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'''URL routing for expense app.'''
from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^nova-verba/', views.new_budget, name='new_budget'),
url(r'^novo-reembolso/', views.new_reimburse, name='new_reimburse'),
url(r'^reembolsos/todos/', views.list_all_reimburses, name='list_all_reimburses'),
url(r'^verbas/', views.list_budgets, name='list_budgets'),
url(r'^reembolsos/', views.list_reimburses, name='list_reimburses'),
url(r'^aprovacoes/verba/', views.list_budget_approvals, name='list_budget_approvals'),
url(r'^aprovacoes/reembolso/', views.list_reimburse_approvals, name='list_reimburse_approvals'),
url(r'^requisicao/(?P<request_id>[0-9]+)/(?P<approval>(aprovar|negar){1})/', views.set_approval,
name='set_approval'),
url(r'^requisicao/(?P<request_id>[0-9]+)/', views.show_request, name='show_request')
]
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from ROOT import TH1D, TF1, TCanvas, TPad, TFile, TPaveLabel, TPaveText
from ROOT import gROOT, gStyle
from array import array
import psrchive
import sys
import time
import numpy as np
from optparse import OptionParser
full_usage = """
"""
usage = "usage: %prog [options]"
gStyle.SetOptFit()
parser = OptionParser(usage)
parser.add_option("-n", "--ncomp", type="int", dest="ncomp", default=1,
help="Number of components")
parser.add_option("-t", "--template", type="string", dest="template",
help="Template of gaussians")
parser.add_option("-s", "--save", action="store_true", dest="save",
default=False, help="Save gaussians to file")
parser.add_option("-r", "--rotate", action="store_true", dest="rotate",
default=False, help="Rotate profile by 180deg for display and fit")
parser.add_option("-c", "--bscr", type="int", dest="bscr", default=1,
help="Bscrunch by this factor (power of 2)")
(opts, args) = parser.parse_args()
if len(sys.argv) <= 1:
print usage
sys.exit()
if opts.template:
gaussian_id, phase, heigth, width = np.loadtxt(opts.template, unpack=True, dtype=float, skiprows=3)
print phase, heigth, width
fn = args[0]
ar = psrchive.Archive_load(fn)
#ar.dedisperse()
if opts.rotate:
ar.rotate_phase(.5)
ar.tscrunch()
ar.fscrunch()
if opts.bscr>1:
print "Will scrunch the profile by ", opts.bscr, " bins"
ar.bscrunch(opts.bscr)
ar.pscrunch()
ar.remove_baseline()
# Get I
#prof = ar.get_Profile(0,0,0)
nbin = ar.get_nbin()
#x = prof.get_amps()
data = ar.get_data()[0][0][0]
# NO scrunching for flux calibrated data
#x = data / data.max()
x = data
#for a,b in enumerate(x):
# print a,b
#max_bin = prof.find_max_bin()
#max_bin = nbin/2
#lowbin = max_bin-30
#hibin = max_bin+30
# For interpulse
#lowbin = max_bin-40
#hibin = max_bin+40
#low_phase = 0.47
#hi_phase = 0.6
low_phase = 0.42
hi_phase = 0.6
lowbin = int(low_phase * nbin)
hibin = int(hi_phase * nbin)
gROOT.Reset()
# Canvas
c1 = TCanvas( 'c1', 'Histogram Drawing Options', 200, 10, 700, 900 )
pad1 = TPad( 'pad1', 'The pad with the function', 0.03, 0.62, 0.50, 0.92, 21 )
pad2 = TPad( 'pad2', 'The pad with the histogram', 0.51, 0.62, 0.98, 0.92, 21 )
pad3 = TPad( 'pad3', 'The pad with the histogram', 0.03, 0.02, 0.97, 0.57, 21 )
pad1.Draw()
pad2.Draw()
pad3.Draw()
npts = len(x)
h1 = TH1D( 'h1', 'Full pulse profile', npts, 0, npts)
h2 = TH1D( 'h2', 'Diff with template', hibin-lowbin, lowbin, hibin)
h3 = TH1D( 'h3', 'Fitted pulse profile', hibin-lowbin, lowbin, hibin)
for i in xrange(npts):
h1.SetBinContent( i+1, x[i] )
#h2.SetBinContent( i+1, x[i] )
for i,xx in enumerate(x[lowbin:hibin]):
h2.SetBinContent( i+1, xx )
h3.SetBinContent( i+1, xx )
par = array( 'd', 3*[0.] )
total = TF1( 'total', 'gaus', lowbin, hibin )
# Set initial conditions
par[0] = h2.GetMaximum()
par[1] = h2.GetMaximumBin() + lowbin
par[2] = 5
print "Initial conditions:", par[0], par[1], par[2]
total.SetParameters( par )
#total.FixParameter(0, par[0])
#total.FixParameter(1, par[1])
#total.FixParameter(2, par[2])
#g2 = TF1( 'g2', 'gaus', 98, 108 )
#g3 = TF1( 'g3', 'gaus', 110, 121 )
#total = TF1( 'total', 'gaus(0)+gaus(3)+gaus(6)', 85, 125 )
#total.SetLineColor( 2 )
# Draw histogram hpx in first pad with the default option.
pad1.cd()
h1.Draw()
pad3.cd()
h3.Draw()
#fit = h3.Fit( total, 'RS')
fit = h3.Fit( total, 'RS' , '',lowbin, hibin)
par1 = total.GetParameters()
#print par1[0], par1[1], par1[2]
icomp = 1
# residual plot
for i in range(h3.GetNbinsX()):
h2.SetBinContent( i+1, h3.GetBinContent(i) - total.Eval(h3.GetBinCenter(i)) )
#print i+1, h3.GetBinContent(i) - total.Eval(h3.GetBinCenter(i))
pad2.cd()
h2.Draw()
if opts.template:
ncomp = len(gaussian_id)
print "Ncomponents: ", ncomp
# init the model to a single gaussian
model = 'gaus(0)'
for i in range(ncomp-1):
model += '+gaus(%d)'%((i+1)*3)
print model
par = array( 'd', 3*ncomp*[0.] )
for i in range(ncomp):
par[3*(i)] = heigth[i]
par[3*(i)+1] = phase[i] /360. * nbin
par[3*(i)+2] = width[i]
print par
total = TF1( 'total', model, lowbin, hibin )
total.SetParameters( par )
# fit and display
pad3.Clear()
h3.Fit(total, 'ER')
par1 = total.GetParameters()
# residual plot
pad2.Clear()
for i in range(h3.GetNbinsX()):
h2.SetBinContent( i+1, h3.GetBinContent(i) - total.Eval(h3.GetBinCenter(i)) )
pad2.cd()
h2.Draw()
icomp = ncomp
#if 1:
else:
# init the model to a single gaussian
model = 'gaus(0)'
ncomp = opts.ncomp
while icomp < ncomp:
par = array( 'd', 3*(icomp+1)*[0.] )
model += '+gaus(%d)'%(icomp*3)
#print model
total = TF1( 'total', model, lowbin, hibin )
# Set params
print "Max residual bin: ", h2.GetMaximumBin() + lowbin, "Value: ", h2.GetMaximum()
for i in range(icomp):
par[3*i],par[3*i+1],par[3*i+2] = par1[3*i],par1[3*i+1],par1[3*i+2]
par[3*(i+1)] = h2.GetMaximum()
par[3*(i+1)+1] = h2.GetMaximumBin() + lowbin
par[3*(i+1)+2] = 4.
total.SetParameters( par )
# fit and display
pad3.Clear()
h3.Fit(total, 'RS')
par1 = total.GetParameters()
# residual plot
pad2.Clear()
for i in range(h3.GetNbinsX()):
h2.SetBinContent( i+1, h3.GetBinContent(i) - total.Eval(h3.GetBinCenter(i)) )
#print i+1, h3.GetBinContent(i) - total.Eval(h3.GetBinCenter(i))
pad2.cd()
h2.Draw()
icomp +=1
#time.sleep(1)
if opts.rotate:
offset = 180.
else:
offset = 0.
par = total.GetParameters()
if opts.save:
pfo = open(fn + '.gaussian', 'w')
pfo.write('#### Gaussian Template #####\n')
pfo.write('MJD: %s\n'%ar.start_time().strtempo())
pfo.write('Components: %d\n'%ncomp)
for icomp in range(ncomp):
pfo.write('%d %f %f %f\n'%(icomp, par[3*icomp+1]*360./float(ar.get_nbin()) - offset, par[3*icomp], par[3*icomp+2]*360./float(ar.get_nbin())))
pfo.close()
print "\nSummary of fit"
for icomp in range(ncomp):
print par[3*icomp+1]
print 'Component #%d %f %f %fi (%f)\n'%(icomp, par[3*icomp+1]*360/float(ar.get_nbin()) - offset, par[3*icomp], par[3*icomp+2]*360/float(ar.get_nbin()), par[3*icomp+1]/float(ar.get_nbin()) - offset/360.)
raw_input("Press enter to continue...")
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/****************************************************************************
* drivers/sensors/bh1750fvi.c
*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership. The
* ASF licenses this file to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance with the
* License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
****************************************************************************/
/* Character driver for the Rohm Ambient Light Sensor BH1750FVI */
/****************************************************************************
* Included Files
****************************************************************************/
#include <nuttx/config.h>
#include <assert.h>
#include <errno.h>
#include <debug.h>
#include <stdlib.h>
#include <nuttx/kmalloc.h>
#include <nuttx/fs/fs.h>
#include <nuttx/i2c/i2c_master.h>
#include <nuttx/sensors/bh1750fvi.h>
#include <nuttx/random.h>
#if defined(CONFIG_I2C) && defined(CONFIG_SENSORS_BH1750FVI)
/****************************************************************************
* Pre-process Definitions
****************************************************************************/
#ifndef CONFIG_BH1750FVI_I2C_FREQUENCY
# define CONFIG_BH1750FVI_I2C_FREQUENCY 400000
#endif
/****************************************************************************
* Private Types
****************************************************************************/
struct bh1750fvi_dev_s
{
FAR struct i2c_master_s *i2c; /* I2C interface */
uint8_t addr; /* I2C address */
};
/****************************************************************************
* Private Function Prototypes
****************************************************************************/
/* I2C Helpers */
static int bh1750fvi_read16(FAR struct bh1750fvi_dev_s *priv,
FAR uint16_t *regval);
static int bh1750fvi_write8(FAR struct bh1750fvi_dev_s *priv,
uint8_t regval);
/* Character driver methods */
static int bh1750fvi_open(FAR struct file *filep);
static int bh1750fvi_close(FAR struct file *filep);
static ssize_t bh1750fvi_read(FAR struct file *filep, FAR char *buffer,
size_t buflen);
static ssize_t bh1750fvi_write(FAR struct file *filep,
FAR const char *buffer, size_t buflen);
static int bh1750fvi_ioctl(FAR struct file *filep, int cmd,
unsigned long arg);
/****************************************************************************
* Private Data
****************************************************************************/
static const struct file_operations g_bh1750fvi_fops =
{
bh1750fvi_open, /* open */
bh1750fvi_close, /* close */
bh1750fvi_read, /* read */
bh1750fvi_write, /* write */
NULL, /* seek */
bh1750fvi_ioctl, /* ioctl */
NULL /* poll */
#ifndef CONFIG_DISABLE_PSEUDOFS_OPERATIONS
, NULL /* unlink */
#endif
};
/****************************************************************************
* Private Functions
****************************************************************************/
/****************************************************************************
* Name: bh1750fvi_read16
*
* Description:
* Read 16-bit register
*
****************************************************************************/
static int bh1750fvi_read16(FAR struct bh1750fvi_dev_s *priv,
FAR uint16_t *regval)
{
struct i2c_config_s config;
uint8_t buffer[2];
int ret = -1;
/* Set up the I2C configuration */
config.frequency = CONFIG_BH1750FVI_I2C_FREQUENCY;
config.address = priv->addr;
config.addrlen = 7;
/* Read 16-bits from the device */
ret = i2c_read(priv->i2c, &config, buffer, 2);
if (ret < 0)
{
snerr ("i2c_read failed: %d\n", ret);
return ret;
}
/* Copy the content of the buffer to the location of the uint16_t pointer */
*regval = (uint16_t)((buffer[0] << 8) | (buffer[1]));
sninfo("value: %08x ret: %d\n", *regval, ret);
return OK;
}
/****************************************************************************
* Name: bh1750fvi_write8
*
* Description:
* Write from an 8-bit register
*
****************************************************************************/
static int bh1750fvi_write8(FAR struct bh1750fvi_dev_s *priv, uint8_t regval)
{
struct i2c_config_s config;
int ret;
sninfo("value: %02x\n", regval);
/* Set up the I2C configuration */
config.frequency = CONFIG_BH1750FVI_I2C_FREQUENCY;
config.address = priv->addr;
config.addrlen = 7;
/* Write 8 bits to device */
ret = i2c_write(priv->i2c, &config, ®val, 1);
if (ret < 0)
{
snerr("ERROR: i2c_write failed: %d\n", ret);
}
return ret;
}
/****************************************************************************
* Name: bh1750fvi_open
*
* Description:
* This function is called whenever the BH1750FVI device is opened.
*
****************************************************************************/
static int bh1750fvi_open(FAR struct file *filep)
{
return OK;
}
/****************************************************************************
* Name: bh1750fvi_close
*
* Description:
* This routine is called when the BH1750FVI device is closed.
*
****************************************************************************/
static int bh1750fvi_close(FAR struct file *filep)
{
return OK;
}
/****************************************************************************
* Name: bh1750fvi_read
****************************************************************************/
static ssize_t bh1750fvi_read(FAR struct file *filep, FAR char *buffer,
size_t buflen)
{
int ret;
FAR struct inode *inode;
FAR struct bh1750fvi_dev_s *priv;
uint16_t lux = 0;
DEBUGASSERT(filep);
inode = filep->f_inode;
DEBUGASSERT(inode && inode->i_private);
priv = (FAR struct bh1750fvi_dev_s *)inode->i_private;
/* Check if the user is reading the right size */
if (buflen != 2)
{
snerr("ERROR: You need to read 2 bytes from this sensor!\n");
return -EINVAL;
}
ret = bh1750fvi_read16(priv, &lux);
if (ret < 0)
{
snerr("ERROR: Error reading light sensor!\n");
return ret;
}
buffer[0] = lux & 0xff;
buffer[1] = (lux & 0xff00) >> 8;
add_sensor_randomness(lux);
return buflen;
}
/****************************************************************************
* Name: bh1750fvi_write
****************************************************************************/
static ssize_t bh1750fvi_write(FAR struct file *filep,
FAR const char *buffer, size_t buflen)
{
return -ENOSYS;
}
/****************************************************************************
* Name: bh1750fvi_ioctl
****************************************************************************/
static int bh1750fvi_ioctl(FAR struct file *filep, int cmd,
unsigned long arg)
{
FAR struct inode *inode = filep->f_inode;
FAR struct bh1750fvi_dev_s *priv = inode->i_private;
int ret = OK;
switch (cmd)
{
/* Set device to Continuously H-Resolution Mode */
case SNIOC_CHRM:
{
ret = bh1750fvi_write8(priv, BH1750FVI_CONTINUOUS_HRM);
if (ret < 0)
{
snerr("ERROR:");
snerr(" Cannot change to Continuously H-Resolution Mode!\n");
}
}
break;
/* Set device to Continuously H-Resolution Mode2 */
case SNIOC_CHRM2:
{
ret = bh1750fvi_write8(priv, BH1750FVI_CONTINUOUS_HRM2);
if (ret < 0)
{
snerr("ERROR:");
snerr(" Cannot change to Continuously H-Resolution Mode2!\n");
}
}
break;
/* Set device to Continuously L-Resolution Mode */
case SNIOC_CLRM:
{
ret = bh1750fvi_write8(priv, BH1750FVI_CONTINUOUS_LRM);
if (ret < 0)
{
snerr("ERROR:");
snerr(" Cannot change to Continuously L-Resolution Mode!\n");
}
}
break;
/* Set device to One Time H-Resolution Mode */
case SNIOC_OTHRM:
{
ret = bh1750fvi_write8(priv, BH1750FVI_ONETIME_HRM);
if (ret < 0)
{
snerr("ERROR: Cannot change to One Time H-Resolution Mode!\n");
}
}
break;
/* Set device to One Time H-Resolution Mode 2 */
case SNIOC_OTHRM2:
{
ret = bh1750fvi_write8(priv, BH1750FVI_ONETIME_HRM2);
if (ret < 0)
{
snerr("ERROR:");
snerr(" Cannot change to One Time H-Resolution Mode2!\n");
}
}
break;
/* Set device to One Time L-Resolution Mode */
case SNIOC_OTLRM:
{
ret = bh1750fvi_write8(priv, BH1750FVI_ONETIME_LRM);
if (ret < 0)
{
snerr("ERROR: Cannot change to One Time L-Resolution Mode!\n");
}
}
break;
/* Change the Measurement Time */
case SNIOC_CHMEATIME:
{
FAR uint8_t *ptr = (FAR uint8_t *)((uintptr_t)arg);
uint8_t reg;
DEBUGASSERT(ptr != NULL);
reg = BH1750FVI_MEASURE_TIMEH | ((*ptr & 0xe0) >> 5);
ret = bh1750fvi_write8(priv, reg);
if (ret < 0)
{
snerr("ERROR: Cannot Change Measure Time at MEASURE_TIMEH!\n");
}
reg = BH1750FVI_MEASURE_TIMEL | (*ptr & 0x1f);
ret = bh1750fvi_write8(priv, reg);
if (ret < 0)
{
snerr("ERROR: Cannot Change Measure Time at MEASURE_TIMEL!\n");
}
}
break;
default:
snerr("ERROR: Unrecognized cmd: %d\n", cmd);
ret = -ENOTTY;
break;
}
return ret;
}
/****************************************************************************
* Public Functions
****************************************************************************/
/****************************************************************************
* Name: bh1750fvi_register
*
* Description:
* Register the BH1750FVI character device as 'devpath'
*
* Input Parameters:
* devpath - The full path to the driver to register. E.g., "/dev/light0"
* i2c - An instance of the I2C interface to use to communicate with
* BH1750FVI
* addr - The I2C address of the BH1750FVI.
*
* Returned Value:
* Zero (OK) on success; a negated errno value on failure.
*
****************************************************************************/
int bh1750fvi_register(FAR const char *devpath, FAR struct i2c_master_s *i2c,
uint8_t addr)
{
int ret;
/* Sanity check */
DEBUGASSERT(i2c != NULL);
/* Initialize the BH1750FVI device structure */
FAR struct bh1750fvi_dev_s *priv =
(FAR struct bh1750fvi_dev_s *)kmm_malloc(sizeof(struct bh1750fvi_dev_s));
if (priv == NULL)
{
snerr("ERROR: Failed to allocate instance\n");
return -ENOMEM;
}
priv->i2c = i2c;
priv->addr = addr;
/* Power on the device */
ret = bh1750fvi_write8(priv, BH1750FVI_POWERON);
if (ret < 0)
{
snerr("ERROR: Failed to power-on the BH1750FVI!\n");
return ret;
}
/* Set Continuously H-Resolution Mode */
ret = bh1750fvi_write8(priv, BH1750FVI_CONTINUOUS_HRM);
if (ret < 0)
{
snerr("ERROR: Failed to enable the Continuously H-Resolution Mode!\n");
return ret;
}
/* Register the character driver */
ret = register_driver(devpath, &g_bh1750fvi_fops, 0666, priv);
if (ret < 0)
{
snerr("ERROR: Failed to register driver: %d\n", ret);
kmm_free(priv);
}
return ret;
}
#endif /* CONFIG_I2C && CONFIG_SENSORS_BH1750FVI */
|
from sqlalchemy.testing import fixtures
from sqlalchemy.engine.ddl import SchemaGenerator, SchemaDropper
from sqlalchemy.engine import default
from sqlalchemy import MetaData, Table, Column, Integer, Sequence
from sqlalchemy import schema
class EmitDDLTest(fixtures.TestBase):
def _mock_connection(self, item_exists):
_canary = []
class MockDialect(default.DefaultDialect):
supports_sequences = True
def has_table(self, connection, name, schema):
return item_exists(name)
def has_sequence(self, connection, name, schema):
return item_exists(name)
class MockConnection(object):
dialect = MockDialect()
canary = _canary
def execute(self, item):
_canary.append(item)
return MockConnection()
def _mock_create_fixture(self, checkfirst, tables,
item_exists=lambda item: False):
connection = self._mock_connection(item_exists)
return SchemaGenerator(connection.dialect, connection,
checkfirst=checkfirst,
tables=tables)
def _mock_drop_fixture(self, checkfirst, tables,
item_exists=lambda item: True):
connection = self._mock_connection(item_exists)
return SchemaDropper(connection.dialect, connection,
checkfirst=checkfirst,
tables=tables)
def _table_fixture(self):
m = MetaData()
return (m, ) + tuple(
Table('t%d' % i, m, Column('x', Integer))
for i in range(1, 6)
)
def _table_seq_fixture(self):
m = MetaData()
s1 = Sequence('s1')
s2 = Sequence('s2')
t1 = Table('t1', m, Column("x", Integer, s1, primary_key=True))
t2 = Table('t2', m, Column("x", Integer, s2, primary_key=True))
return m, t1, t2, s1, s2
def test_create_seq_checkfirst(self):
m, t1, t2, s1, s2 = self._table_seq_fixture()
generator = self._mock_create_fixture(True, [t1, t2],
item_exists=lambda t: t not in ("t1", "s1")
)
self._assert_create([t1, s1], generator, m)
def test_drop_seq_checkfirst(self):
m, t1, t2, s1, s2 = self._table_seq_fixture()
generator = self._mock_drop_fixture(True, [t1, t2],
item_exists=lambda t: t in ("t1", "s1")
)
self._assert_drop([t1, s1], generator, m)
def test_create_collection_checkfirst(self):
m, t1, t2, t3, t4, t5 = self._table_fixture()
generator = self._mock_create_fixture(True, [t2, t3, t4],
item_exists=lambda t: t not in ("t2", "t4")
)
self._assert_create_tables([t2, t4], generator, m)
def test_drop_collection_checkfirst(self):
m, t1, t2, t3, t4, t5 = self._table_fixture()
generator = self._mock_drop_fixture(True, [t2, t3, t4],
item_exists=lambda t: t in ("t2", "t4")
)
self._assert_drop_tables([t2, t4], generator, m)
def test_create_collection_nocheck(self):
m, t1, t2, t3, t4, t5 = self._table_fixture()
generator = self._mock_create_fixture(False, [t2, t3, t4],
item_exists=lambda t: t not in ("t2", "t4")
)
self._assert_create_tables([t2, t3, t4], generator, m)
def test_create_empty_collection(self):
m, t1, t2, t3, t4, t5 = self._table_fixture()
generator = self._mock_create_fixture(True, [],
item_exists=lambda t: t not in ("t2", "t4")
)
self._assert_create_tables([], generator, m)
def test_drop_empty_collection(self):
m, t1, t2, t3, t4, t5 = self._table_fixture()
generator = self._mock_drop_fixture(True, [],
item_exists=lambda t: t in ("t2", "t4")
)
self._assert_drop_tables([], generator, m)
def test_drop_collection_nocheck(self):
m, t1, t2, t3, t4, t5 = self._table_fixture()
generator = self._mock_drop_fixture(False, [t2, t3, t4],
item_exists=lambda t: t in ("t2", "t4")
)
self._assert_drop_tables([t2, t3, t4], generator, m)
def test_create_metadata_checkfirst(self):
m, t1, t2, t3, t4, t5 = self._table_fixture()
generator = self._mock_create_fixture(True, None,
item_exists=lambda t: t not in ("t2", "t4")
)
self._assert_create_tables([t2, t4], generator, m)
def test_drop_metadata_checkfirst(self):
m, t1, t2, t3, t4, t5 = self._table_fixture()
generator = self._mock_drop_fixture(True, None,
item_exists=lambda t: t in ("t2", "t4")
)
self._assert_drop_tables([t2, t4], generator, m)
def test_create_metadata_nocheck(self):
m, t1, t2, t3, t4, t5 = self._table_fixture()
generator = self._mock_create_fixture(False, None,
item_exists=lambda t: t not in ("t2", "t4")
)
self._assert_create_tables([t1, t2, t3, t4, t5], generator, m)
def test_drop_metadata_nocheck(self):
m, t1, t2, t3, t4, t5 = self._table_fixture()
generator = self._mock_drop_fixture(False, None,
item_exists=lambda t: t in ("t2", "t4")
)
self._assert_drop_tables([t1, t2, t3, t4, t5], generator, m)
def _assert_create_tables(self, elements, generator, argument):
self._assert_ddl(schema.CreateTable, elements, generator, argument)
def _assert_drop_tables(self, elements, generator, argument):
self._assert_ddl(schema.DropTable, elements, generator, argument)
def _assert_create(self, elements, generator, argument):
self._assert_ddl(
(schema.CreateTable, schema.CreateSequence),
elements, generator, argument)
def _assert_drop(self, elements, generator, argument):
self._assert_ddl(
(schema.DropTable, schema.DropSequence),
elements, generator, argument)
def _assert_ddl(self, ddl_cls, elements, generator, argument):
generator.traverse_single(argument)
for c in generator.connection.canary:
assert isinstance(c, ddl_cls)
assert c.element in elements, "element %r was not expected"\
% c.element
elements.remove(c.element)
assert not elements, "elements remain in list: %r" % elements
|
class Tau {
static TAU = 2 * Math.PI
static TAU_2 = this.TAU/2
static TAU_4 = this.TAU/4
static TAU_6 = this.TAU/6
static TAU_8 = this.TAU/8
static TAU_12 = this.TAU/12
constructor(){
// This consists solely of static methods and constants
}
// converts a revolution to degrees
static toDegrees(rev){return rev * 360}
// converts a revolution to radians
static toRadians(rev){return rev * this.TAU}
// converts from degrees to revolutions
static fromDegrees(deg){return deg / 360}
// converts from radians to revolutions
static fromRadians(rad){return rad / this.TAU}
// returns the sine value of the given revolution
static sin(rev){
return Math.sin(rev * this.TAU)
}
// returns the cosine value of the given revolution
static cos(rev){
return Math.cos(rev * this.TAU)
}
// returns the tangent value of the given revolution
static tan(rev){
return Math.tan(rev * this.TAU)
}
// returns the arcsine value of the given revolution
static asin(rev){
return this.fromRadians(Math.asin(rev))
}
// returns the arccosine value of the given revolution
static acos(rev){
return this.fromRadians(Math.acos(rev))
}
// For a given x,y value, returns the corresponding revolution from -0.5 to 0.5.
static atan(x,y){
return this.fromRadians(Math.atan2(y,x)) }
}
class TauComplex{
// Indicates the number of significant digits complex numbers are displayed using.
static SIGDIGITS = 5;
constructor(x,y){
this.x = x
this.y = y
return this
}
// toString() generates a complex number of the form "a+bi" for string output
toString(){
let minX = Math.abs(this.x)<1e-5?0:TauComplex.trim(this.x);
let minY = Math.abs(this.y)<1e-5?0:TauComplex.trim(this.y);
return `${minX} ${Math.sign(this.y)>=0?'+':'-'} ${Math.abs(minY)}i`
}
// generates the length of the complex number vector
get modulus(){
return Math.sqrt(this.x*this.x + this.y*this.y);
}
// generates the square of the length of the complex number vector. This avoids the need to take the square root
get modsquare(){
return this.x*this.x + this.y*this.y;
}
// retrieves the angle relative to the positive x axis of the complex number, in revolutions
get theta(){
let angle = Tau.atan(this.x,this.y);
let ySgn = Math.sign(this.y);
let adjAngle = ySgn<0?1+angle:angle;
return adjAngle;
}
// retrieves the complex conjugate (a-bi) of the complex number (a+bi)
get conjugate(){
return new TauComplex(this.x,-this.y)
}
// retrieves the complex inverse of the number (a+bi).
get inverse(){
return (this.conjugate).scale(1/this.modsquare)
}
// rotates the complex number through the angle, expressed in revolutions.
rotate(angle){
let newX = this.x * Tau.cos(angle) - this.y * Tau.sin(angle);
let newY = this.x * Tau.sin(angle) + this.y * Tau.cos(angle)
return new TauComplex(newX,newY)
}
// Multiplies the complex number by a scalar value (or values if two arguments are supplied)
scale(x,y=x){
let newX = this.x * x;
let newY = this.y * y;
return new TauComplex(newX,newY)
}
// translates the complex number by the given amount. Equivalent to adding two complex numbers
translate(x,y=x){
let newX = this.x + x;
let newY = this.y + y;
return new TauComplex(newX,newY)
}
// Adds two or more complex numbers together.
static sum(...c){
let reducer = (acc, cur) => new TauComplex(acc.x+cur.x,acc.y+cur.y)
return c.reduce(reducer)
}
// Multiples two or more complex numbers together.
static mult(...c){
let reducer = (acc, cur) => new TauComplex(acc.x*cur.x-acc.y*cur.y,acc.x*cur.y+acc.y*cur.x)
return c.reduce(reducer)
}
// Divides the first complex number by the second
static div(c1,c2){
return TauComplex.mult(c1,c2.inverse)
}
// Takes the complex number to the given power. Power MUST be a non-negative integer.
pow(power){
let arr = [];
for (var index=0;index!=power;index++){
arr.push(this);
}
if (arr.length>0) {
return TauComplex.mult(...arr)
}
else {
return new TauComplex(1,0);
}
}
// Returns the real portion of a complex number
get re(){
return this.x
}
// Returns the imaginary portion of a complex number
get im(){
return this.y
}
// Returns the complex number associated with a unit vector rotated by the revolution amount
static tau(rev){
return new TauComplex(Tau.cos(rev),Tau.sin(rev));
}
// Returns the complex exponent of the given complex number
get exp(){
return TauComplex.tau(this.y).scale(Math.exp(this.x))
}
// Creates a string representation of a number to the given significant digits, default being 5.
static trim(value,sigDigits=this.SIGDIGITS){
return value.toLocaleString("en-us",{maximumSignificantDigits:sigDigits})
}
static array(...arr){
return arr.map((subArr,index)=>new TauComplex(...subArr))
}
}
const _TauComplex = TauComplex;
exports.TauComplex = _TauComplex;
const _Tau = Tau;
exports.Tau = _Tau;
|
// David Eberly, Geometric Tools, Redmond WA 98052
// Copyright (c) 1998-2021
// Distributed under the Boost Software License, Version 1.0.
// https://www.boost.org/LICENSE_1_0.txt
// https://www.geometrictools.com/License/Boost/LICENSE_1_0.txt
// Version: 4.0.2019.08.13
#pragma once
#include <algorithm>
#include <atomic>
// Implementations of atomic minimum and atomic maximum computations. These
// are based on std::atomic_compare_exchange_strong.
namespace gte
{
template <typename T>
T AtomicMin(std::atomic<T>& v0, T const& v1)
{
T vInitial, vMin;
do
{
vInitial = v0;
vMin = std::min(vInitial, v1);
}
while (!std::atomic_compare_exchange_strong(&v0, &vInitial, vMin));
return vInitial;
}
template <typename T>
T AtomicMax(std::atomic<T>& v0, T const& v1)
{
T vInitial, vMax;
do
{
vInitial = v0;
vMax = std::max(vInitial, v1);
}
while (!std::atomic_compare_exchange_strong(&v0, &vInitial, vMax));
return vInitial;
}
}
|
define([
'angularAMD'
], function(angularAMD) {
'use strict';
/**
* @ngdoc directive
* @name contextMenuApp.directive:contextMenu
* @description
* # contextMenu
* Custom Context Menu Directive
*/
angular.module('contextMenuApp', [])
.directive('ngRightClick', function($parse) {
return function(scope, element, attrs) {
var fn = $parse(attrs.ngRightClick);
element.bind('contextmenu', function(event) {
scope.$apply(function() {
event.preventDefault();
fn(scope, {$event:event});
});
});
};
});
});
