index int64 0 1,000k | blob_id stringlengths 40 40 | code stringlengths 7 10.4M |
|---|---|---|
13,800 | a80de19d410872eb28e884f17815c1fffb29d156 | import numpy as np
def is_ccw(points):
"""
Check if connected planar points are counterclockwise.
Parameters
-----------
points: (n,2) float, connected points on a plane
Returns
----------
ccw: bool, True if points are counterclockwise
"""
points = np.asanyarray(points, dtype=np.float64)
if (len(points.shape) != 2 or
points.shape[1] != 2):
raise ValueError('CCW is only defined for 2D')
xd = np.diff(points[:, 0])
yd = np.column_stack((
points[:, 1],
points[:, 1])).reshape(-1)[1:-1].reshape((-1, 2)).sum(axis=1)
area = np.sum(xd * yd) * .5
ccw = area < 0
return ccw
def concatenate(paths):
"""
Concatenate multiple paths into a single path.
Parameters
-------------
paths: list of Path, Path2D, or Path3D objects
Returns
-------------
concat: Path, Path2D, or Path3D object
"""
# if only one path object just return copy
if len(paths) == 1:
return paths[0].copy()
# length of vertex arrays
vert_len = np.array([len(i.vertices) for i in paths])
# how much to offset each paths vertex indices by
offsets = np.append(0.0, np.cumsum(vert_len))[:-1].astype(np.int64)
# resulting entities
entities = []
# resulting vertices
vertices = []
# resulting metadata
metadata = {}
for path, offset in zip(paths, offsets):
# update metadata
metadata.update(path.metadata)
# copy vertices, we will stack later
vertices.append(path.vertices.copy())
# copy entity then reindex points
for entity in path.entities:
entities.append(entity.copy())
entities[-1].points += offset
# generate the single new concatenated path
# use input types so we don't have circular imports
concat = type(path)(metadata=metadata,
entities=entities,
vertices=np.vstack(vertices))
return concat
|
13,801 | e8870f0e2ee8ddfb45cf9847dc870e8711991f76 | #! /usr/bin/env python
from sys import stdin
import numpy as np
from bisect import bisect
ntest = input()
for test in xrange(ntest):
n = input()
y = 0
z = 0
naomi = sorted([float(i) for i in stdin.readline().strip().split(' ')])
ken = sorted([float(i) for i in stdin.readline().strip().split(' ')])
ken2 = ken[:]
for chosen_n in naomi:
min_k = ken[0]
if chosen_n > min_k:
ken.pop(0)
y += 1
else:
ken.pop()
ken = ken2
for chosen_n in naomi:
k = bisect(ken, chosen_n) % len(ken)
chosen_k = ken.pop(k)
if chosen_n > chosen_k:
z += 1
print "Case #{}: {} {}".format(test+1, y, z) |
13,802 | 82f5e2c2120104fd0529b8cf33da57343c86261e | # Generated by Django 3.0.2 on 2021-05-07 15:10
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='CsvImportado1',
fields=[
('id', models.TextField(db_column='ID', max_length=255, primary_key=True, serialize=False)),
('grupo_asignado', models.TextField(db_column='Grupo_Asignado')),
('estado', models.TextField(db_column='Estado')),
('status_reason_hidden', models.TextField(db_column='Status_Reason_Hidden')),
('id_cliente', models.TextField(db_column='ID_Cliente')),
('razon_social', models.TextField(db_column='Razon_Social')),
('cliente_sidi', models.TextField(db_column='Cliente_SIDI')),
('ci', models.TextField(db_column='CI')),
('clase_ci', models.TextField(db_column='CLASE_CI')),
('fecha_envio', models.TextField(db_column='Fecha_Envio')),
('tipo_incidencia', models.TextField(db_column='Tipo_Incidencia')),
('segmento', models.TextField(db_column='Segmento')),
('n2_categ_prod', models.TextField(db_column='N2_Categ_Prod')),
('n3_categ_prod', models.TextField(db_column='N3_Categ_Prod')),
('n1_cat_ope', models.TextField(db_column='N1_Cat_Ope')),
('n2_cat_ope', models.TextField(db_column='N2_Cat_Ope')),
('n3_cat_ope', models.TextField(db_column='N3_Cat_Ope')),
('urgencia', models.TextField(db_column='Urgencia')),
('prioridad', models.TextField(db_column='Prioridad')),
('fecha_cierre', models.TextField(db_column='Fecha_Cierre')),
('assigned_company', models.TextField(db_column='Assigned_Company')),
('assigned_org', models.TextField(db_column='Assigned_Org')),
('usuario_asignado', models.TextField(db_column='Usuario_Asignado')),
('remitente', models.TextField(db_column='Remitente')),
('ultima_mod', models.TextField(db_column='Ultima_Mod')),
('celula_n', models.IntegerField(db_column='Celula_N')),
],
options={
'db_table': 'CSV_Importado1',
'managed': False,
},
),
migrations.CreateModel(
name='CsvImportado2',
fields=[
('id', models.TextField(db_column='ID', max_length=255, primary_key=True, serialize=False)),
('grupo_asignado', models.TextField(db_column='Grupo_Asignado')),
('estado', models.TextField(db_column='Estado')),
('status_reason_hidden', models.TextField(db_column='Status_Reason_Hidden')),
('id_cliente', models.TextField(db_column='ID_Cliente')),
('razon_social', models.TextField(db_column='Razon_Social')),
('cliente_sidi', models.TextField(db_column='Cliente_SIDI')),
('ci', models.TextField(db_column='CI')),
('clase_ci', models.TextField(db_column='CLASE_CI')),
('fecha_envio', models.TextField(db_column='Fecha_Envio')),
('tipo_incidencia', models.TextField(db_column='Tipo_Incidencia')),
('segmento', models.TextField(db_column='Segmento')),
('n2_categ_prod', models.TextField(db_column='N2_Categ_Prod')),
('n3_categ_prod', models.TextField(db_column='N3_Categ_Prod')),
('n1_cat_ope', models.TextField(db_column='N1_Cat_Ope')),
('n2_cat_ope', models.TextField(db_column='N2_Cat_Ope')),
('n3_cat_ope', models.TextField(db_column='N3_Cat_Ope')),
('urgencia', models.TextField(db_column='Urgencia')),
('prioridad', models.TextField(db_column='Prioridad')),
('fecha_cierre', models.TextField(db_column='Fecha_Cierre')),
('assigned_company', models.TextField(db_column='Assigned_Company')),
('assigned_org', models.TextField(db_column='Assigned_Org')),
('usuario_asignado', models.TextField(db_column='Usuario_Asignado')),
('remitente', models.TextField(db_column='Remitente')),
('ultima_mod', models.TextField(db_column='Ultima_Mod')),
('celula_n', models.IntegerField(db_column='Celula_N')),
],
options={
'db_table': 'CSV_Importado2',
'managed': False,
},
),
migrations.CreateModel(
name='Eventostkt',
fields=[
('sk', models.AutoField(db_column='SK', primary_key=True, serialize=False)),
('id', models.CharField(blank=True, db_column='ID', max_length=250, null=True)),
('grupo_asignado', models.CharField(blank=True, db_column='Grupo_Asignado', max_length=250, null=True)),
('grupo_asignado_anterior', models.CharField(blank=True, db_column='Grupo_Asignado_anterior', max_length=250, null=True)),
('estado', models.CharField(blank=True, db_column='Estado', max_length=250, null=True)),
('estado_anterior', models.CharField(blank=True, db_column='Estado_anterior', max_length=250, null=True)),
('status_reason_hidden', models.CharField(blank=True, db_column='Status_Reason_Hidden', max_length=250, null=True)),
('status_reason_hidden_anterior', models.CharField(blank=True, db_column='Status_Reason_Hidden_anterior', max_length=250, null=True)),
('tipo_incidencia', models.CharField(blank=True, db_column='Tipo_Incidencia', max_length=250, null=True)),
('tipo_incidencia_anterior', models.CharField(blank=True, db_column='Tipo_Incidencia_anterior', max_length=250, null=True)),
('fecha_envio', models.CharField(blank=True, db_column='Fecha_Envio', max_length=250, null=True)),
('tiempo_acumulado', models.CharField(blank=True, db_column='Tiempo_Acumulado', max_length=250, null=True)),
('horario', models.DateTimeField(db_column='Horario')),
],
options={
'db_table': 'eventostkt',
'managed': False,
},
),
migrations.CreateModel(
name='Llamadas',
fields=[
('id_llamadas', models.AutoField(db_column='ID_Llamadas', primary_key=True, serialize=False)),
('encola', models.IntegerField(db_column='EnCola')),
('aux', models.IntegerField(db_column='AUX')),
('presentes', models.IntegerField(db_column='Presentes')),
('disponibles', models.IntegerField(db_column='Disponibles')),
('enllamada', models.IntegerField(db_column='EnLlamada')),
('acw', models.IntegerField(db_column='ACW')),
('otros', models.IntegerField(db_column='Otros')),
('hora', models.TextField(db_column='Hora')),
('otro', models.IntegerField()),
('ring', models.IntegerField(db_column='Ring')),
],
options={
'db_table': 'Llamadas',
'managed': False,
},
),
migrations.CreateModel(
name='Llamadasssdd',
fields=[
('id_llamadas', models.AutoField(db_column='ID_Llamadas', primary_key=True, serialize=False)),
('encola', models.IntegerField(db_column='EnCola')),
('aux', models.IntegerField(db_column='AUX')),
('presentes', models.IntegerField(db_column='Presentes')),
('disponibles', models.IntegerField(db_column='Disponibles')),
('enllamada', models.IntegerField(db_column='EnLlamada')),
('acw', models.IntegerField(db_column='ACW')),
('otros', models.IntegerField(db_column='Otros')),
('hora', models.TextField(db_column='Hora')),
('otro', models.IntegerField()),
('ring', models.IntegerField(db_column='Ring')),
],
options={
'db_table': 'LlamadasSSDD',
'managed': False,
},
),
migrations.CreateModel(
name='Tablaseguimiento',
fields=[
('orderid', models.AutoField(db_column='OrderId', primary_key=True, serialize=False)),
('detalle', models.CharField(blank=True, default='', max_length=200, null=True)),
('reactivar', models.DateField(blank=True, default=None, null=True)),
('reactivar_hora', models.TimeField(blank=True, null=True)),
('prioridad', models.CharField(blank=True, choices=[('0', 'URGENTE'), ('1', 'PRIORITARIO'), ('2', 'NORMAL')], default='2', max_length=100, null=True)),
('tkt', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='tkt', to='pasaje.CsvImportado1')),
],
options={
'db_table': 'TablaSeguimiento',
'managed': True,
},
),
]
|
13,803 | ce6c8a99e4dad584e41bd4e661f3c199cbaebd81 | """
* get simple rms vs mag stats for CDIPS LCs
* plot them.
* assess how many all-nan LCs there are.
* move allnan light curves to a graveyard directory to collect dust
* supplement the statsfile by matching against Gaia DR2 and CDIPS catalogs.
usage:
$ (cdips) python -u get_cdips_lc_stats.py |& tee logs/s6_stats_overview_log.txt
NOTE: depends on pipe-trex (--> run in environment with aperturephot on path)
"""
import sys
sys.path.append('/nfs/phtess1/ar1/TESS/PROJ/jhartman/202106_CDIPS/cdips-pipeline')
import pandas as pd, numpy as np
import aperturephot as ap
import os, subprocess, shlex, shutil
from glob import glob
from os.path import join
from cdips.utils import collect_cdips_lightcurves as ccl
def get_cdips_lc_stats(
sector=6,
cdipssource_vnum=None,
nworkers=32,
overwrite=0,
filesystem='phtess2'
):
if filesystem in ['phtess2', 'php1']:
fs = f"/nfs/{filesystem}"
projdir = f'{fs}/ar0/TESS/PROJ/lbouma/cdips'
lcdirectory = f'/nfs/phtess2/ar0/TESS/PROJ/lbouma/CDIPS_LCS/sector-{sector}/'
catdir = '/nfs/phtess1/ar1/TESS/PROJ/lbouma/'
elif filesystem in ['wh1', 'wh2']:
projdir = "/ar1/PROJ/luke/proj/cdips"
lcdirectory = f'/ar1/PROJ/luke/proj/CDIPS_LCS/sector-{sector}/'
catdir = '/ar1/local/cdips/catalogs/'
statsdir = join(projdir, 'results', 'cdips_lc_stats', f'sector-{sector}')
if not os.path.exists(statsdir): os.mkdir(statsdir)
statsfile = os.path.join(statsdir,'cdips_lc_statistics.txt')
if os.path.exists(statsfile) and not overwrite:
print("found statsfile and not overwrite. skip")
return
lcglob = 'cam?_ccd?/*_llc.fits'
# a cut on OC_MG_FINAL_GaiaRp_lt_16_v0.4.csv to be genfromtxt readable
catalogfile = join(catdir,
f'sourceid_and_photrpmeanmag_v{cdipssource_vnum}.csv' )
if not os.path.exists(catalogfile):
if cdipssource_vnum < 0.6:
cfile = join(catdir,
f'OC_MG_FINAL_GaiaRp_lt_16_v{cdipssource_vnum}.csv')
cdipsdf = pd.read_csv(cfile, sep=';')
else:
cfile = join(catdir,
f'cdips_targets_v{cdipssource_vnum}_gaiasources_Rplt16_orclose.csv')
cdipsdf = pd.read_csv(cfile, sep=',')
outdf = cdipsdf[['source_id','phot_rp_mean_mag']].dropna(axis=0, how='any')
outdf.to_csv(catalogfile, sep=' ', index=False, header=False)
ap.parallel_lc_statistics(lcdirectory, lcglob,
catalogfile, tfalcrequired=True,
epdlcrequired=False,
fitslcnottxt=True,
fovcatcols=(0,1), # objectid, magcol to use
fovcatmaglabel='GRp', outfile=statsfile,
nworkers=nworkers,
workerntasks=500, rmcols=None,
epcols=None, tfcols=None,
rfcols=None, correctioncoeffs=None,
sigclip=5.0, fovcathasgaiaids=True)
ap.plot_stats_file(statsfile, statsdir,
f'sector-{sector} cdips',
binned=False, logy=True, logx=False,
correctmagsafter=None, rangex=(5.9,16),
observatory='tess', fovcathasgaiaids=True,
yaxisval='RMS')
print('Finished get_cdips_lc_stats!')
def supplement_stats_file(
cdipssource_vnum=None,
sector=6,
filesystem=None):
"""
add crossmatching info per line:
* all gaia mags. also gaia extinction and parallax. (also parallax upper
and lower bounds).
* calculated T mag from TICv8 relations
* all the gaia info (especially teff, rstar, etc if available. but also
position ra,dec and x,y, for sky-map plots. rstar to then be used when
applying rstar>5rsun cut in vetting)
* all the CDIPS catalog info (especially the name of the damn cluster)
* all the TIC info (the CROWDING metric, the TICID, and the Tmag)
"""
if filesystem in ['phtess2', 'php1']:
fs = f"/nfs/{filesystem}"
projdir = f'{fs}/ar0/TESS/PROJ/lbouma/cdips'
lcdirectory = f'/nfs/phtess2/ar0/TESS/PROJ/lbouma/CDIPS_LCS/sector-{sector}/'
catdir = '/nfs/phtess1/ar1/TESS/PROJ/lbouma/'
elif filesystem in ['wh1', 'wh2']:
projdir = "/ar1/PROJ/luke/proj/cdips"
lcdirectory = f'/ar1/PROJ/luke/proj/CDIPS_LCS/sector-{sector}/'
catdir = '/ar1/local/cdips/catalogs/'
statsdir = join(projdir, 'results', 'cdips_lc_stats', f'sector-{sector}')
if not os.path.exists(statsdir): os.mkdir(statsdir)
statsfile = os.path.join(statsdir,'cdips_lc_statistics.txt')
outpath = statsfile.replace('cdips_lc_statistics',
'supplemented_cdips_lc_statistics')
outdir = os.path.dirname(outpath)
stats = ap.read_stats_file(statsfile, fovcathasgaiaids=True)
df = pd.DataFrame(stats)
del stats
lcobjcsv = os.path.join(outdir, 'sector{}_lcobj.csv'.format(sector))
lcobjtxt = os.path.join(outdir, 'sector{}_lcobj.txt'.format(sector))
df['lcobj'].to_csv(lcobjcsv, index=False, header=False)
# run the gaia2read on this list
if not os.path.exists(lcobjtxt):
gaia2readcmd = (
"gaia2read --header --extra --idfile {} > {}".format(
lcobjcsv, lcobjtxt
)
)
returncode = os.system(gaia2readcmd)
if returncode != 0:
raise AssertionError('gaia2read cmd failed!!')
else:
print('ran {}'.format(gaia2readcmd))
# merge statsfile against (most of) gaia dr2
gdf = pd.read_csv(lcobjtxt, delim_whitespace=True)
desiredcols = ['#Gaia-ID[1]', 'RA[deg][2]', 'Dec[deg][3]',
'RAError[mas][4]', 'DecError[mas][5]',
'Parallax[mas][6]', 'Parallax_error[mas][7]',
'PM_RA[mas/yr][8]', 'PM_Dec[mas/year][9]',
'PMRA_error[mas/yr][10]', 'PMDec_error[mas/yr][11]',
'Ref_Epoch[yr][12]', 'phot_g_mean_mag[20]',
'phot_bp_mean_mag[25]', 'phot_rp_mean_mag[30]',
'radial_velocity[32]', 'radial_velocity_error[33]',
'teff_val[35]', 'teff_percentile_lower[36]',
'teff_percentile_upper[37]', 'a_g_val[38]',
'a_g_percentile_lower[39]', 'a_g_percentile_upper[40]',
'e_bp_min_rp_val[41]',
'e_bp_min_rp_percentile_lower[42]',
'e_bp_min_rp_percentile_upper[43]', 'radius_val[44]',
'radius_percentile_lower[45]',
'radius_percentile_upper[46]', 'lum_val[47]',
'lum_percentile_lower[48]', 'lum_percentile_upper[49]']
cgdf = gdf[desiredcols]
df['lcobj'] = df['lcobj'].astype(np.int64)
mdf = df.merge(cgdf, how='left', left_on='lcobj', right_on='#Gaia-ID[1]')
if np.all(pd.isnull(mdf['RA[deg][2]'])):
errmsg = (
'ERR! probably merging against bad temp files!! check gaia2read '
'call, perhaps.'
)
raise AssertionError(errmsg)
del df, cgdf, gdf
# merge against CDIPS catalog info
cdips_df = ccl.get_cdips_pub_catalog(ver=cdipssource_vnum)
if cdipssource_vnum < 0.6:
dcols = (
'cluster;ext_catalog_name;reference;source_id;unique_cluster_name;logt;logt_provenance;comment'
)
dcols = dcols.split(';')
else:
dcols = (
'source_id,ra,dec,parallax,parallax_error,pmra,pmdec,phot_g_mean_mag,phot_rp_mean_mag,phot_bp_mean_mag,cluster,age,mean_age,reference_id,reference_bibcode'
)
dcols = dcols.split(',')
ccdf = cdips_df[dcols]
ccdf['source_id'] = ccdf['source_id'].astype(np.int64)
megadf = mdf.merge(ccdf, how='left', left_on='lcobj', right_on='source_id')
# finally save
megadf.to_csv(outpath, index=False, sep=';')
print('made {}'.format(outpath))
print('Finished supplement_stats_file!')
def print_metadata_stats(sector=6, filesystem=None):
"""
how many LCs?
how many all nan LCs?
"""
assert isinstance(filesystem, str)
if filesystem in ['phtess2', 'php1']:
fs = f"/nfs/{filesystem}"
projdir = f'{fs}/ar0/TESS/PROJ/lbouma/cdips'
lcdirectory = f'/nfs/phtess2/ar0/TESS/PROJ/lbouma/CDIPS_LCS/sector-{sector}/'
catdir = '/nfs/phtess1/ar1/TESS/PROJ/lbouma/'
elif filesystem in ['wh1', 'wh2']:
projdir = "/ar1/PROJ/luke/proj/cdips"
lcdirectory = f'/ar1/PROJ/luke/proj/CDIPS_LCS/sector-{sector}/'
catdir = '/ar1/local/cdips/catalogs/'
statsdir = join(projdir, 'results', 'cdips_lc_stats', f'sector-{sector}')
if not os.path.exists(statsdir): os.mkdir(statsdir)
statsfile = os.path.join(statsdir,'cdips_lc_statistics.txt')
stats = ap.read_stats_file(statsfile, fovcathasgaiaids=True)
N_lcs = len(stats)
print('CDIPS LIGHTCURVES STATS FOR SECTOR {}'.format(sector))
print(42*'-')
print('total N_lcs: {}'.format(N_lcs))
for apn in [1,2,3]:
N_nan = len(stats[stats['ndet_tf{}'.format(apn)]==0])
print('for ap {}, {} ({:.1f}%) are all nan, leaving {} ok lcs'.
format(apn, N_nan, N_nan/N_lcs*100, N_lcs-N_nan))
print('\nsanity check: {} TF1 LCs have stdev > 0'.
format(len(stats[stats['stdev_tf1'] > 0])))
print('Finished print_metadata_stats!')
def move_allnan_lcs(sector=None, cdipsvnum=None, filesystem=None):
assert isinstance(filesystem, str)
if filesystem in ['phtess2', 'php1']:
fs = f"/nfs/{filesystem}"
projdir = f'{fs}/ar0/TESS/PROJ/lbouma/cdips'
lcdirectory = f'/nfs/phtess2/ar0/TESS/PROJ/lbouma/CDIPS_LCS/sector-{sector}/'
catdir = '/nfs/phtess1/ar1/TESS/PROJ/lbouma/'
elif filesystem in ['wh1', 'wh2']:
projdir = "/ar1/PROJ/luke/proj/cdips"
lcdirectory = f'/ar1/PROJ/luke/proj/CDIPS_LCS/sector-{sector}/'
catdir = '/ar1/local/cdips/catalogs/'
statsdir = join(projdir, 'results', 'cdips_lc_stats', f'sector-{sector}')
if not os.path.exists(statsdir): os.mkdir(statsdir)
statsfile = os.path.join(statsdir,'cdips_lc_statistics.txt')
stats = ap.read_stats_file(statsfile, fovcathasgaiaids=True)
N_lcs = len(stats)
print('CDIPS LIGHTCURVES STATS FOR SECTOR {}'.format(sector))
print(42*'-')
print('total N_lcs: {}'.format(N_lcs))
for apn in [1,2,3]:
N_nan = len(stats[stats['ndet_rm{}'.format(apn)]==0])
print('for ap {}, {} ({:.1f}%) are all nan, leaving {} ok lcs'.
format(apn, N_nan, N_nan/N_lcs*100, N_lcs-N_nan))
print(42*'-')
print('BEGINNING MOVE OF ALLNAN LIGHT CURVES')
sel = (
(stats['ndet_rm1']==0) &
(stats['ndet_rm2']==0) &
(stats['ndet_rm3']==0)
)
nanobjs = stats[sel]['lcobj']
lcdirectory = join(lcdirectory, "cam?_ccd?")
lcnames = [(
'hlsp_cdips_tess_ffi_'
'gaiatwo{zsourceid}-{zsector}-cam{cam}-ccd{ccd}_'
'tess_v{zcdipsvnum}_llc.fits'
).format(
cam='?',
ccd='?',
zsourceid=str(lcgaiaid).zfill(22),
zsector=str(sector).zfill(4),
zcdipsvnum=str(cdipsvnum).zfill(2)
)
for lcgaiaid in nanobjs
]
lcglobs = [os.path.join(lcdirectory, lcname) for lcname in lcnames]
lcpaths = []
for l in lcglobs:
try:
lcpaths.append(glob(l)[0])
except:
pass
dstpaths = [os.path.join(os.path.dirname(l),
'allnanlcs',
os.path.basename(l))
for l in lcpaths]
for src,dst in zip(lcpaths,dstpaths):
dstdir = os.path.dirname(dst)
if not os.path.exists(dstdir):
os.mkdir(dstdir)
try:
shutil.move(src,dst)
print('moved {} -> {}'.format(src,dst))
except FileNotFoundError as e:
if os.path.exists(dst):
pass
else:
print(repr(e))
raise FileNotFoundError
def main(sector, cdipssource_vnum, cdipsvnum, overwrite, get_stats=1,
make_supp_stats=0, print_metadata=1, move_allnan=1, filesystem=None):
assert isinstance(filesystem, str)
if get_stats:
get_cdips_lc_stats(
sector=sector,
cdipssource_vnum=cdipssource_vnum,
nworkers=40,
overwrite=overwrite,
filesystem=filesystem
)
if make_supp_stats:
supplement_stats_file(
cdipssource_vnum=cdipssource_vnum,
sector=sector,
filesystem=filesystem
)
if print_metadata:
print_metadata_stats(
sector=sector,
filesystem=filesystem
)
if move_allnan:
move_allnan_lcs(
sector=sector, cdipsvnum=cdipsvnum, filesystem=filesystem
)
if __name__ == "__main__":
sector=40
cdipssource_vnum=0.6
cdipsvnum=1
overwrite=0
get_stats=0
make_supp_stats=1
print_metadata=0
move_allnan=1
filesystem='wh1'
main(sector, cdipssource_vnum, cdipsvnum, overwrite, get_stats=get_stats,
make_supp_stats=make_supp_stats, print_metadata=print_metadata,
move_allnan=move_allnan, filesystem=filesystem)
|
13,804 | 1c958beb4d01d57ae32dc6a9bc4d11b4400f8061 | height = float(input("Chiều cao?"))
weight = float(input("Cân nặng?"))
