graph(calibGraph) with gfile.FastGFile(output_prefix+'.INT8.pb','wb') as f: f.write(trt_graph.SerializeToString()) return trt_graph #main if "__main__" in __name__: P=argparse.ArgumentParser(prog="trt_convert") P.add_argument('--FP32',action='store_true') P.add_argument('--FP16',action='store_true') P.add_argument('--INT8',action='store_true') P.add_argument('--input_file',type=str) P.add_argument('--input_path_calibration',type=str,default='./',help="path to read input files from for calibration mode") P.add_argument('--output_prefix',type=str) P.add_argument('--batch_size',type=int, default=32) P.add_argument('--num_calibration_runs',type=int, default=10) P.add_argument('--workspace_size',type=int, default=1<<20,help="workspace size in MB") P.add_argument('--gpu', type=int, default=0) #P.add_argument('--update_graphdef',action='store_true') #parse args f,unparsed=P.parse_known_args() #select the GPU os.environ["CUDA_VISIBLE_DEVICES"]=str(f.gpu) #selects a specific device #create a session just in case sess = tf.Session() #print graph print_graph(f.input_file) #do the conversion if f.FP32: getFP32(input_file=f.input_file, output_prefix=f.output_prefix, output=["Softmax"], batch_size=f.batch_size, workspace_size=f.workspace_size) if f.FP16: getFP16(input_file=f.input_file, output_prefix=f.output_prefix, output=["Softmax"], batch_size=f.batch_size, workspace_size=f.workspace_size) if f.INT8: calibGraph = getINT8CalibGraph(input_file=f.input_file, output_prefix=f.output_prefix, output=["Softmax"], batch_size=f.batch_size, workspace_size=f.workspace_size) print('Calibrating Graph...') #run graph runGraph(calibGraph, f.batch_size, f.num_calibration_runs, "Placeholder", ["Softmax"], dtype=np.float32, input_data=f.input_path_calibration) print('done...') #get int8 graph getINT8InferenceGraph(output_prefix=f.output_prefix, calibGraph=calibGraph) sys.exit(0)
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2.35107
1,823
import Adafruit_SSD1306 import Image import ImageDraw import ImageFont # I2C ADDRESS / BITS SSD1306_ADDRESS = 0x3C class Ssd1306(object): _display = None _draw = None _image = None _font = None _height = 0 _width = 0 def __init__(self, i2c_bus = 0, ssd1306_rst = "22"): """ :type i2c_bus: int specifying i2c bus number :type ssd1306_rst: string specifying GPIO pin for RST """ # 128x32 display with hardware I2C: self._display = Adafruit_SSD1306.SSD1306_128_32(rst=ssd1306_rst, i2c_bus=i2c_bus) # Initialize library. self._display.begin() # Clear display. self._display.clear() self._display.display() # Create blank image for drawing. # Make sure to create image with mode '1' for 1-bit color. self._width = self._display.width self._height = self._display.height self._image = Image.new('1', (self._width, self._height)) # Get drawing object to draw on image. self._draw = ImageDraw.Draw(self._image) # Load default font. self._font = ImageFont.load_default() def clear_display(self): self._draw.rectangle((0, 0, self._width, self._height), outline=0, fill=0) def draw_text(self, texttowrite, x, y): self._draw.text((x, y), texttowrite, font=self._font, fill=255) def display_image(self): self._display.image(self._image) self._display.display() def image_width(self): width, height = self._image.size return width def get_text_width(self, text): width, height = self._font.getsize(text) return width
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2.281843
738
def arg_count(inference_rule): # these should be determined based on string match in CSV file num_input=2 num_feed=1 num_output=1 return num_input,num_feed,num_output if __name__ == '__main__': print compute(1, 0.1) # default values
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2.626263
99
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import numpy as np from ..optimization import discretization from ..common.decorators import Registry registry = Registry() def _onemax(x: np.ndarray) -> float: return len(x) - sum(1 if int(round(w)) == 1 else 0 for w in x) def _leadingones(x: np.ndarray) -> float: for i, x_ in enumerate(list(x)): if int(round(x_)) != 1: return len(x) - i return 0 def _jump(x: np.ndarray) -> float: # TODO: docstring? n = len(x) m = n // 4 o = n - _onemax(x) if o == n or o <= n - m: return n - m - o return o # Deceptive part. def _styblinksitang(x: np.ndarray, noise: float) -> float: x = np.asarray(x) val = np.sum(np.power(x, 4) - 16 * np.power(x, 2) + 5 * x) # return a positive value for maximization return float(39.16599 * len(x) + 1 * 0.5 * val + noise * np.random.normal(size=val.shape)) # following functions using discretization should not be used with translation/rotation
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2.580994
463
string = input() d = {} for i in string: if i in d: d[i] += 1 else: d[i] = 1 s = sorted(sorted(d), key = d.get, reverse = True) for i in s[:3]: print(i, d[i])
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1.888889
99
def solve(n): result = [[0 for _ in range(n)] for __ in range(n)] counter = 1 iteration = 0 while counter <= n * n: i = j = iteration while j < n - iteration: result[i][j] = counter counter += 1 j += 1 j -= 1 i += 1 while i < n - iteration: result[i][j] = counter counter += 1 i += 1 i -= 1 j -= 1 while j >= iteration: result[i][j] = counter counter += 1 j -= 1 j += 1 i -= 1 while i > iteration: result[i][j] = counter counter += 1 i -= 1 iteration += 1 return result for row in solve(10): print(row)
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1.836493
422
def solution(text): letters = string.ascii_letters s = '' for c in text: s = '{}{}'.format(s, c if c in letters else ' ') return max(s.split(), key=len)
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2.207317
82
import random import string from subprocess import run from types import SimpleNamespace import psycopg2 import versions from docker_helpers import get_image_name, pull, exec_safely from service_config import api_db_user from settings import get_secret root_user = "vimc" # these tables should only be modified via sql migrations protected_tables = ["gavi_support_level", "activity_type", "burden_outcome", "gender", "responsibility_set_status", "impact_outcome", "gavi_support_level", "support_type", "touchstone_status", "permission", "role", "role_permission"] def user_configs(password_group): # Later, read these from a yml file? return [ UserConfig(api_db_user, 'all', VaultPassword(password_group, api_db_user)), UserConfig('import', 'all', VaultPassword(password_group, 'import')), UserConfig('orderly', 'all', VaultPassword(password_group, 'orderly')), UserConfig('readonly', 'readonly', VaultPassword(password_group, 'readonly')), ] class GeneratePassword: def get(self): return ''.join(random.SystemRandom().choice( string.ascii_uppercase + string.digits) for _ in range(50)) def __str__(self): return "Generated" class VaultPassword: def __init__(self, password_group, username): self.password_group = password_group self.username = username def get(self): if self.password_group is None: return "changeme" if self.username == "vimc" else self.username else: return get_secret(self._path(), field="password") def _path(self): if self.password_group is None: raise Exception("_path() is not defined without a password group") else: return "database/{password_group}/users/{username}".format( password_group=self.password_group, username=self.username) def __str__(self): if self.password_group is None: return "Using default password value" else: return "From vault at " + self._path() class UserConfig: def __init__(self, name, permissions, password_source, option=None): self.name = name self.permissions = permissions # Currently, this can only be 'all', but the idea is to extend this config later self.password_source = password_source self.option = option.upper() if option else "" self._password = None # Lazy password resolution def set_root_password(service, password): query = "ALTER USER {user} WITH PASSWORD '{password}'".format( user=root_user, password=password) service.db.exec_run( 'psql -U {user} -d postgres -c "{query}"'.format(user=root_user, query=query)) def connect(user, password, host="localhost", port=5432): conn_settings = { "host": host, "port": port, "name": "montagu", "user": user, "password": password } conn_string_template = "host='{host}' port='{port}' dbname='{name}' user='{user}' password='{password}'" conn_string = conn_string_template.format(**conn_settings) return psycopg2.connect(conn_string) def create_user(db, user): sql = """DO $body$ BEGIN IF NOT EXISTS (SELECT FROM pg_catalog.pg_user WHERE usename = '{name}') THEN CREATE ROLE {name} {option} LOGIN PASSWORD '{password}'; END IF; END $body$""".format(name=user.name, password=user.password, option=user.option) db.execute(sql) def set_password(db, user): db.execute( "ALTER USER {name} WITH PASSWORD '{password}'".format(name=user.name, password=user.password)) def revoke_all(db, user): def revoke_all_on(what): db.execute( "REVOKE ALL PRIVILEGES ON ALL {what} IN SCHEMA public FROM {name}".format( name=user.name, what=what)) revoke_all_on("tables") revoke_all_on("sequences") revoke_all_on("functions") def revoke_write_on_protected_tables(db, user): def revoke_specific_on(what): db.execute( "REVOKE INSERT, UPDATE, DELETE ON {what} FROM {name}".format( name=user.name, what=what)) for table in protected_tables: revoke_specific_on(table) def grant_all(db, user): def grant_all_on(what): db.execute( "GRANT ALL PRIVILEGES ON ALL {what} IN SCHEMA public TO {name}".format( name=user.name, what=what)) print(" - Granting all permissions to {name}".format(name=user.name)) grant_all_on("tables") grant_all_on("sequences") grant_all_on("functions") def grant_readonly(db, user): print(" - Granting readonly permissions to {name}".format(name=user.name)) db.execute("GRANT SELECT ON ALL TABLES IN SCHEMA public TO {name}".format( name=user.name)) def set_permissions(db, user): revoke_all(db, user) if user.permissions == 'all': grant_all(db, user) elif user.permissions == 'readonly': grant_readonly(db, user) elif user.permissions == 'pass': pass else: template = "Unhandled permission type '{permissions}' for user '{name}'" raise Exception( template.format(name=user.name, permissions=user.permissions)) def migrate_schema_core(service, root_password): network_name = service.network_name print("- migrating schema") image = "vimc/{name}:{version}".format(name="montagu-migrate", version=versions.db) pull(image) cmd = ["docker", "run", "--rm", "--network=" + network_name, image] + \ ["-user=vimc", "-password=" + root_password, "migrate"] run(cmd, check=True) def setup_user(db, user): print(" - " + user.name) create_user(db, user) set_password(db, user) set_permissions(db, user) def for_each_user(root_password, users, operation): """Operation is a callback (function) that takes the connection cursor and a UserConfig object""" with connect(root_user, root_password) as conn: with conn.cursor() as cur: for user in users: operation(cur, user) conn.commit() def setup(service): print("Waiting for the database to accept connections") exec_safely(service.db, ["montagu-wait.sh", "7200"], check=True) password_group = service.settings["password_group"] print("Setting up database users") print("- Scrambling root password") if password_group is not None: root_password = GeneratePassword().get() else: root_password = 'changeme' set_root_password(service, root_password) print("- Getting user configurations") users = user_configs(password_group) print("- Getting user passwords") passwords = {} for user in users: print(" - {name}: {source}".format(name=user.name, source=user.password_source)) passwords[user.name] = user.password # NOTE: As I work through this - why not set up users *after* the # schema migration? This is because the migration user needs to # exist, though in practice we don't use them so this could be # reordered later. print("- Updating database users") for_each_user(root_password, users, setup_user) print("- Migrating database schema") migrate_schema_core(service, root_password) print("- Refreshing permissions") # The migrations may have added new tables, so we should set the permissions # again, in case users need to have permissions on these new tables for_each_user(root_password, users, set_permissions) # Revoke specific permissions now that all tables have been created. for_each_user(root_password, users, revoke_write_on_protected_tables) setup_streaming_replication(root_password, service) return passwords # NOTE: it might be worth revisiting this to not run this script # directly (that requires corresponding changes in montagu-db to move # the inline sql into a standalone .sql file and then getting psql to # run it via docker exec - it must run as the vimc user). The # passwords might move directly under control here using set_password # (but these are special users so we'd not want to use the rest of the # user machinery). But I suggest waiting until the restore is done # VIMC-1560) because that is likely to affect how we deal with users def setup_streaming_replication(root_password, service): if service.settings['enable_db_replication']: print("Setting up streaming replication") password_group = service.settings['password_group'] barman = UserConfig.create("barman", "pass", password_group, "superuser") streaming_barman = UserConfig.create("streaming_barman", "pass", password_group, "replication") with connect(root_user, root_password) as conn: with conn.cursor() as db: create_user(db, barman) create_user(db, streaming_barman) pw_barman = VaultPassword(password_group, "barman").get() pw_stream = VaultPassword(password_group, "streaming_barman").get() cmd = ["enable-replication.sh", pw_barman, pw_stream] exec_safely(service.db, cmd, check=True) def prepare_db_for_import(service): print("Preparing databse for import") ## NOTE: this could otherwise be done by connecting using the ## connection function, but that that requires further changes to ## the connect function to allow connection to the postgres ## maintenance database. This way works for now. This also ## allows us to avoid working out what the root password will be ## because we're interating without passwords over exec. db = service.db print("- deleting and recreating database") db.exec_run(["dropdb", "-U", "vimc", "--if-exists", "montagu"]) db.exec_run(["createdb", "-U", "vimc", "montagu"]) print("- configuring users") users = user_configs(service.settings["password_group"]) for user in users: db.exec_run(["createuser", "-U", "vimc", user.name])
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#!/usr/bin/env python3 import poplib import argparse def main(hostname,port,user,password): mailbox = poplib.POP3_SSL(hostname,port) try: mailbox.user(user) mailbox.pass_(password) response, listings, octet_count = mailbox.list() for listing in listings: number, size = listing.decode('ascii').split() print("Message %s has %s bytes" % (number, size)) except poplib.error_proto as exception: print("Login failed:", exception) finally: mailbox.quit() if __name__ == '__main__': parser = argparse.ArgumentParser(description='MailBox basic params') parser.add_argument('--hostname', action="store", dest="hostname") parser.add_argument('--port', action="store", dest="port") parser.add_argument('--user', action="store", dest="user") given_args = parser.parse_args() hostname = given_args.hostname port = given_args.port user = given_args.user import getpass password = getpass.getpass(prompt='Enter your password:') main(hostname,port,user,password)
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import sys import time import redis from openob.rtp.tx import RTPTransmitter from openob.rtp.rx import RTPReceiver import gst from colorama import Fore, Back, Style # OpenOB Link Manager # One of these runs at each end and negotiates everything (RX pushes config info to TX), reconnects when links fail, and so on. class Manager: '''OpenOB Manager. Handles management of links, mostly recovery from failures.''' def run(self, opts): print("-- OpenOB Audio Link") print(" -- Starting Up") print(" -- Parameters: %s" % opts) # We're now entering the realm where we should desperately try and maintain a link under all circumstances forever. while True: try: # Set up redis and connect config = None while True: try: config = redis.Redis(opts.config_host) print(" -- Connected to configuration server") break except Exception, e: print(Fore.BLACK + Back.RED + " -- Couldn't connect to Redis! Ensure your configuration host is set properly, and you can connect to the default Redis port on that host from here (%s)." % e) print(" Waiting half a second and attempting to connect again." + Fore.RESET + Back.RESET) time.sleep(0.5) # So if we're a transmitter, let's set the options the receiver needs to know about link_key = "openob2:"+opts.link_name+":" if opts.mode == 'tx': if opts.encoding == 'celt' and int(opts.bitrate) > 192: print(Fore.BLACK + Back.YELLOW + " -- WARNING: Can't use bitrates higher than 192kbps for CELT, limiting" + Fore.RESET + Back.RESET) opts.bitrate = 192 # We're a transmitter! config.set(link_key+"port", opts.port) config.set(link_key+"ipv6", opts.ipv6) config.set(link_key+"jitter_buffer", opts.jitter_buffer) config.set(link_key+"encoding", opts.encoding) config.set(link_key+"bitrate", opts.bitrate) print(" -- Configured receiver with:") print(" - Base Port: %s" % config.get(link_key+"port")) print(" - Jitter Buffer: %s ms" % config.get(link_key+"jitter_buffer")) print(" - Encoding: %s" % config.get(link_key+"encoding")) print(" - Bitrate: %s kbit/s" % config.get(link_key+"bitrate")) # Okay, we can't set caps yet - we need to configure ourselves first. opus_opts = {'audio': True, 'bandwidth': -1000, 'frame-size': opts.framesize, 'complexity': opts.complexity, 'constrained-vbr': True, 'inband-fec': opts.fec, 'packet-loss-percentage': opts.loss, 'dtx': opts.dtx} try: transmitter = RTPTransmitter(audio_input=opts.audio_input, audio_device=opts.device, base_port=opts.port, ipv6=opts.ipv6, encoding=opts.encoding, bitrate=opts.bitrate, jack_name=("openob_tx_%s" % opts.link_name), receiver_address=opts.receiver_host, opus_options=opus_opts) # Set it up, get caps try: transmitter.run() config.set(link_key+"caps", transmitter.get_caps()) print(" - Caps: %s" % config.get(link_key+"caps")) transmitter.loop() except Exception, e: print(Fore.BLACK + Back.RED + " -- Lost connection or otherwise had the transmitter fail on us, restarting (%s)" % e) time.sleep(0.5) except gst.ElementNotFoundError, e: print(Fore.BLACK + Back.RED + (" -- Couldn't fulfill our gstreamer module dependencies! You don't have the following element available: %s" % e) + Fore.RESET + Back.RESET) sys.exit(1) else: # We're a receiver! # Default values. port = 3000 caps = '' jitter_buffer = 150 encoding = 'opus' bitrate = '96' while True: try: if config.get(link_key+"port") == None: print(Fore.BLACK + Back.YELLOW + " -- Unable to configure myself from the configuration host; has the transmitter been started yet, and have you got the same link name on each end?") print(" Waiting half a second and attempting to reconfigure myself." + Fore.RESET + Back.RESET) time.sleep(0.5) port = int(config.get(link_key+"port")) ipv6 = int(config.get(link_key+"ipv6")) caps = config.get(link_key+"caps") jitter_buffer = int(config.get(link_key+"jitter_buffer")) encoding = config.get(link_key+"encoding") bitrate = int(config.get(link_key+"bitrate")) print(" -- Configured from transmitter with:") print(" - Base Port: %s" % port) print(" - Jitter Buffer: %s ms" % caps) print(" - Encoding: %s" % encoding) print(" - Bitrate: %s kbit/s" % bitrate) print(" - Caps: %s" % caps) break except Exception, e: print(Fore.BLACK + Back.YELLOW + " -- Unable to configure myself from the configuration host; has the transmitter been started yet? (%s)" % e) print(" Waiting half a second and attempting to reconfigure myself." + Fore.RESET + Back.RESET) time.sleep(0.5) #raise # Okay, we can now configure ourself receiver = RTPReceiver(audio_output=opts.audio_output, audio_device=opts.device, base_port=port, ipv6=ipv6, encoding=encoding, caps=caps, bitrate=bitrate, jitter_buffer=jitter_buffer, jack_name=("openob_tx_%s" % opts.link_name) ) try: receiver.run() receiver.loop() except Exception, e: print(Fore.BLACK + Back.RED + (" -- Lost connection or otherwise had the receiver fail on us, restarting (%s)" % e) + Fore.RESET + Back.RESET) time.sleep(0.5) except Exception, e: print(Fore.BLACK + Back.RED + " -- Unhandled exception occured, please report this as a bug!" + Fore.RESET + Back.RESET) raise