|
import os
import random
import stat
import string
import unittest
import nvmet.nvme as nvme
# Default test devices are ram disks, but allow user to specify different
# block devices or files.
NVMET_TEST_DEVICES = os.getenv("NVMET_TEST_DEVICES",
"/dev/ram0,/dev/ram1").split(',')
def test_devices_present():
return len([x for x in NVMET_TEST_DEVICES
if os.path.exists(x) and
(stat.S_ISBLK(os.stat(x).st_mode) or os.path.isfile(x))]) >= 2
class TestNvmet(unittest.TestCase):
def test_subsystem(self):
root = nvme.Root()
root.clear_existing()
for s in root.subsystems:
self.assertTrue(False, 'Found Subsystem after clear')
# create mode
s1 = nvme.Subsystem(nqn='testnqn1', mode='create')
self.assertIsNotNone(s1)
self.assertEqual(len(list(root.subsystems)), 1)
# any mode, should create
s2 = nvme.Subsystem(nqn='testnqn2', mode='any')
self.assertIsNotNone(s2)
self.assertEqual(len(list(root.subsystems)), 2)
# random name
s3 = nvme.Subsystem(mode='create')
self.assertIsNotNone(s3)
self.assertEqual(len(list(root.subsystems)), 3)
# duplicate
self.assertRaises(nvme.CFSError, nvme.Subsystem,
nqn='testnqn1', mode='create')
self.assertEqual(len(list(root.subsystems)), 3)
# lookup using any, should not create
s = nvme.Subsystem(nqn='testnqn1', mode='any')
self.assertEqual(s1, s)
self.assertEqual(len(list(root.subsystems)), 3)
# lookup only
s = nvme.Subsystem(nqn='testnqn2', mode='lookup')
self.assertEqual(s2, s)
self.assertEqual(len(list(root.subsystems)), 3)
# lookup without nqn
self.assertRaises(nvme.CFSError, nvme.Subsystem, mode='lookup')
# and delete them all
for s in root.subsystems:
s.delete()
self.assertEqual(len(list(root.subsystems)), 0)
def test_namespace(self):
root = nvme.Root()
root.clear_existing()
s = nvme.Subsystem(nqn='testnqn', mode='create')
for n in s.namespaces:
self.assertTrue(False, 'Found Namespace in new Subsystem')
# create mode
n1 = nvme.Namespace(s, nsid=3, mode='create')
self.assertIsNotNone(n1)
self.assertEqual(len(list(s.namespaces)), 1)
# any mode, should create
n2 = nvme.Namespace(s, nsid=2, mode='any')
self.assertIsNotNone(n2)
self.assertEqual(len(list(s.namespaces)), 2)
# create without nsid, should pick lowest available
n3 = nvme.Namespace(s, mode='create')
self.assertIsNotNone(n3)
self.assertEqual(n3.nsid, 1)
self.assertEqual(len(list(s.namespaces)), 3)
n4 = nvme.Namespace(s, mode='create')
self.assertIsNotNone(n4)
self.assertEqual(n4.nsid, 4)
self.assertEqual(len(list(s.namespaces)), 4)
# duplicate
self.assertRaises(nvme.CFSError, nvme.Namespace, 1, mode='create')
self.assertEqual(len(list(s.namespaces)), 4)
# lookup using any, should not create
n = nvme.Namespace(s, nsid=3, mode='any')
self.assertEqual(n1, n)
self.assertEqual(len(list(s.namespaces)), 4)
# lookup only
n = nvme.Namespace(s, nsid=2, mode='lookup')
self.assertEqual(n2, n)
self.assertEqual(len(list(s.namespaces)), 4)
# lookup without nsid
self.assertRaises(nvme.CFSError, nvme.Namespace, None, mode='lookup')
# and delete them all
for n in s.namespaces:
n.delete()
self.assertEqual(len(list(s.namespaces)), 0)
@unittest.skipUnless(test_devices_present(),
"Devices %s not available or suitable" % ','.join(NVMET_TEST_DEVICES))
def test_namespace_attrs(self):
root = nvme.Root()
root.clear_existing()
s = nvme.Subsystem(nqn='testnqn', mode='create')
n = nvme.Namespace(s, mode='create')
self.assertFalse(n.get_enable())
self.assertTrue('device' in n.attr_groups)
self.assertTrue('path' in n.list_attrs('device'))
# no device set yet, should fail
self.assertRaises(nvme.CFSError, n.set_enable, 1)
# now set a path and enable
n.set_attr('device', 'path', NVMET_TEST_DEVICES[0])
n.set_enable(1)
self.assertTrue(n.get_enable())
# test double enable
n.set_enable(1)
# test that we can't write to attrs while enabled
self.assertRaises(nvme.CFSError, n.set_attr, 'device', 'path',
NVMET_TEST_DEVICES[1])
self.assertRaises(nvme.CFSError, n.set_attr, 'device', 'nguid',
'15f7767b-50e7-4441-949c-75b99153dea7')
# disable: once and twice
n.set_enable(0)
n.set_enable(0)
# enable again, and remove while enabled
n.set_enable(1)
n.delete()
def test_recursive_delete(self):
root = nvme.Root()
root.clear_existing()
s = nvme.Subsystem(nqn='testnqn', mode='create')
n1 = nvme.Namespace(s, mode='create')
n2 = nvme.Namespace(s, mode='create')
s.delete()
self.assertEqual(len(list(root.subsystems)), 0)
def test_port(self):
root = nvme.Root()
root.clear_existing()
for p in root.ports:
self.assertTrue(False, 'Found Port after clear')
# create mode
p1 = nvme.Port(portid=0, mode='create')
self.assertIsNotNone(p1)
self.assertEqual(len(list(root.ports)), 1)
# any mode, should create
p2 = nvme.Port(portid=1, mode='any')
self.assertIsNotNone(p2)
self.assertEqual(len(list(root.ports)), 2)
# duplicate
self.assertRaises(nvme.CFSError, nvme.Port,
portid=0, mode='create')
self.assertEqual(len(list(root.ports)), 2)
# lookup using any, should not create
p = nvme.Port(portid=0, mode='any')
self.assertEqual(p1, p)
self.assertEqual(len(list(root.ports)), 2)
# lookup only
p = nvme.Port(portid=1, mode='lookup')
self.assertEqual(p2, p)
self.assertEqual(len(list(root.ports)), 2)
# and delete them all
for p in root.ports:
p.delete()
self.assertEqual(len(list(root.ports)), 0)
def test_loop_port(self):
root = nvme.Root()
root.clear_existing()
s = nvme.Subsystem(nqn='testnqn', mode='create')
p = nvme.Port(portid=0, mode='create')
# subsystem doesn't exists, should fail
self.assertRaises(nvme.CFSError, p.add_subsystem, 'invalidnqn')
self.assertTrue('addr' in p.attr_groups)
# no trtype set yet, should fail
self.assertRaises(nvme.CFSError, p.add_subsystem, 'testnqn')
# now set trtype to loop and other attrs and enable
p.set_attr('addr', 'trtype', 'loop')
p.set_attr('addr', 'adrfam', 'ipv4')
p.set_attr('addr', 'traddr', '192.168.0.1')
p.set_attr('addr', 'treq', 'not required')
p.set_attr('addr', 'trsvcid', '1023')
p.add_subsystem('testnqn')
# test double add
self.assertRaises(nvme.CFSError, p.add_subsystem, 'testnqn')
# test that we can't write to attrs while enabled
self.assertRaises(nvme.CFSError, p.set_attr, 'addr', 'trtype',
'rdma')
self.assertRaises(nvme.CFSError, p.set_attr, 'addr', 'adrfam',
'ipv6')
self.assertRaises(nvme.CFSError, p.set_attr, 'addr', 'traddr',
'10.0.0.1')
self.assertRaises(nvme.CFSError, p.set_attr, 'addr', 'treq',
'required')
self.assertRaises(nvme.CFSError, p.set_attr, 'addr', 'trsvcid',
'21')
# remove: once and twice
p.remove_subsystem('testnqn')
self.assertRaises(nvme.CFSError, p.remove_subsystem, 'testnqn')
# check that the attrs haven't been tampered with
self.assertEqual(p.get_attr('addr', 'trtype'), 'loop')
self.assertEqual(p.get_attr('addr', 'adrfam'), 'ipv4')
self.assertEqual(p.get_attr('addr', 'traddr'), '192.168.0.1')
self.assertEqual(p.get_attr('addr', 'treq'), 'not required')
self.assertEqual(p.get_attr('addr', 'trsvcid'), '1023')
# add again, and try to remove while enabled
p.add_subsystem('testnqn')
p.delete()
def test_host(self):
root = nvme.Root()
root.clear_existing()
for p in root.hosts:
self.assertTrue(False, 'Found Host after clear')
# create mode
h1 = nvme.Host(nqn='foo', mode='create')
self.assertIsNotNone(h1)
self.assertEqual(len(list(root.hosts)), 1)
# any mode, should create
h2 = nvme.Host(nqn='bar', mode='any')
self.assertIsNotNone(h2)
self.assertEqual(len(list(root.hosts)), 2)
# duplicate
self.assertRaises(nvme.CFSError, nvme.Host,
'foo', mode='create')
self.assertEqual(len(list(root.hosts)), 2)
# lookup using any, should not create
h = nvme.Host('foo', mode='any')
self.assertEqual(h1, h)
self.assertEqual(len(list(root.hosts)), 2)
# lookup only
h = nvme.Host('bar', mode='lookup')
self.assertEqual(h2, h)
self.assertEqual(len(list(root.hosts)), 2)
# and delete them all
for h in root.hosts:
h.delete()
self.assertEqual(len(list(root.hosts)), 0)
def test_referral(self):
root = nvme.Root()
root.clear_existing()
# create port
p = nvme.Port(portid=1, mode='create')
self.assertEqual(len(list(p.referrals)), 0)
# create mode
r1 = nvme.Referral(p, name="1", mode='create')
self.assertIsNotNone(r1)
self.assertEqual(len(list(p.referrals)), 1)
# any mode, should create
r2 = nvme.Referral(p, name="2", mode='any')
self.assertIsNotNone(r2)
self.assertEqual(len(list(p.referrals)), 2)
# duplicate
self.assertRaises(nvme.CFSError, nvme.Referral,
p, name="2", mode='create')
self.assertEqual(len(list(p.referrals)), 2)
# lookup using any, should not create
r = nvme.Referral(p, name="1", mode='any')
self.assertEqual(r1, r)
self.assertEqual(len(list(p.referrals)), 2)
# lookup only
r = nvme.Referral(p, name="2", mode='lookup')
self.assertEqual(r2, r)
self.assertEqual(len(list(p.referrals)), 2)
# non-existant lookup
self.assertRaises(nvme.CFSError, nvme.Referral, p, name="foo",
mode='lookup')
# basic state
self.assertTrue('addr' in r.attr_groups)
self.assertFalse(r.get_enable())
# now set trtype to loop and other attrs and enable
r.set_attr('addr', 'trtype', 'loop')
r.set_attr('addr', 'adrfam', 'ipv4')
r.set_attr('addr', 'traddr', '192.168.0.1')
r.set_attr('addr', 'treq', 'not required')
r.set_attr('addr', 'trsvcid', '1023')
r.set_enable(1)
# test double enable
r.set_enable(1)
# test that we can't write to attrs while enabled
self.assertRaises(nvme.CFSError, r.set_attr, 'addr', 'trtype',
'rdma')
self.assertRaises(nvme.CFSError, r.set_attr, 'addr', 'adrfam',
'ipv6')
self.assertRaises(nvme.CFSError, r.set_attr, 'addr', 'traddr',
'10.0.0.1')
self.assertRaises(nvme.CFSError, r.set_attr, 'addr', 'treq',
'required')
self.assertRaises(nvme.CFSError, r.set_attr, 'addr', 'trsvcid',
'21')
# disable: once and twice
r.set_enable(0)
r.set_enable(0)
# check that the attrs haven't been tampered with
self.assertEqual(r.get_attr('addr', 'trtype'), 'loop')
self.assertEqual(r.get_attr('addr', 'adrfam'), 'ipv4')
self.assertEqual(r.get_attr('addr', 'traddr'), '192.168.0.1')
self.assertEqual(r.get_attr('addr', 'treq'), 'not required')
self.assertEqual(r.get_attr('addr', 'trsvcid'), '1023')
# enable again, and try to remove while enabled
r.set_enable(1)
r.delete()
# remove the other one while disabled:
r1.delete()
self.assertEqual(len(list(p.referrals)), 0)
def test_allowed_hosts(self):
root = nvme.Root()
h = nvme.Host(nqn='hostnqn', mode='create')
s = nvme.Subsystem(nqn='testnqn', mode='create')
# add allowed_host
s.add_allowed_host(nqn='hostnqn')
# duplicate
self.assertRaises(nvme.CFSError, s.add_allowed_host, 'hostnqn')
# invalid
self.assertRaises(nvme.CFSError, s.add_allowed_host, 'invalid')
# remove again
s.remove_allowed_host('hostnqn')
# duplicate removal
self.assertRaises(nvme.CFSError, s.remove_allowed_host, 'hostnqn')
# invalid removal
self.assertRaises(nvme.CFSError, s.remove_allowed_host, 'foobar')
def test_invalid_input(self):
root = nvme.Root()
root.clear_existing()
self.assertRaises(nvme.CFSError, nvme.Subsystem,
nqn='', mode='create')
self.assertRaises(nvme.CFSError, nvme.Subsystem,
nqn='/', mode='create')
for l in [ 257, 512, 1024, 2048 ]:
toolong = ''.join(random.choice(string.ascii_lowercase)
for i in range(l))
self.assertRaises(nvme.CFSError, nvme.Subsystem,
nqn=toolong, mode='create')
discover_nqn = "nqn.2014-08.org.nvmexpress.discovery"
self.assertRaises(nvme.CFSError, nvme.Subsystem,
nqn=discover_nqn, mode='create')
self.assertRaises(nvme.CFSError, nvme.Port,
portid=1 << 17, mode='create')
@unittest.skipUnless(test_devices_present(),
"Devices %s not available or suitable" % ','.join(
NVMET_TEST_DEVICES))
def test_save_restore(self):
root = nvme.Root()
root.clear_existing()
h = nvme.Host(nqn='hostnqn', mode='create')
s = nvme.Subsystem(nqn='testnqn', mode='create')
s.add_allowed_host(nqn='hostnqn')
s2 = nvme.Subsystem(nqn='testnqn2', mode='create')
s2.set_attr('attr', 'allow_any_host', 1)
n = nvme.Namespace(s, nsid=42, mode='create')
n.set_attr('device', 'path', NVMET_TEST_DEVICES[0])
n.set_enable(1)
nguid = n.get_attr('device', 'nguid')
p = nvme.Port(portid=66, mode='create')
p.set_attr('addr', 'trtype', 'loop')
p.set_attr('addr', 'adrfam', 'ipv4')
p.set_attr('addr', 'traddr', '192.168.0.1')
p.set_attr('addr', 'treq', 'not required')
p.set_attr('addr', 'trsvcid', '1023')
p.add_subsystem('testnqn')
# save, clear, and restore
root.save_to_file('test.json')
root.clear_existing()
root.restore_from_file('test.json')
# additional restores should fai
self.assertRaises(nvme.CFSError, root.restore_from_file,
'test.json', False)
# ... unless forced!
root.restore_from_file('test.json', True)
# rebuild our view of the world
h = nvme.Host(nqn='hostnqn', mode='lookup')
s = nvme.Subsystem(nqn='testnqn', mode='lookup')
s2 = nvme.Subsystem(nqn='testnqn2', mode='lookup')
n = nvme.Namespace(s, nsid=42, mode='lookup')
p = nvme.Port(portid=66, mode='lookup')
self.assertEqual(s.get_attr('attr', 'allow_any_host'), "0")
self.assertEqual(s2.get_attr('attr', 'allow_any_host'), "1")
self.assertIn('hostnqn', s.allowed_hosts)
# and check everything is still the same
self.assertTrue(n.get_enable())
self.assertEqual(n.get_attr('device', 'path'), NVMET_TEST_DEVICES[0])
self.assertEqual(n.get_attr('device', 'nguid'), nguid)
self.assertEqual(p.get_attr('addr', 'trtype'), 'loop')
self.assertEqual(p.get_attr('addr', 'adrfam'), 'ipv4')
self.assertEqual(p.get_attr('addr', 'traddr'), '192.168.0.1')
self.assertEqual(p.get_attr('addr', 'treq'), 'not required')
self.assertEqual(p.get_attr('addr', 'trsvcid'), '1023')
self.assertIn('testnqn', p.subsystems)
self.assertNotIn('testtnqn2', p.subsystems)
|
$(function() {
$(document).on('click', '[data-tab-track]', function() {
var $this = $(this);
var field = $this.data('tab-track');
var value = $this.data('tab-track-id');
$('#' + field).val(value);
});
});
|
/****************************************************************************
* Copyright 2021 EPAM Systems
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
***************************************************************************/
import { RxnArrowMode, SimpleObjectMode } from 'ketcher-core'
import { bond as bondSchema } from '../data/schema/struct-schema'
import isHidden from './isHidden'
import { toBondType } from '../data/convert/structconv'
const toolActions = {
'select-lasso': {
title: 'Lasso Selection',
shortcut: 'Escape',
action: { tool: 'select', opts: 'lasso' }
},
'select-rectangle': {
title: 'Rectangle Selection',
shortcut: 'Escape',
action: { tool: 'select', opts: 'rectangle' }
},
'select-fragment': {
title: 'Fragment Selection',
shortcut: 'Escape',
action: { tool: 'select', opts: 'fragment' }
},
erase: {
title: 'Erase',
shortcut: ['Delete', 'Backspace'],
action: { tool: 'eraser', opts: 1 } // TODO last selector mode is better
},
chain: {
title: 'Chain',
action: { tool: 'chain' }
},
'enhanced-stereo': {
shortcut: 'Alt+e',
title: 'Stereochemistry',
action: { tool: 'enhancedStereo' },
hidden: options => isHidden(options, 'enhanced-stereo')
},
'charge-plus': {
shortcut: '5',
title: 'Charge Plus',
action: { tool: 'charge', opts: 1 }
},
'charge-minus': {
shortcut: '5',
title: 'Charge Minus',
action: { tool: 'charge', opts: -1 }
},
'transform-rotate': {
shortcut: 'Alt+r',
title: 'Rotate Tool',
action: { tool: 'rotate' }
},
'transform-flip-h': {
shortcut: 'Alt+h',
title: 'Horizontal Flip',
action: { tool: 'rotate', opts: 'horizontal' }
},
'transform-flip-v': {
shortcut: 'Alt+v',
title: 'Vertical Flip',
action: { tool: 'rotate', opts: 'vertical' }
},
sgroup: {
shortcut: 'Mod+g',
title: 'S-Group',
action: { tool: 'sgroup' },
hidden: options => isHidden(options, 'sgroup')
},
'sgroup-data': {
shortcut: 'Mod+g',
title: 'Data S-Group',
action: { tool: 'sgroup', opts: 'DAT' },
hidden: options => isHidden(options, 'sgroup-data')
},
'reaction-arrows': {
hidden: options => isHidden(options, 'reaction-arrows')
},
'reaction-arrow-open-angle': {
title: 'Arrow Open Angle Tool',
action: { tool: 'reactionarrow', opts: RxnArrowMode.OpenAngle },
hidden: options => isHidden(options, 'reaction-arrow-open-angle')
},
'reaction-arrow-filled-triangle': {
title: 'Arrow Filled Triangle',
action: { tool: 'reactionarrow', opts: RxnArrowMode.FilledTriangle },
hidden: options => isHidden(options, 'reaction-arrow-filled-triangle')
},
'reaction-arrow-filled-bow': {
title: 'Arrow Filled Bow Tool',
action: { tool: 'reactionarrow', opts: RxnArrowMode.FilledBow },
hidden: options => isHidden(options, 'reaction-arrow-filled-bow')
},
'reaction-arrow-dashed-open-angle': {
title: 'Arrow Dashed Open Angle Tool',
action: { tool: 'reactionarrow', opts: RxnArrowMode.DashedOpenAngle },
hidden: options => isHidden(options, 'reaction-arrow-dashed-open-angle')
},
'reaction-arrow-failed': {
title: 'Failed Arrow Tool',
action: { tool: 'reactionarrow', opts: RxnArrowMode.Failed },
hidden: options => isHidden(options, 'reaction-arrow-failed')
},
'reaction-arrow-both-ends-filled-triangle': {
title: 'Arrow Both Ends Filled Triangle Tool',
action: {
tool: 'reactionarrow',
opts: RxnArrowMode.BothEndsFilledTriangle
},
hidden: options =>
isHidden(options, 'reaction-arrow-both-ends-filled-triangle')
},
'reaction-arrow-equilibrium-filled-half-bow': {
title: 'Arrow Equilibrium Filled Half Bow Tool',
action: {
tool: 'reactionarrow',
opts: RxnArrowMode.EquilibriumFilledHalfBow
},
hidden: options =>
isHidden(options, 'reaction-arrow-equilibrium-filled-half-bow')
},
'reaction-arrow-equilibrium-filled-triangle': {
title: 'Arrow Equilibrium Filled Triangle Tool',
action: {
tool: 'reactionarrow',
opts: RxnArrowMode.EquilibriumFilledTriangle
},
hidden: options =>
isHidden(options, 'reaction-arrow-equilibrium-filled-triangle')
},
'reaction-arrow-equilibrium-open-angle': {
title: 'Arrow Equilibrium Open Angle Tool',
action: { tool: 'reactionarrow', opts: RxnArrowMode.EquilibriumOpenAngle },
hidden: options =>
isHidden(options, 'reaction-arrow-equilibrium-open-angle')
},
'reaction-arrow-unbalanced-equilibrium-filled-half-bow': {
title: 'Arrow Unbalanced Equilibrium Filled Half Bow Tool',
action: {
tool: 'reactionarrow',
opts: RxnArrowMode.UnbalancedEquilibriumFilledHalfBow
},
hidden: options =>
isHidden(options, 'reaction-arrow-unbalanced-equilibrium-filled-half-bow')
},
'reaction-arrow-unbalanced-equilibrium-open-half-angle': {
title: 'Arrow Unbalanced Equilibrium Open Half Angle Tool',
action: {
tool: 'reactionarrow',
opts: RxnArrowMode.UnbalancedEquilibriumOpenHalfAngle
},
hidden: options =>
isHidden(options, 'reaction-arrow-unbalanced-equilibrium-open-half-angle')
},
'reaction-arrow-unbalanced-equilibrium-large-filled-half-bow': {
title: 'Arrow Unbalanced Equilibrium Large Filled Half Bow Tool',
action: {
tool: 'reactionarrow',
opts: RxnArrowMode.UnbalancedEquilibriumLargeFilledHalfBow
},
hidden: options =>
isHidden(
options,
'reaction-arrow-unbalanced-equilibrium-large-filled-half-bow'
)
},
'reaction-arrow-unbalanced-equilibrium-fille-half-triangle': {
title: 'Arrow Unbalanced Equilibrium Filled Half Triangle Tool',
action: {
tool: 'reactionarrow',
opts: RxnArrowMode.UnbalancedEquilibriumFilleHalfTriangle
},
hidden: options =>
isHidden(
options,
'reaction-arrow-unbalanced-equilibrium-fille-half-triangle'
)
},
'reaction-plus': {
title: 'Reaction Plus Tool',
action: { tool: 'reactionplus' },
hidden: options => isHidden(options, 'reaction-plus')
},
'reaction-mapping-tools': {
hidden: options => isHidden(options, 'reaction-mapping-tools')
},
'reaction-map': {
title: 'Reaction Mapping Tool',
action: { tool: 'reactionmap' },
hidden: options => isHidden(options, 'reaction-map')
},
'reaction-unmap': {
title: 'Reaction Unmapping Tool',
action: { tool: 'reactionunmap' },
hidden: options => isHidden(options, 'reaction-unmap')
},
rgroup: {
hidden: options => isHidden(options, 'rgroup')
},
'rgroup-label': {
shortcut: 'Mod+r',
title: 'R-Group Label Tool',
action: { tool: 'rgroupatom' },
hidden: options => isHidden(options, 'rgroup-label')
},
'rgroup-fragment': {
shortcut: ['Mod+Shift+r', 'Mod+r'],
title: 'R-Group Fragment Tool',
action: { tool: 'rgroupfragment' },
hidden: options => isHidden(options, 'rgroup-fragment')
},
'rgroup-attpoints': {
shortcut: 'Mod+r',
title: 'Attachment Point Tool',
action: { tool: 'apoint' },
hidden: options => isHidden(options, 'rgroup-attpoints')
},
shape: {
hidden: options => isHidden(options, 'shape')
},
'shape-ellipse': {
title: 'Shape Ellipse',
action: { tool: 'simpleobject', opts: SimpleObjectMode.ellipse },
hidden: options => isHidden(options, 'shape-ellipse')
},
'shape-rectangle': {
title: 'Shape Rectangle',
action: { tool: 'simpleobject', opts: SimpleObjectMode.rectangle },
hidden: options => isHidden(options, 'shape-rectangle')
},
'shape-line': {
title: 'Shape Line',
action: { tool: 'simpleobject', opts: SimpleObjectMode.line },
hidden: options => isHidden(options, 'shape-line')
},
text: {
title: 'Add text',
action: { tool: 'text' },
hidden: options => isHidden(options, 'text')
}
}
const bondCuts = {
single: '1',
double: '2',
triple: '3',
up: '1',
down: '1',
updown: '1',
crossed: '2',
any: '0',
aromatic: '4'
}
const typeSchema = bondSchema.properties.type
export default typeSchema.enum.reduce((res, type, i) => {
res[`bond-${type}`] = {
title: `${typeSchema.enumNames[i]} Bond`,
shortcut: bondCuts[type],
action: {
tool: 'bond',
opts: toBondType(type)
}
}
return res
}, toolActions)
|
joo.classLoader.prepare("package flashx.textLayout.formats",/* {*/
/**
* Defines values for setting the <code>direction</code> property of the <code>TextLayoutFormat</code> class. Left-to-right reading order is used in Latin-style scripts. Right-to-left reading order is used with scripts such as Arabic or Hebrew.
* @see TextLayoutFormat#direction
*
*/
"public final class Direction",1,function($$private){;return[
/**
* Specifies left-to-right direction for text.
*/
"public static const",{ LTR/*:String*/ : "ltr"},
/**
* Specifies right-to-left direction for text.
*/
"public static const",{ RTL/*:String*/ : "rtl"},
];},[],[], "0.8.0", "0.9.6"
);
|
#!/usr/bin/env python
my_name = 'Zed A. Shaw'
my_age = 35 # not a lie
my_height = 74 # inches
my_weight = 180 # lbs
my_eyes = 'Blue'
my_teeth = 'White'
my_hair = 'Brown'
print "Let's talk about %s." % my_name
print "He's %d inches tall." % my_height
print "He's %d pounds heavy." % my_weight
print "Actually that's not too heavy."
print "He's got %s eyes and %s hair." % (my_eyes, my_hair)
print "His teeth are usually %s depending on the coffee." % my_teeth
# this line is tricky, try to get it exactly right
print "If I add %d, %d, and %d I get %d." % (my_age, my_height, my_weight, my_age + my_height + my_weight)
|
/*eslint no-console: 0*/
import test from "ava";
import { post } from "../../build/browser/index.umd.js";
test("Creating a resource in jsonplaceholder. (Content-Type)", async t => {
t.plan(2);
try {
const _post = {
title: "foo",
body: "bar",
userId: Math.random(),
};
const {
response,
data,
} = await post`https://jsonplaceholder.typicode.com/posts
Content-type: application/json
${JSON.stringify(_post)}`;
t.is(response.status, 201);
t.is(data.constructor, Object);
} catch (err) {
console.log(err);
}
});
test("Creating a resource in jsonplaceholder.", async t => {
t.plan(2);
try {
const {
response,
data,
} = await post`https://jsonplaceholder.typicode.com/posts ${{
title: "foo",
body: "bar",
userId: Math.random(),
}}`;
t.is(response.status, 201);
t.is(data.constructor, Object);
} catch (err) {
console.log(err);
}
});
test("Creating a resource in jsonplaceholder with expression interpolations", async t => {
t.plan(2);
const sub = "jsonplaceholder";
try {
const {
response,
data,
} = await post`https://${sub}.${"typicode"}.com/${"posts"}/ ${{
title: "foo",
body: "bar",
userId: Math.random(),
}}`;
t.is(response.status, 201);
t.is(data.constructor, Object);
} catch (err) {
console.log(err);
}
});
test("Creating a basic resource in jsonplaceholder", async t => {
t.plan(2);
try {
const {
response,
data,
} = await post`https://jsonplaceholder.typicode.com/posts/ ${{
title: "foo",
body: "bar",
userId: Math.random(),
}}`;
t.is(response.status, 201);
t.is(data.constructor, Object);
} catch (err) {
console.log(err);
}
});
|
import { utilGetAllNodes } from '@id-sdk/util';
import { t } from '../core/localizer';
import { behaviorOperation } from '../behavior/operation';
import { prefs } from '../core/preferences';
import { modeMove } from '../modes/move';
import { utilTotalExtent } from '../util/util';
export function operationMove(context, selectedIDs) {
var multi = (selectedIDs.length === 1 ? 'single' : 'multiple');
var nodes = utilGetAllNodes(selectedIDs, context.graph());
var coords = nodes.map(function(n) { return n.loc; });
var extent = utilTotalExtent(selectedIDs, context.graph());
var operation = function() {
context.enter(modeMove(context, selectedIDs));
};
operation.available = function() {
return selectedIDs.length > 0;
};
operation.disabled = function() {
const allowLargeEdits = prefs('rapid-internal-feature.allowLargeEdits') === 'true';
if (!allowLargeEdits && extent.percentContainedIn(context.map().extent()) < 0.8) {
return 'too_large';
} else if (someMissing()) {
return 'not_downloaded';
} else if (selectedIDs.some(context.hasHiddenConnections)) {
return 'connected_to_hidden';
} else if (selectedIDs.some(incompleteRelation)) {
return 'incomplete_relation';
}
return false;
function someMissing() {
if (context.inIntro()) return false;
var osm = context.connection();
if (osm) {
var missing = coords.filter(function(loc) { return !osm.isDataLoaded(loc); });
if (missing.length) {
missing.forEach(function(loc) { context.loadTileAtLoc(loc); });
return true;
}
}
return false;
}
function incompleteRelation(id) {
var entity = context.entity(id);
return entity.type === 'relation' && !entity.isComplete(context.graph());
}
};
operation.tooltip = function() {
var disable = operation.disabled();
return disable ?
t('operations.move.' + disable + '.' + multi) :
t('operations.move.description.' + multi);
};
operation.annotation = function() {
return selectedIDs.length === 1 ?
t('operations.move.annotation.' + context.graph().geometry(selectedIDs[0])) :
t('operations.move.annotation.feature', { n: selectedIDs.length });
};
operation.id = 'move';
operation.keys = [t('operations.move.key')];
operation.title = t('operations.move.title');
operation.behavior = behaviorOperation(context).which(operation);
operation.mouseOnly = true;
return operation;
}
|
from .fdio import * # yapf: disable # NOQA
# version detector. Precedence: installed dist, git, 'UNKNOWN'
try:
from ._dist_ver import __version__
except ImportError:
try:
from setuptools_scm import get_version
__version__ = get_version(root="..", relative_to=__file__)
except (ImportError, LookupError):
__version__ = "UNKNOWN"
|
import sqlite3 as sq
import datetime as dt
from contextlib import contextmanager
from rapidfuzz import fuzz
from rapidfuzz import process
@contextmanager
def cursor():
try:
conn = sq.connect('bot.db')
cur = conn.cursor()
yield cur
conn.commit()
finally:
conn.close()
def addRip(type, rip, channel, creator):
with cursor() as cur:
date = dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
cur.execute('INSERT INTO Rip values(?, ?, ?, ?, ?)',
(rip, type, date, channel, creator))
def delRip(delrip):
with cursor() as cur:
cur.execute('DELETE FROM Rip WHERE type = ? and rip = ?', (delrip))
def readRips():
with cursor() as cur:
cur.execute('SELECT type, rip, channel from Rip')
rows = cur.fetchall()
data = {}
for row in rows:
type, rip, channel = row
if channel not in data:
data[channel] = set()
data[channel].add((type, rip))
return data
def readViisaudet():
with cursor() as cur:
cur.execute('SELECT viisaus from Viisaus')
rows = cur.fetchall()
return set(rows)
def readSanat():
with cursor() as cur:
cur.execute('SELECT sana from Sana')
rows = cur.fetchall()
return set(rows)
def upsertOppi(keyword, definition, channel, creator):
with cursor() as cur:
date = dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
cur.execute('INSERT OR REPLACE INTO Oppi values(?, ?, ?, ?, ?)',
(keyword, definition, date, channel, creator))
def findOppi(keyword, channel):
with cursor() as cur:
cur.execute('SELECT definition FROM Oppi WHERE keyword=? and channel=?', (keyword, channel))
return cur.fetchone()
def searchOppi(keyword, user, channels):
search = '%' + keyword + '%'
results = []
for channel in channels:
with cursor() as cur:
cur.execute('SELECT keyword, definition FROM Oppi WHERE (keyword LIKE ? OR definition LIKE ?) AND channel=? LIMIT 50', (search, search, channel))
results = results + [(item[0], item[1]) for item in cur.fetchall()]
opis = {}
keys = []
for item in results:
opis[item[0]] = item[1]
keys.append(item[0])
keys = list(set(keys))
fuzzed = process.extract(keyword, keys, limit=50)
output = []
for item in fuzzed:
output.append((item[0], opis[item[0]]))
return output
def getChannels():
with cursor() as cur:
cur.execute('SELECT DISTINCT channel FROM Oppi')
return [item[0] for item in cur.fetchall()]
def countOpis(channel):
with cursor() as cur:
cur.execute('SELECT COUNT(*) AS count FROM Oppi WHERE channel=?', (channel,))
count = cur.fetchone()
return count
def randomOppi(channel):
with cursor() as cur:
cur.execute('SELECT keyword, definition FROM Oppi WHERE channel=? ORDER BY RANDOM() LIMIT 1', (channel,))
return cur.fetchone()
def insertQuote(quote, quotee, channel, creator):
with cursor() as cur:
date = dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
cur.execute('INSERT INTO Quote values(?, ?, ?, ?, ?)',
(quote, quotee, date, channel, creator))
def findQuotes(channel, quotee=None):
with cursor() as cur:
if quotee is not None:
cur.execute('SELECT quote, quotee FROM Quote WHERE channel=? AND upper(quotee) = upper(?)', (channel, quotee))
return cur.fetchall()
else:
cur.execute('SELECT quote, quotee FROM Quote WHERE channel=?', (channel,))
return cur.fetchall()
def countQuotes(channel):
with cursor() as cur:
cur.execute('SELECT count(quote) FROM Quote WHERE channel=?', (channel,))
return cur.fetchone()[0]
def readDiagnoosit():
with cursor() as cur:
cur.execute('SELECT diagnoosi from Diagnoosi')
rows = cur.fetchall()
return set(rows)
def readMaidot():
with cursor() as cur:
cur.execute('SELECT maito from Maito')
rows = cur.fetchall()
return set(rows)
def readNimet():
with cursor() as cur:
cur.execute('SELECT nimi from Nimi')
rows = cur.fetchall()
return set(rows)
def readKalat():
with cursor() as cur:
cur.execute('SELECT kala from Kalat')
rows = cur.fetchall()
return set(rows)
def readVihanneet():
with cursor() as cur:
cur.execute('SELECT nimi from Vihannes')
rows = cur.fetchall()
return set(rows)
def readPlanetoidit():
with cursor() as cur:
cur.execute('SELECT nimi from Planetoidi')
rows = cur.fetchall()
return set(rows)
def readKulkuneuvot():
with cursor() as cur:
cur.execute('SELECT nimi from Kulkuneuvo')
rows = cur.fetchall()
return set(rows)
def readLinnut():
with cursor() as cur:
cur.execute('SELECT nimi from Linnut')
rows = cur.fetchall()
return set(rows)
def readSotilasarvot():
with cursor() as cur:
cur.execute('SELECT nimi from Arvonimet')
rows = cur.fetchall()
return set(rows)
def readSotilasnimet():
with cursor() as cur:
cur.execute('SELECT nimi from Sotilasnimet')
rows = cur.fetchall()
return set(rows)
def readKasvinimet():
with cursor() as cur:
cur.execute('SELECT nimi from Kasvinimet')
rows = cur.fetchall()
return set(rows)
def readEnnustukset():
with cursor() as cur:
cur.execute('SELECT rivi from Ennustus')
rows = cur.fetchall()
return set(rows)
def readNakutukset():
with cursor() as cur:
cur.execute('SELECT nakutus from Nakutukset')
rows = cur.fetchall()
return set(rows)
def readDefinitions(channel):
with cursor() as cur:
cur.execute('SELECT definition, keyword from Oppi where channel=?', (channel, ))
rows = cur.fetchall()
return rows
def upsertTag(tag, target, channel, creator):
with cursor() as cur:
date = dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
cur.execute('INSERT OR REPLACE INTO Tagit values(?, ?, ?, ?, ?)',
(tag, target, channel, creator, date))
def findTagged(tag, channel):
with cursor() as cur:
cur.execute('SELECT target FROM Tagit WHERE tag=? and channel=?', (tag, channel))
rows = cur.fetchall()
return rows
def findTargetTags(target, channel):
with cursor() as cur:
cur.execute('SELECT tag FROM Tagit WHERE target=? and channel=?', (target, channel))
rows = cur.fetchall()
return rows
def addUrheilu(uid, chatid, km, lajinnimi, date):
with cursor() as cur:
query = ("INSERT INTO Urheilut (uid, chatid, km, type, date) VALUES (?, ?, ?, "
"(SELECT l.id FROM Urheilulajit AS l WHERE l.nimi = ?), ?)")
params = (uid, chatid, km, lajinnimi, date)
cur.execute(query, params)
def getKayttajanUrheilut(uid, chatid, earliest_date):
with cursor() as cur:
query = ("SELECT up.lajinnimi AS lajinnimi, SUM(up.km) AS km, SUM(up.pisteet) AS pisteet "
"FROM UrheilutPisteilla AS up "
"WHERE up.uid = ? AND up.chatid = ? AND up.date >= ? "
"GROUP BY up.lajinnimi, up.uid")
params = (uid, chatid, earliest_date)
cur.execute(query, params)
return cur.fetchall()
def getTopUrheilut(chatid, lajinnimi, earliest_date, limit):
with cursor() as cur:
query = ("SELECT uid, km from (SELECT up.uid AS uid, SUM(up.km) AS km "
"FROM UrheilutPisteilla AS up "
"WHERE up.chatid = ? AND up.date >= ? AND up.lajinnimi = ? "
"GROUP BY up.lajinnimi, up.uid) "
"ORDER BY km DESC LIMIT ?")
params = (chatid, earliest_date, lajinnimi, limit)
cur.execute(query, params)
return cur.fetchall()
def getPisteet(chatid, earliest_date, limit):
with cursor() as cur:
query = ("SELECT uid, pisteet from (SELECT up.uid AS uid, SUM(up.pisteet) AS pisteet "
"FROM UrheilutPisteilla AS up "
"WHERE up.chatid = ? AND up.date >= ? "
"GROUP BY up.uid) "
"ORDER BY pisteet DESC LIMIT ?")
params = (chatid, earliest_date, limit)
cur.execute(query, params)
return cur.fetchall()
def lisaaUrheilulaji(nimi, kerroin):
with cursor() as cur:
cur.execute("INSERT INTO Urheilulajit (nimi, kerroin) VALUES (?, ?) ON CONFLICT (nimi) DO UPDATE SET kerroin = ?",
(nimi, kerroin, kerroin))
def readSelitykset():
with cursor() as cur:
cur.execute('SELECT kortti, selitys, rev from Korttiselitykset')
rows = cur.fetchall()
return (rows)
|
import logging
# Setup Basic Configuration
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
logger = logging.getLogger(__name__)
|
# Copyright (c) 2016 Ansible, Inc.
# All Rights Reserved.
import json
from django.utils.encoding import smart_text
from django.core.mail.backends.base import BaseEmailBackend
from django.utils.translation import ugettext_lazy as _
class AWXBaseEmailBackend(BaseEmailBackend):
def format_body(self, body):
if "body" in body:
body_actual = body['body']
else:
body_actual = smart_text(_("{} #{} had status {}, view details at {}\n\n").format(
body['friendly_name'], body['id'], body['status'], body['url'])
)
body_actual += json.dumps(body, indent=4)
return body_actual
|
(window.webpackJsonp=window.webpackJsonp||[]).push([[27],{157:function(e,t,n){"use strict";n.r(t),n.d(t,"frontMatter",(function(){return i})),n.d(t,"metadata",(function(){return c})),n.d(t,"rightToc",(function(){return l})),n.d(t,"default",(function(){return s}));var r=n(2),o=n(9),a=(n(0),n(323)),i={id:"login",title:"This should be the Login UI"},c={id:"version-v0.2/fallback/login",title:"This should be the Login UI",description:"You ended up here because you did not set the following configuration value:",source:"@site/versioned_docs/version-v0.2/fallback/login.mdx",permalink:"/kratos/docs/v0.2/fallback/login",editUrl:"https://github.com/ory/kratos/edit/master/docs/versioned_docs/version-v0.2/fallback/login.mdx",version:"v0.2",lastUpdatedBy:"aeneasr",lastUpdatedAt:1588605498},l=[],u={rightToc:l};function s(e){var t=e.components,n=Object(o.a)(e,["components"]);return Object(a.b)("wrapper",Object(r.a)({},u,n,{components:t,mdxType:"MDXLayout"}),Object(a.b)("p",null,"You ended up here because you did not set the following configuration value:"),Object(a.b)("pre",null,Object(a.b)("code",Object(r.a)({parentName:"pre"},{className:"language-yaml",metastring:'title="path/to/kratos/config.yml',title:'"path/to/kratos/config.yml'}),"urls:\n login_ui: http://my-app.com/login\n")),Object(a.b)("p",null,"You can set this configuration value using environment variable ",Object(a.b)("inlineCode",{parentName:"p"},"URLS_LOGIN_UI"),"\nas well!"),Object(a.b)("p",null,"If you don't know what that means, head over to\n",Object(a.b)("a",Object(r.a)({parentName:"p"},{href:"/kratos/docs/v0.2/self-service/flows/user-login-user-registration"}),"User Login and Registration"),"!"))}s.isMDXComponent=!0},323:function(e,t,n){"use strict";n.d(t,"a",(function(){return p})),n.d(t,"b",(function(){return b}));var r=n(0),o=n.n(r);function a(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function i(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function c(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?i(Object(n),!0).forEach((function(t){a(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):i(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function l(e,t){if(null==e)return{};var n,r,o=function(e,t){if(null==e)return{};var n,r,o={},a=Object.keys(e);for(r=0;r<a.length;r++)n=a[r],t.indexOf(n)>=0||(o[n]=e[n]);return o}(e,t);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);for(r=0;r<a.length;r++)n=a[r],t.indexOf(n)>=0||Object.prototype.propertyIsEnumerable.call(e,n)&&(o[n]=e[n])}return o}var u=o.a.createContext({}),s=function(e){var t=o.a.useContext(u),n=t;return e&&(n="function"==typeof e?e(t):c(c({},t),e)),n},p=function(e){var t=s(e.components);return o.a.createElement(u.Provider,{value:t},e.children)},f={inlineCode:"code",wrapper:function(e){var t=e.children;return o.a.createElement(o.a.Fragment,{},t)}},d=o.a.forwardRef((function(e,t){var n=e.components,r=e.mdxType,a=e.originalType,i=e.parentName,u=l(e,["components","mdxType","originalType","parentName"]),p=s(n),d=r,b=p["".concat(i,".").concat(d)]||p[d]||f[d]||a;return n?o.a.createElement(b,c(c({ref:t},u),{},{components:n})):o.a.createElement(b,c({ref:t},u))}));function b(e,t){var n=arguments,r=t&&t.mdxType;if("string"==typeof e||r){var a=n.length,i=new Array(a);i[0]=d;var c={};for(var l in t)hasOwnProperty.call(t,l)&&(c[l]=t[l]);c.originalType=e,c.mdxType="string"==typeof e?e:r,i[1]=c;for(var u=2;u<a;u++)i[u]=n[u];return o.a.createElement.apply(null,i)}return o.a.createElement.apply(null,n)}d.displayName="MDXCreateElement"}}]);
|
// Copyright (c) 2009-2010 Satoshi Nakamoto
// Copyright (c) 2009-2012 The Bitcoin Developers
// Distributed under the MIT/X11 software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
//
// Why base-58 instead of standard base-64 encoding?
// - Don't want 0OIl characters that look the same in some fonts and
// could be used to create visually identical looking account numbers.
// - A string with non-alphanumeric characters is not as easily accepted as an account number.
// - E-mail usually won't line-break if there's no punctuation to break at.
// - Double-clicking selects the whole number as one word if it's all alphanumeric.