BMI = weight/(height**2)
print(BMI)
if BMI < 16:
print("Severly underweight")
elif BMI >= 16 and BMI < 18.5:
print("Underweight")
elif BMI >= 18.5 and BMI < 25:
print("Normal")
elif BMI >= 25 and BMI < 30:
print("Overweight")
else:
print("Obese") |
13,805 | 3be78019786bb386b09a2be94298c8f51fb63b3a | from sys import stdin
s = str(stdin.readline().strip())
result = []
while(s != '.'):
array = []
flag = 0
for i in range(len(s)):
if s[i] == '(' or ')' or '[' or ']':
if len(array) == 0 and s[i] == '(' or s[i] == '[':
array.append(s[i])
print(i, array)
elif len(array) == 0 and s[i] == ')' or s[i] == ']':
flag = 1
break
elif array[-1] != s[i]:
if array[-1] == '(' or array[-1] == ')' and s[i] == '[' or s[i] == ']':
array.append(s[i])
print(i, array)
elif array[-1] == '[' or array[-1] == ']' and s[i] == '(' or s[i] == ')':
array.append(s[i])
print(i, array)
else:
array.pop()
print(i, array)
else:
array.append(s[i])
print(i, array)
else:
continue
if len(array) == 0 and flag == 0:
result.append('yes')
else:
result.append('no')
s = str(stdin.readline().strip())
print(result)
|
13,806 | e4bb3f1f91b1b2c38a065d2d6b02b95eea0d6ef3 | # -*- coding: utf-8 -*-
# -*- coding: utf-8 -*-
from __future__ import print_function
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from sklearn.feature_extraction import FeatureHasher
from sklearn.feature_extraction import DictVectorizer
from sklearn import preprocessing
import numpy as np
import pandas as pd
import os
def save_epoch(nn_model, epoch):
if not os.path.exists('models/'):
os.makedirs('models/')
nn_model.save_weights('models/weights_epoch_%d.h5' % epoch, overwrite=True)
def load_epoch(nn_model, epoch):
assert os.path.exists('models/weights_epoch_%d.h5' % epoch), 'Weights at epoch %d not found' % epoch
nn_model.load_weights('models/weights_epoch_%d.h5' % epoch)
seed = 7
np.random.seed(seed)
h = FeatureHasher(n_features=2048)
vec = DictVectorizer()
le = preprocessing.LabelEncoder()
nb_epoch = 500
batch_size = 2048
attr_name = ['taxiID', 'point', 'time', 'dst', 'direc', 'distance', 'wth', 'FX']
train = pd.read_csv("train.txt", header=None)
train_set = train.values[:, [0, 1, 2, 3, 4, 5, 6, 7, 8]]
print(train_set[0])
test = pd.read_csv("test.txt")
test_set = test.values[:, [0, 1, 2, 3, 4, 5, 6, 7, 8]]
print(test_set[0])
dataset = train.values[:, [0, 1, 2, 3, 4, 5, 6, 7]]
samples = list()
for sample in dataset:
sample_dict = dict()
for index, attr in enumerate(sample):
sample_dict[attr_name[index]] = attr
samples.append(sample_dict)
h.fit(samples)
X_train = list()
y_train = list()
X_test = list()
y_test = list()
for sample in train_set:
sample_dict = dict()
for index, attr in enumerate(sample):
attr = str(attr)
if index == 8:
y_train.append(int(attr))
continue
sample_dict[attr_name[index]] = attr
X_train.append(sample_dict)
for sample in test_set:
sample_dict = dict()
for index, attr in enumerate(sample):
attr = str(attr)
if index == 8:
y_test.append(int(attr))
continue
sample_dict[attr_name[index]] = attr
X_test.append(sample_dict)
X_train = h.transform(X_train).toarray()
X_test = h.transform(X_test).toarray()
print(X_train[0])
print(X_test[0])
print(X_train.shape)
print(X_test.shape)
y_train = np.asarray(y_train, dtype='int16')
y_test = np.asarray(y_test, dtype='int16')
nb_classes = np.max(y_train) + 1
nb_test_classes = np.max(y_test) + 1
print('nb_classes: ', nb_classes)
print('nb_test_classes: ', nb_test_classes)
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test, nb_classes)
print(y_train.shape)
print(y_test.shape)
model = Sequential()
model.add(Dense(2048, input_dim=X_train.shape[1], init='uniform', activation='relu'))
model.add(Dense(1024, init='uniform', activation='relu'))
model.add(Dense(nb_classes, init='uniform', activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, nb_epoch=nb_epoch, batch_size=batch_size, verbose=1, validation_data=(X_test, y_test))
# # save_epoch(model, nb_epoch)
#
# # load_epoch(model, nb_epoch)
# # results = model.predict_classes(X_test, batch_size=128)
# # for index, product in enumerate(list(le.inverse_transform(results))):
# # print(index, product)
# # print(results[0:2])
|
13,807 | 8fbbcc9add056ad90b5d8158c36cb2b85fb5b046 | from unittest import TestCase
from mock import patch, Mock, mock_open
from docker.errors import ImageNotFound, BuildError, APIError
from samcli.local.docker.lambda_image import LambdaImage
from samcli.commands.local.cli_common.user_exceptions import ImageBuildException
class TestLambdaImage(TestCase):
def test_initialization_without_defaults(self):
lambda_image = LambdaImage("layer_downloader", False, False, docker_client="docker_client")
self.assertEquals(lambda_image.layer_downloader, "layer_downloader")
self.assertFalse(lambda_image.skip_pull_image)
self.assertFalse(lambda_image.force_image_build)
self.assertEquals(lambda_image.docker_client, "docker_client")
@patch("samcli.local.docker.lambda_image.docker")
def test_initialization_with_defaults(self, docker_patch):
docker_client_mock = Mock()
docker_patch.from_env.return_value = docker_client_mock
lambda_image = LambdaImage("layer_downloader", False, False)
self.assertEquals(lambda_image.layer_downloader, "layer_downloader")
self.assertFalse(lambda_image.skip_pull_image)
self.assertFalse(lambda_image.force_image_build)
self.assertEquals(lambda_image.docker_client, docker_client_mock)
def test_building_image_with_no_layers(self):
docker_client_mock = Mock()
lambda_image = LambdaImage("layer_downloader", False, False, docker_client=docker_client_mock)
self.assertEquals(lambda_image.build("python3.6", []), "lambci/lambda:python3.6")
@patch("samcli.local.docker.lambda_image.LambdaImage._build_image")
@patch("samcli.local.docker.lambda_image.LambdaImage._generate_docker_image_version")
def test_not_building_image_that_already_exists(self,
generate_docker_image_version_patch,
build_image_patch):
layer_downloader_mock = Mock()
layer_mock = Mock()
layer_mock.name = "layers1"
layer_mock.is_defined_within_template = False
layer_downloader_mock.download_all.return_value = [layer_mock]
generate_docker_image_version_patch.return_value = "image-version"
docker_client_mock = Mock()
docker_client_mock.images.get.return_value = Mock()
lambda_image = LambdaImage(layer_downloader_mock, False, False, docker_client=docker_client_mock)
actual_image_id = lambda_image.build("python3.6", [layer_mock])
self.assertEquals(actual_image_id, "samcli/lambda:image-version")
layer_downloader_mock.download_all.assert_called_once_with([layer_mock], False)
generate_docker_image_version_patch.assert_called_once_with([layer_mock], "python3.6")
docker_client_mock.images.get.assert_called_once_with("samcli/lambda:image-version")
build_image_patch.assert_not_called()
@patch("samcli.local.docker.lambda_image.LambdaImage._build_image")
@patch("samcli.local.docker.lambda_image.LambdaImage._generate_docker_image_version")
def test_force_building_image_that_doesnt_already_exists(self,
generate_docker_image_version_patch,
build_image_patch):
layer_downloader_mock = Mock()
layer_downloader_mock.download_all.return_value = ["layers1"]
generate_docker_image_version_patch.return_value = "image-version"
docker_client_mock = Mock()
docker_client_mock.images.get.side_effect = ImageNotFound("image not found")
lambda_image = LambdaImage(layer_downloader_mock, False, True, docker_client=docker_client_mock)
actual_image_id = lambda_image.build("python3.6", ["layers1"])
self.assertEquals(actual_image_id, "samcli/lambda:image-version")
layer_downloader_mock.download_all.assert_called_once_with(["layers1"], True)
generate_docker_image_version_patch.assert_called_once_with(["layers1"], "python3.6")
docker_client_mock.images.get.assert_called_once_with("samcli/lambda:image-version")
build_image_patch.assert_called_once_with("lambci/lambda:python3.6", "samcli/lambda:image-version", ["layers1"])
@patch("samcli.local.docker.lambda_image.LambdaImage._build_image")
@patch("samcli.local.docker.lambda_image.LambdaImage._generate_docker_image_version")
def test_not_force_building_image_that_doesnt_already_exists(self,
generate_docker_image_version_patch,
build_image_patch):
layer_downloader_mock = Mock()
layer_downloader_mock.download_all.return_value = ["layers1"]
generate_docker_image_version_patch.return_value = "image-version"
docker_client_mock = Mock()
docker_client_mock.images.get.side_effect = ImageNotFound("image not found")
lambda_image = LambdaImage(layer_downloader_mock, False, False, docker_client=docker_client_mock)
actual_image_id = lambda_image.build("python3.6", ["layers1"])
self.assertEquals(actual_image_id, "samcli/lambda:image-version")
layer_downloader_mock.download_all.assert_called_once_with(["layers1"], False)
generate_docker_image_version_patch.assert_called_once_with(["layers1"], "python3.6")
docker_client_mock.images.get.assert_called_once_with("samcli/lambda:image-version")
build_image_patch.assert_called_once_with("lambci/lambda:python3.6", "samcli/lambda:image-version", ["layers1"])
@patch("samcli.local.docker.lambda_image.hashlib")
def test_generate_docker_image_version(self, hashlib_patch):
haslib_sha256_mock = Mock()
hashlib_patch.sha256.return_value = haslib_sha256_mock
haslib_sha256_mock.hexdigest.return_value = "thisisahexdigestofshahash"
layer_mock = Mock()
layer_mock.name = 'layer1'
image_version = LambdaImage._generate_docker_image_version([layer_mock], 'runtime')
self.assertEquals(image_version, "runtime-thisisahexdigestofshahash")
hashlib_patch.sha256.assert_called_once_with(b'layer1')
@patch("samcli.local.docker.lambda_image.docker")
def test_generate_dockerfile(self, docker_patch):
docker_client_mock = Mock()
docker_patch.from_env.return_value = docker_client_mock
expected_docker_file = "FROM python\nADD --chown=sbx_user1051:495 layer1 /opt\n"
layer_mock = Mock()
layer_mock.name = "layer1"
self.assertEquals(LambdaImage._generate_dockerfile("python", [layer_mock]), expected_docker_file)
@patch("samcli.local.docker.lambda_image.create_tarball")
@patch("samcli.local.docker.lambda_image.uuid")
@patch("samcli.local.docker.lambda_image.Path")
@patch("samcli.local.docker.lambda_image.LambdaImage._generate_dockerfile")
def test_build_image(self, generate_dockerfile_patch, path_patch, uuid_patch, create_tarball_patch):
uuid_patch.uuid4.return_value = "uuid"
generate_dockerfile_patch.return_value = "Dockerfile content"
docker_full_path_mock = Mock()
docker_full_path_mock.exists.return_value = True
path_patch.return_value = docker_full_path_mock
docker_client_mock = Mock()
layer_downloader_mock = Mock()
layer_downloader_mock.layer_cache = "cached layers"
tarball_fileobj = Mock()
create_tarball_patch.return_value.__enter__.return_value = tarball_fileobj
layer_version1 = Mock()
layer_version1.codeuri = "somevalue"
layer_version1.name = "name"
dockerfile_mock = Mock()
m = mock_open(dockerfile_mock)
with patch("samcli.local.docker.lambda_image.open", m):
LambdaImage(layer_downloader_mock, True, False, docker_client=docker_client_mock)\
._build_image("base_image", "docker_tag", [layer_version1])
handle = m()
handle.write.assert_called_with("Dockerfile content")
path_patch.assert_called_once_with("cached layers", "dockerfile_uuid")
docker_client_mock.images.build.assert_called_once_with(fileobj=tarball_fileobj,
rm=True,
tag="docker_tag",
pull=False,
custom_context=True)
docker_full_path_mock.unlink.assert_called_once()
@patch("samcli.local.docker.lambda_image.create_tarball")
@patch("samcli.local.docker.lambda_image.uuid")
@patch("samcli.local.docker.lambda_image.Path")
@patch("samcli.local.docker.lambda_image.LambdaImage._generate_dockerfile")
def test_build_image_fails_with_BuildError(self,
generate_dockerfile_patch,
path_patch,
uuid_patch,
create_tarball_patch):
uuid_patch.uuid4.return_value = "uuid"
generate_dockerfile_patch.return_value = "Dockerfile content"
docker_full_path_mock = Mock()
docker_full_path_mock.exists.return_value = False
path_patch.return_value = docker_full_path_mock
docker_client_mock = Mock()
docker_client_mock.images.build.side_effect = BuildError("buildError", "buildlog")
layer_downloader_mock = Mock()
layer_downloader_mock.layer_cache = "cached layers"
tarball_fileobj = Mock()
create_tarball_patch.return_value.__enter__.return_value = tarball_fileobj
layer_version1 = Mock()
layer_version1.codeuri = "somevalue"
layer_version1.name = "name"
dockerfile_mock = Mock()
m = mock_open(dockerfile_mock)
with patch("samcli.local.docker.lambda_image.open", m):
with self.assertRaises(ImageBuildException):
LambdaImage(layer_downloader_mock, True, False, docker_client=docker_client_mock) \
._build_image("base_image", "docker_tag", [layer_version1])
handle = m()
handle.write.assert_called_with("Dockerfile content")
path_patch.assert_called_once_with("cached layers", "dockerfile_uuid")
docker_client_mock.images.build.assert_called_once_with(fileobj=tarball_fileobj,
rm=True,
tag="docker_tag",
pull=False,
custom_context=True)
docker_full_path_mock.unlink.assert_not_called()
@patch("samcli.local.docker.lambda_image.create_tarball")
@patch("samcli.local.docker.lambda_image.uuid")
@patch("samcli.local.docker.lambda_image.Path")
@patch("samcli.local.docker.lambda_image.LambdaImage._generate_dockerfile")
def test_build_image_fails_with_ApiError(self,
generate_dockerfile_patch,
path_patch,
uuid_patch,
create_tarball_patch):
uuid_patch.uuid4.return_value = "uuid"
generate_dockerfile_patch.return_value = "Dockerfile content"
docker_full_path_mock = Mock()
path_patch.return_value = docker_full_path_mock
docker_client_mock = Mock()
docker_client_mock.images.build.side_effect = APIError("apiError")
layer_downloader_mock = Mock()
layer_downloader_mock.layer_cache = "cached layers"
tarball_fileobj = Mock()
create_tarball_patch.return_value.__enter__.return_value = tarball_fileobj
layer_version1 = Mock()
layer_version1.codeuri = "somevalue"
layer_version1.name = "name"
dockerfile_mock = Mock()
m = mock_open(dockerfile_mock)
with patch("samcli.local.docker.lambda_image.open", m):
with self.assertRaises(ImageBuildException):
LambdaImage(layer_downloader_mock, True, False, docker_client=docker_client_mock) \
._build_image("base_image", "docker_tag", [layer_version1])
handle = m()
handle.write.assert_called_with("Dockerfile content")
path_patch.assert_called_once_with("cached layers", "dockerfile_uuid")
docker_client_mock.images.build.assert_called_once_with(fileobj=tarball_fileobj,
rm=True,
tag="docker_tag",
pull=False,
custom_context=True)
docker_full_path_mock.unlink.assert_called_once()
|
13,808 | 74af5fe080bec8675684fcaa342d72babe380a87 | # -*- coding: utf-8 -*-
import configparser
from klass.singleton import Singleton
class AppConfig(metaclass=Singleton):
__config = None
def __init__(self, file):
self.__config = configparser.ConfigParser()
self.__config.read(file)
def section(self, name):
return self.__config[name]
|
13,809 | 09ef2592fe5d231dd1dd404209a5e457e996d4a7 | #!/usr/bin/python
# -*- coding: UTF-8 -*-
# 读配置文件
import ConfigParser
config = ConfigParser.ConfigParser()
config.read("ODBC.ini")
sections = config.sections() # 返回所有的配置块
print "配置块：", sections
o = config.options("ODBC 32 bit Data Sources") # 返回所有的配置项
print "配置项：", o
v = config.items("ODBC 32 bit Data Sources")
print "内容：", v
# 根据配置块和配置项返回内容
access = config.get("ODBC 32 bit Data Sources", "MS Access Database")
print access
excel = config.get("ODBC 32 bit Data Sources", "Excel Files")
print excel
dBASE = config.get("ODBC 32 bit Data Sources", "dBASE Files")
print dBASE
# 写配置文件
import ConfigParser
config = ConfigParser.ConfigParser()
config.add_section("ODBC Driver Count") # 添加新的配置块
config.set("ODBC Driver Count", "count", 2) # 添加新的配置项
f = open("ODBC.ini", "a+")
config.write(f)
f.close()
# 修改配置文件
import ConfigParser
config = ConfigParser.ConfigParser()
config.read("ODBC.ini")
config.set("ODBC Driver Count", "count", 3)
f = open("ODBC.ini", "r+")
config.write(f)
f.close()
# 删除配置文件
import ConfigParser
config = ConfigParser.ConfigParser()
config.read("ODBC.ini")
config.remove_option("ODBC Driver Count", "count") # 删除配置项
config.remove_section("ODBC Driver Count") # 删除配置块
f = open("ODBC.ini", "w+")
config.write(f)
f.close()
|
13,810 | ec91c37aeeb77ac62043c15d7d628007424ec514 | # split(' ') preserves multiple spaces
def capitalize(string):
return ' '.join([word.capitalize() for word in string.split(' ')])
'''
# changes need to be done in same list
# as indefinite number of spaces
def capitalize(string):
words=string.split()
res=[]
for word in words:
capital = word[0].upper()
if(len(word)>1):
capital += word[1:]
res += [capital]
return ' '.join(res)
'''
if __name__ == '__main__':
string = "1 s 2 3g"
capitalized_string = capitalize(string)
print(capitalized_string)
|
13,811 | 3e04f3ffa056a5deee860d837706b2bebfdc89fe | # Generated by Django 2.1.1 on 2018-10-17 01:10
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('course', '0001_initial'),
('shop', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='AddCourse',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='course.Course')),
],
),
migrations.CreateModel(
name='AddGood',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('good', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='shop.Goods')),
],
),
migrations.CreateModel(
name='Address',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('province', models.CharField(max_length=50, null=True)),
('city', models.CharField(max_length=50, null=True)),
('area', models.CharField(max_length=50, null=True)),
('detailaddress', models.CharField(max_length=50, null=True)),
],
),
migrations.CreateModel(
name='Icon',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('icon_url', models.CharField(max_length=254)),
],
),
migrations.CreateModel(
name='Intergral',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('intergral', models.CharField(max_length=50)),
],
),
migrations.CreateModel(
name='Sex',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=50)),
],
),
migrations.CreateModel(
name='User',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('telephone', models.CharField(max_length=50, unique=True)),
('password', models.CharField(max_length=255)),
('regist_time', models.DateTimeField(auto_now_add=True)),
],
),
migrations.CreateModel(
name='UserInfo',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=50)),
('height', models.CharField(max_length=50)),
('width', models.CharField(max_length=50)),
('birth', models.DateField()),
('note1', models.CharField(max_length=50, null=True)),
('note2', models.CharField(max_length=50, null=True)),
('icon', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='user.Icon')),
('sex', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='user.Sex')),
],
),
migrations.AddField(
model_name='intergral',
name='user',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='user.User'),
),
migrations.AddField(
model_name='address',
name='user',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='user.User'),
),
migrations.AddField(
model_name='addgood',
name='user',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='user.User'),
),
migrations.AddField(
model_name='addcourse',
name='user',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='user.User'),
),
]
|
13,812 | cb6847d2fb9b42734ba39b34b4fdcc4064d931ad | version='3.0.2'
|
13,813 | c544c792f31f44ac724330d2833293fc984848a5 | # Generated by Django 2.1 on 2019-04-26 21:34
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('policies', '0002_auto_20190424_1622'),
]
operations = [
migrations.AddField(
model_name='policy',
name='cash_back_days',
field=models.PositiveSmallIntegerField(default=6, help_text='Indicate the number of days without claiming qualifies a cash back.', verbose_name='Cash back days'),
),
]
|
13,814 | abf67b937bfb8b41bc8c23067063505df958729c | t = int(input())
ans = []
"""
def find_fib(index):
if index == 1:
return 0
else:
#index += 1
print(index)
a = (round(((1.618**index)-(-0.618**index))/2.236)) % 10
return a
"""
def find_fib(n):
# fibo = 2.078087 * math.log(n) + 1.672276
"""phi = (1 + 5**0.5)/2.0
return int(round((phi**n - (1-phi)**n) / 5**0.5))"""
return 0 # fibo
fiblist = [0, 1]
last = 0
next = 1
max = 0
for testcase in range(t):
n = int(input())
j = 1
while j < n:
j = j << 1
if max < j:
for i in range(max, j):
if i == 0 or i == 1:
continue
temp = next
next = last + next
last = temp
if (i + 1) and (not ((i + 1) & (i))):
fiblist.append(next)
max = j
ans.append(find_fib(2**j))
print(fiblist)
for i in ans:
print(i)
# code to find n in 2 rais to #
que = []
for testcase in range(t):
n = int(input())
j = 1
temp = 0
while j < n:
temp += 1
j = j << 1
que.append(temp)
if max < temp:
max = temp
|
13,815 | 5ce0bf3328fa59c72f7ed35dbf9dc404fec4d0e1 | import pygame
# NEVER START THE PROGRAM!!!
# pygame.init()
# screen = pygame.display.set_mode((640, 480))
#
# while True:
# pygame.display.flip()
import pygame.examples.stars
pygame.examples.stars.main()
|
13,816 | c1df02d19dbbbae2bb6d255318cabe938d9b113c | import os, openpyxl
os.chdir('C:\\Users\\Zig0n\\Documents\\VSCode Projects\\atbs_excercises\\Ch_13')
wb = openpyxl.load_workbook('example.xlsx')
sheet = wb['Sheet1'] # Get a sheet from the workbook.
print(sheet['A1']) # Get a cell from the sheet.
print(sheet['A1'].value) # Get the value from the cell.
# Get another cell from the sheet.
c = sheet['B1']
print(c.value)
# Get the row, column, and value from the cell.
print('Row %s, Column %s is %s' % (c.row, c.column, c.value))
print('Cell %s is %s' % (c.coordinate, c.value))
print(sheet['C1'].value)
# Get coordinates then value
print(sheet.cell(row=1, column=2))
print(sheet.cell(row=1, column=2).value)
for i in range(1,8,2): # Print every other row
print(i, sheet.cell(row=i, column=2).value)
# Getting max row & column number
print(sheet.max_row)
print(sheet.max_column)
|
13,817 | 54f61ddac94ac8c346329612bb5819075972c11b | #!/usr/bin/python3
# -*-coding:utf-8 -
"""
Labyrinth constants
"""
import pygame
pygame.init()
# Dimensions of labyrinth
NB_SPRITE = 15
SPRITE_SIZE = 30
SCREEN_SIDE = NB_SPRITE * SPRITE_SIZE
# Customization window...
WINDOW = pygame.display.set_mode((SCREEN_SIDE + 90, SCREEN_SIDE))
# Without this line, 'ICON' will be a misstake.
# + 60 px for items scoreboard.
WINDOW_TITLE = "Help Mac Gyver to escape !"