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# -*- coding: utf-8 -*- # ------------------------------------------------------------------------------ # # Copyright 2018-2019 Fetch.AI Limited # # 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. # # ------------------------------------------------------------------------------ """This module contains the tests of the strategy class of the confirmation aw2 skill.""" import datetime import logging from pathlib import Path from typing import cast from unittest.mock import Mock, patch import pytest from packages.fetchai.skills.confirmation_aw2.registration_db import RegistrationDB from packages.fetchai.skills.confirmation_aw2.strategy import Strategy from tests.conftest import ROOT_DIR from tests.test_packages.test_skills.test_confirmation_aw2.intermediate_class import ( ConfirmationAW2TestCase, ) class TestStrategy(ConfirmationAW2TestCase): """Test Strategy of confirmation aw2.""" path_to_skill = Path(ROOT_DIR, "packages", "fetchai", "skills", "confirmation_aw2") def test__init__i(self): """Test the __init__ of Strategy class.""" assert self.strategy.aw1_aea == self.aw1_aea assert self.strategy.minimum_hours_between_txs == self.minimum_hours_between_txs assert ( self.strategy.minimum_minutes_since_last_attempt == self.minimum_minutes_since_last_attempt ) def test__init__ii(self): """Test the __init__ of Strategy class where aw1_aea is None.""" with pytest.raises(ValueError, match="aw1_aea must be provided!"): Strategy( aw1_aea=None, mininum_hours_between_txs=self.minimum_hours_between_txs, minimum_minutes_since_last_attempt=self.minimum_minutes_since_last_attempt, name="strategy", skill_context=self.skill.skill_context, ) def test_get_acceptable_counterparties(self): """Test the get_acceptable_counterparties method of the Strategy class.""" # setup couterparties = ("couterparty_1", "couterparty_2", "couterparty_3") is_valid_counterparty = [True, False, True] # operation with patch.object( self.strategy, "is_valid_counterparty", side_effect=is_valid_counterparty ): actual_acceptable_counterparties = self.strategy.get_acceptable_counterparties( couterparties ) # after assert actual_acceptable_counterparties == ("couterparty_1", "couterparty_3") def test_is_enough_time_since_last_attempt_i(self): """Test the is_enough_time_since_last_attempt method of the Strategy class where now IS greater than last attempt + min minutes.""" # setup counterparty_last_attempt_time_str = "2020-12-22 20:30:00.000000" counterparty_last_attempt_time = datetime.datetime.strptime( counterparty_last_attempt_time_str, "%Y-%m-%d %H:%M:%S.%f" ) mocked_now_greater_than_last_plus_minimum = "2020-12-22 20:33:00.000000" datetime_mock = Mock(wraps=datetime.datetime) datetime_mock.now.return_value = datetime.datetime.strptime( mocked_now_greater_than_last_plus_minimum, "%Y-%m-%d %H:%M:%S.%f" ) self.strategy.last_attempt = {self.counterparty: counterparty_last_attempt_time} # operation with patch("datetime.datetime", new=datetime_mock): is_enough_time = self.strategy.is_enough_time_since_last_attempt( self.counterparty ) # after assert is_enough_time is True def test_is_enough_time_since_last_attempt_ii(self): """Test the is_enough_time_since_last_attempt method of the Strategy class where now is NOT greater than last attempt + min minutes.""" # setup counterparty_last_attempt_time_str = "2020-12-22 20:30:00.000000" counterparty_last_attempt_time = datetime.datetime.strptime( counterparty_last_attempt_time_str, "%Y-%m-%d %H:%M:%S.%f" ) mocked_now_less_than_last_plus_minimum = "2020-12-22 20:31:00.000000" datetime_mock = Mock(wraps=datetime.datetime) datetime_mock.now.return_value = datetime.datetime.strptime( mocked_now_less_than_last_plus_minimum, "%Y-%m-%d %H:%M:%S.%f" ) self.strategy.last_attempt = {self.counterparty: counterparty_last_attempt_time} # operation with patch("datetime.datetime", new=datetime_mock): is_enough_time = self.strategy.is_enough_time_since_last_attempt( self.counterparty ) # after assert is_enough_time is False def test_is_enough_time_since_last_attempt_iii(self): """Test the is_enough_time_since_last_attempt method of the Strategy class where now counterparty is NOT in last_attempt.""" # setup self.strategy.last_attempt = {} # operation is_enough_time = self.strategy.is_enough_time_since_last_attempt( self.counterparty ) # after assert is_enough_time is True def test_is_valid_counterparty_i(self): """Test the is_valid_counterparty method of the Strategy class where is_registered is False.""" # operation with patch.object(self.db, "is_registered", return_value=False): with patch.object(self.logger, "log") as mock_logger: is_valid = self.strategy.is_valid_counterparty(self.counterparty) # after mock_logger.assert_any_call( logging.INFO, f"Invalid counterparty={self.counterparty}, not registered!", ) assert is_valid is False def test_is_valid_counterparty_ii(self): """Test the is_valid_counterparty method of the Strategy class where is_enough_time_since_last_attempt is False.""" # operation with patch.object(self.db, "is_registered", return_value=True): with patch.object( self.strategy, "is_enough_time_since_last_attempt", return_value=False ): with patch.object(self.logger, "log") as mock_logger: is_valid = self.strategy.is_valid_counterparty(self.counterparty) # after mock_logger.assert_any_call( logging.DEBUG, f"Not enough time since last attempt for counterparty={self.counterparty}!", ) assert is_valid is False def test_is_valid_counterparty_iii(self): """Test the is_valid_counterparty method of the Strategy class where is_allowed_to_trade is False.""" # operation with patch.object(self.db, "is_registered", return_value=True): with patch.object( self.strategy, "is_enough_time_since_last_attempt", return_value=True ): with patch.object(self.db, "is_allowed_to_trade", return_value=False): is_valid = self.strategy.is_valid_counterparty(self.counterparty) # after assert is_valid is False def test_is_valid_counterparty_iv(self): """Test the is_valid_counterparty method of the Strategy class where it succeeds.""" # operation with patch.object(self.db, "is_registered", return_value=True): with patch.object( self.strategy, "is_enough_time_since_last_attempt", return_value=True ): with patch.object(self.db, "is_allowed_to_trade", return_value=True): is_valid = self.strategy.is_valid_counterparty(self.counterparty) # after assert is_valid is True def test_successful_trade_with_counterparty(self): """Test the successful_trade_with_counterparty method of the Strategy class.""" # setup data = {"some_key_1": "some_value_1", "some_key_2": "some_value_2"} mocked_now_str = "2020-12-22 20:33:00.000000" mock_now = datetime.datetime.strptime(mocked_now_str, "%Y-%m-%d %H:%M:%S.%f") datetime_mock = Mock(wraps=datetime.datetime) datetime_mock.now.return_value = mock_now # operation with patch.object(self.db, "set_trade") as mock_set_trade: with patch("datetime.datetime", new=datetime_mock): with patch.object(self.logger, "log") as mock_logger: self.strategy.successful_trade_with_counterparty( self.counterparty, data ) # after mock_set_trade.assert_any_call(self.counterparty, mock_now, data) mock_logger.assert_any_call( logging.INFO, f"Successful trade with={self.counterparty}. Data acquired={data}!", ) def test_register_counterparty(self): """Test the register_counterparty method of the Strategy class.""" # setup developer_handle = "some_developer_handle" # operation with patch.object(self.db, "set_registered") as mock_set_registered: self.strategy.register_counterparty(self.counterparty, developer_handle) # after mock_set_registered.assert_any_call(self.counterparty, developer_handle)
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class DummyMPICom(object): rank = 0 size = 1 def barrier(self): pass try: from mpi4py import MPI # @UnusedImport @IgnorePep8 This is imported before NEURON to avoid a bug in NEURON except ImportError: mpi_comm = DummyMPICom() else: mpi_comm = MPI.COMM_WORLD MPI_ROOT = 0 def is_mpi_master(): return (mpi_comm.rank == MPI_ROOT)
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# -*- coding: utf-8 -*- """Unpacks Raw API data from zkillboard into victim files that contain TEST - 10/02/2019 Params: 10000002201505.csv | 61MB | 28208 rows x 8 columns Output: ``` (+0.000s|t:0.000s) Importing modules... (+2.209s|t:2.209s) Loading CSV data from local file... (+1.132s|t:3.341s) Converting DataFrame column value types... (+18.746s|t:22.087s) Loading YAML files into memory... (+3.88m|t:4.25m) Unpacking DataFrame values... (+2.30m|t:6.55m) Writing results to CSV... (+8.008s|t:6.68m) Exit ``` Written By: Adam Coscia Updated On: 11/09/2019 """ # Start timing import time start = time.time() total = 0 def lap(msg): """Records time elapsed.""" global start, total elapsed = (time.time() - start) - total total = time.time() - start if elapsed > 3600: print(f'(+{elapsed/3600:.2f}h|t:{total/3600:.2f}h) {msg}') elif elapsed > 60: if total > 3600: print(f'(+{elapsed/60:.2f}m|t:{total/3600:.2f}h) {msg}') else: print(f'(+{elapsed/60:.2f}m|t:{total/60:.2f}m) {msg}') else: if total > 3600: print(f'(+{elapsed:.3f}s|t:{total/3600:.2f}h) {msg}') elif total > 60: print(f'(+{elapsed:.3f}s|t:{total/60:.2f}m) {msg}') else: print(f'(+{elapsed:.3f}s|t:{total:.3f}s) {msg}') lap("Importing modules...") from ast import literal_eval import os import sys import numpy as np import pandas as pd import yaml def load_yaml(file_loc, encoding='utf-8'): """Loads yaml file at file_loc and returns Python object based on yaml structure. """ data = None with open(file_loc, 'r', encoding=encoding) as stream: try: data = yaml.safe_load(stream) except yaml.YAMLError as exc: print(exc) return data # Specify S3 parameters and SQL query bucket='dilabevetrajectorymining' key='eve-trajectory-mining/Killmail_Fetching/killmail_scrapes/byregion/10000002/10000002201505.csv' query=""" SELECT * FROM s3Object s LIMIT 5 """ # Let amazon do the api calls # print('Querying s3 bucket...') # df = select(bucket, key, query) # # Open YAML file of typeIDs to get names of items # typeIDs.yaml -> dictionary of typeID keys which contain attributes # ex. typeIDs[11317] -> {'description': {'en': 'blah', ...}, ...} # typeIDs[11317]['name']['en'] == '800mm Rolled Tungsten Compact Plates' # typeIDs[11317]['groupID'] == 329 # groupIDs[329] -> {'name': {'en': 'blah', ...}, ...} # groupIDs[329]['name']['en'] == 'Armor Reinforcer' # lap("Loading YAML files into memory...") root = "../Trajectory_Mining/docs/eve files" # YAML file location typeIDs = load_yaml(os.path.join(root, 'typeIDs.yaml')) groupIDs = load_yaml(os.path.join(root, 'groupIDs.yaml')) # invFlags = load_yaml(os.path.join(root, 'invFlags.yaml')) # invMarketGroups = load_yaml(os.path.join(root, 'invMarketGroups.yaml')) # categoryIDs = load_yaml(os.path.join(root, 'categoryIDs.yaml')) # Sequentially load CSV's from file lap("Loading CSV data from killmail_scrapes...") victims = [] # list of victim dataframes generated from CSV's attackers = [] # list of victim dataframes generated from CSV's for root, dirs, files in os.walk("../Killmail_Fetching/killmail_scrapes/byregion", topdown=False): count = 0 num_files = len(files) # number of CSV files for file in sorted(files): print(f"Progress {count/num_files:2.1%} ", end="\r") df = pd.read_csv(os.path.join(root, file), encoding='utf-8') # Convert all timestamp strings to numpy.datetime64 # print("> Converting DataFrame column value types ", end="") df['killmail_time'] = pd.to_datetime(df['killmail_time'], # Turn errors into NaT errors='coerce', # Use this format to parse str format='%Y-%m-%dT%H:%M:%SZ') # Convert all numeric values in 'solar_system_id' to smallest int type # Convert all non-numeric values in 'solar_system_id' to NaN df['solar_system_id'] = pd.to_numeric(df['solar_system_id'], # Turn errors into NaN errors='coerce', # Convert to smallest int type downcast='integer') # Convert values in columns to python objects df['victim'] = df['victim'].apply(literal_eval) df['attackers'] = df['attackers'].apply(literal_eval) df['zkb'] = df['zkb'].apply(literal_eval) # Unpack DataFrame subset containing lists and dicts # print("> Unpacking DataFrame values ", end="") victim_rows = [] attacker_rows = [] a_col = ['final_blow', 'damage_done', 'ship_type_id'] v_col = ['killmail_time', 'solar_system_id', 'character_id', 'ship_type_id', 'items'] for v_row, a_rows, k_id in unpack(df): if v_row is not None: # If no character ID, don't append victim victim_rows.append(pd.DataFrame( [v_row], columns=v_col, index=pd.Index([k_id], name='killmail_id') )) if a_rows: attacker_rows.extend([pd.DataFrame( [a_row], columns=a_col, index=pd.MultiIndex.from_tuples( [(k_id, a_id)], names=('killmail_id', 'character_id') ) ) for a_id, a_row in a_rows]) # Concat victim_rows together # print("> Concating victim rows ", end="\r") victims.append(pd.concat(victim_rows, sort=False)) # attackers.append(pd.concat(attacker_rows, sort=False)) count += 1 # Save victim and attacker info to CSV lap("Writing results to CSV...") df_victims = pd.concat(victims) df_victims.to_csv('data/all_victims_items.csv') # df_attackers = pd.concat(attackers) # df_attackers.to_csv('data/all_attackers.csv') lap("Exit")
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from django.shortcuts import render, get_object_or_404 from django.contrib import messages from .forms import ReportForm from data.models import Paper def reportPaper(request, paperId): paper = get_object_or_404(Paper, pk=paperId) form = None try: assert request.method == "POST" form = ReportForm(request.POST) assert form.is_valid() report = form.save(commit=False) report.paper = paper report.save() messages.add_message(request, messages.INFO, "Report Successful!") return render(request, "reportform.html", { "form": ReportForm(), "paper": paper }) except: if form is None: form = ReportForm() return render(request, "reportform.html", { "form": form, "paper": paper })
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#!/usr/bin/python #stats.py ''' Classes and functons to implement calculation and output of statistics ''' #geonomics imports from geonomics.utils.io import (_append_array2d_to_array_stack, _append_row_to_csv, _write_dict_to_csv) from geonomics.ops.selection import _calc_fitness from geonomics.utils.viz import _check_display #other imports import numpy as np from scipy.stats.stats import pearsonr from collections import Counter as C import os import matplotlib as mpl _check_display() import matplotlib.pyplot as plt ###################################### # -----------------------------------# # CLASSES ---------------------------# # -----------------------------------# ###################################### #a StatsCollector class, to parameterize and manage calculation #and collection of stats, then write them to file at the end of #each model iteration class _StatsCollector: def __init__(self, model_name, params): #set model_name self.model_name = model_name #set total model time self.T = params.model.T #grab the stats parameters stats_params = params.model.stats #a dictionary to link the stats' names in the params dict #to the functions to be called to calculate them self.calc_fn_dict = {'Nt': _calc_Nt, 'ld': _calc_ld, 'het': _calc_het, 'maf': _calc_maf, 'mean_fit': _calc_mean_fitness, } #a dictionary to link the stats' names in the params dict #to the functions to be called to write them to disk self.write_fn_dict = {'ld': self._write_array_to_stack, 'het': self._write_row_to_csv, 'maf': self._write_row_to_csv, } #a dictionary to link stats to the file extensions that #should be used to write them to disk self.file_suffix_dict = {'Nt': 'OTHER_STATS.csv', 'ld': 'LD.txt', 'het': 'HET.csv', 'maf': 'MAF.csv', 'mean_fit': 'OTHER_STATS.csv', } #get the species names spps_with_wout_genomes = {str(k):('gen_arch' in v.keys()) for k, v in params.comm.species.items()} #list stats that cannot be calculated for species without genomes stats_invalid_wout_genomes = ['ld', 'het', 'maf', 'mean_fit'] #create a stats attribute, to store all stats calculated self.stats = {} for spp_name, genome in spps_with_wout_genomes.items(): self.stats[spp_name] = {} for stat, stat_params in stats_params.items(): #skip species without genomes for stats that need genomes if not genome and stat in stats_invalid_wout_genomes: break #each spp gets a subdict else: #each subdict gets a key for each stat to be calculated if stat_params.calc: #create a subdictionary for each stat, with a list of #NaNs self.T items long, which will be filled in for #each whenever it is sampled (NOTE: this forces all #stats to have the same length so that they all fit #into one pd.DataFrame at the end, and so that plots #easily line up on the same timeframe) self.stats[spp_name][stat]= { 'vals': [np.nan]*self.T, 'freq': stat_params.freq, #add a 'filepath' key, whose value will be updated #to contain to correct filepaths for each stat 'filepath': None, #create tuple of other, stat-specific parameters, #to later be unpacked as arguments to #the appropriate stat function 'other_params': dict([(k,v) for k,v in stat_params.items() if k not in ['calc', 'freq']]) } #if the freq value is 0, change it to self.T -1, so #that it collects only on the first and last timestep if self.stats[spp_name][stat]['freq'] == 0: self.stats[spp_name][stat]['freq'] = self.T-1 #create a master method, to be called each timestep, which will make a list #of all stats that need to be calculated that timestep (based on the #calculation-frequencies provided in the params dicts), and then calls the #functions to calculate them all and adds the results to self.stats def _calc_stats(self, community, t, iteration): #set the filepaths, if this is the first timestep of the model #iteration if t == 0: self._set_filepaths(iteration) #for each species for spp in community.values(): #list the stats to be calculated this timestep if t == self.T-1: #calculate all, if it's the last timestep calc_list = [*self.stats[spp.name]] else: #or else only calculate based on the parameterized frequencies #for each stat calc_list = [k for k,v in self.stats[spp.name].items() if ( t % v['freq'] == 0)] #then calculate each stat for stat in calc_list: vals = self.calc_fn_dict[stat](spp, **self.stats[spp.name][stat]['other_params']) #and add each stat to the right location (by timestep) #in its list try: self.stats[spp.name][stat]['vals'][t] = vals #unless the list isn't long enough (which happens if mod.walk #has been used to run the model past its initially stipulated #length of time), in which case make it long enough and make #the last value the stat just calculated except IndexError: stats_list = self.stats[spp.name][stat]['vals'] stats_list.extend([np.nan] * (t-len(stats_list)) + [vals]) #and write whichever stats are necessary to file self._write_stats(t) #a method to make the filenames for all of the stats to be saved def _set_filepaths(self, iteration): #get the directory name for this model and iteration dirname = os.path.join('GNX_mod-%s' % self.model_name, 'it-%i' % iteration) #for each species for spp_name in [*self.stats]: #get the subdirectory name and filename for this species subdirname = os.path.join(dirname, 'spp-%s' % spp_name) #make this subdir, and any parent dirs as necessary os.makedirs(subdirname, exist_ok = True) #create the filename and filepath for this spp, for each stat for stat in [*self.stats[spp_name]]: filename = 'mod-%s_it-%i_spp-%s_%s' % (self.model_name, iteration, spp_name, self.file_suffix_dict[stat]) filepath = os.path.join(subdirname, filename) #add the filepath for this stat to self.stats self.stats[spp_name][stat]['filepath'] = filepath #wrapper around io.append_array2d_to_array_stack #TODO WHAT TO DO WITH t IN THIS CASE?? CAN'T ADD TO txt 3D ARRAY FILE def _write_array_to_stack(self, filepath, array, t): _append_array2d_to_array_stack(filepath, array) #wrapper around io.append_row_to_csv def _write_row_to_csv(self, filepath, array, t): _append_row_to_csv(filepath, array, t) #use io._write_dict_to_csv to write to disk all "other stats", i.e. #all stats that collect only a single value per species per timestep #TODO: CHANGE THE 'OTHER STATS' NAMING CONVENTION TO SOMETING MORE #DESCRIPTIVE def _write_other_stats(self): for spp, spp_stats in self.stats.items(): #get a dictionary of the data values for all stats that are to be #written just once at the end of the iteration data_dict = {k:v['vals'] for k,v in spp_stats.items() if 'OTHER_STATS' in v['filepath']} #they all have the same filepath, so just grab the first filepath = [*spp_stats.values()][0]['filepath'] #write to disk _write_dict_to_csv(filepath, data_dict) #method to write stats to files, in the appropriate directory (by model #and iteration number), and with the appropriate spp names in the filenames def _write_stats(self, t): #for each species for spp_name, spp_stats in self.stats.items(): #for each stat write_list = [k for k,v in spp_stats.items() if t % v['freq'] == 0] for stat, stat_dict in spp_stats.items(): #get the filepath filepath = stat_dict['filepath'] #if the filepath does not contain "OTHER_STATS" then it is a #stat that produces more than a single value per species per #timestep it is collected, so write the data to disk #intermittently and then delete the data from memory (if it was #collected this timestep) if "OTHER_STATS" not in filepath and stat in write_list: #get the correct write_fn for this stat write_fn = self.write_fn_dict[stat] #call the write_fn to write the data to disk write_fn(filepath, stat_dict['vals'][t], t) #then replace the last data collected prior to this #timestep's data with None, to free up memory but still #maintain the latest data in case of plotting rev_nonnull = [n for n, v in enumerate( stat_dict['vals'][::-1]) if (v is not np.nan and v is not None)] nonnull = [range(len( stat_dict['vals']))[::-1][n] for n in rev_nonnull] nonnull = [v for v in nonnull if v != t] for v in nonnull: stat_dict['vals'][v] = None #or write all 'other stats' to disk, if it's the last timestep if t == self.T-1: self._write_other_stats() #method to plot whichever stat as a function of runtime def _plot_stat(self, stat, spp_name=None): #check that the stat argument is valid assert type(stat) is str, "The 'stat' argument must be a string." assert stat in [*self.stats.values()][0].keys(), ("The 'stat' " "argument must name a statistic that was collected. Valid values: " "%s.") % (','.join(["'%s'" % val for val in [*self.stats.values()][0].keys()])) #get the list of spps to plot if spp_name is None: spp_names = [*self.stats] elif (spp_name is