//
#ifndef BITCOIN_BASE58_H
#define BITCOIN_BASE58_H
#include <string>
#include <vector>
#include "bignum.h"
#include "key.h"
#include "script.h"
static const char* pszBase58 = "123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz";
// Encode a byte sequence as a base58-encoded string
inline std::string EncodeBase58(const unsigned char* pbegin, const unsigned char* pend)
{
CAutoBN_CTX pctx;
CBigNum bn58 = 58;
CBigNum bn0 = 0;
// Convert big endian data to little endian
// Extra zero at the end make sure bignum will interpret as a positive number
std::vector<unsigned char> vchTmp(pend-pbegin+1, 0);
reverse_copy(pbegin, pend, vchTmp.begin());
// Convert little endian data to bignum
CBigNum bn;
bn.setvch(vchTmp);
// Convert bignum to std::string
std::string str;
// Expected size increase from base58 conversion is approximately 137%
// use 138% to be safe
str.reserve((pend - pbegin) * 138 / 100 + 1);
CBigNum dv;
CBigNum rem;
while (bn > bn0)
{
if (!BN_div(&dv, &rem, &bn, &bn58, pctx))
throw bignum_error("EncodeBase58 : BN_div failed");
bn = dv;
unsigned int c = rem.getulong();
str += pszBase58[c];
}
// Leading zeroes encoded as base58 zeros
for (const unsigned char* p = pbegin; p < pend && *p == 0; p++)
str += pszBase58[0];
// Convert little endian std::string to big endian
reverse(str.begin(), str.end());
return str;
}
// Encode a byte vector as a base58-encoded string
inline std::string EncodeBase58(const std::vector<unsigned char>& vch)
{
return EncodeBase58(&vch[0], &vch[0] + vch.size());
}
// Decode a base58-encoded string psz into byte vector vchRet
// returns true if decoding is successful
inline bool DecodeBase58(const char* psz, std::vector<unsigned char>& vchRet)
{
CAutoBN_CTX pctx;
vchRet.clear();
CBigNum bn58 = 58;
CBigNum bn = 0;
CBigNum bnChar;
while (isspace(*psz))
psz++;
// Convert big endian string to bignum
for (const char* p = psz; *p; p++)
{
const char* p1 = strchr(pszBase58, *p);
if (p1 == NULL)
{
while (isspace(*p))
p++;
if (*p != '\0')
return false;
break;
}
bnChar.setulong(p1 - pszBase58);
if (!BN_mul(&bn, &bn, &bn58, pctx))
throw bignum_error("DecodeBase58 : BN_mul failed");
bn += bnChar;
}
// Get bignum as little endian data
std::vector<unsigned char> vchTmp = bn.getvch();
// Trim off sign byte if present
if (vchTmp.size() >= 2 && vchTmp.end()[-1] == 0 && vchTmp.end()[-2] >= 0x80)
vchTmp.erase(vchTmp.end()-1);
// Restore leading zeros
int nLeadingZeros = 0;
for (const char* p = psz; *p == pszBase58[0]; p++)
nLeadingZeros++;
vchRet.assign(nLeadingZeros + vchTmp.size(), 0);
// Convert little endian data to big endian
reverse_copy(vchTmp.begin(), vchTmp.end(), vchRet.end() - vchTmp.size());
return true;
}
// Decode a base58-encoded string str into byte vector vchRet
// returns true if decoding is successful
inline bool DecodeBase58(const std::string& str, std::vector<unsigned char>& vchRet)
{
return DecodeBase58(str.c_str(), vchRet);
}
// Encode a byte vector to a base58-encoded string, including checksum
inline std::string EncodeBase58Check(const std::vector<unsigned char>& vchIn)
{
// add 4-byte hash check to the end
std::vector<unsigned char> vch(vchIn);
uint256 hash = Hash(vch.begin(), vch.end());
vch.insert(vch.end(), (unsigned char*)&hash, (unsigned char*)&hash + 4);
return EncodeBase58(vch);
}
// Decode a base58-encoded string psz that includes a checksum, into byte vector vchRet
// returns true if decoding is successful
inline bool DecodeBase58Check(const char* psz, std::vector<unsigned char>& vchRet)
{
if (!DecodeBase58(psz, vchRet))
return false;
if (vchRet.size() < 4)
{
vchRet.clear();
return false;
}
uint256 hash = Hash(vchRet.begin(), vchRet.end()-4);
if (memcmp(&hash, &vchRet.end()[-4], 4) != 0)
{
vchRet.clear();
return false;
}
vchRet.resize(vchRet.size()-4);
return true;
}
// Decode a base58-encoded string str that includes a checksum, into byte vector vchRet
// returns true if decoding is successful
inline bool DecodeBase58Check(const std::string& str, std::vector<unsigned char>& vchRet)
{
return DecodeBase58Check(str.c_str(), vchRet);
}
/** Base class for all base58-encoded data */
class CBase58Data
{
protected:
// the version byte
unsigned char nVersion;
// the actually encoded data
std::vector<unsigned char> vchData;
CBase58Data()
{
nVersion = 0;
vchData.clear();
}
~CBase58Data()
{
// zero the memory, as it may contain sensitive data
if (!vchData.empty())
memset(&vchData[0], 0, vchData.size());
}
void SetData(int nVersionIn, const void* pdata, size_t nSize)
{
nVersion = nVersionIn;
vchData.resize(nSize);
if (!vchData.empty())
memcpy(&vchData[0], pdata, nSize);
}
void SetData(int nVersionIn, const unsigned char *pbegin, const unsigned char *pend)
{
SetData(nVersionIn, (void*)pbegin, pend - pbegin);
}
public:
bool SetString(const char* psz)
{
std::vector<unsigned char> vchTemp;
DecodeBase58Check(psz, vchTemp);
if (vchTemp.empty())
{
vchData.clear();
nVersion = 0;
return false;
}
nVersion = vchTemp[0];
vchData.resize(vchTemp.size() - 1);
if (!vchData.empty())
memcpy(&vchData[0], &vchTemp[1], vchData.size());
memset(&vchTemp[0], 0, vchTemp.size());
return true;
}
bool SetString(const std::string& str)
{
return SetString(str.c_str());
}
std::string ToString() const
{
std::vector<unsigned char> vch(1, nVersion);
vch.insert(vch.end(), vchData.begin(), vchData.end());
return EncodeBase58Check(vch);
}
int CompareTo(const CBase58Data& b58) const
{
if (nVersion < b58.nVersion) return -1;
if (nVersion > b58.nVersion) return 1;
if (vchData < b58.vchData) return -1;
if (vchData > b58.vchData) return 1;
return 0;
}
bool operator==(const CBase58Data& b58) const { return CompareTo(b58) == 0; }
bool operator<=(const CBase58Data& b58) const { return CompareTo(b58) <= 0; }
bool operator>=(const CBase58Data& b58) const { return CompareTo(b58) >= 0; }
bool operator< (const CBase58Data& b58) const { return CompareTo(b58) < 0; }
bool operator> (const CBase58Data& b58) const { return CompareTo(b58) > 0; }
};
/** base58-encoded addresses.
* Public-key-hash-addresses have version 25 (or 111 testnet).
* The data vector contains RIPEMD160(SHA256(pubkey)), where pubkey is the serialized public key.
* Script-hash-addresses have version 85 (or 196 testnet).
* The data vector contains RIPEMD160(SHA256(cscript)), where cscript is the serialized redemption script.
*/
class CBitcoinAddress;
class CBitcoinAddressVisitor : public boost::static_visitor<bool>
{
private:
CBitcoinAddress *addr;
public:
CBitcoinAddressVisitor(CBitcoinAddress *addrIn) : addr(addrIn) { }
bool operator()(const CKeyID &id) const;
bool operator()(const CScriptID &id) const;
bool operator()(const CNoDestination &no) const;
};
class CBitcoinAddress : public CBase58Data
{
public:
enum
{
PUBKEY_ADDRESS = 63, // pskbank
SCRIPT_ADDRESS = 85, // pskbank
PUBKEY_ADDRESS_TEST = 111,
SCRIPT_ADDRESS_TEST = 196,
};
bool Set(const CKeyID &id) {
SetData(fTestNet ? PUBKEY_ADDRESS_TEST : PUBKEY_ADDRESS, &id, 20);
return true;
}
bool Set(const CScriptID &id) {
SetData(fTestNet ? SCRIPT_ADDRESS_TEST : SCRIPT_ADDRESS, &id, 20);
return true;
}
bool Set(const CTxDestination &dest)
{
return boost::apply_visitor(CBitcoinAddressVisitor(this), dest);
}
bool IsValid() const
{
unsigned int nExpectedSize = 20;
bool fExpectTestNet = false;
switch(nVersion)
{
case PUBKEY_ADDRESS:
nExpectedSize = 20; // Hash of public key
fExpectTestNet = false;
break;
case SCRIPT_ADDRESS:
nExpectedSize = 20; // Hash of CScript
fExpectTestNet = false;
break;
case PUBKEY_ADDRESS_TEST:
nExpectedSize = 20;
fExpectTestNet = true;
break;
case SCRIPT_ADDRESS_TEST:
nExpectedSize = 20;
fExpectTestNet = true;
break;
default:
return false;
}
return fExpectTestNet == fTestNet && vchData.size() == nExpectedSize;
}
CBitcoinAddress()
{
}
CBitcoinAddress(const CTxDestination &dest)
{
Set(dest);
}
CBitcoinAddress(const std::string& strAddress)
{
SetString(strAddress);
}
CBitcoinAddress(const char* pszAddress)
{
SetString(pszAddress);
}
CTxDestination Get() const {
if (!IsValid())
return CNoDestination();
switch (nVersion) {
case PUBKEY_ADDRESS:
case PUBKEY_ADDRESS_TEST: {
uint160 id;
memcpy(&id, &vchData[0], 20);
return CKeyID(id);
}
case SCRIPT_ADDRESS:
case SCRIPT_ADDRESS_TEST: {
uint160 id;
memcpy(&id, &vchData[0], 20);
return CScriptID(id);
}
}
return CNoDestination();
}
bool GetKeyID(CKeyID &keyID) const {
if (!IsValid())
return false;
switch (nVersion) {
case PUBKEY_ADDRESS:
case PUBKEY_ADDRESS_TEST: {
uint160 id;
memcpy(&id, &vchData[0], 20);
keyID = CKeyID(id);
return true;
}
default: return false;
}
}
bool IsScript() const {
if (!IsValid())
return false;
switch (nVersion) {
case SCRIPT_ADDRESS:
case SCRIPT_ADDRESS_TEST: {
return true;
}
default: return false;
}
}
};
bool inline CBitcoinAddressVisitor::operator()(const CKeyID &id) const { return addr->Set(id); }
bool inline CBitcoinAddressVisitor::operator()(const CScriptID &id) const { return addr->Set(id); }
bool inline CBitcoinAddressVisitor::operator()(const CNoDestination &id) const { return false; }
/** A base58-encoded secret key */
class CBitcoinSecret : public CBase58Data
{
public:
void SetSecret(const CSecret& vchSecret, bool fCompressed)
{
assert(vchSecret.size() == 32);
SetData(128 + (fTestNet ? CBitcoinAddress::PUBKEY_ADDRESS_TEST : CBitcoinAddress::PUBKEY_ADDRESS), &vchSecret[0], vchSecret.size());
if (fCompressed)
vchData.push_back(1);
}
CSecret GetSecret(bool &fCompressedOut)
{
CSecret vchSecret;
vchSecret.resize(32);
memcpy(&vchSecret[0], &vchData[0], 32);
fCompressedOut = vchData.size() == 33;
return vchSecret;
}
bool IsValid() const
{
bool fExpectTestNet = false;
switch(nVersion)
{
case (128 + CBitcoinAddress::PUBKEY_ADDRESS):
break;
case (128 + CBitcoinAddress::PUBKEY_ADDRESS_TEST):
fExpectTestNet = true;
break;
default:
return false;
}
return fExpectTestNet == fTestNet && (vchData.size() == 32 || (vchData.size() == 33 && vchData[32] == 1));
}
bool SetString(const char* pszSecret)
{
return CBase58Data::SetString(pszSecret) && IsValid();
}
bool SetString(const std::string& strSecret)
{
return SetString(strSecret.c_str());
}
CBitcoinSecret(const CSecret& vchSecret, bool fCompressed)
{
SetSecret(vchSecret, fCompressed);
}
CBitcoinSecret()
{
}
};
#endif
|
var express = require('express')
var app = express()
var path = require('path')
var bodyParser = require('body-parser')
var cookieSession = require('cookie-session')
var apiRoute = require('./routes/api.js')
// 设置静态路径
app.use(express.static(path.join(__dirname, 'public')))
// 配置body解析器
app.use(bodyParser.json())
app.use(bodyParser.urlencoded({extended: false}))
// 使用服务器客户端数据通信的中间件
app.use(cookieSession({
name: 'session',
secret: 'some random charactors',
maxAge: 1000 * 60 * 60 * 24
}))
// 使用路由
app.use('/api', apiRoute)
// 监听端口号
app.listen(process.env.PORT || '8080')
|
/* eslint-disable @typescript-eslint/no-var-requires */
process.env.NODE_ENV = 'test';
module.exports = wallaby => ({
files: [
'src/**/*.ts',
'jest.config.js',
'test/avatar.png',
'test/profile.jpg',
],
tests: ['test/**/*.test.ts'],
testFramework: 'jest',
env: {
type: 'node',
runner: 'node',
},
// setup: function(wallaby) {
// const jestConfig = require('./backend/jest.config');
// wallaby.testFramework.configure(jestConfig);
// },
setup: function(wallaby) {
const jestConfig = require('./jest.config');
wallaby.testFramework.configure(jestConfig);
},
workers: {
initial: 1,
regular: 1,
},
});
|
export default {
lang: {
colorpicker: {
confirm: 'OK',
clear: 'Tøm'
},
datepicker: {
now: 'Nå',
today: 'I dag',
cancel: 'Avbryt',
clear: 'Tøm',
confirm: 'OK',
selectDate: 'Velg dato',
selectTime: 'Velg tidspunkt',
startDate: 'Start Dato',
startTime: 'Start Tidspunkt',
endDate: 'Sluttdato',
endTime: 'Sluttidspunkt',
prevYear: 'Previous Year', // to be translated
nextYear: 'Next Year', // to be translated
prevMonth: 'Previous Month', // to be translated
nextMonth: 'Next Month', // to be translated
year: '',
month1: 'Januar',
month2: 'Februar',
month3: 'Mars',
month4: 'April',
month5: 'Mai',
month6: 'Juni',
month7: 'Juli',
month8: 'August',
month9: 'September',
month10: 'Oktober',
month11: 'November',
month12: 'Desember',
// week: 'week',
weeks: {
sun: 'Søn',
mon: 'Man',
tue: 'Tir',
wed: 'Ons',
thu: 'Tor',
fri: 'Fre',
sat: 'Lør'
},
months: {
jan: 'Jan',
feb: 'Feb',
mar: 'Mar',
apr: 'Apr',
may: 'Mai',
jun: 'Jun',
jul: 'Jul',
aug: 'Aug',
sep: 'Sep',
oct: 'Okt',
nov: 'Nov',
dec: 'Des'
}
},
select: {
loading: 'Laster',
noMatch: 'Ingen samsvarende data',
noData: 'Ingen data',
placeholder: 'Velg'
},
cascader: {
noMatch: 'Ingen samsvarende data',
loading: 'Laster',
placeholder: 'Velg',
noData: 'Ingen data'
},
pagination: {
goto: 'Gå til',
pagesize: '/side',
total: 'Total {total}',
pageClassifier: ''
},
messagebox: {
confirm: 'OK',
cancel: 'Avbryt',
error: 'Ugyldig input'
},
upload: {
deleteTip: 'press delete to remove', // to be translated
delete: 'Slett',
preview: 'Forhåndsvisning',
continue: 'Fortsett'
},
table: {
emptyText: 'Ingen Data',
confirmFilter: 'Bekreft',
resetFilter: 'Tilbakestill',
clearFilter: 'Alle',
sumText: 'Sum' // to be translated
},
tree: {
emptyText: 'Ingen Data'
},
transfer: {
noMatch: 'Ingen samsvarende data',
noData: 'Ingen data',
titles: ['List 1', 'List 2'], // to be translated
filterPlaceholder: 'Enter keyword', // to be translated
noCheckedFormat: '{total} items', // to be translated
hasCheckedFormat: '{checked}/{total} checked' // to be translated
},
image: {
error: 'FAILED' // to be translated
},
pageHeader: {
title: 'Back' // to be translated
},
popconfirm: {
confirmButtonText: 'Yes', // to be translated
cancelButtonText: 'No' // to be translated
},
empty: {
description: 'Ingen Data'
}
}
};
|
import { __rest } from "tslib";
import * as React from 'react';
import { css } from '@patternfly/react-styles';
import styles from '@patternfly/react-styles/css/components/Wizard/wizard';
export const WizardNavItem = (_a) => {
var { children = null, content = '', isCurrent = false, isDisabled = false, step, onNavItemClick = () => undefined, navItemComponent = 'button', href = null } = _a, rest = __rest(_a, ["children", "content", "isCurrent", "isDisabled", "step", "onNavItemClick", "navItemComponent", "href"]);
const NavItemComponent = navItemComponent;
if (navItemComponent === 'a' && !href && process.env.NODE_ENV !== 'production') {
// eslint-disable-next-line no-console
console.error('WizardNavItem: When using an anchor, please provide an href');
}
const btnProps = {
disabled: isDisabled
};
const linkProps = {
tabIndex: isDisabled ? -1 : undefined,
href
};
return (React.createElement("li", { className: css(styles.wizardNavItem) },
React.createElement(NavItemComponent, Object.assign({}, rest, (navItemComponent === 'a' ? Object.assign({}, linkProps) : Object.assign({}, btnProps)), { onClick: () => onNavItemClick(step), className: css(styles.wizardNavLink, isCurrent && 'pf-m-current', isDisabled && 'pf-m-disabled'), "aria-disabled": isDisabled ? true : null, "aria-current": isCurrent && !children ? 'page' : false }), content),
children));
};
WizardNavItem.displayName = 'WizardNavItem';
//# sourceMappingURL=WizardNavItem.js.map
|
"""
General image database
An image database creates a list of relative image path called image_set_index and
transform index to absolute image path. As to training, it is necessary that ground
truth and proposals are mixed together for training.
roidb
basic format [image_index]
['image', 'height', 'width', 'flipped',
'boxes', 'gt_classes', 'gt_overlaps', 'max_classes', 'max_overlaps', 'bbox_targets']
"""
import os
import cPickle
import numpy as np
from ..processing.bbox_transform import bbox_overlaps
class IMDB(object):
def __init__(self, name, image_set, root_path, dataset_path):
"""
basic information about an image database
:param name: name of image database will be used for any output
:param root_path: root path store cache and proposal data
:param dataset_path: dataset path store images and image lists
"""
self.name = name + '_' + image_set
self.image_set = image_set
self.root_path = root_path
self.data_path = dataset_path
# abstract attributes
self.classes = []
self.num_classes = 0
self.image_set_index = []
self.num_images = 0
self.config = {}
def image_path_from_index(self, index):
raise NotImplementedError
def gt_roidb(self):
raise NotImplementedError
def evaluate_detections(self, detections):
raise NotImplementedError
@property
def cache_path(self):
"""
make a directory to store all caches
:return: cache path
"""
cache_path = os.path.join(self.root_path, 'cache_no_dff')
if not os.path.exists(cache_path):
os.mkdir(cache_path)
return cache_path
def image_path_at(self, index):
"""
access image at index in image database
:param index: image index in image database
:return: image path
"""
return self.image_path_from_index(self.image_set_index[index])
def load_rpn_data(self, full=False):
if full:
rpn_file = os.path.join(self.root_path, 'rpn_data', self.name + '_full_rpn.pkl')
else:
rpn_file = os.path.join(self.root_path, 'rpn_data', self.name + '_rpn.pkl')
print 'loading {}'.format(rpn_file)
assert os.path.exists(rpn_file), 'rpn data not found at {}'.format(rpn_file)
with open(rpn_file, 'rb') as f:
box_list = cPickle.load(f)
return box_list
def load_rpn_roidb(self, gt_roidb):
"""
turn rpn detection boxes into roidb
:param gt_roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
:return: roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
"""
box_list = self.load_rpn_data()
return self.create_roidb_from_box_list(box_list, gt_roidb)
def rpn_roidb(self, gt_roidb, append_gt=False):
"""
get rpn roidb and ground truth roidb
:param gt_roidb: ground truth roidb
:param append_gt: append ground truth
:return: roidb of rpn
"""
if append_gt:
print 'appending ground truth annotations'
rpn_roidb = self.load_rpn_roidb(gt_roidb)
roidb = IMDB.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self.load_rpn_roidb(gt_roidb)
return roidb
def create_roidb_from_box_list(self, box_list, gt_roidb):
"""
given ground truth, prepare roidb
:param box_list: [image_index] ndarray of [box_index][x1, x2, y1, y2]
:param gt_roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
:return: roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
"""
assert len(box_list) == self.num_images, 'number of boxes matrix must match number of images'
roidb = []
for i in range(self.num_images):
roi_rec = dict()
roi_rec['image'] = gt_roidb[i]['image']
roi_rec['height'] = gt_roidb[i]['height']
roi_rec['width'] = gt_roidb[i]['width']
boxes = box_list[i]
if boxes.shape[1] == 5:
boxes = boxes[:, :4]
num_boxes = boxes.shape[0]
overlaps = np.zeros((num_boxes, self.num_classes), dtype=np.float32)
if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0:
gt_boxes = gt_roidb[i]['boxes']
gt_classes = gt_roidb[i]['gt_classes']
# n boxes and k gt_boxes => n * k overlap
gt_overlaps = bbox_overlaps(boxes.astype(np.float), gt_boxes.astype(np.float))
# for each box in n boxes, select only maximum overlap (must be greater than zero)
argmaxes = gt_overlaps.argmax(axis=1)
maxes = gt_overlaps.max(axis=1)
I = np.where(maxes > 0)[0]
overlaps[I, gt_classes[argmaxes[I]]] = maxes[I]
roi_rec.update({'boxes': boxes,
'gt_classes': np.zeros((num_boxes,), dtype=np.int32),
'gt_overlaps': overlaps,
'max_classes': overlaps.argmax(axis=1),
'max_overlaps': overlaps.max(axis=1),
'flipped': False})
# background roi => background class
zero_indexes = np.where(roi_rec['max_overlaps'] == 0)[0]
assert all(roi_rec['max_classes'][zero_indexes] == 0)
# foreground roi => foreground class
nonzero_indexes = np.where(roi_rec['max_overlaps'] > 0)[0]
assert all(roi_rec['max_classes'][nonzero_indexes] != 0)
roidb.append(roi_rec)
return roidb
def append_flipped_images(self, roidb):
"""
append flipped images to an roidb
flip boxes coordinates, images will be actually flipped when loading into network
:param roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
:return: roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
"""
print 'append flipped images to roidb'
assert self.num_images == len(roidb)
for i in range(self.num_images):
roi_rec = roidb[i]
boxes = roi_rec['boxes'].copy()
oldx1 = boxes[:, 0].copy()
oldx2 = boxes[:, 2].copy()
boxes[:, 0] = roi_rec['width'] - oldx2 - 1
boxes[:, 2] = roi_rec['width'] - oldx1 - 1
assert (boxes[:, 2] >= boxes[:, 0]).all(),\
'img_name %s, width %d\n' % (roi_rec['image'], roi_rec['width']) + \
np.array_str(roi_rec['boxes'], precision=3, suppress_small=True)
entry = {'image': roi_rec['image'],
'height': roi_rec['height'],
'width': roi_rec['width'],
'boxes': boxes,
'gt_classes': roidb[i]['gt_classes'],
'gt_overlaps': roidb[i]['gt_overlaps'],
'max_classes': roidb[i]['max_classes'],
'max_overlaps': roidb[i]['max_overlaps'],
'flipped': True}
roidb.append(entry)
self.image_set_index *= 2
return roidb
def evaluate_recall(self, roidb, candidate_boxes=None, thresholds=None):
"""
evaluate detection proposal recall metrics
record max overlap value for each gt box; return vector of overlap values
:param roidb: used to evaluate
:param candidate_boxes: if not given, use roidb's non-gt boxes
:param thresholds: array-like recall threshold
:return: None
ar: average recall, recalls: vector recalls at each IoU overlap threshold
thresholds: vector of IoU overlap threshold, gt_overlaps: vector of all ground-truth overlaps
"""
area_names = ['all', '0-25', '25-50', '50-100',
'100-200', '200-300', '300-inf']
area_ranges = [[0**2, 1e5**2], [0**2, 25**2], [25**2, 50**2], [50**2, 100**2],
[100**2, 200**2], [200**2, 300**2], [300**2, 1e5**2]]
area_counts = []
for area_name, area_range in zip(area_names[1:], area_ranges[1:]):
area_count = 0
for i in range(self.num_images):
if candidate_boxes is None:
# default is use the non-gt boxes from roidb
non_gt_inds = np.where(roidb[i]['gt_classes'] == 0)[0]
boxes = roidb[i]['boxes'][non_gt_inds, :]
else:
boxes = candidate_boxes[i]
boxes_areas = (boxes[:, 2] - boxes[:, 0] + 1) * (boxes[:, 3] - boxes[:, 1] + 1)
valid_range_inds = np.where((boxes_areas >= area_range[0]) & (boxes_areas < area_range[1]))[0]
area_count += len(valid_range_inds)
area_counts.append(area_count)
total_counts = float(sum(area_counts))
for area_name, area_count in zip(area_names[1:], area_counts):
print 'percentage of', area_name, area_count / total_counts
print 'average number of proposal', total_counts / self.num_images
for area_name, area_range in zip(area_names, area_ranges):
gt_overlaps = np.zeros(0)
num_pos = 0
for i in range(self.num_images):
# check for max_overlaps == 1 avoids including crowd annotations
max_gt_overlaps = roidb[i]['gt_overlaps'].max(axis=1)
gt_inds = np.where((roidb[i]['gt_classes'] > 0) & (max_gt_overlaps == 1))[0]
gt_boxes = roidb[i]['boxes'][gt_inds, :]
gt_areas = (gt_boxes[:, 2] - gt_boxes[:, 0] + 1) * (gt_boxes[:, 3] - gt_boxes[:, 1] + 1)
valid_gt_inds = np.where((gt_areas >= area_range[0]) & (gt_areas < area_range[1]))[0]
gt_boxes = gt_boxes[valid_gt_inds, :]
num_pos += len(valid_gt_inds)
if candidate_boxes is None:
# default is use the non-gt boxes from roidb
non_gt_inds = np.where(roidb[i]['gt_classes'] == 0)[0]
boxes = roidb[i]['boxes'][non_gt_inds, :]
else:
boxes = candidate_boxes[i]
if boxes.shape[0] == 0:
continue
overlaps = bbox_overlaps(boxes.astype(np.float), gt_boxes.astype(np.float))
_gt_overlaps = np.zeros((gt_boxes.shape[0]))
# choose whatever is smaller to iterate
rounds = min(boxes.shape[0], gt_boxes.shape[0])
for j in range(rounds):
# find which proposal maximally covers each gt box
argmax_overlaps = overlaps.argmax(axis=0)
# get the IoU amount of coverage for each gt box
max_overlaps = overlaps.max(axis=0)
# find which gt box is covered by most IoU
gt_ind = max_overlaps.argmax()
gt_ovr = max_overlaps.max()
assert (gt_ovr >= 0), '%s\n%s\n%s' % (boxes, gt_boxes, overlaps)
# find the proposal box that covers the best covered gt box
box_ind = argmax_overlaps[gt_ind]
# record the IoU coverage of this gt box
_gt_overlaps[j] = overlaps[box_ind, gt_ind]
assert (_gt_overlaps[j] == gt_ovr)
# mark the proposal box and the gt box as used
overlaps[box_ind, :] = -1
overlaps[:, gt_ind] = -1
# append recorded IoU coverage level
gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))
gt_overlaps = np.sort(gt_overlaps)
if thresholds is None:
step = 0.05
thresholds = np.arange(0.5, 0.95 + 1e-5, step)
recalls = np.zeros_like(thresholds)
# compute recall for each IoU threshold
for i, t in enumerate(thresholds):
recalls[i] = (gt_overlaps >= t).sum() / float(num_pos)
ar = recalls.mean()
# print results
print 'average recall for {}: {:.3f}'.format(area_name, ar)
for threshold, recall in zip(thresholds, recalls):
print 'recall @{:.2f}: {:.3f}'.format(threshold, recall)
@staticmethod
def merge_roidbs(a, b):
"""
merge roidbs into one
:param a: roidb to be merged into
:param b: roidb to be merged
:return: merged imdb
"""
assert len(a) == len(b)
for i in range(len(a)):
a[i]['boxes'] = np.vstack((a[i]['boxes'], b[i]['boxes']))
a[i]['gt_classes'] = np.hstack((a[i]['gt_classes'], b[i]['gt_classes']))
a[i]['gt_overlaps'] = np.vstack((a[i]['gt_overlaps'], b[i]['gt_overlaps']))
a[i]['max_classes'] = np.hstack((a[i]['max_classes'], b[i]['max_classes']))
a[i]['max_overlaps'] = np.hstack((a[i]['max_overlaps'], b[i]['max_overlaps']))
return a
|
from typing import Dict
from datetime import datetime
from botocore.paginate import Paginator
class ListDeviceEvents(Paginator):
def paginate(self, DeviceId: str, FromTimeStamp: datetime, ToTimeStamp: datetime, PaginationConfig: Dict = None) -> Dict:
"""
Creates an iterator that will paginate through responses from :py:meth:`IoT1ClickDevicesService.Client.list_device_events`.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/devices-2018-05-14/ListDeviceEvents>`_
**Request Syntax**
::
response_iterator = paginator.paginate(
DeviceId='string',
FromTimeStamp=datetime(2015, 1, 1),
ToTimeStamp=datetime(2015, 1, 1),
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
**Response Syntax**
::
{
'Events': [
{
'Device': {
'Attributes': {},
'DeviceId': 'string',
'Type': 'string'
},
'StdEvent': 'string'
},
],
}
**Response Structure**
- *(dict) --*
200 response
- **Events** *(list) --*
An array of zero or more elements describing the event(s) associated with the device.
- *(dict) --*
- **Device** *(dict) --*
An object representing the device associated with the event.
- **Attributes** *(dict) --*
The user specified attributes associated with the device for an event.
- **DeviceId** *(string) --*
The unique identifier of the device.
- **Type** *(string) --*
The device type, such as "button".
- **StdEvent** *(string) --*
A serialized JSON object representing the device-type specific event.
:type DeviceId: string
:param DeviceId: **[REQUIRED]**
The unique identifier of the device.
:type FromTimeStamp: datetime
:param FromTimeStamp: **[REQUIRED]**
The start date for the device event query, in ISO8061 format. For example, 2018-03-28T15:45:12.880Z
:type ToTimeStamp: datetime
:param ToTimeStamp: **[REQUIRED]**
The end date for the device event query, in ISO8061 format. For example, 2018-03-28T15:45:12.880Z
:type PaginationConfig: dict
:param PaginationConfig:
A dictionary that provides parameters to control pagination.
- **MaxItems** *(integer) --*
The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.
- **PageSize** *(integer) --*
The size of each page.
- **StartingToken** *(string) --*
A token to specify where to start paginating. This is the ``NextToken`` from a previous response.
:rtype: dict
:returns:
"""
pass
class ListDevices(Paginator):
def paginate(self, DeviceType: str = None, PaginationConfig: Dict = None) -> Dict:
"""
Creates an iterator that will paginate through responses from :py:meth:`IoT1ClickDevicesService.Client.list_devices`.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/devices-2018-05-14/ListDevices>`_
**Request Syntax**
::
response_iterator = paginator.paginate(
DeviceType='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
**Response Syntax**
::
{
'Devices': [
{
'Arn': 'string',
'Attributes': {
'string': 'string'
},
'DeviceId': 'string',
'Enabled': True|False,
'RemainingLife': 123.0,
'Type': 'string',
'Tags': {
'string': 'string'
}
},
],
}
**Response Structure**
- *(dict) --*
200 response
- **Devices** *(list) --*
A list of devices.
- *(dict) --*
- **Arn** *(string) --*
The ARN of the device.
- **Attributes** *(dict) --*
An array of zero or more elements of DeviceAttribute objects providing user specified device attributes.
- *(string) --*
- *(string) --*
- **DeviceId** *(string) --*
The unique identifier of the device.
- **Enabled** *(boolean) --*
A Boolean value indicating whether or not the device is enabled.
- **RemainingLife** *(float) --*
A value between 0 and 1 inclusive, representing the fraction of life remaining for the device.
- **Type** *(string) --*
The type of the device, such as "button".
- **Tags** *(dict) --*
The tags currently associated with the AWS IoT 1-Click device.
- *(string) --*
- *(string) --*
:type DeviceType: string
:param DeviceType:
The type of the device, such as \"button\".
:type PaginationConfig: dict
:param PaginationConfig:
A dictionary that provides parameters to control pagination.
- **MaxItems** *(integer) --*
The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.
- **PageSize** *(integer) --*
The size of each page.
- **StartingToken** *(string) --*
A token to specify where to start paginating. This is the ``NextToken`` from a previous response.
:rtype: dict
:returns:
"""
pass
|
// eslint-disable-next-line @typescript-eslint/no-var-requires
const path = require('path')
// If your port is set to 80,
// use administrator privileges to execute the command line.
// For example, on Mac: sudo npm run / sudo yarn
const devServerPort = 9527 // TODO: get this variable from setting.ts
const mockServerPort = 9528 // TODO: get this variable from setting.ts
const name = '邻药优选后台' // TODO: get this variable from setting.ts
module.exports = {
publicPath: process.env.NODE_ENV === 'production' ? '/vue-typescript-admin-template/' : '/',
lintOnSave: process.env.NODE_ENV === 'development',
productionSourceMap: false,
devServer: {
port: devServerPort,
open: true,
overlay: {
warnings: false,
errors: true
},
progress: false,
proxy: {
// change xxx-api/login => /mock-api/v1/login
// detail: https://cli.vuejs.org/config/#devserver-proxy
'/we-shop-mgmt': {
target: 'http://test.ysbang.cn/',
changeOrigin: true // needed for virtual hosted sites
// ws: true // proxy websockets
// pathRewrite: {
// ['^' + process.env.VUE_APP_BASE_API]: ''
// }
}
}
},
configureWebpack: {
// provide the app's title in webpack's name field, so that
// it can be accessed in index.html to inject the correct title.
name: name
},
pwa: {
name: name,
workboxPluginMode: 'InjectManifest',
workboxOptions: {
swSrc: path.resolve(__dirname, 'src/pwa/service-worker.js')
}
},
pluginOptions: {
'style-resources-loader': {
preProcessor: 'scss',
patterns: [
path.resolve(__dirname, 'src/styles/_variables.scss'),
path.resolve(__dirname, 'src/styles/_mixins.scss')
]
}
},
chainWebpack(config) {
// provide the app's title in html-webpack-plugin's options list so that
// it can be accessed in index.html to inject the correct title.
// https://cli.vuejs.org/guide/webpack.html#modifying-options-of-a-plugin
config.plugin('html').tap(args => {
args[0].title = name
return args
})
// it can improve the speed of the first screen, it is recommended to turn on preload
config.plugin('preload').tap(() => [
{
rel: 'preload',
// to ignore runtime.js
// https://github.com/vuejs/vue-cli/blob/dev/packages/@vue/cli-service/lib/config/app.js#L171
fileBlacklist: [/\.map$/, /hot-update\.js$/, /runtime\..*\.js$/],
include: 'initial'
}
])
// when there are many pages, it will cause too many meaningless requests
config.plugins.delete('prefetch')
// https://webpack.js.org/configuration/devtool/#development
// Change development env source map if you want.
// The default in vue-cli is 'eval-cheap-module-source-map'.
// config
// .when(process.env.NODE_ENV === 'development',
// config => config.devtool('eval-cheap-source-map')
// )
config
.when(process.env.NODE_ENV !== 'development',
config => {
config
.optimization.splitChunks({
chunks: 'all',
cacheGroups: {
libs: {
name: 'chunk-libs',
test: /[\\/]node_modules[\\/]/,
priority: 10,
chunks: 'initial' // only package third parties that are initially dependent
},
elementUI: {
name: 'chunk-elementUI', // split elementUI into a single package
priority: 20, // the weight needs to be larger than libs and app or it will be packaged into libs or app
test: /[\\/]node_modules[\\/]_?element-ui(.*)/ // in order to adapt to cnpm
},
commons: {
name: 'chunk-commons',
test: path.resolve(__dirname, 'src/components'),
minChunks: 3, // minimum common number
priority: 5,
reuseExistingChunk: true
}
}
})
// https://webpack.js.org/configuration/optimization/#optimizationruntimechunk
config.optimization.runtimeChunk('single')
}
)
}
}
|
# -*- coding: utf-8 -*-
"""@package Methods.Machine.SlotW21.comp_surface
SlotW21 Computation of surface method
@date Created on Mon Dec 08 17:34:45 2014
@copyright (C) 2014-2015 EOMYS ENGINEERING.
@author pierre_b
"""
from numpy import sin, tan
def comp_surface(self):
"""Compute the Slot total surface (by analytical computation).
Caution, the bottom of the Slot is an Arc
Parameters
----------
self : SlotW21
A SlotW21 object
Returns
-------
S: float
Slot total surface [m**2]
"""
Rbo = self.get_Rbo()
if self.H1_is_rad: # H1 in rad
H1 = (self.W1 - self.W0) / 2.0 * tan(self.H1) # H1 in m
else: # H1 in m
H1 = self.H1
S2 = 0.5 * (self.W0 + self.W1) * H1
S1 = self.H0 * self.W0
Swind = self.comp_surface_wind()
# The bottom is an arc
alpha = self.comp_angle_opening()
Sarc = (Rbo ** 2.0) / 2.0 * (alpha - sin(alpha))
# Because Slamination = S - Zs * Sslot
if self.is_outwards():
return S1 + S2 + Swind - Sarc
else:
return S1 + S2 + Swind + Sarc
|
import { _ as __vue_normalize__, r as registerComponent, u as use } from './chunk-cca88db8.js';
import { T as Tag } from './chunk-3c2169d7.js';
export { T as BTag } from './chunk-3c2169d7.js';
//
//
//
//
//
//
var script = {
name: 'BTaglist',
props: {
attached: Boolean
}
};
/* script */
const __vue_script__ = script;
/* template */
var __vue_render__ = function () {var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"tags",class:{ 'has-addons': _vm.attached }},[_vm._t("default")],2)};
var __vue_staticRenderFns__ = [];
/* style */
const __vue_inject_styles__ = undefined;
/* scoped */
const __vue_scope_id__ = undefined;
/* module identifier */
const __vue_module_identifier__ = undefined;
/* functional template */
const __vue_is_functional_template__ = false;
/* style inject */
/* style inject SSR */
var Taglist = __vue_normalize__(
{ render: __vue_render__, staticRenderFns: __vue_staticRenderFns__ },
__vue_inject_styles__,
__vue_script__,
__vue_scope_id__,
__vue_is_functional_template__,
__vue_module_identifier__,
undefined,
undefined
);
var Plugin = {
install: function install(Vue) {
registerComponent(Vue, Tag);
registerComponent(Vue, Taglist);
}
};
use(Plugin);
export default Plugin;
export { Taglist as BTaglist };
|
//
// File: rtGetInf.h
//
// Code generated for Simulink model 'est_estimator'.