ICON = pygame.image.load("images/macgyver.png").convert_alpha()
# Customization window home
WINDOW_HOME = pygame.Surface((540, 450))
FONT = pygame.font.Font('freesansbold.ttf', 40)
SENTENCE1 = "- Mac Gyver Game -"
SENTENCE2 = "Play : press 'SPACE'"
SENTENCE3 = "QUIT : press 'ESCAPE'"
HOME_WINDOW_TITLE1 = FONT.render(SENTENCE1, True, (255, 255, 255))
HOME_WINDOW_TITLE_RECT1 = HOME_WINDOW_TITLE1.get_rect()
HOME_WINDOW_TITLE_RECT1.center = (270, 50)
HOME_WINDOW_TITLE2 = FONT.render(SENTENCE2, False, (255, 200, 200))
HOME_WINDOW_TITLE_RECT2 = HOME_WINDOW_TITLE2.get_rect()
HOME_WINDOW_TITLE_RECT2.center = (270, 200)
HOME_WINDOW_TITLE3 = FONT.render(SENTENCE3, False, (255, 200, 200))
HOME_WINDOW_TITLE_RECT3 = HOME_WINDOW_TITLE3.get_rect()
HOME_WINDOW_TITLE_RECT3.center = (270, 300)
# Customization items window
FONT = pygame.font.Font('freesansbold.ttf', 10)
ITEMS_WINDOW_TITLE = FONT.render("Items collected:", True, (255, 255, 255))
SURFACE_ETHER = pygame.Surface((30, 30))
SURFACE_ETHER.fill((255, 255, 255))
SURFACE_TUBE = pygame.Surface((30, 30))
SURFACE_TUBE.fill((255, 255, 255))
SURFACE_SYRINGUE = pygame.Surface((30, 30))
SURFACE_SYRINGUE.fill((255, 255, 255))
# Labyrinth elements
BACKGROUND = pygame.image.load("images/fond.jpg").convert()
WALL = pygame.image.load("images/mur.jpg").convert()
WALL = pygame.transform.scale(WALL, (SPRITE_SIZE, SPRITE_SIZE))
DEPARTURE = pygame.image.load("images/depart.jpg").convert()
DEPARTURE = pygame.transform.scale(DEPARTURE, (SPRITE_SIZE, SPRITE_SIZE))
ARRIVAL = pygame.image.load("images/arrivee.jpg").convert()
ARRIVAL = pygame.transform.scale(ARRIVAL, (SPRITE_SIZE, SPRITE_SIZE))
# Labyrinth items
TUBE = pygame.image.load("images/tube_plastique.png").convert_alpha()
TUBE = pygame.transform.scale(TUBE, (SPRITE_SIZE, SPRITE_SIZE))
ETHER = pygame.image.load("images/ether.png").convert_alpha()
ETHER = pygame.transform.scale(ETHER, (SPRITE_SIZE, SPRITE_SIZE))
SYRINGUE = pygame.image.load("images/seringue.png").convert_alpha()
SYRINGUE = pygame.transform.scale(SYRINGUE, (SPRITE_SIZE, SPRITE_SIZE))
# Display characters
MG = pygame.image.load("images/MacGyver.png").convert_alpha()
MG = pygame.transform.scale(MG, (SPRITE_SIZE, SPRITE_SIZE))
GUARDIAN = pygame.image.load("images/Gardien.png").convert_alpha()
GUARDIAN = pygame.transform.scale(GUARDIAN, (SPRITE_SIZE, SPRITE_SIZE))
# Win & Lose
WIN = pygame.image.load("images/win.jpg").convert()
WIN = pygame.transform.scale(WIN, (SCREEN_SIDE + 90, SCREEN_SIDE))
GAMEOVER = pygame.image.load("images/gameover.png").convert()
GAMEOVER = pygame.transform.scale(GAMEOVER, (SCREEN_SIDE + 90, SCREEN_SIDE))
# Structure labyrinthe
FILE = "map/N1.txt"
# Sounds
SOUNDTRACK = pygame.mixer.Sound('sounds/sound_game.wav')
SOUNDTRACK.set_volume(.1)
|
13,818 | 63efe6806f761ea3c12b45a63e211bfa0a8085c1 | # -*- coding: utf-8 -*-
__author__ = 'damon'
from common.utils import *
class Solution:
def rotateRight(self, head: ListNode, k: int) -> ListNode:
if not head:
return head
cur = head
n = 0
while cur:
n += 1
cur = cur.next
k %= n
slow = head
fast = head
i = 0
while i < k and fast:
fast = fast.next
i += 1
if not fast:
return head
while fast.next:
fast = fast.next
slow = slow.next
fast.next = head
fast = slow.next
slow.next = None
return fast
class Solution:
def rotateRight(self, head: ListNode, k: int) -> ListNode:
if head is None:
return head
dummy = ListNode(None)
dummy.next = head
list_len = 0
fast = dummy
while fast.next is not None:
list_len += 1
fast = fast.next
if list_len == 1:
return head
k_step = k % list_len
if k_step == 0:
return dummy.next
fast = dummy
slow = dummy
for i in range(k_step):
fast = fast.next
while fast.next is not None:
fast = fast.next
slow = slow.next
fast.next = dummy.next
dummy.next = slow.next
slow.next = None
return dummy.next
if __name__ == '__main__':
sl = Solution()
# print(sl.rotateRight(genLinkList([1,2,3,4,5]),2))
print(sl.rotateRight(genLinkList([0,1,2]), 4))
print(sl.rotateRight(genLinkList([]), 0))
|
13,819 | cc33365a37cb4ba51d574253d4a2690887c6b179 | import os
from django.core.mail import send_mail
os.environ['DJANGO_SETTINGS_MODULE'] = 'mysite.settings'
if __name__ == '__main__':
send_mail(
'来自django练手项目的测试邮件',
'欢迎访问django项目，我们在测试邮件发送！',
'xxxx@163.com',
['xxxxx@qq.com'],
) |
13,820 | d9354389a89dfdd30e3022e5cf246eea4ee9d43f | import time
def dynamo_create_save_token_func(dynamo, token_model):
""" dynamo save_token function for authlib.integrations.flask_oauth2.AuthorizationServer's save_token param """
def save_token(token, request):
if request.user:
user_id = request.user.get_user_id()
else:
user_id = None
client = request.client
item = token_model(
client_id= client.client_id,
user_id= user_id,
issued_at= int(time.time()),
token_type= token["token_type"],
access_token= token["access_token"],
expires_in= token["expires_in"],
refresh_token= token["refresh_token"],
scope= token["scope"] if "scope" in token else ""
)
dynamo.save_token(item)
return save_token
def dynamo_create_query_client_func(dynamo):
""" dynamo query_client for authlib.integrations.flask_oauth2.AuthorizationServer's query_client """
def query_client(client_id):
return dynamo.get_client(client_id)
return query_client
def dynamo_create_bearer_token_validator(dynamo):
"""Token validator. dynamo version of authlib.oauth2.rfc6750.BearerTokenValidator"""
from authlib.oauth2.rfc6750 import BearerTokenValidator
class _BearerTokenValidator(BearerTokenValidator):
def authenticate_token(self, token_string):
return dynamo.get_token(token_string)
def request_invalid(self, request):
return False
def token_revoked(self, token):
return token.revoked
return _BearerTokenValidator
def dynamo_create_revocation_endpoint(dynamo):
""" dynamo version of revocation endpoint class from uthlib.oauth2.rfc7009.RevocationEndpoint"""
from authlib.oauth2.rfc7009 import RevocationEndpoint
class _RevocationEndpoint(RevocationEndpoint):
def query_token(self, token_string, token_type_hint, client):
return dynamo.get_token(token_string)
def revoke_token(self, token):
token.revoked = True
dynamo.save_token(token)
return _RevocationEndpoint
|
13,821 | 93d248a2e21d6ec200abaaaf6749d1dc74e4ced3 | import itertools
import sympy as sp
from means.approximation.mea.mea_helpers import get_one_over_n_factorial, derive_expr_from_counter_entry
def generate_dmu_over_dt(species, propensity, n_counter, stoichiometry_matrix):
r"""
Calculate :math:`\frac{d\mu_i}{dt}` in eq. 6 (see Ale et al. 2013).
.. math::
\frac{d\mu_i}{dt} = S \begin{bmatrix} \sum_{l} \sum_{n_1=0}^{\infty} ...
\sum_{n_d=0}^{\infty}
\frac{1}{\mathbf{n!}}
\frac{\partial^n \mathbf{n}a_l(\mathbf{x})}{\partial \mathbf{x^n}} |_{x=\mu}
\mathbf{M_{x^n}} \end{bmatrix}
:param species: the name of the species/variables (typically `['y_0', 'y_1', ..., 'y_n']`)
:type species: list[`sympy.Symbol`]
:param propensity: the reactions describes by the model
:param n_counter: a list of :class:`~means.core.descriptors.Moment`\s representing central moments
:type n_counter: list[:class:`~means.core.descriptors.Moment`]
:param stoichiometry_matrix: the stoichiometry matrix
:type stoichiometry_matrix: `sympy.Matrix`
:return: a matrix in which each row corresponds to a reaction, and each column to an element of counter.
"""
# compute derivatives :math:`\frac{\partial^n \mathbf{n}a_l(\mathbf{x})}{\partial \mathbf{x^n}}`
# for EACH REACTION and EACH entry in COUNTER
derives =[derive_expr_from_counter_entry(reac, species, c.n_vector) for (reac, c) in itertools.product(propensity, n_counter)]
# Computes the factorial terms (:math:`\frac{1}{\mathbf{n!}}`) for EACH REACTION and EACH entry in COUNTER
# this does not depend of the reaction, so we just repeat the result for each reaction
factorial_terms = [get_one_over_n_factorial(tuple(c.n_vector)) for c in n_counter] * len(propensity)
# we make a matrix in which every element is the entry-wise multiplication of `derives` and factorial_terms
taylor_exp_matrix = sp.Matrix(len(propensity), len(n_counter), [d*f for (d, f) in zip(derives, factorial_terms)])
# dmu_over_dt is the product of the stoichiometry matrix by the taylor expansion matrix
return stoichiometry_matrix * taylor_exp_matrix
|
13,822 | 66e6359405eb4cc180305664f8090fe6b42c88dd | h ,w = map(int,input().split())
a = [list(map(int, input().split())) for i in range(h)]
minlist = []
suma = []
for i in range(h):
minlist.append(min(a[i]))
suma.append(sum(a[i]))
print(sum(suma)-(min(minlist)*h*w)) |
13,823 | b9eb367e30fa62b32300d7fdbb09f4d1a62ae182 |
class ApiException(Exception):
def __init__(self, code):
self.code = code
print("We got a problem! {}".format(self.code))
|
13,824 | 2fa916805abe3e1f654f1de35e73dc0c5afb06c4 | import serial
import matplotlib.pyplot as plt
import numpy as np
from time import sleep, time
values = []
plt.ion()
s = serial.Serial('COM7',9600) # check your arduino code baudrate
#plt.ion()
#plt.show()
#while True:
# print([float(item) for item in str(s.readline()).split(',')[1:-1]])
data = np.random.random(50)
def live_update_demo():
global data
plot, = plt.plot(np.random.randn(50))
redraw_figure()
t_start = time()
for i in range(1000):
data = np.roll(data, -1)
try:
serialdata = str(s.readline())
print(serialdata)
read = serialdata.split(',')[1:-1:2]
#nums = [float(item.split(':')[1]) for item in read]
data[-1] = read[0]
print(read)
#data[-1] = num[0]
except:
data[-1] = 0
plot.set_ydata(data)
redraw_figure()
print('Mean Frame Rate: %.3gFPS' % ((i+1) / (time() - t_start)))
def redraw_figure():
plt.draw()
plt.pause(0.05)
live_update_demo()
|
13,825 | aa1d0a3d822939fc6ea9588f084314b3fd227734 | # -*- coding: utf-8 -*-
"""
Created on Thu Dec 1 21:22:55 2016
"""
#==============================================================================
# The function get unprocessed email text, removes the header and returns
# only the body of the email for further processing.
#==============================================================================
#import numpy as np
#import itertools
def get_email_text(filename):
# Import the email modules we'll need
from email.parser import Parser
import email
parser = Parser()
file = open(filename, 'r', encoding='ISO-8859-1')
emailText = file.read()
email = parser.parsestr(emailText)
# Getting the fields of email. Commented out.
#print(email.get('From'))
#print(email.get('To'))
#print(email.get('Subject'))
# Getting the body of the email
textlist = []
if email.is_multipart():
for part in email.get_payload():
textlist.extend(email.get_payload())
text = [items.as_string() for items in textlist]
email_text = '\n'.join(text)
else:
email_text = email.get_payload()
file.close()
return email_text
#==============================================================================
# The function below takes an email file and looks for matches in a spam
# vocabulary and returns a feature vector of the matches. The vocabulary
# file can be changed foe later use.
#==============================================================================
def process_email(filename):
import re
import numpy as np
import pandas as pd
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
#==============================================================================
# Process email function
#==============================================================================
email_pattern = r'[A-Z0-9._%+-]+@[A-Z0-9._%+-]+\.[A-Z]{2,4}'
email_regex = re.compile(email_pattern, flags = re.IGNORECASE)
#url_pattern = r'(https?:\/\/)?([\da-z\.-]+)\.([a-z\.]{2,6})([\/\w\.-]*)*\/?'
url_pattern = r'(http|https)://[^\s]*'
url_regex = re.compile(url_pattern, flags = re.IGNORECASE)
number_pattern = r'[0-9]+'
number_regex = re.compile(number_pattern)
dollar_pattern = r'[$]+'
dollar_regex = re.compile(dollar_pattern)
http_pattern = r'<[^<>]+>'
http_regex = re.compile(http_pattern)
nonword_pattern = r'[^a-zA-Z0-9]'
nonword_regex = re.compile(nonword_pattern)
email_list = []
# Get the body of the email
line = get_email_text(filename)
# process the body of the email.
line = line.lower()
line = http_regex.sub(' ',line)
line = email_regex.sub('emailaddr',line)
line = url_regex.sub('httpaddr',line)
line = number_regex.sub('number',line)
line = dollar_regex.sub('dollar',line)
line = nonword_regex.sub(' ',line)
listline = line.split()
newline = []
for word in listline:
word = word.strip()
word = stemmer.stem(word)
newline.append(word)
# print(line)
email_list.extend(newline)
# print(email_list)
vocab_filename = '../vocab.txt'
b = pd.read_table(vocab_filename, header = None)
vocab = pd.DataFrame(b)
vocab = pd.Series(vocab[1])
invocab = np.array(vocab[vocab.isin(email_list)].index)
# print(invocab)
x = np.zeros(len(vocab))
x[invocab] = 1
# Sanity checks
# print(invocab.shape)
# print(x.shape)
# print(x[x==1].shape)
return x
|
13,826 | ad41ebe98df5aad61776c19f7fe6eaf292f43391 | import unicornhathd
from random import randint
from flask import Flask, request, jsonify, render_template
app = Flask(__name__)
width, height = unicornhathd.get_shape()
unicornhathd.brightness(1.0)
@app.route('/', methods=['GET'])
def index():
return render_template('index.html')
@app.route('/paint', methods=['POST'])
def paint():
try:
result = request.json
for x in range(width):
for y in range(height):
if result[x][y] == '1':
r, g, b = randint(0, 255), randint(0, 255), randint(0, 255)
else:
r, g, b = 0, 0, 0
unicornhathd.set_pixel(x, y, r, g, b)
# change flash timming
unicornhathd.show()
return jsonify({'result': 0})
except:
unicornhathd.off()
return jsonify({'result': 1})
if __name__ == '__main__':
try:
app.run(debug=True, host='0.0.0.0', port=8888)
except KeyboardInterrupt:
unicornhathd.off()
|
13,827 | 223be7cea879796c52940d78afc7b1d5fe1e181e |
in_file = 'A-large.in'
Type = 'large'
out_file = 'A-{0}.out'.format(Type)
with open(in_file,'r') as f:
data = f.readlines()
Tt = int(data[0])
del data[0]
OUT = []
for k in range(Tt):
# Simple loop through (both sides???)
Seq, K = data[k].split()
Seq = [1 if l == "+" else 0 for l in Seq]
Copy_Seq = Seq.copy()
K = int(K)
Count = 0
for P in range(len(Seq) - K + 1):
if Seq[P] == 0:
Count += 1
Seq[P:(P+K)] = [1 - el for el in Seq[P:(P+K)]]
if sum(Seq) == len(Seq): OUT.append(Count)
else: OUT.append("IMPOSSIBLE")
with open(out_file,'w') as f:
for i in range(Tt):
f.write('Case #{0}: {1}\n'.format(i+1,OUT[i]))
|
13,828 | 40f8762b270a0ba2548fd57200dcf6fe45c2eb26 | # -*- coding: utf-8 -*-
"""proj.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1HZAHtZ5EyfBGqT_9kt3rIWTo1YS9iYpg
"""
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
# import keras
from tensorflow.keras.optimizers import Adam
from keras.utils.np_utils import to_categorical
from tensorflow.keras.layers import Dropout, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
import cv2
from sklearn.model_selection import train_test_split
import pickle
import os
import pandas as pd
import random
from keras.preprocessing.image import ImageDataGenerator
# imagefile = r'myData/'
# ################# Parameters #####################
# path = imagefile # folder with all the class folders
# labelFile = 'labelsmod5.csv' # file with all names of classes
# batch_size_val=50 # how many to process together
# steps_per_epoch_val=2000
# epochs_val=10
# imageDimesions = (32,32,3)
# testRatio = 0.2 # if 1000 images split will 200 for testing
# validationRatio = 0.2 # if 1000 images 20% of remaining 800 will be 160 for validation
# ###################################################
# ############################### Importing of the Images
# count = 0
# images = []
# classNo = []
# myList = os.listdir(path)
# print("Total Classes Detected:",len(myList))
# noOfClasses=len(myList)
# print("Importing Classes.....")
# for x in range (0,len(myList)):
# myPicList = os.listdir(path+"/"+str(count))
# for y in myPicList:
# curImg = cv2.imread(path+"/"+str(count)+"/"+y)
# images.append(curImg)
# classNo.append(count)
# print(count, end =" ")
# count +=1
# print(" ")
# images = np.array(images)
# classNo = np.array(classNo)
imagefile = r'myData/'
################# Parameters #####################
path = imagefile # folder with all the class folders
batch_size_val=50 # how many to process together
steps_per_epoch_val=2000
epochs_val= 30
imageDimesions = (32,32,3)
testRatio = 0.2 # if 1000 images split will 200 for testing
validationRatio = 0.2 # if 1000 images 20% of remaining 800 will be 160 for validation
###################################################
############################### Importing of the Images
count = 0
images = []
classNo = []
myList = os.listdir(path)
print("Total Classes Detected:",len(myList))
noOfClasses=len(myList)
print("Importing Classes.....")
for x in range (0,len(myList)):
myPicList = os.listdir(path+"/"+str(count))
for y in myPicList:
curImg = cv2.imread(path+"/"+str(count)+"/"+y)
images.append(curImg)
classNo.append(count)
print(count, end =" ")
count +=1
print(" ")
images = np.array(images)
classNo = np.array(classNo)
############################### Split Data
X_train, X_test, y_train, y_test = train_test_split(images, classNo, test_size=testRatio)
X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validationRatio)
# X_train = ARRAY OF IMAGES TO TRAIN
# y_train = CORRESPONDING CLASS ID
############################### TO CHECK IF NUMBER OF IMAGES MATCHES TO NUMBER OF LABELS FOR EACH DATA SET
print("Data Shapes")
print("Train",end = "");print(X_train.shape,y_train.shape)
print("Validation",end = "");print(X_validation.shape,y_validation.shape)
print("Test",end = "");print(X_test.shape,y_test.shape)
assert(X_train.shape[0]==y_train.shape[0]), "The number of images in not equal to the number of lables in training set"
assert(X_validation.shape[0]==y_validation.shape[0]), "The number of images in not equal to the number of lables in validation set"
assert(X_test.shape[0]==y_test.shape[0]), "The number of images in not equal to the number of lables in test set"
assert(X_train.shape[1:]==(imageDimesions))," The dimesions of the Training images are wrong "
assert(X_validation.shape[1:]==(imageDimesions))," The dimesionas of the Validation images are wrong "
assert(X_test.shape[1:]==(imageDimesions))," The dimesionas of the Test images are wrong"
############################### READ CSV FILE
labelFile = 'labels.csv' # file with all names of classes
data=pd.read_csv(labelFile)
print("data shape ",data.shape,type(data))
############################### DISPLAY SOME SAMPLES IMAGES OF ALL THE CLASSES
num_of_samples = []
cols = 5
num_classes = noOfClasses
fig, axs = plt.subplots(nrows=num_classes, ncols=cols, figsize=(5, 300))
fig.tight_layout()
for i in range(cols):
for j,row in data.iterrows():
x_selected = X_train[y_train == j]
axs[j][i].imshow(x_selected[random.randint(0, len(x_selected)- 1), :, :], cmap=plt.get_cmap("gray"))
axs[j][i].axis("off")
if i == 2:
axs[j][i].set_title(str(j)+ "-"+row["Name"])
num_of_samples.append(len(x_selected))
############################### DISPLAY A BAR CHART SHOWING NO OF SAMPLES FOR EACH CATEGORY
print(num_of_samples)
plt.figure(figsize=(12, 4))
plt.bar(range(0, num_classes), num_of_samples)
plt.title("Distribution of the training dataset")
plt.xlabel("Class number")
plt.ylabel("Number of images")
plt.show()
############################### PREPROCESSING THE IMAGES
def grayscale(img):
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
return img
def equalize(img):
img =cv2.equalizeHist(img)
return img
def preprocessing(img):
img = grayscale(img) # CONVERT TO GRAYSCALE
img = equalize(img) # STANDARDIZE THE LIGHTING IN AN IMAGE
img = img/255 # TO NORMALIZE VALUES BETWEEN 0 AND 1 INSTEAD OF 0 TO 255
return img
X_train=np.array(list(map(preprocessing,X_train))) # TO IRETATE AND PREPROCESS ALL IMAGES
X_validation=np.array(list(map(preprocessing,X_validation)))
X_test=np.array(list(map(preprocessing,X_test)))
# cv2.imshow("GrayScale Images",X_train[random.randint(0,len(X_train)-1)]) # TO CHECK IF THE TRAINING IS DONE PROPERLY
############################### ADD A DEPTH OF 1
X_train=X_train.reshape(X_train.shape[0],X_train.shape[1],X_train.shape[2],1)
X_validation=X_validation.reshape(X_validation.shape[0],X_validation.shape[1],X_validation.shape[2],1)
X_test=X_test.reshape(X_test.shape[0],X_test.shape[1],X_test.shape[2],1)
############################### AUGMENTATAION OF IMAGES: TO MAKEIT MORE GENERIC
dataGen= ImageDataGenerator(width_shift_range=0.1, # 0.1 = 10% IF MORE THAN 1 E.G 10 THEN IT REFFERS TO NO. OF PIXELS EG 10 PIXELS
height_shift_range=0.1,
zoom_range=0.2, # 0.2 MEANS CAN GO FROM 0.8 TO 1.2
shear_range=0.1, # MAGNITUDE OF SHEAR ANGLE
rotation_range=10) # DEGREES
dataGen.fit(X_train)
batches= dataGen.flow(X_train,y_train,batch_size=20) # REQUESTING DATA GENRATOR TO GENERATE IMAGES BATCH SIZE = NO. OF IMAGES CREAED EACH TIME ITS CALLED
X_batch,y_batch = next(batches)
# TO SHOW AGMENTED IMAGE SAMPLES
fig,axs=plt.subplots(1,15,figsize=(20,5))
fig.tight_layout()
for i in range(15):
axs[i].imshow(X_batch[i].reshape(imageDimesions[0],imageDimesions[1]))
axs[i].axis('off')
plt.show()
y_train = to_categorical(y_train,noOfClasses)
y_validation = to_categorical(y_validation,noOfClasses)
y_test = to_categorical(y_test,noOfClasses)
############################### CONVOLUTION NEURAL NETWORK MODEL
def myModel():
no_Of_Filters=60
size_of_Filter=(5,5)
size_of_Filter2=(3,3)
size_of_pool=(2,2)
no_Of_Nodes = 500
model= Sequential()
model.add((Conv2D(no_Of_Filters,size_of_Filter,input_shape=(imageDimesions[0],imageDimesions[1],1),activation='relu')))
model.add((Conv2D(no_Of_Filters, size_of_Filter, activation='relu')))
model.add(MaxPooling2D(pool_size=size_of_pool))
model.add((Conv2D(no_Of_Filters//2, size_of_Filter2,activation='relu')))
model.add((Conv2D(no_Of_Filters // 2, size_of_Filter2, activation='relu')))
model.add(MaxPooling2D(pool_size=size_of_pool))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(no_Of_Nodes,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(noOfClasses,activation='softmax'))
# COMPILE MODEL
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
############################### TRAIN
model = myModel()
print(model.summary())
history=model.fit_generator(dataGen.flow(X_train,y_train,batch_size=batch_size_val),epochs=epochs_val,validation_data=(X_validation,y_validation),shuffle=1)
# no_Of_Filters=60
# size_of_Filter=(5,5) # THIS IS THE KERNEL THAT MOVE AROUND THE IMAGE TO GET THE FEATURES.
# # THIS WOULD REMOVE 2 PIXELS FROM EACH BORDER WHEN USING 32 32 IMAGE
# size_of_Filter2=(3,3)
# size_of_pool=(2,2) # SCALE DOWN ALL FEATURE MAP TO GERNALIZE MORE, TO REDUCE OVERFITTING
# no_Of_Nodes = 500 # NO. OF NODES IN HIDDEN LAYERS
# model= Sequential()
# model.add((Conv2D(no_Of_Filters,size_of_Filter,input_shape=(imageDimesions[0],imageDimesions[1],1),activation='relu'))) # ADDING MORE CONVOLUTION LAYERS = LESS FEATURES BUT CAN CAUSE ACCURACY TO INCREASE
# model.add((Conv2D(no_Of_Filters, size_of_Filter, activation='relu')))
# model.add(MaxPooling2D(pool_size=size_of_pool)) # DOES NOT EFFECT THE DEPTH/NO OF FILTERS
# model.add((Conv2D(no_Of_Filters//2, size_of_Filter2,activation='relu')))
# model.add((Conv2D(no_Of_Filters // 2, size_of_Filter2, activation='relu')))
# model.add(MaxPooling2D(pool_size=size_of_pool))
# model.add(Dropout(0.5))
# model.add(Flatten())
# model.add(Dense(no_Of_Nodes,activation='relu'))
# model.add(Dropout(0.5)) # INPUTS NODES TO DROP WITH EACH UPDATE 1 ALL 0 NONE
# model.add(Dense(noOfClasses,activation='softmax')) # OUTPUT LAYER
# # COMPILE MODEL
# model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])
# print(model.summary())
# history=model.fit_generator(dataGen.flow(X_train,y_train,batch_size=batch_size_val),steps_per_epoch=steps_per_epoch_val,epochs=epochs_val,validation_data=(X_validation,y_validation),shuffle=1)
############################### PLOT
plt.figure(1)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.legend(['training','validation'])
plt.title('loss')
plt.xlabel('epoch')
plt.figure(2)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.legend(['training','validation'])
plt.title('Acurracy')
plt.xlabel('epoch')
plt.show()
score =model.evaluate(X_test,y_test,verbose=0)
print('Test Score:',score[0])
print('Test Accuracy:',score[1])
# #STORE THE MODEL AS A PICKLE OBJECT
# pickle_out= open("model_trained.p","wb") # wb = WRITE BYTE
# pickle.dump(model,pickle_out)
# pickle_out.close()
# cv2.waitKey(0)
# import weakref
# #STORE THE MODEL AS A PICKLE OBJECT
# pickle_out= open("model_trained.p","wb") # wb = WRITE BYTE
# pickle.dump(model,pickle_out)
# pickle_out.close()
# cv2.waitKey(0)
model.save("./training/TrainedModule_epoch30.h5")
# print(data)
# print(data.Name[0])
from keras.models import load_model
model = load_model('./training/TrainedModule_epoch30.h5')
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
def test_on_img(img):
data=[]
image = Image.open(img)
image = image.resize((30,30))
data.append(np.array(image))
X_test=np.array(data)
# Y_pred = model.predict_classes(X_test)
predict_x=model.predict(X_test)
Y_pred=np.argmax(predict_x,axis=1)
return image,Y_pred
# # plot,prediction = test_on_img(r'D:\Traffic_Sign_Recognition\Test\00500.png')
# plot,prediction = test_on_img(r'./Test/001.png')
# s = [str(i) for i in prediction]
# a = int("".join(s))
# print("Predicted traffic sign is: ", s)
# # classes[a]
# plt.imshow(plot)
# plt.show()
classes = { 0:'Speed limit (20km/h)',
1:'Speed limit (30km/h)',
2:'Speed limit (50km/h)',
3:'Speed limit (60km/h)',
4:'Speed limit (70km/h)',
5:'Speed limit (80km/h)',
6:'End of speed limit (80km/h)',
7:'Speed limit (100km/h)',
8:'Speed limit (120km/h)',
9:'No passing',
10:'No passing veh over 3.5 tons',
11:'Right-of-way at intersection',
12:'Priority road',
13:'Yield',
14:'Stop',
15:'No vehicles',
16:'Veh > 3.5 tons prohibited',
17:'No entry',
18:'General caution',
19:'Dangerous curve left',
20:'Dangerous curve right',
21:'Double curve',
22:'Bumpy road',
23:'Slippery road',
24:'Road narrows on the right',
25:'Road work',
26:'Traffic signals',
27:'Pedestrians',
28:'Children crossing',
29:'Bicycles crossing',
30:'Beware of ice/snow',
31:'Wild animals crossing',
32:'End speed + passing limits',
33:'Turn right ahead',
34:'Turn left ahead',
35:'Ahead only',
36:'Go straight or right',
37:'Go straight or left',
38:'Keep right',
39:'Keep left',
40:'Roundabout mandatory',
41:'End of no passing',
42:'End no passing veh > 3.5 tons' }
def grayscale(img):
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
return img
def equalize(img):
img =cv2.equalizeHist(img)
return img
def preprocessing(img):
img = grayscale(img)
img = equalize(img)
img = img/255
return img
import cv2
import matplotlib.pyplot as plt
import numpy as np
testImgPath = r'Test/006.jpeg'
imgOrignal = cv2.imread(testImgPath)
# cv2.imshow("Processed Image", imgOrignal)
plt.imshow(imgOrignal)
plt.show()
img = np.array(imgOrignal)
img = cv2.resize(img, (32, 32))
img = preprocessing(img)
# cv2.imshow("Processed Image", img)
plt.imshow(img)
plt.show()
img = img.reshape(1, 32, 32, 1)
predictions = model.predict(img)
classnum = np.argmax(predictions,axis=1)
s = [str(i) for i in classnum]
probabilityValue =np.amax(predictions)
print(classes[classnum[0]])
if probabilityValue > 0.90:
print("Predicted sign : ", classes[classnum[0]])
print("Predicted accuracy : ", probabilityValue)
# classIndex = model.predict_classes(img)
|
13,829 | 2113e51af36f616cac3d40e176f1bfe39dcb32e4 | import os
import torch
import torch.nn as nn
from torchvision.utils import save_image
class FaceGAN:
def __init__(self):
if not os.path.exists(f'models/FaceGAN_dir/faces'):
os.makedirs('models/FaceGAN_dir/faces')
self.device = torch.device('cpu')
self.latent_size = 512
self.generator = nn.Sequential(
# in: latent_size x 1 x 1
nn.ConvTranspose2d(self.latent_size, 1024, kernel_size=4, stride=1, padding=0, bias=False),
nn.BatchNorm2d(1024),
# nn.ReLU(inplace=True),
nn.LeakyReLU(0.2, inplace=True),
# out: 1024 x 4 x 4
nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(512),
# nn.ReLU(inplace=True),
nn.LeakyReLU(0.2, inplace=True),
# out: 512 x 16 x 16
nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(256),
# nn.ReLU(inplace=True),
nn.LeakyReLU(0.2, inplace=True),
# out: 256 x 32 x 32
nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
# nn.ReLU(inplace=True),
nn.LeakyReLU(0.2, inplace=True),
# out: 128 x 64 x 64
nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(64),
# nn.ReLU(inplace=True),
nn.LeakyReLU(0.2, inplace=True),
# out: 128 x 128 x 128
nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1, bias=False),
# nn.Tanh()
nn.Sigmoid()
# out: 3 x 128 x 128
)
self.model = self.generator.to(device=self.device)
self.model.load_state_dict(torch.load('models/model_weights/genr3m128p10e.model', map_location=self.device))
self.model.eval()
async def get_image(self):
"""Generating a random tensor,
passing through the model and saving the picture"""
# generating
fixed_latent = torch.randn(1, 512, 1, 1, device=self.device)
with torch.no_grad():
# passing through
fake_images = self.model(fixed_latent)
# saving
save_image(fake_images, f'models/FaceGAN_dir/faces/fake.jpg')
async def remove_image(self):
if os.path.isfile(f'models/FaceGAN_dir/faces/fake.jpg'):
os.remove(f'models/FaceGAN_dir/faces/fake.jpg')
if __name__ == '__main__':
inst = FaceGAN()
#inst.get_image()
inst.remove_image()
|
13,830 | 9cc62b9bee9503a5c9137c8ba1f59ec2e042f521 | #script iterates through each juror. Transcribes speech to text so that it can be implemented for NLP
#to predict sentiment of each juror towards specific topics.