not None and type(spp_name) is str and spp_name in [*self.stats]): spp_names = [spp_name] else: raise ValueError(("The 'spp_name' argument, if provided, " "must be a string containing a valid species name.")) #create the figure fig = plt.figure() #plot each species for the chosen statistic for n, spp_name in enumerate(spp_names): #get the stat values to plot vals = self.stats[spp_name][stat]['vals'] #plot 'Nt' or 'mean_fit' if stat in ['Nt', 'mean_fit']: #add axes objects horizontally across ax = fig.add_subplot(1, len(spp_names), n+1) #get the indices of non-NaN values to be plotted indices_to_plot = np.array(np.where( np.invert(np.isnan(vals)))[0]) #get the timesteps at the non-NaN values x = np.arange(0, len(vals))[indices_to_plot] #get the non-NaN values y = np.array(vals)[indices_to_plot] #plot a dotted line (which necessarily linearly interpolates #between collected timesteps if not all timesteps #were collected) plt.plot(x, y, ':') #and plot dots at each of the collected timesteps plt.plot(x, y, '.') #set the title to the stat and the species' name ax.set_title("SPP: '%s'" % (spp_name)) #set the x- and y-labels plt.xlabel('timestep') plt.ylabel(stat) #or plot 'maf' or 'het' elif stat in ['het', 'maf']: #add axes objects horizontally across ax = fig.add_subplot(1, len(spp_names), n+1) #get the reversed-list index of the last set of values #calculated rev_idx_last_vals = [n for n,v in enumerate(vals[::-1]) if ( v is not None and v is not np.nan)][0] #get the last set of values calculated last_vals = vals[::-1][rev_idx_last_vals] #get the timestep of the last set of values t_last_vals = range(len(vals))[::-1][rev_idx_last_vals] #plot the values plt.plot(range(len(last_vals)), last_vals, '-') #set the title to the species' name and timestep of the #values plotted ax.set_title("SPP: '%s'; T: %i" % (spp_name, t_last_vals)) #set the x- and y-labels plt.xlabel('locus') plt.ylabel(stat) #or plot 'ld' elif stat in ['ld']: #get the reversed-list index of the last set of values #calculated rev_idx_last_vals = [n for n,v in enumerate(vals[::-1]) if ( v is not None and v is not np.nan)][0] #get the last set of values (i.e. r^2 array) calculated r2_mat = vals[::-1][rev_idx_last_vals] #get the timestep of the last set of values t_last_vals = range(len(vals))[::-1][rev_idx_last_vals] #add axes objects horizontally across, in two rows ax = fig.add_subplot(2, len(spp_names), n+1) #plot the LD matrix in row 1 plt.imshow(np.clip(r2_mat, a_min = 0, a_max = None), interpolation = 'nearest') plt.colorbar() #set plot title ax.set_title(("SPP: '%s'; T: %i\nLocus-wise " "linkage matrix") % (spp_name, t_last_vals)) #set the x- and y-labels plt.xlabel('locus') plt.ylabel('locus') ax = fig.add_subplot(2, len(spp_names), n+1+len(spp_names)) #plot of mean linkage values r2_list = [r2_mat[0,1]] L = r2_mat.shape[0] for i in range(1,L-1): r2_list.append(np.mean([r2_mat[i-1,i], r2_mat[i,i+1]])) r2_list.append(r2_mat[L-2,L-1]) plt.scatter(range(L), r2_list, c = 'red', marker = 'o', s=25) #set plot title ax.set_title("Locus-wise mean linkage values") #set the x- and y-labels plt.xlabel('locus') plt.ylabel('mean linkage') #or else return informative error message else: raise ValueError(("The value provided for the 'stat' argument " "is not a valid statistic. Valid values include: %s\n\n")%( ','.join(['%s' % k for k in [*self.calc_fn_dict]]))) #set the main title to the stat plotted fig.suptitle('STAT: %s' % stat) #show the image fig.show() ###################################### # -----------------------------------# # FUNCTIONS -------------------------# # -----------------------------------# ###################################### #method to get pop size (NOTE: not actually calculating it) def _calc_Nt(spp): Nt = spp.Nt[-1] return(Nt) def _calc_ld(spp, plot = False): #TODO: I should also include (either as an alternative within this fn, #or as separate fn) the option to calculate D' #TODO: I keep getting warnings like the following, which could just be #due to divison of small floating-point numbers, but I should figure out #exactly what's going on and be sure everything checks out. WARNING: # stats.py:117: RuntimeWarning: invalid value encountered in double_scalars speciome = spp._get_genotypes() n = np.shape(speciome)[0] #num individs x = np.shape(speciome)[2] #ploidy N = n*x L = spp.gen_arch.L assert L == np.shape(speciome)[1], ("The length of the 1st dimension " "of speciome doesn't equal spp.genomic_arch.L") r2_mat = np.zeros([L]*2) * np.nan # vals default to NaN for i in range(L): for j in range(i+1, L): #calculate freq of allele 1 at locus i f1_i = np.sum(speciome[:,i,:], axis = None)/(N) #calculate freq of allele 1 at locus j f1_j = np.sum(speciome[:,j,:], axis = None)/(N) #calculate freq of chroms with 1_1 haplotype at loci i and j f11_ij = float(np.sum(speciome[:,[i,j],:].sum(axis = 1) ==2, axis = None))/(N) D_1_1 = f11_ij - (f1_i * f1_j) r2 = (D_1_1**2)/(f1_i*(1-f1_i)*f1_j*(1-f1_j)) # add to both triangular halves, to produce a symmetric matrix r2_mat[i,j] = r2 r2_mat[j,i] = r2 return(r2_mat) #function to calculate the locus-wise (if mean == False) or mean (if #mean == True) heterozygosity of the species def _calc_het(spp, mean=False): #get pop size N = len(spp) #get the speciome speciome = spp._get_genotypes() #calculate the frequency of heterozygotes, locus-wise het = np.sum(np.mean(speciome, axis = 2) == 0.5, axis = 0)/N #get the mean heterozygosity, if mean argument is True if mean: het = mean(het) return(het) #function to calculate the locus-wise minor allele frequency of the species def _calc_maf(spp): #get two times the pop size two_N = 2*len(spp) #get the speciome speciome = spp._get_genotypes() #get the frequencies of 1-alleles for all loci freqs_1 = np.sum(np.sum(speciome, axis = 2), axis = 0)/two_N #find all loci where the 1-allele is the major allele majors = np.where(freqs_1 > 0.5) #replace the locations where 1 is the major allele with 0-allele freq maf = freqs_1[:] maf[majors] = 1 - freqs_1[majors] return(maf) #function to calculate the mean fitness of the species def _calc_mean_fitness(spp): #calculate the mean fitness, if this species has traits if spp.gen_arch.traits is not None: mean_fit = np.mean(_calc_fitness(spp)) #or else return NaN else: mean_fit = np.nan return(mean_fit)
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2.004317
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from eve import Eve from .db_domains import db_domains import os def isInDocker(): return os.environ.get('AM_I_IN_A_DOCKER_CONTAINER', False) SETTINGS = { 'DOMAIN': db_domains, 'MONGO_HOST': 'localhost', 'MONGO_PORT': 27017, # MONGO_USERNAME': os.environ.get(...), # MONGO_PASSWORD': os.environ.get(...), 'MONGO_DBNAME': 'quantz', 'RENDERERS': [ 'eve.render.JSONRenderer' # 'eve.render.XMLRenderer' ], 'ALLOW_UNKNOWN': True, # 'X_DOMAINS_RE': r'.*', 'X_DOMAINS': [r'*.zhangyuzheng.com'], 'IF_MATCH': False, 'ENFORCE_IF_MATCH': False, 'HATEOAS': False, # 修改数据域名称,从 _items 改为 items,避免前端语法检查严格不能使用_开头的变量 # 'ITEMS': 'items', # 'META': 'meta', # 'DATE_CREATED': 'created', # 'ID_FIELD': 'id', # FIXME: not working, Y? # 'LAST_UPDATED': 'updated', # 'ETAG': 'etag', 'PAGINATION_DEFAULT': 10000, 'PAGINATION_LIMIT': 99999999, # 'OPTIMIZE_PAGINATION_FOR_SPEED': True, 'RESOURCE_METHODS': ['GET'], 'ITEM_METHODS': ['GET'] } def exclude_fields(resource, response): excluded_fields = ['_id', '_created', '_updated', '_etag'] for doc in response['_items']: for field in excluded_fields: # Better ask forgiveness than permission try: del doc[field] except KeyError as e: pass def on_fetched_resource(resource_name, response): print('on_fetched_resource:%s' % resource_name) exclude_fields(resource_name, response) app = Eve(settings=SETTINGS) app.on_fetched_resource += on_fetched_resource if __name__ == '__main__': app.run(host='0.0.0.0', port=80)
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2.021739
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import datetime as dt import calendar import time def parse_timedelta(s): valid_units = [ "weeks", "days", "hours", "seconds", "minutes", "miliseconds", "microseconds", ] try: if s == "0": return dt.timedelta() value, unit = s.split(" ") if unit[-1] != "s": unit += "s" value = float(value) delta = dt.timedelta(**{unit: value}) return delta except: raise ValueError( "Could not parse '{}'. Timedelta format is '<number> <unit> | 0', where `unit` is one of {} (tailing 's' is optional).".format( s, ", ".join(valid_units) ) ) def parse_datetime(s): try: date = dt.datetime.strptime(s, "%Y-%m-%d") except: try: date = dt.datetime.strptime(s, "%Y-%m-%d %H:%M:%S") except: raise ValueError( "Could not parse '{}'. Time format is '%Y-%m-%d' or '%Y-%m-%d %H:%M:%S'.".format( s ) ) return date def month_last_day(date): """Return the last date of the month for the month containing date.""" _, last_day = calendar.monthrange(date.year, date.month) return dt.datetime(date.year, date.month, last_day) def month_first_day(date): """Return the first date of the month (always 01) for the month containing date.""" return dt.datetime(date.year, date.month, 1) def iso_date(date): return date.strftime("%Y-%m-%d") def normalize_datetime(date): return dt.datetime.fromtimestamp(time.mktime(date.timetuple()))
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2.012092
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# https://leetcode.com/problems/remove-all-adjacent-duplicates-in-string def remove_duplicates(S): if len(S) <= 1: return S start, end = 0, 1 while end < len(S): if S[start] != S[end]: start = end end = start + 1 elif S[start] == S[end] and end + 1 == len(S): S = S[0:start] elif S[start] == S[end]: S = S[0:start] + S[end+1:] start, end = 0, 1 return S def remove_duplicates(S): stack = [] for char in S: if len(stack) and stack[-1] == char: stack.pop() else: stack.append(char) return ''.join(stack)
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1.913295
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#-*- coding: utf-8 -*- from py3wirecard.entities.lib.wireentity import * from py3wirecard.entities.taxdocument import TaxDocument class AcquirerDetails(WireEntity):
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2.964286
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chinese_strings = [ '京', '津', '冀', '晋', '蒙', '辽', '吉', '黑', '沪', '苏', '浙', '皖', '闽', '赣', '鲁', '豫', '鄂', '湘', '粤', '桂', '琼', '渝', '川', '贵', '云', '藏', '陕', '甘', '青', '宁', '新', '港', '澳', '台', '警', 'WJ'] # 36 chinese = [ 'Beijing', 'Tianjin', 'Hebei', 'Shanxi', 'InnerMongolia', 'Liaoning', 'Jilin', 'Heilongjiang', 'Shanghai', 'Jiangsu', 'Zhejiang', 'Anhui', 'Fujian', 'Jiangxi', 'Shandong', 'Henan', 'Hubei', 'Hunan', 'Guangdong', 'Guangxi', 'Hainan', 'Chongqing', 'Sichuan', 'Guizhou', 'Yunnan', 'Xizang', 'Shaanxi', 'Gansu', 'Qinghai', 'Ningxia', 'Xinjiang', 'HongKong', 'Macau', 'Tibet', 'police', 'WJ'] # 26 alphabet = [ 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'I', 'O'] # 10 number = [ '0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] blank = ['-'] CHARS = blank + chinese + alphabet + number SHOW_CHARS = blank + chinese_strings + alphabet + number
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# Generated by Django 3.1.7 on 2021-04-05 14:35 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Customer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('address', models.CharField(max_length=50)), ('active', models.BooleanField(default=True)), ('doc_num', models.CharField(blank=True, max_length=12, null=True, unique=True)), ], ), migrations.CreateModel( name='DataSheet', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('description', models.CharField(max_length=50)), ('historical_data', models.TextField()), ], ), migrations.CreateModel( name='Profession', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('description', models.CharField(max_length=50)), ], ), migrations.CreateModel( name='Document', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('dtype', models.CharField(choices=[('PP', 'Passport'), ('ID', 'Identity card'), ('OT', 'Others')], max_length=2)), ('doc_number', models.CharField(max_length=50)), ('customer', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.customer')), ], ), migrations.AddField( model_name='customer', name='datasheet', field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.datasheet'), ), migrations.AddField( model_name='customer', name='profession', field=models.ManyToManyField(to='core.Profession'), ), ]
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"""Main definitions for Virtool Workflows.""" from typing import Any, Callable, Coroutine, Iterable, Optional, Sequence from virtool_workflow.utils import coerce_to_coroutine_function from fixtures import FixtureScope WorkflowStep = Callable[..., Coroutine[Any, Any, None]] class Workflow: """ A Workflow is a step-wise, long-running operation. A workflow is comprised of: 1. a set of functions to be executed on startup (.on_startup) 2. a set of step functions which will be executed in order (.steps) 3. a set of functions to be executed once all steps are completed (.on_cleanup) """ on_startup: Sequence[WorkflowStep] on_cleanup: Sequence[WorkflowStep] steps: Sequence[WorkflowStep] def __new__( cls, *args, startup: Optional[Iterable[WorkflowStep]] = None, cleanup: Optional[Iterable[WorkflowStep]] = None, steps: Optional[Iterable[WorkflowStep]] = None, **kwargs ): """ :param startup: An initial set of startup steps. :param cleanup: An initial set of cleanup steps. :param steps: An initial set of steps. """ obj = super().__new__(cls) obj.on_startup = [] obj.on_cleanup = [] obj.steps = [] if startup: obj.on_startup.extend(startup) if cleanup: obj.on_cleanup.extend(cleanup) if steps: obj.steps.extend(steps) return obj def startup(self, action: Callable) -> Callable: """Decorator for adding a step to workflow startup.""" self.on_startup.append(coerce_to_coroutine_function(action)) return action def cleanup(self, action: Callable) -> Callable: """Decorator for adding a step to workflow cleanup.""" self.on_cleanup.append(coerce_to_coroutine_function(action)) return action def step(self, step: Callable) -> Callable: """Decorator for adding a step to the workflow.""" self.steps.append(coerce_to_coroutine_function(step)) return step def merge(self, *workflows: "Workflow"): """Merge steps from other workflows into this workflow.""" self.steps.extend(step for w in workflows for step in w.steps) self.on_startup.extend( step for w in workflows for step in w.on_startup) self.on_cleanup.extend( step for w in workflows for step in w.on_cleanup) return self
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#coding:utf-8 import threading import time import pytest from .connect import DataClient, DataBase from ..psrc import ConnPool from concurrent.futures import ProcessPoolExecutor,ThreadPoolExecutor,as_completed app = None DATABASECONFIG = { "test":{ "host": "localhost", "port": 3306, "username": "root", "password": "", "schema" : "test" } } class JJApp: def __init__(self): self.obj_pool = {} self.init_mysql_pool() def init_mysql_pool(self): debug = False for key in DATABASECONFIG: mysql_pool = ConnPool() mysql_pool.add_obj(DataBase, DATABASECONFIG[key], debug) self.obj_pool.setdefault(key, mysql_pool) def __getattr__(self, name): obj = None if name in DATABASECONFIG: pool = self.obj_pool[name] obj = pool.get_obj() if not obj: time.sleep(10) obj = pool.get_obj() return obj def release(self, name): if name in self.obj_pool: pool = self.obj_pool[name] pool.release_obj() def print_func(lock): global app sql = u""" select * from test limit 10; """ data = app.test.query(sql) if lock.acquire(): for item in data: print(item['name']) lock.release() app.release("test") time.sleep(20) def test_pool(): global app app = JJApp() lock = threading.Lock() with ThreadPoolExecutor(3) as executor: for _ in range(5): executor.submit(print_func, lock)
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2.03607
804
from . import parseutil
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4
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# package org.apache.helix #from org.apache.helix import * #from java.lang.reflect import Constructor #from java.util import ArrayList #from java.util import Collection #from java.util import Collections #from java.util import HashMap #from java.util import List #from java.util import Map from org.apache.helix.util.misc import enum from org.apache.helix.ZNRecord import ZNRecord import traceback HelixPropertyAttribute = enum('BUCKET_SIZE', 'GROUP_MESSAGE_MODE') class HelixProperty(object): def __init__(self, *args): self._record = ZNRecord(*args) # """ # # Parameters: # String id # """ # def __init__(self, id): # self._record = ZNRecord(id) # # # """ # # Parameters: # ZNRecord record # """ # def __init__(self, record): # self._record = ZNRecord(record) def getId(self): """ Returns String Java modifiers: final """ return self._record.getId() def getRecord(self): """ Returns ZNRecord Java modifiers: final """ return self._record def setDeltaList(self, deltaList): """ Returns void Parameters: deltaList: List<ZNRecordDelta> Java modifiers: final """ self._record.setDeltaList(deltaList) def toString(self): """ Returns String @Override """ return self._record.toString() def getBucketSize(self): """ Returns int """ # String bucketSizeStr = self._record.getSimpleField('BUCKET_SIZE') # int bucketSize = 0 if bucketSizeStr != None: try: bucketSize = int(bucketSizeStr) except ValueError, e: pass return bucketSize def setBucketSize(self, bucketSize): """ Returns void Parameters: bucketSize: int """ if bucketSize <= 0: bucketSize = 0 self._record.setSimpleField('BUCKET_SIZE', "" + str(bucketSize)) def setGroupMessageMode(self, enable): """ Returns void Parameters: enable: boolean """ self._record.setSimpleField('GROUP_MESSAGE_MODE', "" + str(enable)) def getGroupMessageMode(self): """ Returns boolean """ # String enableStr = self._record.getSimpleField('GROUP_MESSAGE_MODE') if enableStr == None: return False try: groupMode = eval(enableStr.lower().capitalize()) except: return False if not groupMode: return False return groupMode def isValid(self): """ Returns boolean """ return False def __eq__(self, obj): """ Returns boolean Parameters: obj: Object @Override """ if obj == None: return False if type(obj) == HelixProperty: # HelixProperty that = obj if that.getRecord() != None: return (that.getRecord() == self.getRecord()) return False
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# Copyright (c) 2019 fortiss GmbH # # This software is released under the MIT License. # https://opensource.org/licenses/MIT import unittest import matplotlib.pyplot as plt from bark.world.agent import * from bark.models.behavior import * from bark.world import * from bark.geometry import * from bark.models.dynamic import * from bark.models.execution import * from bark.geometry import * from bark.geometry.standard_shapes import * from modules.runtime.commons.parameters import ParameterServer from bark.world.opendrive import * from bark.world.map import * from modules.runtime.commons.xodr_parser import XodrParser class ImporterTests(unittest.TestCase): def test_python_map(self): pass # xodr_parser = XodrParser("modules/runtime/tests/data/Crossing8Course.xodr") # xodr_parser.print_python_map() def test_map(self): xodr_parser = XodrParser("modules/runtime/tests/data/city_highway_straight.xodr") # xodr_parser = XodrParser("modules/runtime/tests/data/CulDeSac.xodr") params = ParameterServer() world = World(params) map_interface = MapInterface() map_interface.set_open_drive_map(xodr_parser.map) map_interface.set_roadgraph(xodr_parser.roadgraph) world.set_map(map_interface) for _, road in xodr_parser.map.get_roads().items(): for lane_section in road.lane_sections: for _, lane in lane_section.get_lanes().items(): line_np = lane.line.toArray() plt.text(line_np[-1, 0], line_np[-1, 1], 'center_{i}_{j}'.format(i=lane.lane_id,j=lane.lane_position)) plt.plot( line_np[:, 0], line_np[:, 1], color="grey", alpha=1.0) plt.axis("equal") plt.show() # driving corridor calculation test #lanes = map_interface.find_nearest_lanes(Point2d(-11,-8),1) #left_line, right_line, center_line = map_interface.calculate_driving_corridor(lanes[0].lane_id,2) #plt.plot(center_line.toArray()[:,0],center_line.toArray()[:,1]) #plt.show() # TODO: plot cpp map #cwd = os.getcwd() #print (cwd) roadgraph = xodr_parser.roadgraph roadgraph.print_graph("/home/bernhard/"+"test1234.dot") if __name__ == '__main__': unittest.main()
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2.180269
1,115
if sm.hasQuest(1666): sm.warpInstanceIn(931050429) sm.createClock(6*60) sm.invokeAfterDelay(6*60*1000, "warpInstanceOut", 230040410, 0) else: map = 230040420 portal = 2 sm.warp(map, portal)
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2.087379
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from typing import List, Union from pysbr.queries.query import Query import pysbr.utils as utils class MarketsByMarketIds(Query): """Get information about a number of leagues from their league ids. Market name, description, and market type id are included in the response. Args: market_ids: SBR market id or list of market ids. sport_id: SBR sport id. """
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3.023077
130
from py_uci import UCIEngine e = UCIEngine() e.new_game() e.set_position(moves=["e2e4"]) e.find_best_move()
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2.204082
49
name = "branch_bound" if __name__ == "__main__": print("branch bound installed!")