//
// Model version : 1.1142
// Simulink Coder version : 8.11 (R2016b) 25-Aug-2016
// C/C++ source code generated on : Mon Dec 4 08:33:06 2017
//
#ifndef RTW_HEADER_rtGetInf_h_
#define RTW_HEADER_rtGetInf_h_
#include <stddef.h>
#include "rtwtypes.h"
#include "rt_nonfinite.h"
#ifdef __cplusplus
extern "C" {
#endif
extern real_T rtGetInf(void);
extern real32_T rtGetInfF(void);
extern real_T rtGetMinusInf(void);
extern real32_T rtGetMinusInfF(void);
#ifdef __cplusplus
} // extern "C"
#endif
#endif // RTW_HEADER_rtGetInf_h_
//
// File trailer for generated code.
//
// [EOF]
//
|
/* Copyright (c) 2001, Stanford University
* All rights reserved
*
* See the file LICENSE.txt for information on redistributing this software.
*/
#include "cr_error.h"
#include "cr_mem.h"
#include "cr_string.h"
#include "packspu.h"
#include "packspu_proto.h"
static void packspu_GetHostBufferSubDataARB( GLenum target, GLintptrARB offset, GLsizeiptrARB size, void * data )
{
GET_THREAD(thread);
int writeback = 1;
crPackGetBufferSubDataARB(target, offset, size, data, &writeback);
packspuFlush((void *) thread);
CRPACKSPU_WRITEBACK_WAIT(thread, writeback);
}
void * PACKSPU_APIENTRY
packspu_MapBufferARB( GLenum target, GLenum access )
{
GET_CONTEXT(ctx);
void *buffer;
CRBufferObject *pBufObj;
CRASSERT(GL_TRUE == ctx->clientState->bufferobject.retainBufferData);
buffer = crStateMapBufferARB(target, access);
#ifdef CR_ARB_pixel_buffer_object
if (buffer)
{
pBufObj = crStateGetBoundBufferObject(target, &ctx->clientState->bufferobject);
CRASSERT(pBufObj);
if (pBufObj->bResyncOnRead &&
access != GL_WRITE_ONLY_ARB)
{
/*fetch data from host side*/
packspu_GetHostBufferSubDataARB(target, 0, pBufObj->size, buffer);
}
}
#endif
return buffer;
}
void PACKSPU_APIENTRY packspu_GetBufferSubDataARB( GLenum target, GLintptrARB offset, GLsizeiptrARB size, void * data )
{
GET_CONTEXT(ctx);
#ifdef CR_ARB_pixel_buffer_object
CRBufferObject *pBufObj;
pBufObj = crStateGetBoundBufferObject(target, &ctx->clientState->bufferobject);
if (pBufObj && pBufObj->bResyncOnRead)
{
packspu_GetHostBufferSubDataARB(target, offset, size, data);
return;
}
#endif
crStateGetBufferSubDataARB(target, offset, size, data);
}
GLboolean PACKSPU_APIENTRY
packspu_UnmapBufferARB( GLenum target )
{
GET_CONTEXT(ctx);
#if CR_ARB_vertex_buffer_object
CRBufferObject *bufObj;
bufObj = crStateGetBoundBufferObject(target, &ctx->clientState->bufferobject);
/* send new buffer contents to server */
crPackBufferDataARB( target, bufObj->size, bufObj->pointer, bufObj->usage );
#endif
CRASSERT(GL_TRUE == ctx->clientState->bufferobject.retainBufferData);
crStateUnmapBufferARB( target );
return GL_TRUE;
}
void PACKSPU_APIENTRY
packspu_BufferDataARB(GLenum target, GLsizeiptrARB size, const GLvoid * data, GLenum usage)
{
/*crDebug("packspu_BufferDataARB size:%d", size);*/
crStateBufferDataARB(target, size, data, usage);
crPackBufferDataARB(target, size, data, usage);
}
void PACKSPU_APIENTRY
packspu_BufferSubDataARB(GLenum target, GLintptrARB offset, GLsizeiptrARB size, const GLvoid * data)
{
/*crDebug("packspu_BufferSubDataARB size:%d", size);*/
crStateBufferSubDataARB(target, offset, size, data);
crPackBufferSubDataARB(target, offset, size, data);
}
void PACKSPU_APIENTRY
packspu_GetBufferPointervARB(GLenum target, GLenum pname, GLvoid **params)
{
crStateGetBufferPointervARB( target, pname, params );
}
void PACKSPU_APIENTRY
packspu_GetBufferParameterivARB( GLenum target, GLenum pname, GLint * params )
{
crStateGetBufferParameterivARB( target, pname, params );
}
/*
* Need to update our local state for vertex arrays.
*/
void PACKSPU_APIENTRY
packspu_BindBufferARB( GLenum target, GLuint buffer )
{
crStateBindBufferARB(target, buffer);
crPackBindBufferARB(target, buffer);
}
void PACKSPU_APIENTRY packspu_GenBuffersARB( GLsizei n, GLuint * buffer )
{
GET_THREAD(thread);
int writeback = 1;
if (!CRPACKSPU_IS_WDDM_CRHGSMI() && !(pack_spu.thread[pack_spu.idxThreadInUse].netServer.conn->actual_network))
{
crError( "packspu_GenBuffersARB doesn't work when there's no actual network involved!\nTry using the simplequery SPU in your chain!" );
}
if (pack_spu.swap)
{
crPackGenBuffersARBSWAP( n, buffer, &writeback );
}
else
{
crPackGenBuffersARB( n, buffer, &writeback );
}
packspuFlush( (void *) thread );
CRPACKSPU_WRITEBACK_WAIT(thread, writeback);
crStateRegBuffers(n, buffer);
}
void PACKSPU_APIENTRY packspu_DeleteBuffersARB( GLsizei n, const GLuint * buffer )
{
crStateDeleteBuffersARB( n, buffer );
crPackDeleteBuffersARB(n, buffer);
}
|
import React, { Component } from 'react';
import { Link } from 'react-router-dom';
import axios from 'axios';
class Register extends Component {
constructor(props){
super(props);
this.state ={
username: "",
password: ""
}
}
//handleChange
handleChange = (e) => {
this.setState({[e.target.name]: e.target.value})
}
//handleClick
handleClick = (e) => {
axios.post("http://localhost:25851/auth/register", this.state)
.then(response => {
localStorage.setItem("jwt", response.data.token);
this.props.history.push("/notes");
})
}
render() {
console.log(this.state);
return (
<div className="main-form-container">
<div className="form-wrapper">
<h1>New User? Register Here</h1>
<div className="input-wrapper">
<label className="label">Username:</label>
<input onChange={this.handleChange} name="username" type="text" value={this.state.username}/>
</div>
<div className="input-wrapper">
<label className="label">Password:</label>
<input onChange={this.handleChange} name="password" type="password" value={this.state.password}/>
</div>
<div className="button login-button"onClick={this.handleClick}>Register</div>
<Link to="/login"><div className="register-here">Already Have An Account? Login Here!</div></Link>
</div>
</div>
);
}
}
export default Register;
|
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String
Base = declarative_base()
class SeatReservation(Base):
__tablename__ = "seat_reservation"
__table_args__ = {"sqlite_autoincrement": True}
id = Column(Integer, primary_key=True)
trainid = Column(String)
seatid = Column(Integer)
startstation = Column(String)
endstation = Column(String)
def __repr__(self):
return "<SeatReservation(trainid='{}', seatid='{}', startstation={}, endstation={})>".format(
self.trainid, self.seatid, self.startstation, self.endstation
)
|
define(['angular'], function(angular) {
var hello = angular.module('home', []);
});
|
import React from 'react'
import { Provider } from 'react-redux'
import { render } from 'react-dom'
import { configureStore } from 'redux-starter-kit'
import App from './components/App'
import rootReducer from './ducks'
import * as serviceWorker from './serviceWorker'
import './styles.scss'
const store = configureStore({
reducer: rootReducer
})
render(
<Provider store={store}>
<App />
</Provider>,
document.getElementById('root')
)
// If you want your app to work offline and load faster, you can change
// unregister() to register() below. Note this comes with some pitfalls.
// Learn more about service workers: https://bit.ly/CRA-PWA
serviceWorker.unregister()
|
# flake8: noqa
# import all models into this package
# if you have many models here with many references from one model to another this may
# raise a RecursionError
# to avoid this, import only the models that you directly need like:
# from from fds.sdk.ProcuretoPayProvisioning.model.pet import Pet
# or import this package, but before doing it, use:
# import sys
# sys.setrecursionlimit(n)
from fds.sdk.ProcuretoPayProvisioning.model.cancel_individual import CancelIndividual
from fds.sdk.ProcuretoPayProvisioning.model.create_individual import CreateIndividual
from fds.sdk.ProcuretoPayProvisioning.model.get_individual import GetIndividual
from fds.sdk.ProcuretoPayProvisioning.model.inline_response202 import InlineResponse202
from fds.sdk.ProcuretoPayProvisioning.model.list_individuals import ListIndividuals
from fds.sdk.ProcuretoPayProvisioning.model.manage_product_ids import ManageProductIds
from fds.sdk.ProcuretoPayProvisioning.model.modify_individual import ModifyIndividual
from fds.sdk.ProcuretoPayProvisioning.model.transaction_status import TransactionStatus
|
# -*- coding: utf-8 -*-
#
# This file is part of the pyFDA project hosted at https://github.com/chipmuenk/pyfda
#
# Copyright © pyFDA Project Contributors
# Licensed under the terms of the MIT License
# (see file LICENSE in root directory for details)
"""
Widget for plotting \|H(z)\| in 3D
"""
import logging
logger = logging.getLogger(__name__)
from ..compat import (QCheckBox, QWidget, QComboBox, QLabel, QLineEdit, QDial,
QGridLayout, QFrame, pyqtSlot, pyqtSignal)
import numpy as np
from numpy import pi, ones, sin, cos, log10
import scipy.signal as sig
import pyfda.filterbroker as fb
from pyfda.pyfda_rc import params
from pyfda.pyfda_lib import H_mag, mod_version, safe_eval
from pyfda.pyfda_qt_lib import qget_cmb_box
from pyfda.plot_widgets.mpl_widget import MplWidget
from mpl_toolkits import mplot3d # import to register 3D projection
from matplotlib import cm # Colormap
from matplotlib.colors import LightSource
classes = {'Plot_3D':'3D'} #: Dict containing class name : display name
#http://docs.enthought.com/mayavi/mayavi/mlab_running_scripts.html#running-mlab-scripts
#http://docs.enthought.com/mayavi/mayavi/auto/mlab_helper_functions.html
#http://docs.enthought.com/mayavi/mayavi/mlab.html#simple-scripting-with-mlab
if mod_version('mayavi'):
from mayavi import mlab
MLAB = True
else:
MLAB = False
if mod_version('vispy'):
from vispy import plot #(?)
HAS_VISPY = True
else:
HAS_VISPY = False
class Plot_3D(QWidget):
"""
Class for various 3D-plots:
- lin / log line plot of H(f)
- lin / log surf plot of H(z)
- optional display of poles / zeros
"""
# incoming, connected in sender widget (locally connected to self.process_signals() )
sig_rx = pyqtSignal(object)
# sig_tx = pyqtSignal(object) # outgoing from process_signals
def __init__(self, parent):
super(Plot_3D, self).__init__(parent)
self.zmin = 0
self.zmax = 4
self.zmin_dB = -80
self.cmap_default = 'RdYlBu_r'
self.data_changed = True # flag whether data has changed
self.size_changed = True # flag whether widget has been resized
self.tool_tip = "3D magnitude response |H(z)|"
self.tab_label = "3D"
self._construct_UI()
#------------------------------------------------------------------------------
def process_signals(self, dict_sig=None):
"""
Process signals coming from the navigation toolbar and from ``sig_rx``
"""
logger.debug("Processing {0} | data_changed = {1}, visible = {2}"\
.format(dict_sig, self.data_changed, self.isVisible()))
if self.isVisible():
if 'data_changed' in dict_sig or 'home' in dict_sig or self.data_changed:
self.draw()
self.data_changed = False
self.size_changed = False
elif 'ui_changed' in dict_sig and dict_sig['ui_changed'] == 'resized'\
or self.size_changed:
self.redraw()
self.size_changed = False
else:
if 'data_changed' in dict_sig:
self.data_changed = True
if 'ui_changed' in dict_sig and dict_sig['ui_changed'] == 'resized':
self.size_changed = True
#------------------------------------------------------------------------------
def _construct_UI(self):
self.chkLog = QCheckBox("Log.", self)
self.chkLog.setObjectName("chkLog")
self.chkLog.setToolTip("Logarithmic scale")
self.chkLog.setChecked(False)
self.chkPolar = QCheckBox("Polar", self)
self.chkPolar.setObjectName("chkPolar")
self.chkPolar.setToolTip("Polar coordinate range")
self.chkPolar.setChecked(False)
self.lblBottom = QLabel("Bottom:", self)
self.ledBottom = QLineEdit(self)
self.ledBottom.setObjectName("ledBottom")
self.ledBottom.setText(str(self.zmin))
self.ledBottom.setToolTip("Minimum display value.")
self.lblBottomdB = QLabel("dB", self)
self.lblBottomdB.setVisible(self.chkLog.isChecked())
self.lblTop = QLabel("Top:", self)
self.ledTop = QLineEdit(self)
self.ledTop.setObjectName("ledTop")
self.ledTop.setText(str(self.zmax))
self.ledTop.setToolTip("Maximum display value.")
self.lblTopdB = QLabel("dB", self)
self.lblTopdB.setVisible(self.chkLog.isChecked())
self.chkUC = QCheckBox("UC", self)
self.chkUC.setObjectName("chkUC")
self.chkUC.setToolTip("Plot unit circle")
self.chkUC.setChecked(True)
self.chkPZ = QCheckBox("P/Z", self)
self.chkPZ.setObjectName("chkPZ")
self.chkPZ.setToolTip("Plot poles and zeros")
self.chkPZ.setChecked(True)
self.chkHf = QCheckBox("H(f)", self)
self.chkHf.setObjectName("chkHf")
self.chkHf.setToolTip("Plot H(f) along the unit circle")
self.chkHf.setChecked(True)
modes = ['None', 'Mesh', 'Surf', 'Contour']
self.cmbMode3D = QComboBox(self)
self.cmbMode3D.addItems(modes)
self.cmbMode3D.setObjectName("cmbShow3D")
self.cmbMode3D.setToolTip("Select 3D-plot mode.")
self.cmbMode3D.setCurrentIndex(0)
self.cmbMode3D.setSizeAdjustPolicy(QComboBox.AdjustToContents)
self.chkColormap_r = QCheckBox("reverse", self)
self.chkColormap_r.setToolTip("reverse colormap")
self.chkColormap_r.setChecked(True)
self.cmbColormap = QComboBox(self)
self._init_cmb_colormap()
self.cmbColormap.setToolTip("Select colormap")
self.chkColBar = QCheckBox("Colorbar", self)
self.chkColBar.setObjectName("chkColBar")
self.chkColBar.setToolTip("Show colorbar")
self.chkColBar.setChecked(False)
self.chkLighting = QCheckBox("Lighting", self)
self.chkLighting.setObjectName("chkLighting")
self.chkLighting.setToolTip("Enable light source")
self.chkLighting.setChecked(False)
self.lblAlpha = QLabel("Alpha", self)
self.diaAlpha = QDial(self)
self.diaAlpha.setRange(0., 10.)
self.diaAlpha.setValue(10)
self.diaAlpha.setTracking(False) # produce less events when turning
self.diaAlpha.setFixedHeight(30)
self.diaAlpha.setFixedWidth(30)
self.diaAlpha.setWrapping(False)
self.diaAlpha.setToolTip("<span>Set transparency for surf and contour plots.</span>")
self.lblHatch = QLabel("Stride", self)
self.diaHatch = QDial(self)
self.diaHatch.setRange(0., 9.)
self.diaHatch.setValue(5)
self.diaHatch.setTracking(False) # produce less events when turning
self.diaHatch.setFixedHeight(30)
self.diaHatch.setFixedWidth(30)
self.diaHatch.setWrapping(False)
self.diaHatch.setToolTip("Set line density for various plots.")
self.chkContour2D = QCheckBox("Contour2D", self)
self.chkContour2D.setObjectName("chkContour2D")
self.chkContour2D.setToolTip("Plot 2D-contours at z =0")
self.chkContour2D.setChecked(False)
#----------------------------------------------------------------------
# LAYOUT for UI widgets
#----------------------------------------------------------------------
layGControls = QGridLayout()
layGControls.addWidget(self.chkLog, 0, 0)
layGControls.addWidget(self.chkPolar, 1, 0)
layGControls.addWidget(self.lblTop, 0, 2)
layGControls.addWidget(self.ledTop, 0, 4)
layGControls.addWidget(self.lblTopdB, 0, 5)
layGControls.addWidget(self.lblBottom, 1, 2)
layGControls.addWidget(self.ledBottom, 1, 4)
layGControls.addWidget(self.lblBottomdB, 1, 5)
layGControls.setColumnStretch(5,1)
layGControls.addWidget(self.chkUC, 0, 6)
layGControls.addWidget(self.chkHf, 1, 6)
layGControls.addWidget(self.chkPZ, 0, 8)
layGControls.addWidget(self.cmbMode3D, 0, 10)
layGControls.addWidget(self.chkContour2D, 1, 10)
layGControls.addWidget(self.cmbColormap, 0,12,1,1)
layGControls.addWidget(self.chkColormap_r, 1,12)
layGControls.addWidget(self.chkLighting, 0, 14)
layGControls.addWidget(self.chkColBar, 1, 14)
layGControls.addWidget(self.lblAlpha, 0, 15)
layGControls.addWidget(self.diaAlpha, 0, 16)
layGControls.addWidget(self.lblHatch, 1, 15)
layGControls.addWidget(self.diaHatch, 1, 16)
# This widget encompasses all control subwidgets
self.frmControls = QFrame(self)
self.frmControls.setObjectName("frmControls")
self.frmControls.setLayout(layGControls)
#----------------------------------------------------------------------
# mplwidget
#----------------------------------------------------------------------
# This is the plot pane widget, encompassing the other widgets
self.mplwidget = MplWidget(self)
self.mplwidget.layVMainMpl.addWidget(self.frmControls)
self.mplwidget.layVMainMpl.setContentsMargins(*params['wdg_margins'])
self.setLayout(self.mplwidget.layVMainMpl)
self._init_grid() # initialize grid and do initial plot
#----------------------------------------------------------------------
# GLOBAL SIGNALS & SLOTs
#----------------------------------------------------------------------
self.sig_rx.connect(self.process_signals)
#----------------------------------------------------------------------
# LOCAL SIGNALS & SLOTs
#----------------------------------------------------------------------
self.chkLog.clicked.connect(self._log_clicked)
self.ledBottom.editingFinished.connect(self._log_clicked)
self.ledTop.editingFinished.connect(self._log_clicked)
self.chkPolar.clicked.connect(self._init_grid)
self.chkUC.clicked.connect(self.draw)
self.chkHf.clicked.connect(self.draw)
self.chkPZ.clicked.connect(self.draw)
self.cmbMode3D.currentIndexChanged.connect(self.draw)
self.chkColBar.clicked.connect(self.draw)
self.cmbColormap.currentIndexChanged.connect(self.draw)
self.chkColormap_r.clicked.connect(self._init_cmb_colormap)
self.chkLighting.clicked.connect(self.draw)
self.diaAlpha.valueChanged.connect(self.draw)
self.diaHatch.valueChanged.connect(self.draw)
self.chkContour2D.clicked.connect(self.draw)
self.mplwidget.mplToolbar.sig_tx.connect(self.process_signals)
#self.mplwidget.mplToolbar.enable_plot(state = False) # disable initially
#------------------------------------------------------------------------------
def _init_cmb_colormap(self):
""" (Re-)Load combobox with available colormaps"""
if self.chkColormap_r.isChecked():
cmap_list = [m for m in cm.datad if m.endswith("_r")]
else:
cmap_list = [m for m in cm.datad if not m.endswith("_r")]
# *_r colormaps reverse the color order
cmap_list.sort()
self.cmbColormap.blockSignals(True) # don't send signal "indexChanged"
self.cmbColormap.clear()
self.cmbColormap.addItems(cmap_list)
self.cmbColormap.blockSignals(False)
idx = self.cmbColormap.findText(self.cmap_default)
if idx == -1:
idx = 0
self.cmbColormap.setCurrentIndex(idx)
#------------------------------------------------------------------------------
def _init_grid(self):
""" Initialize (x,y,z) coordinate grid + (re)draw plot."""
phi_UC = np.linspace(0, 2*pi, 400, endpoint=True) # angles for unit circle
self.xy_UC = np.exp(1j * phi_UC) # x,y coordinates of unity circle
steps = 100 # number of steps for x, y, r, phi
#
self.xmin = -1.5; self.xmax = 1.5 # cartesian range limits
self.ymin = -1.5; self.ymax = 1.5
rmin = 0; rmax = self.xmin # polar range limits
# Calculate grids for 3D-Plots
dr = rmax / steps * 2 # grid size for polar range
dx = (self.xmax - self.xmin) / steps
dy = (self.ymax - self.ymin) / steps # grid size cartesian range
if self.chkPolar.isChecked(): # # Plot circular range in 3D-Plot
[r, phi] = np.meshgrid(np.arange(rmin, rmax, dr),
np.linspace(0, 2 * pi, steps, endpoint=True))
self.x = r * cos(phi)
self.y = r * sin(phi)
else: # cartesian grid
[self.x, self.y] = np.meshgrid(np.arange(self.xmin, self.xmax, dx),
np.arange(self.ymin, self.ymax, dy))
self.z = self.x + 1j*self.y # create coordinate grid for complex plane
self.draw() # initial plot
#------------------------------------------------------------------------------
def init_axes(self):
"""
Initialize and clear the axes to get rid of colorbar
The azimuth / elevation / distance settings of the camera are restored
after clearing the axes. See
http://stackoverflow.com/questions/4575588/matplotlib-3d-plot-with-pyqt4-in-qtabwidget-mplwidget
"""
self._save_axes()
self.mplwidget.fig.clf() # needed to get rid of colorbar
self.ax3d = self.mplwidget.fig.add_subplot(111, projection='3d')
#self.ax3d = self.mplwidget.fig.subplots(nrows=1, ncols=1, projection='3d')
self._restore_axes()
#------------------------------------------------------------------------------
def _save_axes(self):
"""
Store x/y/z - limits and camera position
"""
try:
self.azim = self.ax3d.azim
self.elev = self.ax3d.elev
self.dist = self.ax3d.dist
self.xlim = self.ax3d.get_xlim3d()
self.ylim = self.ax3d.get_ylim3d()
self.zlim = self.ax3d.get_zlim3d()
except AttributeError: # not yet initialized, set standard values
self.azim = -65
self.elev = 30
self.dist = 10
self.xlim = (self.xmin, self.xmax)
self.ylim = (self.ymin, self.ymax)
self.zlim = (self.zmin, self.zmax)
#------------------------------------------------------------------------------
def _restore_axes(self):
"""
Restore x/y/z - limits and camera position
"""
if self.mplwidget.mplToolbar.a_lk.isChecked():
self.ax3d.set_xlim3d(self.xlim)
self.ax3d.set_ylim3d(self.ylim)
self.ax3d.set_zlim3d(self.zlim)
self.ax3d.azim = self.azim
self.ax3d.elev = self.elev
self.ax3d.dist = self.dist
#------------------------------------------------------------------------------
def _log_clicked(self):
"""
Change scale and settings to log / lin when log setting is changed
Update min / max settings when lineEdits have been edited
"""
self.log = self.chkLog.isChecked()
if self.sender().objectName() == 'chkLog': # clicking chkLog triggered the slot
if self.log:
self.ledBottom.setText(str(self.zmin_dB))
self.zmax_dB = np.round(20 * log10(self.zmax), 2)
self.ledTop.setText(str(self.zmax_dB))
self.lblTopdB.setVisible(True)
self.lblBottomdB.setVisible(True)
else:
self.ledBottom.setText(str(self.zmin))
self.zmax = np.round(10**(self.zmax_dB / 20), 2)
self.ledTop.setText(str(self.zmax))
self.lblTopdB.setVisible(False)
self.lblBottomdB.setVisible(False)
else: # finishing a lineEdit field triggered the slot
if self.log:
self.zmin_dB = safe_eval(self.ledBottom.text(), self.zmin_dB, return_type='float')
self.ledBottom.setText(str(self.zmin_dB))
self.zmax_dB = safe_eval(self.ledTop.text(), self.zmax_dB, return_type='float')
self.ledTop.setText(str(self.zmax_dB))
else:
self.zmin = safe_eval(self.ledBottom.text(), self.zmin, return_type='float')
self.ledBottom.setText(str(self.zmin))
self.zmax = safe_eval(self.ledTop.text(), self.zmax, return_type='float')
self.ledTop.setText(str(self.zmax))
self.draw()
#------------------------------------------------------------------------------
def draw(self):
"""
Main drawing entry point: perform the actual plot
"""
self.draw_3d()
#------------------------------------------------------------------------------
def draw_3d(self):
"""
Draw various 3D plots
"""
self.init_axes()
bb = fb.fil[0]['ba'][0]
aa = fb.fil[0]['ba'][1]
zz = np.array(fb.fil[0]['zpk'][0])
pp = np.array(fb.fil[0]['zpk'][1])
wholeF = fb.fil[0]['freqSpecsRangeType'] != 'half' # not used
f_S = fb.fil[0]['f_S']
N_FFT = params['N_FFT']
alpha = self.diaAlpha.value()/10.
cmap = cm.get_cmap(str(self.cmbColormap.currentText()))
# Number of Lines /step size for H(f) stride, mesh, contour3d:
stride = 10 - self.diaHatch.value()
NL = 3 * self.diaHatch.value() + 5
surf_enabled = qget_cmb_box(self.cmbMode3D, data=False) in {'Surf', 'Contour'}
self.cmbColormap.setEnabled(surf_enabled)
self.chkColormap_r.setEnabled(surf_enabled)
self.chkLighting.setEnabled(surf_enabled)
self.chkColBar.setEnabled(surf_enabled)
self.diaAlpha.setEnabled(surf_enabled or self.chkContour2D.isChecked())
#cNorm = colors.Normalize(vmin=0, vmax=values[-1])
#scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
#-----------------------------------------------------------------------------
# Calculate H(w) along the upper half of unity circle
#-----------------------------------------------------------------------------
[w, H] = sig.freqz(bb, aa, worN=N_FFT, whole=True)
H = np.nan_to_num(H) # replace nans and inf by finite numbers
H_abs = abs(H)
H_max = max(H_abs)
H_min = min(H_abs)
#f = w / (2 * pi) * f_S # translate w to absolute frequencies
#F_min = f[np.argmin(H_abs)]
plevel_rel = 1.05 # height of plotted pole position relative to zmax
zlevel_rel = 0.1 # height of plotted zero position relative to zmax
if self.chkLog.isChecked(): # logarithmic scale
bottom = np.floor(max(self.zmin_dB, 20*log10(H_min)) / 10) * 10
top = self.zmax_dB
top_bottom = top - bottom
zlevel = bottom - top_bottom * zlevel_rel
if self.cmbMode3D.currentText() == 'None': # "Poleposition" for H(f) plot only
plevel_top = 2 * bottom - zlevel # height of displayed pole position
plevel_btm = bottom
else:
plevel_top = top + top_bottom * (plevel_rel - 1)
plevel_btm = top
else: # linear scale
bottom = max(self.zmin, H_min) # min. display value
top = self.zmax # max. display value
top_bottom = top - bottom
# top = zmax_rel * H_max # calculate display top from max. of H(f)
zlevel = bottom + top_bottom * zlevel_rel # height of displayed zero position
if self.cmbMode3D.currentText() == 'None': # "Poleposition" for H(f) plot only
#H_max = np.clip(max(H_abs), 0, self.zmax)
# make height of displayed poles same to zeros
plevel_top = bottom + top_bottom * zlevel_rel
plevel_btm = bottom
else:
plevel_top = plevel_rel * top
plevel_btm = top
# calculate H(jw)| along the unity circle and |H(z)|, each clipped
# between bottom and top
H_UC = H_mag(bb, aa, self.xy_UC, top, H_min=bottom, log=self.chkLog.isChecked())
Hmag = H_mag(bb, aa, self.z, top, H_min=bottom, log=self.chkLog.isChecked())
#===============================================================
## plot Unit Circle (UC)
#===============================================================
if self.chkUC.isChecked():
# Plot unit circle and marker at (1,0):
self.ax3d.plot(self.xy_UC.real, self.xy_UC.imag,
ones(len(self.xy_UC)) * bottom, lw=2, color='k')
self.ax3d.plot([0.97, 1.03], [0, 0], [bottom, bottom], lw=2, color='k')
#===============================================================
## plot ||H(f)| along unit circle as 3D-lineplot
#===============================================================
if self.chkHf.isChecked():
self.ax3d.plot(self.xy_UC.real, self.xy_UC.imag, H_UC, alpha = 0.5)
# draw once more as dashed white line to improve visibility
self.ax3d.plot(self.xy_UC.real, self.xy_UC.imag, H_UC, 'w--')
if stride < 10: # plot thin vertical line every stride points on the UC
for k in range(len(self.xy_UC[::stride])):
self.ax3d.plot([self.xy_UC.real[::stride][k], self.xy_UC.real[::stride][k]],
[self.xy_UC.imag[::stride][k], self.xy_UC.imag[::stride][k]],
[np.ones(len(self.xy_UC[::stride]))[k]*bottom, H_UC[::stride][k]],
linewidth=1, color=(0.5, 0.5, 0.5))
#===============================================================
## plot Poles and Zeros
#===============================================================
if self.chkPZ.isChecked():
PN_SIZE = 8 # size of P/N symbols
# Plot zero markers at |H(z_i)| = zlevel with "stems":
self.ax3d.plot(zz.real, zz.imag, ones(len(zz)) * zlevel, 'o',
markersize=PN_SIZE, markeredgecolor='blue', markeredgewidth=2.0,
markerfacecolor='none')
for k in range(len(zz)): # plot zero "stems"
self.ax3d.plot([zz[k].real, zz[k].real], [zz[k].imag, zz[k].imag],
[bottom, zlevel], linewidth=1, color='b')
# Plot the poles at |H(z_p)| = plevel with "stems":
self.ax3d.plot(np.real(pp), np.imag(pp), plevel_top,
'x', markersize=PN_SIZE, markeredgewidth=2.0, markeredgecolor='red')
for k in range(len(pp)): # plot pole "stems"
self.ax3d.plot([pp[k].real, pp[k].real], [pp[k].imag, pp[k].imag],
[plevel_btm, plevel_top], linewidth=1, color='r')
#===============================================================
## 3D-Plots of |H(z)| clipped between |H(z)| = top
#===============================================================
m_cb = cm.ScalarMappable(cmap=cmap) # normalized proxy object that is mappable
m_cb.set_array(Hmag) # for colorbar
#---------------------------------------------------------------
## 3D-mesh plot
#---------------------------------------------------------------
if self.cmbMode3D.currentText() == 'Mesh':
# fig_mlab = mlab.figure(fgcolor=(0., 0., 0.), bgcolor=(1, 1, 1))
# self.ax3d.set_zlim(0,2)
self.ax3d.plot_wireframe(self.x, self.y, Hmag, rstride=5,
cstride=stride, linewidth=1, color='gray')
#---------------------------------------------------------------
## 3D-surface plot
#---------------------------------------------------------------
# http://stackoverflow.com/questions/28232879/phong-shading-for-shiny-python-3d-surface-plots
elif self.cmbMode3D.currentText() == 'Surf':
if MLAB:
## Mayavi
surf = mlab.surf(self.x, self.y, H_mag, colormap='RdYlBu', warp_scale='auto')
# Change the visualization parameters.
surf.actor.property.interpolation = 'phong'
surf.actor.property.specular = 0.1
surf.actor.property.specular_power = 5
# s = mlab.contour_surf(self.x, self.y, Hmag, contour_z=0)
mlab.show()
else:
if self.chkLighting.isChecked():
ls = LightSource(azdeg=0, altdeg=65) # Create light source object
rgb = ls.shade(Hmag, cmap=cmap) # Shade data, creating an rgb array
cmap_surf = None
else:
rgb = None
cmap_surf = cmap
# s = self.ax3d.plot_surface(self.x, self.y, Hmag,
# alpha=OPT_3D_ALPHA, rstride=1, cstride=1, cmap=cmap,
# linewidth=0, antialiased=False, shade=True, facecolors = rgb)
# s.set_edgecolor('gray')
s = self.ax3d.plot_surface(self.x, self.y, Hmag,
alpha=alpha, rstride=1, cstride=1,
linewidth=0, antialiased=False, facecolors=rgb, cmap=cmap_surf, shade=True)
s.set_edgecolor(None)
#---------------------------------------------------------------
## 3D-Contour plot
#---------------------------------------------------------------
elif self.cmbMode3D.currentText() == 'Contour':
s = self.ax3d.contourf3D(self.x, self.y, Hmag, NL, alpha=alpha, cmap=cmap)
#---------------------------------------------------------------
## 2D-Contour plot
# TODO: 2D contour plots do not plot correctly together with 3D plots in
# current matplotlib 1.4.3 -> disable them for now
# TODO: zdir = x / y delivers unexpected results -> rather plot max(H)
# along the other axis?
# TODO: colormap is created depending on the zdir = 'z' contour plot
# -> set limits of (all) other plots manually?
if self.chkContour2D.isChecked():
# self.ax3d.contourf(x, y, Hmag, 20, zdir='x', offset=xmin,
# cmap=cmap, alpha = alpha)#, vmin = bottom)#, vmax = top, vmin = bottom)
# self.ax3d.contourf(x, y, Hmag, 20, zdir='y', offset=ymax,
# cmap=cmap, alpha = alpha)#, vmin = bottom)#, vmax = top, vmin = bottom)
s = self.ax3d.contourf(self.x, self.y, Hmag, NL, zdir='z',
offset=bottom - (top - bottom) * 0.05,
cmap=cmap, alpha=alpha)
# plot colorbar for suitable plot modes
if self.chkColBar.isChecked() and (self.chkContour2D.isChecked() or
str(self.cmbMode3D.currentText()) in {'Contour', 'Surf'}):
self.colb = self.mplwidget.fig.colorbar(m_cb,
ax=self.ax3d, shrink=0.8, aspect=20,
pad=0.02, fraction=0.08)
#----------------------------------------------------------------------
## Set view limits and labels
#----------------------------------------------------------------------
if not self.mplwidget.mplToolbar.a_lk.isChecked():
self.ax3d.set_xlim3d(self.xmin, self.xmax)
self.ax3d.set_ylim3d(self.ymin, self.ymax)
self.ax3d.set_zlim3d(bottom, top)
else:
self._restore_axes()
self.ax3d.set_xlabel('Re')#(fb.fil[0]['plt_fLabel'])
self.ax3d.set_ylabel('Im') #(r'$ \tau_g(\mathrm{e}^{\mathrm{j} \Omega}) / T_S \; \rightarrow $')
# self.ax3d.set_zlabel(r'$|H(z)|\; \rightarrow $')
self.ax3d.set_title(r'3D-Plot of $|H(\mathrm{e}^{\mathrm{j} \Omega})|$ and $|H(z)|$')
self.redraw()
#------------------------------------------------------------------------------
def redraw(self):
"""
Redraw the canvas when e.g. the canvas size has changed
"""
self.mplwidget.redraw()
#------------------------------------------------------------------------------
def main():
import sys
from ..compat import QApplication
app = QApplication(sys.argv)
mainw = Plot_3D(None)
app.setActiveWindow(mainw)
mainw.show()
sys.exit(app.exec_())
if __name__ == "__main__":
main()
# module test using python -m pyfda.plot_widgets.plot_3d
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r&&(r=[[e.id,r,""]]),n(14246)(r,{}),r.locals&&(e.exports=r.locals)},18845:(e,t,n)=>{e.exports=n.p+"MapStore2/web/client/components/style/vector/iconNotFound.png"},70290:e=>{e.exports="data:image/png;base64,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"},45507:e=>{e.exports="data:image/png;base64,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"}}]);
|
class Solution:
def canVisitAllRooms(self, rooms: List[List[int]]) -> bool:
# we use this below list for checking whether all list have been visited or not
visited=[False]*len(rooms)
# per condition room 0 is always unlocked
visited[0]=True
#we use stack for acquiring key to next room so as to visit that room
# we have initialised stack with room 0
stack=[0]
#we then start visiting other rooms
while stack:
# imagine rooms as nodes of graphs and keys as edge?
node=stack.pop()
# we at any given time stack will contain the next room number
for key in rooms[node]:
# we check whether the current room was already visited or not
if not visited[key]:
# we set the flag true since it was not previously visited
visited[key]=True
#we add the key to stack
stack.append(key)
return all(visited)
|
# -*- coding: utf-8 -*-
from datetime import datetime
import numpy as np
import pytest
from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical, CategoricalIndex, DatetimeIndex, Index, Interval,
IntervalIndex, NaT, Series, Timestamp, date_range, period_range,
timedelta_range)
import pandas.util.testing as tm
class TestCategoricalConstructors(object):
def test_validate_ordered(self):
# see gh-14058
exp_msg = "'ordered' must either be 'True' or 'False'"
exp_err = TypeError
# This should be a boolean.
ordered = np.array([0, 1, 2])
with pytest.raises(exp_err, match=exp_msg):
Categorical([1, 2, 3], ordered=ordered)
with pytest.raises(exp_err, match=exp_msg):
Categorical.from_codes([0, 0, 1], categories=['a', 'b', 'c'],
ordered=ordered)
def test_constructor_empty(self):
# GH 17248
c = Categorical([])
expected = Index([])
tm.assert_index_equal(c.categories, expected)
c = Categorical([], categories=[1, 2, 3])
expected = pd.Int64Index([1, 2, 3])
tm.assert_index_equal(c.categories, expected)
def test_constructor_empty_boolean(self):
# see gh-22702
cat = pd.Categorical([], categories=[True, False])
categories = sorted(cat.categories.tolist())
assert categories == [False, True]
def test_constructor_tuples(self):
values = np.array([(1,), (1, 2), (1,), (1, 2)], dtype=object)
result = Categorical(values)
expected = Index([(1,), (1, 2)], tupleize_cols=False)
tm.assert_index_equal(result.categories, expected)
assert result.ordered is False
def test_constructor_tuples_datetimes(self):
# numpy will auto reshape when all of the tuples are the
# same len, so add an extra one with 2 items and slice it off
values = np.array([(Timestamp('2010-01-01'),),
(Timestamp('2010-01-02'),),
(Timestamp('2010-01-01'),),
(Timestamp('2010-01-02'),),
('a', 'b')], dtype=object)[:-1]
result = Categorical(values)
expected = Index([(Timestamp('2010-01-01'),),
(Timestamp('2010-01-02'),)], tupleize_cols=False)
tm.assert_index_equal(result.categories, expected)
def test_constructor_unsortable(self):
# it works!
arr = np.array([1, 2, 3, datetime.now()], dtype='O')
factor = Categorical(arr, ordered=False)
assert not factor.ordered
# this however will raise as cannot be sorted
with pytest.raises(TypeError):
Categorical(arr, ordered=True)
def test_constructor_interval(self):
result = Categorical([Interval(1, 2), Interval(2, 3), Interval(3, 6)],
ordered=True)
ii = IntervalIndex([Interval(1, 2), Interval(2, 3), Interval(3, 6)])
exp = Categorical(ii, ordered=True)
tm.assert_categorical_equal(result, exp)
tm.assert_index_equal(result.categories, ii)
def test_constructor(self):
exp_arr = np.array(["a", "b", "c", "a", "b", "c"], dtype=np.object_)
c1 = Categorical(exp_arr)
tm.assert_numpy_array_equal(c1.__array__(), exp_arr)
c2 = Categorical(exp_arr, categories=["a", "b", "c"])
tm.assert_numpy_array_equal(c2.__array__(), exp_arr)
c2 = Categorical(exp_arr, categories=["c", "b", "a"])
tm.assert_numpy_array_equal(c2.__array__(), exp_arr)
# categories must be unique
with pytest.raises(ValueError):
Categorical([1, 2], [1, 2, 2])
with pytest.raises(ValueError):
Categorical(["a", "b"], ["a", "b", "b"])
# The default should be unordered
c1 = Categorical(["a", "b", "c", "a"])
assert not c1.ordered
# Categorical as input
c1 = Categorical(["a", "b", "c", "a"])
c2 = Categorical(c1)
tm.assert_categorical_equal(c1, c2)
c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
c2 = Categorical(c1)
tm.assert_categorical_equal(c1, c2)
c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"])
c2 = Categorical(c1)
tm.assert_categorical_equal(c1, c2)
c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"])
c2 = Categorical(c1, categories=["a", "b", "c"])
tm.assert_numpy_array_equal(c1.__array__(), c2.__array__())
tm.assert_index_equal(c2.categories, Index(["a", "b", "c"]))
# Series of dtype category
c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
c2 = Categorical(Series(c1))
tm.assert_categorical_equal(c1, c2)
c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"])
c2 = Categorical(Series(c1))
tm.assert_categorical_equal(c1, c2)
# Series
c1 = Categorical(["a", "b", "c", "a"])
c2 = Categorical(Series(["a", "b", "c", "a"]))
tm.assert_categorical_equal(c1, c2)
c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
c2 = Categorical(Series(["a", "b", "c", "a"]),
categories=["a", "b", "c", "d"])
tm.assert_categorical_equal(c1, c2)
# This should result in integer categories, not float!
cat = Categorical([1, 2, 3, np.nan], categories=[1, 2, 3])
assert is_integer_dtype(cat.categories)
# https://github.com/pandas-dev/pandas/issues/3678
cat = Categorical([np.nan, 1, 2, 3])
assert is_integer_dtype(cat.categories)
# this should result in floats
cat = Categorical([np.nan, 1, 2., 3])
assert is_float_dtype(cat.categories)
cat = Categorical([np.nan, 1., 2., 3.])