import speech_recognition as sr
import pyaudio
import pandas as pd
from helperFunctions import *
import numpy as np
#load model
loaded_model = load_model(r'C:\Users\19712\my_model')
#transcribes speech to text
jurors = ['Zack', 'Ben']
storage = []
while len(storage) < len(jurors):
print('Juror' + ' ' + jurors[len(storage)] + ' ' + 'is speaking:')
init_rec = sr.Recognizer()
with sr.Microphone() as source:
audio_string = []
audio_data = init_rec.adjust_for_ambient_noise(source)
audio_data = init_rec.listen(source) #each juror speaks for 10 seconds
audio_text = init_rec.recognize_google(audio_data)
print('End of juror' + ' ' + jurors[len(storage)] + ' ' + 'speech')
#storage of all spoken text (maybe convert to dict for key, value with juror name)
storage.append(audio_text)
audio_string.append(audio_text)
#funtions
cleaned = clean_text(audio_string)
tokenized = tokenize_text(audio_string)
padded_text = padding(audio_string, tokenized) #fix padded text elongating rows
#sentiment
loaded_score = loaded_model.predict(padded_text)
y_loaded_pred = np.argmax(loaded_score, axis = 1).reshape(-1,1)
y_sentiment = np.vectorize(label_dict.get)(y_loaded_pred)
|
13,831 | d4a8c684fa1190741a57e2f580f5e8e0f2d41c8c | import numpy as np
from sklearn import metrics
from model.sklearn_multiclass import sklearn_multiclass_prediction
from model.self_multiclass import MulticlassSVM
if __name__ == '__main__':
print('Loading data...')
mnist = np.loadtxt('data/mnist_test.csv', delimiter=',')
X_train = mnist[:len(mnist)//2, 1:]
y_train = mnist[:len(mnist)//2, 0].astype(np.int)
X_test = mnist[len(mnist)//2:, 1:]
y_test = mnist[len(mnist)//2:, 0].astype(np.int)
print('Training Sklearn OVR...')
y_pred_train, y_pred_test = sklearn_multiclass_prediction(
'ovr', X_train, y_train, X_test)
print('Sklearn OVR Accuracy (train):',
metrics.accuracy_score(y_train, y_pred_train))
print('Sklearn OVR Accuracy (test) :',
metrics.accuracy_score(y_test, y_pred_test))
print('Training Sklearn OVO...')
y_pred_train, y_pred_test = sklearn_multiclass_prediction(
'ovo', X_train, y_train, X_test)
print('Sklearn OVO Accuracy (train):',
metrics.accuracy_score(y_train, y_pred_train))
print('Sklearn OVO Accuracy (test) :',
metrics.accuracy_score(y_test, y_pred_test))
print('Training Sklearn Crammer-Singer...')
y_pred_train, y_pred_test = sklearn_multiclass_prediction(
'crammer', X_train, y_train, X_test)
print('Sklearn Crammer-Singer Accuracy (train):',
metrics.accuracy_score(y_train, y_pred_train))
print('Sklearn Crammer-Singer Accuracy (test) :',
metrics.accuracy_score(y_test, y_pred_test))
print('Training self OVR...')
self_ovr = MulticlassSVM('ovr')
self_ovr.fit(X_train, y_train)
print('Self OVR Accuracy (train):',
metrics.accuracy_score(y_train, self_ovr.predict(X_train)))
print('Self OVR Accuracy (test) :',
metrics.accuracy_score(y_test, self_ovr.predict(X_test)))
print('Training self OVO...')
self_ovo = MulticlassSVM('ovo')
self_ovo.fit(X_train, y_train)
print('Self OVO Accuracy (train):',
metrics.accuracy_score(y_train, self_ovo.predict(X_train)))
print('Self OVO Accuracy (test) :',
metrics.accuracy_score(y_test, self_ovo.predict(X_test)))
print('Training self Crammer-Singer...')
self_cs = MulticlassSVM('crammer-singer')
self_cs.fit(X_train, y_train)
print('Self Crammer-Singer Accuracy (train):',
metrics.accuracy_score(y_train, self_cs.predict(X_train)))
print('Self Crammer-Singer Accuracy (test) :',
metrics.accuracy_score(y_test, self_cs.predict(X_test)))
|
13,832 | 63562b3789c1e6a5c003d5733f568e14111c94bb | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from zabbix_api import ZabbixAPI
zapi = ZabbixAPI(server="http://192.168.25.3")
zapi.login("Admin", "zabbix")
hosts = zapi.host.get({
"output": [
"hostid",
"host"
],
"sortfield": "host"
})
for x in hosts:
print x["hostid"], "- ", x["host"]
|
13,833 | 8bb30f74c42eb1fb94acd365ad7d816bf89895ee | # Generated by Django 2.0.8 on 2018-10-08 14:13
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='permisos',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
],
options={
'permissions': (('usuarios', 'Permiso al modulo de usuarios'), ('academico', 'Permiso al modulo academico'), ('conf_evaluaion', 'Permiso al modulo de inilisacion de evaluacion'), ('evaluacion', 'Permiso al modulo de evaluacion')),
},
),
]
|
13,834 | db0017a862a785d30768995752b1c453649ab02c | from sqlalchemy import Boolean, Column, ForeignKey, Integer, String, DateTime
from sqlalchemy.orm import relationship
from db_models.base_class import Base
class DimField(Base):
__tablename__ = "dim_field"
field_key = Column(Integer, nullable=False, primary_key=True)
project_id = Column(String(255), nullable=False)
name = Column(String(255), nullable=False)
control_type = Column(String(255), nullable=True, default=None)
default_value = Column(String(255), nullable=True, default=None)
counted_character = Column(Boolean, nullable=False, default=True)
counted_character_date_from_key = Column(Integer, nullable=False)
counted_character_time_from_key = Column(Integer, nullable=False)
counted_character_date_to_key = Column(Integer, nullable=False)
counted_character_time_to_key = Column(Integer, nullable=False)
is_sub_field = Column(Boolean, nullable=False, default=False)
__table_args__ = {"schema": "dwh_development_analytic"} |
13,835 | adaa6e582fe533affd05303a07256280f910d889 | from django.contrib.auth.models import AbstractUser
from django.db import models
from django.conf import settings
from django.db.models.signals import post_save
from django.dispatch import receiver
from rest_framework.authtoken.models import Token
# from django.contrib.auth.models import User
@receiver(post_save, sender=settings.AUTH_USER_MODEL)
def create_auth_token(sender, instance=None, created=False, **kwargs):
if created:
Token.objects.create(user=instance)
user_obj = User.objects.get(username=instance)
token_obj = Token.objects.get(user=user_obj)
user_obj.token = str(token_obj.key)
user_obj.save()
class User(AbstractUser):
class Meta:
db_table = 'users'
unique_together = ('email',)
addedBy = models.ForeignKey("self", blank=True, null=True, on_delete=models.CASCADE)
def __str__(self):
return self.username |
13,836 | b4bba7196c2d700a3f23a8c47d2d7178cce1a3e6 | # Write a program to sort a list of tuples using Lambda.
# Original list of tuples: [('English', 88), ('Science', 90), ('Maths', 97), ('Social sciences', 82)] Sorting the List of Tuples: [('Social sciences', 82), ('English', 88), ('Science', 90), ('Maths', 97)]
marks = [('English', 88), ('Science', 90), ('Maths', 97), ('Social sciences', 82)]
print("Original list of tuples:", marks)
marks.sort(key = lambda x: x[1])
print("Sorting the List of Tuples:", marks)
|
13,837 | e6a62a9f023489e25084fa8c5a9fd77f75340639 | from base import Base
from globals import PEOPLE, presence_state
from typing import Tuple, Union
"""
Class LowBatteryManager manages the low battery warning TTS
"""
class LowBatteryManager(Base):
def initialize(self) -> None:
"""Initialize."""
super().initialize() # Always call base class
self._people = self.args.get("people", {})
self._low_bat_level = int(self.args.get("battery_level_low", "15"))
self._tts_device = self.args.get("tts_device", "media_player.house")
for person in self._people:
self.log("Setup tracker {}".format(PEOPLE[person]['device_tracker']))
self.listen_state(
self.__on_tracker_changed,
entity=PEOPLE[person]['device_tracker'],
attribute="all",
person=person
)
def __on_tracker_changed(
self, entity: Union[str, dict], attribute: str, old: dict,
new: dict, kwargs: dict) -> None:
if old is None:
return
person = kwargs['person']
batt_level = int(new["attributes"].get("battery_level", "100"))
old_bat_lev = int(old["attributes"].get("battery_level", "100"))
state = new["state"]
if batt_level != old_bat_lev and self.now_is_between("07:00:00", "22:30:00"):
self.log("{} changed battery status from {} to {}".format(entity, old_bat_lev, batt_level))
if old_bat_lev > self._low_bat_level and \
batt_level<=self._low_bat_level and \
state==presence_state["home"] and \
self.now_is_between("07:00:00", "22:30:00"):
# Battery level went from over min level to under min level and the person is home, lets warn!
self.tts_manager.speak("{}, dags att ladda din mobil. {} ladda din mobil nu!".format(person, person), media_player=self._tts_device) |
13,838 | 9784f69a3356e9cf372353e92ad48256e46e37ef | class Test():
def __init__(self,name,duration,machines,resources):
self.name=name
self.duration=duration
self.machines=machines
self.resources=resources
self.run_time=0
def can_run(self,target_machine):
if not self.machines:
return True
if target_machine in self.machines:
return True
else: return False
"""
slower but supports multiple resources of the same type
"""
def resources_available(self,resources):
if not self.resources:
return True
cnt=0
for i in set(self.resources):
cur_res=resources[resources.index(i)]
if cur_res.current+len(filter(lambda x: x==cur_res.name,self.resources))<=cur_res.n:
cnt+=1
if cnt==len(set(self.resources)):
return True
else: return False
"""
faster but assumes only one resource of each type is available
"""
def resources_available2(self,resources):
if not self.resources:
return True
cnt=0
for i in self.resources:
for j in resources:
if i==j.name and j.current<j.n:
cnt+=1
if cnt==len(self.resources):
return True
else: return False
def unlock_resources(self,resources):
if self.resources:
for i in self.resources:
resources[resources.index(i)].current-=1
def __repr__(self):
return self.name
|
13,839 | 6aceac3cdbb1e47a96edb5bf6e5aae1b94d3f0bf | #P4HW1
#CTI110
#Fatmata Dumbuya
#07/05/2018
speed = int(input("what is the speed of the vechicle in mph:"))
hours = int(input("how many hours has it travelled:"))
count = 0
print ("Hours Distance travelled")
print ("........................")
while count < hours:
count = count + 1
print(" ", count, " " ,speed * count )
#print ("distance:", (speed * hour))
|
13,840 | b37c4ac9d938f0364aa08774214e29f730509a7c | from method import df_banknote
def main():
df = df_banknote
# question 1
data = df['class'].tolist()
# set the color
color = []
for row in data:
if row == 0:
color.append('green')
else:
color.append('red')
# add color column
df.insert(5, 'Color', color)
# question 2
result1 = df.groupby(['class'])[['variance', 'skewness']].agg(['mean', 'std'])
result2 = df.groupby(['class'])[['curtosis', 'entropy']].agg(['mean', 'std'])
f1 = df['variance'].agg(['mean', 'std'])
f2 = df['skewness'].agg(['mean', 'std'])
f3 = df['curtosis'].agg(['mean', 'std'])
f4 = df['entropy'].agg(['mean', 'std'])
print(result1)
print(result2)
print('\nmean and standard deviation for all')
print(f1, '\n', f2, '\n', f3, '\n', f4)
# save file
df.to_csv('../datasets/data_banknote_color.csv', index=False)
main()
|
13,841 | 372651d7a91a6c704044d1a9ae1cae94f4d996b5 | # -*- coding: utf-8 -*-
"""
YOUR HEADER COMMENT HERE
@author: Sarah Barden
"""
import random
import math
from load import load_seq
random.seed(5845)
from amino_acids import aa, codons, aa_table # you may find these useful
def shuffle_string(s):
"""Shuffles the characters in the input string
NOTE: this is a terhelper function, you do not
have to modify this in any way """
return ''.join(random.sample(s, len(s)))
# YOU WILL START YOUR IMPLEMENTATION FROM HERE DOWN ###
def get_complement(nucleotide):
""" Returns the complementary nucleotide
nucleotide: a nucleotide (A, C, G, or T) represented as a string
returns: the complementary nucleotide
>>> get_complement('A')
'T'
>>> get_complement('C')
'G'
"""
if nucleotide == 'A':
return 'T'
elif nucleotide == 'T':
return 'A'
elif nucleotide == 'C':
return 'G'
elif nucleotide == 'G':
return 'C'
def get_reverse_complement(dna):
""" Computes the reverse complementary sequence of DNA for the specfied DNA
sequence
dna: a DNA sequence represented as a string
returns: the reverse complementary DNA sequence represented as a string
>>> get_reverse_complement("ATGCCCGCTTT")
'AAAGCGGGCAT'
>>> get_reverse_complement("CCGCGTTCA")
'TGAACGCGG'
"""
rev_comp = ''
for i in range(0, len(dna)):
nucleo = dna[i]
comp = get_complement(nucleo)
rev_comp = comp + rev_comp
return rev_comp
def split_into_codons(dna):
""" Takes a DNA sequence (a string) and splits it into a list of codons
as a list of strings.
dna: a DNA sequence
returns: DNA sequence split into codons
>>> split_into_codons("ATGTGATAG")
['ATG', 'TGA', 'TAG']
>>> split_into_codons("ATGTGATAGCC")
['ATG', 'TGA', 'TAG', 'CC']
"""
dna_split = []
length = math.ceil(len(dna)/3)
for i in range(0, length):
j = 3*i
codon = dna[j:j+3]
dna_split += [codon]
return dna_split
def rest_of_ORF(dna):
""" Takes a DNA sequence that is assumed to begin with a start
codon and returns the sequence up to but not including the
first in frame stop codon (TGA, TAA, or TAG). If there is
no in frame stop codon, returns the whole string.
dna: a DNA sequence
returns: the open reading frame represented as a string
>>> rest_of_ORF("ATGTGAA")
'ATG'
>>> rest_of_ORF("ATGAGATAGG")
'ATGAGA'
"""
index = -1
dna_split = split_into_codons(dna)
for i, j in enumerate(dna_split):
if j == "TAG" or j == "TGA" or j == "TAA":
index = i
if index == -1:
return ''.join(dna_split) # if there is no stop codon
return ''.join(dna_split[:index])
def find_all_ORFs_oneframe(dna):
""" Finds all non-nested open reading frames in the given DNA
sequence and returns them as a list. This function should
only find ORFs that are in the default frame of the sequence
(i.e. they start on indices that are multiples of 3).
By non-nested we mean that if an ORF occurs entirely within
another ORF, it should not be included in the returned list of ORFs.
dna: a DNA sequence
returns: a list of non-nested ORFs
>>> find_all_ORFs_oneframe("ATGCATGAATGTAGATAGATGTGCCC")
['ATGCATGAATGTAGA', 'ATGTGCCC']
>>> find_all_ORFs_oneframe("ATGCCCATGTTTTAG")
['ATGCCCATGTTT']
"""
dna_split = split_into_codons(dna)
i = 0
all_ORFs_oneframe = []
length_of_orf = 0
while i < len(dna_split):
if dna_split[i] == "ATG":
orf = rest_of_ORF(dna[int(i)*3:])
all_ORFs_oneframe += [orf]
length_of_orf = math.ceil(len(orf)/3)
i += length_of_orf
else:
i += 1
return all_ORFs_oneframe
def find_all_ORFs(dna):
""" Finds all non-nested open reading frames in the given DNA sequence in
all 3 possible frames and returns them as a list. By non-nested we
mean that if an ORF occurs entirely within another ORF and they are
both in the same frame, it should not be included in the returned list
of ORFs.
dna: a DNA sequence
returns: a list of non-nested ORFs
>>> find_all_ORFs("ATGCATGAATGTAG")
['ATGCATGAATGTAG', 'ATGAATGTAG', 'ATG']
"""
all_ORFs = []
for i in range(0, 3):
dna_new = dna[i:]
all_ORFs += find_all_ORFs_oneframe(dna_new)
return all_ORFs
def find_all_ORFs_both_strands(dna):
""" Finds all non-nested open reading frames in the given DNA sequence on both
strands.
dna: a DNA sequence
returns: a list of non-nested ORFs
>>> find_all_ORFs_both_strands("ATGCGAATGTAGCATCAAA")
['ATGCGAATG', 'ATGCTACATTCGCAT']
"""
orfs = []
orfs = find_all_ORFs(dna) + find_all_ORFs(get_reverse_complement(dna))
return orfs
def longest_ORF(dna):
""" Finds the longest ORF on both strands of the specified DNA and returns it
as a string
>>> longest_ORF("ATGCGAATGTAGCATCAAA")
'ATGCTACATTCGCAT'
"""
orfs = find_all_ORFs_both_strands(dna)
longest = max(orfs, key=len)
return longest
def longest_ORF_noncoding(dna, num_trials):
""" Computes the maximum length of the longest ORF over num_trials shuffles
of the specfied DNA sequence
dna: a DNA sequence
num_trials: the number of random shuffles
returns: the maximum length longest ORF
"""
i = 0
longest_each_trial = []
while i < num_trials:
shuffled_dna = shuffle_string(dna)
longest_each_trial.append(longest_ORF(shuffled_dna))
i += 1
longest_longest = max(longest_each_trial, key=len)
return len(longest_longest)
def coding_strand_to_AA(dna):
""" Computes the Protein encoded by a sequence of DNA. This function
does not check for start and stop codons (it assumes that the input
DNA sequence represents an protein coding region).
dna: a DNA sequence represented as a string
returns: a string containing the sequence of amino acids encoded by the
the input DNA fragment
>>> coding_strand_to_AA("ATGCGA")
'MR'
>>> coding_strand_to_AA("ATGCCCGCTTT")
'MPA'
"""
dna_codons = split_into_codons(dna)
i = 0
aa_string = ''
while i < len(dna_codons):
if len(dna_codons[i]) == 3:
amino_acid = aa_table[dna_codons[i]]
aa_string += amino_acid
i += 1
return aa_string
def gene_finder(dna):
""" Returns the amino acid sequences that are likely coded by the specified dna
dna: a DNA sequence
returns: a list of all amino acid sequences coded by the sequence dna.
"""
orfs = find_all_ORFs_both_strands(dna)
print(orfs)
threshold = longest_ORF_noncoding(dna, 1000)
print('threshold is', threshold)
print('number of orfs:', len(orfs))
aa_sequences = []
i = 0
while i < len(orfs):
print(len(orfs[i]))
if len(orfs[i]) > threshold:
print('if')
aa_sequences += [coding_strand_to_AA(orfs[i])]
i += 1
print(aa_sequences)
if __name__ == "__main__":
import doctest
dna = load_seq("./data/X73525.fa")
gene_finder(dna)
# doctest.testmod(verbose=True)
# doctest.run_docstring_examples(coding_strand_to_AA, globals())
|
13,842 | 1980f25fa5af936a160ec78a7c8cdb1e9e4f9bab | import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
def preprocess( data ):
data = data[["density","ratio_suger","label"]]
mean = data.groupby( "label" ).mean().values
data_mat = data[["density","ratio_suger"]].values
label_mat = data[["label"]].values
return data_mat, label_mat, mean
#############################################
# linear discriminant for 2 class #
# return value are the weights for the line #
#############################################
def LDA( data_mat, label_mat, mean ):
_,n = data_mat.shape
S_w = np.zeros((n,n))
for x,label in zip(data_mat, label_mat):
temp = x-mean[label]
S_w = S_w+(temp.T @ temp)
S_w = S_w+0.01*np.ones((n,n))
weights = np.linalg.inv(S_w)@(mean[0]-mean[1]).T
return weights
def display( data_mat, label_mat, weights):
m,_ = label_mat.shape
x_cord1 = []
y_cord1 = []
x_cord2 = [] # to show different type of label
y_cord2 = []
for i in range( m ):
if label_mat[i] == 1:
x_cord1.append( data_mat[i,0])
y_cord1.append( data_mat[i,1])
else :
x_cord2.append( data_mat[i,0])
y_cord2.append( data_mat[i,1])
plt.plot( x_cord1,y_cord1,'bo',x_cord2,y_cord2,'ro')
# plt.axis( [0,1,0,1])
x = np.arange(0,1.2,0.1)
y = (weights[0]*x)/weights[1]
plt.plot( x , y )
plt.xlabel( 'density' )
plt.ylabel( 'ratio_suger' )
plt.show()
if __name__ =='__main__':
# read data
df = pd.read_csv("watermelon3.csv")
data_mat, label_mat, mean = preprocess( df )
weights = LDA( data_mat, label_mat, mean )
print( weights)
#display
display( data_mat, label_mat, weights)
|
13,843 | 463622420370c80760f47d48c5b95887cb93a112 | #!/bin/python
def evalue(n):
l = [x for x in str(n)]
return len(set(l)) == len(l)
def evaluate(x, y, z):
l = [x for x in str(x)+str(y)+str(z)]
return len(set(l)) == 9 and len(l) == 9
s = set()
for x in range(10000):
if '0' not in str(x) and evalue(x):
for y in range(10000):
if '0' not in str(y) and '0' not in str(x*y) and evalue(y) and evaluate(x, y, x*y):
s.add(x*y)
print x
print sum(s) |
13,844 | 3959e1163c18ca054b2800a76dc4003bbe14d569 | from __future__ import unicode_literals
from httoop import URI
def test_simple_uri_comparision(uri):
u1 = URI(b'http://abc.com:80/~smith/home.html')
u2 = URI(b'http://ABC.com/%7Esmith/home.html')
u3 = URI(b'http://ABC.com:/%7esmith/home.html')
u4 = URI(b'http://ABC.com:/%7esmith/./home.html')
u5 = URI(b'http://ABC.com:/%7esmith/foo/../home.html')
assert u1 == u2
assert u2 == u3
assert u1 == u3
assert u1 == u4
assert u1 == u5
def test_request_uri_maxlength():
pass
def test_request_uri_is_star():
pass
def test_request_uri_containig_fragment():
pass
def test_invalid_uri_scheme():
pass
def test_invalid_port():
pass
def test_normalized_uri_redirects():
pass
def test_uri_composing_username_and_password():
assert bytes(URI(b'http://username@example.com')) == b'http://username@example.com'
assert bytes(URI(b'http://username:password@example.com')) == b'http://username:password@example.com'
|
13,845 | ff4ab4b0a3a8c119fa97f7fc6a0acbdf017d9e40 | timeout = 0
workers = 2
|
13,846 | 9f86709b223ef19744d54896175dd2267b8167a0 | from django.contrib import admin
from .models import Blog
from .models import Portfolio
admin.site.register(Blog)
admin.site.register(Portfolio) |
13,847 | 0e377f2c78e91552f4464e0ce43c01e20644ffd3 | #!/usr/bin/env python
import sys
for input_line in sys.stdin:
#line = input_line.strip ()
print '%s%s%d' % ("a", "\t", 1)
|
13,848 | f9a277512e0318cd29c5707f723b7b0bc98ac154 | """
:Author: Pauli Virtanen <pauli@ltl.tkk.fi>
:Organization: Low Temperature Laboratory, Helsinki University of Technology
:Date: 2005-2006
A solver for Keldysh-Usadel 1D circuit equations.