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2.636364
33
# coding:utf-8 # tensorflow version1.13.1 import tensorflow as tf saver = tf.train.import_meta_graph('models/model.ckpt-667589.meta', clear_devices=True) with tf.Session() as sess: chpt_state = tf.train.get_checkpoint_state('models/model.ckpt-667589') # if chpt_state: # last_model = chpt_state.model_checkpoint_path last_model = "models/model.ckpt-667589" saver.restore(sess,last_model) print ("model was loaded",last_model) # else: # print ("model cannot loaded") # exit(1) graph = tf.get_default_graph() graph_def = graph.as_graph_def() x = graph.get_tensor_by_name('x:0') out = graph.get_tensor_by_name('reduce/out:0') tf.saved_model.simple_save(sess, './models', inputs={"x": x}, outputs={"reduce/out": out})
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2.491694
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'''Running subprocesses asynchronously.''' from __future__ import print_function import fcntl import os import os.path import subprocess import functools import datetime import pty import signal import atexit import tornado.ioloop from tornado.ioloop import IOLoop, PeriodicCallback import tornado.process from seesaw.event import Event from seesaw.task import Task from seesaw.config import realize import time _all_procs = set() class AsyncPopen(object): '''Asynchronous version of :class:`subprocess.Popen`. Deprecated. ''' def __init__(self, *args, **kwargs): self.args = args self.kwargs = kwargs self.ioloop = None self.master_fd = None self.master = None self.pipe = None self.stdin = None self.on_output = Event() self.on_end = Event() def run(self): self.ioloop = IOLoop.instance() (master_fd, slave_fd) = pty.openpty() # make stdout, stderr non-blocking fcntl.fcntl(master_fd, fcntl.F_SETFL, fcntl.fcntl(master_fd, fcntl.F_GETFL) | os.O_NONBLOCK) self.master_fd = master_fd self.master = os.fdopen(master_fd) # listen to stdout, stderr self.ioloop.add_handler(master_fd, self._handle_subprocess_stdout, self.ioloop.READ) slave = os.fdopen(slave_fd) self.kwargs["stdout"] = slave self.kwargs["stderr"] = slave self.kwargs["close_fds"] = True self.kwargs["preexec_fn"] = self.ignore_sigint self.pipe = subprocess.Popen(*self.args, **self.kwargs) self.stdin = self.pipe.stdin # check for process exit self.wait_callback = PeriodicCallback(self._wait_for_end, 250) self.wait_callback.start() _all_procs.add(self.pipe) def _handle_subprocess_stdout(self, fd, events): if not self.master.closed and (events & IOLoop._EPOLLIN) != 0: data = self.master.read() self.on_output(data) self._wait_for_end(events) def _wait_for_end(self, events=0): self.pipe.poll() if self.pipe.returncode is not None or \ (events & tornado.ioloop.IOLoop._EPOLLHUP) > 0: self.wait_callback.stop() self.master.close() self.ioloop.remove_handler(self.master_fd) self.on_end(self.pipe.returncode) _all_procs.remove(self.pipe) class AsyncPopen2(object): '''Adapter for the legacy AsyncPopen''' def __init__(self, *args, **kwargs): self.args = args self.kwargs = kwargs self.on_output = Event() self.on_end = Event() self.pipe = None def run(self): self.kwargs["stdout"] = tornado.process.Subprocess.STREAM self.kwargs["stderr"] = tornado.process.Subprocess.STREAM self.kwargs["preexec_fn"] = AsyncPopen.ignore_sigint self.pipe = tornado.process.Subprocess(*self.args, **self.kwargs) self.pipe.stdout.read_until_close( callback=self._handle_subprocess_stdout, streaming_callback=self._handle_subprocess_stdout) self.pipe.stderr.read_until_close( callback=self._handle_subprocess_stdout, streaming_callback=self._handle_subprocess_stdout) self.pipe.set_exit_callback(self._end_callback) _all_procs.add(self.pipe) def _handle_subprocess_stdout(self, data): self.on_output(data) def _end_callback(self, return_code): self.on_end(return_code) _all_procs.remove(self.pipe) class ExternalProcess(Task): '''External subprocess runner.''' def __init__(self, name, args, max_tries=1, retry_delay=2, kill_pipeline_on_error=False, accept_on_exit_code=None, retry_on_exit_code=None, env=None): Task.__init__(self, name) self.args = args self.max_tries = max_tries self.retry_delay = retry_delay if accept_on_exit_code is not None: self.accept_on_exit_code = accept_on_exit_code else: self.accept_on_exit_code = [0] if kill_pipeline_on_error is True: self.hard_exit = True else: self.hard_exit = False self.retry_on_exit_code = retry_on_exit_code self.env = env or {} if 'PYTHONIOENCODING' not in self.env: self.env['PYTHONIOENCODING'] = 'utf8:replace' def enqueue(self, item): self.start_item(item) item.log_output("Starting %s for %s\n" % (self, item.description())) item["tries"] = 0 item["ExternalProcess.stdin_write_error"] = False item["ExternalProcess.running"] = False self.process(item) def stdin_data(self, item): return b"" def process(self, item): with self.task_cwd(): p = AsyncPopen2( args=realize(self.args, item), env=realize(self.env, item), stdin=subprocess.PIPE, close_fds=True ) p.on_output += functools.partial(self.on_subprocess_stdout, p, item) p.on_end += functools.partial(self.on_subprocess_end, item) p.run() item["ExternalProcess.running"] = True try: p.stdin.write(self.stdin_data(item)) except Exception as error: # FIXME: We need to properly propagate errors item.log_output("Error writing to process: %s" % str(error)) item["ExternalProcess.stdin_write_error"] = True p.stdin.close() def fail_item(self, item): # Don't allow the item to fail until the external process completes if item["ExternalProcess.running"]: return if self.hard_exit == True: Task.hard_fail_item(self, item) else: Task.fail_item(self, item) def on_subprocess_stdout(self, pipe, item, data): item.log_output(data, full_line=False) def on_subprocess_end(self, item, returncode): item["ExternalProcess.running"] = False if returncode in self.accept_on_exit_code and \ not item["ExternalProcess.stdin_write_error"]: self.handle_process_result(returncode, item) else: self.handle_process_error(returncode, item) def handle_process_result(self, exit_code, item): item.log_output("Finished %s for %s\n" % (self, item.description())) self.complete_item(item) def handle_process_error(self, exit_code, item): item["tries"] += 1 item.log_output( "Process %s returned exit code %d for %s\n" % (self, exit_code, item.description()) ) item.log_error(self, exit_code) retry_acceptable = self.max_tries is None or \ item["tries"] < self.max_tries exit_status_indicates_retry = self.retry_on_exit_code is None or \ exit_code in self.retry_on_exit_code or \ item["ExternalProcess.stdin_write_error"] if retry_acceptable and exit_status_indicates_retry: item.log_output( "Retrying %s for %s after %d seconds...\n" % (self, item.description(), self.retry_delay) ) IOLoop.instance().add_timeout( datetime.timedelta(seconds=self.retry_delay), functools.partial(self.process, item) ) else: item.log_output("Failed %s for %s\n" % (self, item.description())) self.fail_item(item) class WgetDownload(ExternalProcess): '''Download with Wget process runner.''' def __init__(self, args, max_tries=1, accept_on_exit_code=None, kill_pipeline_on_error=False, retry_on_exit_code=None, env=None, stdin_data_function=None): ExternalProcess.__init__( self, "WgetDownload", args=args, max_tries=max_tries, accept_on_exit_code=(accept_on_exit_code if accept_on_exit_code is not None else [0]), retry_on_exit_code=retry_on_exit_code, kill_pipeline_on_error=kill_pipeline_on_error, env=env) self.stdin_data_function = stdin_data_function def stdin_data(self, item): if self.stdin_data_function: return self.stdin_data_function(item) else: return b"" class RsyncUpload(ExternalProcess): '''Upload with Rsync process runner.''' def __init__(self, target, files, target_source_path="./", bwlimit="0", max_tries=None, extra_args=None): args = [ "rsync", "-rltv", "--timeout=300", "--contimeout=300", "--progress", "--bwlimit", bwlimit ] if extra_args is not None: args.extend(extra_args) args.extend([ "--files-from=-", target_source_path, target ]) ExternalProcess.__init__(self, "RsyncUpload", args=args, max_tries=max_tries) self.files = files self.target_source_path = target_source_path def stdin_data(self, item): return "".join( [ "%s\n" % os.path.relpath( realize(f, item), realize(self.target_source_path, item) ) for f in realize(self.files, item) ]).encode('utf-8') class CurlUpload(ExternalProcess): '''Upload with Curl process runner.''' def __init__(self, target, filename, connect_timeout="60", speed_limit="1", speed_time="900", max_tries=None): args = [ "curl", "--fail", "--output", "/dev/null", "--connect-timeout", str(connect_timeout), "--speed-limit", str(speed_limit), # minimum upload speed 1B/s # stop if speed < speed-limit for 900 seconds "--speed-time", str(speed_time), "--header", "X-Curl-Limits: inf,%s,%s" % (str(speed_limit), str(speed_time)), "--write-out", "Upload server: %{url_effective}\\n", "--location", "--upload-file", filename, target ] ExternalProcess.__init__(self, "CurlUpload", args=args, max_tries=max_tries)
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2.019359
5,269
from fastai.vision.all import * import gc import torch from typing import Callable __all__ = ['EMAAverager', 'EMACallback', 'add_ema_to_gan_learner', 'custom_save_model', 'custom_load_model', 'SaveCheckpointsCallback', 'clean_mem'] class EMAAverager(): """Callable class that calculates the EMA of a parameter. It can be used as the `avg_fn` parameter of `torch.optim.swa_utils.AveragedModel` Args: decay (float): weight of averaged value. The new value of the parameter is multiplied by 1 - decay. """ def __init__(self, decay=0.999): self.decay = decay def __call__(self, averaged_model_parameter, model_parameter, num_averaged): return self.decay * averaged_model_parameter + (1 - self.decay) * model_parameter def _default_forward_batch(model, batch, device): input = batch if isinstance(input, (list, tuple)): input = input[0] if device is not None: input = input.to(device) model(input) class FullyAveragedModel(torch.optim.swa_utils.AveragedModel): """Extension of AveragedModel that also averages the buffers. To update both the parameters and the buffers, the method `update_all` should be called instead of `update_parameters`.""" def _update_buffers(self, model): for b_swa, b_model in zip(self.module.buffers(), model.buffers()): device = b_swa.device b_model_ = b_model.detach().to(device) if self.n_averaged == 0: b_swa.detach().copy_(b_model_) else: b_swa.detach().copy_(self.avg_fn(b_swa.detach(), b_model_, self.n_averaged.to(device))) def update_all(self, model): # Buffers must be updated first, because this method relies on n_averaged, # which is updated by super().update_parameters() self._update_buffers(model) self.update_parameters(model) class EMACallback(Callback): """Updates the averaged weights of the generator of a GAN after every opt step. It's meant to be used only with a GANLearner; i.e., an instance of this callback is assumed to be attached to a GANLearner. Args: ema_model: AveragedModel that wraps the averaged generator module. orig_model: active (not averaged) generator module, the one that's included in learner.model and updated by the optimizer. dl: dataloader needed to iterate over all data and make forward passes over the ema_model in order to update the running statistic of BN layers. update_buffers: if True, not only parameters, but also buffers, of ema_model are averaged and updated, forward_batch (Callable): Method with params (model, batch, device) that chooses how to extract the input from every element of `dl`, transfers it to the proper device and finally makes a forward pass on the model (here `ema_model`). It's needed for updating the running statistics of BN layers. """ def __init__(self, ema_model:FullyAveragedModel, orig_model:nn.Module, dl, update_buffers=True, forward_batch=None): self.ema_model = ema_model self.orig_model = orig_model self.dl = dl self.update_buffers = update_buffers self.update_bn_pending = False self.forward_batch = forward_batch def after_step(self): if self.gan_trainer.gen_mode: update_method = (self.ema_model.update_all if self.update_buffers else self.ema_model.update_parameters) update_method(self.orig_model) self.update_bn_pending = True def after_fit(self): if not self.update_bn_pending: return #torch.optim.swa_utils.update_bn(self.dl, self.ema_model) _update_bn(self.dl, self.ema_model, forward_batch=self.forward_batch) self.update_bn_pending = False def add_ema_to_gan_learner(gan_learner, dblock, decay=0.999, update_bn_dl_bs=64, forward_batch=None): """"Creates and setups everything needed to update an alternative EMA generator. It stores the EMA generator in `ema_model` attribute of `gan_learner`. Args: gan_learner (GANLearner): the learner to add the EMA generator to. dblock (DataBlock): needed to create dataloaders that are independent of those of `gan_learner`, used after fit to update BN running stats of the EMA G. decay: weight that multiplies averaged parameter every update. update_bn_dl_bs: batch size used to update BN running stats. forward_batch (Callable): Method with params (model, batch, device) that chooses how to extract the input from every element of the dataloader, transfers it to the proper device and finally makes a forward pass on the ema model. It's needed for updating the running statistics of BN layers. """ generator = gan_learner.model.generator ema_avg_fn = EMAAverager(decay=decay) gan_learner.ema_model = FullyAveragedModel(generator, avg_fn=ema_avg_fn) ds_path = gan_learner.dls.path clean_dls = dblock.dataloaders(ds_path, path=ds_path, bs=update_bn_dl_bs) gan_learner.ema_model.eval().to(clean_dls.device) gan_learner.add_cb(EMACallback(gan_learner.ema_model, generator, clean_dls.train, forward_batch=forward_batch)) def custom_save_model(learner, filename, base_path='.'): """Saves the model and optimizer state of the learner. The path of the generated file is base_path/learner.model_dir/filename with ".pth" extension. If the learner has an EMA G model attached too, a similar file with the suffix "_ema" is generated too. """ if isinstance(base_path, str): base_path = Path(base_path) if not isinstance(base_path, Path): raise Exception('Invalid base_path') file = join_path_file(filename, base_path/learner.model_dir, ext='.pth') save_model(file, learner.model, learner.opt) if getattr(learner, 'ema_model', None) is not None: _save_ema_model(learner, base_path, filename) def custom_load_model(learner, filename, with_opt=True, device=None, base_path='./models', with_ema=False, **kwargs): """Loads the model and optimizer state of the learner. The file is expected to be placed in `base_path/filename` with ".pth" extension. `kwargs` are forwarded to fastai's `load_model` method. """ if isinstance(base_path, str): base_path = Path(base_path) if not isinstance(base_path, Path): raise Exception('Invalid base_path') if device is None and hasattr(learner.dls, 'device'): device = learner.dls.device if learner.opt is None: learner.create_opt() #file = join_path_file(filename, base_path/learner.model_dir, ext='.pth') file = base_path/f'{filename}.pth' load_model(file, learner.model, learner.opt, with_opt=with_opt, device=device, **kwargs) if with_ema: _load_ema_model(learner, base_path, filename) def _load_ema_model(learner, base_path, filename, device=None): ema_filename = base_path/f'{filename}_ema.pth' load_model(ema_filename, learner.ema_model, None, with_opt=False, device=device) #state_dict = torch.load(ema_filename) #learner.ema_model.load_state_dict(state_dict) def _save_ema_model(learner, base_path, filename): file = join_path_file(filename+'_ema', base_path/learner.model_dir, ext='.pth') save_model(file, learner.ema_model, None, with_opt=False) #torch.save(file, learner.ema_model.state_dict()) class SaveCheckpointsCallback(Callback): "Callback that saves the model at the end of each epoch." def __init__(self, fn_prefix, base_path=Path('.'), initial_epoch=1, save_cycle_len=1): self.fn_prefix = fn_prefix self.base_path = base_path self.epoch = initial_epoch self.save_cycle_len = save_cycle_len def after_epoch(self): if (self.epoch % self.save_cycle_len) == 0: fn = f'{self.fn_prefix}_{self.epoch}ep' custom_save_model(self.learn, fn, base_path=self.base_path) self.epoch += 1 def clean_mem(): if torch.cuda.is_available(): torch.cuda.empty_cache() gc.collect()
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2.429963
3,491
"""A set of sudoku puzzles to experiment with the spinnaker_csp package. the puzzles are containned on the dictionary puzzles, keys are the name of the puzzle and values are tuples with the puzzle as first element and solution as second element. """ puzzles={ #--------------------------------------------------------------------- 'Dream': ("dream", #--------------------------------------------------------------------- [[0 for x in range(9)] for y in range(9)], None), #--------------------------------------------------------------------- 'easy':("easy", # easy from doi:10.1038/srep00725 #--------------------------------------- [[0, 4, 0, 8, 0, 5, 2, 0, 0], [0, 2, 0, 0, 4, 0, 0, 5, 0], [5, 0, 0, 0, 0, 0, 0, 0, 4], [0, 9, 0, 0, 0, 3, 1, 2, 0], [1, 0, 6, 0, 7, 8, 0, 0, 3], [3, 7, 0, 9, 0, 4, 0, 8, 0], [0, 0, 0, 0, 0, 6, 7, 0, 0], [0, 0, 8, 3, 5, 9, 0, 1, 0], [0, 1, 9, 0, 0, 7, 6, 0, 0]], #--------------------------------------- [[9, 4, 7, 8, 3, 5, 2, 6, 1], [6, 2, 3, 7, 4, 1, 8, 5, 9], [5, 8, 1, 6, 9, 2, 3, 7, 4], [8, 9, 4, 5, 6, 3, 1, 2, 7], [1, 5, 6, 2, 7, 8, 9, 4, 3], [3, 7, 2, 9, 1, 4, 5, 8, 6], [4, 3, 5, 1, 2, 6, 7, 9, 8], [7, 6, 8, 3, 5, 9, 4, 1, 2], [2, 1, 9, 4, 8, 7, 6, 3, 5]]), #--------------------------------------------------------------------- 'hard':('hard', # hard puzzle from https://doi.org/10.1371/journal.pcbi.1003311 #--------------------------------------------------------------------- [[8, 0, 5, 0, 0, 0, 0, 3, 0], [0, 3, 0, 9, 0, 0, 0, 0, 0], [4, 0, 6, 0, 3, 0, 0, 0, 0], [6, 0, 0, 0, 1, 0, 9, 0, 0], [0, 5, 0, 3, 0, 8, 0, 7, 0], [0, 0, 9, 0, 4, 0, 0, 0, 1], [0, 0, 0, 0, 2, 0, 3, 0, 8], [0, 0, 0, 0, 0, 9, 0, 2, 0], [0, 7, 0, 0, 0, 0, 5, 0, 4]], #--------------------------------------------------------------------- [[8, 1, 5, 6, 7, 4, 2, 3, 9], [7, 3, 2, 9, 5, 1, 4, 8, 6], [4, 9, 6, 8, 3, 2, 7, 1, 5], [6, 8, 7, 2, 1, 5, 9, 4, 3], [1, 5, 4, 3, 9, 8, 6, 7, 2], [3, 2, 9, 7, 4, 6, 8, 5, 1], [9, 4, 1, 5, 2, 7, 3, 6, 8], [5, 6, 3, 4, 8, 9, 1, 2, 7], [2, 7, 8, 1, 6, 3, 5, 9, 4]]), #--------------------------------------------------------------------- 'AI_escargot': ('AI_escargot', #--------------------------------------------------------------------- [[1, 0, 0, 0, 0, 7, 0, 9, 0], [0, 3, 0, 0, 2, 0, 0, 0, 8], [0, 0, 9, 6, 0, 0, 5, 0, 0], [0, 0, 5, 3, 0, 0, 9, 0, 0], [0, 1, 0, 0, 8, 0, 0, 0, 2], [6, 0, 0, 0, 0, 4, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 1, 0], [0, 4, 0, 0, 0, 0, 0, 0, 7], [0, 0, 7, 0, 0, 0, 3, 0, 0]], #--------------------------------------------------------------------- [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]), #--------------------------------------------------------------------- 'platinum_blonde':('platinum_blonde', # hard from doi:10.1038/srep00725 #--------------------------------------------------------------------- [[0, 0, 0, 0, 0, 0, 0, 1, 2], [0, 0, 0, 0, 0, 0, 0, 0, 3], [0, 0, 2, 3, 0, 0, 4, 0, 0], [0, 0, 1, 8, 0, 0, 0, 0, 5], [0, 6, 0, 0, 7, 0, 8, 0, 0], [0, 0, 0, 0, 0, 9, 0, 0, 0], [0, 0, 8, 5, 0, 0, 0, 0, 0], [9, 0, 0, 0, 4, 0, 5, 0, 0], [4, 7, 0, 0, 0, 6, 0, 0, 0]], #--------------------------------------------------------------------- [[8, 3, 9, 4, 6, 5, 7, 1, 2], [1, 4, 6, 7, 8, 2, 9, 5, 3], [7, 5, 2, 3, 9, 1, 4, 8, 6], [3, 9, 1, 8, 2, 4, 6, 7, 5], [5, 6, 4, 1, 7, 3, 8, 2, 9], [2, 8, 7, 6, 5, 9, 3, 4, 1], [6, 2, 8, 5, 3, 7, 1, 9, 4], [9, 1, 3, 2, 4, 8, 5, 6, 7], [4, 7, 5, 9, 1, 6, 2, 3, 8]]) } #-----------------TEMPLATE--------------------------------------------- ##--------------------------------------------------------------------- # [[0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0]] #-----------------TEMPLATE 16X16---------------------------------------- # #--------------------------------------------------------------------- # [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
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2.015638
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#!/usr/bin/env python from __future__ import print_function import os print( """[busco] out_path = {0} tmp_path = {0}/tmp [tblastn] # path to tblastn path = /usr/bin/ [makeblastdb] # path to makeblastdb path = /usr/bin/ [augustus] # path to augustus path = /opt/augustus/bin/ [etraining] # path to augustus etraining path = /opt/augustus/bin/ # path to augustus perl scripts, redeclare it for each new script [gff2gbSmallDNA.pl] path = /usr/bin/ [new_species.pl] path = /usr/bin/ [optimize_augustus.pl] path = /usr/bin/ [hmmsearch] # path to HMMsearch executable path = /usr/local/bin/ [Rscript] # path to Rscript, if you wish to use the plot tool path = /usr/bin/""".format(os.environ['PWD']) )
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2.429553
291
from flask import Flask, current_app import jwt from disasterpets import db from disasterpets.Pets.models import Pets class PetImage(db.Model): __tablename__ = 'petimage' id = db.Column(db.Integer, primary_key = True, autoincrement = True) image_url =db.Column(db.String(200), nullable = False) def __init__ (self, image_url): self.image_url = image_url class PetImageJoin(db.Model): __tablename__ = 'petimagejoin' id = db.Column(db.Integer, primary_key = True, autoincrement = True) pet_id = db.Column(db.Integer, db.ForeignKey('pets.id')) pet= db.relationship("Pets", uselist=False, lazy='select') petimage_id = db.Column(db.Integer, db.ForeignKey('petimage.id')) petimage = db.relationship("PetImage", uselist=False, lazy='select') def __init__ (self, pet_id, petimage_id): self.pet_id = pet_id self.petimage_id = petimage_id
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2.594828
348
# Copyright (c) 2017-present, Facebook, Inc. # All rights reserved. # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. An additional grant # of patent rights can be found in the PATENTS file in the same directory. """ Utilities for downloading and building data. These can be replaced if your particular file system does not support them. """ import datetime import os import requests import shutil import wget def built(path): """Checks if '.built' flag has been set for that task.""" return os.path.isfile(os.path.join(path, '.built')) def download(path, url, redownload=True): """Downloads file using `wget`. If redownload is set to false, then will not download tar file again if it is present (default true). """ if redownload or not os.path.isfile(path): filename = wget.download(url, out=path) print() # wget prints download status, without newline def download_request(url, path, fname): """Downloads file using `requests`.""" with requests.Session() as session: response = session.get(url, stream=True) CHUNK_SIZE = 32768 with open(os.path.join(path, fname), 'wb') as f: for chunk in response.iter_content(CHUNK_SIZE): if chunk: # filter out keep-alive new chunks f.write(chunk) response.close() def make_dir(path): """Makes the directory and any nonexistent parent directories.""" os.makedirs(path, exist_ok=True) def mark_done(path): """Marks the path as done by adding a '.built' file with the current timestamp. """ with open(os.path.join(path, '.built'), 'w') as write: write.write(str(datetime.datetime.today())) def move(path1, path2): """Renames the given file.""" shutil.move(path1, path2) def remove_dir(path): """Removes the given directory, if it exists.""" shutil.rmtree(path, ignore_errors=True) def untar(path, fname, deleteTar=True): """Unpacks the given archive file to the same directory, then (by default) deletes the archive file. """ print('unpacking ' + fname) fullpath = os.path.join(path, fname) shutil.unpack_archive(fullpath, path) if deleteTar: os.remove(fullpath) def _get_confirm_token(response): for key, value in response.cookies.items(): if key.startswith('download_warning'): return value return None def download_from_google_drive(gd_id, destination): """Uses the requests package to download a file from Google Drive.""" URL = 'https://docs.google.com/uc?export=download' with requests.Session() as session: response = session.get(URL, params={'id': gd_id}, stream=True) token = _get_confirm_token(response) if token: response.close() params = {'id': gd_id, 'confirm': token} response = session.get(URL, params=params, stream=True) CHUNK_SIZE = 32768 with open(destination, 'wb') as f: for chunk in response.iter_content(CHUNK_SIZE): if chunk: # filter out keep-alive new chunks f.write(chunk) response.close()
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from datetime import datetime import pytz import firebase_admin from firebase_admin import credentials from firebase_admin import firestore def setup_firebase(service_account_path): cred = credentials.Certificate(service_account_path) firebase_admin.initialize_app(cred) db = firestore.client() return db def upload_to_firebase(db, pi_id, temperature, humidity): now = datetime.utcnow().replace(tzinfo=pytz.utc) firebase_id = str(now) print(firebase_id + " :: temperature= "+ str(temperature), flush=True) doc_ref = db.collection(pi_id).document(firebase_id) doc_ref.set({ 'pi_id': pi_id, 'datetime': now, 'temperature': temperature, 'humidity': humidity })