assert is_float_dtype(cat.categories)
# This doesn't work -> this would probably need some kind of "remember
# the original type" feature to try to cast the array interface result
# to...
# vals = np.asarray(cat[cat.notna()])
# assert is_integer_dtype(vals)
# corner cases
cat = Categorical([1])
assert len(cat.categories) == 1
assert cat.categories[0] == 1
assert len(cat.codes) == 1
assert cat.codes[0] == 0
cat = Categorical(["a"])
assert len(cat.categories) == 1
assert cat.categories[0] == "a"
assert len(cat.codes) == 1
assert cat.codes[0] == 0
# Scalars should be converted to lists
cat = Categorical(1)
assert len(cat.categories) == 1
assert cat.categories[0] == 1
assert len(cat.codes) == 1
assert cat.codes[0] == 0
# two arrays
# - when the first is an integer dtype and the second is not
# - when the resulting codes are all -1/NaN
with tm.assert_produces_warning(None):
c_old = Categorical([0, 1, 2, 0, 1, 2],
categories=["a", "b", "c"]) # noqa
with tm.assert_produces_warning(None):
c_old = Categorical([0, 1, 2, 0, 1, 2], # noqa
categories=[3, 4, 5])
# the next one are from the old docs
with tm.assert_produces_warning(None):
c_old2 = Categorical([0, 1, 2, 0, 1, 2], [1, 2, 3]) # noqa
cat = Categorical([1, 2], categories=[1, 2, 3])
# this is a legitimate constructor
with tm.assert_produces_warning(None):
c = Categorical(np.array([], dtype='int64'), # noqa
categories=[3, 2, 1], ordered=True)
def test_constructor_not_sequence(self):
# https://github.com/pandas-dev/pandas/issues/16022
with pytest.raises(TypeError):
Categorical(['a', 'b'], categories='a')
def test_constructor_with_null(self):
# Cannot have NaN in categories
with pytest.raises(ValueError):
Categorical([np.nan, "a", "b", "c"],
categories=[np.nan, "a", "b", "c"])
with pytest.raises(ValueError):
Categorical([None, "a", "b", "c"],
categories=[None, "a", "b", "c"])
with pytest.raises(ValueError):
Categorical(DatetimeIndex(['nat', '20160101']),
categories=[NaT, Timestamp('20160101')])
def test_constructor_with_index(self):
ci = CategoricalIndex(list('aabbca'), categories=list('cab'))
tm.assert_categorical_equal(ci.values, Categorical(ci))
ci = CategoricalIndex(list('aabbca'), categories=list('cab'))
tm.assert_categorical_equal(ci.values,
Categorical(ci.astype(object),
categories=ci.categories))
def test_constructor_with_generator(self):
# This was raising an Error in isna(single_val).any() because isna
# returned a scalar for a generator
xrange = range
exp = Categorical([0, 1, 2])
cat = Categorical((x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical(xrange(3))
tm.assert_categorical_equal(cat, exp)
# This uses xrange internally
from pandas.core.index import MultiIndex
MultiIndex.from_product([range(5), ['a', 'b', 'c']])
# check that categories accept generators and sequences
cat = Categorical([0, 1, 2], categories=(x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical([0, 1, 2], categories=xrange(3))
tm.assert_categorical_equal(cat, exp)
@pytest.mark.parametrize("dtl", [
date_range("1995-01-01 00:00:00", periods=5, freq="s"),
date_range("1995-01-01 00:00:00", periods=5,
freq="s", tz="US/Eastern"),
timedelta_range("1 day", periods=5, freq="s")
])
def test_constructor_with_datetimelike(self, dtl):
# see gh-12077
# constructor with a datetimelike and NaT
s = Series(dtl)
c = Categorical(s)
expected = type(dtl)(s)
expected.freq = None
tm.assert_index_equal(c.categories, expected)
tm.assert_numpy_array_equal(c.codes, np.arange(5, dtype="int8"))
# with NaT
s2 = s.copy()
s2.iloc[-1] = NaT
c = Categorical(s2)
expected = type(dtl)(s2.dropna())
expected.freq = None
tm.assert_index_equal(c.categories, expected)
exp = np.array([0, 1, 2, 3, -1], dtype=np.int8)
tm.assert_numpy_array_equal(c.codes, exp)
result = repr(c)
assert "NaT" in result
def test_constructor_from_index_series_datetimetz(self):
idx = date_range('2015-01-01 10:00', freq='D', periods=3,
tz='US/Eastern')
result = Categorical(idx)
tm.assert_index_equal(result.categories, idx)
result = Categorical(Series(idx))
tm.assert_index_equal(result.categories, idx)
def test_constructor_from_index_series_timedelta(self):
idx = timedelta_range('1 days', freq='D', periods=3)
result = Categorical(idx)
tm.assert_index_equal(result.categories, idx)
result = Categorical(Series(idx))
tm.assert_index_equal(result.categories, idx)
def test_constructor_from_index_series_period(self):
idx = period_range('2015-01-01', freq='D', periods=3)
result = Categorical(idx)
tm.assert_index_equal(result.categories, idx)
result = Categorical(Series(idx))
tm.assert_index_equal(result.categories, idx)
def test_constructor_invariant(self):
# GH 14190
vals = [
np.array([1., 1.2, 1.8, np.nan]),
np.array([1, 2, 3], dtype='int64'),
['a', 'b', 'c', np.nan],
[pd.Period('2014-01'), pd.Period('2014-02'), NaT],
[Timestamp('2014-01-01'), Timestamp('2014-01-02'), NaT],
[Timestamp('2014-01-01', tz='US/Eastern'),
Timestamp('2014-01-02', tz='US/Eastern'), NaT],
]
for val in vals:
c = Categorical(val)
c2 = Categorical(c)
tm.assert_categorical_equal(c, c2)
@pytest.mark.parametrize('ordered', [True, False])
def test_constructor_with_dtype(self, ordered):
categories = ['b', 'a', 'c']
dtype = CategoricalDtype(categories, ordered=ordered)
result = Categorical(['a', 'b', 'a', 'c'], dtype=dtype)
expected = Categorical(['a', 'b', 'a', 'c'], categories=categories,
ordered=ordered)
tm.assert_categorical_equal(result, expected)
assert result.ordered is ordered
def test_constructor_dtype_and_others_raises(self):
dtype = CategoricalDtype(['a', 'b'], ordered=True)
with pytest.raises(ValueError, match="Cannot"):
Categorical(['a', 'b'], categories=['a', 'b'], dtype=dtype)
with pytest.raises(ValueError, match="Cannot"):
Categorical(['a', 'b'], ordered=True, dtype=dtype)
with pytest.raises(ValueError, match="Cannot"):
Categorical(['a', 'b'], ordered=False, dtype=dtype)
@pytest.mark.parametrize('categories', [
None, ['a', 'b'], ['a', 'c'],
])
@pytest.mark.parametrize('ordered', [True, False])
def test_constructor_str_category(self, categories, ordered):
result = Categorical(['a', 'b'], categories=categories,
ordered=ordered, dtype='category')
expected = Categorical(['a', 'b'], categories=categories,
ordered=ordered)
tm.assert_categorical_equal(result, expected)
def test_constructor_str_unknown(self):
with pytest.raises(ValueError, match="Unknown dtype"):
Categorical([1, 2], dtype="foo")
def test_constructor_from_categorical_with_dtype(self):
dtype = CategoricalDtype(['a', 'b', 'c'], ordered=True)
values = Categorical(['a', 'b', 'd'])
result = Categorical(values, dtype=dtype)
# We use dtype.categories, not values.categories
expected = Categorical(['a', 'b', 'd'], categories=['a', 'b', 'c'],
ordered=True)
tm.assert_categorical_equal(result, expected)
def test_constructor_from_categorical_with_unknown_dtype(self):
dtype = CategoricalDtype(None, ordered=True)
values = Categorical(['a', 'b', 'd'])
result = Categorical(values, dtype=dtype)
# We use values.categories, not dtype.categories
expected = Categorical(['a', 'b', 'd'], categories=['a', 'b', 'd'],
ordered=True)
tm.assert_categorical_equal(result, expected)
def test_constructor_from_categorical_string(self):
values = Categorical(['a', 'b', 'd'])
# use categories, ordered
result = Categorical(values, categories=['a', 'b', 'c'], ordered=True,
dtype='category')
expected = Categorical(['a', 'b', 'd'], categories=['a', 'b', 'c'],
ordered=True)
tm.assert_categorical_equal(result, expected)
# No string
result = Categorical(values, categories=['a', 'b', 'c'], ordered=True)
tm.assert_categorical_equal(result, expected)
def test_constructor_with_categorical_categories(self):
# GH17884
expected = Categorical(['a', 'b'], categories=['a', 'b', 'c'])
result = Categorical(
['a', 'b'], categories=Categorical(['a', 'b', 'c']))
tm.assert_categorical_equal(result, expected)
result = Categorical(
['a', 'b'], categories=CategoricalIndex(['a', 'b', 'c']))
tm.assert_categorical_equal(result, expected)
def test_from_codes(self):
# too few categories
with pytest.raises(ValueError):
Categorical.from_codes([1, 2], [1, 2])
# no int codes
with pytest.raises(ValueError):
Categorical.from_codes(["a"], [1, 2])
# no unique categories
with pytest.raises(ValueError):
Categorical.from_codes([0, 1, 2], ["a", "a", "b"])
# NaN categories included
with pytest.raises(ValueError):
Categorical.from_codes([0, 1, 2], ["a", "b", np.nan])
# too negative
with pytest.raises(ValueError):
Categorical.from_codes([-2, 1, 2], ["a", "b", "c"])
exp = Categorical(["a", "b", "c"], ordered=False)
res = Categorical.from_codes([0, 1, 2], ["a", "b", "c"])
tm.assert_categorical_equal(exp, res)
# Not available in earlier numpy versions
if hasattr(np.random, "choice"):
codes = np.random.choice([0, 1], 5, p=[0.9, 0.1])
Categorical.from_codes(codes, categories=["train", "test"])
def test_from_codes_with_categorical_categories(self):
# GH17884
expected = Categorical(['a', 'b'], categories=['a', 'b', 'c'])
result = Categorical.from_codes(
[0, 1], categories=Categorical(['a', 'b', 'c']))
tm.assert_categorical_equal(result, expected)
result = Categorical.from_codes(
[0, 1], categories=CategoricalIndex(['a', 'b', 'c']))
tm.assert_categorical_equal(result, expected)
# non-unique Categorical still raises
with pytest.raises(ValueError):
Categorical.from_codes([0, 1], Categorical(['a', 'b', 'a']))
def test_from_codes_with_nan_code(self):
# GH21767
codes = [1, 2, np.nan]
categories = ['a', 'b', 'c']
with pytest.raises(ValueError):
Categorical.from_codes(codes, categories)
def test_from_codes_with_float(self):
# GH21767
codes = [1.0, 2.0, 0] # integer, but in float dtype
categories = ['a', 'b', 'c']
with tm.assert_produces_warning(FutureWarning):
cat = Categorical.from_codes(codes, categories)
tm.assert_numpy_array_equal(cat.codes, np.array([1, 2, 0], dtype='i1'))
codes = [1.1, 2.0, 0] # non-integer
with pytest.raises(ValueError):
Categorical.from_codes(codes, categories)
@pytest.mark.parametrize('dtype', [None, 'category'])
def test_from_inferred_categories(self, dtype):
cats = ['a', 'b']
codes = np.array([0, 0, 1, 1], dtype='i8')
result = Categorical._from_inferred_categories(cats, codes, dtype)
expected = Categorical.from_codes(codes, cats)
tm.assert_categorical_equal(result, expected)
@pytest.mark.parametrize('dtype', [None, 'category'])
def test_from_inferred_categories_sorts(self, dtype):
cats = ['b', 'a']
codes = np.array([0, 1, 1, 1], dtype='i8')
result = Categorical._from_inferred_categories(cats, codes, dtype)
expected = Categorical.from_codes([1, 0, 0, 0], ['a', 'b'])
tm.assert_categorical_equal(result, expected)
def test_from_inferred_categories_dtype(self):
cats = ['a', 'b', 'd']
codes = np.array([0, 1, 0, 2], dtype='i8')
dtype = CategoricalDtype(['c', 'b', 'a'], ordered=True)
result = Categorical._from_inferred_categories(cats, codes, dtype)
expected = Categorical(['a', 'b', 'a', 'd'],
categories=['c', 'b', 'a'],
ordered=True)
tm.assert_categorical_equal(result, expected)
def test_from_inferred_categories_coerces(self):
cats = ['1', '2', 'bad']
codes = np.array([0, 0, 1, 2], dtype='i8')
dtype = CategoricalDtype([1, 2])
result = Categorical._from_inferred_categories(cats, codes, dtype)
expected = Categorical([1, 1, 2, np.nan])
tm.assert_categorical_equal(result, expected)
def test_construction_with_ordered(self):
# GH 9347, 9190
cat = Categorical([0, 1, 2])
assert not cat.ordered
cat = Categorical([0, 1, 2], ordered=False)
assert not cat.ordered
cat = Categorical([0, 1, 2], ordered=True)
assert cat.ordered
@pytest.mark.xfail(reason="Imaginary values not supported in Categorical")
def test_constructor_imaginary(self):
values = [1, 2, 3 + 1j]
c1 = Categorical(values)
tm.assert_index_equal(c1.categories, Index(values))
tm.assert_numpy_array_equal(np.array(c1), np.array(values))
|
from __future__ import absolute_import # Avoid importing `importlib` from this package.
import copy
import imp
from importlib import import_module
import os
import sys
import warnings
from django.core.exceptions import ImproperlyConfigured
from django.utils import six
from django.utils.deprecation import RemovedInDjango19Warning
def import_string(dotted_path):
"""
Import a dotted module path and return the attribute/class designated by the
last name in the path. Raise ImportError if the import failed.
"""
try:
module_path, class_name = dotted_path.rsplit('.', 1)
except ValueError:
msg = "%s doesn't look like a module path" % dotted_path
six.reraise(ImportError, ImportError(msg), sys.exc_info()[2])
module = import_module(module_path)
try:
return getattr(module, class_name)
except AttributeError:
msg = 'Module "%s" does not define a "%s" attribute/class' % (
dotted_path, class_name)
six.reraise(ImportError, ImportError(msg), sys.exc_info()[2])
def import_by_path(dotted_path, error_prefix=''):
"""
Import a dotted module path and return the attribute/class designated by the
last name in the path. Raise ImproperlyConfigured if something goes wrong.
"""
warnings.warn(
'import_by_path() has been deprecated. Use import_string() instead.',
RemovedInDjango19Warning, stacklevel=2)
try:
attr = import_string(dotted_path)
except ImportError as e:
msg = '%sError importing module %s: "%s"' % (
error_prefix, dotted_path, e)
six.reraise(ImproperlyConfigured, ImproperlyConfigured(msg),
sys.exc_info()[2])
return attr
def autodiscover_modules(*args, **kwargs):
"""
Auto-discover INSTALLED_APPS modules and fail silently when
not present. This forces an import on them to register any admin bits they
may want.
You may provide a register_to keyword parameter as a way to access a
registry. This register_to object must have a _registry instance variable
to access it.
"""
from django.apps import apps
register_to = kwargs.get('register_to')
for app_config in apps.get_app_configs():
# Attempt to import the app's module.
try:
if register_to:
before_import_registry = copy.copy(register_to._registry)
for module_to_search in args:
import_module('%s.%s' % (app_config.name, module_to_search))
except:
# Reset the model registry to the state before the last import as
# this import will have to reoccur on the next request and this
# could raise NotRegistered and AlreadyRegistered exceptions
# (see #8245).
if register_to:
register_to._registry = before_import_registry
# Decide whether to bubble up this error. If the app just
# doesn't have an admin module, we can ignore the error
# attempting to import it, otherwise we want it to bubble up.
if module_has_submodule(app_config.module, module_to_search):
raise
def module_has_submodule(package, module_name):
"""See if 'module' is in 'package'."""
name = ".".join([package.__name__, module_name])
try:
# None indicates a cached miss; see mark_miss() in Python/import.c.
return sys.modules[name] is not None
except KeyError:
pass
try:
package_path = package.__path__ # No __path__, then not a package.
except AttributeError:
# Since the remainder of this function assumes that we're dealing with
# a package (module with a __path__), so if it's not, then bail here.
return False
for finder in sys.meta_path:
if finder.find_module(name, package_path):
return True
for entry in package_path:
try:
# Try the cached finder.
finder = sys.path_importer_cache[entry]
if finder is None:
# Implicit import machinery should be used.
try:
file_, _, _ = imp.find_module(module_name, [entry])
if file_:
file_.close()
return True
except ImportError:
continue
# Else see if the finder knows of a loader.
elif finder.find_module(name):
return True
else:
continue
except KeyError:
# No cached finder, so try and make one.
for hook in sys.path_hooks:
try:
finder = hook(entry)
# XXX Could cache in sys.path_importer_cache
if finder.find_module(name):
return True
else:
# Once a finder is found, stop the search.
break
except ImportError:
# Continue the search for a finder.
continue
else:
# No finder found.
# Try the implicit import machinery if searching a directory.
if os.path.isdir(entry):
try:
file_, _, _ = imp.find_module(module_name, [entry])
if file_:
file_.close()
return True
except ImportError:
pass
# XXX Could insert None or NullImporter
else:
# Exhausted the search, so the module cannot be found.
return False
|
#ifndef __MTKFB_H
#define __MTKFB_H
#define MTK_FB_OVERLAY_SUPPORT
#define MTK_FB_SYNC_SUPPORT
#define MTK_FB_ION_SUPPORT
#define MTK_FB_NO_ION_FD ((int)(~0U>>1))
/* IOCTL commands. */
#define MTK_IOW(num, dtype) _IOW('O', num, dtype)
#define MTK_IOR(num, dtype) _IOR('O', num, dtype)
#define MTK_IOWR(num, dtype) _IOWR('O', num, dtype)
#define MTK_IO(num) _IO('O', num)
// --------------------------------------------------------------------------
#define MTKFB_SET_OVERLAY_LAYER MTK_IOW(45, struct fb_overlay_layer)
#define MTKFB_TRIG_OVERLAY_OUT MTK_IO(46)
#define MTKFB_SET_VIDEO_LAYERS MTK_IOW(51, struct fb_overlay_layer)
#define MTKFB_CAPTURE_FRAMEBUFFER MTK_IOW(52, unsigned long)
#define MTKFB_CONFIG_IMMEDIATE_UPDATE MTK_IOW(53, unsigned long)
#define MTKFB_SET_MULTIPLE_LAYERS MTK_IOW(54, struct fb_overlay_layer)
#define MTKFB_REGISTER_OVERLAYBUFFER MTK_IOW(55, struct fb_overlay_buffer_info)
#define MTKFB_UNREGISTER_OVERLAYBUFFER MTK_IOW(56, unsigned int)
#define MTKFB_SET_ORIENTATION MTK_IOW(57, unsigned long)
#define MTKFB_FBLAYER_ENABLE MTK_IOW(58,unsigned int)
#define MTKFB_LOCK_FRONT_BUFFER MTK_IO(59)
#define MTKFB_UNLOCK_FRONT_BUFFER MTK_IO(60)
#define MTKFB_POWERON MTK_IO(61)
#define MTKFB_POWEROFF MTK_IO(62)
// -------------------------------------------------Fence/Ion, OVL decoupling
#define MTKFB_PREPARE_OVERLAY_BUFFER MTK_IOW(66, struct fb_overlay_buffer)
#define MTKFB_SWITCH_DISPLAY_MODE MTK_IOW(67, struct fb_overlay_mode)
// -----------------------------------------------------------------S3D control
#define MTKFB_SET_COMPOSING3D MTK_IOW(70, unsigned long)
#define MTKFB_SET_S3D_FTM MTK_IOW(71, unsigned long)
//-------------------------------------------FM De-sense for EM and Normal mode
#define MTKFB_GET_DEFAULT_UPDATESPEED MTK_IOR(72, unsigned long)
#define MTKFB_GET_CURR_UPDATESPEED MTK_IOR(73, unsigned long)
#define MTKFB_CHANGE_UPDATESPEED MTK_IOW(74, unsigned long) //for EM, not called change writecycle because DPI change pll ckl
#define MTKFB_GET_INTERFACE_TYPE MTK_IOR(77, unsigned long) ///0 DBI, 1 DPI, 2 MIPI
#define MTKFB_GET_POWERSTATE MTK_IOR(78, unsigned long) ///0: power off 1: power on
#define MTKFB_GET_DISPLAY_IF_INFORMATION MTK_IOR(90, mtk_dispif_info_t)
#define MTKFB_AEE_LAYER_EXIST MTK_IOR(92, unsigned long) //called before SET_OVERLAY each time, if true, hwc will not use FB_LAYER again
#define MTKFB_GET_OVERLAY_LAYER_INFO MTK_IOR(93, struct fb_overlay_layer_info)
#define MTKFB_FACTORY_AUTO_TEST MTK_IOR(94, unsigned long)
#define MTKFB_GET_FRAMEBUFFER_MVA MTK_IOR(95, unsigned int)
#define MTKFB_SLT_AUTO_CAPTURE MTK_IOWR(96, struct fb_slt_catpure)
//---------------------------------------------------------------error handling
#define MTKFB_META_RESTORE_SCREEN MTK_IOW(101, unsigned long)
#define MTKFB_ERROR_INDEX_UPDATE_TIMEOUT MTK_IO(103)
#define MTKFB_ERROR_INDEX_UPDATE_TIMEOUT_AEE MTK_IO(104)
//----------------------------------------------------------------------
#define FBCAPS_GENERIC_MASK 0x00000fff
#define FBCAPS_LCDC_MASK 0x00fff000
#define FBCAPS_PANEL_MASK 0xff000000
#define FBCAPS_MANUAL_UPDATE 0x00001000
#define FBCAPS_SET_BACKLIGHT 0x01000000
#define MAKE_MTK_FB_FORMAT_ID(id, bpp) (((id) << 8) | (bpp))
typedef enum
{
MTK_FB_FORMAT_UNKNOWN = 0,
MTK_FB_FORMAT_RGB565 = MAKE_MTK_FB_FORMAT_ID(1, 2),
MTK_FB_FORMAT_RGB888 = MAKE_MTK_FB_FORMAT_ID(2, 3),
MTK_FB_FORMAT_BGR888 = MAKE_MTK_FB_FORMAT_ID(3, 3),
MTK_FB_FORMAT_ARGB8888 = MAKE_MTK_FB_FORMAT_ID(4, 4),
MTK_FB_FORMAT_ABGR8888 = MAKE_MTK_FB_FORMAT_ID(5, 4),
MTK_FB_FORMAT_YUV422 = MAKE_MTK_FB_FORMAT_ID(6, 2),
MTK_FB_FORMAT_XRGB8888 = MAKE_MTK_FB_FORMAT_ID(7, 4),
MTK_FB_FORMAT_XBGR8888 = MAKE_MTK_FB_FORMAT_ID(8, 4),
MTK_FB_FORMAT_UYVY = MAKE_MTK_FB_FORMAT_ID(9, 2),
MTK_FB_FORMAT_YUV420_P = MAKE_MTK_FB_FORMAT_ID(10, 2),
MTK_FB_FORMAT_BPP_MASK = 0xFF,
} MTK_FB_FORMAT;
#define GET_MTK_FB_FORMAT_BPP(f) ((f) & MTK_FB_FORMAT_BPP_MASK)
// --------------------------------------------------------------------------
typedef enum
{
MTK_FB_ORIENTATION_0 = 0,
MTK_FB_ORIENTATION_90 = 1,
MTK_FB_ORIENTATION_180 = 2,
MTK_FB_ORIENTATION_270 = 3,
} MTK_FB_ORIENTATION;
typedef enum
{
MTK_FB_TV_SYSTEM_NTSC = 0,
MTK_FB_TV_SYSTEM_PAL = 1,
} MTK_FB_TV_SYSTEM;
typedef enum
{
MTK_FB_TV_FMT_RGB565 = 0,
MTK_FB_TV_FMT_YUV420_SEQ = 1,
MTK_FB_TV_FMT_UYUV422 = 2,
MTK_FB_TV_FMT_YUV420_BLK = 3,
} MTK_FB_TV_SRC_FORMAT;
typedef struct _disp_dfo_item
{
char name[32];
int value;
}disp_dfo_item_t;
// --------------------------------------------------------------------------
struct fb_slt_catpure
{
MTK_FB_FORMAT format;
volatile char* outputBuffer;
unsigned int wdma_width;
unsigned int wdma_height;
};
struct fb_scale {
unsigned int xscale, yscale;
};
struct fb_frame_offset {
unsigned int idx;
unsigned long offset;
};
struct fb_update_window {
unsigned int x, y;
unsigned int width, height;
};
typedef enum
{
LAYER_2D = 0,
LAYER_3D_SBS_0 = 0x1,
LAYER_3D_SBS_90 = 0x2,
LAYER_3D_SBS_180 = 0x3,
LAYER_3D_SBS_270 = 0x4,
LAYER_3D_TAB_0 = 0x10,
LAYER_3D_TAB_90 = 0x20,
LAYER_3D_TAB_180 = 0x30,
LAYER_3D_TAB_270 = 0x40,
} MTK_FB_LAYER_TYPE;
typedef enum {
DISP_DIRECT_LINK_MODE,
DISP_DECOUPLE_MODE
}MTK_DISP_MODE;
struct fb_overlay_mode {
MTK_DISP_MODE mode;
};
struct fb_overlay_buffer{
// Input
int layer_id;
unsigned int layer_en;
int ion_fd;
unsigned int cache_sync;
// Output
unsigned int index;
int fence_fd;
};
struct fb_overlay_layer {
unsigned int layer_id;
unsigned int layer_enable;
void* src_base_addr;
void* src_phy_addr;
unsigned int src_direct_link;
MTK_FB_FORMAT src_fmt;
unsigned int src_use_color_key;
unsigned int src_color_key;
unsigned int src_pitch;
unsigned int src_offset_x, src_offset_y;
unsigned int src_width, src_height;
unsigned int tgt_offset_x, tgt_offset_y;
unsigned int tgt_width, tgt_height;
MTK_FB_ORIENTATION layer_rotation;
MTK_FB_LAYER_TYPE layer_type;
MTK_FB_ORIENTATION video_rotation;
unsigned int isTdshp; // set to 1, will go through tdshp first, then layer blending, then to color
int next_buff_idx;
int identity;
int connected_type;
unsigned int security;
};
struct fb_overlay_buffer_info{
unsigned int src_vir_addr;
unsigned int size;
};
struct fb_overlay_layer_info {
unsigned int layer_id;
unsigned int layer_enabled; // TO BE DEL
unsigned int curr_en;
unsigned int next_en;
unsigned int hw_en;
int curr_idx;
int next_idx;
int hw_idx;
int curr_identity;
int next_identity;
int hw_identity;
int curr_conn_type;
int next_conn_type;
int hw_conn_type;
};
#define MTKFB_ERROR_IS_EARLY_SUSPEND 0x12000000
// --------------------------------------------------------------------------
struct fb_post_video_buffer {
void* phy_addr;
void* vir_addr;
MTK_FB_TV_SRC_FORMAT format;
unsigned int width, height;
};
// --------------------------------------------------------------------------
#define HW_OVERLAY_COUNT (4)
// Top layer is assigned to Debug Layer
// Second layer is assigned to UI
#define RESERVED_LAYER_COUNT (2)
#define VIDEO_LAYER_COUNT (HW_OVERLAY_COUNT - RESERVED_LAYER_COUNT)
#ifdef __KERNEL__
#include <linux/completion.h>
#include <linux/interrupt.h>
#define MTKFB_DRIVER "mtkfb"
enum mtkfb_state {
MTKFB_DISABLED = 0,
MTKFB_SUSPENDED = 99,
MTKFB_ACTIVE = 100
};
typedef enum {
MTKFB_LAYER_ENABLE_DIRTY = (1 << 0),
MTKFB_LAYER_FORMAT_DIRTY = (1 << 1),
MTKFB_LAYER_SET_DIRTY = (1 << 2),
} MTKFB_LAYER_CONFIG_DIRTY;
struct mtkfb_device {
int state;
void *fb_va_base; /* MPU virtual address */
dma_addr_t fb_pa_base; /* Bus physical address */
unsigned long fb_size_in_byte;
unsigned long layer_enable;
MTK_FB_FORMAT layer_format[HW_OVERLAY_COUNT];
unsigned int layer_config_dirty;
int xscale, yscale, mirror; /* transformations.
rotate is stored in fb_info->var */
u32 pseudo_palette[17];
struct fb_info *fb_info; /* Linux fbdev framework data */
struct device *dev;
};
#endif /* __KERNEL__ */
#endif /* __MTKFB_H */
|
$(document).ready(function (){
toasterOptions();
initialLoad();
setDateRangePicker();
function toasterOptions()
{
toastr.options = {
"closeButton": true,
"debug": false,
"newestOnTop": true,
"progressBar": true,
"positionClass": "toast-center-center",
"preventDuplicates": false,
"onclick": null,
"showDuration": "300",
"hideDuration": "1000",
"timeOut": "5000",
"extendedTimeOut": "1000",
"showEasing": "swing",
"hideEasing": "linear",
"showMethod": "fadeIn",
"hideMethod": "fadeOut",
"preventDuplicates":true,
}
}
function initialLoad()
{
var date = new Date();
var start = new Date(date.getFullYear(), date.getMonth(), 1);
var end = new Date(date.getFullYear(), date.getMonth() + 1, 0);
start = moment(start).format('YYYY-MM-DD');
end = moment(end).format('YYYY-MM-DD');
fetch(start,end)
}
//calculate Gross pay
function grossPay()
{
}
function fetch(start,end)
{
start = moment(start).format('YYYY-MM-DD');
end = moment(end).format('YYYY-MM-DD');
$.ajax({
type: "POST",
url: baseurl + "PayrollControl/fetch" ,
cache:false,
data:{
StartDate:start,
EndDate:end,
},
dataType: "JSON",
success: function (data) {
if(data.response == "success"){
var table;
setters = data.posts;
console.log(setters)
if($.fn.dataTable.isDataTable('#payrollRecords')) {
console.log("drew it!");
table = $('#payrollRecords').DataTable();
table.clear().draw();
table.rows.add(setters); // Add new data
table.columns.adjust().draw();
}
else{
console.log("generate it!");
table = $('#payrollRecords').DataTable({
"destroy": true,
responsive:true,
dom: //'Blfrtip', // if you remove this line you will see the show entries dropdown
'B'+
"<'row'<'col-sm-12 col-md-6'l><'col-sm-12 col-md-6'f>>"+
"rtip",
buttons: [
// 'copy', 'csv', 'excel', 'pdf', 'print',
{
text:'Copy Table to Clipboard',
className: "copy spaceButtons",
extend:'copy',
},
{
text:'Export Table To Excel',
extend:'excel',
className:"excel spaceButtons"
},
{
text:'Export Table To CSV',
extend:'csv',
className:"csv spaceButtons"
},
{
text:'Export Table To PDF',
extend:'pdf',
className:"pdf spaceButtons",
orientation : 'landscape',
pageSize : 'LEGAL',
},
],
data:data.posts,
columns:[
{
title:"Image",
"data": "Image",
"render": function ( data, type, row, meta ) {
return '<img src="'+baseurl+data+"?time"+new Date().getTime()+'"alt="Error load" class="img-fluid"></img>'
}
},
{
title:"Employee Name",
data:null,
render:function(data,type,row,meta){
return row.FirstName + " " + row.LastName;
}
},
{
title:"Employee Number",
data:"EmployeeNumber"
},
{
title:"Position",
data:"Position"
},
{
title:"Total Days Worked",
data:"DaysWorked"
},
{
title:"Base Salary",
data:"BaseSalary",
},
{
title:"Dental Commissions",
data:"TotalCommissions",
render:function(data,type,row,meta){
if(data == null){
return "";
}
else{
return data;
}
}
},
{
title:"Gross pay",
data:null,
render:function(data,type,row,meta){
let daysWorked = Number(row.DaysWorked);
let dailyRate = Number(row.BaseSalary);
let overTime = Number(row.OverTime);
let commissions = Number(row.TotalCommissions);
let baseRate = Number((daysWorked * dailyRate) + commissions);
let overTimeRate = Number(((dailyRate/8/60) * overTime));
let grossPay = Number(baseRate + overTimeRate);
grossPay = grossPay.toFixed(2);
return grossPay;
},
},
{
title:"Net Pay",
data:null,
render: function(data,type,row,meta){
let daysWorked = Number(row.DaysWorked);
let dailyRate = Number(row.BaseSalary);
let overTime = Number(row.OverTime);
let commissions = Number(row.TotalCommissions);
let deductions = Number(row.SSS) + Number(row.PagIbig) + Number(row.PhilHealth) + Number(row.CashAdvance);
let baseRate = Number((daysWorked * dailyRate) + commissions);
let overTimeRate = Number(((dailyRate/8/60) * overTime));
let grossPay = Number(baseRate + overTimeRate);
let netPay = Number(grossPay - deductions);
netPay = netPay.toFixed(2)
return netPay;
},
},
/*
{
title:"Actions",
data:null,
render:function(data,type,row,meta){
let viewButton = '<a href="#" value = "'+'" class = "btn btn-outline-info viewButton"><i class="far fa-eye"></i></a>'
return viewButton;
},
},
*/
],
columnDefs: [
{ "targets": 0,
"width":"6%",
},
],
})
}
}
else if(data.response == "none"){
toastr["info"]("Alert",data.message);
console.log("no records");
}
else if (data.resposne == "failed"){
toastr["error"]("Alert",data.message);
console.log("Failed to retrieve");
}
}
});
}
//Initialize Date Range Picker
function setDateRangePicker()
{
var date = new Date();
var start = new Date(date.getFullYear(), date.getMonth(), 1);
var end = new Date(date.getFullYear(), date.getMonth() + 1, 0);
$('#dateRangePicker').val(date);
$('#dateRangePicker').daterangepicker({
startDate:start,
endDate:end,
"applyButtonClasses": "btn-success",
"cancelClass": "btn-danger",
locale: {
cancelLabel: 'Clear'
}
});
}
// When apply button is hit
$('#dateRangePicker').on('apply.daterangepicker', function(ev, picker) {
var startDate = picker.startDate.format('YYYY-MM-DD');
var endDate = picker.endDate.format('YYYY-MM-DD');
fetch(startDate,endDate)
});
// when Clear/Cancel button is hit
$('#dateRangePicker').on('cancel.daterangepicker', function(ev, picker) {
$(this).val('');
});
$('#applyDate').on('click', function (e,picker) {
e.preventDefault();
var start = $('#dateRangePicker').data('daterangepicker').startDate;
var end = $('#dateRangePicker').data('daterangepicker').endDate;
start = moment(start).format('YYYY-MM-DD');
end = moment(end).format('YYYY-MM-DD');
fetch(start,end);
});
//View More info
$(document).on('click','tbody .viewButton', function () {
alert("Clicked View Button")
});
});
|
export { default } from "./FilterSelector";
|
const DeedCard = require('../../deedcard.js');
const GameActions = require('../../GameActions/index.js');
class RedRiverRoulette extends DeedCard {
setupCardAbilities(ability) {
this.action({
title: 'Red River Roulette',
playType: ['resolution'],
cost: ability.costs.bootSelf(),
handler: context => {
if(!context.player.isCheatin()) {
context.ability.selectAnotherTarget(context.player, context, {
activePromptTitle: 'Choose a dude',
waitingPromptTitle: 'Waiting for opponent to select dude',
cardCondition: card => card.location === 'play area' &&
card.controller === context.player &&
card.gamelocation === this.gamelocation,
cardType: 'dude',
gameAction: 'boot',
onSelect: (player, card) => {
this.game.resolveGameAction(GameActions.bootCard({ card }), context).thenExecute(() => {
this.game.getPlayers().forEach(player => player.modifyCasualties(-999));
this.game.addMessage('{0} uses {1} and boots {2} to prevent all casualties',
player, this, card);
});
return true;
}
});
} else {
this.game.addMessage('{0} uses {1} but it does not have any effect because they do not have a legal hand',
context.player, this);
}
this.lastingEffect(context.ability, ability => ({
until: {
onShootoutRoundFinished: () => true
},
condition: () => true,
match: card => card.getType() === 'dude' && card.isParticipating(),
effect: ability.effects.cannotFlee()
}));
const eventHandler = () => {
this.game.getPlayers().forEach(player => player.modifyCasualties(player.casualties));
};
const currentShootoutRound = this.game.shootout.round;
this.game.onceConditional('onShootoutCasualtiesStepStarted', {
until: 'onShootoutPhaseFinished',
condition: event => event.shootoutRound === currentShootoutRound + 1
}, eventHandler);
}
});
}
}
RedRiverRoulette.code = '22032';
module.exports = RedRiverRoulette;
|
import io
import sys
"""
Normalize the input text to output ascii-only text
by removing any line that contains non-ascii
Also lowercase all text
"""
printable_chars = ' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~'
def main():
with io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8') as f:
for line in f:
line = line.rstrip()
skip = False
for c in line:
if c not in printable_chars:
skip = True
continue
if not skip:
print(line.lower())
if __name__ == '__main__':
main()
|
'''LastPass Vault search'''
import subprocess
import re
import os
from shutil import which
from albertv0 import *
__iid__ = 'PythonInterface/v0.1'
__prettyname__ = 'LastPass'
__version__ = '1.0'
__trigger__ = 'lp '
__author__ = 'David Piçarra'
__dependencies__ = ['lpass']
if not which('lpass'):
raise Exception("`lpass` is not in $PATH.")