To find out how this library can be used, you should peek at
- `solver.Geometry`: How to specify a geometry
- `currents`: Simple interface to calculating currents
- `selfconsistentiteration`: A self-consistent iteration
- `solver`: Low-level solver interface
"""
# Modules to import
from solver import *
from selfconsistentiteration import *
from nonlinearsolver import *
from currents import *
from util import *
from error import *
from version import __version__
__docformat__ = "restructuredtext en"
|
13,849 | 49285b20e9ca63b0b76404da0cfd0b3eb22c8dd3 |
from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^$', views.home, name="home"),
url(r'^login/$', views.login, name="login"),
url(r'^logout/$', views.logout, name="logout"),
url(r'^create/account/$', views.new_account, name="newaccount"),
url(r'^chats/$', views.chats, name="chats"),
url(r'^chats/(?P<chat_id>)/$', views.viewchat, name="viewchat"),
url(r'^chats/(?P<chat_id>)/invite/$', views.invite, name="invite"),
url(r'^create/chat/$', views.new_chat, name="newchat"),
url(r'^create/post/(?P<chat_id>)/$', views.new_post, name="newpost"),
] |
13,850 | 85aed7b348f2e0a57515349e87d3b82a899a0285 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# for localized messages
from . import _
from collections import OrderedDict
from Components.ActionMap import ActionMap
from Components.Sources.List import List
from xStaticText import StaticText
from Screens.Screen import Screen
from Screens.MessageBox import MessageBox
from plugin import skin_path, hdr, cfg, common_path
from Tools.LoadPixmap import LoadPixmap
#download / parse
import urllib2
import xml.etree.ElementTree as ET
import gzip
import xstreamity_globals as glob
import base64
from Tools.BoundFunction import boundFunction
import os
import json
from StringIO import StringIO
class XStreamity_Menu(Screen):
def __init__(self, session):
Screen.__init__(self, session)
self.session = session
skin = skin_path + 'menu.xml'
with open(skin, 'r') as f:
self.skin = f.read()
self.startList = []
self.list = []
self.drawList = []
self["list"] = List(self.drawList)
self.setup_title = (_('Stream Selection'))
self['key_red'] = StaticText(_('Back'))
self.tempcategorytypepath = "/tmp/xstreamity/categories.xml"
self['actions'] = ActionMap(['XStreamityActions'], {
'red': self.quit,
'cancel': self.quit,
'ok' : self.next,
}, -2)
ref = str(glob.current_playlist['playlist_info']['enigma2_api'])
self.protocol = glob.current_playlist['playlist_info']['protocol']
self.domain = glob.current_playlist['playlist_info']['domain']
self.host = glob.current_playlist['playlist_info']['host']
self.username = glob.current_playlist['playlist_info']['username']
self.password = glob.current_playlist['playlist_info']['password']
self.live_categories = "%s/enigma2.php?username=%s&password=%s&type=get_live_categories" % (self.host, self.username, self.password)
self.live_streams = "%s/player_api.php?username=%s&password=%s&action=get_live_streams" % (self.host, self.username, self.password)
if ref:
if not ref.startswith(self.host):
ref = str(ref.replace(self.protocol + self.domain ,self.host))
self.onFirstExecBegin.append(boundFunction(self.downloadEnigma2API, ref))
self.onLayoutFinish.append(self.__layoutFinished)
def __layoutFinished(self):
self.setTitle(self.setup_title)
def quit(self):
if os.path.exists(self.tempcategorytypepath):
os.remove(self.tempcategorytypepath)
self.close()
def next(self):
import categories
import catchup
if self["list"].getCurrent():
self.currentList = {"title": self["list"].getCurrent()[1], "playlist_url": self["list"].getCurrent()[3], "index": self["list"].getCurrent()[0]}
if self["list"].getCurrent()[2] == "3":
self.session.open(catchup.XStreamity_Catchup, self.startList, self.currentList )
else:
self.session.open(categories.XStreamity_Categories, self.startList, self.currentList )
def downloadEnigma2API(self, url):
self.list = []
response = ''
valid = False
if not os.path.exists(self.tempcategorytypepath):
try:
response = checkGZIP(url)
if response != '':
valid = True
try:
content = response.read()
except:
content = response
with open(self.tempcategorytypepath, 'w') as f:
f.write(content)
except Exception as e:
print(e)
pass
except:
pass
else:
valid = True
with open(self.tempcategorytypepath, "r") as f:
content = f.read()
if valid == True and content != '':
root = ET.fromstring(content)
self.list = []
index = 0
for channel in root.findall('channel'):
title64 = channel.findtext('title')
category_id = str(channel.findtext('category_id'))
playlist_url = channel.findtext('playlist_url')
#check if correct port in url
if playlist_url:
if not playlist_url.startswith(self.host):
playlist_url = str(playlist_url.replace(self.protocol + self.domain ,self.host))
title = base64.b64decode(title64).decode('utf-8')
if cfg.showlive.value == True and category_id == "0":
self.startList.append({"title": str(title),"playlist_url": str(playlist_url)})
self.list.append([index, str(title), str(category_id), str(playlist_url)])
if cfg.showvod.value == True and category_id == "1":
self.startList.append({"title": str(title),"playlist_url": str(playlist_url)})
self.list.append([index, str(title), str(category_id), str(playlist_url)])
if cfg.showseries.value == True and category_id == "2":
self.startList.append({"title": str(title),"playlist_url": str(playlist_url)})
self.list.append([index, str(title), str(category_id), str(playlist_url)])
index += 1
if cfg.showcatchup.value == True:
hascatchup = self.checkCatchup()
if hascatchup:
self.list.append([index, "Catch Up TV", "3", self.live_categories])
self.drawList = []
self.drawList = [buildListEntry(x[0],x[1],x[2],x[3]) for x in self.list]
self["list"].setList(self.drawList)
if len(self.list) == 1:
self.next()
self.close()
else:
self.session.openWithCallback(self.close ,MessageBox, _('No data, blocked or playlist not compatible with XStreamity plugin.'), MessageBox.TYPE_WARNING, timeout=5)
def checkCatchup(self):
url = self.live_streams
valid = False
response = ''
self.catchup_all = []
try:
response = checkGZIP(url)
if response != '':
valid = True
except Exception as e:
print(e)
pass
except:
pass
if valid == True and response != '':
try:
self.catchup_all = json.load(response, object_pairs_hook=OrderedDict)
except:
try:
self.catchup_all = json.loads(response, object_pairs_hook=OrderedDict)
except:
pass
for item in self.catchup_all:
if "tv_archive" and "tv_archive_duration" in item :
if int(item["tv_archive"]) == 1 and int(item["tv_archive_duration"]) > 0:
return True
break
return False
def buildListEntry(index, title, category_id, playlisturl):
png = None
if category_id == "0": png = LoadPixmap(common_path + "live.png")
if category_id == "1": png = LoadPixmap(common_path + "vod.png")
if category_id == "2": png = LoadPixmap(common_path + "series.png")
if category_id == "3": png = LoadPixmap(common_path + "catchup.png")
return (index, str(title), str(category_id), str(playlisturl), png)
def checkGZIP(url):
response = ''
request = urllib2.Request(url, headers=hdr)
try:
response= urllib2.urlopen(request)
if response.info().get('Content-Encoding') == 'gzip':
print "*** content is gzipped %s " % url
buffer = StringIO( response.read())
deflatedContent = gzip.GzipFile(fileobj=buffer)
return deflatedContent
else:
return response
except:
pass
return response
|
13,851 | 8d8c64d0e4abce069198ca94e87510059f6f5981 | game_txt = [ '게임 제목을 입력하세요\n',
'아무것도 입력되지 않았습니다. 다시 진행하세요\n',
'입력하신 "%s"게임 제목은 최대 20자를 초과할 수 없습니다. 다시 입력하세요\n',
'v1.0.0\n',
'게이머의 이름을 입력하세요?\n',
'이름이 입력되지 않았습니다. 다시~\n',
'다시 게임을 할까요?(yes/no) 대소문자 관계 없이 입력\n',
'정확하게 (yes/no)로 입력하세요\n',
'game over !! bye bye~'
]
game_txtEx = { 'GAME_INTRO':'0 ~ 99사이의 값으로만 AI의 값을 예측하여 입력하세요',
'INPUT_EMPTY':'값을 정확하게 입력하세요',
'INPUT_NOT_NUM':'숫자가 아닙니다',
'out_of_bound':'값이 범위를 넘었습니다. 0~99 사이로 다시 입력하세요',
'check_err01':'값이 크다',
'check_err02':'값이 작다',
'check_success':'''
정답입니다. 게이머:{0}, AI:{1}
{name}님의 총 시도 횟수는 {cnt}회 입니다.
획득 점수는 {score}점 입니다.
'''
}
while True :
game_title = input(game_txt[0])
if not game_title :
print(game_txt[1])
elif len(game_title) > 20 :
print(game_txt[2] % ( game_title, len(game_title) ))
else :
break
print('[%s]' % game_title)
cell_amt = 40
form = '={0:^%s}=' % (cell_amt-2)
print('='*cell_amt)
print(form.format(game_title))
print(form.format(game_txt[3]))
print('='*cell_amt)
nameCheck = True
while nameCheck :
gamer_name = input(game_txt[4])
if not gamer_name :
print(game_txt[5])
continue
nameCheck = False
print('정상입력', gamer_name)
game_run = True
while game_run :
ai_num = None
try_count = 0
while True :
while True :
gamer_num = input(game_txtEx['GAME_INTRO']).strip()
if not gamer_num :
print(game_txtEx['INPUT_EMPTY'])
continue
elif not gamer_num.isnumeric() :
print(game_txtEx['INPUT_NOT_NUM'])
continue
gamer_num = int(gamer_num)
if gamer_num < 0 or gamer_num > 99 :
print(game_txtEx['out_of_bound'])
continue
break
import random
if not ai_num :
ai_num = random.randint(0,99)
print('ai_num',ai_num)
try_count +=1
if gamer_num > ai_num :
print(game_txtEx['check_err01'])
elif gamer_num < ai_num :
print(game_txtEx['check_err02'])
else :
print(game_txtEx['check_success'].format(
gamer_num,
ai_num,
score=(100-try_count*10),
cnt=try_count,
name=gamer_name
))
break
while True :
ans = input(game_txt[6]).strip().upper()
if ans == 'YES' :
break
elif ans == 'NO' :
print(game_txt[8])
game_run = False
break
else :
print(game_txt[7])
import sys
sys.exit |
13,852 | a42bcbb9b1f0a1bd08cbac5cd705b118aac81028 | # -*- coding: utf-8 -*-
'''
Created on 2018. 12. 31.
@author: Taehyoung Yim
'''
import unittest
from pathlib import Path
import pathlib
class TestUtils(unittest.TestCase):
def setUp(self):
unittest.TestCase.setUp(self)
self.data_path = str(pathlib.Path(__file__).resolve().parents[3]) + '/data'
def test_check_file_exists(self):
file_path = self.data_path + '/' + 'test' + '.' + 'csv'
self.assertTrue(Path(file_path).is_file(), 'That file is exists.')
def test_check_file_size(self):
file_path = self.data_path + '/' + 'test' + '.' + 'csv'
self.assertNotEqual(Path(file_path).stat().st_size, 0)
|
13,853 | 3ac27ead79db13c03756e47f56855f8dd0bd1a86 | # coding: utf-8
# Tem Vogais Adjacentes | UFCG - PROGRAMAÇÃO 1
# (C) | Alessandro Santos, 2015
def tem_vogais_adjacentes(palavra):
for i_palavra in range(len(palavra) - 1):
if palavra[i_palavra].lower() in "aeiou" \
and palavra[i_palavra + 1].lower() in "aeiou":
return "sim"
return "nao"
palavra = raw_input()
print tem_vogais_adjacentes(palavra)
if __name__ == "__main__":
assert tem_vogais_adjacentes("orfeu") == "sim"
assert tem_vogais_adjacentes("brasil") == "nao"
assert tem_vogais_adjacentes("voo") == "sim"
|
13,854 | c36f9d7d6e70bd5f5e3a91780efb6108ff70fad3 | A,B,C,D=map(int,input().split()) #1行で1スペースあけて入力
l=A+B
r=C+D
if l>r:
print('Left')
elif r==l:
print('Balanced')
else:
print('Right') |
13,855 | 2de6c08d2d616c9ea19d1bba13ac26e275d6c187 |
class StorageRepos(object):
def list(self, filters):
raise NotImplementedError
class Filters(object):
def items(self):
raise NotImplementedError
|
13,856 | d9ead81b9aadb9d80006e6041af3a1e43295fef0 | import random
from Player import Player
from Gamestate import Gamestate
from TreeNode import TreeNode
class MCTSPlayer(Player):
"""AI Player that uses MCTS for choosing action"""
def __init__(self, DISPLAYSURF, actions, actionTime):
super().__init__(DISPLAYSURF)
self.actions = actions
self.time = actionTime
# Chooses next action
def act(self, state):
# Copy the game state
rootState = state.copy()
# Create tree root node
rootNode = TreeNode(None, -1, self.actions, self.time)
rootNode.setRootState(rootState)
# Perform MCTS
rootNode.mcts()
# return the action with highest average score
return rootNode.bestAction()
def update(self, state):
action = self.act(state)
self.winds()
self.move(action)
self.draw() |
13,857 | ba06cd26581ff47817f8f25e82037938bb1e373c | string = "这是新增的文件 为的是看不同commit的效果" |
13,858 | 224508cc10f8c7908af46c243b1d1107f9325729 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on 2017/12/27 14:51
@author: Pete
@email: yuwp_1985@163.com
@file: cdfdemo.py
@software: PyCharm Community Edition
"""
import math
import matplotlib.pyplot as plt
import numpy as np
import sys
"""
The empirical CDF is usually defined as
CDF(x) = "number of samples <= x" / "number of samples"
"""
def cdf(data, hasDuplicate=True):
sortedData = np.sort(data)
dataLength = len(sortedData)
cdfData = np.arange(1, dataLength + 1) / float(dataLength)
retValue = (sortedData, cdfData)
if hasDuplicate:
distinctData = [sortedData[-1]]
distinctCDF = [1.0]
for i in range(dataLength - 2, -1, -1):
if math.fabs(distinctData[-1] - sortedData[i]) > sys.float_info.epsilon:
distinctData.append(sortedData[i])
distinctCDF.append(cdfData[i])
retValue = (np.array(distinctData), np.array(distinctCDF))
return retValue
if __name__ == "__main__":
# some test data
data = [0.02, 0.02, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.4, 0.6, 0.8, 1., 1.2, 1.2, 1.4]
# Calculate the CDF
X, Y = cdf(data)
X2, Y2 = cdf(data, False)
# Plot the function
plt.grid(True)
plt.plot(X, Y, "r--", marker="o")
plt.plot(X2, Y2, marker="*")
plt.show()
print("Done.") |
13,859 | 85be602315f29abca01845a5195d6fd57cb3df2f | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChildSum(nn.Module):
def __init__(self, dim_hidden):
super(ChildSum, self).__init__()
self.i1 = nn.Linear(dim_hidden, dim_hidden)
self.i2 = nn.Linear(dim_hidden, dim_hidden)
self.g1 = nn.Linear(dim_hidden, dim_hidden)
self.g2 = nn.Linear(dim_hidden, dim_hidden)
self.f1 = nn.Linear(dim_hidden, dim_hidden)
self.f2 = nn.Linear(dim_hidden, dim_hidden)
self.o1 = nn.Linear(dim_hidden, dim_hidden)
self.o2 = nn.Linear(dim_hidden, dim_hidden)
def forward(self, h1, h2, c1, c2):
i = F.sigmoid(self.i1(h1)+self.i2(h2))
g = F.tanh(self.g1(h1)+self.g2(h2))
f_1 = F.sigmoid(self.f1(h1))
f_2 = F.sigmoid(self.f2(h2))
o = F.sigmoid(self.o1(h1)+self.o2(h2))
c = i * g + f_1 * c1 + f_2 * c2
h = o * F.tanh(c)
return (h, c)
|
13,860 | 56b2ed5f44a7cc2e09def1e90158e61527b1c960 | print('Здрасьте') |
13,861 | a779757b77fb5964b9a613d4f406fe47c1f9a183 | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
np.random.seed(0)
def linear_regression(X, y, W_init, lr, max_nsteps):
N, D = X.shape
# Assign bias values
X_ = np.ones((N, D+1))
X_[:, 1:] = X
# Initialize weights
W = W_init
# Log history
history = []
step = 0
while True:
# Forward:
# h_i = sum_{j=1}^{D} W_j * X_{i,j}
# === Vectorize ===
# h = X.T W
h = X_ @ W
# Loss:
# L = 1/N sum_{i=1}^{N} 1/2 (h_i - y_i)^2
# === Vectorize ===
# L = 1/2N (h - y).T (h - y)
loss = ((h - y) ** 2).mean() / 2
# Gradient:
# dL/dW_j = 1/N sum_{i=1}^{N} dh_i/dW_j * (h_i - y_i)
# = 1/N sum_{i=1}^{N} X_{i, j} * (h_i - y_i)
# === Vectorize ===
# dL/dW = 1/N (h - y).T dh/dW = (h - y).T X / N
dW = (h - y) @ X_ / N
# Update:
# W_j := W_j - \alpha * dL/dW_j
# === Vectorize ===
# W := W - \alpha * dL/dW
W = W - lr * dW
# Stop condition
history.append((W, loss))
step += 1
if step > max_nsteps:
break
return W, history
|
13,862 | b849237821dcc49283a06b65ce94e3d5bc030d0e | # encoding='utf-8'
import requests
import re
import time
import copy
from bs4 import BeautifulSoup
from collections import defaultdict
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)'
'AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36'}
WIKI_PREFIX = 'http://prts.wiki/w/'
T5_MATERIAL = ['D32钢', '双极纳米片', '聚合剂', '晶体电子单元']
T4_MATERIAL = ['白马醇', '三水锰矿', '五水研磨石', 'RMA70-24', '提纯源岩', '改量装置', '聚酸酯块', '糖聚块', '异铁块',
'酮阵列', '聚合凝胶', '炽合金块', '晶体电路']
T3_MATERIAL = ['扭转醇', '轻锰矿', '研磨石', 'RMA70-12', '固源岩组', '全新装置', '聚酸酯组', '糖组', '异铁组', '酮凝集组',
'凝胶', '炽合金', '晶体元件', '技巧概要·卷3']
# OWNED = {'D32钢': 6, '双极纳米片': 0, '聚合剂': 4,
# '白马醇': 6, '扭转醇': 0, '三水锰矿': 3, '轻锰矿': 25, '五水研磨石': 8, '研磨石': 25, 'RMA70-24': 17, 'RMA70-12': 0,
# '提纯源岩': 6, '固源岩组': 16, '改量装置': 3, '全新装置': 3, '聚酸酯块': 3, '聚酸酯组': 62, '糖聚块': 4, '糖组': 15,
# '异铁块': 12, '异铁组': 8, '酮阵列': 9, '酮凝集组': 12, '聚合凝胶': 1, '凝胶': 6, '炽合金块': 7, '炽合金': 106,
# '晶体电子单元': 4, '晶体电路': 0, '晶体元件': 25,
# '技巧概要·卷3': 9}
memory = {}
def master_material(op_name):
"""
given operator's name, return materials his or her skill mastering needs
:param op_name: name of operator, in Chinese, e.g. '阿米娅', '阿米娅(近卫)'
:return: list of length 1, 2 or 3
"""
if op_name in memory:
return memory[op_name]
r = requests.get(WIKI_PREFIX + op_name)
while r.status_code != 200:
print('http get for {0} failed: {1}'.format(WIKI_PREFIX + op_name, r.status_code))
r = requests.get(WIKI_PREFIX + op_name)
time.sleep(10)
soup = BeautifulSoup(r.content, features='html.parser')
# skill_t is a soup object, `tag`
skill_t = soup.find_all(text=re.compile('达到精英阶段2后解锁'))[-1].parent.parent.parent
# a list of lists of soup object, rank_table[rank][skill]
rank_table = [skill_t.find_all(text=re.compile('等级' + str(x))) for x in range(1, 4)]
# skill_table[skill][master_rank] is the soup object
skill_table = [[rank[skill].parent.next_sibling.next_sibling for rank in rank_table]
for skill in range(len(rank_table[0]))]
memory[op_name] = [[{a['title']: int(a.next_sibling.string) for a in rank.find_all('a')}
for rank in skill] for skill in skill_table]
return memory[op_name]
def material_compound(material):
"""
given a purple or gold material, try to decompose it to blue ones
:param material: standard name of material in Chinese, e.g. '双极纳米片', '五水研磨石'
:return:
"""
if material in T3_MATERIAL:
return {material: 1}
if material in memory:
return memory[material]
r = requests.get(WIKI_PREFIX + material)
while r.status_code != 200:
print('requests get failed', r.status_code)
time.sleep(10)
r = requests.get(WIKI_PREFIX + material)
soup = BeautifulSoup(r.content, features='html.parser')
table = soup.find(text=re.compile('副产物'))
lines = table.parent.parent.parent.find_all('tr')
composition_tag = lines[1]
re0 = {a['title']: int(a.next_sibling.string) for a in composition_tag.find_all('a')}
# print('{0} needs {1}'.format(material, re0))
ret = defaultdict(int)
for mat, num in re0.items():
p_ret = material_compound(mat)
for p_ma, p_num in p_ret.items():
ret[p_ma] += num * p_num
memory[material] = ret
return memory[material]
def op_master_material(op_skills):
"""
calculate materials needed for operators. will NOT decompose the high lv materials into t3 materials
:param op_skills:
:return:
"""
# left = copy.deepcopy(owned)
needed = defaultdict(int)
for operator, skills in op_skills.items():
# op_ret = defaultdict(int)
materials = master_material(operator)
for skill, rank in skills.items():
if isinstance(rank, int):
target_rank = rank
present_rank = 1
else:
target_rank, present_rank = rank
for mat_dict in materials[skill - 1][present_rank: target_rank]:
for mat, num in mat_dict.items():
needed[mat] += num
return dict(needed)
def op_master_needed(op_skills, owned=None):
"""
in t3 materials.