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#!/usr/bin/env python # -*- coding: utf-8 -*- from bacpypes.iocb import IOCB from bacpypes.pdu import Address, GlobalBroadcast from bacpypes.apdu import WhoIsRequest, ReadPropertyRequest, ReadPropertyACK from bacpypes.object import get_object_class, get_datatype from bacpypes.object import ObjectType, registered_object_types from bacpypes.basetypes import PropertyIdentifier from eyed.driver.bacnet import definition # # BACnet Client # class BACnetClient: # # BACnetClient 初期化処理 # def __init__(self, application, auto_device_discovery = True): # # アプリケーションの取得 # self.application = application # # デバイス の 探索を自動で実行するか? # self.auto_device_discovery = auto_device_discovery # # getAddressByDeviceID # def getAddressByDeviceID(self, device_id): # # デバイスマップの返却 # device_map = self.application.getDeviceMap() if device_id in device_map: return device_map[device_id] return None # # WhoIsRequest # def WhoIsRequest(self, low_limit = 1, high_limit = 65535): # # WhoIsRequest の レスポンス(IAmRequest) を保存するキューをクリア # #self.application.clear() # # WhoIsRequest の 送信 # self.application.who_is(low_limit, high_limit, GlobalBroadcast()) return True # # IamRequest の 受信待ち # - 例外: Empty (タイムアウト時) # def receiveIamRequest(self, timeout): # # タイムアウト秒の間受信待ち # device_queue = self.application.getDeviceQueue() device_id = device_queue.get(timeout = timeout) return { 'device_id' : device_id } # # ReadProperty # def _ReadPropertyRequest(self, device_id, objectIdentifier, propertyIdentifier): # # デバイスID から IPの取得 # address = self.getAddressByDeviceID(device_id) if not address: # # デバイスの探索オプションの確認 # if self.auto_device_discovery == False: return None # # デバイスの探索 # self.WhoIsRequest() # # リクエスト作成 # request = ReadPropertyRequest( destination = address, objectIdentifier = objectIdentifier, propertyIdentifier = propertyIdentifier, ) # # リクエストを送信 & 結果取得待ち # iocb = IOCB(request) self.application.request_io(iocb) iocb.wait() # # エラーがあるかを確認 # if iocb.ioError: return None # # レスポンスの確認 # elif iocb.ioResponse: # # レスポンスデータの取得 # apdu = iocb.ioResponse # # ACKであるかの確認 # if not isinstance(apdu, ReadPropertyACK): print 'ACK is not contain...' return None # # データタイプの取得 # datatype = get_datatype(apdu.objectIdentifier[0], apdu.propertyIdentifier) if not datatype: print 'Unknown datatype...' return None # # データ種別と値の取得 # return apdu, datatype # # 例外 # else: print 'Response seems something wrong...' return None # # ReadProperty # def ReadPropertyRequest(self, device_id, object_id, instance_id, property_id): # # リクエストの作成 # result = BACnetClient._ReadPropertyRequest( self, device_id = device_id, objectIdentifier = (object_id, instance_id), propertyIdentifier = property_id ) # # レスポンスの確認 # if result == None: return None # # キャスト # apdu, datatype = result return apdu.propertyValue.cast_out(datatype) # # ReadDeviceProperty (デバイス関連の情報読み出し) # def _ReadDevicePropertyRequest(self, device_id, propertyIdentifier): # # リクエストの作成 # result = BACnetClient._ReadPropertyRequest( self, device_id = device_id, objectIdentifier = ('device', device_id), propertyIdentifier = propertyIdentifier ) # # レスポンスの確認 # if result == None: return None # # キャスト # apdu, datatype = result return apdu.propertyValue.cast_out(datatype) # # addObject (オブジェクト の 登録) # def addObject(self, name, object_id, instance_id): # # オブジェクト識別子の取得 # objectIdentifier = self.getObjectIdentifier(object_id, instance_id) if objectIdentifier == None: return False # # オブジェクトクラス の 取得 # Object = definition.findObjectClassByType(objectIdentifier[0]) # # オブジェクト の 定義 # new_object = Object( objectName = name, objectIdentifier = objectIdentifier, ) # # オブジェクト の 登録 # self.application.add_object(new_object) return True # # addProperty (プロパティ の 登録) # def addProperty(self, name, property_instance): # # オブジェクトを名前から検索 # obj = self.application.get_object_name(name) if obj == None: return False # # プロパティの登録 # obj.add_property(property_instance) return True # # getProperty (プロパティ の 登録) # def getProperty(self, name, property_name): obj = self.getObjectByName(name) return obj._properties.get(property_name) # # getObjectByID (オブジェクト の 取得) # def getObjectIdentifier(self, object_id, instance_id): # # オブジェクト識別子の作成 # obj_type = definition.findObjectByID(object_id) if obj_type == None: return None objectType = obj_type['name'] # # オブジェクト識別子の作成 # return (objectType, instance_id) # # getObjectByID (オブジェクト の 取得 [ID 検索]) # def getObjectByID(self, objectIdentifier, instance_id): # # 登録されているオブジェクトの検索 # return self.application.get_object_id((objectIdentifier, instance_id)) # # getObjectByName (オブジェクト の 取得 [名前で検索]) # def getObjectByName(self, name): # # オブジェクトを名前から検索 # return self.application.get_object_name(name)
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1.956127
2,644
#!/usr/bin/env python # -*- coding: utf-8 -*- from bson.objectid import ObjectId from pymongo import MongoClient from validate_email import validate_email from views.base import base import config import hashlib ''' forms constructor. * Es necesario crear una variable tipo dict() que debe llevar la siguiente estructura. { 'config(requerido)':{ 'method(requerido)': 'valores POST o GET', 'action(requerido)': 'url para enviar la data', 'class' : 'Clases de css', 'error-class': 'Clase para el error' }, fields(requerido): [ { 'name(requerido)': 'nombre del campo', 'widget(requerido)': 'Tipo de input', 'class': 'Clases de css', 'id': 'Valor del ID', 'label'(*Requiere que el ID del campo este seteado.): { 'attributes': 'Cualquier otro valor que no este disponible. ejemplo: data-*= "" ', 'class': 'Clases de css' } 'placeholder': 'Valor del placeholder', 'required': 'Valores True o False', 'value': 'valor default del campo.' } ] } ''' class users(): def form(self): _form = { 'config' : { 'method': 'POST', 'action' : '/admin/users', 'class' : 'form-horizontal', 'error-class' : '' }, 'fields': [ { 'required':True, 'widget':'text', 'attributes': { 'class': 'form-control floating-label', 'data-hint':'Por favor escriba el usuario que usara para ingresar', 'name': 'username', 'placeholder': 'Username' }, 'form-group-class': 'col-md-12', }, { 'required':True, 'widget':'text', 'attributes': { 'class': 'form-control floating-label', 'data-hint':'Escriba el nombre del usuario', 'name': 'first_name', 'placeholder': 'Nombre' }, 'form-group-class': 'col-md-12', }, { 'required':True, 'widget':'text', 'attributes':{ 'class': 'form-control floating-label', 'data-hint':'Escriba el apellido del usuario', 'name': 'last_name', 'placeholder': 'Last Name' }, 'form-group-class': 'col-md-12', }, { 'required':True, 'widget':'email', 'attributes':{ 'class': 'form-control floating-label', 'data-hint':'Escriba el correo electronico del Usuario', 'name': 'email', 'placeholder': 'Email' }, 'form-group-class': 'col-md-12', }, { 'required':True, 'widget':'select', 'attributes':{ 'name': 'rol', 'class': 'form-control', 'placeholder' : 'Seleccione un Rol de Usuario', }, 'label_class':'col-lg-1 control-label', 'form-group-class': 'col-md-12', 'options': list() }, { 'required':True, 'widget':'password', 'attributes': { 'data-hint':"Escriba la contraseña para el usuario", 'name': 'password', 'placeholder': 'Password', 'class': 'form-control floating-label', }, 'form-group-class': 'col-md-12', }, { 'required':True, 'widget':'password', 'attributes': { 'data-hint':'Confirme la contraseña del usuario', 'class': 'form-control floating-label', 'placeholder': 'Confirm Password', 'name': 'password_confirm', }, 'form-group-class': 'col-md-12', }, { 'widget':'submit', 'attributes':{ 'name': 'submit', 'class': 'btn btn-primary', 'value': 'Crear nuevo Usuario' }, 'form-group-class': 'col-md-6' }, { 'widget':'reset', 'attributes':{ 'name': 'submit', 'class': 'btn btn-default', 'value': 'Limpiar formulario' }, 'form-group-class': 'col-md-6' } ] } rols = self.db.rols.find() for rol in rols: data ={ 'name':rol['name'] } _form['fields'][4]['options'].append(data) return _form def form_edit(self,id): user = self.db.users.find_one({'_id':ObjectId(id)}) _form = { 'config' : { 'method': 'POST', 'action' : '/admin/users/edit/'+id, 'class' : 'form-horizontal', 'error-class' : '' }, 'fields': [ { 'required':True, 'widget':'text', 'attributes': { 'class': 'form-control floating-label', 'data-hint':'Por favor escriba el usuario que usara para ingresar', 'name': 'username', 'placeholder': 'Username', 'value' : user['username'] }, 'form-group-class': 'col-md-12', }, { 'required':True, 'widget':'text', 'attributes': { 'class': 'form-control floating-label', 'data-hint':'Escriba el nombre del usuario', 'name': 'first_name', 'placeholder': 'Nombre', 'value' : user['first_name'] }, 'form-group-class': 'col-md-12', }, { 'required':True, 'widget':'text', 'attributes':{ 'class': 'form-control floating-label', 'data-hint':'Escriba el apellido del usuario', 'name': 'last_name', 'placeholder': 'Last Name', 'value': user['last_name'] }, 'form-group-class': 'col-md-12', }, { 'required':True, 'widget':'email', 'attributes':{ 'class': 'form-control floating-label', 'data-hint':'Escriba el correo electronico del Usuario', 'name': 'email', 'placeholder': 'Email', 'value': user['email'] }, 'form-group-class': 'col-md-12', }, { 'required':True, 'widget':'select', 'attributes':{ 'name': 'rol', 'class': 'form-control', 'placeholder' : 'Seleccione un Rol de Usuario', }, 'label_class':'col-lg-1 control-label', 'form-group-class': 'col-md-12', 'options': list() }, { 'required':True, 'widget':'password', 'attributes': { 'data-hint':"Escriba la contraseña para el usuario", 'name': 'password', 'placeholder': 'Password', 'class': 'form-control floating-label', }, 'form-group-class': 'col-md-12', }, { 'required':True, 'widget':'password', 'attributes': { 'data-hint':'Confirme la contraseña del usuario', 'class': 'form-control floating-label', 'placeholder': 'Confirm Password', 'name': 'password_confirm', }, 'form-group-class': 'col-md-12', }, { 'widget':'hidden', 'attributes': { 'value': id, 'name':'id' } }, { 'widget':'submit', 'attributes':{ 'name': 'submit', 'class': 'btn btn-primary', 'value': 'Crear nuevo Usuario' }, 'form-group-class': 'col-md-6' }, { 'widget':'reset', 'attributes':{ 'name': 'submit', 'class': 'btn btn-default', 'value': 'Limpiar formulario' }, 'form-group-class': 'col-md-6' } ] } rols = self.db.rols.find() for rol in rols: data ={ 'name':rol['name'], 'selected': False } if user['rol'] == rol['name']: print user['rol'] print rol['name'] data['selected'] = True _form['fields'][4]['options'].append(data) return _form def validation(self,data,edit=False): form = self.form() validation = {'status':True, 'errors': list() } if 'username' in data: user = self.db.users.find_one({'username': data['username']}) if len(data['username']) < 3: validation['status'] = False validation['errors'].append('El campo nombre debe poseer al menos 3 caracteres.') if user != None: if edit == False: validation['status'] = False validation['errors'].append('El nombre de usuario ya existe.') else: if data['id'] != str(user['_id']): validation['status'] = False validation['errors'].append('El nombre de usuario ya existe.') else: validation['status'] = False validation['errors'].append('El campo nombre es Obligatorio.') if 'first_name' in data: if len(data['first_name']) < 3: validation['status'] = False validation['errors'].append({'field':'first_name','value':'El campo nombre debe poseer al menos 3 caracteres.'}) else: validation['status'] = False validation['errors'].append('El campo nombre es Obligatorio.') if 'last_name' in data: if len(data['last_name']) < 3: validation['status'] = False validation['errors'].append('El campo Apellido debe poseer al menos 3 caracteres.') else: validation['status'] = False validation['errors'].append('El campo Apellido es Obligatorio.') if 'email' in data: if validate_email(data['email']) == False: validation['status'] = False validation['errors'].append('Inserte un email valido.') else: if edit == False: if self.db.users.find_one({'email':data['email']}) != None: validation['status'] = False validation['errors'].append('Ya existe un usuario con este email.') else: email = self.db.users.find_one({'email':data['email']}) print data['id'] print str(email['_id']) if email != None and data['id'] != str(email['_id']): validation['status'] = False validation['errors'].append('Otro usuario ya tiene este email.') else: validation['status'] = False validation['errors'].append('El campo Email es Obligatorio.') if 'rol' in data: rols = self.db.rols.find_one({'name':data['rol']}) if rols == None: if self.db.users.find().count() <= 0: if data['rol'] != 'admin': validation['status'] = False validation['errors'].append('El Primer usuario debe ser Admin') else: validation['status'] = False validation['errors'].append('Seleccione un rol valido') password = False if len(data['password']) > 0: password = True if len(data['password']) < 4: validation['status'] = False validation['errors'].append('La Contraseña debe tener al menos 4 Caracteres') password = False if password == True: if data['password_confirm'] != data['password']: validation['status'] = False validation['errors'].append('Las Contraseñas no coinciden') if validation['status'] == True: if edit == False: if self.db.users.find().count() <= 0: self.insert(data,admin=True) else: self.insert(data) return 'Nuevo usuario '+data['username']+' Creado' else: return self.edit(data) else: return validation def insert(self,data,admin=False): _INSERT = { 'username': data['username'].lower(), 'first_name': data['first_name'], 'last_name': data['last_name'], 'email': data['email'], 'password': hashlib.md5(data['password']).hexdigest(), 'rol' : data['rol'], 'status' : True } if admin == True: _INSERT['block'] = True self.db.users.insert(_INSERT) def edit(self, data): old_data = self.db.users.find_one({'_id':ObjectId(data['id'])}) new_data = { 'username': data['username'].lower(), 'first_name': data['first_name'], 'last_name': data['last_name'], 'email': data['email'], 'password': hashlib.md5(data['password']).hexdigest(), 'rol' : data['rol'], 'status' : old_data['status'] } if new_data['rol'] == 'admin': new_data['block'] = True self.db.users.update(old_data,new_data) return 'Usuario '+old_data['first_name'] + ' ' + old_data['last_name'] +' editado correctamente.'
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2.102197
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import traceback from twisted.internet import reactor def stack(): print("The Python Stack.") traceback.print_stack() reactor.callWhenRunning(stack) reactor.run()
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3.240741
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# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import logging from ralph.util import plugin from ralph.util.api_scrooge import get_blade_servers from ralph_scrooge.models import ( AssetInfo, DailyAssetInfo, DailyUsage, UsageType, ) logger = logging.getLogger(__name__) class AssetInfoNotFoundError(Exception): pass class DailyAssetInfoNotFoundError(Exception): pass def update_usage(daily_asset_info, date, value, usage_type): """ Saves single record to model """ usage, created = DailyUsage.objects.get_or_create( date=date, type=usage_type, daily_pricing_object=daily_asset_info, defaults=dict( service_environment=daily_asset_info.service_environment, ) ) usage.service_environment = daily_asset_info.service_environment usage.value = value usage.save() return created def update_blade_server(data, date, usage_type): """ Updates single Blade Server usage type record """ try: asset_info = AssetInfo.objects.get(device_id=data['device_id']) daily_asset_info = asset_info.dailyassetinfo_set.get(date=date) return update_usage( daily_asset_info, date, 1, usage_type, ) except AssetInfo.DoesNotExist: raise AssetInfoNotFoundError() except DailyAssetInfo.DoesNotExist: raise DailyAssetInfoNotFoundError() def get_usage_type(): """ Returns Blade Server usage type """ return UsageType.objects.get_or_create( symbol='blade_server', defaults=dict( name='Blade server', ) )[0]
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from django.db import models from django.contrib.auth.models import ( AbstractUser, BaseUserManager ) class AppUserManager(BaseUserManager): def create_user(self, email, password=None): """ Creates and saves a User with the given email and password. """ if not email: raise ValueError('Users must have an email address') user = self.model( email=self.normalize_email(email), username=self.normalize_email(email), ) user.set_password(password) user.save(using=self._db) return user def create_staffuser(self, email, password): """ Creates and saves a staff user with the given email and password. """ user = self.create_user( email, password=password, ) user.is_staff = True user.save(using=self._db) return user def create_superuser(self, email, password): """ Creates and saves a superuser with the given email and password. """ user = self.create_user( email, password=password, ) user.is_staff = True user.is_superuser = True user.save(using=self._db) return user class User(AbstractUser): email = models.EmailField( max_length=255, unique=True, ) username = models.TextField(blank=True, null=True) phone_number = models.CharField( blank=True, null=True, unique=True, max_length=25) address = models.TextField(blank=True, null=True) objects = AppUserManager() USERNAME_FIELD = 'email' REQUIRED_FIELDS = [] # Email & Password are required by default.
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#!/usr/bin/env python3 # # Convert Pavlick's dictionary to hunalign # import argparse import re def main(): parser = argparse.ArgumentParser() parser.add_argument("-d", "--dict", default="/home/bhaddow/data/pavlick-dicts/dict.hi") args = parser.parse_args() brackets = re.compile("\[[^\]]*\]") delim = re.compile("[\t,/]") with open(args.dict) as ifh: for line in ifh: line = brackets.sub("", line[:-1]) fields = delim.split(line) for e in fields[1:]: e = e.strip() if e and fields[0]: if e == "fullstop": e = "." print("{} @ {}".format(fields[0],e)) if __name__ == "__main__": main()
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# Standard Library from typing import List, Union, Optional from ipaddress import IPv4Network, IPv6Network # Third Party from pydantic import StrictStr # Project from rp.models._common import Flag, RPModel class RouteFilterEntry(RPModel): """JunOS route-filter-list item JSON model.""" address: Union[IPv4Network, IPv6Network] longer: Flag orlonger: Flag exact: Flag prefix_length_range: Optional[StrictStr] through: Optional[StrictStr] upto: Optional[StrictStr] class Config: """Pydantic config overrides.""" fields = {"prefix_length_range": "prefix-length-range"} class RouteFilterList(RPModel): """JunOS route-filter-list JSON model.""" name: StrictStr rf_list: List[RouteFilterEntry]
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# -*- coding: utf-8 -*- """ Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community Edition) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import json from django.utils.translation import ugettext_lazy as _ from jsonschema import SchemaError from jsonschema import ValidationError as JsonValidationError from jsonschema import validate as json_validate from rest_framework.exceptions import ValidationError from .constants import KEY_PATTERN, NUM_VAR_ERROR_MSG, REAL_NUM_VAR_PATTERN from .models import VersionedEntity, get_model_class_by_resource_name def get_name_from_config(config): return config.get('metadata', {}).get('name') or '' def is_name_duplicate(resource_name, resource_id, name, version_id): """同一类资源的名称不能重复""" # 判断新名称与老名称是否一致,如果一致,则不会重复 model_class = get_model_class_by_resource_name(resource_name) try: resource = model_class.objects.get(id=resource_id) if name == resource.name: return False except model_class.DoesNotExist: pass # 只校验当前版本内是否重复 try: version_entity = VersionedEntity.objects.get(id=version_id) except VersionedEntity.DoesNotExist: return False else: entity = version_entity.get_entity() resource_ids = entity.get(resource_name, '') if not resource_ids: return False if model_class.objects.filter(name=name, id__in=resource_ids.split(',')): return True return False def validate_variable_inconfig(config): """校验配置文件中的变量名是否合法""" search_list = KEY_PATTERN.findall(json.dumps(config)) search_keys = set(search_list) for ikey in search_keys: if not REAL_NUM_VAR_PATTERN.match(ikey): raise ValidationError(_('变量[{}]不合法, {}').format(ikey, NUM_VAR_ERROR_MSG)) def validate_res_config(config, resource_name, schema): err_prefix = '{resource_name} {suffix_msg}'.format(resource_name=resource_name, suffix_msg=_("配置信息格式错误")) try: json_validate(config, schema) except JsonValidationError as e: raise ValidationError(f'{err_prefix}:{e.message}') except SchemaError as e: raise ValidationError(f'{err_prefix}:{e}') def validate_name_duplicate(data): resource_id = data.get('resource_id', None) version_id = data.get('version_id', None) if resource_id is None or version_id is None: return resource_name = data['resource_name'] name = data['name'] is_duplicate = is_name_duplicate(resource_name, resource_id, name, version_id) if is_duplicate: raise ValidationError(_('{}名称:{}已经在项目模板中被占用,请重新填写').format(resource_name, name))