ICON_PATH = os.path.dirname(__file__)+"/lastpass.svg"
def handleQuery(query):
"""
Handle query
:param str query: Query
:return list
"""
if not query.isTriggered:
return None
stripped = query.string.strip()
try:
lpass = subprocess.check_output(['lpass', 'status'])
except Exception as e:
return Item(
id=__prettyname__,
icon=ICON_PATH,
text=f'Not logged in.',
subtext=f'Please enter your lastpass email address',
completion=query.rawString,
actions=[
ProcAction("lpass login with given email", ["lpass", "login", stripped]),
]
)
if stripped:
try:
lpass = subprocess.Popen(['lpass', 'ls', '--long'], stdout=subprocess.PIPE)
try:
output = subprocess.check_output(['grep', '-i', stripped], stdin=lpass.stdout)
except subprocess.CalledProcessError as e:
return Item(
id=__prettyname__,
icon=ICON_PATH,
text=__prettyname__,
subtext=f'No results found for {stripped}',
completion=query.rawString
)
items = []
for line in output.splitlines():
match = re.match(r'\d{4}-\d{2}-\d{2} \d{2}:\d{2} (.*) \[id: (\d*)\] \[username: (.*)\]', line.decode("utf-8"))
items.append(Item(
id=__prettyname__,
icon=ICON_PATH,
text=match.group(1),
subtext=match.group(3),
completion=query.rawString,
actions=[
ProcAction("Copy password to clipboard", ["lpass", "show", "-cp", match.group(2)]),
ProcAction("Copy username to clipboard", ["lpass", "show", "-cu", match.group(2)]),
ProcAction("Copy notes to clipboard", ["lpass", "show", "-c", "--notes", match.group(2)])
]
))
return items
except subprocess.CalledProcessError as e:
return Item(
id=__prettyname__,
icon=ICON_PATH,
text=f'Error: {str(e.output)}',
subtext=str(e),
completion=query.rawString,
actions=[ClipAction('Copy CalledProcessError to clipboard', str(e))]
)
except Exception as e:
return Item(
id=__prettyname__,
icon=ICON_PATH,
text=f'Generic Exception: {str(e)}',
subtext=str(e),
completion=query.rawString,
actions=[ClipAction('Copy Exception to clipboard', str(e))]
)
else:
return Item(
id=__prettyname__,
icon=ICON_PATH,
text=__prettyname__,
subtext='Search the LastPass vault',
completion=query.rawString,
)
|
"""
Quantiphyse - widget which allows a data sets view metadata to be changed
The widget automatically switches to show the 'current' data, whether it is
currently visible or not.
Copyright (c) 2013-2020 University of Oxford
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import division, unicode_literals, absolute_import, print_function
import logging
from PySide2 import QtGui, QtCore, QtWidgets
from quantiphyse.utils import get_icon, sf
from quantiphyse.utils.enums import Visibility
from quantiphyse.gui.widgets import RoiCombo, OverlayCombo
from quantiphyse.gui.options import OptionBox, DataOption, ChoiceOption, NumericOption, TextOption
from quantiphyse.gui.colors import CMAPS, DEFAULT_CMAP
LOG = logging.getLogger(__name__)
def no_update(f):
def wrapper(self, *args, **kwargs):
try:
self._no_update = True
f(self, *args, **kwargs)
finally:
self._no_update = False
return wrapper
class ViewParamsWidget(OptionBox):
def __init__(self, ivl, rois=True, data=True):
OptionBox.__init__(self, border=True)
self.ivl = ivl
self.ivm = ivl.ivm
self._qpdata = None
self._no_update = False
self.grid.setVerticalSpacing(2)
self._view_btn = QtWidgets.QPushButton()
self._view_btn.setIcon(QtGui.QIcon(get_icon("visible.png")))
self._view_btn.setFixedSize(16, 16)
self._view_btn.setToolTip("Visibility")
self._view_btn.clicked.connect(self._view_btn_clicked)
self._data = self.add("Data" if data else "ROI", DataOption(self.ivm, data=data, rois=rois, follow_current=True), self._view_btn, key="data")
self._view_roi = self.add("View ROI", DataOption(self.ivm, data=False, rois=True), checked=True, key="view_roi")
self._levels_btn = QtWidgets.QPushButton()
self._levels_btn.setIcon(QtGui.QIcon(get_icon("levels.png")))
self._levels_btn.setFixedSize(16, 16)
self._levels_btn.setToolTip("Adjust colour map levels")
self._levels_btn.clicked.connect(self._levels_clicked)
self._cmap = self.add("Colour map", ChoiceOption(CMAPS, default=DEFAULT_CMAP), self._levels_btn, key="cmap")
self._alpha = self.add("Alpha", NumericOption(minval=0, maxval=255, default=255, edit=False, intonly=True), key="alpha")
self._value_label = QtWidgets.QLabel()
self.add("Value", self._value_label)
self.add("", stretch=2)
self._data.sig_changed.connect(self._data_changed)
self._view_roi.sig_changed.connect(self._view_roi_changed)
self._cmap.sig_changed.connect(self._cmap_changed)
self._alpha.sig_changed.connect(self._alpha_changed)
self.ivl.sig_focus_changed.connect(self._focus_changed)
self.qpdata = None
@property
def qpdata(self):
return self._qpdata
@qpdata.setter
def qpdata(self, qpdata):
if self._qpdata is not None:
self._qpdata.view.sig_changed.disconnect(self._view_md_changed)
self._qpdata = qpdata
self.option("alpha").setEnabled(qpdata is not None)
self._view_btn.setEnabled(qpdata is not None)
self.set_visible("cmap", qpdata is not None and not qpdata.roi)
self._levels_btn.setVisible(qpdata is not None and not qpdata.roi)
self.set_visible("view_roi", qpdata is not None and not qpdata.roi)
if self._qpdata is not None:
self._qpdata.view.sig_changed.connect(self._view_md_changed)
self._view_md_changed()
self.option("data").value = self._qpdata.name
def _view_md_changed(self, _key=None, _value=None):
if not self._no_update:
try:
self.blockSignals(True)
if self._qpdata is not None:
self.option("alpha").value = self._qpdata.view.alpha
if not self._qpdata.roi:
self.set_checked("view_roi", bool(self._qpdata.view.roi))
if self._qpdata.view.roi:
self.option("view_roi").value = self._qpdata.view.roi
self.option("cmap").value = self._qpdata.view.cmap
self._view_btn.setIcon(self._get_visibility_icon())
finally:
self.blockSignals(False)
def _data_changed(self):
self.qpdata = self.ivm.data.get(self.option("data").value, None)
if self.qpdata is not None:
self.ivm.set_current(self.qpdata.name)
@no_update
def _view_roi_changed(self):
if self._qpdata is not None:
self._qpdata.view.roi = None if not self.option("view_roi").isEnabled() else self.option("view_roi").value
@no_update
def _cmap_changed(self):
if self._qpdata is not None:
self._qpdata.view.cmap = self.option("cmap").value
@no_update
def _alpha_changed(self):
if self._qpdata is not None:
self._qpdata.view.alpha = self.option("alpha").value
def _levels_clicked(self):
dlg = LevelsDialog(self, self.ivm, self.ivl, self._qpdata)
dlg.exec_()
def _focus_changed(self, focus):
if self._qpdata is not None:
value = self._qpdata.value(focus, self.ivl.grid)
if self._qpdata.roi:
region = int(value)
if region == 0:
text = "(outside ROI)"
else:
text = self._qpdata.regions.get(region, "")
if text == "":
text = "1"
else:
text = sf(value, 4)
self._value_label.setText(text)
def _view_btn_clicked(self):
if self._qpdata.roi:
if self._qpdata.view.visible == Visibility.HIDE:
vis, shade, contour = Visibility.SHOW, True, False
elif self._qpdata.view.shade and not self._qpdata.view.contour:
vis, shade, contour = Visibility.SHOW, True, True
elif self._qpdata.view.shade and self._qpdata.view.contour:
vis, shade, contour = Visibility.SHOW, False, True
else:
vis, shade, contour = Visibility.HIDE, True, False
self._qpdata.view.visible = vis
self._qpdata.view.shade = shade
self._qpdata.view.contour = contour
else:
currently_visible = self._qpdata.view.visible == Visibility.SHOW
self._qpdata.view.visible = Visibility.SHOW if not currently_visible else Visibility.HIDE
def _get_visibility_icon(self):
if self._qpdata.view.visible == Visibility.HIDE:
icon = "invisible.png"
elif self._qpdata.roi:
if self._qpdata.view.contour and self._qpdata.view.shade:
icon = "shade_contour.png"
elif self._qpdata.view.contour:
icon = "contour.png"
elif self._qpdata.view.shade:
icon = "shade.png"
else:
icon = "invisible.png"
else:
icon = "visible.png"
return QtGui.QIcon(get_icon(icon))
class LevelsDialog(QtWidgets.QDialog):
"""
Dialog box used to set the colourmap max/min for a data view
"""
def __init__(self, parent, ivm, ivl, qpdata):
super(LevelsDialog, self).__init__(parent)
self.ivm = ivm
self.ivl = ivl
self._qpdata = qpdata
self.setWindowTitle("Levels for %s" % self._qpdata.name)
vbox = QtWidgets.QVBoxLayout()
grid = QtWidgets.QGridLayout()
self.min_spin = self._add_spin(grid, "Minimum", 0)
self.max_spin = self._add_spin(grid, "Maximum", 1)
grid.addWidget(QtWidgets.QLabel("Percentage of data range"), 2, 0)
hbox = QtWidgets.QHBoxLayout()
self.percentile_spin = QtWidgets.QSpinBox()
self.percentile_spin.setMaximum(100)
self.percentile_spin.setMinimum(1)
self.percentile_spin.setValue(100)
hbox.addWidget(self.percentile_spin)
btn = QtWidgets.QPushButton("Reset")
btn.clicked.connect(self._reset)
hbox.addWidget(btn)
grid.addLayout(hbox, 2, 1)
grid.addWidget(QtWidgets.QLabel("Values outside range are"), 4, 0)
self.combo = QtWidgets.QComboBox()
self.combo.addItem("Transparent")
self.combo.addItem("Clamped to max/min colour")
self.combo.addItem("Transparent at lower, clamped at upper")
self.combo.addItem("Clamped at lower, transparent at upper")
self.combo.setCurrentIndex(self._qpdata.view.boundary)
self.combo.currentIndexChanged.connect(self._bound_changed)
grid.addWidget(self.combo, 4, 1)
vbox.addLayout(grid)
bbox = QtWidgets.QDialogButtonBox(QtWidgets.QDialogButtonBox.Ok)
bbox.accepted.connect(self.close)
vbox.addWidget(bbox)
self.setLayout(vbox)
def _add_spin(self, grid, label, row):
grid.addWidget(QtWidgets.QLabel(label), row, 0)
spin = QtWidgets.QDoubleSpinBox()
spin.setMaximum(1e20)
spin.setMinimum(-1e20)
spin.setValue(self._qpdata.view.cmap_range[row])
spin.valueChanged.connect(self._val_changed(row))
grid.addWidget(spin, row, 1)
return spin
def _val_changed(self, row):
def _changed(val):
cmap_range = list(self._qpdata.view.cmap_range)
cmap_range[row] = val
self._qpdata.view.cmap_range = cmap_range
return _changed
def _bound_changed(self, idx):
self._qpdata.view.boundary = idx
def _reset(self):
percentile = self.percentile_spin.value()
if self._qpdata.view.roi:
roi = self.ivm.data[self._qpdata.view.roi]
else:
roi = None
cmin, cmax = self._qpdata.suggest_cmap_range(vol=self.ivl.focus()[3], percentile=percentile, roi=roi)
self.min_spin.setValue(cmin)
self.max_spin.setValue(cmax)
self._qpdata.view.cmap_range = [cmin, cmax]
|
import './index.css';
import 'bootstrap/dist/css/bootstrap.min.css';
import React, { useEffect } from 'react';
import { BrowserRouter as Router, Route, Switch } from 'react-router-dom';
import Footer from './components/layout/Footer';
import Register from './components/auth/Register';
import SubscribePlan from './components/layout/SubscribePlan';
import ClientNavbar from './components/Dashboard/User/ClientNavbar';
import Login from './components/auth/Login';
import Welcome from './components/layout/Welcome';
import AdminHeader from './components/Dashboard/Admin/AdminHeader';
import AdminMain from './components/layout/AdminMain';
import PrivateRoute from './components/routing/PrivateRoute';
import Profile from './components/layout/Profile';
import UserDashboard from '../src/components/page/user/UserDashboard';
import UserLearning from '../src/components/page/user/UserLearning';
import Landing from './components/layout/Landing';
import About from './components/layout/About';
import Courses from './components/courses/Courses';
import Course from './components/course/Course';
import Navbar from './components/layout/Navbar';
// Redux
import { Provider } from 'react-redux';
import store from './store';
import { loadUser } from './actions/auth';
import setAuthToken from './utils/setAuthToken';
import FileUpload from './components/page/course/FileUpload';
import ViewCoursePage from './components/layout/ViewCoursePage';
import VideoStream from './components/layout/VideoStream';
if (localStorage.token) {
setAuthToken(localStorage.token);
}
// Parent component
const App = () => {
// useEffect Hook
useEffect(() => {
window.scrollTo(0, 0);
store.dispatch(loadUser());
}, []);
return (
<Provider store={store}>
<Router>
<div className='landing'>
<div>
<Switch>
<Route exact path='/'>
<Landing />
</Route>
<PrivateRoute path='/profile' component={Profile} />
<PrivateRoute path='/upload-course' component={FileUpload} />
<PrivateRoute path='/course/:id' component={Course} />
<PrivateRoute path='/stream' component={VideoStream} />
<Route exact path='/about'>
<ClientNavbar />
<About />
</Route>
<Route path='/register'>
<ClientNavbar />
<Welcome />
<Register />
</Route>
<Route path='/login'>
<ClientNavbar />
<Welcome />
<Login />
</Route>
<Route path='/Admin'>
<AdminHeader />
<AdminMain />
</Route>
<Route path='/subscribe'>
<ClientNavbar />
<Welcome />
<SubscribePlan />
</Route>
<Route exact path='/learning'>
<Navbar />
<UserDashboard />
<Courses />
</Route>
<Route exact path='/user'>
<ClientNavbar />
<UserDashboard />
<UserLearning />
</Route>
{/* Added a route to View Course page */}
<Route exact path='/courses'>
<ClientNavbar />
<AdminMain />
</Route>
</Switch>
</div>
<Footer />
</div>
</Router>
</Provider>
);
};
export default App;
|
from chainer.backends import cuda
from chainer.backends import intel64
from chainer import function_node
from chainer.utils import type_check
_kern = None
def _get_kern():
global _kern
if _kern is None:
_kern = cuda.elementwise(
'T cond, T x, T slope', 'T y',
'y = cond <= 0 ? (T)(slope * x) : x', 'lrelu')
return _kern
class LeakyReLU(function_node.FunctionNode):
"""Leaky rectifier unit."""
def __init__(self, slope=0.2):
self.slope = slope
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x',))
x_type, = in_types
type_check.expect(x_type.dtype.kind == 'f')
def forward_cpu(self, inputs):
if (intel64.should_use_ideep('>=auto')
and intel64.inputs_all_ready(inputs)):
return self.forward_ideep(inputs)
x, = inputs
y = x.copy()
y[x <= 0] *= self.slope
if self.slope >= 0:
self.retain_outputs((0,))
else:
self.retain_inputs((0,))
return y,
def forward_ideep(self, inputs):
x, = inputs
y = intel64.ideep.relu.Forward(
intel64.ideep.array(x), self.slope)
if self.slope >= 0:
self.retain_outputs((0,))
else:
self.retain_inputs((0,))
return y,
def forward_gpu(self, inputs):
x, = inputs
y = _get_kern()(x, x, self.slope)
if self.slope >= 0:
self.retain_outputs((0,))
else:
self.retain_inputs((0,))
return y,
def backward(self, indexes, grad_outputs):
if self.slope >= 0:
cond = self.get_retained_outputs()[0].array
else:
cond = self.get_retained_inputs()[0].array
return _LeakyReLUGrad(cond, self.slope).apply(grad_outputs)
class _LeakyReLUGrad(function_node.FunctionNode):
def __init__(self, cond, slope):
self.cond = cond
self.slope = slope
def forward_cpu(self, inputs):
if (intel64.should_use_ideep('>=auto')
and intel64.inputs_all_ready(inputs)):
return self.forward_ideep(inputs)
gy, = inputs
gy = gy.copy()
gy[self.cond <= 0] *= self.slope
return gy,
def forward_ideep(self, inputs):
gy, = inputs
gy = intel64.ideep.relu.Backward(
intel64.ideep.array(self.cond),
intel64.ideep.array(gy), self.slope)
return gy,
def forward_gpu(self, inputs):
gy, = inputs
gy = _get_kern()(self.cond, gy, self.slope)
return gy,
def backward(self, indexes, grad_outputs):
return _LeakyReLUGrad(self.cond, self.slope).apply(grad_outputs)
def leaky_relu(x, slope=0.2):
"""Leaky Rectified Linear Unit function.
This function is expressed as
.. math::
f(x) = \\left \\{ \\begin{array}{ll}
x & {\\rm if}~ x \\ge 0 \\\\
ax & {\\rm if}~ x < 0,
\\end{array} \\right.
where :math:`a` is a configurable slope value.
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`):
Input variable. A :math:`(s_1, s_2, ..., s_N)`-shaped float array.
slope (float): Slope value :math:`a`.
Returns:
~chainer.Variable: Output variable. A
:math:`(s_1, s_2, ..., s_N)`-shaped float array.
.. admonition:: Example
>>> x = np.array([[-1, 0], [2, -3], [-2, 1]], np.float32)
>>> x
array([[-1., 0.],
[ 2., -3.],
[-2., 1.]], dtype=float32)
>>> F.leaky_relu(x, slope=0.2).data
array([[-0.2, 0. ],
[ 2. , -0.6],
[-0.4, 1. ]], dtype=float32)
"""
return LeakyReLU(slope).apply((x,))[0]
|
/*
HLSL shader compiler implementation
*/
#pragma once
#include "backend.h"
#include <string>
#include <tscore/system/memory.h>
/////////////////////////////////////////////////////////////////////////////////////////////////
namespace ts
{
class HLSLCompiler : public IShaderBackend
{
private:
void* m_module = nullptr;
public:
HLSLCompiler();
~HLSLCompiler();
EShaderBackend getId() const override { return eBackendHLSL_SM5; }
bool compile(const std::string& code, ts::MemoryBuffer& codebuffer, const char* entrypoint, EShaderStage stage, std::string& errors, const char* sourcePath) override;
};
}
/////////////////////////////////////////////////////////////////////////////////////////////////
|
import math
def pairs(k, arr):
arr=set(arr)
brr=set(x+k for x in arr)
count=arr.intersection(brr)
return count.__len__()
|
import faulthandler
faulthandler.enable()
from tensor2tensor.data_generators import problem
from tensor2tensor.data_generators import text_problems
from tensor2tensor.data_generators import translate
from tensor2tensor.utils import registry
from tensor2tensor import problems
import tensorflow as tf
import os
import logging
logger = logging.getLogger()
tf.logging.set_verbosity(tf.logging.DEBUG)
import sentencepiece as spm
vocab = 'sp10m.cased.t5.model'
sp = spm.SentencePieceProcessor()
sp.Load(vocab)
class Encoder:
def __init__(self, sp):
self.sp = sp
self.vocab_size = sp.GetPieceSize() + 100
def encode(self, s):
return self.sp.EncodeAsIds(s)
def decode(self, ids, strip_extraneous = False):
return self.sp.DecodeIds(list(ids))
encoder = Encoder(sp)
from tqdm import tqdm
from glob import glob
@registry.register_problem
class Seq2Seq(text_problems.Text2TextProblem):
@property
def approx_vocab_size(self):
return 32100
@property
def is_generate_per_split(self):
return False
@property
def dataset_splits(self):
return [
{'split': problem.DatasetSplit.TRAIN, 'shards': 100},
{'split': problem.DatasetSplit.EVAL, 'shards': 1},
]
def feature_encoders(self, data_dir):
encoder = Encoder(sp)
return {'inputs': encoder, 'targets': encoder}
DATA_DIR = 'gs://mesolitica-tpu-general/t2t/data'
TRAIN_DIR = 'gs://mesolitica-tpu-general/t2t-small'
PROBLEM = 'seq2_seq'
t2t_problem = problems.problem(PROBLEM)
train_steps = 500000
eval_steps = 10
batch_size = 32
save_checkpoints_steps = 25000
ALPHA = 0.003
schedule = 'continuous_train_and_eval'
MODEL = 'transformer'
HPARAMS = 'transformer_base'
from tensor2tensor.utils.trainer_lib import create_run_config, create_experiment
from tensor2tensor.utils.trainer_lib import create_hparams
from tensor2tensor.utils import registry
from tensor2tensor import models
from tensor2tensor import problems
hparams = create_hparams(HPARAMS)
hparams.batch_size = batch_size
hparams.learning_rate = ALPHA
hparams.train_batch_size = batch_size
hparams.filter_size = 3072
hparams.hidden_size = 768
hparams.num_heads = 12
hparams.num_hidden_layers = 8
hparams.vocab_divisor = 128
hparams.dropout = 0.1
# LM
hparams.label_smoothing = 0.0
hparams.shared_embedding_and_softmax_weights = False
hparams.eval_drop_long_sequences = True
hparams.max_length = 1024
hparams.multiproblem_mixing_schedule = 'pretrain'
hparams.use_fixed_batch_size = True
# tpu
hparams.symbol_modality_num_shards = 1
hparams.attention_dropout_broadcast_dims = '0,1'
hparams.relu_dropout_broadcast_dims = '1'
hparams.layer_prepostprocess_dropout_broadcast_dims = '1'
hparams.optimizer = 'Adafactor'
hparams.learning_rate_warmup_steps = 10000
hparams.learning_rate_schedule = 'rsqrt_decay'
print(hparams)
RUN_CONFIG = create_run_config(
model_dir = TRAIN_DIR,
model_name = MODEL,
save_checkpoints_steps = save_checkpoints_steps,
use_tpu = True,
cloud_tpu_name = 'node-6',
iterations_per_loop = 100,
schedule = 'train',
)
tensorflow_exp_fn = create_experiment(
run_config = RUN_CONFIG,
hparams = hparams,
model_name = MODEL,
problem_name = PROBLEM,
data_dir = DATA_DIR,
train_steps = train_steps,
eval_steps = eval_steps,
use_tpu = True,
use_tpu_estimator = False,
schedule = 'train',
# use_xla=True # For acceleration
)
tensorflow_exp_fn.train()
|
/**
* Copyright 2017 IBM Corp. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the 'License'); you may not
* use this file except in compliance with the License. You may obtain a copy of
* the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations under
* the License.
*/
const parseData = data => ({
topics: data.result.aggregations[0].results,
rawData: data
});
const topicStory = item => item.aggregations[0].hits.hits[0];
const objectWithoutProperties = (object, properties) => {
'use strict';
var obj = {};
var keys = Object.keys(object);
keys.forEach(key => {
if (!~properties.indexOf(key)) {
obj[key] = object[key];
}
});
return obj;
};
module.exports = {
parseData,
topicStory,
objectWithoutProperties
};
|
# Copyrights. All rights reserved.
# ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE, Switzerland,
# Space Center (eSpace), 2018
# See the LICENSE.TXT file for more details.
import os
import time
import torch as th
from practical_deep_stereo import visualization
def get_learning_rate(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
def _is_on_cuda(network):
return next(network.parameters()).is_cuda
def _is_logging_required(example_index, number_of_examples):
"""Returns True only if logging is required.
Logging is performed after 10%, 20%, ... 100% percents of examples
is processed.
"""
return (example_index + 1) % max(1, number_of_examples // 10) == 0
def _set_fastest_cuda_mode():
th.backends.cudnn.fastest = True
th.backends.cudnn.benchmark = True
def _move_tensors_to_cuda(dictionary_of_tensors):
if isinstance(dictionary_of_tensors, dict):
return {
key: _move_tensors_to_cuda(value)
for key, value in dictionary_of_tensors.items()
}
return dictionary_of_tensors.cuda()
class Trainer(object):
def __init__(self, parameters):
"""Returns initialized trainer object.
Args:
parameters: dictionary with parameters, that
should have the same names as
attributes of the class (but without
underscore).
"""
if th.cuda.is_available():
_set_fastest_cuda_mode()
self._experiment_folder = None
self._current_epoch = 0
self._end_epoch = None
self._learning_rate_scheduler = None
self._network = None
self._training_set_loader = None
self._test_set_loader = None
self._optimizer = None
self._criterion = None
self._current_losses = []
self._current_errors = []
self._current_processing_times = []
self._training_losses = []
self._test_errors = []
self._number_of_examples_to_visualize = 3
self.from_dictionary(parameters)
def from_dictionary(self, parameters):
attributes = vars(self)
for key, value in parameters.items():
_key = '_{0}'.format(key)
attributes[_key] = value
def _initialize_filenames(self):
self._log_filename = os.path.join(self._experiment_folder, 'log.txt')
self._plot_filename = os.path.join(self._experiment_folder, 'plot.png')
self._checkpoint_template = os.path.join(self._experiment_folder,
'{0:03d}_checkpoint.bin')
def load_checkpoint(self, filename, load_only_network=False):
"""Initilizes trainer from checkpoint.
Args:
filename: file with the checkpoint.
load_only_network: if the flag is set, the function only loads
the network (can be usefull for
fine-tuning).
"""
checkpoint = th.load(filename)
self._network.load_state_dict(checkpoint['network'])
if load_only_network:
return
parameters = {
'current_epoch': len(checkpoint['training_losses']),
'training_losses': checkpoint['training_losses'],
'test_errors': checkpoint['test_errors']
}
self.from_dictionary(parameters)
self._optimizer.load_state_dict(checkpoint['optimizer'])
self._learning_rate_scheduler.load_state_dict(
checkpoint['learning_rate_scheduler'])
def _save_checkpoint(self):
th.save({
'training_losses':
self._training_losses,
'test_errors':
self._test_errors,
'network':
self._network.state_dict(),
'optimizer':
self._optimizer.state_dict(),
'learning_rate_scheduler':
self._learning_rate_scheduler.state_dict()
}, self._checkpoint_template.format(self._current_epoch + 1))
def train(self):
"""Trains network and returns validation error of last epoch."""
self._initialize_filenames()
self._logger = visualization.Logger(self._log_filename)
start_epoch = self._current_epoch
if start_epoch == self._end_epoch:
return None
self._logger.log("Training started.")
for self._current_epoch in range(start_epoch, self._end_epoch):
self._training_losses.append(self._train_for_epoch())
self._test_errors.append(self._test()[0])
self._report_training_progress()
self._learning_rate_scheduler.step()
self._save_checkpoint()
self._current_epoch = self._end_epoch
return self._test_errors[-1]
def _run_network_and_measure_time(self, example):
if th.cuda.is_available():
th.cuda.synchronize()
start_time = time.time()
self._run_network(example)
if th.cuda.is_available():
th.cuda.synchronize()
example['processing_time'] = float(time.time() - start_time)
def _report_test_results(self, error, processing_time):
"""Reports test results."""
raise NotImplementedError('"_report_test_results" method should '
'be implemented in a child class.')
def _run_network(self, batch_or_example):
"""Runs network and adds output to "batch_or_example"."""
raise NotImplementedError('"_run_network" method should '
'be implemented in a child class.')
def _compute_gradients_wrt_loss(self, batch):
"""Computes loss, gradients w.r.t loss and saves loss.
The loss should be saved to "loss" item of "batch".
"""
raise NotImplementedError('"_compute_loss" method should '
'be implemented in a child class.')
def _compute_error(self, example):
"""Computes error and adds it to "example" as an "error" item."""
raise NotImplementedError('"_compute_error" method should '
'be implemented in a child class.')
def _visualize_example(self, example, example_index):
"""Visualize result for the example during validation and test.
Args:
example: should include network input and output necessary for
the visualization.
example_index: index of the example.
"""
raise NotImplementedError('"_visualize_example" method should '
'be implemented in a child class.')
def _average_errors(self, errors):
"""Returns average error."""
raise NotImplementedError('"_average_errors" method should '
'be implemented in a child class.')
def _average_losses(self, losses):
"""Returns average loss."""
raise NotImplementedError('"_average_losses" method should '
'be implemented in a child class.')
def _average_processing_time(self, processing_times):
"""Returns average processing time."""
raise NotImplementedError('"_average_processing_time" method should '
'be implemented in a child class.')
def _report_training_progress(self):
"""Report current training progress after current epoch.
The report, for example, may include training plot and log update.
"""
raise NotImplementedError('"_report_training_progress" method should '
'be implemented in a child class.')
def _train_for_epoch(self):
"""Returns training set losses."""
self._network.train()
self._current_losses = []
number_of_batches = len(self._training_set_loader)
for batch_index, batch in enumerate(self._training_set_loader):
if _is_logging_required(batch_index, number_of_batches):
self._logger.log('epoch {0:02d} ({1:02d}) : '
'training: {2:05d} ({3:05d})'.format(
self._current_epoch + 1, self._end_epoch,
batch_index + 1, number_of_batches))
self._optimizer.zero_grad()
if _is_on_cuda(self._network):
batch = _move_tensors_to_cuda(batch)
self._run_network(batch)
self._compute_gradients_wrt_loss(batch)
self._optimizer.step()
self._current_losses.append(batch['loss'])
del batch
th.cuda.empty_cache()
return self._average_losses(self._current_losses)
def _test(self):
"""Returns test set errors."""
self._network.eval()
self._current_errors = []
self._current_processing_times = []
number_of_examples = len(self._test_set_loader)
for example_index, example in enumerate(self._test_set_loader):
if _is_logging_required(example_index, number_of_examples):
self._logger.log('epoch: {0:02d} ({1:02d}) : '
'validation: {2:05d} ({3:05d})'.format(
self._current_epoch + 1, self._end_epoch,
example_index + 1, number_of_examples))
if _is_on_cuda(self._network):
example = _move_tensors_to_cuda(example)
with th.no_grad():
self._run_network_and_measure_time(example)
self._compute_error(example)
self._current_errors.append(example['error'])
self._current_processing_times.append(example['processing_time'])
self._visualize_example(example, example_index)
del example
th.cuda.empty_cache()
return (self._average_errors(self._current_errors),
self._average_processing_time(self._current_processing_times))
def test(self):
"""Test network and reports average errors and execution time."""
self._initialize_filenames()
self._logger = visualization.Logger(self._log_filename)
average_errors, average_processing_time = self._test()
self._report_test_results(average_errors, average_processing_time)
return average_errors, average_processing_time
|
import os
import time
import torch
import numpy as np
"""[ElegantRL](https://github.com/AI4Finance-LLC/ElegantRL)"""
class Evaluator:
def __init__(self, cwd, agent_id, device, env, eval_times1, eval_times2, eval_gap, ):
self.recorder = list() # total_step, r_avg, r_std, obj_c, ...