:param op_skills:
:param owned:
:return:
"""
needed = op_master_material(op_skills)
left = copy.deepcopy(owned)
if left:
for m in needed:
needed[m], left[m] = max(0, needed[m] - owned[m]), max(0, owned[m] - needed[m])
t3_needed = defaultdict(int)
for m, n in needed.items():
t3_mats = material_compound(m)
for t3m, t3n in t3_mats.items():
t3_needed[t3m] += n * t3n
if left:
for m in t3_needed:
t3_needed[m] = max(0, t3_needed[m] - left[m])
return dict(t3_needed), left
else:
return dict(t3_needed)
if __name__ == '__main__':
print('testing utils.py')
# print(material_compound('晶体电子单元'))
# print(material_compound('D32钢'))
# print(material_compound('双极纳米片'))
# print(material_compound('聚合剂'))
# print(master_material('白面鸮'))
# print(master_material('阿米娅'))
# print(master_material('阿米娅(近卫)'))
# print(op_master_mater({'能天使': {3: (3, 2)}}))
# print(op_master_mater({'塞雷娅': {1: (3, 1), 2: (3, 1)}}))
# print(op_master_mater({'白面鸮': {2: (3, 2)}}))
# print(op_master_mater({'夜莺': {3: 3}}))
# print(op_master_mater({'能天使': {3: (3, 2)},
# '塞雷娅': {1: (3, 1), 2: (3, 1)},
# '白面鸮': {2: (3, 2)},
# '夜莺': {3: 3}}))
# print(op_master_material({'能天使': {3: (3, 2)},
# '塞雷娅': {1: (3, 1), 2: (3, 1)},
# '白面鸮': {2: (3, 1)},
# '夜莺': {3: 3}}))
# print(op_master_needed({'能天使': {3: (3, 2)},
# '塞雷娅': {1: (3, 1), 2: (3, 1)},
# '白面鸮': {2: (3, 1)},
# '夜莺': {3: 3}}))
# print(op_master_needed({'能天使': {3: (3, 2)},
# '塞雷娅': {1: (3, 1), 2: (3, 1)},
# '白面鸮': {2: (3, 1)},
# '夜莺': {3: 3}}, OWNED))
# print(t3_owned)
|
13,863 | 483f928bbd14ffdd20cf21d077f3158bbdbec619 | import os
import cv2
import random
import numpy as np
from glob import glob
from pathlib import Path
video_count = 0
img_file_count = 0
save_path = "data"
def get_basename(file_path) -> str:
file_name = os.path.basename(file_path).split(".")[0]
return file_name
def get_total_frames_num(path:str) -> int:
cap = cv2.VideoCapture(path)
frames_num = cap.get(7)
return int(frames_num)
def get_video_fps(path:str) -> int:
cap = cv2.VideoCapture(path)
fps = cap.get(5)
return int(fps)
def get_frame_list(num_pick, num_pick_max, video_fps, frame_start, frame_end) -> list:
x_list = list()
per_frame_skip_frame = round(float(video_fps)/float(num_pick))
min_per_frame_skip_frame = round(float(video_fps)/float(num_pick_max))
if per_frame_skip_frame > (frame_end - frame_start):
# raise print("frames are not enough")
return [[],[]]
scope_end = frame_end - per_frame_skip_frame
for i in range(frame_start, scope_end):
skip_frame = random.randrange(min_per_frame_skip_frame, per_frame_skip_frame, 1)
x_list.append([i, i+skip_frame])
random.shuffle(x_list)
split_train = int(len(x_list) * 0.8)
train_x = x_list[:split_train]
test_x = x_list[split_train:]
return (train_x, test_x)
def get_frame_from_video(video_path, frame_num):
cap = cv2.VideoCapture(video_path)
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_num)
success, frame = cap.read()
if not success:
raise print("wrong frame number: {}".format(frame_num))
# cv2.imwrite("img_0.jpg", frame)
# cv2.imshow("preview", frame)
# cv2.waitKey(0)
return frame
def merge_imgs(img_list: list):
img = cv2.vconcat(img_list)
# cv2.imshow("preview", img)
# cv2.waitKey(0)
return img
def save_imgs(save_root:str, action:str, imgs) -> int:
global img_file_count
Path(os.path.join(save_root, action)).mkdir(parents=True, exist_ok=True)
img_file_count += 1
img_path = os.path.join("{}/{}/video_{:03d}_{:06d}.jpg".format(save_root, action, video_count, img_file_count))
cv2.imwrite(img_path, imgs)
return img_file_count
def gen_img_by_frame_num_list(video_path:str, frame_list:list):
print(video_path)
for x in frame_list:
img_stack = list()
for i in x:
img_stack.append(get_frame_from_video(video_path, i))
merged_img = merge_imgs(img_stack)
file_name = get_basename(video_path)
action_name = file_name.split('_')[2]
save_imgs(save_path, action_name, merged_img)
if __name__ == '__main__':
for i in glob("C:/dataset/fall_video/*.avi"):
# global video_count
video_count += 1
video_path = i
print(video_path)
total_frame_num = get_total_frames_num(video_path)
fps = get_video_fps(video_path)
train_x, test_x = get_frame_list(0.5, 1.5, fps, 0, total_frame_num)
gen_img_by_frame_num_list(video_path, train_x)
img_file_count = 0
|
13,864 | c42330e46ae7efcad240d123a9ad9e54059e81a0 | '''
execfile('work11.py')
execfile('work16.py')
def indexx2(indl):
sum=0
for i in range(len(inmap)):
sum=sum+len(inmap[i])
if(indl>sum):
continue
else:
indl=indl-sum
break
return (i)
innmap=[]
for w in inmap:
for x in w:
innmap.append(x)
present=[]
for key in citnet.keys():
present.append(innmap[g.index(key)])
def common(p,q):
p=str(re.sub(r'[^\w]',' ',p))
p=p.lower()
q=str(re.sub(r'[^\w]',' ',q))
q=q.lower()
temp=len(list(set(p.split()).intersection(set(q.split()))))
return temp/(min(len(p.split()),len(q.split()))*1.0)
f=open("nohistry.txt",'w')
citedtimes={}
for w in present:
flag=0
print(present.index(w))
citedtimes[citnet.keys()[present.index(w)]]=[]
val=0.0
for i in range(len(refname2)):
for j in range(len(refname2[i])):
value=0.0
if(len(refname2[i][j].split())>=8 and len(refname2[i][j].split())<=50):
value=common(refname2[i][j],w)
if(value>val):
val=value
stri=refname2[i][j]
if (value>0.7):
flag=1
print("HOOLLAH")
citedtimes[citnet.keys()[present.index(w)]].append(time2[i][j])
if(flag==0):
web=indexx2(innmap.index(w))
f.write("\n"+str(r[web])+"\n"+str(w)+"\n"*2)
print(val)
print("\n"+stri+"\n"*2+w+"\n")
f=open("tired.txt",'w')
for m in citedtimes.keys():
for x in citedtimes[m]:
f.write(m+" "+x+'\n')
'''
biggy={}
for i in range(33):
print(i)
if(i<9):
root="disk"+str(i+1)+".gsd00"+str(i+1)
f=open(root)
ss=f.read()
asb=ss.split('\n'*2)
for w in asb:
flag=0
ww=w.split('\n')
h=[]
for x in ww:
if x.startswith('#index'):
biggy[x[6:]]=[]
inde=x[6:]
if x.startswith('#y'):
biggy[inde].append(x[2:])
else:
root="disk"+str(i+1)+".gsd0"+str(i+1)
f=open(root)
ss=f.read()
asb=ss.split('\n'*2)
for w in asb:
flag=0
ww=w.split('\n')
h=[]
for x in ww:
if x.startswith('#index'):
biggy[x[6:]]=[]
inde=x[6:]
if x.startswith('#y'):
biggy[inde].append(x[2:])
|
13,865 | 11b1189b86b5ebd0d155f21260751aaa0e610966 |
# coding: utf-8
# In[61]:
import requests
import time
import lxml
from lxml import etree
# In[62]:
def Get_Html(url):
headers = {'user-agent': 'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.183 Mobile Safari/537.36 Edg/86.0.622.63'}
response = requests.get(url,headers = headers)
if response.status_code == 200:
response.encoding = 'utf-8'
return response.text
else:
print("请求失败")
# In[63]:
def Get_Info(html):
html = etree.HTML(html)
Mlt = html.xpath('//*[@id="__layout"]/div/div[2]/div[3]/table/tbody/tr')
for ls in Mlt:
rank = ls.xpath('./td[1]/text()')[0]
name = ls.xpath('./td[2]/a/text()')[0]
province = ls.xpath('./td[3]/text()')[0]
score = ls.xpath('./td[4]/text()')[0]
data = {'rank':rank.replace('\n','').replace(' ',''),'name':name,'province':province.replace('\n','').replace(' ',''),
'score':score.replace('\n','').replace(' ','')}
print(data)
# In[64]:
url = 'https://www.shanghairanking.cn/rankings/bcur/2020'
html = Get_Html(url)
Get_Info(html)
time.sleep(1)
|
13,866 | fb11f1003cb16bc7523db874d461d63d4bec5409 | CONFIG = {
'kafka_broker': 'brokerip',
'port': 'port',
'topics': ['array_of_topics']
} |
13,867 | 7225c3c6f470d3df7d24e0b6098262a4d3aee285 | from django.conf import settings
from django.http import HttpResponse, get_host
SETTINGS = getattr(settings, "SITE_ACCESS_SETTINGS", {})
class BasicAuthMiddleware(object):
def process_request(self, request):
if get_host(request).startswith(SETTINGS["basic-auth"]["domain"]):
return self.process_auth(request, SETTINGS["basic-auth"]["realm"])
def process_auth(self, request, realm):
if "HTTP_AUTHORIZATION" in request.META:
auth_method, auth = request.META["HTTP_AUTHORIZATION"].split(" ", 1)
if "basic" == auth_method.lower():
auth = auth.strip().decode("base64")
username, password = auth.split(":", 1)
if username == SETTINGS["basic-auth"]["username"] \
and password == SETTINGS["basic-auth"]["password"]:
return None
response = HttpResponse("Authorization Required", mimetype="text/plain")
response.status_code = 401
response["WWW-Authenticate"] = 'Basic realm="%s"' % realm
return response
|
13,868 | e6c4fbc73eb197439ed83a79e9e57798cbd0431a | from abc import ABC, abstractmethod
from data_type.sentence import Sentence, ProcessSentence
class AbstractPreProcessor(ABC):
@abstractmethod
def transform(self, sent: Sentence, process_sent: ProcessSentence = None) -> Sentence:
return NotImplementedError |
13,869 | 236f6fa78e09d53e460c712e0666e1b892aba5f8 | #the pass keyword can be used to leave areas of the code empty so that they can be worked on later
bool=True
if bool==True:
print("Python in Easy steps")
else:
pass
#if the pass keyword was not inserted after else and a set of commands were also not present then the
#program would have displayed a syntax error
#pass is a placeholder
# the continue keyword is another place holder which can be used at the end of a if statement
|
13,870 | 2718ba4ff6b4624296e30b6b2742c124c259d5bb | from __future__ import division, print_function, absolute_import
import pickle
import numpy as np
import config
import os.path
import codecs
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
import preprocessing_RCNN as prep
import cv2
def load_data(datafile, num_class, save=False, save_path='dataset.pkl'):
fr = codecs.open(datafile, 'r', 'utf-8')#https://blog.csdn.net/weay/article/details/80946152
train_list = fr.readlines()#读取所有的行到数组
labels = []
images = []
for line in train_list:
tmp = line.strip().split(' ')
fpath = tmp[0]
img = cv2.imread(fpath)
img = prep.resize_image(img, config.IMAGE_SIZE, config.IMAGE_SIZE)
np_img = np.asarray(img, dtype="float32")
images.append(np_img)
index = int(tmp[1])
label = np.zeros(num_class)
label[index] = 1
labels.append(label)
if save:
pickle.dump((images, labels), open(save_path, 'wb')) #序列化对象，将对象obj保存到文件file中
fr.close()
return images, labels
def load_from_pkl(dataset_file):
X, Y = pickle.load(open(dataset_file, 'rb')) #反序列化对象，将文件中的数据解析为一个python对象
return X,Y
# Building 'AlexNet'
def create_alexnet(num_classes):
network = input_data(shape=[None, config.IMAGE_SIZE, config.IMAGE_SIZE, 3])
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, num_classes, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
return network
def train(network, X, Y, save_model_path):
# Training
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='output')
if os.path.isfile(save_model_path + '.index'):
model.load(save_model_path)
print('load model...')
for _ in range(5):
model.fit(X, Y, n_epoch=1, validation_set=0.1, shuffle=True,
show_metric=True, batch_size=64, snapshot_step=200,
snapshot_epoch=False, run_id='alexnet_oxflowers17') # epoch = 1000
# Save the model
model.save(save_model_path)
print('save model...')
def predict(network, modelfile, images):
model = tflearn.DNN(network)
model.load(modelfile)
return model.predict(images)
if __name__ == '__main__':
X, Y = load_data(config.TRAIN_LIST, config.TRAIN_CLASS)
net = create_alexnet(config.TRAIN_CLASS)
train(net, X, Y, config.SAVE_MODEL_PATH)
|
13,871 | 011d7062b113e5933daf14fd4782821c37f312a9 | """
Dependency-Injected HTTP metadata.
"""
from typing import Any, Dict, Mapping, Sequence, Type, Union, cast
import attr
from hyperlink import DecodedURL
from zope.interface import Interface, implementer, provider
from twisted.python.components import Componentized
from twisted.web.iweb import IRequest
from .interfaces import (
IDependencyInjector,
IRequestLifecycle,
IRequiredParameter,
)
def urlFromRequest(request: IRequest) -> DecodedURL:
sentHeader = request.getHeader(b"host")
if sentHeader is not None:
sentHeader = sentHeader.decode("charmap")
if ":" in sentHeader:
host, port = sentHeader.split(":")
port = int(port)
else:
host = sentHeader
port = None
else:
client = request.client # type: ignore[attr-defined]
host = client.host
port = client.port
url = DecodedURL.fromText(request.uri.decode("charmap"))
url = url.replace(
scheme="https" if request.isSecure() else "http",
host=host,
port=port,
)
return url
@provider(IRequiredParameter, IDependencyInjector)
class RequestURL:
"""
Require a hyperlink L{DecodedURL} object from a L{Requirer}.
@since: Klein NEXT
"""
@classmethod
def registerInjector(
cls,
injectionComponents: Componentized,
parameterName: str,
requestLifecycle: IRequestLifecycle,
) -> IDependencyInjector:
# typing note: https://github.com/Shoobx/mypy-zope/issues/39
return cast(IDependencyInjector, cls())
@classmethod
def injectValue(
cls,
instance: Any,
request: IRequest,
routeParams: Dict[str, Any],
) -> DecodedURL:
return urlFromRequest(request)
@classmethod
def finalize(cls) -> None:
"Nothing to do upon finalization."
@implementer(IRequiredParameter, IDependencyInjector)
@attr.frozen
class RequestComponent:
"""
Require a hyperlink L{DecodedURL} object from a L{Requirer}.
@since: Klein NEXT
"""
interface: Type[Interface]
def registerInjector(
self,
injectionComponents: Componentized,
parameterName: str,
requestLifecycle: IRequestLifecycle,
) -> IDependencyInjector:
return self
def injectValue(
self, instance: Any, request: IRequest, routeParams: Dict[str, Any]
) -> DecodedURL:
return cast(
DecodedURL,
cast(Componentized, request).getComponent(self.interface),
)
def finalize(cls) -> None:
"Nothing to do upon finalization."
@attr.s(auto_attribs=True, frozen=True)
class Response:
"""
Metadata about an HTTP response, with an object that Klein knows how to
understand.
This includes:
- an HTTP response code
- some HTTP headers
- a body object, which can be anything else Klein understands; for
example, an IResource, an IRenderable, str, bytes, etc.
@since: Klein NEXT
"""
code: int = 200
headers: Mapping[
Union[str, bytes], Union[str, bytes, Sequence[Union[str, bytes]]]
] = attr.ib(factory=dict)
body: Any = ""
def _applyToRequest(self, request: IRequest) -> Any:
"""
Apply this L{Response} to the given L{IRequest}, setting its response
code and headers.
Private because:
- this should only ever be applied by Klein, and
- hopefully someday soon this will be replaced with something that
actually creates a txrequest-style response object.
"""
request.setResponseCode(self.code)
for headerName, headerValueOrValues in self.headers.items():
if not isinstance(headerValueOrValues, (str, bytes)):
headerValues = headerValueOrValues
else:
headerValues = [headerValueOrValues]
request.responseHeaders.setRawHeaders(headerName, headerValues)
return self.body
|
13,872 | 27ac3895691276cf545da7147b8eb2195213dba9 | #!bin/python3
def is_hamiltonian_path(vert_con, v, l, n, s):
if len(l) == n:
return 1
try:
for i in vert_con[v]:
if i not in l:
s += is_hamiltonian_path(vert_con, i, l+[i], n, 0)
else:
pass
return s
except:
return 0
num_T = int(input())
for i in range(num_T):
n, m = list(map(int, input().strip().split(' ')))
verts = list(map(int, input().strip().split(' ')))
vert_con = {}
for i in range(0, m*2, 2):
a = min(verts[i], verts[i+1])
b = max(verts[i], verts[i+1])
try:
if b not in vert_con[a]:
vert_con[a].append(b)
except:
vert_con[a] = [b]
try:
if a not in vert_con[b]:
vert_con[b].append(a)
except:
vert_con[b] = [a]
strt = []
for i, j in vert_con.items():
if len(j) == 1:
strt.append(i)
bool_is_ham = []
for i in [1] + strt:
g = is_hamiltonian_path(vert_con, i, [i], n, 0)
bool_is_ham.append(g >= 1)
if any(bool_is_ham) is True:
print(1)
else:
print(0)
|
13,873 | c764f80a12851abc3f8c42fc58296038fff3192c | """
Django settings for webapp project.
For more information on this file, see
https://docs.djangoproject.com/en/dev/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/dev/ref/settings/
"""
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
import os
BASE_DIR = os.path.dirname(os.path.dirname(__file__))
VERSION = "0.2.0"
API_VERSION = "1"
LOGIN_URL = '/login'
LOGIN_REDIRECT_URL = '/orgs'
LOGOUT_REDIRECT_URL = '/login'
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/dev/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'kb*bltoh!tg%fix9ujft*#6ln2o3!q%2(1x+a@0qf2e5-07%2e'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
TEMPLATE_DEBUG = True
ALLOWED_HOSTS = []
CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',
}
}
# Application definition
INSTALLED_APPS = (
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'bootstrap3',
'django_extensions',
'pytz',
'rest_framework',
'rest_framework.authtoken',
'main',
'account',
)
#REST_FRAMEWORK = {
#'DEFAULT_PERMISSION_CLASSES': ('rest_framework.permissions.IsAdminUser',),
# 'PAGINATE_BY': 10
#}
MIDDLEWARE_CLASSES = (
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
'api.middleware.XsSharing',
'main.middleware.RequireMembershipMiddleware',
#'django.middleware.timezone.TimeZoneMiddleware',
'main.middleware.TimezoneMiddleware',
)
TEMPLATE_CONTEXT_PROCESSORS = (
"django.contrib.auth.context_processors.auth",
"django.core.context_processors.debug",
"django.core.context_processors.i18n",
"django.core.context_processors.media",
"django.contrib.messages.context_processors.messages",
"main.middleware.membership_context_processor",
'django.core.context_processors.request',
)
ROOT_URLCONF = 'bikecounter.urls'
WSGI_APPLICATION = 'bikecounter.wsgi.application'
# Database
# https://docs.djangoproject.com/en/dev/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Internationalization
# https://docs.djangoproject.com/en/dev/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/dev/howto/static-files/
STATIC_URL = '/static/'
# REST Framework Config
REST_FRAMEWORK = {
'DEFAULT_AUTHENTICATION_CLASSES': (
'rest_framework.authentication.TokenAuthentication',
'rest_framework.authentication.SessionAuthentication',
),
# Use hyperlinked styles by default.
# Only used if the `serializer_class` attribute is not set on a view.
'DEFAULT_MODEL_SERIALIZER_CLASS':
'rest_framework.serializers.HyperlinkedModelSerializer',
# Use Django's standard `django.contrib.auth` permissions,
# or allow read-only access for unauthenticated users.
#'DEFAULT_PERMISSION_CLASSES': [
# 'rest_framework.permissions.DjangoModelPermissionsOrAnonReadOnly'
#]
}
BOOTSTRAP3 = {
'jquery_url': '//code.jquery.com/jquery.min.js',
'base_url': '//netdna.bootstrapcdn.com/bootstrap/3.0.3/',
'css_url': None,
'theme_url': '/static/css/bootstrap-flatly.css',
'javascript_url': None,
'horizontal_label_class': 'col-md-2',
'horizontal_field_class': 'col-md-4',
}
|
13,874 | 3d0bdebf29a522508c423272a82679cdb597f517 | from temboo.Library.PagerDuty.Alerts.ListAlerts import ListAlerts, ListAlertsInputSet, ListAlertsResultSet, ListAlertsChoreographyExecution
|
13,875 | aa261aba8e8daab64e60e438fc24bb88f322e7f4 | #!/usr/bin/env python
import ROOT
from array import array
from CMGTools.VVResonances.plotting.TreePlotter import TreePlotter
from CMGTools.VVResonances.plotting.MergedPlotter import MergedPlotter
from CMGTools.VVResonances.plotting.StackPlotter import StackPlotter
from CMGTools.VVResonances.statistics.Fitter import Fitter
from math import log
import os, sys, re, optparse,pickle,shutil,json
ROOT.gROOT.SetBatch(True)
ROOT.gStyle.SetOptStat(0)
parser = optparse.OptionParser()
parser.add_option("-s","--sample",dest="sample",default='',help="Type of sample")
parser.add_option("-c","--cut",dest="cut",help="Cut to apply for shape",default='')
parser.add_option("-o","--output",dest="output",help="Output JSON",default='')
parser.add_option("-m","--min",dest="mini",type=float,help="min MJJ",default=40)
parser.add_option("-M","--max",dest="maxi",type=float,help="max MJJ",default=160)
parser.add_option("--store",dest="store",type=str,help="store fitted parameters in this file",default="")
parser.add_option("--corrFactorW",dest="corrFactorW",type=float,help="add correction factor xsec",default=0.205066345)
parser.add_option("--corrFactorZ",dest="corrFactorZ",type=float,help="add correction factor xsec",default=0.09811023622)
parser.add_option("-f","--fix",dest="fixPars",help="Fixed parameters",default="1")
parser.add_option("--minMVV","--minMVV",dest="minMVV",type=float,help="mVV variable",default=1)
parser.add_option("--maxMVV","--maxMVV",dest="maxMVV",type=float, help="mVV variable",default=1)
parser.add_option("--binsMVV",dest="binsMVV",help="use special binning",default="")
parser.add_option("-t","--triggerweight",dest="triggerW",action="store_true",help="Use trigger weights",default=False)
(options,args) = parser.parse_args()
samples={}
def getBinning(binsMVV,minx,maxx,bins):
l=[]
if binsMVV=="":
for i in range(0,bins+1):
l.append(minx + i* (maxx - minx)/bins)
else:
s = binsMVV.split(",")
for w in s:
l.append(int(w))
return l
def returnString(func,ftype,varname):
if ftype.find("pol")!=-1:
st='0'
for i in range(0,func.GetNpar()):
st=st+"+("+str(func.GetParameter(i))+")"+("*{varname}".format(varname=varname)*i)
return st
else:
return ""
def doFit(fitter,histo,histo_nonRes,label,leg):
params={}
print "fitting "+histo.GetName()+" contribution "
exp = ROOT.TF1("gaus" ,"gaus",55,215)
histo_nonRes.Fit(exp,"R")
gauss = ROOT.TF1("gauss" ,"gaus",74,94)
if histo.GetName().find("Z")!=-1:
gauss = ROOT.TF1("gauss","gaus",80,100)
histo.Fit(gauss,"R")
mean = gauss.GetParameter(1)
sigma = gauss.GetParameter(2)
print "____________________________________"
print "mean "+str(mean)
print "sigma "+str(sigma)
print "set paramters of double CB constant aground the ones from gaussian fit"
fitter.w.var("mean").setVal(mean)
fitter.w.var("mean").setConstant(1)
#fitter.w.var("sigma").setVal(sigma)
#fitter.w.var("sigma").setConstant(1)
print "_____________________________________"
fitter.importBinnedData(histo,['x'],'data')
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(1),ROOT.RooFit.Save(1),ROOT.RooFit.Range(55,120)]) #55,140 works well with fitting only the resonant part
#ROOT.RooFit.Minos(ROOT.kTRUE)
fitter.projection("model","data","x","debugJ"+leg+"_"+label+"_Res.pdf",0,False,"m_{jet}")
c= getCanvas(label)
histo_nonRes.SetMarkerStyle(1)
histo_nonRes.SetMarkerColor(ROOT.kBlack)
histo_nonRes.GetXaxis().SetTitle("m_{jet}")
histo_nonRes.GetYaxis().SetTitleOffset(1.5)
histo_nonRes.GetYaxis().SetTitle("events")
histo_nonRes.Draw("p")
exp.SetLineColor(ROOT.kRed)
exp.Draw("same")
text = ROOT.TLatex()
text.DrawLatexNDC(0.13,0.92,"#font[62]{CMS} #font[52]{Simulation}")
c.SaveAs("debugJ"+leg+"_"+label+"_nonRes.pdf")
params[label+"_Res_"+leg]={"mean": {"val": fitter.w.var("mean").getVal(), "err": fitter.w.var("mean").getError()}, "sigma": {"val": fitter.w.var("sigma").getVal(), "err": fitter.w.var("sigma").getError()}, "alpha":{ "val": fitter.w.var("alpha").getVal(), "err": fitter.w.var("alpha").getError()},"alpha2":{"val": fitter.w.var("alpha2").getVal(),"err": fitter.w.var("alpha2").getError()},"n":{ "val": fitter.w.var("n").getVal(), "err": fitter.w.var("n").getError()},"n2": {"val": fitter.w.var("n2").getVal(), "err": fitter.w.var("n2").getError()}}
params[label+"_nonRes_"+leg]={"mean": {"val":exp.GetParameter(1),"err":exp.GetParError(1)},"sigma": {"val":exp.GetParameter(2),"err":exp.GetParError(2)}}
return params
def getCanvas(name):
c=ROOT.TCanvas(name,name)
c.cd()
c.SetFillColor(0)
c.SetBorderMode(0)
c.SetFrameFillStyle(0)
c.SetFrameBorderMode(0)
c.SetLeftMargin(0.13)
c.SetRightMargin(0.08)
c.SetTopMargin( 0.1 )
c.SetBottomMargin( 0.12 )
return c
label = options.output.split(".root")[0]
t = label.split("_")
el=""
for words in t:
if words.find("HP")!=-1 or words.find("LP")!=-1:
continue
el+=words+"_"
label = el
samplenames = options.sample.split(",")
for filename in os.listdir(args[0]):
for samplename in samplenames:
if not (filename.find(samplename)!=-1):
continue
fnameParts=filename.split('.')
fname=fnameParts[0]
ext=fnameParts[1]
if ext.find("root") ==-1:
continue
name = fname.split('_')[0]
samples[name] = fname
print 'found',filename
sigmas=[]
params={}
legs=["l1","l2"]
plotters=[]
names = []
for name in samples.keys():
plotters.append(TreePlotter(args[0]+'/'+samples[name]+'.root','tree'))
plotters[-1].setupFromFile(args[0]+'/'+samples[name]+'.pck')
plotters[-1].addCorrectionFactor('xsec','tree')
plotters[-1].addCorrectionFactor('genWeight','tree')
plotters[-1].addCorrectionFactor('puWeight','tree')
if options.triggerW: plotters[-1].addCorrectionFactor('triggerWeight','tree')
corrFactor = options.corrFactorW
if samples[name].find('Z') != -1: corrFactor = options.corrFactorZ
if samples[name].find('W') != -1: corrFactor = options.corrFactorW
plotters[-1].addCorrectionFactor(corrFactor,'flat')
names.append(samples[name])
print 'Fitting Mjet:'
histos2D_l2={}
histos2D={}
histos2D_nonRes={}
histos2D_nonRes_l2={}
for p in range(0,len(plotters)):
key ="Wjets"
if str(names[p]).find("ZJets")!=-1: key = "Zjets"
if str(names[p]).find("TT")!=-1: key = "TTbar"
print "make histo for "+key
histos2D_nonRes [key] = plotters[p].drawTH2("jj_l1_softDrop_mass:jj_l2_softDrop_mass",options.cut+"*(jj_l1_mergedVTruth==0)*(jj_l1_softDrop_mass>55&&jj_l1_softDrop_mass<215)","1",80,55,215,80,55,215)
histos2D_nonRes [key].SetName(key+"_nonResl1")
histos2D [key] = plotters[p].drawTH2("jj_l1_softDrop_mass:jj_l2_softDrop_mass",options.cut+"*(jj_l1_mergedVTruth==1)*(jj_l1_softDrop_mass>55&&jj_l1_softDrop_mass<215)","1",80,55,215,80,55,215)
histos2D [key].SetName(key+"_Resl1")
histos2D_nonRes_l2 [key] = plotters[p].drawTH2("jj_l2_softDrop_mass:jj_l1_softDrop_mass",options.cut+"*(jj_l2_mergedVTruth==0)*(jj_l2_softDrop_mass>55&&jj_l2_softDrop_mass<215)","1",80,55,215,80,55,215)
histos2D_nonRes_l2 [key].SetName(key+"_nonResl2")
histos2D_l2 [key] = plotters[p].drawTH2("jj_l2_softDrop_mass:jj_l1_softDrop_mass",options.cut+"*(jj_l2_mergedVTruth==1)*(jj_l2_softDrop_mass>55&&jj_l2_softDrop_mass<215)","1",80,55,215,80,55,215)
histos2D_l2 [key].SetName(key+"_Resl2")
histos2D[key].Scale(35900.)
histos2D_l2[key].Scale(35900.)
histos2D_nonRes[key].Scale(35900.)
histos2D_nonRes_l2[key].Scale(35900.)