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''' DeviceManager: a Component that manages different device families e.g. Telescope, Camera, FilterWheel via a GUI element that permits selection/connection/disconnection DeviceFamily: superclass of e.g. Camera, Telescope, FilterWheel handles communication with devices for generic functions such as select, connect, disconnect as well as common error handling Device: superclass of device instances e.g. SXCamera, ASCOMFilterWheel ''' import json import importlib from functools import partial from kivy.app import App from loguru import logger from kivy.metrics import dp from kivy.uix.spinner import Spinner from kivy.uix.button import Button from kivy.uix.label import Label from kivy.uix.boxlayout import BoxLayout from kivy.event import EventDispatcher from kivy.core.window import Window from kivy.properties import ( ObjectProperty, StringProperty, BooleanProperty, DictProperty ) from kivy.clock import Clock from jocular.component import Component from jocular.settingsmanager import SettingsBase from jocular.widgets import jicon, LabelL from jocular.formwidgets import configurable_to_widget from kivy.lang import Builder Builder.load_string(''' <DeviceManager>: canvas: Color: rgba: .2, .2, .2, .7 Ellipse: pos: self.x + dp(58) + (self.width - self.height) / 2, dp(58) size: self.height - dp(116), self.height - dp(116) orientation: 'vertical' pos_hint: {'center_x': 10, 'center_y': .5} ''') class DeviceManager(Component, BoxLayout): devices = {'Camera': 'Camera', 'Telescope': 'Telescope', 'FilterWheel': 'Filter wheel'} def __init__(self, **args): super().__init__(**args) self.app = App.get_running_app() self.status = {} self.connect_buttons = {} self.connect_dots = {} self.size = Window.size self.app.gui.add_widget(self) def show(self, *args): Component.get('SettingsManager').hide() if self.pos_hint['center_x'] > 1: self.show_device_manager() self.pos_hint = {'center_x': .5, 'center_y': .5} def hide(self, *args): if self.pos_hint['center_x'] < 1: self.pos_hint = {'center_x': 10, 'center_y': .5} def show_device_manager(self): ''' Main device manager panel that handles mode selection and connection, and links to configuration of current devices. ''' self.clear_widgets() self.add_widget(Label(size_hint=(1, None), height=dp(90))) self.add_widget(Label(size_hint=(1, None), height=dp(60), text='Your devices', font_size='24sp')) self.add_widget(Label(size_hint=(1, 1))) for device, name in self.devices.items(): current_device = Component.get(device).device bh = BoxLayout(size_hint=(1, None), height=dp(40)) bh.add_widget(Label(size_hint=(1, 1))) # connection status lab = self.connect_dots[device] = LabelL(size_hint=(None, 1), width=dp(24), markup=True, text=jicon('dot', color='g' if current_device.connected else 'r')) bh.add_widget(lab) # device family bh.add_widget(LabelL(text=name, size_hint=(None, 1), width=dp(120))) # device chooser spinner = Spinner(size_hint=(None, 1), width=dp(120), text=Component.get(device).settings['current_mode'], values=Component.get(device).modes.keys()) spinner.bind(text=partial(self.mode_changed, device)) bh.add_widget(spinner) # mid spacer bh.add_widget(Label(size_hint=(None, 1), width=dp(40))) # connect/disconnect button but = self.connect_buttons[device] = Button(size_hint=(None, 1), width=dp(120), text='disconnect...' if current_device.connected else 'connect...', on_press=partial(self.connect, device)) bh.add_widget(but) # configure icon lab = Button(size_hint=(None, 1), width=dp(140), markup=True, background_color=(0, 0, 0, 0), text=jicon('settings'), on_press=partial(self.config, device)) bh.add_widget(lab) bh.add_widget(Label(size_hint=(1, 1))) self.add_widget(bh) # connection status message bh = BoxLayout(padding=(10, 1), size_hint=(1, None), height=dp(40)) status = self.status[device] = Label(text=current_device.status, size_hint=(1, 1), color=(.5, .5, .5, 1)) bh.add_widget(status) self.add_widget(bh) # inter-device spacer # self.add_widget(Label(size_hint=(1, None), height=dp(40))) self.add_widget(Label(size_hint=(1, 1))) # done button hb = BoxLayout(size_hint=(1, None), height=dp(30)) hb.add_widget(Label(size_hint=(1, 1))) hb.add_widget(Button(size_hint=(None, 1), width=dp(100), text='close', on_press=self.hide)) hb.add_widget(Label(size_hint=(1, 1))) self.add_widget(hb) self.add_widget(Label(size_hint=(1, None), height=dp(90))) def mode_changed(self, device, spinner, mode): Component.get(device).set_mode(mode) def connect(self, device, widget=None): try: if self.connect_buttons[device].text == 'connect...': Component.get(device).connect() else: Component.get(device).disconnect() Component.get(device).save() except Exception as e: logger.exception(e) def status_changed(self, device, status): if device in self.status: self.status[device].text = status def connection_changed(self, device, connected): if device in self.connect_dots: self.connect_dots[device].text = jicon('dot', color=('g' if connected else 'r')) Component.get(device).info('not connected') if device in self.connect_buttons: self.connect_buttons[device].text = 'disconnect...' if connected else 'connect...' Component.get(device).info('connected') def config(self, device, *args): ''' user wants to configure device ''' logger.debug('Configuring {:} device'.format(device)) try: self.current_device = Component.get(device).device self.changed_settings = {} if self.current_device is not None: self.show_device_config_panel(name=device, device=self.current_device) except Exception as e: logger.exception(e) def show_device_config_panel(self, name=None, device=None): ''' Build device settings panel ''' self.clear_widgets() self.add_widget(Label(size_hint=(1, None), height=dp(75))) self.add_widget(Label(text=device.name, size_hint=(1, None), height=dp(60), font_size='24sp')) self.add_widget(Label(size_hint=(1, 1))) # spacer for pname, pspec in device.configurables: self.add_widget(configurable_to_widget( text=pspec.get('name', pname), name=pname, spec=pspec, helptext=pspec.get('help', ''), initval=getattr(self.current_device, pname), changed=device.setting_changed)) self.add_widget(Label(size_hint=(1, 1))) # spacer # done button hb = BoxLayout(size_hint=(1, None), height=dp(30)) hb.add_widget(Label(size_hint=(1, 1))) hb.add_widget(Button(size_hint=(None, 1), width=dp(150), text='back to devices', on_press=self._save_settings)) hb.add_widget(Label(size_hint=(1, 1))) self.add_widget(hb) self.add_widget(Label(size_hint=(1, None), height=dp(75))) def on_touch_down(self, touch): handled = super().on_touch_down(touch) if self.collide_point(*touch.pos): return True return handled class DeviceFamily: device = ObjectProperty(None) # these three need to be set in each subclass family = StringProperty('Unknown') modes = DictProperty({}) default_mode = StringProperty('') def __init__(self, **kwargs): self.app = App.get_running_app() try: with open(self.app.get_path('{:}.json'.format(self.family)), 'r') as f: self.settings = json.load(f) except: self.settings = {} Clock.schedule_once(self.post_init, 0) def post_init(self, dt): self.set_mode(self.settings.get('current_mode', self.default_mode)) self.connect() def save(self): with open(self.app.get_path('{:}.json'.format(self.family)), 'w') as f: json.dump(self.settings, f, indent=1) def set_mode(self, mode): self.disconnect() try: if mode in self.modes: devmod = importlib.import_module('jocular.{:}'.format(self.family.lower())) devclass = getattr(devmod, self.modes[mode]) self.device = devclass() self.settings['current_mode'] = mode self.device.settings_have_changed() # self.save() except Exception as e: logger.exception(e) def get_configurables(self): if self.device is not None: return self.device.configurables def configure(self): if self.device is not None: logger.debug('family {:} settings {:}'.format(self.family, self.settings['current_mode'])) self.device.configure() def connect(self): logger.debug('Connecting {:} (current mode: {:})'.format( self.family, self.settings['current_mode'])) if self.device is not None: self.device.connect() # only save current mode if we are able to connect if self.device.connected: self.save() self.device_connected() self.device.on_new_object() def disconnect(self): if self.device is None: return if self.connected(): self.device.disconnect() self.device_disconnected() def connected(self): if self.device is None: return False return self.device.connected def device_connected(self): pass def device_disconnected(self): pass def on_close(self, *args): if self.connected(): self.disconnect() def choose(self, *args): if self.device is not None: self.device.choose() ''' Each actual device e.g. ASCOMTelescope, ManualFilterwheel etc is a subclass of this ''' class Device(EventDispatcher, SettingsBase): connected = BooleanProperty(False) status = StringProperty('') family = StringProperty('unknown family') def on_close(self): pass def on_new_object(self): pass def on_previous_object(self): pass def connect(self): self.status = 'Not implemented for this {:}'.format(self.family) self.connected = False def disconnect(self): self.status = 'not connected' self.connected = False def on_connected(self, *args): Component.get('DeviceManager').connection_changed(self.family, self.connected) def on_status(self, *args): Component.get('DeviceManager').status_changed(self.family, self.status) def select(self, f): return None def choose(self): pass def handle_failure(self, message='problem'): logger.error('{:}: failure {:}'.format(self.family, message)) self.disconnect() self.connected = False self.status = message if hasattr(self, 'on_failure') and self.on_failure is not None: self.on_failure()
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2.637927
3,897
class Rule: def __init__(self, exprs, label): self.antec = exprs # list of Expression self.cons = label def searchantec(self, f): lst = [] for i in self.antec: if f == i.f: lst.append(i) return lst def __str__(self): res = "" for i in range(len(self.antec)): res = res + str(self.antec[i]) if i < len(self.antec) - 1: res = res + " & " return res + " ==> " + str(self.cons) def __eq__(self, other): return self.__dict__ == other.__dict__
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1.883648
318
import numpy as np import cv2 import time def connect( channel): return cv2.VideoCapture(channel) def capture_image (device,exposition): cam= connect(device) for i in range(exposition): ret, bgr_img = cam.read() cam.release() return bgr_img #Test unit if __name__ == '__main__': while True: img = capture_image(0,10) print(img) time.sleep(2) cv2.imshow("c",img) cv2.waitKey(0) cv2.destroyAllWindows()
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1.846906
307
from ._Encode import *
[ 6738, 47540, 4834, 8189, 1330, 1635, 198 ]
3.285714
7
a = float(2) b = int(2.0) c = bool(a) d = float(None) # fails e = int(None) # fails f = bool(None) # fails # show_store()
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2.047619
63
from db import nova_conexao from mysql.connector.errors import ProgrammingError sql = ''' SELECT A.NOME, A.TEL, B.DESCRICAO FROM CONTATOS A INNER JOIN GRUPOS B ON A.IDGRUPO = B.ID ORDER BY B.DESCRICAO, A.NOME ''' with nova_conexao() as conexao: try: cursor = conexao.cursor() cursor.execute(sql) contatos = cursor.fetchall() except ProgrammingError as e: print(f'Erro: {e.msg}') else: for contato in contatos: print(f'Nome: {contato[0]:10s} tel: {contato[1]:15s} grupo: {contato[2]}')
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1.964286
308
class Solution: def searchInsert(self, nums: List[int], target: int) -> int: l,r = 0, len(nums) while l < r: mid = (l+r)//2 if target > nums[mid]: l = mid+1 else: r = mid return l
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1.676829
164
#!/usr/bin/env python3 import json import logging import socket import sys from os.path import dirname, realpath; sys.path.append(dirname(dirname(dirname(realpath(__file__))))) from logger.utils.formats import Text from logger.readers.reader import Reader # Don't barf if they don't have redis installed. Only complain if # they actually try to use it, below try: import paho.mqtt.client as mqtt # import the client | $ pip installing paho-mqtt is necessary PAHO_ENABLED = True except ModuleNotFoundError: PAHO_ENABLED = False ################################################################################ class MQTTReader(Reader): """ Read messages from an mqtt broker """ def __init__(self, broker, channel, client_name): """ Read text records from the channel subscription. ``` broker MQTT broker to connect, broker format[###.###.#.#] channel MQTT channel to read from, channel format[@broker/path_of_subscripton] ``` Instructions on how to start an MQTT broker: 1. First install the Mosquitto Broker : ``` sudo apt-get update sudo apt-get install mosquitto sudo apt-get install mosquitto-clients ``` 2. The mosquitto service starts automatically when downloaded but use : ``` sudo service mosquitto start sudo service mosquitto stop ``` to start and stop the service. 3. To test the install use: ``` netstat -at ``` and you should see the MQTT broker which is the port 1883 4. In order to manually subscribe to a client use : ``` mosquitto_sub -t "example/topic" ``` and publish a message by using ``` mosquitto_pub -m "published message" -t "certain/channel" ``` 5. Mosquitto uses a configuration file "mosquitto.conf" which you can find in /etc/mosquitto folder ``` """ super().__init__(output_format=Text) if not PAHO_ENABLED: raise ModuleNotFoundError('MQTTReader(): paho-mqtt is not installed. Please ' 'try "pip install paho-mqtt" prior to use.') self.broker = broker self.channel = channel self.client_name = client_name try: self.paho = mqtt.Client(client_name) self.paho.connect(broker) self.paho.subscribe(channel) while paho.loop() == 0: pass except mqtt.WebsocketConnectionError as e: logging.error('Unable to connect to broker at %s:%s', self.broker, self.channel) raise e ############################ def read(self): while True: try: self.paho.loop_forever() message = next(iter(self.paho.listen())) logging.debug('Got message "%s"', message) if message.get('type', None) == 'message': data = message.get('data', None) if data: return data except KeyboardInterrupt: self.paho.disconnect() exit(0) ################################################################################
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import logging from synch.enums import ClickHouseEngine from synch.factory import Global from synch.replication.etl import etl_full from synch.writer.collapsing_merge_tree import ClickHouseCollapsingMergeTree from synch.writer.merge_tree import ClickHouseMergeTree logger = logging.getLogger("synch.replication.consumer") def consume(args): settings = Global.settings reader = Global.reader broker = Global.broker schema = args.schema engine = settings.schema_settings.get(schema).get("clickhouse_engine") if engine == ClickHouseEngine.merge_tree: writer_cls = ClickHouseMergeTree elif engine == ClickHouseEngine.collapsing_merge_tree: writer_cls = ClickHouseCollapsingMergeTree else: raise NotImplementedError writer = writer_cls(settings, broker) tables = settings.schema_settings.get(schema).get("tables") # try etl full if settings.auto_full_etl: etl_full(reader, writer, schema, tables) tables_pk = {} for table in tables: tables_pk[table] = reader.get_primary_key(schema, table) writer.start_consume(schema, tables_pk, args.last_msg_id, args.skip_error)
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# Authors: Lukas Breuer <[email protected]> """ ---------------------------------------------------------------------- --- jumeg.decompose.fourier_ica_plot --------------------------------- ---------------------------------------------------------------------- autor : Lukas Breuer email : [email protected] last update: 17.11.2016 version : 1.1 ---------------------------------------------------------------------- This is a simple implementation to plot the results achieved by applying FourierICA ---------------------------------------------------------------------- """ ####################################################### # # # plotting functions for FourierICA # # # ####################################################### # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Simple function to adjust axis in plots # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ def adjust_spines(ax, spines, labelsize=10): """ Simple function to adjust axis in plots Parameters ---------- ax: axis object Plot object which should be adjusted spines: list of strings ['bottom', 'left'] Name of the axis which should be adjusted labelsize: integer Font size for the x- and y-axis labels """ for loc, spine in list(ax.spines.items()): if loc in spines: spine.set_position(('outward', 4)) # outward by 4 points # spine.set_smart_bounds(True) else: spine.set_color('none') # don't draw spine # turn off ticks where there is no spine if 'left' in spines: ax.yaxis.set_ticks_position('left') else: # no yaxis ticks ax.yaxis.set_ticks([]) if 'bottom' in spines: ax.xaxis.set_ticks_position('bottom') else: # no xaxis ticks ax.xaxis.set_ticks([]) ax.tick_params(axis='x', labelsize=labelsize) ax.tick_params(axis='y', labelsize=labelsize) # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ # function to generate automatically combined labels # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ def get_combined_labels(subject='fsaverage', subjects_dir=None, parc='aparc.a2009s'): """ Helper function to combine labels automatically according to previous studies. Parameters ---------- subject: string containing the subjects name default: subject='fsaverage' subjects_dir: Subjects directory. If not given the system variable SUBJECTS_DIR is used default: subjects_dir=None parc: name of the parcellation to use for reading in the labels default: parc='aparc.a2009s' Return ------ label_keys: names of the new labels labels: list containing the combined labels """ # ------------------------------------------ # import necessary modules # ------------------------------------------ from mne import read_labels_from_annot import numpy as np from os.path import join # ------------------------------------------ # define labels based on previous studies # ------------------------------------------ # to get more information about the label names and their # locations check the following publication: # Destrieux et al. (2010), Automatic parcellation of human # cortical gyri and sulci using standard anatomical nomenclature, # NeuroImage, DOI: 10.1016/j.neuroimage.2010.06.010 label_combinations = { 'auditory': ['G_temp_sup-G_T_transv', 'G_temp_sup-Plan_polar', 'Lat_Fis-post'], 'broca': ['G_front_inf-Opercular', 'G_front_inf-Triangul', 'Lat_Fis-ant-Vertical'], 'cingulate': ['G_cingul-Post-dorsal', 'G_cingul-Post-ventral', 'G_and_S_cingul-Ant', 'G_and_S_cingul-Mid-Ant', 'G_and_S_cingul-Mid-Post', 'S_pericallosal', 'cingul-Post-ventral'], 'frontal': ['G_and_S_frontomargin', 'G_and_S_transv_frontopol', 'G_front_inf-Orbital', 'G_front_middle', 'G_front_sup', 'G_orbital', 'G_rectus', 'G_subcallosal', 'Lat_Fis-ant-Horizont', 'S_front_inf', 'S_front_middle', 'S_front_sup', 'S_orbital_lateral', 'S_orbital-H_Shaped', 'S_suborbital'], 'gustatory': ['G_and_S_subcentral'], 'insula': ['S_circular_insula_ant', 'S_circular_insula_inf', 'S_circular_insula_sup', 'G_Ins_lg_and_S_cent_ins', 'G_insular_short'], 'motor': ['G_precentral', 'S_precentral-sup-part', 'S_precentral-inf-part', 'S_central'], 'olfactory': ['S_temporal_transverse'], 'somatosensory': ['G_postcentral', 'S_postcentral'], 'somatosensory associated': ['G_and_S_paracentral', 'G_pariet_inf-Angular', 'G_parietal_sup', 'S_cingul-Marginalis', 'S_intrapariet_and_P_trans'], 'temporal': ['G_oc-temp_lat-fusifor', 'G_oc-temp_med-Parahip', 'G_temp_sup-Plan_polar', 'G_temporal_inf', 'G_temporal_middle', 'G_temp_sup-Lateral', 'Pole_temporal', 'S_collat_transv_ant', 'S_oc-temp_lat', 'S_oc-temp_med_and_Lingual', 'S_temporal_inf', 'S_temporal_sup'], 'vision': ['G_and_S_occipital_inf', 'G_occipital_middle', 'G_oc-temp_med-Lingual', 'S_collat_transv_post', 'S_oc_sup_and_transversal', 'S_occipital_ant', 'S_oc_middle_and_Lunatus'], 'visual': ['G_cuneus', 'G_precuneus', 'S_calcarine', 'S_parieto_occipital', 'G_occipital_sup', 'Pole_occipital', 'S_subparietal'], 'wernicke': ['G_pariet_inf-Supramar', 'G_temp_sup-Plan_tempo', 'S_interm_prim-Jensen'] } label_keys = list(label_combinations.keys()) labels = [] # ------------------------------------------ # combine labels # ------------------------------------------ # loop over both hemispheres for hemi in ['lh', 'rh']: # read all labels in labels_all = read_labels_from_annot(subject, parc=parc, hemi=hemi, surf_name='inflated', subjects_dir=subjects_dir, verbose=False) # loop over all labels to extract label names label_names = [] for label in labels_all: label_names.append(label.name) # ------------------------------------------ # now generate labels based on previous # studies # ------------------------------------------ # loop over all previously defined labels for label_key in label_keys: # get name of all labels related to the current one label_members = label_combinations[label_key] label_members = [x+'-'+hemi for x in label_members] # check which labels we need for the current one idx_labels_want = np.where(np.in1d(label_names, label_members))[0] labels_want = [labels_all[i] for i in idx_labels_want] # combine labels label_new = np.sum(labels_want) label_new.name = label_key + '-' + hemi # fill the surface between sources label_new.values.fill(1.0) label_new.smooth(subject=subject, subjects_dir=subjects_dir) # save new label fnout = join(subjects_dir, subject, 'label', hemi + '.' + label_key + '.label') label_new.save(fnout) labels.append(label_new) return label_keys, labels # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ # function to get the anatomical label to a given vertex # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ def get_anat_label_name(vertex, hemi, labels=None, subject='fsaverage', subjects_dir=None, parc='aparc.a2009s'): """ Helper function to get to a given vertex the name of the anatomical label Parameters ---------- vertex: integer containing the vertex number hemi: string containing the information in which hemisphere the vertex is located. Should be either 'lh' or 'rh' labels: labels to use for checking. If not given the labels are read from the subjects directory default: labels=None subject: string containing the subjects name default: subject='fsaverage' subjects_dir: Subjects directory. If not given the system variable SUBJECTS_DIR is used default: subjects_dir=None parc: name of the parcellation to use for reading in the labels default: parc='aparc.a2009s' Return ------ name: string containing the name of the anatomical label related to the given vertex """ # ------------------------------------------ # import necessary modules # ------------------------------------------ from mne import read_labels_from_annot import numpy as np # ------------------------------------------ # check input parameter # ------------------------------------------ # check if labels are given or must be read if not labels: labels = read_labels_from_annot(subject, parc=parc, hemi=hemi, surf_name='inflated', subjects_dir=subjects_dir, verbose=False) # ------------------------------------------ # loop over labels to find corresponding # label # ------------------------------------------ name = '' for label in labels: if label.hemi == hemi: # get vertices of current label label_vert = np.in1d(np.array(vertex), label.vertices) if label_vert: name = label.name break if name == '': name = 'unknown-' + hemi return name # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ # function to get the MNI-coordinate(s) to a given # FourierICA component # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ def get_mni_coordinates(A_orig, subject='fsaverage', subjects_dir=None, parc='aparc.a2009s', percentile=97, combine_labels=True): """ Helper function to get the MNI-coordinate(s) to a given FourierICA component. The selection if a component has activation in both hemispheres or only in one is made like follows: estimate for each component an activation threshold based on the given percentile. Next, estimate the total number of voxels in the component which are above the estimated threshold. Now check if at least 20% of the total number of voxels above threshold are in each hemisphere. If yes both hemispheres are marked as active, otherwise only one. Parameters ---------- A_orig: array 2D-mixing-array (nvoxel, ncomp) estimated when applying FourierICA subject: string containing the subjects name default: subject='fsaverage' subjects_dir: Subjects directory. If not given the system variable SUBJECTS_DIR is used default: subjects_dir=None parc: name of the parcellation to use for reading in the labels default: parc='aparc.a2009s' percentile: integer value between 0 and 100 used to set a lower limit for the shown intensity range of the spatial plots combine_labels: if set labels are combined automatically according to previous studies default: combine_labels=True Return ------ mni_coords: dictionary The dictionary contains two elements: 'rh' and 'lh', each of which containing a list with the MNI coordinates as string. Note, each list contains the same number of elements as components are given. If there is no MNI coordinate for a component an empty string is used, e.g. for two components {'rh': ['(37.55, 1.58, -21.71)', '(44.78, -10.41, 27.89)'], 'lh': ['(-39.43, 5.60, -27.80)', '']} hemi_loc_txt: list containing for each FourierICA component to which region it spatially belongs ('left', 'right' or 'both') classification: dictionary classification object. It is a dictionary containing two sub-dictionaries 'lh' and 'rh' (for left and right hemisphere). In both sub-dictionaries the information about the groups is stored, i.e. a group/region name + the information which components are stored in this group (as indices). An example for 6 components might look like this: {'rh': {'somatosensory': [1, 3], 'cingulate': [4, 5]}, 'lh': {'somatosensory': [1, 2], 'cingulate': [0, 5]}} labels: list of strings names of the labels which are involved in this data set """ # ------------------------------------------ # import necessary modules # ------------------------------------------ from mne import vertex_to_mni import numpy as np from os import environ import types # ------------------------------------------- # check input parameter # ------------------------------------------- if