self.recorder_path = f'{cwd}/recorder.npy'
self.env = env
self.cwd = cwd
self.device = device
self.agent_id = agent_id
self.eval_gap = eval_gap
self.eval_times1 = eval_times1
self.eval_times2 = eval_times2
self.target_return = env.target_return
self.used_time = None
self.start_time = time.time()
self.eval_time = 0
self.total_step = 0
self.r_max = -np.inf
print(f"{'#' * 80}\n"
f"{'ID':<3}{'Step':>8}{'maxR':>8} |"
f"{'avgR':>8}{'stdR':>7}{'avgS':>7}{'stdS':>6} |"
f"{'expR':>8}{'objC':>7}{'etc.':>7}")
def evaluate_and_save(self, act, steps, r_exp, log_tuple) -> bool:
self.total_step += steps # update total training steps
if time.time() - self.eval_time < self.eval_gap:
return False # if_reach_goal
self.eval_time = time.time()
'''evaluate first time'''
rewards_steps_list = [get_episode_return_and_step(self.env, act, self.device) for _ in
range(self.eval_times1)]
r_avg, r_std, s_avg, s_std = self.get_r_avg_std_s_avg_std(rewards_steps_list)
'''evaluate second time'''
if r_avg > self.r_max: # evaluate actor twice to save CPU Usage and keep precision
rewards_steps_list += [get_episode_return_and_step(self.env, act, self.device)
for _ in range(self.eval_times2 - self.eval_times1)]
r_avg, r_std, s_avg, s_std = self.get_r_avg_std_s_avg_std(rewards_steps_list)
'''save the policy network'''
if r_avg > self.r_max: # save checkpoint with highest episode return
self.r_max = r_avg # update max reward (episode return)
act_save_path = f'{self.cwd}/actor.pth'
torch.save(act.state_dict(), act_save_path) # save policy network in *.pth
print(f"{self.agent_id:<3}{self.total_step:8.2e}{self.r_max:8.2f} |") # save policy and print
self.recorder.append((self.total_step, r_avg, r_std, r_exp, *log_tuple)) # update recorder
'''print some information to Terminal'''
if_reach_goal = bool(self.r_max > self.target_return) # check if_reach_goal
if if_reach_goal and self.used_time is None:
self.used_time = int(time.time() - self.start_time)
print(f"{'ID':<3}{'Step':>8}{'TargetR':>8} |"
f"{'avgR':>8}{'stdR':>7}{'avgS':>7}{'stdS':>6} |"
f"{'UsedTime':>8} ########\n"
f"{self.agent_id:<3}{self.total_step:8.2e}{self.target_return:8.2f} |"
f"{r_avg:8.2f}{r_std:7.1f}{s_avg:7.0f}{s_std:6.0f} |"
f"{self.used_time:>8} ########")
print(f"{self.agent_id:<3}{self.total_step:8.2e}{self.r_max:8.2f} |"
f"{r_avg:8.2f}{r_std:7.1f}{s_avg:7.0f}{s_std:6.0f} |"
f"{r_exp:8.2f}{''.join(f'{n:7.2f}' for n in log_tuple)}")
self.draw_plot()
return if_reach_goal
@staticmethod
def get_r_avg_std_s_avg_std(rewards_steps_list):
rewards_steps_ary = np.array(rewards_steps_list, dtype=np.float32)
r_avg, s_avg = rewards_steps_ary.mean(axis=0) # average of episode return and episode step
r_std, s_std = rewards_steps_ary.std(axis=0) # standard dev. of episode return and episode step
return r_avg, r_std, s_avg, s_std
def save_or_load_recoder(self, if_save):
if if_save:
np.save(self.recorder_path, self.recorder)
elif os.path.exists(self.recorder_path):
recorder = np.load(self.recorder_path)
self.recorder = [tuple(i) for i in recorder] # convert numpy to list
self.total_step = self.recorder[-1][0]
def draw_plot(self):
if len(self.recorder) == 0:
print("| save_npy_draw_plot() WARNNING: len(self.recorder)==0")
return None
np.save(self.recorder_path, self.recorder)
'''draw plot and save as png'''
train_time = int(time.time() - self.start_time)
total_step = int(self.recorder[-1][0])
save_title = f"step_time_maxR_{int(total_step)}_{int(train_time)}_{self.r_max:.3f}"
save_learning_curve(self.recorder, self.cwd, save_title)
def get_episode_return_and_step(env, act, device) -> (float, int):
episode_return = 0.0 # sum of rewards in an episode
episode_step = 1
max_step = env.max_step
if_discrete = env.if_discrete
state = env.reset()
for episode_step in range(max_step):
s_tensor = torch.as_tensor((state,), device=device)
a_tensor = act(s_tensor)
if if_discrete:
a_tensor = a_tensor.argmax(dim=1)
action = a_tensor.detach().cpu().numpy()[0] # not need detach(), because with torch.no_grad() outside
state, reward, done, _ = env.step(action)
episode_return += reward
if done:
break
episode_return = getattr(env, 'episode_return', episode_return)
return episode_return, episode_step
def save_learning_curve(recorder=None, cwd='.', save_title='learning curve', fig_name='plot_learning_curve.jpg'):
if recorder is None:
recorder = np.load(f"{cwd}/recorder.npy")
recorder = np.array(recorder)
steps = recorder[:, 0] # x-axis is training steps
r_avg = recorder[:, 1]
r_std = recorder[:, 2]
r_exp = recorder[:, 3]
obj_c = recorder[:, 4]
obj_a = recorder[:, 5]
'''plot subplots'''
import matplotlib as mpl
mpl.use('Agg')
"""Generating matplotlib graphs without a running X server [duplicate]
write `mpl.use('Agg')` before `import matplotlib.pyplot as plt`
https://stackoverflow.com/a/4935945/9293137
"""
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2)
'''axs[0]'''
ax00 = axs[0]
ax00.cla()
ax01 = axs[0].twinx()
color01 = 'darkcyan'
ax01.set_ylabel('Explore AvgReward', color=color01)
ax01.plot(steps, r_exp, color=color01, alpha=0.5, )
ax01.tick_params(axis='y', labelcolor=color01)
color0 = 'lightcoral'
ax00.set_ylabel('Episode Return')
ax00.plot(steps, r_avg, label='Episode Return', color=color0)
ax00.fill_between(steps, r_avg - r_std, r_avg + r_std, facecolor=color0, alpha=0.3)
ax00.grid()
'''axs[1]'''
ax10 = axs[1]
ax10.cla()
ax11 = axs[1].twinx()
color11 = 'darkcyan'
ax11.set_ylabel('objC', color=color11)
ax11.fill_between(steps, obj_c, facecolor=color11, alpha=0.2, )
ax11.tick_params(axis='y', labelcolor=color11)
color10 = 'royalblue'
ax10.set_xlabel('Total Steps')
ax10.set_ylabel('objA', color=color10)
ax10.plot(steps, obj_a, label='objA', color=color10)
ax10.tick_params(axis='y', labelcolor=color10)
for plot_i in range(6, recorder.shape[1]):
other = recorder[:, plot_i]
ax10.plot(steps, other, label=f'{plot_i}', color='grey', alpha=0.5)
ax10.legend()
ax10.grid()
'''plot save'''
plt.title(save_title, y=2.3)
plt.savefig(f"{cwd}/{fig_name}")
plt.close('all') # avoiding warning about too many open figures, rcParam `figure.max_open_warning`
# plt.show() # if use `mpl.use('Agg')` to draw figures without GUI, then plt can't plt.show()
def check_save_learning_curve():
cwd = './AgentPPO_Pendulum-v0_0'
save_learning_curve(recorder=None, cwd=cwd)
if __name__ == '__main__':
check_save_learning_curve()
|
const initialState = {
data: null,
userLogin: false
};
const userAccountReducer = (state = initialState, action) => {
switch(action.type) {
case 'USER_ACCOUNT' :
return {
...state,
data : action.payload,
userLogin : action.status
};
case 'USER_LOGOUT' :
return {
data: null,
userLogin: false
}
default :
return state;
}
}
export default userAccountReducer;
|
/***************************************************************************
*cr
*cr (C) Copyright 2007 The Board of Trustees of the
*cr University of Illinois
*cr All Rights Reserved
*cr
***************************************************************************/
#ifndef __FILEH__
#define __FILEH__
void inputData(char* fName, int* len, int* depth, int* dim,int *nzcnt_len,int *pad,
float** h_data, int** h_indices, int** h_ptr,
int** h_perm, int** h_nzcnt);
void input_vec(char* fNanme, float *h_vec,int dim);
void outputData(char* fName, float *h_Ax_vector,int dim);
#endif
|
import unittest
from concurrent.futures import ThreadPoolExecutor
from unittest.mock import mock_open
from dirhunt import __version__
from dirhunt.tests._compat import Mock, patch
from dirhunt.crawler import Crawler
from dirhunt.crawler_url import CrawlerUrl
from dirhunt.processors import GenericProcessor
from dirhunt.tests.base import CrawlerTestBase
REPORT_DATA = {
"version": __version__,
"processed": [
{
"crawler_url": {
"depth": 3,
"exists": None,
"flags": [
"302",
"redirect"
],
"type": "directory",
"url": {
"address": "https://site.com/",
"domain": "site.com"
}
},
"line": "",
"processor_class": "ProcessRedirect",
"status_code": 302
},
],
"processing": [
"https://site.com/path/",
]
}
class TestCrawler(CrawlerTestBase, unittest.TestCase):
def test_print_results(self):
crawler = self.get_crawler()
crawler_url = CrawlerUrl(crawler, self.url)
crawler.results.put(GenericProcessor(None, crawler_url))
crawler.print_results()
@patch('dirhunt.crawler.json.dump')
@patch('builtins.open')
def test_create_report(self, _, mock_dump):
crawler = self.get_crawler()
crawler.results.put(GenericProcessor(None, CrawlerUrl(crawler, self.url)))
crawler.create_report(crawler.get_resume_file())
mock_dump.assert_called_once()
@patch('dirhunt.crawler.json.load', return_value=REPORT_DATA)
@patch('dirhunt.crawler.Crawler.echo', return_value=REPORT_DATA)
@patch('dirhunt.crawler.Crawler.add_url', return_value=REPORT_DATA)
@patch('builtins.open')
def test_resume(self, _, m1, m2, m3):
crawler = self.get_crawler()
crawler.resume(crawler.get_resume_file())
m3.assert_called_once()
m2.assert_called_once()
m1.assert_called_once_with(REPORT_DATA['processing'][0], lock=False)
def test_print_results_limit(self):
crawler = self.get_crawler(limit=1)
crawler.current_processed_count = 1
crawler_url = CrawlerUrl(crawler, self.url)
crawler.results.put(GenericProcessor(None, crawler_url))
crawler.print_results()
self.assertTrue(crawler.closing)
def test_add_url(self):
crawler = self.get_crawler()
crawler.domains.add('domain.com')
crawler_url = CrawlerUrl(crawler, self.url)
with patch.object(ThreadPoolExecutor, 'submit') as mock_method:
crawler.add_url(crawler_url)
def test_add_init_urls(self):
crawler = self.get_crawler()
with patch.object(Crawler, 'add_url') as m:
crawler.add_init_urls(self.url)
m.assert_called_once()
self.assertEqual(crawler.domains, {'domain.com'})
def test_erase_tty(self):
crawler = self.get_crawler()
crawler.std = Mock(**{'isatty.return_value': True})
crawler.erase()
@patch('dirhunt.crawler.Crawler.create_report')
@patch('dirhunt.crawler.unregister')
def test_close(self, m1, m2):
crawler = self.get_crawler()
crawler.close(True)
m2.assert_called_once()
m1.assert_called_once()
|
# -*- coding: utf-8 -*-
'''
Error generator to enable integration testing of salt runner error handling
'''
# Import python libs
# Import salt libs
import salt.utils.error
def error(name=None, message=''):
'''
If name is None Then return empty dict
Otherwise raise an exception with __name__ from name, message from message
CLI Example:
.. code-block:: bash
salt-run error
salt-run error.error name="Exception" message="This is an error."
'''
ret = {}
if name is not None:
salt.utils.error.raise_error(name=name, message=message)
return ret
|
// @flow
type O = {};
type F = () => void;
type T = [boolean, ?number];
type ObjMapAlias = $ObjMap<O, F>;
type ObjMapAliasLiteral = $ObjMap<{}, () => number>;
type ObjMapiAlias = $ObjMapi<O, F>;
type TupleMapAliasTuple = $ObjMapi<T, F>;
type TupleMapAliasObj = $ObjMapi<O, F>;
let ObjMapTypeAnnotation: $ObjMap<O, F>;
|
# -*- encoding: utf-8 -*-
import functools as ft
from inspect import getdoc
import re
import h2o
from h2o.automl._base import H2OAutoMLBaseMixin
from h2o.automl._h2o_automl_output import H2OAutoMLOutput
from h2o.base import Keyed
from h2o.estimators import H2OEstimator
from h2o.exceptions import H2OResponseError, H2OValueError
from h2o.frame import H2OFrame
from h2o.job import H2OJob
from h2o.utils.shared_utils import check_id
from h2o.utils.typechecks import assert_is_type, is_type, numeric
_params_doc_ = dict() # holds the doc per param extracted from H2OAutoML constructor
def _extract_params_doc(docstr):
pat = re.compile(r"^:param (\w+ )?(?P<name>\w+):\s?(?P<doc>.*)") # match param doc-start in Sphinx format ":param type name: description"
lines = docstr.splitlines()
param, doc = None, None
for l in lines:
m = pat.match(l)
if m:
if param:
_params_doc_[param] = "\n".join(doc)
param = m.group('name')
doc = [m.group('doc')]
elif param:
doc.append(l)
def _aml_property(param_path, name=None, types=None, validate_fn=None, freezable=False, set_input=True):
path = param_path.split('.')
name = name or path[-1]
def attr_name(self, attr):
return ("_"+self.__class__.__name__+attr) if attr.startswith('__') and not attr.endswith('__') else attr
def _fget(self):
_input = getattr(self, attr_name(self, '__input'))
return _input.get(name)
def _fset(self, value):
if freezable and getattr(self, attr_name(self, '__frozen'), False):
raise H2OValueError("Param ``%s`` can not be modified after the first call to ``train``." % name, name)
if types is not None:
assert_is_type(value, *types)
input_val = value
if validate_fn:
value = validate_fn(self, value)
_input = getattr(self, attr_name(self, '__input'))
_input[name] = input_val if set_input else value
group = getattr(self, attr_name(self, path[0]))
if group is None:
group = {}
setattr(self, attr_name(self, path[0]), group)
obj = group
for t in path[1:-1]:
tmp = obj.get(t)
if tmp is None:
tmp = obj[t] = {}
obj = tmp
obj[path[-1]] = value
return property(fget=_fget, fset=_fset, doc=_params_doc_.get(name, None))
class H2OAutoML(H2OAutoMLBaseMixin, Keyed):
"""
Automatic Machine Learning
The Automatic Machine Learning (AutoML) function automates the supervised machine learning model training process.
The current version of AutoML trains and cross-validates the following algorithms (in the following order):
three pre-specified XGBoost GBM (Gradient Boosting Machine) models,
a fixed grid of GLMs,
a default Random Forest (DRF),
five pre-specified H2O GBMs,
a near-default Deep Neural Net,
an Extremely Randomized Forest (XRT),
a random grid of XGBoost GBMs,
a random grid of H2O GBMs,
and a random grid of Deep Neural Nets.
In some cases, there will not be enough time to complete all the algorithms, so some may be missing from the
leaderboard. AutoML then trains two Stacked Ensemble models, one of all the models, and one of only the best
models of each kind.
:examples:
>>> import h2o
>>> from h2o.automl import H2OAutoML
>>> h2o.init()
>>> # Import a sample binary outcome train/test set into H2O
>>> train = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
>>> test = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")
>>> # Identify the response and set of predictors
>>> y = "response"
>>> x = list(train.columns) #if x is defined as all columns except the response, then x is not required
>>> x.remove(y)
>>> # For binary classification, response should be a factor
>>> train[y] = train[y].asfactor()
>>> test[y] = test[y].asfactor()
>>> # Run AutoML for 30 seconds
>>> aml = H2OAutoML(max_runtime_secs = 30)
>>> aml.train(x = x, y = y, training_frame = train)
>>> # Print Leaderboard (ranked by xval metrics)
>>> aml.leaderboard
>>> # (Optional) Evaluate performance on a test set
>>> perf = aml.leader.model_performance(test)
>>> perf.auc()
"""
def __init__(self,
nfolds=5,
balance_classes=False,
class_sampling_factors=None,
max_after_balance_size=5.0,
max_runtime_secs=None,
max_runtime_secs_per_model=None,
max_models=None,
stopping_metric="AUTO",
stopping_tolerance=None,
stopping_rounds=3,
seed=None,
project_name=None,
exclude_algos=None,
include_algos=None,
exploitation_ratio=0,
modeling_plan=None,
preprocessing=None,
monotone_constraints=None,
keep_cross_validation_predictions=False,
keep_cross_validation_models=False,
keep_cross_validation_fold_assignment=False,
sort_metric="AUTO",
export_checkpoints_dir=None,
verbosity="warn",
**kwargs):
"""
Create a new H2OAutoML instance.
:param int nfolds: Number of folds for k-fold cross-validation.
Use ``0`` to disable cross-validation; this will also disable Stacked Ensemble (thus decreasing the overall model performance).
Defaults to ``5``.
:param bool balance_classes: Balance training data class counts via over/under-sampling (for imbalanced data).
Defaults to ``False``.
:param class_sampling_factors: Desired over/under-sampling ratios per class (in lexicographic order).
If not specified, sampling factors will be automatically computed to obtain class balance during training.
:param float max_after_balance_size: Maximum relative size of the training data after balancing class counts (can be less than 1.0).
Requires ``balance_classes``.
Defaults to ``5.0``.
:param int max_runtime_secs: Specify the maximum time that the AutoML process will run for, prior to training the final Stacked Ensemble models.
If neither ``max_runtime_secs`` nor ``max_models`` are specified by the user, then ``max_runtime_secs``.
Defaults to 3600 seconds (1 hour).
:param int max_runtime_secs_per_model: Controls the max time the AutoML run will dedicate to each individual model.
Defaults to ``0`` (disabled: no time limit).
:param int max_models: Specify the maximum number of models to build in an AutoML run, excluding the Stacked Ensemble models.
Defaults to ``0`` (disabled: no limitation).
:param str stopping_metric: Specifies the metric to use for early stopping.
The available options are:
``"AUTO"`` (This defaults to ``"logloss"`` for classification, ``"deviance"`` for regression),
``"deviance"``, ``"logloss"``, ``"mse"``, ``"rmse"``, ``"mae"``, ``"rmsle"``, ``"auc"``, ``aucpr``, ``"lift_top_group"``,
``"misclassification"``, ``"mean_per_class_error"``, ``"r2"``.
Defaults to ``"AUTO"``.
:param float stopping_tolerance: Specify the relative tolerance for the metric-based stopping to stop the AutoML run if the improvement is less than this value.
Defaults to ``0.001`` if the dataset is at least 1 million rows;
otherwise it defaults to a value determined by the size of the dataset and the non-NA-rate, in which case the value is computed as 1/sqrt(nrows * non-NA-rate).
:param int stopping_rounds: Stop training new models in the AutoML run when the option selected for
stopping_metric doesn't improve for the specified number of models, based on a simple moving average.
To disable this feature, set it to ``0``.
Defaults to ``3`` and must be an non-negative integer.
:param int seed: Set a seed for reproducibility.
AutoML can only guarantee reproducibility if ``max_models`` or early stopping is used because ``max_runtime_secs`` is resource limited,
meaning that if the resources are not the same between runs, AutoML may be able to train more models on one run vs another.
Defaults to ``None``.
:param str project_name: Character string to identify an AutoML project.
Defaults to ``None``, which means a project name will be auto-generated based on the training frame ID.
More models can be trained on an existing AutoML project by specifying the same project name in multiple calls to the AutoML function
(as long as the same training frame, or a sample, is used in subsequent runs).
:param exclude_algos: List the algorithms to skip during the model-building phase.
The full list of options is:
``"DRF"`` (Random Forest and Extremely-Randomized Trees),
``"GLM"``,
``"XGBoost"``,
``"GBM"``,
``"DeepLearning"``,
``"StackedEnsemble"``.
Defaults to ``None``, which means that all appropriate H2O algorithms will be used, if the search stopping criteria allow. Optional.
Usage example: ``exclude_algos = ["GLM", "DeepLearning", "DRF"]``.
:param include_algos: List the algorithms to restrict to during the model-building phase.
This can't be used in combination with `exclude_algos` param.
Defaults to ``None``, which means that all appropriate H2O algorithms will be used, if the search stopping criteria allow. Optional.
:param exploitation_ratio: The budget ratio (between 0 and 1) dedicated to the exploitation (vs exploration) phase.
By default, the exploitation phase is disabled (exploitation_ratio=0) as this is still experimental;
to activate it, it is recommended to try a ratio around 0.1.
Note that the current exploitation phase only tries to fine-tune the best XGBoost and the best GBM found during exploration.
:param modeling_plan: List of modeling steps to be used by the AutoML engine (they may not all get executed, depending on other constraints).
Defaults to None (Expert usage only).
:param preprocessing: List of preprocessing steps to run. Only 'target_encoding' is currently supported.
:param monotone_constraints: Dict representing monotonic constraints.
Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint.
:param keep_cross_validation_predictions: Whether to keep the predictions of the cross-validation predictions.
This needs to be set to ``True`` if running the same AutoML object for repeated runs because CV predictions are required to build
additional Stacked Ensemble models in AutoML.
Defaults to ``False``.
:param keep_cross_validation_models: Whether to keep the cross-validated models.
Keeping cross-validation models may consume significantly more memory in the H2O cluster.
Defaults to ``False``.
:param keep_cross_validation_fold_assignment: Whether to keep fold assignments in the models.
Deleting them will save memory in the H2O cluster.
Defaults to ``False``.
:param sort_metric: Metric to sort the leaderboard by.
For binomial classification choose between ``"auc"``, ``"aucpr"``, ``"logloss"``, ``"mean_per_class_error"``, ``"rmse"``, ``"mse"``.
For multinomial classification choose between ``"mean_per_class_error"``, ``"logloss"``, ``"rmse"``, ``"mse"``.
For regression choose between ``"deviance"``, ``"rmse"``, ``"mse"``, ``"mae"``, ``"rmlse"``.
Defaults to ``"AUTO"`` (This translates to ``"auc"`` for binomial classification, ``"mean_per_class_error"`` for multinomial classification, ``"deviance"`` for regression).
:param export_checkpoints_dir: Path to a directory where every model will be stored in binary form.
:param verbosity: Verbosity of the backend messages printed during training.
Available options are None (live log disabled), ``"debug"``, ``"info"`` or ``"warn"``.
Defaults to ``"warn"``.
"""
# early validate kwargs, extracting hidden parameters:
algo_parameters = {}
for k in kwargs:
if k == 'algo_parameters':
algo_parameters = kwargs[k] or {}
else:
raise TypeError("H2OAutoML got an unexpected keyword argument '%s'" % k)
# Check if H2O jar contains AutoML
try:
h2o.api("GET /3/Metadata/schemas/AutoMLV99")
except h2o.exceptions.H2OResponseError as e:
print(e)
print("*******************************************************************\n" \
"*Please verify that your H2O jar has the proper AutoML extensions.*\n" \
"*******************************************************************\n" \
"\nVerbose Error Message:")
self._job = None
self._leader_id = None
self._leaderboard = None
self._verbosity = verbosity
self._event_log = None
self._training_info = None
self._state_json = None
self._build_resp = None # contains all the actual parameters used on backend
self.__frozen = False
self.__input = dict() # contains all the input params as entered by the user
# Make bare minimum params containers
self.build_control = dict()
self.build_models = dict()
self.input_spec = dict()
self.project_name = project_name
self.nfolds = nfolds
self.balance_classes = balance_classes
self.class_sampling_factors = class_sampling_factors
self.max_after_balance_size = max_after_balance_size
self.keep_cross_validation_models = keep_cross_validation_models
self.keep_cross_validation_fold_assignment = keep_cross_validation_fold_assignment
self.keep_cross_validation_predictions = keep_cross_validation_predictions
self.export_checkpoints_dir = export_checkpoints_dir
self.max_runtime_secs = max_runtime_secs
self.max_runtime_secs_per_model = max_runtime_secs_per_model
self.max_models = max_models
self.stopping_metric = stopping_metric
self.stopping_tolerance = stopping_tolerance
self.stopping_rounds = stopping_rounds
self.seed = seed
self.exclude_algos = exclude_algos
self.include_algos = include_algos
self.exploitation_ratio = exploitation_ratio
self.modeling_plan = modeling_plan
self.preprocessing = preprocessing
if monotone_constraints is not None:
algo_parameters['monotone_constraints'] = monotone_constraints
self._algo_parameters = algo_parameters
self.sort_metric = sort_metric
#---------------------------------------------------------------------------
# AutoML params
#---------------------------------------------------------------------------
def __validate_not_set(self, val, prop=None, message=None):
assert val is None or getattr(self, prop, None) is None, message
return val
def __validate_project_name(self, project_name):
check_id(project_name, "H2OAutoML")
return project_name
def __validate_nfolds(self, nfolds):
assert nfolds == 0 or nfolds > 1, "nfolds set to %s; use nfolds >=2 if you want cross-validated metrics and Stacked Ensembles or use nfolds = 0 to disable." % nfolds
return nfolds
def __validate_modeling_plan(self, modeling_plan):
if modeling_plan is None:
return None
supported_aliases = ['all', 'defaults', 'grids']
def assert_is_step_def(sd):
assert 'name' in sd, "each definition must have a 'name' key"
assert 0 < len(sd) < 3, "each definition must have only 1 or 2 keys: name, name+alias or name+steps"
assert len(sd) == 1 or 'alias' in sd or 'steps' in sd, "steps definitions support only the following keys: name, alias, steps"
assert 'alias' not in sd or sd['alias'] in supported_aliases, "alias must be one of %s" % supported_aliases
assert 'steps' not in sd or (is_type(sd['steps'], list) and all(assert_is_step(s) for s in sd['steps']))
def assert_is_step(s):
assert is_type(s, dict), "each step must be a dict with an 'id' key and an optional 'weight' key"
assert 'id' in s, "each step must have an 'id' key"
assert len(s) == 1 or ('weight' in s and is_type(s['weight'], int)), "weight must be an integer"
return True
plan = []
for step_def in modeling_plan:
assert_is_type(step_def, dict, tuple, str)
if is_type(step_def, dict):
assert_is_step_def(step_def)
plan.append(step_def)
elif is_type(step_def, str):
plan.append(dict(name=step_def))
else:
assert 0 < len(step_def) < 3
assert_is_type(step_def[0], str)
name = step_def[0]
if len(step_def) == 1:
plan.append(dict(name=name))
else:
assert_is_type(step_def[1], str, list)
ids = step_def[1]
if is_type(ids, str):
assert_is_type(ids, *supported_aliases)
plan.append(dict(name=name, alias=ids))
else:
plan.append(dict(name=name, steps=[dict(id=i) for i in ids]))
return plan
def __validate_preprocessing(self, preprocessing):
if preprocessing is None:
return
assert all(p in ['target_encoding'] for p in preprocessing)
return [dict(type=p.replace("_", "")) for p in preprocessing]
def __validate_monotone_constraints(self, monotone_constraints):
if monotone_constraints is None:
self._algo_parameters.pop('monotone_constraints', None)
else:
self._algo_parameters['monotone_constraints'] = monotone_constraints
return self.__validate_algo_parameters(self._algo_parameters)
def __validate_algo_parameters(self, algo_parameters):
if algo_parameters is None:
return None
algo_parameters_json = []
for k, v in algo_parameters.items():
scope, __, name = k.partition('__')
if len(name) == 0:
name, scope = scope, 'any'
value = [dict(key=k, value=v) for k, v in v.items()] if isinstance(v, dict) else v # we can't use stringify_dict here as this will be converted into a JSON string
algo_parameters_json.append(dict(scope=scope, name=name, value=value))
return algo_parameters_json
def __validate_frame(self, fr, name=None, required=False):
return H2OFrame._validate(fr, name, required=required)
_extract_params_doc(getdoc(__init__))
project_name = _aml_property('build_control.project_name', types=(None, str), freezable=True,
validate_fn=__validate_project_name)
nfolds = _aml_property('build_control.nfolds', types=(int,), freezable=True,
validate_fn=__validate_nfolds)
balance_classes = _aml_property('build_control.balance_classes', types=(bool,), freezable=True)
class_sampling_factors = _aml_property('build_control.class_sampling_factors', types=(None, [numeric]), freezable=True)
max_after_balance_size = _aml_property('build_control.max_after_balance_size', types=(None, numeric), freezable=True)
keep_cross_validation_models = _aml_property('build_control.keep_cross_validation_models', types=(bool,), freezable=True)
keep_cross_validation_fold_assignment = _aml_property('build_control.keep_cross_validation_fold_assignment', types=(bool,), freezable=True)
keep_cross_validation_predictions = _aml_property('build_control.keep_cross_validation_predictions', types=(bool,), freezable=True)
export_checkpoints_dir = _aml_property('build_control.export_checkpoints_dir', types=(None, str), freezable=True)
max_runtime_secs = _aml_property('build_control.stopping_criteria.max_runtime_secs', types=(None, int), freezable=True)
max_runtime_secs_per_model = _aml_property('build_control.stopping_criteria.max_runtime_secs_per_model', types=(None, int), freezable=True)
max_models = _aml_property('build_control.stopping_criteria.max_models', types=(None, int), freezable=True)
stopping_metric = _aml_property('build_control.stopping_criteria.stopping_metric', types=(None, str), freezable=True)
stopping_tolerance = _aml_property('build_control.stopping_criteria.stopping_tolerance', types=(None, numeric), freezable=True)
stopping_rounds = _aml_property('build_control.stopping_criteria.stopping_rounds', types=(None, int), freezable=True)
seed = _aml_property('build_control.stopping_criteria.seed', types=(None, int), freezable=True)
exclude_algos = _aml_property('build_models.exclude_algos', types=(None, [str]), freezable=True,
validate_fn=ft.partial(__validate_not_set, prop='include_algos',
message="Use either `exclude_algos` or `include_algos`, not both."))
include_algos = _aml_property('build_models.include_algos', types=(None, [str]), freezable=True,
validate_fn=ft.partial(__validate_not_set, prop='exclude_algos',
message="Use either `exclude_algos` or `include_algos`, not both."))
exploitation_ratio = _aml_property('build_models.exploitation_ratio', types=(None, numeric), freezable=True)
modeling_plan = _aml_property('build_models.modeling_plan', types=(None, list), freezable=True,
validate_fn=__validate_modeling_plan)
preprocessing = _aml_property('build_models.preprocessing', types=(None, [str]), freezable=True,
validate_fn=__validate_preprocessing)
monotone_constraints = _aml_property('build_models.algo_parameters', name='monotone_constraints', types=(None, dict), freezable=True,
validate_fn=__validate_monotone_constraints)
_algo_parameters = _aml_property('build_models.algo_parameters', types=(None, dict), freezable=True,
validate_fn=__validate_algo_parameters)
sort_metric = _aml_property('input_spec.sort_metric', types=(None, str))
fold_column = _aml_property('input_spec.fold_column', types=(None, int, str))
weights_column = _aml_property('input_spec.weights_column', types=(None, int, str))
training_frame = _aml_property('input_spec.training_frame', set_input=False,
validate_fn=ft.partial(__validate_frame, name='training_frame', required=True))
validation_frame = _aml_property('input_spec.validation_frame', set_input=False,
validate_fn=ft.partial(__validate_frame, name='validation_frame'))
leaderboard_frame = _aml_property('input_spec.leaderboard_frame', set_input=False,
validate_fn=ft.partial(__validate_frame, name='leaderboard_frame'))
blending_frame = _aml_property('input_spec.blending_frame', set_input=False,
validate_fn=ft.partial(__validate_frame, name='blending_frame'))
response_column = _aml_property('input_spec.response_column', types=(str,))
#---------------------------------------------------------------------------
# Basic properties
#---------------------------------------------------------------------------
@property
def key(self):
return self._job.dest_key if self._job else self.project_name
@property
def leader(self):
return None if self._leader_id is None else h2o.get_model(self._leader_id)
@property
def leaderboard(self):
return H2OFrame([]) if self._leaderboard is None else self._leaderboard
@property
def event_log(self):
return H2OFrame([]) if self._event_log is None else self._event_log
@property
def training_info(self):
return dict() if self._training_info is None else self._training_info
@property
def modeling_steps(self):
"""
expose the modeling steps effectively used by the AutoML run.
This executed plan can be directly reinjected as the `modeling_plan` property of a new AutoML instance
to improve reproducibility across AutoML versions.
:return: a list of dictionaries representing the effective modeling plan.
"""
# removing alias key to be able to reinject result to a new AutoML instance
return list(map(lambda sdef: dict(name=sdef['name'], steps=sdef['steps']), self._state_json['modeling_steps']))
#---------------------------------------------------------------------------
# Training AutoML
#---------------------------------------------------------------------------
def train(self, x=None, y=None, training_frame=None, fold_column=None,
weights_column=None, validation_frame=None, leaderboard_frame=None, blending_frame=None):
"""
Begins an AutoML task, a background task that automatically builds a number of models
with various algorithms and tracks their performance in a leaderboard. At any point
in the process you may use H2O's performance or prediction functions on the resulting
models.
:param x: A list of column names or indices indicating the predictor columns.
:param y: An index or a column name indicating the response column.
:param fold_column: The name or index of the column in training_frame that holds per-row fold
assignments.
:param weights_column: The name or index of the column in training_frame that holds per-row weights.
:param training_frame: The H2OFrame having the columns indicated by x and y (as well as any
additional columns specified by fold_column or weights_column).
:param validation_frame: H2OFrame with validation data. This argument is ignored unless the user sets
nfolds = 0. If cross-validation is turned off, then a validation frame can be specified and used
for early stopping of individual models and early stopping of the grid searches. By default and
when nfolds > 1, cross-validation metrics will be used for early stopping and thus validation_frame will be ignored.
:param leaderboard_frame: H2OFrame with test data for scoring the leaderboard. This is optional and
if this is set to None (the default), then cross-validation metrics will be used to generate the leaderboard
rankings instead.
:param blending_frame: H2OFrame used to train the the metalearning algorithm in Stacked Ensembles (instead of relying on cross-validated predicted values).
This is optional, but when provided, it is also recommended to disable cross validation
by setting `nfolds=0` and to provide a leaderboard frame for scoring purposes.
:returns: An H2OAutoML object.
:examples:
>>> # Set up an H2OAutoML object
>>> aml = H2OAutoML(max_runtime_secs=30)
>>> # Launch an AutoML run
>>> aml.train(y=y, training_frame=train)
"""
# Minimal required arguments are training_frame and y (response)
self.training_frame = training_frame
ncols = self.training_frame.ncols
names = self.training_frame.names
if y is None and self.response_column is None:
raise H2OValueError('The response column (y) is not set; please set it to the name of the column that you are trying to predict in your data.')
elif y is not None:
assert_is_type(y, int, str)
if is_type(y, int):
if not (-ncols <= y < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % y)
y = names[y]
else:
if y not in names:
raise H2OValueError("Column %s does not exist in the training frame" % y)
self.response_column = y
self.fold_column = fold_column
self.weights_column = weights_column
self.validation_frame = validation_frame
self.leaderboard_frame = leaderboard_frame
self.blending_frame = blending_frame
if x is not None:
assert_is_type(x, list)
xset = set()
if is_type(x, int, str): x = [x]
for xi in x:
if is_type(xi, int):
if not (-ncols <= xi < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % xi)
xset.add(names[xi])
else:
if xi not in names:
raise H2OValueError("Column %s not in the training frame" % xi)
xset.add(xi)
ignored_columns = set(names) - xset
for col in [y, fold_column, weights_column]:
if col is not None and col in ignored_columns:
ignored_columns.remove(col)
if ignored_columns is not None:
self.input_spec['ignored_columns'] = list(ignored_columns)
def clean_params(params):
return ({k: clean_params(v) for k, v in params.items() if v is not None} if isinstance(params, dict)
else H2OEstimator._keyify(params))
automl_build_params = clean_params(dict(
build_control=self.build_control,
build_models=self.build_models,
input_spec=self.input_spec,
))
resp = self._build_resp = h2o.api('POST /99/AutoMLBuilder', json=automl_build_params)
if 'job' not in resp:
raise H2OResponseError("Backend failed to build the AutoML job: {}".format(resp))
if not self.project_name:
self.project_name = resp['build_control']['project_name']
self.__frozen = True
self._job = H2OJob(resp['job'], "AutoML")
poll_updates = ft.partial(self._poll_training_updates, verbosity=self._verbosity, state={})
try:
self._job.poll(poll_updates=poll_updates)
finally:
poll_updates(self._job, 1)
self._fetch()
#---------------------------------------------------------------------------
# Predict with AutoML
#---------------------------------------------------------------------------
def predict(self, test_data):
leader = self.leader
if leader is None:
self._fetch()
leader = self.leader
if leader is not None:
return leader.predict(test_data)
print("No model built yet...")
#-------------------------------------------------------------------------------------------------------------------
# Overrides
#-------------------------------------------------------------------------------------------------------------------
def detach(self):
self.__frozen = False
self.project_name = None
h2o.remove(self.leaderboard)
h2o.remove(self.event_log)
#-------------------------------------------------------------------------------------------------------------------
# Private
#-------------------------------------------------------------------------------------------------------------------
def _fetch(self):
state = H2OAutoML._fetch_state(self.key)
self._leader_id = state['leader_id']
self._leaderboard = state['leaderboard']
self._event_log = el = state['event_log']
self._training_info = { r[0]: r[1]
for r in el[el['name'] != '', ['name', 'value']]
.as_data_frame(use_pandas=False, header=False)
}
self._state_json = state['json']
return self._leader_id is not None
def _poll_training_updates(self, job, bar_progress=0, verbosity=None, state=None):
"""
the callback function used to print verbose info when polling AutoML job.
"""
levels = ['Debug', 'Info', 'Warn']
if verbosity is None or verbosity.capitalize() not in levels:
return
levels = levels[levels.index(verbosity.capitalize()):]
try:
if job.progress > state.get('last_job_progress', 0):
# print("\nbar_progress={}, job_progress={}".format(bar_progress, job.progress))
events = H2OAutoML._fetch_state(job.dest_key, properties=['event_log'])['event_log']
events = events[events['level'].isin(levels), :]
last_nrows = state.get('last_events_nrows', 0)
if events.nrows > last_nrows:
fr = events[last_nrows:, ['timestamp', 'message']].as_data_frame(use_pandas=False, header=False)
print('')
for r in fr:
print("{}: {}".format(r[0], r[1]))
print('')
state['last_events_nrows'] = events.nrows
state['last_job_progress'] = job.progress
except Exception as e:
print("Failed polling AutoML progress log: {}".format(e))
@staticmethod
def _fetch_leaderboard(aml_id, extensions=None):
assert_is_type(extensions, None, str, [str])
extensions = ([] if extensions is None
else [extensions] if is_type(extensions, str)
else extensions)
resp = h2o.api("GET /99/Leaderboards/%s" % aml_id, data=dict(extensions=extensions))
dest_key = resp['project_name'].split('@', 1)[0]+"_custom_leaderboard"
lb = H2OAutoML._fetch_table(resp['table'], key=dest_key, progress_bar=False)
return h2o.assign(lb[1:], dest_key)
@staticmethod
def _fetch_table(table, key=None, progress_bar=True):
try:
# Intentionally mask the progress bar here since showing multiple progress bars is confusing to users.
# If any failure happens, revert back to user's original setting for progress and display the error message.
ori_progress_state = H2OJob.__PROGRESS_BAR__
H2OJob.__PROGRESS_BAR__ = progress_bar
# Parse leaderboard H2OTwoDimTable & return as an H2OFrame
return h2o.H2OFrame(table.cell_values, destination_frame=key, column_names=table.col_header, column_types=table.col_types)
finally:
H2OJob.__PROGRESS_BAR__ = ori_progress_state
@staticmethod
def _fetch_state(aml_id, properties=None):
state_json = h2o.api("GET /99/AutoML/%s" % aml_id)
project_name = state_json["project_name"]
if project_name is None:
raise H2OValueError("No AutoML instance with id {}.".format(aml_id))
leaderboard_list = [key["name"] for key in state_json['leaderboard']['models']]
leader_id = leaderboard_list[0] if (leaderboard_list is not None and len(leaderboard_list) > 0) else None
should_fetch = lambda prop: properties is None or prop in properties
leader = None
if should_fetch('leader'):
leader = h2o.get_model(leader_id) if leader_id is not None else None
leaderboard = None
if should_fetch('leaderboard'):
leaderboard = H2OAutoML._fetch_table(state_json['leaderboard_table'], key=project_name+"_leaderboard", progress_bar=False)
leaderboard = h2o.assign(leaderboard[1:], project_name+"_leaderboard") # removing index and reassign id to ensure persistence on backend
event_log = None
if should_fetch('event_log'):
event_log = H2OAutoML._fetch_table(state_json['event_log_table'], key=project_name+"_eventlog", progress_bar=False)
event_log = h2o.assign(event_log[1:], project_name+"_eventlog") # removing index and reassign id to ensure persistence on backend
return dict(
project_name=project_name,
json=state_json,
leader_id=leader_id,
leader=leader,
leaderboard=leaderboard,
event_log=event_log,
)
def get_automl(project_name):
"""
Retrieve information about an AutoML instance.
:param str project_name: A string indicating the project_name of the automl instance to retrieve.
:returns: A dictionary containing the project_name, leader model, leaderboard, event_log.
"""
state = H2OAutoML._fetch_state(project_name)
return H2OAutoMLOutput(state)
def get_leaderboard(aml, extra_columns=None):
"""
Retrieve the leaderboard from the AutoML instance.
Contrary to the default leaderboard attached to the automl instance, this one can return columns other than the metrics.
:param H2OAutoML aml: the instance for which to return the leaderboard.
:param extra_columns: a string or a list of string specifying which optional columns should be added to the leaderboard. Defaults to None.
Currently supported extensions are:
- 'ALL': adds all columns below.
- 'training_time_ms': column providing the training time of each model in milliseconds (doesn't include the training of cross validation models).
- 'predict_time_per_row_ms`: column providing the average prediction time by the model for a single row.
- 'algo': column providing the algorithm name for each model.
:return: An H2OFrame representing the leaderboard.