############################
tmpfile = ROOT.TFile("test.root","RECREATE")
for key in histos2D.keys():
histos2D_l2[key].Write()
histos2D_nonRes[key].Write()
histos2D_nonRes_l2[key].Write()
histos2D[key].Write()
###########################
for leg in legs:
histos = {}
histos_nonRes = {}
scales={}
scales_nonRes={}
purity = "LPLP"
if options.output.find("HPHP")!=-1:purity = "HPHP"
if options.output.find("HPLP")!=-1:purity = "HPLP"
fitter=Fitter(['x'])
fitter.jetResonanceVjets('model','x')
if options.fixPars!="1":
fixedPars =options.fixPars.split(',')
if len(fixedPars) > 0:
print " - Fix parameters: ", fixedPars
for par in fixedPars:
parVal = par.split(':')
fitter.w.var(parVal[0]).setVal(float(parVal[1]))
fitter.w.var(parVal[0]).setConstant(1)
for key in histos2D.keys():
if leg=="l1":
histos_nonRes [key] = histos2D_nonRes[key].ProjectionY()
histos [key] = histos2D[key].ProjectionY()
else:
histos_nonRes [key] = histos2D_nonRes_l2[key].ProjectionY()
histos [key] = histos2D_l2[key].ProjectionY()
histos_nonRes[key].SetName(key+"_nonRes")
histos [key].SetName(key)
scales [key] = histos[key].Integral()
scales_nonRes [key] = histos_nonRes[key].Integral()
# combine ttbar and wjets contributions:
Wjets = histos["Wjets"]
Wjets_nonRes = histos_nonRes["Wjets"]
if 'TTbar' in histos.keys(): Wjets.Add(histos["TTbar"]); Wjets_nonRes.Add(histos_nonRes["TTbar"])
keys = ["Wjets"]
Wjets_params = doFit(fitter,Wjets,Wjets_nonRes,"Wjets_TTbar",leg)
params.update(Wjets_params)
params["ratio_Res_nonRes_"+leg]= {'ratio':scales["Wjets"]/scales_nonRes["Wjets"] }
if 'Zjets' in histos.keys():
keys.append("Zjets")
fitterZ=Fitter(['x'])
fitterZ.jetResonanceVjets('model','x')
Zjets_params = doFit(fitterZ,histos["Zjets"],histos_nonRes["Zjets"],"Zjets",leg)
params.update(Wjets_params)
params.update(Zjets_params)
params["ratio_Res_nonRes_"+leg]= {'ratio': scales["Wjets"]/scales_nonRes["Wjets"] , 'ratio_Z': scales["Zjets"]/scales_nonRes["Zjets"]}
if "Zjets" in histos.keys() and "TTbar" in histos.keys():
params["ratio_Res_nonRes_"+leg]= {'ratio': scales["Wjets"]/scales_nonRes["Wjets"] , 'ratio_Z': scales["Zjets"]/scales_nonRes["Zjets"],'ratio_TT': scales["TTbar"]/scales_nonRes["TTbar"]}
fitter.drawVjets("Vjets_mjetRes_"+leg+"_"+purity+".pdf",histos,histos_nonRes,scales,scales_nonRes)
del histos,histos_nonRes,fitter,fitterZ
graphs={}
projections=[[1,3],[4,6],[7,10],[11,15],[16,20],[21,26],[27,35],[36,50],[51,61],[62,75],[76,80]]
for key in keys:
graphs[key]=ROOT.TGraphErrors()
n=0
for p in projections:
i1 = histos2D[key].ProjectionY("tmp1",p[0],p[1]).Integral()
i2 = histos2D_nonRes_l2[key].ProjectionY("tmp2",p[0],p[1]).Integral()
i1_l2 = histos2D_l2[key].ProjectionY("tmp1",p[0],p[1]).Integral()
i2_l2 = histos2D_nonRes[key].ProjectionY("tmp2",p[0],p[1]).Integral()
graphs[key].SetPoint(n,55+p[0]*2+(p[1]-p[0]),(i1/i2 +i1_l2/i2_l2)/2.)
if (key=="Wjets") and ("TTbar" in histos2D.keys()):
norm = histos2D["TTbar"].Integral()/histos2D["Wjets"].Integral()
tt_i1 = histos2D["TTbar"].ProjectionY("tmp1",p[0],p[1]).Integral()*norm
tt_i2 = histos2D_nonRes_l2["TTbar"].ProjectionY("tmp2",p[0],p[1]).Integral()
tt_i1_l2 = histos2D_l2["TTbar"].ProjectionY("tmp1",p[0],p[1]) .Integral()*norm
tt_i2_l2 = histos2D_nonRes["TTbar"].ProjectionY("tmp2",p[0],p[1]).Integral()
graphs[key].SetPoint(n,55+p[0]*2+(p[1]-p[0]),(i1/i2 +i1_l2/i2_l2)/2.+(tt_i1/tt_i2 + tt_i1_l2/tt_i2_l2)/2.)
err = ROOT.TMath.Sqrt(pow(ROOT.TMath.Sqrt(i1)/i2 + ROOT.TMath.Sqrt(i2)*i1/(i2*i2),2)+pow(ROOT.TMath.Sqrt(i1_l2)/i2_l2 + ROOT.TMath.Sqrt(i2_l2)*i1_l2/(i2_l2*i2_l2),2))
graphs[key].SetPointError(n,0,err)
print "set point errors "+str(err)
n+=1
func=ROOT.TF1("pol","pol6",55,215)
func2=ROOT.TF1("pol","pol6",55,215)
l="ratio"
for key in graphs.keys():
if key.find("Z")!=-1:
l="ratio_Z"
if key.find("T")!=-1:
l="ratio_TT"
if key.find("W")!=-1:
l="ratio"
if key.find("W")!=-1:
graphs[key].Fit(func)
st = returnString(func,"pol","MJ2")
params["ratio_Res_nonRes_l1"][l] = st
st = returnString(func,"pol","MJ1")
params["ratio_Res_nonRes_l2"][l] = st
else:
graphs[key].Fit(func2)
st = returnString(func2,"pol","MJ2")
params["ratio_Res_nonRes_l1"][l] = st
st = returnString(func2,"pol","MJ1")
params["ratio_Res_nonRes_l2"][l] = st
graphs[key].SetMarkerColor(ROOT.kBlack)
graphs[key].SetMarkerStyle(1)
graphs[key].SetMarkerColor(ROOT.kBlue)
graphs[key].SetMarkerStyle(2)
graphs[key].GetXaxis().SetTitle("m_{jet1}")
graphs[key].GetYaxis().SetTitle("res/nonRes")
graphs[key].GetFunction("pol").SetLineColor(ROOT.kBlack)
graphs[key].GetXaxis().SetRangeUser(55,215)
graphs[key].SetMinimum(0)
c =getCanvas("c")
graphs["Wjets"].Draw("AP")
graphs["Wjets"].GetFunction("pol").Draw("same")
legend = ROOT.TLegend(0.5607383,0.2063123,0.85,0.3089701)
legend.SetLineWidth(2)
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetTextFont(42)
legend.SetTextSize(0.04)
legend.SetTextAlign(12)
legend.AddEntry(graphs["Wjets"],"ratio W+jets + t#bar{t}","lp")
legend.AddEntry(graphs["Wjets"].GetFunction("pol"),"fit ","lp")
legend.Draw("same")
text = ROOT.TLatex()
text.DrawLatexNDC(0.13,0.92,"#font[62]{CMS} #font[52]{Simulation}")
c.SaveAs("debug_corr_l1_l2_Wjets.pdf")
if 'Zjets' in graphs.keys():
c = getCanvas("zjets")
graphs["Zjets"].Draw("AP")
graphs["Zjets"].GetFunction("pol").Draw("same")
legend = ROOT.TLegend(0.5607383,0.2063123,0.85,0.3089701)
legend.SetLineWidth(2)
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetTextFont(42)
legend.SetTextSize(0.04)
legend.SetTextAlign(12)
legend.AddEntry(graphs["Zjets"],"ratio Z+jets m_{jet1}","lp")
legend.AddEntry(graphs["Zjets"].GetFunction("pol"),"fit m_{jet1}","lp")
legend.Draw("same")
text.DrawLatexNDC(0.13,0.92,"#font[62]{CMS} #font[52]{Simulation}")
c.SaveAs("debug_corr_l1_l2_Zjets.pdf")
if 'TTbar' in graphs.keys():
c=getCanvas("ttbar")
graphs["TTbar"].Draw("AP")
graphs["TTbar"].GetFunction("pol").Draw("same")
legend = ROOT.TLegend(0.5607383,0.2063123,0.85,0.3089701)
legend.SetLineWidth(2)
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetTextFont(42)
legend.SetTextSize(0.04)
legend.SetTextAlign(12)
legend.AddEntry(graphs["TTbar"],"ratio TTbar m_{jet1}","lp")
legend.AddEntry(graphs["TTbar"].GetFunction("pol"),"fit m_{jet1}","lp")
legend.Draw("same")
text.DrawLatexNDC(0.13,0.92,"#font[62]{CMS} #font[52]{Simulation}")
c.SaveAs("debug_corr_l1_l2_TTbar.pdf")
if options.store!="":
print "write to file "+options.store
f=open(options.store,"w")
for par in params:
f.write(str(par)+ " = " +str(params[par])+"\n")
|
13,876 | 5de9e72a52fc2dd0d5043eb48f1f922574c84dab | #!/usr/bin/env python
#import unicornhat as unicorn
import sys
import os
path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
if not path in sys.path:
sys.path.insert(1, path)
del path
import Lifi.UnicornWrapper as unicorn
import weakref
from Lifi.tx import LifiTx
if __name__ == "__main__":
unicorn.set_layout(unicorn.AUTO)
unicorn.rotation(0)
unicorn.brightness(0.85)
mymsg = 69
tx = LifiTx(weakref.ref(unicorn))
tx.run(mymsg)
|
13,877 | 2c6cfd2a3a8c67c2ec4ffc804c61bee487d82a40 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
#packeges
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# In[3]:
# Get the Data
#
# Avg. Session Length: Average session of in-store style advice sessions.
# Time on App: Average time spent on App in minutes
# Time on Website: Average time spent on Website in minutes
# Length of Membership: How many years the customer has been a member.
customers = pd.read_csv("EcommerceCustomers.csv")
print(customers.head())
# In[4]:
print(customers.describe())
# In[5]:
print(customers.info())
# In[6]:
# Compare Website vs App on impact to Yearly Spending
sns.set_palette("GnBu_d")
#sns.set_style('whitegrid')
sns.jointplot(x='Time on Website',y='Yearly Amount Spent',data=customers)
sns.plt.show()
# In[7]:
sns.jointplot(x='Time on App',y='Yearly Amount Spent',data=customers)
sns.plt.show()
# In[8]:
# ** Use jointplot to create a 2D hex bin plot comparing Time on App and Length of Membership.**
sns.jointplot(x='Time on App',y='Length of Membership',kind='hex',data=customers)
sns.plt.show()
# In[9]:
# ** Explore relationships across the entire data set.
sns.pairplot(customers)
sns.plt.show()
# In[10]:
# **Create a linear model plot of Yearly Amount Spent vs. Length of Membership. **
sns.lmplot(x='Length of Membership',y='Yearly Amount Spent',data=customers)
sns.plt.show()
# In[11]:
# Training and Testing Data
# Split the data into training and testing sets.
y = customers['Yearly Amount Spent']
X = customers[['Avg. Session Length', 'Time on App','Time on Website', 'Length of Membership']]
# In[15]:
# ** Use model_selection.train_test_split from sklearn to split the data into training and testing sets. Set test_size=0.3 and random_state=101**
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)
# In[16]:
# Training the Model
from sklearn.linear_model import LinearRegression
lm = LinearRegression()
lm.fit(X_train,y_train)
# In[17]:
#The coefficients
print('Coefficients: \n', lm.coef_)
# In[18]:
# ## Predicting Test Data
predictions = lm.predict( X_test)
# In[19]:
# Create a scatterplot of the real test values versus the predicted values. **
plt.scatter(y_test,predictions)
plt.xlabel('Y Test')
plt.ylabel('Predicted Y')
plt.show()
# In[20]:
# Calculate the Mean Absolute Error, Mean Squared Error, and the Root Mean Squared Error.
from sklearn import metrics
print('MAE:', metrics.mean_absolute_error(y_test, predictions))
print('MSE:', metrics.mean_squared_error(y_test, predictions))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))
# In[21]:
# Residuals
#
# Explore the residuals to make sure everything was okay with our data.
#
# Plot a histogram of the residuals and make sure it looks normally distributed.
sns.distplot((y_test-predictions),bins=50)
# In[ ]:
|
13,878 | 495207596f2a11011c74f1d029117adeea6f4677 | import vega.algorithms.nas.modnas.compat
from .compat import ModNasArchSpace
|
13,879 | 10b0b6b973999dda814dcf2ca3e19001008b095a | #!/usr/bin/env python
from PyQt5.QtGui import QColor
from PyQt5.QtWidgets import QColorDialog, QFileDialog
class SettingsController(object):
def __init__(self, model):
self.model = model
def update_default_directory(self, directory = None):
"""Change default directory."""
if not directory:
new_directory = QFileDialog.getExistingDirectory(None, "Change default directory", '/')
else:
new_directory = directory
if not new_directory:
return
self.model.set('Directory', 'default', str(new_directory))
self.model.announce_update()
def update_hotkey(self, selected_option, new_shortcut):
"""Update choosen hotkey."""
self.model.set('Hotkeys', selected_option, new_shortcut)
self.model.announce_update()
def update_background(self):
"""Update background via colorpicker."""
color = QColorDialog().getColor()
self.model.set('Look', 'background', str(color.name(QColor.HexRgb)))
self.model.announce_update()
def update_boolean(self, option, state):
state = True if state == 2 else False
self.model.set('Misc', option, str(state))
self.model.announce_update()
def load_defaults(self):
"""Load defaults."""
defaults = """
[Hotkeys]
Exit = Ctrl+X
Fullscreen = F
Directory = D
Next = Right
Previous = Left
Zoom in = Ctrl++
Zoom out = Ctrl+-
Zoom original = Ctrl+0
"""
self.model.read_string(defaults)
self.model.announce_update()
def apply_settings(self):
"""Override settings."""
with open('config.ini', 'w', encoding='utf-8') as configfile:
self.model.write(configfile)
|
13,880 | d62131efd3f628b6a78d52a9f6c6b4b6c5404708 | import unittest
from osrf_pycommon.terminal_color import impl
assert impl._enabled is True
class TestTerminalColorImpl(unittest.TestCase):
test_format_str = "@{r}red @!bold red @|normal @{rb}red bg"
test_str = "\x1b[31mred \033[1mbold red \x1b[0mnormal \x1b[41mred bg"
def test_ansi(self):
ansi = impl.ansi
self.assertEqual('\x1b[0m', ansi('reset'))
impl.disable_ansi_color_substitution_globally()
self.assertNotEqual('\x1b[0m', ansi('reset'))
self.assertEqual('@|', ansi('atbar'))
impl.enable_ansi_color_substitution_globally()
self.assertEqual('\x1b[0m', ansi('reset'))
def test_get_ansi_dict(self):
get_ansi_dict = impl.get_ansi_dict
ansi_dict = get_ansi_dict()
self.assertNotEqual({}, ansi_dict)
self.assertEqual('\x1b[0m', ansi_dict['reset'])
impl.disable_ansi_color_substitution_globally()
ansi_dict = get_ansi_dict()
self.assertEqual('\x1b[0m', ansi_dict['reset'])
self.assertEqual('@|', ansi_dict['atbar'])
self.assertNotEqual({}, ansi_dict)
impl.enable_ansi_color_substitution_globally()
ansi_dict = get_ansi_dict()
self.assertNotEqual({}, ansi_dict)
self.assertEqual('\x1b[0m', ansi_dict['reset'])
def test_enable_and_disable_ansi_color_substitution_globally(self):
enable = impl.enable_ansi_color_substitution_globally
disable = impl.disable_ansi_color_substitution_globally
is_windows = impl._is_windows
enabled = impl._enabled
try:
impl._is_windows = False
impl._enabled = True
enable()
self.assertEqual('\x1b[0m', impl.ansi('reset'))
self.assertEqual('\x1b[0m', impl.format_color('@|'))
disable()
self.assertEqual('', impl.ansi('reset'))
self.assertEqual('', impl.format_color('@|'))
enable()
self.assertEqual('\x1b[0m', impl.ansi('reset'))
self.assertEqual('\x1b[0m', impl.format_color('@|'))
finally:
impl._is_windows = is_windows
impl._enabled = enabled
def test_format_color(self):
is_windows = impl._is_windows
try:
impl._is_windows = False
format_color = impl.format_color
self.assertEqual(self.test_str, format_color(self.test_format_str))
sanitized_str = "|@@ Notice @{atbar}"
self.assertEqual("|@ Notice @|", format_color(sanitized_str))
finally:
impl._is_windows = is_windows
def test_sanitize(self):
sanitize = impl.sanitize
test_str = "Email: {email}@{org}"
self.assertEqual("Email: {{email}}@@{{org}}", sanitize(test_str))
test_str = "|@ Notice @|"
self.assertEqual("|@@ Notice @{atbar}", sanitize(test_str))
|
13,881 | 370fadbfea65fa486468493caae3fbc81a41256b | import pytest
from glom import glom, Path, T, Spec, Glommer, PathAssignError
from glom.core import UnregisteredTarget
from glom.mutable import Assign, assign
def test_assign():
class Foo(object):
pass
assert glom({}, Assign(T['a'], 1)) == {'a': 1}
assert glom({'a': {}}, Assign(T['a']['a'], 1)) == {'a': {'a': 1}}
assert glom({'a': {}}, Assign('a.a', 1)) == {'a': {'a': 1}}
assert glom(Foo(), Assign(T.a, 1)).a == 1
assert glom({}, Assign('a', 1)) == {'a': 1}
assert glom(Foo(), Assign('a', 1)).a == 1
assert glom({'a': Foo()}, Assign('a.a', 1))['a'].a == 1
def r():
r = {}
r['r'] = r
return r
assert glom(r(), Assign('r.r.r.r.r.r.r.r.r', 1)) == {'r': 1}
assert glom(r(), Assign(T['r']['r']['r']['r'], 1)) == {'r': 1}
assert glom(r(), Assign(Path('r', 'r', T['r']), 1)) == {'r': 1}
assert assign(r(), Path('r', 'r', T['r']), 1) == {'r': 1}
with pytest.raises(TypeError, match='path argument must be'):
Assign(1, 'a')
with pytest.raises(ValueError, match='path must have at least one element'):
Assign(T, 1)
def test_assign_spec_val():
output = glom({'b': 'c'}, Assign('a', Spec('b')))
assert output['a'] == output['b'] == 'c'
def test_unregistered_assign():
# test with bare target registry
glommer = Glommer(register_default_types=False)
with pytest.raises(UnregisteredTarget, match='assign'):
glommer.glom({}, Assign('a', 'b'))
# test for unassignable tuple
with pytest.raises(UnregisteredTarget, match='assign'):
glom({'a': ()}, Assign('a.0', 'b'))
def test_bad_assign_target():
class BadTarget(object):
def __setattr__(self, name, val):
raise Exception("and you trusted me?")
# sanity check
spec = Assign('a', 'b')
ok_target = lambda: None
glom(ok_target, spec)
assert ok_target.a == 'b'
with pytest.raises(PathAssignError, match='could not assign'):
glom(BadTarget(), spec)
return
def test_sequence_assign():
target = {'alist': [0, 1, 2]}
assign(target, 'alist.2', 3)
assert target['alist'][2] == 3
with pytest.raises(PathAssignError, match='could not assign') as exc_info:
assign(target, 'alist.3', 4)
# the following test is because pypy's IndexError is different than CPython's:
# E - PathAssignError(IndexError('list index out of range',), Path('alist'), '3')
# E + PathAssignError(IndexError('list assignment index out of range',), Path('alist'), '3')
# E ? +++++++++++
exc_repr = repr(exc_info.value)
assert exc_repr.startswith('PathAssignError(')
assert exc_repr.endswith("'3')")
return
def test_invalid_assign_op_target():
target = {'afunc': lambda x: 'hi %s' % x}
spec = T['afunc'](x=1)
with pytest.raises(ValueError):
assign(target, spec, None)
|
13,882 | fb705628b9c18fb313f8c8bf1c66afdf04f40924 | import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
matplotlib.rcParams['text.usetex'] = True
matplotlib.rc('font', size=12)
def plot_learning(result_dir, ax, smooth=50, interval=50, error_bar=True, **kwargs):
data = np.load(result_dir)
data = pd.DataFrame(data).rolling(window=smooth, axis=1).mean().values
mean = data.mean(axis=0)[::interval]
episode = np.arange(data.shape[1])[::interval]
ax.semilogy(episode, mean, **kwargs)
if error_bar:
std = data.std(axis=0)[::interval]
if 'color' in kwargs.keys():
ax.fill_between(episode, mean - std, mean + std,
alpha=0.3, facecolor=kwargs['color'])
else:
ax.fill_between(episode, mean - std, mean + std,
alpha=0.3)
fig, ax = plt.subplots(figsize=[5, 4])
plot_learning('results/train/dvn_50/return_sum_gpsr.npy',
ax, label='GPSR', linestyle=':', color='tab:orange')
plot_learning('results/train/dqn_100/return_sum_DQN.npy',
ax, label='DQN', linestyle='-.', color='tab:cyan')
plot_learning('results/train/dvn_50/return_sum_DVN.npy',
ax, label='DVN', linestyle='--', color='tab:green')
plot_learning('results/train/dvn_50/return_sum_opt.npy',
ax, label='Optimal', linestyle='-', color='tab:red')
ax.set_xlim([100, 2500])
ax.set_ylim([60, 450])
ax.set_xlabel('Episode')
ax.set_xticks(np.arange(500, 3000, 500))
ax.set_yticks([60, 70, 80, 90, 100, 200, 300, 400])
ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.set_ylabel('E2E Delay (ms)')
ax.grid()
ax.legend(handlelength=2.3)
fig.tight_layout()
fig.show()
|
13,883 | c3eaabdc5fbae076d00732f6d3a827a67e7aa912 |
class RouterResult(object):
def __init__(self, data, params):
self.data = data
self.params = params
@staticmethod
def not_match(params={}):
return RouterResult(None, params)
@property
def match(self):
return self.data is not None
|
13,884 | 2dc99069416dd72d92cf3e30295a0cfb33063c6e | #!/usr/bin/python3
"""Module for Blueprint"""
from flask import Blueprint, make_response, jsonify
from models.user import User
def get_view(view, view_id):
"""GET view"""
obj_v = storage.get(view, view_id)
if not obj_v:
abort(404)
return jsonify(obj_v.to_dict())
def get_view_parent(view_parent, view_parent_id, view_child):
"""GET view parent"""
parent = storage.get(view_parent, view_parent_id)
if not parent:
abort(404)
return jsonify([v.to_dict() for v in getattr(parent, view_child)])
def delete_view(view, view_id):
"""DELETE view"""
obj_v = storage.get(view, view_id)
if not obj_v:
abort(404)
storage.delete(obj_v)
storage.save()
return make_response(jsonify({}), 200)
def post_view(view, view_parent, view_parent_id, required):
"""POST /model api route"""
if view_parent:
parent = storage.get(view_parent, view_parent_id)
if not parent:
abort(404)
data = request.get_json()
if not data:
return make_response(jsonify({'error': "Not a JSON"}), 400)
for req in required:
if req not in data.keys():
message = "Missing " + req
return make_response(jsonify({'error': message}), 400)
if "user_id" in required:
if not storage.get(User, data.get("user_id")):
abort(404)
if view_parent:
data[view_parent.__name__.lower() + '_id'] = view_parent_id
obj_v = view(**data)
obj_v.save()
return make_response(jsonify(obj_v.to_dict()), 201)
def put_view(view, view_id, ignore):
"""PUT view"""
obj_v = storage.get(view, view_id)
if not obj_v:
abort(404)
data = request.get_json()
if not data:
return make_response(jsonify({'error': "Not a JSON"}), 400)
for k, v in data.items():
if k not in ignore:
setattr(obj_v, k, v)
obj_v.save()
return make_response(jsonify(obj_v.to_dict()), 200)
app_views = Blueprint("app_views", __name__, url_prefix="/api/v1")
from api.v1.views.index import *
from api.v1.views.cities import *
from api.v1.views.amenities import *
from api.v1.views.users import *
from api.v1.views.states import *
from api.v1.views.places import *
from api.v1.views.places_reviews import *
from api.v1.views.places_amenities import *
|
13,885 | 32f2af45e6d7f4380175702a3127fe7cd721e71b | # Generated by Django 3.0.8 on 2021-02-11 09:57
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('home', '0002_discountadvertisement'),
]
operations = [
migrations.CreateModel(
name='Comments',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('userName', models.CharField(max_length=1000)),
('userID', models.CharField(max_length=1000)),
('productID', models.CharField(max_length=1000)),
('dateTime', models.CharField(max_length=1000)),
('comment', models.CharField(max_length=100000)),
],
),
migrations.AlterField(
model_name='discountadvertisement',
name='caption',
field=models.CharField(max_length=1000, null=True),
),
migrations.AlterField(
model_name='discountadvertisement',
name='name',
field=models.CharField(max_length=500, null=True),
),
]
|
13,886 | 993eafb084f7dc2a552143dddbb3d9120a3ca1a7 | import math
import numpy as np
import cv2 as cv
import urllib.request
import IPython
import base64
import html
# Utility function to show an image
def show(*images, enlarge_small_images = True, max_per_row = -1, font_size = 0):
if len(images) == 2 and type(images[1])==str:
images = [(images[0], images[1])]
def convert_for_display(img):
if img.dtype!=np.uint8:
a, b = img.min(), img.max()
if a==b:
offset, mult, d = 0, 0, 1
elif a<0:
offset, mult, d = 128, 127, max(abs(a), abs(b))
else:
offset, mult, d = 0, 255, b
img = np.clip(offset + mult*(img.astype(float))/d, 0, 255).astype(np.uint8)
return img
def convert(imgOrTuple):
try:
img, title = imgOrTuple
if type(title)!=str:
img, title = imgOrTuple, ''
except ValueError:
img, title = imgOrTuple, ''
if type(img)==str:
data = img
else:
img = convert_for_display(img)
if enlarge_small_images:
REF_SCALE = 100
h, w = img.shape[:2]
if h<REF_SCALE or w<REF_SCALE:
scale = max(1, min(REF_SCALE//h, REF_SCALE//w))
img = cv.resize(img,(w*scale,h*scale), interpolation=cv.INTER_NEAREST)
data = 'data:image/png;base64,' + base64.b64encode(cv.imencode('.png', img)[1]).decode('utf8')
return data, title
if max_per_row == -1:
max_per_row = len(images)
rows = [images[x:x+max_per_row] for x in range(0, len(images), max_per_row)]
font = f"font-size: {font_size}px;" if font_size else ""
html_content = ""
for r in rows:
l = [convert(t) for t in r]
html_content += "".join(["<table><tr>"]
+ [f"<td style='text-align:center;{font}'>{html.escape(t)}</td>" for _,t in l]
+ ["</tr><tr>"]
+ [f"<td style='text-align:center;'><img src='{d}'></td>" for d,_ in l]
+ ["</tr></table>"])
IPython.display.display(IPython.display.HTML(html_content))
# Utility function to load an image from an URL
def load_from_url(url):
resp = urllib.request.urlopen(url)
image = np.asarray(bytearray(resp.read()), dtype=np.uint8)
return cv.imdecode(image, cv.IMREAD_GRAYSCALE)
# Utility function to draw orientations over an image
def draw_orientations(fingerprint, orientations, strengths, mask, scale = 3, step = 8, border = 0):
if strengths is None:
strengths = np.ones_like(orientations)
h, w = fingerprint.shape
sf = cv.resize(fingerprint, (w*scale, h*scale), interpolation = cv.INTER_NEAREST)
res = cv.cvtColor(sf, cv.COLOR_GRAY2BGR)
d = (scale // 2) + 1
sd = (step+1)//2
c = np.round(np.cos(orientations) * strengths * d * sd).astype(int)
s = np.round(-np.sin(orientations) * strengths * d * sd).astype(int) # minus for the direction of the y axis
thickness = 1 + scale // 5
for y in range(border, h-border, step):
for x in range(border, w-border, step):
if mask is None or mask[y, x] != 0:
ox, oy = c[y, x], s[y, x]
cv.line(res, (d+x*scale-ox,d+y*scale-oy), (d+x*scale+ox,d+y*scale+oy), (255,0,0), thickness, cv.LINE_AA)
return res
# Utility function to draw a set of minutiae over an image
def draw_minutiae(fingerprint, minutiae, termination_color = (255,0,0), bifurcation_color = (0,0,255)):
res = cv.cvtColor(fingerprint, cv.COLOR_GRAY2BGR)
for x, y, t, *d in minutiae:
color = termination_color if t else bifurcation_color
if len(d)==0:
cv.drawMarker(res, (x,y), color, cv.MARKER_CROSS, 8)
else:
d = d[0]
ox = int(round(math.cos(d) * 7))
oy = int(round(math.sin(d) * 7))
cv.circle(res, (x,y), 3, color, 1, cv.LINE_AA)
cv.line(res, (x,y), (x+ox,y-oy), color, 1, cv.LINE_AA)
return res
# Utility function to generate gabor filter kernels
_sigma_conv = (3.0/2.0)/((6*math.log(10))**0.5)
# sigma is adjusted according to the ridge period, so that the filter does not contain more than three effective peaks
def _gabor_sigma(ridge_period):
return _sigma_conv * ridge_period
def _gabor_size(ridge_period):
p = int(round(ridge_period * 2 + 1))
if p % 2 == 0:
p += 1
return (p, p)
def gabor_kernel(period, orientation):
f = cv.getGaborKernel(_gabor_size(period), _gabor_sigma(period), np.pi/2 - orientation, period, gamma = 1, psi = 0)
f /= f.sum()
f -= f.mean()
return f
# Utility functions for minutiae
def angle_abs_difference(a, b):
return math.pi - abs(abs(a - b) - math.pi)
def angle_mean(a, b):
return math.atan2((math.sin(a)+math.sin(b))/2, ((math.cos(a)+math.cos(b))/2))
# Utility functions for MCC
def draw_minutiae_and_cylinder(fingerprint, origin_cell_coords, minutiae, values, i, show_cylinder = True):
def _compute_actual_cylinder_coordinates(x, y, t, d):
c, s = math.cos(d), math.sin(d)
rot = np.array([[c, s],[-s, c]])
return (rot@origin_cell_coords.T + np.array([x,y])[:,np.newaxis]).T
res = draw_minutiae(fingerprint, minutiae)
if show_cylinder:
for v, (cx, cy) in zip(values[i], _compute_actual_cylinder_coordinates(*minutiae[i])):
cv.circle(res, (int(round(cx)), int(round(cy))), 3, (0,int(round(v*255)),0), 1, cv.LINE_AA)
return res
def draw_match_pairs(f1, m1, v1, f2, m2, v2, cells_coords, pairs, i, show_cylinders = True):
#nd = _current_parameters.ND
h1, w1 = f1.shape
h2, w2 = f2.shape
p1, p2 = pairs
res = np.full((max(h1,h2), w1+w2, 3), 255, np.uint8)
res[:h1,:w1] = draw_minutiae_and_cylinder(f1, cells_coords, m1, v1, p1[i], show_cylinders)
res[:h2,w1:w1+w2] = draw_minutiae_and_cylinder(f2, cells_coords, m2, v2, p2[i], show_cylinders)
for k, (i1, i2) in enumerate(zip(p1, p2)):
(x1, y1, *_), (x2, y2, *_) = m1[i1], m2[i2]
cv.line(res, (int(x1), int(y1)), (w1+int(x2), int(y2)), (0,0,255) if k!=i else (0,255,255), 1, cv.LINE_AA)
return res |
13,887 | ac3e7ddf6e1aa4cab763234f815e9a0de3296401 | import logging
from pprint import pprint
from sys import stdout as STDOUT
# Example 1
def gcd(pair):
a, b = pair
low = min(a, b)
for i in range(low, 0, -1):
if a % i == 0 and b % i == 0:
return i
# Example 2
from time import time
numbers = [(1963309, 2265973), (2030677, 3814172),
(1551645, 2229620), (2039045, 2020802)]
start = time()
results = list(map(gcd, numbers))
end = time()
print('Took %.3f seconds' % (end - start))
# Example 3
from concurrent.futures import ThreadPoolExecutor
start = time()
pool = ThreadPoolExecutor(max_workers=2)
results = list(pool.map(gcd, numbers))
end = time()
print('Took %.3f seconds' % (end - start))
# Example 4
from concurrent.futures import ProcessPoolExecutor
start = time()
pool = ProcessPoolExecutor(max_workers=2) # The one change
results = list(pool.map(gcd, numbers))
end = time()
print('Took %.3f seconds' % (end - start))
|
13,888 | 604f087bfe5510361df689bfce3a8b40fde078b6 | import cv2,serial,time
import numpy as np
def pid(gp, gi, gd, e, ePast):
i += e*gi
p = e*gp
d = (e-ePast)*gd
pidReturn = p+i+d
return pidReturn
#arduino = serial.Serial('COM9', 9600, timeout=.1)
#arduino.write()
cap = cv2.VideoCapture(0)
appleLocation=[0,0]
while True:
a = raw_input('scanDestination, moveBall, or quit? ')
if a.lower() == 'scandestination':
allApples = []
appleAverageX = 0
appleAverageY = 0