not subjects_dir: subjects_dir = environ.get('SUBJECTS_DIR') # ------------------------------------------- # generate spatial profiles # (using magnitude and phase) # ------------------------------------------- if isinstance(A_orig[0, 0], complex): A_orig_mag = np.abs(A_orig) else: A_orig_mag = A_orig # ------------------------------------------- # set some parameters # ------------------------------------------- nvoxel, ncomp = A_orig_mag.shape nvoxel_half = int(nvoxel / 2) hemi = ['lh', 'rh'] hemi_names = ['left ', 'right', 'both '] hemi_indices = [[0, nvoxel_half], [nvoxel_half, -1]] hemi_loc_txt = np.array([' '] * ncomp) hemi_loc = np.zeros(ncomp) # ------------------------------------------- # generate structures to save results # ------------------------------------------- # generate dictionary to save MNI coordinates mni_coords = {'rh': [''] * ncomp, 'lh': [''] * ncomp} # ------------------------------------------ # check if labels should be combined # automatically # ------------------------------------------ if combine_labels: label_names, labels = get_combined_labels(subject=subject, subjects_dir=subjects_dir, parc=parc) # generate empty classification dictionary class_keys = label_names[:] class_keys.append('unknown') classification = {'lh': {key: [] for key in class_keys}, 'rh': {key: [] for key in class_keys}} # if not generate empty variables else: label_names, labels = None, None classification = {} # ------------------------------------------ # loop over all components # ------------------------------------------ for icomp in range(ncomp): # ------------------------------------------ # extract maxima in the spatial profile of # the current component separately for both # hemispheres # ------------------------------------------ idx_ver_max_lh = np.argmax(A_orig_mag[:nvoxel_half, icomp]) idx_ver_max_rh = np.argmax(A_orig_mag[nvoxel_half:, icomp]) # ------------------------------------------ # check for both maxima if they are # significant # ------------------------------------------ # set some paremeter threshold = np.percentile(A_orig_mag[:, icomp], percentile) nidx_above = len(np.where(A_orig_mag[:, icomp] > threshold)[0]) cur_label_name = [] # loop over both hemispheres for idx_hemi, idx_vertex_max in enumerate([idx_ver_max_lh, idx_ver_max_rh]): # get the number of vertices above the threshold # in the current hemisphere nidx_above_hemi = len(np.where(A_orig_mag[hemi_indices[idx_hemi][0]:hemi_indices[idx_hemi][1], icomp] > threshold)[0]) # check if at least 20% of all vertices above the threshold # are in the current hemisphere if nidx_above_hemi * 5 > nidx_above: # get MNI-coordinate mni_coord = vertex_to_mni(idx_vertex_max, idx_hemi, subject, subjects_dir=subjects_dir)[0] # store results in structures mni_coords[hemi[idx_hemi]][icomp] = \ '(' + ', '.join(["%2.2f" % x for x in mni_coord]) + ')' # store hemisphere information hemi_loc[icomp] += idx_hemi + 1.0 # ------------------------------------------ # get MNI-coordinate to vertex as well as # the name of the corresponding anatomical # label # ------------------------------------------ anat_name = get_anat_label_name(idx_vertex_max, hemi[idx_hemi], subject=subject, subjects_dir=subjects_dir, parc=parc, labels=labels) cur_label_name.append(anat_name[:-3]) else: cur_label_name.append(' ') # ------------------------------------------ # check which results must be saved # ------------------------------------------ if combine_labels: # check if activation was found in both hemispheres # --> if not we can directly save the results if ' ' in cur_label_name: # adjust classification dictionary if cur_label_name[0] == ' ': classification[hemi[1]][cur_label_name[1]].append(icomp) else: classification[hemi[0]][cur_label_name[0]].append(icomp) # --> otherwise we have to make sure that we group the # component only into one region else: # check if both vertices are in the same anatomical location # --> then we have no problem if cur_label_name[0] == cur_label_name[1]: classification[hemi[0]][cur_label_name[0]].append(icomp) classification[hemi[1]][cur_label_name[1]].append(icomp) else: # check if we have an unknown region being involved # --> if yes chose the other one if cur_label_name[0] == 'unknown': classification[hemi[1]][cur_label_name[1]].append(icomp) hemi_loc[icomp], mni_coords[hemi[0]][icomp] = 2, '' elif cur_label_name[1] == 'unknown': classification[hemi[0]][cur_label_name[0]].append(icomp) hemi_loc[icomp], mni_coords[hemi[1]][icomp] = 1, '' # otherwise chose the region with the strongest vertex else: if A_orig_mag[idx_ver_max_lh, icomp] > A_orig_mag[idx_ver_max_rh, icomp]: classification[hemi[0]][cur_label_name[0]].append(icomp) hemi_loc[icomp], mni_coords[hemi[1]][icomp] = 1, '' else: classification[hemi[1]][cur_label_name[1]].append(icomp) hemi_loc[icomp], mni_coords[hemi[0]][icomp] = 2, '' # ------------------------------------------ # adjust hemi_loc_txt if activity was found # in both hemispheres # ------------------------------------------ for idx, hemi_name in enumerate(hemi_names): idx_change = np.where(hemi_loc == (idx + 1.0))[0] hemi_loc_txt[idx_change] = hemi_name # ------------------------------------------ # adjust label_names to only contain regions # being involved in processing the current # data # ------------------------------------------ labels = [] for cur_hemi in hemi: for key in label_names: if classification[cur_hemi][key]: labels.append(key) labels = np.unique(labels).tolist() return mni_coords, hemi_loc_txt, classification, labels # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ # helper function to check if classification was # performed prior to plotting # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ def _check_classification(classification, ncomp): """ Helper function to check if classification was performed prior to plotting Parameters ---------- classification: dictionary classification object from the group_ica_object. It is a dictionary containing two sub-dictionaries 'lh' and 'rh' (for left and right hemisphere). In both sub-dictionaries the information about the groups is stored, i.e. a group/region name + the information which components are stored in this group ncomp: integer number of components Return ------ keys: list containing the group names key_borders: list containing the group borders, i.e. the information where to plot a new group name idx_sort: array containing the plotting order of the components, i.e. components beloning to one group are plotted together """ # ------------------------------------------ # import necessary modules # ------------------------------------------ import numpy as np # ------------------------------------------ # check if classification was done # ------------------------------------------ key_borders = [] if np.any(classification): # initialize empty lists idx_sort = [] keys_hemi = list(classification.keys()) # sort keys keys = list(classification[keys_hemi[0]].keys()) keys.sort(key=lambda v: v.upper()) # set 'unknown' variables to the end keys.remove('unknown') keys.append('unknown') # remove keys with empty entries keys_want = [] for key in keys: if classification[keys_hemi[0]][key] or\ classification[keys_hemi[1]][key]: keys_want.append(key) # loop over all keys for key in keys_want: # get indices to each class idx_lh = classification[keys_hemi[0]][key] idx_rh = classification[keys_hemi[1]][key] # get indices of components in both hemispheres idx_both = np.intersect1d(idx_lh, idx_rh) # get indices of components only in right hemisphere idx_only_rh = np.setdiff1d(idx_rh, idx_lh) # get indices of components only in left hemisphere idx_only_lh = np.setdiff1d(idx_lh, idx_rh) # add components to list of sorted indices idx_all = np.concatenate((idx_both, idx_only_rh, idx_only_lh)) idx_sort += idx_all.tolist() key_borders.append(len(idx_all)) # add first border and estimate cumulative sum to # have the right borders key_borders = np.insert(key_borders, 0, 1) key_borders = np.cumsum(key_borders)[:-1] # ------------------------------------------ # if classification was not performed set # some default values # ------------------------------------------ else: idx_sort = np.arange(ncomp) keys_want = [] return keys_want, key_borders, idx_sort # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ # helper function to handle time courses for plotting # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ def _get_temporal_envelopes(fourier_ica_obj, W_orig, temporal_envelope=[], src_loc_data=[], tICA=False, global_scaling=True, win_length_sec=None, tpre=None, flow=None): """ Helper function to check if classification was performed prior to plotting Parameters ---------- fourier_ica_obj: FourierICA object generated when applying jumeg.decompose.fourier_ica W_orig: array 2D-demixing-array (ncomp x nvoxel) estimated when applying FourierICA temporal_envelope: list of arrays containing the temporal envelopes. If the temporal envelopes are already given here z-scoring and mean estimation is performed src_loc_data: array 3D array containing the source localization data used for FourierICA estimation (nfreq x nepochs x nvoxel). Only necessary if temporal_envelope is not given. tICA: bool If set we know that temporal ICA was applied when estimating the FourierICA, i.e. when generating the temporal-envelopes the data must not be transformed from the Fourier domain to the time-domain global_scaling: bool If set all temporal-envelopes are globally scaled. Otherwise each component is scaled individually win_length_sec: float or None Length of the epoch window in seconds tpre: float or None Lower border (in seconds) of the time-window used for generating/showing the epochs. If 'None' the value stored in 'fourier_ica_obj' is used flow: float, integer or None Lower frequency border for generating the temporal-envelope. If 'None' the frequency border stored in 'fourier_ica_obj' is used Return ------ temporal_envelope_mean: list containing the 2D arrays of the mean temporal envelopes of the components temporal_envelope: list containing the 3D arrays of the temporal envelopes of the components. Necessary for estimating the spectral profiles """ # ------------------------------------------ # import necessary modules # ------------------------------------------ from mne.baseline import rescale import numpy as np from scipy import fftpack # ------------------------------------------- # check input parameter # ------------------------------------------- if tpre == None: tpre = fourier_ica_obj.tpre if flow == None: flow = fourier_ica_obj.flow if not win_length_sec: win_length_sec = fourier_ica_obj.win_length_sec # estimate some simple parameter sfreq = fourier_ica_obj.sfreq ncomp, nvoxel = W_orig.shape win_ntsl = int(np.floor(sfreq * win_length_sec)) startfftind = int(np.floor(flow * win_length_sec)) # ------------------------------------------- # check if temporal envelope is already # given or should be estimated # ------------------------------------------- if temporal_envelope == []: # ------------------------------------------- # check if 'src_loc_data' is given... # if not throw an error # ------------------------------------------- if src_loc_data == []: print(">>> ERROR: You have to provide either the 'temporal_envelope' or") print(">>> 'src_loc_data'. Otherwise no temporal information can be plotted!") import pdb pdb.set_trace() # ------------------------------------------- # get independent components # ------------------------------------------- nfreq, nepochs, nvoxel = src_loc_data.shape act = np.zeros((ncomp, nepochs, nfreq), dtype=np.complex) if tICA: win_ntsl = nfreq temporal_envelope = np.zeros((nepochs, ncomp, win_ntsl)) fft_act = np.zeros((ncomp, win_ntsl), dtype=np.complex) # loop over all epochs to get time-courses from # source localized data by inverse FFT for iepoch in range(nepochs): # normalize data src_loc_zero_mean = (src_loc_data[:, iepoch, :] - np.dot(np.ones((nfreq, 1)), fourier_ica_obj.dmean)) / \ np.dot(np.ones((nfreq, 1)), fourier_ica_obj.dstd) act[:ncomp, iepoch, :] = np.dot(W_orig, src_loc_zero_mean.transpose()) #act[ncomp:, iepoch, :] = np.dot(W_orig, src_loc_zero_mean.transpose()) if tICA: temporal_envelope[iepoch, :, :] = act[:, iepoch, :].real else: # ------------------------------------------- # generate temporal profiles # ------------------------------------------- # apply inverse STFT to get temporal envelope fft_act[:, startfftind:(startfftind + nfreq)] = act[:, iepoch, :] temporal_envelope[iepoch, :, :] = fftpack.ifft(fft_act, n=win_ntsl, axis=1).real # ------------------------------------------- # average temporal envelope # ------------------------------------------- if not isinstance(temporal_envelope, list): temporal_envelope = [[temporal_envelope]] ntemp = len(temporal_envelope) temporal_envelope_mean = np.empty((ntemp, 0)).tolist() times = (np.arange(win_ntsl) / sfreq + tpre) # ------------------------------------------- # perform baseline correction # ------------------------------------------- for itemp in range(ntemp): for icomp in range(ncomp): temporal_envelope[itemp][0][:, icomp, :] = rescale(temporal_envelope[itemp][0][:, icomp, :], times, (None, 0), 'zscore') # ------------------------------------------- # estimate mean from temporal envelopes # ------------------------------------------- for itemp in range(ntemp): temporal_envelope_mean[itemp].append(np.mean(temporal_envelope[itemp][0], axis=0)[:, 5:-5]) # ------------------------------------------- # check if global scaling should be used # ------------------------------------------- # if not scale each component separately between -0.5 and 0.5 if not global_scaling: for icomp in range(ncomp): min_val = np.min([temporal_envelope_mean[0][0][icomp, :], temporal_envelope_mean[1][0][icomp, :]]) max_val = np.max([temporal_envelope_mean[0][0][icomp, :], temporal_envelope_mean[1][0][icomp, :]]) scale_fact = 1.0 / (max_val - min_val) for itemp in range(ntemp): temporal_envelope_mean[itemp][0][icomp, :] = np.clip( scale_fact * temporal_envelope_mean[itemp][0][icomp, :] - scale_fact * min_val - 0.5, -0.5, 0.5) # if global scaling should be used, scale all # data between -0.5 and 0.5 else: # scale temporal envelope between -0.5 and 0.5 min_val = np.min(temporal_envelope_mean) max_val = np.max(temporal_envelope_mean) scale_fact = 1.0 / (max_val - min_val) for itemp in range(ntemp): temporal_envelope_mean[itemp][0] = np.clip(scale_fact * temporal_envelope_mean[itemp][0] - scale_fact * min_val - 0.5, -0.5, 0.5) return temporal_envelope_mean, temporal_envelope # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ # helper function to handle spatial profiles for plotting # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ def _get_spatial_profiles(A_orig, keys, idx_text, vertno=[], subject='fsaverage', subjects_dir=None, labels=None, classification={}, percentile=97, mni_coord=[], add_foci=False, fnout=None): """ Helper function to get/generate the spatial profiles of the FourierICA components for plotting Parameters ---------- A_orig: array 2D-mixing-array (nvoxel, ncomp) estimated when applying FourierICA keys: list containing the group names idx_text: list containing the information in which brain hemisphere a component is mainly located (could be either 'both', 'left', 'right' or ' ' if no classification was performed before plotting) vertno: list list containing two arrays with the order of the vertices. If not given it will be generated in this routine subject: string string containing the subjects ID subjects_dir: string string containing the subjects directory path labels: list of strings names of the labels which should be plotted. Note, the prefix 'lh.' and the suffix '.label' are automatically added classification: dictionary classification object from the group_ica_object. It is a dictionary containing two sub-dictionaries 'lh' and 'rh' (for left and right hemisphere). In both sub-dictionaries the information about the groups is stored, i.e. a group/region name + the information which components are stored in this group percentile: integer value between 0 and 100 used to set a lower limit for the shown intensity range of the spatial plots mni_coord: list of strings if given the MNI coordinates are plotted beneath the spatial profiles add_foci: bool if True and the MNI coordinates are given a foci is plotted at the position of the MNI coordinate fnout: string or None if labels and classification is given the output filename of the brain plot containing all labels. If 'None' the results are not stored Return ------ temp_plot_dir: string directory where the spatial profiles are stored """ # ------------------------------------------ # import necessary modules # ------------------------------------------ from matplotlib import gridspec as grd from matplotlib import pyplot as plt from mayavi import mlab from mne.source_estimate import _make_stc import numpy as np from os import environ, makedirs from os.path import exists, join import re from scipy import misc from surfer import set_log_level import types # set log level to 'WARNING' set_log_level('CRITICAL') import mayavi mayavi.mlab.options.offscreen = True # ------------------------------------------- # create temporary directory to save plots # of spatial profiles # ------------------------------------------- temp_plot_dir = join(subjects_dir, subject, 'temp_plots') if not exists(temp_plot_dir): makedirs(temp_plot_dir) # ------------------------------------------- # generate spatial profiles # (using magnitude and phase) # ------------------------------------------- if not subjects_dir: subjects_dir = environ.get('SUBJECTS_DIR') if isinstance(A_orig[0, 0], complex): A_orig_mag = np.abs(A_orig) else: A_orig_mag = A_orig nvoxel, ncomp = A_orig_mag.shape # ------------------------------------------- # check if vertno is given, otherwise # generate it # ------------------------------------------- if not np.any(vertno): vertno = [np.arange(nvoxel/2), np.arange(nvoxel/2)] # ------------------------------------------- # check if labels should be plotted and if # classification was already performed # --> if yes define some colors for the # labels # ------------------------------------------- if labels and classification: colors = ['green', 'red', 'cyan', 'yellow', 'mediumblue', 'magenta', 'chartreuse', 'indigo', 'sandybrown', 'slateblue', 'purple', 'lightpink', 'springgreen', 'orange', 'sienna', 'cadetblue', 'crimson', 'maroon', 'powderblue', 'deepskyblue', 'olive'] # ------------------------------------------- # loop over all components to generate # spatial profiles # ------------------------------------------- for icomp in range(ncomp): # ------------------------------------------- # plot spatial profile # ------------------------------------------- # generate stc-object from current component A_cur = A_orig_mag[:, icomp] src_loc = _make_stc(A_cur[:, np.newaxis], vertices=vertno, tmin=0, tstep=1, subject=subject) # define current range (Xth percentile) fmin = np.percentile(A_cur, percentile) fmax = np.max(A_cur) fmid = 0.5 * (fmin + fmax) clim = {'kind': 'value', 'lims': [fmin, fmid, fmax]} # plot spatial profiles brain = src_loc.plot(surface='inflated', hemi='split', subjects_dir=subjects_dir, config_opts={'cortex': 'bone'}, views=['lateral', 'medial'], time_label=' ', colorbar=False, clim=clim) # check if foci should be added to the plot if add_foci and np.any(mni_coord): for i_hemi in ['lh', 'rh']: mni_string = mni_coord[i_hemi][icomp] # if 'mni_string' is not empty (it might be empty if activity # can only be found in one hemisphere) plot a foci if mni_string != "": mni_float = list(map(float, re.findall("[-+]?\d*\.\d+|\d+", mni_string))) brain.add_foci(mni_float, coords_as_verts=False, hemi=i_hemi, color='chartreuse', scale_factor=1.5, map_surface='white') # ------------------------------------------- # check if labels should be plotted # ------------------------------------------- if labels and classification: # import module to read in labels from mne import read_label # get path to labels dir_labels = join(subjects_dir, subject, 'label') # identify in which group the IC is classified hemi = 'rh' if idx_text[icomp] == 'right' else 'lh' # read in the corresponding label for idx_key, key in enumerate(keys): if icomp in classification[hemi][key]: label_name = ".%s.label" % key color = colors[idx_key] break # loop over both hemispheres to read the label in and plot it hemi = ['lh', 'rh'] if idx_text[icomp] == 'both ' else [hemi] for hemi_cur in hemi: label = read_label(join(dir_labels, hemi_cur + label_name), subject=subject) brain.add_label(label, borders=False, hemi=hemi_cur, color=color, alpha=0.1) brain.add_label(label, borders=True, hemi=hemi_cur, color=color) # save results fn_base = "IC%02d_spatial_profile.png" % (icomp+1) fnout_img = join(temp_plot_dir, fn_base) brain.save_image(fnout_img) # close mlab figure mlab.close(all=True) # ------------------------------------------- # also generate one plot with all labels # ------------------------------------------- if labels and classification: # set clim in a way that no activity can be seen # (Note: we only want to see the labels) clim = {'kind': 'value', 'lims': [fmax, 1.5 * fmax, 2.0 * fmax]} # generate plot brain = src_loc.plot(surface='inflated', hemi='split', subjects_dir=subjects_dir, config_opts={'cortex': 'bone'}, views=['lateral', 'medial'], time_label=' ', colorbar=False, clim=clim, background='white') # loop over all labels for idx_key, key in enumerate(keys): label_name = ".%s.label" % key color = colors[idx_key] # loop over both hemispheres in order to plotting the labels for hemi in ['lh', 'rh']: label = read_label(join(dir_labels, hemi + label_name), subject=subject) brain.add_label(label, borders=False, hemi=hemi, color=color, alpha=0.6) # save results if fnout: fnout_img = '%s_labels.png' % fnout brain.save_image(fnout_img) # close mlab figure mlab.close(all=True) # ------------------------------------------- # now adjust the label plot appropriate # ------------------------------------------- # read spatial profile image spat_tmp = misc.imread(fnout_img) # rearrange image x_size, y_size, _ = spat_tmp.shape x_half, y_half = x_size / 2, y_size / 2 x_frame, y_frame = int(0.11 * x_half), int(0.01 * y_half) spatial_profile = np.concatenate((spat_tmp[x_frame:(x_half - x_frame), y_frame:(y_half - y_frame), :], spat_tmp[(x_half + x_frame):-x_frame, y_frame:(y_half - y_frame), :], spat_tmp[(x_half + x_frame):-x_frame, (y_half + y_frame):-y_frame, :], spat_tmp[x_frame:(x_half - x_frame), (y_half + y_frame):-y_frame, :]), axis=1) # plot image plt.ioff() fig = plt.figure('Labels plots', figsize=(17, 3)) gs = grd.GridSpec(1, 30, wspace=0.00001, hspace=0.00001, left=0.0, right=1.0, bottom=0.0, top=1.0) # set plot position and plot image p1 = fig.add_subplot(gs[0, 0:26]) p1.imshow(spatial_profile) adjust_spines(p1, []) # add label names keys_fac = 0.8/len(keys) keys_split = 0 p_text = fig.add_subplot(gs[0, 26:30]) keys_sort_idx = np.argsort(keys) for idx_key in range(len(keys)): key = keys[keys_sort_idx[idx_key]] # check if string should be split if len(key) > 21 and ' ' in key: p_text.text(0.0, 0.9-keys_fac*(idx_key+keys_split), key.split()[0]+'-', fontsize=13, color=colors[keys_sort_idx[idx_key]]) keys_split += 1 p_text.text(0.0, 0.9-keys_fac*(idx_key+keys_split), key.split()[1], fontsize=13, color=colors[keys_sort_idx[idx_key]]) else: p_text.text(0.0, 0.9-keys_fac*(idx_key+keys_split), key, fontsize=13, color=colors[keys_sort_idx[idx_key]]) adjust_spines(p_text, []) plt.savefig(fnout_img, dpi=300) # close plot and set plotting back to screen plt.close('FourierICA plots') plt.ion() mayavi.mlab.options.offscreen = False return temp_plot_dir # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ # helper function to get spectral profiles for plotting # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ def _get_spectral_profile(temporal_envelope, tpre, sfreq, flow, fhigh, bar_plot=False, use_multitaper=False): """ Helper function to get the spectral-profile of the temporal-envelopes of the FourierICA components for plotting Parameters ---------- temporal_envelope: list of arrays containing the temporal envelopes. tpre: float Lower border (in seconds) of the time-window used for generating/showing the epochs. If 'None' the value stored in 'fourier_ica_obj' is used sfreq: float Sampling frequency of the data flow: float or integer Lower frequency range for time frequency analysis fhigh: float or integer Upper frequency range for time frequency analysis bar_plot: boolean if set the number of time points for time-frequency estimation is reduced in order to save memory and computing-time use_multitaper: boolean If set 'multitaper' is usewd for time frequency analysis, otherwise 'stockwell' Return ------ average_power_all: list containing the averaged frequency power of all components freqs: array containing the frequencies used to calculate the frequency power vmin: lower frequency range for plotting vmax: upper frequency range for plotting """ # ------------------------------------------ # import necessary modules # ------------------------------------------ from mne.baseline import rescale from mne.time_frequency._stockwell import _induced_power_stockwell import numpy as np # ------------------------------------------ # define some parameter # ------------------------------------------ ntemp = len(temporal_envelope) ncomp = temporal_envelope[0][0].shape[1] win_ntsl = temporal_envelope[0][0].shape[-1] average_power_all = np.empty((ntemp, 0)).tolist() vmin = np.zeros(ncomp) vmax = np.zeros(ncomp) # define some time parameter times = np.arange(win_ntsl) / sfreq + tpre idx_start = np.argmin(np.abs(times - tpre)) idx_end = np.argmin(np.abs(times - (tpre + win_ntsl/sfreq))) if bar_plot: decim = 10 else: decim = 1 # ------------------------------------------ # loop over all time courses, i.e. # conditions, and all components # ------------------------------------------ for itemp in range(ntemp): for icomp in range(ncomp): # extract some information from the temporal_envelope nepochs = temporal_envelope[itemp][0].shape[0] # ------------------------------------------ # perform time frequency analysis # ------------------------------------------ # prepare data for frequency analysis data_stockwell = temporal_envelope[itemp][0][:, icomp, idx_start:idx_end].