stop = False
while True:
_, img = cap.read()
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1,15, param1=80,param2=40, minRadius=1, maxRadius=150)
if circles is not None:
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
apple=[i[0],i[1]]
if apple != [0,0] and len(allApples) < 60:
for a in apple:
allApples.append(a)
if len(allApples)==60:
i = 0
j = 1
while i <=58:
while j <= 59:
x=allApples[i]
y=allApples[j]
appleAverageX += int(x)
appleAverageY += int(y)
i+=2
j+=2
appleAverageX /= 30
appleAverageY /= 30
appleLocation = [appleAverageX, appleAverageY]
print 'Destination coordinates: ' + str(appleLocation)
stop = True
if stop == True:
break;
elif a.lower() == 'moveball':
while True:
_, img = cap.read()
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1,80, param1=80,param2=30, minRadius=10, maxRadius=50)
if circles is not None:
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
if (i[0]!=0 and i[1] !=0):
ball=[i[0],i[1]]
error = [abs(ball[0]-appleLocation[0]),abs(ball[1]-appleLocation[1])]
print 'Error: '+ str(error)
#movePid = pid(1, 1, 1, error, ePast)
cv2.imshow('Image', img)
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
elif a.lower() == 'quit':
quit()
else:
pass
cv2.destroyAllWindow()
|
13,889 | 28e9df2eb041d8355362179361ca82a66261ce43 | import webview
import os
import sys
import socket
import threading
import logging
from random import random
try:
from BaseHTTPServer import HTTPServer
from SimpleHTTPServer import SimpleHTTPRequestHandler
from SocketServer import ThreadingMixIn
except ImportError:
from http.server import SimpleHTTPRequestHandler, HTTPServer
from socketserver import ThreadingMixIn
from webview.util import base_uri
logger = logging.getLogger('pywebview')
port = None
def _get_random_port():
def random_port():
port = int(random() * 64512 + 1023)
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
sock.bind(('localhost', port))
result = port
except:
result = None
logger.warning('Port %s is in use' % port)
finally:
sock.close()
return result
port = random_port()
while not port:
port = random_port()
return port
class HTTPHandler(SimpleHTTPRequestHandler):
def translate_path(self, path):
path = SimpleHTTPRequestHandler.translate_path(self, path)
relpath = os.path.relpath(path, os.getcwd())
fullpath = os.path.join(self.server.base_path, relpath)
return fullpath
def log_message(self, format, *args):
if os.environ.get('PYWEBVIEW_LOG') == 'debug':
super(HTTPHandler, self).log_message(format, *args)
class ThreadedHTTPServer(ThreadingMixIn, HTTPServer):
"""Handle requests in a separate thread."""
def start_server(url):
def _start(httpd):
try:
httpd.serve_forever()
except Exception as e:
logger.exception(e)
global port
base_path = os.path.dirname(url.replace('file://', ''))
if not os.path.exists(base_path):
raise IOError('Directory %s is not found' % base_path)
port = _get_random_port()
server_address = ('localhost', port)
httpd = ThreadedHTTPServer(server_address, HTTPHandler)
httpd.base_path = base_path
t = threading.Thread(target=_start, args=(httpd,))
t.daemon = True
t.start()
new_url = 'http://localhost:{0}/{1}'.format(port, os.path.basename(url))
logger.debug('HTTP server started on http://localhost:{0}'.format(port))
return new_url, httpd |
13,890 | 15bcd6b330bd7c45d459157b990343d02ede1fd5 | # -*- coding: utf-8 -*-
from unittest import main
from tornado.escape import url_escape, xhtml_escape
from knimin.tests.tornado_test_base import TestHandlerBase
from knimin import db
class TestAGEditBarcodeHandler(TestHandlerBase):
def test_get_not_authed(self):
response = self.get('/ag_edit_barcode/')
self.assertEqual(response.code, 200)
port = self.get_http_port()
self.assertEqual(response.effective_url,
'http://localhost:%d/login/?next=%s' %
(port, url_escape('/ag_edit_barcode/')))
def test_get_no_auth(self):
self.mock_login()
response = self.get('/ag_edit_barcode/', {'barcode': '000004216'})
self.assertEqual(response.code, 403)
def test_get(self):
self.mock_login_admin()
# check that error is raised for unknown barcode
response = self.get('/ag_edit_barcode/', {'barcode': 'unknown'})
self.assertEqual(response.code, 500)
# make sure return code 400 is returned, if barcode is not given
response = self.get('/ag_edit_barcode/', {})
self.assertEqual(response.code, 400)
# check if page is rendered properly
barcode = '000004216'
response = self.get('/ag_edit_barcode/', {'barcode': barcode})
self.assertEqual(response.code, 200)
self.assertIn('name="barcode" id="barcode" value="%s"' %
barcode, response.body)
self.assertIn('<option value="Stool" selected>Stool</option>',
response.body)
self.assertIn('2013-10-15', response.body)
hs = db.human_sites
hs.remove('Stool')
for s in hs:
self.assertIn('<option value="%s">%s</option>' %
(str(s), str(s)), response.body)
for e in db.general_sites:
self.assertIn('<option value="%s">%s</option>' %
(str(e), str(e)), response.body)
pname = xhtml_escape(
db.getAGBarcodeDetails(barcode)['participant_name'])
self.assertIn('<option value="%s" selected>%s</option>' %
(pname, pname), response.body)
def test_post(self):
details = db.getAGBarcodeDetails('000004216')
payload = {'barcode': '000004216',
'ag_kit_id': details['ag_kit_id'],
'site_sampled': details['site_sampled'],
'sample_date': details['sample_date'],
'sample_time': details['sample_time'],
'participant_name': details['participant_name'],
'notes': details['notes'],
'environment_sampled': details['environment_sampled'],
'refunded': details['refunded'] or 'N'}
self.mock_login_admin()
# Missing a parameters ('withdrawn')
response = self.post('/ag_edit_barcode/', payload)
self.assertEqual(response.code, 400)
payload['withdrawn'] = details['withdrawn'] or 'N'
payload['notes'] = 'Some new notes'
response = self.post('/ag_edit_barcode/', payload)
self.assertEqual(response.code, 200)
self.assertIn("Barcode was updated successfully", response.body)
self.assertEqual(db.getAGBarcodeDetails('000004216')['notes'],
'Some new notes')
payload['ag_kit_id'] = 'notInDB'
response = self.post('/ag_edit_barcode/', payload)
# TODO: think about returning a non-OK status code to better report
# this error, see issue #139
self.assertEqual(response.code, 200)
self.assertIn("Error Updating Barcode Info", response.body)
if __name__ == "__main__":
main()
|
13,891 | b747ff8aab2c5923c00bbea36b09eb91502a04c0 | acc = {'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': 0.06229813324574369, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': 0.0057692307692307696, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin3': 0.0057692307692307696, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin2': 0.0057692307692307696, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin1': 0.0057692307692307696, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin0': 0.0057692307692307696, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': 0.0728910728910729, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': 0.07841984433618904, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin2': 0.03268900638519543, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': 0.03268900638519543, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin0': 0.03268900638519543, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin1': 0.03268900638519543, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin4': 0.03268900638519543, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin4': 0.0721533153898092, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin1': 0.029854529854529854, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin0': 0.029854529854529854, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin3': 0.029854529854529854, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin2': 0.029854529854529854, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': 0.029854529854529854, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': 0.13444694173904195, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin2': 0.10834873173189988, 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dacc = {'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': 0.0012645071656274313, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': 0.00016668849677814592, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin3': 0.00016668849677814592, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin2': 0.00016668849677814592, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin1': 0.00016668849677814592, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin0': 0.00016668849677814592, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': 0.0008117178152710819, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': 0.001422446623587708, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin2': 0.0003391843923262844, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': 0.0003391843923262844, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin0': 0.0003391843923262844, 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'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': 7.358123490391637e-05, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin4': 7.358123490391637e-05, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': 0.00012792860718308826, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin2': 0.00012792860718308826, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin1': 0.00012792860718308826, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin0': 0.00012792860718308826, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin4': 0.00012792860718308826}
eff = {'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': 0.08473909185005443, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': 0.0292742117557855, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin3': 0.3570256173838094, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin2': 0.2000886044979462, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin1': 0.03319918135213345, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin0': 0.0027880818232174373, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': 0.0, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': 0.0, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin2': 0.4416153378889151, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': 0.054938160306357335, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin0': 0.004101128175601676, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin1': 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inc_wrongfrac = {'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': 0.163673855332451, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': 0.002027735800932217, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin3': 0.002027735800932217, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin2': 0.002027735800932217, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin1': 0.002027735800932217, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin0': 0.002027735800932217, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': 0.041577286046308344, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': 0.21202774354499693, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin2': 0.0036785842927527404, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': 0.0036785842927527404, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin0': 0.0036785842927527404, 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'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': 0.002027735800932217, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin4': 0.002027735800932217, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': 0.21202774354499693, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin2': 0.21202774354499693, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin1': 0.21202774354499693, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin0': 0.21202774354499693, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin4': 0.21202774354499693}
binfrac_wrongfrac = {'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': 0.287962608587294, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': 0.0, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin3': 0.019359721346483935, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin2': 0.07254344982605937, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin1': 0.2069956146671711, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin0': 0.7011012141602856, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': 0.009448024654647185, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': 0.01834574206645972, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin2': 0.10075448752115389, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': 0.0437619495299223, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin0': 0.6607639783361895, 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'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin4': -0.02014563562702322, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin3': 0.024003779096623764, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin2': 0.03573159654092265, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin1': 0.021683841850376688, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin0': 0.0478495934695144, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin0': 0.012235378697751632, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin1': -0.014160711267489707, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin2': -0.02263770516459854, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin3': -0.06334982622466753, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin4': -0.06463300602558542, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': -0.07246535425381848, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin0': 0.019174713875375327, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin1': 0.009244725978297843, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin2': -0.015106654312061929, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin3': -0.019998452960113162, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin0': 0.0478495934695144, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin1': 0.021683841850376688, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin0': 0.02389915949060698, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin1': 0.006801662808826749, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': -0.02014563562702322, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin4': -0.043159148853217456, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin4': -0.07246535425381848, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin3': -0.06334982622466753, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin2': -0.02263770516459854, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin1': -0.014160711267489707, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin0': 0.012235378697751632, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': -0.034506154614274, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin2': -0.021451270319087255, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin2': 0.03573159654092265, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin3': -0.019998452960113162, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin4': -0.02014563562702322, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin3': 0.024003779096623764, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin2': 0.03573159654092265, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin0': 0.019174713875375327, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin2': -0.017470606359343457, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin1': 0.021683841850376688, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin3': -0.04127907159825354, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin1': -0.00029690274851331436, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin4': -0.09117635718669764, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': 0.024003779096623764, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin4': -0.09117635718669764, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin4': -0.0493793027469147, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin1': -0.00029690274851331436, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin0': 0.013613885279991508, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin3': -0.052231990360797204, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin2': -0.021451270319087255, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin1': -0.014160711267489707, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin0': 0.012235378697751632, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin3': -0.06334982622466753, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin2': -0.02263770516459854, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': -0.06463300602558542, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin3': -0.052231990360797204, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin2': -0.021451270319087255, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin2': -0.02263770516459854, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin2': -0.021451270319087255, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin3': -0.052231990360797204, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin0': 0.013613885279991508, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin1': -0.00029690274851331436, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin4': -0.0493793027469147, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin4': -0.043159148853217456, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin0': 0.02389915949060698, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin1': 0.006801662808826749, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin2': -0.017470606359343457, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin3': -0.034506154614274, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin4': -0.06463300602558542, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin2': -0.03312555625667694, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin3': -0.04127907159825354, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin0': 0.010331469467915511, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin1': -0.01764054425390082, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin1': -0.01764054425390082, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin0': 0.010331469467915511, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin3': -0.04127907159825354, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin2': -0.03312555625667694, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': -0.06463300602558542, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin4': -0.0493793027469147, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin0': 0.012235378697751632, 'ggH_NNLOPS_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin1': -0.014160711267489707, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin1': -0.00029690274851331436, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin0': 0.013613885279991508, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin1_recobin4': -0.043159148853217456, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin4': -0.07246535425381848, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin0': 0.02389915949060698, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin1': 0.006801662808826749, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin2': -0.017470606359343457, 'WH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin3': -0.034506154614274, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin1': 0.021683841850376688, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin0': 0.0478495934695144, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin3': 0.024003779096623764, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin3_recobin2': 0.03573159654092265, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin0': 0.013613885279991508, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin1': -0.00029690274851331436, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin2': -0.021451270319087255, 'VBF_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': -0.052231990360797204, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin1': 0.009244725978297843, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin0': 0.019174713875375327, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin3': -0.019998452960113162, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin2': -0.015106654312061929, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin4': -0.09117635718669764, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin2': -0.03312555625667694, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin0': 0.0478495934695144, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin0': 0.010331469467915511, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin0_recobin1': -0.01764054425390082, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin4': -0.02014563562702322, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin0': 0.0478495934695144, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin1': 0.021683841850376688, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin2': 0.03573159654092265, 'ttH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin4_recobin3': 0.024003779096623764, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin0': 0.010331469467915511, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin1': -0.01764054425390082, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin2': -0.03312555625667694, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': -0.04127907159825354, 'ggH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin4': -0.09117635718669764, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin3': -0.019998452960113162, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin2': -0.015106654312061929, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin1': 0.009244725978297843, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin0': 0.019174713875375327, 'ZH_powheg_JHUgen_125_2e2mu_njets_pt30_eta2p5_genbin2_recobin4': -0.07246535425381848}
|
13,892 | 551904adc44347259c75285069a553c0c9c51857 | 成长基金 = 90
月卡 = 60
总价 = 成长基金+月卡
print(总价) |
13,893 | 483e56583886865c5101da12b63b3a5076a5d7b6 | def binadd (A, B):
C = [0]*(len(A)+1)
carry = 0
for i in range(len(A)-1, -1, -1):
C[i+1] = (A[i] + B[i] + carry)%2
if A[i] + B[i] == 2:
carry = 1
else:
carry = 0
C[0] = carry
print(C)
binadd([1,0,1,0,1,0,1,0], [1,1,0,0,1,1,0,0]) |
13,894 | 469581f95d7344d51a9c0270eb12071b7c1f2bce | for i in range(int(input())):
n = int(input());print("1 "*n)
|
13,895 | d3401d1d3998df964c1d315a804af24e6ee4938c | class Diagnostico():
# metodo construtor
def __init__(self,nome_arq):
self.pessoa = []
# abre o arquivo db.txt em modo leitura e passa os dados para
# uma lista de listas de str
self.my_dict = dict()
arquivo = open(nome_arq,'r')
for s,r in [x.replace('\n','').split('-') for x in arquivo]:
if self.my_dict.get(r) == None:
self.my_dict.update({r:[s]})
else:
self.my_dict[r].append(s)
self.resultado = list(self.my_dict.keys())
arquivo.close()
# imprime a quantidade de possibilidades cadastradas
def tamanho(self):
print(len(self.resultado))
# imprime a probabilidade do diagnótico
probabilidade = lambda self: int ((1/int(len(self.resultado)))*100) if len(self.resultado) > 0 else 0
# verifica se diagnóstico pensado tem a caracteristica passada por parametro
busca = lambda self,r,s: True if self.my_dict.get(r).count(s) > 0 else False
# remove os diagnósticos que não possuem o atributo passado por parametro
def excluiquemnaoe(self, atributo):
for r in self.resultado:
if not self.busca(r, atributo):
self.resultado.remove(r)
# remove os diagnosticos que possuem o atributo passado por parametro
def excluiqueme(self, atributo):
for r in self.resultado:
if self.busca(r, atributo):
self.resultado.remove(r)
def pergunta(self,pergunta,caract):
print("Lista resultados: {}".format(self.resultado))
resp = input(pergunta+'(s,n,p): ')
if resp == 's' or resp == 'S':
self.excluiquemnaoe(caract)
self.excluiqueme("n_"+caract)
elif resp == 'n' or resp == 'N':
self.excluiqueme(caract)
#self.excluiquemnaoe("n_"+caract) negação nem sempre está inclusa, então acho responsável comentar essa linha
elif resp == 'p' or resp =='P':
print("pergunta pulada")
def get_sintomas(self,para):
return self.my_dict.get(para)
"""
O seu Pet está agitado?' -> 'agitado'
O seu Pet está comendo bem?' -> 'alimentado'
'O ambiente é adequado?' -> 'ambiente_adequado'
'O seu Pet fez atividade física?' -> 'se_movimenta',
"""
|
13,896 | 6cab252a933ed316a1417ff13f808e0cf71b07ad | from base.interfaces import InterfacesBase
from base.modul import Modul
import math
class MenaraAir(Modul):
name = "Menara Air"
def init_formula(self, interfaces: InterfacesBase):
interfaces.get_float("h1",
brief="Tinggi air diatas lubang",
deskripsi="Tinggi air didalam tangki terhitung dari atas lubang tangki",
posfix="m")
interfaces.get_float("g",
brief="Percepatan gravitasi bumi",
deskripsi="Percepatan gravitasi suatu objek yang berada pada \n\
permukaan laut dikatakan ekuivalen dengan 1 g, yang didefinisikan \n\
memiliki nilai 9,80665 m/s²",
posfix="m/s²")
interfaces.get_float("h2",
brief="Tinggi menara air",
deskripsi="Tinggi menara air yang terhitung dari tanah hingga bagian bawah tangki",
posfix="m")
interfaces.add_func("v", self.menentukan_kecepatan,
brief="Kecepatan air yang mengalir",
posfix="m/s")
interfaces.add_func("x", self.jarak,
brief="Jarak air yang keluar",
posfix="m/s")
def menentukan_kecepatan( self, value : dict):
h1 = value["h1"]
g = value["g"]
return math.sqrt(2*g*h1)
def jarak(self, value: dict):
h1 = value["h1"]
h2 = value["h2"]
return 2*math.sqrt(h1*h2) |
13,897 | 6e363277b7a235a20d9ee0009d09f9f9f22801ba | #coding: utf-8
from mymodule import say_hi, __version__ #不建议使用这种方式，容易出现名称冲突
say_hi()
print('version', __version__)
|
13,898 | d68528f7b180e10984b47290e55fc71e5b58bd9f | from bluelens_log import Logging
options = {
'REDIS_SERVER': "{YOUR_REDIS_SERVER_IP}",
'REDIS_PASSWORD': "{YOUR_PASSWORD}"
}
log = Logging(options)
log.debug("debug log")
log.info("info log")
log.error("error log")
|
13,899 | df9ae7cce24c05295fe0773147b0a48dbd9b248a | import encode
import phoneme_info
import pytest
@pytest.mark.parametrize('tokens,expected', [
('h ai', [('h', 'ai', '')]),
('k A t s', [('k', 'A', 't s')]),
('s p ai i z', [('s p', 'ai', ''),
('', 'i', 'z')]),
])
def test__find_nuclei(tokens, expected):
tokens = tokens.split()
tokens = [phoneme_info.by_kbd[t] for t in tokens]
actual = encode._find_nuclei(tokens)
actual = [display_syllable(n) for n in actual]
assert actual == expected
def display_syllable(n):
return (display_phoneme_list(n.prev),
n.vowel.kbd,
display_phoneme_list(n.next))
def display_phoneme_list(lst):
return ' '.join(t.kbd for t in lst)
@pytest.mark.parametrize('tokens,expected', [
('h ai', [['h'],['ai']]),
('s p ai i z', [['s','p'],['ai'],['i'],['z']]),
])
def test__to_sublists(tokens, expected):
tokens = tokens.split()
tokens = [phoneme_info.by_kbd[t] for t in tokens]
actual = encode._to_sublists(tokens)
actual = [[t.kbd for t in sublist] for sublist in actual]
assert actual == expected
@pytest.mark.parametrize('tokens,expected', [
('k A t s', 'k / A / t s'),
('s p ai i z', 's p / ai / i / z'),
])
def test__add_separators(tokens, expected):
tokens = tokens.split()
tokens = [phoneme_info.by_kbd[t] for t in tokens]
actual = encode._add_separators(tokens)
actual = [t.kbd if t != encode.SEPARATOR else '/' for t in actual]
actual = ' '.join(actual)
assert actual == expected
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