\ reshape((nepochs, 1, idx_end-idx_start)) data_stockwell = data_stockwell.transpose([1, 0, 2]) # mirror data to reduce transient frequencies data_stockwell = np.concatenate((data_stockwell[:, :, 50:0:-1], data_stockwell, data_stockwell[:, :, -1:-51:-1]), axis=-1) n_fft = data_stockwell.shape[-1] # check if 'multitaper' or 'stockwell' should be # used for time-frequency analysis if use_multitaper: from mne.time_frequency.tfr import _compute_tfr n_cycle = 3.0 if (10.0 * n_cycle*sfreq)/(2.0 * np.pi * flow) > n_fft: flow *= ((10.0 * n_cycle*sfreq)/(2.0 * np.pi * flow))/n_fft flow = np.ceil(flow) freqs = np.arange(flow, fhigh) power_data = _compute_tfr(data_stockwell, freqs, sfreq=sfreq, use_fft=True, n_cycles=n_cycle, zero_mean=True, decim=decim, output='power', method='multitaper', time_bandwidth=10) else: power_data, _, freqs = _induced_power_stockwell(data_stockwell, sfreq=sfreq, fmin=flow, fmax=fhigh, width=0.6, decim=1, n_fft=n_fft, return_itc=False, n_jobs=4) # perform baseline correction (and remove mirrored parts from data) power_data = rescale(power_data[:, :, int(50/decim):-int(50/decim)], times[idx_start:idx_end][0:-1:decim], (None, 0), 'mean') average_power = np.mean(power_data, axis=0) # ------------------------------------------ # store all frequency data in one list # ------------------------------------------ average_power_all[itemp].append(average_power) # ------------------------------------------ # estimate frequency thresholds for plotting # ------------------------------------------ vmax[icomp] = np.max((np.percentile(average_power, 98), vmax[icomp])) vmin[icomp] = np.min((np.percentile(average_power, 2), vmin[icomp])) if np.abs(vmax[icomp]) > np.abs(vmin[icomp]): vmin[icomp] = - np.abs(vmax[icomp]) else: vmax[icomp] = np.abs(vmin[icomp]) return average_power_all, freqs, vmin, vmax # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ # plot results when Fourier ICA was applied in the # source space # +++++++++++++++++++++++++++++++++++++++++++++++++++++++ def plot_results_src_space(fourier_ica_obj, W_orig, A_orig, src_loc_data=[], temporal_envelope=[], # parameter for temporal profiles tpre=None, win_length_sec=None, tICA=False, vertno=[], subject='fsaverage', subjects_dir=None, # parameter for spatial profiles percentile=97, add_foci=True, classification={}, mni_coords=[], labels=None, flow=None, fhigh=None, bar_plot=False, # parameter for spectral profiles global_scaling=True, ncomp_per_plot=13, fnout=None, # general plotting parameter temp_profile_names=[]): """ Generate plot containing all results achieved by applying FourierICA in source space, i.e., plot spatial and spectral profiles. Parameters ---------- fourier_ica_obj: FourierICA object generated when applying jumeg.decompose.fourier_ica W_orig: array 2D-demixing-array (ncomp x nvoxel) estimated when applying FourierICA A_orig: array 2D-mixing-array (nvoxel, ncomp) estimated when applying FourierICA **** parameter for temporal profiles **** src_loc_data: array 3D array containing the source localization data used for FourierICA estimation (nfreq x nepochs x nvoxel). Only necessary if temporal_envelope is not given. default: src_loc_data=[] temporal_envelope: list of arrays containing the temporal envelopes. If not given the temporal envelopes are estimated here based on the 'src_loc_data' default: temporal_envelope=[] tpre: float Lower border (in seconds) of the time-window used for generating/showing the epochs. If 'None' the value stored in 'fourier_ica_obj' is used win_length_sec: float or None Length of the epoch window in seconds. If 'None' the value stored in 'fourier_ica_obj' is used tICA: boolean should be True if temporal ICA was applied default: tICA=False **** parameter for spatial profiles **** vertno: list list containing two arrays with the order of the vertices. If list is empty it will be automatically generated default: vertno=[] subject: string subjects ID default: subject='fsaverage' subjects_dir: string or None string containing the subjects directory path default: subjects_dir=None --> system variable SUBJETCS_DIR is used percentile: integer value between 0 and 100 used to set a lower limit for the shown intensity range of the spatial plots default: percentile=97 add_foci: bool if True and the MNI coordinates are given a foci is plotted at the position of the MNI coordinate default: add_foci=True classification: dictionary classification object from the group_ica_object. It is a dictionary containing two sub-dictionaries 'lh' and 'rh' (for left and right hemisphere). In both sub-dictionaries the information about the groups is stored, i.e. a group/region name + the information which components are stored in this group default: classification={} mni_coords: list of strings if given the MNI coordinates are plotted beneath the spatial profiles default: mni_coords=[] labels: list of strings names of the labels which should be plotted. Note, the prefix 'lh.' and the suffix '.label' are automatically added default: labels=None **** parameter for spectral profiles **** flow: float or integer Lower frequency range for time frequency analysis fhigh: float or integer Upper frequency range for time frequency analysis bar_plot: boolean If set the results of the time-frequency analysis are shown as bar plot. This option is recommended when FourierICA was applied to resting-state data default: bar_plot=False **** general plotting parameter **** global_scaling: bool If set spatial, spectral and temporal profiles are globally scaled. Otherwise each component is scaled individually default: global_scaling=True ncomp_per_plot: integer number of components per plot fnout: string default: fnout=None temp_profile_names: list of string The list should have the same number of elements as conditions were used to generate the temporal envelopes. The names given here are used as headline for the temporal profiles in the plot default: temp_profile_name=[] """ # ------------------------------------------ # import necessary modules # ------------------------------------------ from matplotlib import pyplot as plt from matplotlib import gridspec as grd from matplotlib.colors import Normalize import numpy as np from os import remove, rmdir from os.path import exists, join from scipy import misc # ------------------------------------------- # check input parameter # ------------------------------------------- if tpre == None: tpre = fourier_ica_obj.tpre if flow == None: flow = fourier_ica_obj.flow if not fhigh: fhigh = fourier_ica_obj.fhigh if not win_length_sec: win_length_sec = fourier_ica_obj.win_length_sec # check if either 'src_loc_data' or # 'temporal_envelope' is given, otherwise stop if src_loc_data == [] and temporal_envelope == []: print(">>> ERROR: you have either to provide the variable") print(">>> 'src_loc_data' or 'temporal_envelope'.") import pdb pdb.set_trace() # estimate/set some simple parameter sfreq = fourier_ica_obj.sfreq win_ntsl = int(np.floor(sfreq * win_length_sec)) ncomp, nvoxel = W_orig.shape ylim_temp = [-0.55, 0.55] time_range = [tpre, tpre + win_length_sec] # ------------------------------------------- # get temporal envelopes, or rather check if # temporal envelopes already exist or must # be calculated # ------------------------------------------- temporal_envelope_mean, temporal_envelope = \ _get_temporal_envelopes(fourier_ica_obj, W_orig, temporal_envelope=temporal_envelope, src_loc_data=src_loc_data, tICA=tICA, global_scaling=global_scaling, win_length_sec=win_length_sec, tpre=tpre, flow=flow) ntemp = len(temporal_envelope) # ------------------------------------------- # get MNI-coordinates of the FourierICA # components # ------------------------------------------- if not classification and not mni_coords and not labels: mni_coords, hemi_loc_txt, classification, labels = \ get_mni_coordinates(A_orig, subject=subject, subjects_dir=subjects_dir, percentile=percentile) # otherwise we only have to get the 'hemi_loc_txt' variable else: hemi_loc = np.array([int(i != '') for i in mni_coords['lh']]) hemi_loc += np.array([2*int(i != '') for i in mni_coords['rh']]) hemi_loc_txt = np.array([' '] * len(hemi_loc)) for idx, hemi_name in enumerate(['left ', 'right', 'both ']): idx_change = np.where(hemi_loc == (idx + 1.0))[0] hemi_loc_txt[idx_change] = hemi_name # check if classification was performed prior to plotting keys, key_borders, idx_sort = _check_classification(classification, ncomp) # ------------------------------------------- # get spatial profiles of all components # Note: This will take a while # ------------------------------------------- temp_plot_dir = _get_spatial_profiles(A_orig, keys, hemi_loc_txt, vertno=vertno, subject=subject, subjects_dir=subjects_dir, labels=labels, classification=classification, percentile=percentile, mni_coord=mni_coords, add_foci=add_foci, fnout=fnout) # ------------------------------------------- # get spectral profiles of all components # Note: This will take a while # ------------------------------------------- average_power_all, freqs, vmin, vmax = \ _get_spectral_profile(temporal_envelope, tpre, sfreq, flow, fhigh, bar_plot=bar_plot) # check if bar plot should be used # --> if yes estimate histogram data and normalize results if bar_plot: # generate an array to store the results freq_heights = np.zeros((ntemp, ncomp, len(freqs))) # loop over all conditions for i_power, average_power in enumerate(average_power_all): freq_heights[i_power, :, :] = np.sum(np.abs(average_power), axis=2) # normalize to a range between 0 and 1 freq_heights /= np.max(freq_heights) # ------------------------------------------ # now generate plot containing spatial, # spectral and temporal profiles # ------------------------------------------ # set some general parameter plt.ioff() nimg = int(np.ceil(ncomp/(1.0*ncomp_per_plot))) idx_key = 0 nplot = list(range(ncomp_per_plot, nimg*ncomp_per_plot, ncomp_per_plot)) nplot.append(ncomp) # generate image and its layout for plotting fig = plt.figure('FourierICA plots', figsize=(14 + ntemp * 8, 34)) n_keys = len(key_borders) if len(key_borders) > 0 else 1 gs = grd.GridSpec(ncomp_per_plot * 20 + n_keys * 10, 10 + ntemp * 8, wspace=0.1, hspace=0.05, left=0.04, right=0.96, bottom=0.04, top=0.96) # ------------------------------------------ # loop over the estimated number of images # ------------------------------------------ for iimg in range(nimg): # clear figure (to start with a white image in each loop) plt.clf() # estimate how many plots on current image istart_plot = int(ncomp_per_plot * iimg) # set idx_class parameter idx_class = 1 if key_borders == [] else 0 # ------------------------------------------ # loop over all components which should be # plotted on the current image # ------------------------------------------ for icomp in range(istart_plot, nplot[iimg]): # ---------------------------------------------- # check if key_boarders is set and should be # written on the image # ---------------------------------------------- if (icomp == istart_plot and key_borders != []) or \ ((icomp + 1) in key_borders): # adjust key-index if (icomp + 1) in key_borders: idx_key += 1 # add sub-plot with 'key_text' p_text = fig.add_subplot(gs[20 * (icomp - istart_plot) + idx_class * 10: \ 20 * (icomp - istart_plot) + 8 + idx_class * 10, 0:10]) p_text.text(0, 0, keys[idx_key-1], fontsize=25) adjust_spines(p_text, []) # adjust idx_class parameter idx_class += 1 # ---------------------------------------------- # plot spatial profiles # ---------------------------------------------- # read spatial profile image fn_base = "IC%02d_spatial_profile.png" % (idx_sort[icomp] + 1) fnin_img = join(temp_plot_dir, fn_base) spat_tmp = misc.imread(fnin_img) remove(fnin_img) # rearrange image x_size, y_size, _ = spat_tmp.shape x_half, y_half = x_size / 2, y_size / 2 x_frame, y_frame = int(0.11 * x_half), int(0.01 * y_half) spatial_profile = np.concatenate((spat_tmp[x_frame:(x_half - x_frame), y_frame:(y_half - y_frame), :], spat_tmp[(x_half + x_frame):-x_frame, y_frame:(y_half - y_frame), :], spat_tmp[(x_half + x_frame):-x_frame, (y_half + y_frame):-y_frame, :], spat_tmp[x_frame:(x_half - x_frame), (y_half + y_frame):-y_frame, :]), axis=1) # set plot position and plot image p1 = fig.add_subplot( gs[20 * (icomp - istart_plot) + idx_class * 10:20 * (icomp - istart_plot) + 15 + idx_class * 10, 0:10]) p1.imshow(spatial_profile) # set some plotting options p1.yaxis.set_ticks([]) p1.xaxis.set_ticks([]) y_name = "IC#%02d" % (idx_sort[icomp] + 1) p1.set_ylabel(y_name, fontsize=18) # ---------------------------------------------- # if given write MNI coordinates under the image # ---------------------------------------------- if np.any(mni_coords): # left hemisphere plt.text(120, 360, mni_coords['lh'][int(idx_sort[int(icomp)])], color="black", fontsize=18) # right hemisphere plt.text(850, 360, mni_coords['rh'][int(idx_sort[int(icomp)])], color="black", fontsize=18) # add location information of the component # --> if located in 'both', 'left' or 'right' hemisphere plt.text(-220, 100, hemi_loc_txt[int(idx_sort[int(icomp)])], color="red", fontsize=25, rotation=90) # ---------------------------------------------- # temporal/spectral profiles # ---------------------------------------------- # loop over all time courses for itemp in range(ntemp): # ---------------------------------------------- # if given plot a headline above the time # courses of each condition # ---------------------------------------------- if icomp == istart_plot and len(temp_profile_names): # add a sub-plot for the text p_text = fig.add_subplot(gs[(idx_class - 1) * 10: 6 + (idx_class - 1) * 12, (itemp) * 8 + 11:(itemp + 1) * 8 + 9]) # plot the text and adjust spines p_text.text(0, 0, " " + temp_profile_names[itemp], fontsize=30) adjust_spines(p_text, []) # set plot position if bar_plot: p2 = plt.subplot( gs[20 * (icomp - istart_plot) + idx_class * 11:20 * (icomp - istart_plot) + 13 + idx_class * 10, itemp * 8 + 11:(itemp + 1) * 8 + 9]) else: p2 = plt.subplot( gs[20 * (icomp - istart_plot) + idx_class * 10:20 * (icomp - istart_plot) + 15 + idx_class * 10, itemp * 8 + 11:(itemp + 1) * 8 + 9]) # extract temporal plotting information times = (np.arange(win_ntsl) / sfreq + tpre)[5:-5] idx_start = np.argmin(np.abs(times - time_range[0])) idx_end = np.argmin(np.abs(times - time_range[1])) # ---------------------------------------------- # plot spectral profile # ---------------------------------------------- # check if global scaling should be used if global_scaling: vmin_cur, vmax_cur = np.min(vmin), np.max(vmax) else: vmin_cur, vmax_cur = vmin[icomp], vmax[icomp] # show spectral profile if bar_plot: plt.bar(freqs, freq_heights[itemp, int(idx_sort[icomp]), :], width=1.0, color='cornflowerblue') plt.xlim(flow, fhigh) plt.ylim(0.0, 1.0) # set some parameter p2.set_xlabel("freq. [Hz]") p2.set_ylabel("ampl. [a.u.]") # ---------------------------------------------- # plot temporal profile on the some spot # ---------------------------------------------- ax = plt.twiny() ax.set_xlabel("time [s]") ax.plot(times[idx_start:idx_end], 0.5+temporal_envelope_mean[itemp][0][int(idx_sort[icomp]), idx_start:idx_end], color='red', linewidth=3.0) ax.set_xlim(times[idx_start], times[idx_end]) ax.set_ylim(0.0, 1.0) else: average_power = average_power_all[itemp][int(idx_sort[icomp])] extent = (times[idx_start], times[idx_end], freqs[0], freqs[-1]) p2.imshow(average_power, extent=extent, aspect="auto", origin="lower", picker=False, cmap='RdBu_r', vmin=vmin_cur, vmax=vmax_cur) # set some parameter p2.set_xlabel("time [s]") p2.set_ylabel("freq. [Hz]") # ---------------------------------------------- # plot temporal profile on the some spot # ---------------------------------------------- ax = plt.twinx() ax.set_xlim(times[idx_start], times[idx_end]) ax.set_ylim(ylim_temp) ax.set_ylabel("ampl. [a.u.]") ax.plot(times[idx_start:idx_end], temporal_envelope_mean[itemp][0][int(idx_sort[icomp]), idx_start:idx_end], color='black', linewidth=3.0) # ---------------------------------------------- # finally plot a color bar # ---------------------------------------------- if not bar_plot: # first normalize the color table norm = Normalize(vmin=np.round(vmin_cur, 2), vmax=np.round(vmax_cur, 2)) sm = plt.cm.ScalarMappable(cmap='RdBu_r', norm=norm) sm.set_array(np.linspace(vmin_cur, 1.0)) # estimate position of the color bar xpos = 0.405 + 0.5/(ntemp + 1.0) if n_keys > 1: cbaxes = fig.add_axes([xpos, 0.135, 0.2, 0.006]) else: cbaxes = fig.add_axes([xpos, 0.03, 0.2, 0.006]) ticks_fac = (vmax_cur - vmin_cur) * 0.3333 ticks = np.round([vmin_cur, vmin_cur + ticks_fac, vmax_cur - ticks_fac, vmax_cur], 2) # ticks = [-1.0, -0.5, 0.0, 0.5, 1.0] # now plot color bar cb = plt.colorbar(sm, ax=p2, cax=cbaxes, use_gridspec=False, orientation='horizontal', ticks=ticks, format='%1.2g') cb.ax.tick_params(labelsize=18) # ---------------------------------------------- # save image # ---------------------------------------------- if fnout: fnout_complete = '%s_%02d.png' % (fnout, iimg + 1) plt.savefig(fnout_complete, format='png', dpi=300) # close plot and set plotting back to screen plt.close('FourierICA plots') plt.ion() # remove temporary directory for # spatial profile plots if exists(temp_plot_dir): rmdir(temp_plot_dir) return mni_coords, classification, labels
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#!/usr/bin/env python # -*- coding: utf-8 -*- # @Author : qichun tang # @Contact : [email protected] import json import os from pathlib import Path import pandas as pd from joblib import Parallel, delayed from joblib import dump info = { "bohb": ("HpBandSter-BOHB", "r",), "ultraopt_BOHB": ("UltraOpt-BOHB", "g",), "ultraopt_HyperBand": ("HyperBand", "b",), "tpe": ("HyperOpt-TPE", "r",), "ultraopt_ETPE": ("UltraOpt-ETPE", "g",), "ultraopt_Random": ("Random", "b",), } benchmarks = [ "protein_structure", "slice_localization", "naval_propulsion", "parkinsons_telemonitoring" ] def process(benchmark, fname): print(f"start, {benchmark}-{fname}") target_file = f"{benchmark}-{fname}.pkl" if os.path.exists(target_file): print(f"exist, {benchmark}-{fname}") return regret_tests = [] runtimes = [] ts = [] df_t = pd.DataFrame() for file in Path(f"{benchmark}-{fname}").iterdir(): if file.suffix != ".json": continue data = json.loads(file.read_text()) col_name = file.name.split(".")[0] # regret_validation = data["regret_validation"] regret_test = data["regret_test"] for i in range(1, len(regret_test)): regret_test[i] = min(regret_test[i - 1], regret_test[i]) regret_tests.append(regret_test) runtime = data["runtime"] runtimes.append(runtime) ts.extend(runtime) for timestamp, regret in zip(runtime, regret_test): df_t.loc[timestamp, col_name] = regret df_t.sort_index(inplace=True) n_rows = df_t.shape[0] for i, col in enumerate(df_t.columns): pre_max=None for j in range(n_rows): if pd.isna(df_t.iloc[j, i]): if pre_max is not None: df_t.iloc[j, i] = pre_max else: pre_max = df_t.iloc[j, i] print(f"ok, {benchmark}-{fname}") dump(df_t, target_file) args_list = [] for _, benchmark in enumerate(benchmarks): for fname in info.keys(): args_list.append((benchmark, fname)) Parallel(n_jobs=10)( delayed(process)(*args) for args in args_list )
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# coding=utf-8 # Copyright 2021 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 # # https://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. # -*- coding: utf-8 -*- # # Configuration file for the Sphinx documentation builder. # # This file does only contain a selection of the most common options. For a # full list see the documentation: # http://www.sphinx-doc.org/en/master/config import os import sys sys.path.insert(0, os.path.abspath('..')) # -- Project information ----------------------------------------------------- project = 'learned_optimization' copyright = '2021, Google LLC.' author = 'The learned_optimization authors' # The short X.Y version version = '' # The full version, including alpha/beta/rc tags release = '' # -- General configuration --------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # needs_sphinx = '2.1' extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.intersphinx', 'sphinx.ext.mathjax', 'sphinx.ext.napoleon', 'sphinx.ext.viewcode', 'matplotlib.sphinxext.plot_directive', 'sphinx_autodoc_typehints', 'myst_nb', ] intersphinx_mapping = { 'python': ('https://docs.python.org/3/', None), 'numpy': ('https://numpy.org/doc/stable/', None), 'scipy': ('https://docs.scipy.org/doc/scipy/reference/', None), } suppress_warnings = [ 'ref.citation', # Many duplicated citations in numpy/scipy docstrings. 'ref.footnote', # Many unreferenced footnotes in numpy/scipy docstrings ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. source_suffix = ['.rst', '.ipynb', '.md'] # The main toctree document. main_doc = 'index' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = [ # Sometimes sphinx reads its own outputs as inputs! '_build/html', '_build/', '_build/jupyter_execute', 'notebooks/README.md', 'README.md', # Ignore markdown source for notebooks; myst-nb builds from the ipynb # These are kept in sync via jupytext --sync 'notebooks/*.md', ] # The name of the Pygments (syntax highlighting) style to use. pygments_style = None autosummary_generate = True # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'sphinx_rtd_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { 'logo_only': True, } # The name of an image file (relative to this directory) to place at the top # of the sidebar. # TODO(lmetz) add logos! # html_logo = '_static/logo_250px.png' # html_favicon = '_static/favicon.png' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # -- Options for myst ---------------------------------------------- jupyter_execute_notebooks = 'force' execution_allow_errors = False # Notebook cell execution timeout; defaults to 30. execution_timeout = 100 # List of patterns, relative to source directory, that match notebook # files that will not be executed. execution_excludepatterns = ['*'] # -- Extension configuration ------------------------------------------------- # Tell sphinx-autodoc-typehints to generate stub parameter annotations including # types, even if the parameters aren't explicitly documented. always_document_param_types = True # force clear docs every rebuild. import shutil if os.path.exists('_build/'): shutil.rmtree('_build/')
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