:examples:
>>> aml = H2OAutoML(max_runtime_secs=30)
>>> aml.train(y=y, training_frame=train)
>>> lb_all = h2o.automl.get_leaderboard(aml, 'ALL')
>>> lb_custom = h2o.automl.get_leaderboard(aml, ['predict_time_per_row_ms', 'training_time_ms'])
>>> lb_custom_sorted = lb_custom.sort(by='predict_time_per_row_ms')
"""
assert_is_type(aml, H2OAutoML, H2OAutoMLOutput)
return H2OAutoML._fetch_leaderboard(aml.key, extra_columns)
|
var express = require('express');
var path = require('path');
var favicon = require('serve-favicon');
var logger = require('morgan');
var cookieParser = require('cookie-parser');
var bodyParser = require('body-parser');
var compression = require('compression');
var routes = require('./routes/index');
var users = require('./routes/users');
var app = express();
var PORT = process.env.PORT || 3000;
// view engine setup
app.set('views', path.join(__dirname, 'views'));
app.set('view engine', 'ejs');
var options = {
'extended': 'true'
};
app.use(favicon(__dirname + '/public/favicon.ico'));
app.use(logger('dev'));
app.use(bodyParser.json());
app.use(bodyParser.urlencoded(options));
app.use(compression());
app.use(cookieParser());
app.use(express.static(path.join(__dirname, 'public')));
app.use('/javascripts', express.static(__dirname + 'javascripts'));
app.use('/bower_components', express.static(__dirname + '/bower_components'));
app.use('/', routes);
app.use('/users', users);
/// catch 404 and forward to error handler
app.use(function(req, res, next) {
var err = new Error('Not Found');
err.status = 404;
next(err);
});
/// error handlers
// development error handler
// will print stacktrace
if (app.get('env') === 'development') {
app.use(function(err, req, res, next) {
res.status(err.status || 500);
res.render('error', {
message: err.message,
error: err
});
});
}
// production error handler
// no stacktraces leaked to user
app.use(function(err, req, res, next) {
res.status(err.status || 500);
res.render('error', {
message: err.message,
error: {}
});
});
module.exports = app;
var server = app.listen(PORT, function() {
console.log('Express server listening on localhost:' + PORT);
});
process.on('SIGINT', function onSigint() {
console.info('Got SIGINT (aka ctrl-c in docker). Graceful shutdown ', new Date().toISOString());
shutdown();
});
// quit properly on docker stop
process.on('SIGTERM', function onSigterm() {
console.info('Got SIGTERM (docker container stop). Graceful shutdown ', new Date().toISOString());
shutdown();
})
// shut down server
function shutdown() {
server.close(function onServerClosed(err) {
if (err) {
console.error(err);
process.exitCode = 1;
}
process.exit();
})
}
|
#ifndef _MMeshIntersector
#define _MMeshIntersector
//-
// ===========================================================================
// Copyright 2018 Autodesk, Inc. All rights reserved.
//
// Use of this software is subject to the terms of the Autodesk license
// agreement provided at the time of installation or download, or which
// otherwise accompanies this software in either electronic or hard copy form.
// ===========================================================================
//+
//
// CLASS: MMeshIntersector
//
// ****************************************************************************
#if defined __cplusplus
// ****************************************************************************
// INCLUDED HEADER FILES
#include <float.h>
#include <maya/MStatus.h>
#include <maya/MPoint.h>
#include <maya/MFloatPoint.h>
#include <maya/MFloatVector.h>
#include <maya/MMatrix.h>
OPENMAYA_MAJOR_NAMESPACE_OPEN
// ****************************************************************************
// CLASS DECLARATION (MMeshIntersector)
//! \ingroup OpenMaya
//! \brief Mesh intersector.
/*!
The MMeshIntersector class contains methods for efficiently finding
the closest point on a mesh. An octree algorithm is used to
find the closest point.
The create() method builds the internal data required for the algorithm.
As a result, calls to it should be minimized as it is a heavy operation.
This class allows multiple threads to evaluate closest points
simultaneously as the method getClosestPoint is threadsafe.
*/
class OPENMAYA_EXPORT MMeshIntersector
{
public:
MMeshIntersector(void);
virtual ~MMeshIntersector(void);
MStatus create( MObject &meshObject, const MMatrix& matrix = MMatrix::identity );
bool isCreated(void) const;
MStatus getClosestPoint( const MPoint& point, MPointOnMesh& meshPoint, double maxDistance = DBL_MAX ) const;
static const char* className();
protected:
// No protected members
private:
void *instance;
};
// Mesh point information. (OpenMaya) (OpenMaya.py)
//! \ingroup OpenMaya
//! \brief Mesh intersector result.
/*!
This class is used to return information about a point on a mesh: 3D
position, normal, barycentric coordinates, etc. Note that this can be a
point anywhere on the surface of the mesh, not just at vertices.
*/
class OPENMAYA_EXPORT MPointOnMesh
{
public:
MPointOnMesh();
MFloatPoint & getPoint();
MFloatVector & getNormal();
void getBarycentricCoords(float& u, float& v) const;
int faceIndex() const;
int triangleIndex() const;
protected:
// No protected members
private:
friend class MMeshIntersector;
MFloatPoint point;
MFloatVector normal;
int faceId;
int triangleId;
float baryU;
float baryV;
};
OPENMAYA_NAMESPACE_CLOSE
#endif /* __cplusplus */
#endif /* _MMeshIntersector */
|
__all__ = ["GetNextPackage", "PackageDto"]
from shipping.application.queries.package import GetNextPackage, PackageDto
|
/**
* @license
* Visual Blocks Language
*
* Copyright 2012 Google Inc.
* https://developers.google.com/blockly/
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/**
* @fileoverview Generating Python for list blocks.
* @author q.neutron@gmail.com (Quynh Neutron)
*/
'use strict';
goog.provide('Blockly.Python.lists');
goog.require('Blockly.Python');
Blockly.Python['lists_create_empty'] = function(block) {
// Create an empty list.
return ['[]', Blockly.Python.ORDER_ATOMIC];
};
Blockly.Python['lists_create_with'] = function(block) {
// Create a list with any number of elements of any type.
var code = new Array(block.itemCount_);
for (var n = 0; n < block.itemCount_; n++) {
code[n] = Blockly.Python.valueToCode(block, 'ADD' + n,
Blockly.Python.ORDER_NONE) || 'None';
}
code = '[' + code.join(', ') + ']';
return [code, Blockly.Python.ORDER_ATOMIC];
};
Blockly.Python['lists_repeat'] = function(block) {
// Create a list with one element repeated.
var argument0 = Blockly.Python.valueToCode(block, 'ITEM',
Blockly.Python.ORDER_NONE) || 'None';
var argument1 = Blockly.Python.valueToCode(block, 'NUM',
Blockly.Python.ORDER_MULTIPLICATIVE) || '0';
var code = '[' + argument0 + '] * ' + argument1;
return [code, Blockly.Python.ORDER_MULTIPLICATIVE];
};
Blockly.Python['lists_length'] = function(block) {
// List length.
var argument0 = Blockly.Python.valueToCode(block, 'VALUE',
Blockly.Python.ORDER_NONE) || '[]';
return ['len(' + argument0 + ')', Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python['lists_isEmpty'] = function(block) {
// Is the list empty?
var argument0 = Blockly.Python.valueToCode(block, 'VALUE',
Blockly.Python.ORDER_NONE) || '[]';
var code = 'not len(' + argument0 + ')';
return [code, Blockly.Python.ORDER_LOGICAL_NOT];
};
Blockly.Python['lists_indexOf'] = function(block) {
// Find an item in the list.
var argument0 = Blockly.Python.valueToCode(block, 'FIND',
Blockly.Python.ORDER_NONE) || '[]';
var argument1 = Blockly.Python.valueToCode(block, 'VALUE',
Blockly.Python.ORDER_MEMBER) || '\'\'';
var code;
if (block.getFieldValue('END') == 'FIRST') {
var functionName = Blockly.Python.provideFunction_(
'first_index',
['def ' + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + '(myList, elem):',
' try: theIndex = myList.index(elem) + 1',
' except: theIndex = 0',
' return theIndex']);
code = functionName + '(' + argument1 + ', ' + argument0 + ')';
return [code, Blockly.Python.ORDER_FUNCTION_CALL];
} else {
var functionName = Blockly.Python.provideFunction_(
'last_index',
['def ' + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + '(myList, elem):',
' try: theIndex = len(myList) - myList[::-1].index(elem)',
' except: theIndex = 0',
' return theIndex']);
code = functionName + '(' + argument1 + ', ' + argument0 + ')';
return [code, Blockly.Python.ORDER_FUNCTION_CALL];
}
};
Blockly.Python['lists_getIndex'] = function(block) {
// Get element at index.
// Note: Until January 2013 this block did not have MODE or WHERE inputs.
var mode = block.getFieldValue('MODE') || 'GET';
var where = block.getFieldValue('WHERE') || 'FROM_START';
var at = Blockly.Python.valueToCode(block, 'AT',
Blockly.Python.ORDER_UNARY_SIGN) || '1';
var list = Blockly.Python.valueToCode(block, 'VALUE',
Blockly.Python.ORDER_MEMBER) || '[]';
if (where == 'FIRST') {
if (mode == 'GET') {
var code = list + '[0]';
return [code, Blockly.Python.ORDER_MEMBER];
} else {
var code = list + '.pop(0)';
if (mode == 'GET_REMOVE') {
return [code, Blockly.Python.ORDER_FUNCTION_CALL];
} else if (mode == 'REMOVE') {
return code + '\n';
}
}
} else if (where == 'LAST') {
if (mode == 'GET') {
var code = list + '[-1]';
return [code, Blockly.Python.ORDER_MEMBER];
} else {
var code = list + '.pop()';
if (mode == 'GET_REMOVE') {
return [code, Blockly.Python.ORDER_FUNCTION_CALL];
} else if (mode == 'REMOVE') {
return code + '\n';
}
}
} else if (where == 'FROM_START') {
// Blockly uses one-based indicies.
if (Blockly.isNumber(at)) {
// If the index is a naked number, decrement it right now.
at = parseInt(at, 10) - 1;
} else {
// If the index is dynamic, decrement it in code.
at = 'int(' + at + ' - 1)';
}
if (mode == 'GET') {
var code = list + '[' + at + ']';
return [code, Blockly.Python.ORDER_MEMBER];
} else {
var code = list + '.pop(' + at + ')';
if (mode == 'GET_REMOVE') {
return [code, Blockly.Python.ORDER_FUNCTION_CALL];
} else if (mode == 'REMOVE') {
return code + '\n';
}
}
} else if (where == 'FROM_END') {
if (mode == 'GET') {
var code = list + '[-' + at + ']';
return [code, Blockly.Python.ORDER_MEMBER];
} else {
var code = list + '.pop(-' + at + ')';
if (mode == 'GET_REMOVE') {
return [code, Blockly.Python.ORDER_FUNCTION_CALL];
} else if (mode == 'REMOVE') {
return code + '\n';
}
}
} else if (where == 'RANDOM') {
Blockly.Python.definitions_['import_random'] = 'import random';
if (mode == 'GET') {
code = 'random.choice(' + list + ')';
return [code, Blockly.Python.ORDER_FUNCTION_CALL];
} else {
var functionName = Blockly.Python.provideFunction_(
'lists_remove_random_item',
['def ' + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + '(myList):',
' x = int(random.random() * len(myList))',
' return myList.pop(x)']);
code = functionName + '(' + list + ')';
if (mode == 'GET_REMOVE') {
return [code, Blockly.Python.ORDER_FUNCTION_CALL];
} else if (mode == 'REMOVE') {
return code + '\n';
}
}
}
throw 'Unhandled combination (lists_getIndex).';
};
Blockly.Python['lists_setIndex'] = function(block) {
// Set element at index.
// Note: Until February 2013 this block did not have MODE or WHERE inputs.
var list = Blockly.Python.valueToCode(block, 'LIST',
Blockly.Python.ORDER_MEMBER) || '[]';
var mode = block.getFieldValue('MODE') || 'GET';
var where = block.getFieldValue('WHERE') || 'FROM_START';
var at = Blockly.Python.valueToCode(block, 'AT',
Blockly.Python.ORDER_NONE) || '1';
var value = Blockly.Python.valueToCode(block, 'TO',
Blockly.Python.ORDER_NONE) || 'None';
// Cache non-trivial values to variables to prevent repeated look-ups.
// Closure, which accesses and modifies 'list'.
function cacheList() {
if (list.match(/^\w+$/)) {
return '';
}
var listVar = Blockly.Python.variableDB_.getDistinctName(
'tmp_list', Blockly.Variables.NAME_TYPE);
var code = listVar + ' = ' + list + '\n';
list = listVar;
return code;
}
if (where == 'FIRST') {
if (mode == 'SET') {
return list + '[0] = ' + value + '\n';
} else if (mode == 'INSERT') {
return list + '.insert(0, ' + value + ')\n';
}
} else if (where == 'LAST') {
if (mode == 'SET') {
return list + '[-1] = ' + value + '\n';
} else if (mode == 'INSERT') {
return list + '.append(' + value + ')\n';
}
} else if (where == 'FROM_START') {
// Blockly uses one-based indicies.
if (Blockly.isNumber(at)) {
// If the index is a naked number, decrement it right now.
at = parseInt(at, 10) - 1;
} else {
// If the index is dynamic, decrement it in code.
at = 'int(' + at + ' - 1)';
}
if (mode == 'SET') {
return list + '[' + at + '] = ' + value + '\n';
} else if (mode == 'INSERT') {
return list + '.insert(' + at + ', ' + value + ')\n';
}
} else if (where == 'FROM_END') {
if (mode == 'SET') {
return list + '[-' + at + '] = ' + value + '\n';
} else if (mode == 'INSERT') {
return list + '.insert(-' + at + ', ' + value + ')\n';
}
} else if (where == 'RANDOM') {
Blockly.Python.definitions_['import_random'] = 'import random';
var code = cacheList();
var xVar = Blockly.Python.variableDB_.getDistinctName(
'tmp_x', Blockly.Variables.NAME_TYPE);
code += xVar + ' = int(random.random() * len(' + list + '))\n';
if (mode == 'SET') {
code += list + '[' + xVar + '] = ' + value + '\n';
return code;
} else if (mode == 'INSERT') {
code += list + '.insert(' + xVar + ', ' + value + ')\n';
return code;
}
}
throw 'Unhandled combination (lists_setIndex).';
};
Blockly.Python['lists_getSublist'] = function(block) {
// Get sublist.
var list = Blockly.Python.valueToCode(block, 'LIST',
Blockly.Python.ORDER_MEMBER) || '[]';
var where1 = block.getFieldValue('WHERE1');
var where2 = block.getFieldValue('WHERE2');
var at1 = Blockly.Python.valueToCode(block, 'AT1',
Blockly.Python.ORDER_ADDITIVE) || '1';
var at2 = Blockly.Python.valueToCode(block, 'AT2',
Blockly.Python.ORDER_ADDITIVE) || '1';
if (where1 == 'FIRST' || (where1 == 'FROM_START' && at1 == '1')) {
at1 = '';
} else if (where1 == 'FROM_START') {
// Blockly uses one-based indicies.
if (Blockly.isNumber(at1)) {
// If the index is a naked number, decrement it right now.
at1 = parseInt(at1, 10) - 1;
} else {
// If the index is dynamic, decrement it in code.
at1 = 'int(' + at1 + ' - 1)';
}
} else if (where1 == 'FROM_END') {
if (Blockly.isNumber(at1)) {
at1 = -parseInt(at1, 10);
} else {
at1 = '-int(' + at1 + ')';
}
}
if (where2 == 'LAST' || (where2 == 'FROM_END' && at2 == '1')) {
at2 = '';
} else if (where1 == 'FROM_START') {
if (Blockly.isNumber(at2)) {
at2 = parseInt(at2, 10);
} else {
at2 = 'int(' + at2 + ')';
}
} else if (where1 == 'FROM_END') {
if (Blockly.isNumber(at2)) {
// If the index is a naked number, increment it right now.
// Add special case for -0.
at2 = 1 - parseInt(at2, 10);
if (at2 == 0) {
at2 = '';
}
} else {
// If the index is dynamic, increment it in code.
Blockly.Python.definitions_['import_sys'] = 'import sys';
at2 = 'int(1 - ' + at2 + ') or sys.maxsize';
}
}
var code = list + '[' + at1 + ' : ' + at2 + ']';
return [code, Blockly.Python.ORDER_MEMBER];
};
|
from rest_framework import serializers
from hub.models import Project
class ProjectSerializer(serializers.Serializer):
project_name = serializers.CharField(max_length=30)
duration = serializers.IntegerField()
allocated_time = serializers.IntegerField()
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# -----------------------------------------------------------------------------------------------------------
# Name: tinyserver.py
# Purpose: Small server using threads that listens on a default port and returns a response
# with echo of the same message received and additional information.
#
# Author: Gabriel Marti Fuentes
# email: gabimarti at gmail dot com
# GitHub: https://github.com/gabimarti
# Created: 03/08/2019 v1.0
# Version: 1.0.1
# License: MIT License
# -----------------------------------------------------------------------------------------------------------
import argparse
import atexit
import socket
import sys
import threading
import time
import traceback
########################################################
# CONSTANTS
########################################################
DESCRIPTION = 'Tiny Server'
EPILOG = 'Listen and respond. Simply.'
DEFAULT_MAXCLIENTS = 10 # Aka MAXTHREADS, every Client is a thread
DEFAULT_VERBOSE_LEVEL = 1 # Default verbose level
VERBOSE_LEVEL_DESCRIPTION = ['basic',
'a few',
'insane info'] # Verbose levels description
DEFAULT_HOST_BIND = '' # Bind to all ports
DEFAULT_PORT = 6666 # Default port
DEFAULT_KILL_MESSAGE = 'kill' # If this message is received, the server will end.
DEFAULT_MAX_BUFFER_SIZE = 4096 # Default max buffer size
ENCODING = 'utf-8' # Encoding for message communication
SHUTDOWN_RESPONSE = 'Shutting down the server' # Shutdown response to client
########################################################
# VARIABLES
########################################################
host_bind = DEFAULT_HOST_BIND
max_clients = DEFAULT_MAXCLIENTS
server_port = DEFAULT_PORT
max_buffer_size = DEFAULT_MAX_BUFFER_SIZE
kill_message = DEFAULT_KILL_MESSAGE
verbose = DEFAULT_VERBOSE_LEVEL # Verbose level
thread_counter = 0 # Total executed threads / clients
thread_active_counter = 0 # active threads / clients
thread_client_list = [] # List of active threads
time_start = 0 # For Timer counter
shutdown = False # It will be True when the server has to be closed.
server = None # Global server socket
########################################################
# FUNCTIONS
########################################################
# Print the message if the verbose level allows
def print_verbose(msg, verbose_level, established_verbose):
if verbose_level <= established_verbose:
print(msg)
# Kill all client active sockets
def kill_client_sockets():
global thread_client_list
print_verbose('Killing clients ...', 1, verbose)
for client in thread_client_list:
try:
client.shutdown(socket.SHUT_RDWR)
client.close()
except socket.error:
pass # socket already closed. don't worry
# Client input string processing
def do_client_input_string_processing(input_string, verbose):
print_verbose('Processing {}'.format(input_string), 1, verbose)
# Reverse message
input_processed = input_string[::-1]
return input_processed
# Client thread for each new connection
def client_thread(conn, ip, port, buffer_size, verbose, kill_message):
global shutdown, thread_active_counter, thread_client_list, server
# Lock for shared counters operations
lock = threading.Lock()
lock.acquire()
thread_active_counter += 1
thread_client_list.append(conn)
lock.release()
print_verbose('Total clients {} | Current active clients {}'.format(thread_counter, thread_active_counter),
1, verbose)
# the input is in bytes, so decode it
input_from_client_bytes = conn.recv(max_buffer_size)
if not input_from_client_bytes:
print_verbose('No data. from {}:{}'.format(ip, port), 1, verbose)
# max_buffer_size indicates how big the message can be
# this is test if it's sufficiently big
siz = sys.getsizeof(input_from_client_bytes)
if siz >= buffer_size:
print_verbose('The length of input is probably too long: {}'.format(siz), 1, verbose)
# decode input and strip the end of line
input_from_client = input_from_client_bytes.decode(ENCODING).rstrip()
# check if message is received to kill server
if input_from_client == kill_message:
print_verbose('Killing message received \'{}\''.format(input_from_client), 1, verbose)
# response shutdown to client
res = SHUTDOWN_RESPONSE
res_encoded = res.encode(ENCODING)
conn.sendall(res_encoded)
shutdown = True
try:
print_verbose('Shutdown server from Client thread', 1, verbose)
server.close()
except Exception as e:
print_verbose('Can\'t close Server from client thread', 1, verbose)
print_verbose('Exception {}'.format(e), 1, verbose)
kill_client_sockets()
else:
res = do_client_input_string_processing(input_from_client, verbose)
print_verbose('Result of processing {} is: {}'.format(input_from_client, res), 1, verbose)
res_encoded = res.encode(ENCODING) # encode the result string
try:
conn.sendall(res_encoded) # send it to client
conn.close() # close connection
except Exception as e:
print_verbose('Socket Error: {:s} '.format(e), 1, verbose)
# Lock for shared counters operations
lock.acquire()
thread_active_counter -= 1
thread_client_list.remove(conn)
lock.release()
print_verbose('Connection to {}:{} ended'.format(ip, port), 1, verbose)
# Parse command line parameters
def parse_params():
parser = argparse.ArgumentParser(description=DESCRIPTION, epilog=EPILOG)
parser.add_argument('-i', '--interfacebind', type=str, default=DEFAULT_HOST_BIND,
help='What interface is it linked to? Default (empty) to any of this host.')
parser.add_argument('-c', '--maxclients', type=int, default=DEFAULT_MAXCLIENTS,
help='Indicates the maximum number of clients/threads. Default value: {}'
.format(DEFAULT_MAXCLIENTS))
parser.add_argument('-p', '--serverport', type=int, default=DEFAULT_PORT,
help='Port to listen. Default value: {}'.format(DEFAULT_PORT))
parser.add_argument('-b', '--buffersize', type=int, default=DEFAULT_MAX_BUFFER_SIZE,
help='Buffer size. Default value: {}'.format(DEFAULT_MAX_BUFFER_SIZE))
parser.add_argument('-k', '--killmessage', type=str, default=DEFAULT_KILL_MESSAGE,
help='Message to kill server. Default value: \'{}\''.format(DEFAULT_KILL_MESSAGE))
parser.add_argument('-v', '--verbose', type=int, choices=[0, 1, 2], default=DEFAULT_VERBOSE_LEVEL,
help='Increase output verbosity. Default value: {}'.format(DEFAULT_VERBOSE_LEVEL))
args = parser.parse_args()
return args
# Executes on program close
def on_close_program():
global time_start
totaltime = time.perf_counter() - time_start
print('The server has been running for {} seconds.'.format(totaltime))
# Main - Start Server
def start_server():
global host_bind, max_clients, server_port, max_buffer_size, kill_message, verbose, time_start
global shutdown, thread_counter, server
# Handler to do actions when application is closed
atexit.register(on_close_program)
# Check and parse parameters
args = parse_params()
host_bind = args.interfacebind
max_clients = args.maxclients
server_port = args.serverport
max_buffer_size = args.buffersize
kill_message = args.killmessage
verbose = args.verbose
if max_clients < 1: # avoid zero division
max_clients = DEFAULT_MAXCLIENTS
print('Verbose level {}'.format(VERBOSE_LEVEL_DESCRIPTION[verbose]))
print('Host bind {}'.format(host_bind))
print('Max {} Clients / Threads'.format(max_clients))
print('Server Port {}'.format(server_port))
print('Buffer size {} bytes'.format(max_buffer_size))
print('Kill message \'{}\''.format(kill_message))
print('Starting Server ...')
time_start = time.perf_counter()
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
print_verbose('Socket created', 1, verbose)
# Bind socket to local host and port
try:
server.bind((host_bind, server_port))
print_verbose('Socket bind completed', 2, verbose)
except socket.error as msg:
print('Bind failed. Error Code: {} Message: {}'.format(msg[0], msg[1]))
print('Sys Error Info: {}'.format(sys.exc_info()))
sys.exit()
# Start listening on socket
server.listen(max_clients)
print_verbose('Socket now listening', 2, verbose)
# Now keep talking with the client while no shutdown message is received
while not shutdown:
# Wait to accept a connection - blocking call
try:
(conn, (ip, port)) = server.accept()
except:
break # Possibly closed server
print('Accepting connection from {}:{}'.format(ip, port))
thread_counter += 1
try:
client = threading.Thread(target=client_thread,
args=(conn, ip, port, max_buffer_size, verbose, kill_message))
client.start()
except Exception as e:
print('WTF Error! {}'.format(e))
traceback.print_exc()
# Shutting down server
try:
print("Shutdown server")
server.shutdown(socket.SHUT_RDWR)
server.close()
except Exception as e:
print_verbose('Server already closed from client thread', 1, verbose)
print_verbose('Exception {}'.format(e), 2, verbose)
print_verbose('At shutdown, total clients {} | Current active {}'.format(thread_counter,
thread_active_counter),
1, verbose)
sys.exit(1)
if __name__ == '__main__':
start_server()
|
const webpack = require('webpack')
const { merge } = require('webpack-merge')
const common = require('./webpack.common.js')
const paths = require('./paths')
module.exports = merge(common, {
// Set the mode to development or production
mode: 'development',
// Control how source maps are generated
devtool: 'inline-source-map',
// Spin up a server for quick development
devServer: {
historyApiFallback: true,
contentBase: paths.build,
open: true,
compress: true,
hot: true,
port: 8080,
},
module: {
rules: [
// Styles: Inject CSS into the head with source maps
{
test: /\.(scss|css)$/,
use: [
'style-loader',
{
loader: 'css-loader',
options: { sourceMap: true, importLoaders: 1, modules: true },
},
{ loader: 'postcss-loader', options: { sourceMap: true } },
{ loader: 'sass-loader', options: { sourceMap: true } },
],
},
],
},
plugins: [
// Only update what has changed on hot reload
new webpack.HotModuleReplacementPlugin(),
],
})
|
//------------------------------------------------------------------------------
// GB_AxB: hard-coded C=A*B and C<M>=A*B
//------------------------------------------------------------------------------
// SuiteSparse:GraphBLAS, Timothy A. Davis, (c) 2017-2018, All Rights Reserved.
// http://suitesparse.com See GraphBLAS/Doc/License.txt for license.
// If this filename has a double underscore in its name ("__") then it has been
// automatically constructed from Generator/GB_AxB.c, via the Source/axb*.m
// scripts, and should not be editted. Edit the original source file instead.
//------------------------------------------------------------------------------
#include "GB.h"
#ifndef GBCOMPACT
#include "GB_heap.h"
#include "GB_AxB__semirings.h"
// The C=A*B semiring is defined by the following types and operators:
// A*B function (Gustavon): GB_AgusB__plus_second_int64
// A'*B function (dot): GB_AdotB__plus_second_int64
// A*B function (heap): GB_AheapB__plus_second_int64
// Z type: int64_t (the type of C)
// X type: int64_t (the type of x for z=mult(x,y))
// Y type: int64_t (the type of y for z=mult(x,y))
// handle flipxy: 0 (0 if mult(x,y) is commutative, 1 otherwise)
// Identity: 0 (where cij += identity does not change cij)
// Multiply: z = y
// Add: cij += z
#define GB_XTYPE \
int64_t
#define GB_YTYPE \
int64_t
#define GB_HANDLE_FLIPXY \
0
#define GB_MULTOP(z,x,y) \
z = y
//------------------------------------------------------------------------------
// C<M>=A*B and C=A*B: gather/scatter saxpy-based method (Gustavson)
//------------------------------------------------------------------------------
#define GB_IDENTITY \
0
// x [i] = y
#define GB_COPY_SCALAR_TO_ARRAY(x,i,y,s) \
x [i] = y ;
// x = y [i]
#define GB_COPY_ARRAY_TO_SCALAR(x,y,i,s) \
GB_btype x = y [i] ;
// x [i] = y [i]
#define GB_COPY_ARRAY_TO_ARRAY(x,i,y,j,s) \
x [i] = y [j] ;
// mult-add operation (no mask)
#define GB_MULTADD_NOMASK \
{ \
/* Sauna_Work [i] += A(i,k) * B(k,j) */ \
GB_atype aik = Ax [pA] ; \
int64_t t ; \
GB_MULTIPLY (t, aik, bkj) ; \
Sauna_Work [i] += t ; \
}
// mult-add operation (with mask)
#define GB_MULTADD_WITH_MASK \
{ \
/* Sauna_Work [i] += A(i,k) * B(k,j) */ \
GB_atype aik = Ax [pA] ; \
int64_t t ; \
GB_MULTIPLY (t, aik, bkj) ; \
if (mark == hiwater) \
{ \
/* first time C(i,j) seen */ \
Sauna_Mark [i] = hiwater + 1 ; \
Sauna_Work [i] = t ; \
} \
else \
{ \
/* C(i,j) seen before, update it */ \
Sauna_Work [i] += t ; \
} \
}
GrB_Info GB_AgusB__plus_second_int64
(
GrB_Matrix C,
const GrB_Matrix M,
const GrB_Matrix A,
const GrB_Matrix B,
bool flipxy, // if true, A and B have been swapped
GB_Sauna Sauna, // sparse accumulator
GB_Context Context
)
{
int64_t *restrict Sauna_Work = Sauna->Sauna_Work ; // size C->vlen*zsize
int64_t *restrict Cx = C->x ;
GrB_Info info = GrB_SUCCESS ;
#include "GB_AxB_Gustavson_flipxy.c"
return (info) ;
}
//------------------------------------------------------------------------------
// C<M>=A'*B or C=A'*B: dot product
//------------------------------------------------------------------------------
// get A(k,i)
#define GB_DOT_GETA(pA) \
GB_atype aki = Ax [pA] ;
// get B(k,j)
#define GB_DOT_GETB(pB) \
GB_btype bkj = Bx [pB] ;
// t = aki*bkj
#define GB_DOT_MULT(bkj) \
int64_t t ; \
GB_MULTIPLY (t, aki, bkj) ;
// cij += t
#define GB_DOT_ADD \
cij += t ;
// cij = t
#define GB_DOT_COPY \
cij = t ;
// cij is not a pointer but a scalar; nothing to do
#define GB_DOT_REACQUIRE ;
// clear cij
#define GB_DOT_CLEAR \
cij = 0 ;
// save the value of C(i,j)
#define GB_DOT_SAVE \
Cx [cnz] = cij ;
#define GB_DOT_WORK_TYPE \
GB_btype
#define GB_DOT_WORK(k) Work [k]
// Work [k] = Bx [pB]
#define GB_DOT_SCATTER \
Work [k] = Bx [pB] ;
GrB_Info GB_AdotB__plus_second_int64
(
GrB_Matrix *Chandle,
const GrB_Matrix M,
const GrB_Matrix A,
const GrB_Matrix B,
bool flipxy, // if true, A and B have been swapped
GB_Context Context
)
{
GrB_Matrix C = (*Chandle) ;
int64_t *restrict Cx = C->x ;
int64_t cij ;
GrB_Info info = GrB_SUCCESS ;
size_t bkj_size = B->type->size ; // no typecasting here
#include "GB_AxB_dot_flipxy.c"
return (info) ;
}
//------------------------------------------------------------------------------
// C<M>=A*B and C=A*B: heap saxpy-based method
//------------------------------------------------------------------------------
#define GB_CIJ_GETB(pB) \
GB_btype bkj = Bx [pB] ;
// C(i,j) = A(i,k) * bkj
#define GB_CIJ_MULT(pA) \
{ \
GB_atype aik = Ax [pA] ; \
GB_MULTIPLY (cij, aik, bkj) ; \
}
// C(i,j) += A(i,k) * B(k,j)
#define GB_CIJ_MULTADD(pA,pB) \
{ \
GB_atype aik = Ax [pA] ; \
GB_btype bkj = Bx [pB] ; \
int64_t t ; \
GB_MULTIPLY (t, aik, bkj) ; \
cij += t ; \
}
// cij is not a pointer but a scalar; nothing to do
#define GB_CIJ_REACQUIRE ;
// cij = identity
#define GB_CIJ_CLEAR \
cij = 0 ;
// save the value of C(i,j)
#define GB_CIJ_SAVE \
Cx [cnz] = cij ;
GrB_Info GB_AheapB__plus_second_int64
(
GrB_Matrix *Chandle,
const GrB_Matrix M,
const GrB_Matrix A,
const GrB_Matrix B,
bool flipxy, // if true, A and B have been swapped
int64_t *restrict List,
GB_pointer_pair *restrict pA_pair,
GB_Element *restrict Heap,
const int64_t bjnz_max,
GB_Context Context
)
{
GrB_Matrix C = (*Chandle) ;
int64_t *restrict Cx = C->x ;
int64_t cij ;
int64_t cvlen = C->vlen ;
GrB_Info info = GrB_SUCCESS ;
GB_CIJ_CLEAR ;
#include "GB_AxB_heap_flipxy.c"
return (info) ;
}
//------------------------------------------------------------------------------
// clear macro definitions
//------------------------------------------------------------------------------
#undef GB_XTYPE
#undef GB_YTYPE
#undef GB_HANDLE_FLIPXY
#undef GB_MULTOP
#undef GB_IDENTITY
#undef GB_COPY_SCALAR_TO_ARRAY
#undef GB_COPY_ARRAY_TO_SCALAR
#undef GB_COPY_ARRAY_TO_ARRAY
#undef GB_MULTADD_NOMASK
#undef GB_MULTADD_WITH_MASK
#undef GB_DOT_GETA
#undef GB_DOT_GETB
#undef GB_DOT_MULT
#undef GB_DOT_ADD
#undef GB_DOT_COPY
#undef GB_DOT_REACQUIRE
#undef GB_DOT_CLEAR
#undef GB_DOT_SAVE
#undef GB_DOT_WORK_TYPE
#undef GB_DOT_WORK
#undef GB_DOT_SCATTER
#undef GB_CIJ_GETB
#undef GB_CIJ_MULT
#undef GB_CIJ_MULTADD
#undef GB_CIJ_REACQUIRE
#undef GB_CIJ_CLEAR
#undef GB_CIJ_SAVE
#undef GB_MULTIPLY
#endif
|
var searchData=
[
['container_20views_7128',['Container Views',['../group__containerviews.html',1,'']]],
['controls_7129',['Controls',['../group__controls.html',1,'']]]
];
|
# Importa as blibliotecas
import pyautogui
import time
# Abre o executar do Windows e busca o servidor
pyautogui.hotkey('win','r');pyautogui.typewrite('\\\srsdcsp01\\NETLOGON\\Bitdefender\\Setup');pyautogui.press('enter')
time.sleep(2)
# Abre o arquivo de instalação
pyautogui.click(520,479);pyautogui.typewrite('epskit_x64');pyautogui.press('enter')
# Mostra mensagem na tela
pyautogui.alert(title='INFRA - BitDefender', text='Aguarde a instalacao, pode demorar um pouco', button='OK')
|
# Copyright (C) 2019 ZTE. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import mock
from django.test import TestCase
from rest_framework import status
from rest_framework.test import APIClient
from . import const
from lcm.pub.database.models import NfInstModel, JobStatusModel, StorageInstModel, NetworkInstModel, \
SubNetworkInstModel, PortInstModel, FlavourInstModel, VmInstModel, VNFCInstModel
from lcm.pub.utils import restcall
from lcm.pub.vimapi import api
from lcm.pub.utils.jobutil import JobUtil
from lcm.nf.biz.scale_vnf_to_level import ScaleVnfToLevel
class TestNfScaleToLevel(TestCase):
def setUp(self):
self.client = APIClient()
self.req_data = {
"instantiationLevelId": "instantiation_level_2",
"scaleInfo": []
}
NfInstModel(nfinstid='98765',
nf_name='VNF1',
nf_desc="VNF DESC",
vnfdid="1",
netype="XGW",
vendor="ZTE",
vnfSoftwareVersion="V1",
version="V1",
package_id="2",
status='NOT_INSTANTIATED').save()
def tearDown(self):
NfInstModel.objects.filter(nfinstid='98765').delete()
def assert_job_result(self, job_id, job_progress, job_detail):
jobs = JobStatusModel.objects.filter(
jobid=job_id,
progress=job_progress,
descp=job_detail
)
self.assertEqual(1, len(jobs))
def test_scale_vnf_to_level_not_found(self):
url = "/api/vnflcm/v1/vnf_instances/12/scale_to_level"
response = self.client.post(url,
data=self.req_data,
format='json')
self.assertEqual(status.HTTP_404_NOT_FOUND, response.status_code)
def test_scale_vnf_conflict(self):
url = "/api/vnflcm/v1/vnf_instances/98765/scale_to_level"
response = self.client.post(url,
data=self.req_data,
format='json')
self.assertEqual(status.HTTP_409_CONFLICT, response.status_code)
def test_scale_to_level_badreq(self):
NfInstModel(nfinstid='678',
nf_name='VNF1',
nf_desc="VNF DESC",
vnfdid="1",
netype="XGW",
vendor="ZTE",
vnfSoftwareVersion="V1",
version="V1",
package_id="2",
status='INSTANTIATED').save()
url = "/api/vnflcm/v1/vnf_instances/678/scale_to_level"
response = self.client.post(url,
data={},
format='json')
NfInstModel.objects.filter(nfinstid='678').delete()
self.assertEqual(status.HTTP_400_BAD_REQUEST, response.status_code)
@mock.patch.object(restcall, 'call_req')
@mock.patch.object(api, 'call')
def test_scale_out_vnf_success(self, mock_call, mock_call_req):
self.nf_inst_id = '6789'
res_cache = {"volume": {}, "flavor": {}, "port": {}}
res_cache["volume"]["test"] = "test"
# res_cache["port"]["ext_cp"] = "port1"
NfInstModel(nfinstid=self.nf_inst_id,
nf_name='VNF1',
nf_desc="VNF DESC",
vnfdid="1",
netype="XGW",
vendor="ZTE",
vnfSoftwareVersion="V1",
version="V1",
package_id="2",
status='INSTANTIATED',
vnfd_model=json.dumps(const.vnfd_for_scale),
vimInfo=json.dumps({}),
resInfo=json.dumps(res_cache)).save()
StorageInstModel.objects.create(
storageid="1",
vimid="1",
resourceid="11",
insttype=0,
instid=self.nf_inst_id,
is_predefined=1
)
NetworkInstModel.objects.create(
networkid='1',
vimid='1',
resourceid='1',
name='pnet_network',
is_predefined=1,
tenant='admin',
insttype=0,
instid=self.nf_inst_id
)
SubNetworkInstModel.objects.create(
subnetworkid='1',
vimid='1',
resourceid='1',
networkid='1',
is_predefined=1,
name='sub_pnet',
tenant='admin',
insttype=0,
instid=self.nf_inst_id
)
PortInstModel.objects.create(
portid='1',
networkid='1',
subnetworkid='1',
vimid='1',
resourceid='1',
is_predefined=1,
name='ext_cp',
tenant='admin',
insttype=0,
instid=self.nf_inst_id
)
FlavourInstModel.objects.create(
flavourid="1",
vimid="1",
resourceid="11",
instid=self.nf_inst_id,
is_predefined=1,
name="Flavor_sunshine"
)
VmInstModel.objects.create(
vmid="1",
vimid="1",
resourceid="11",
insttype=0,
instid=self.nf_inst_id,
vmname="test_01",
is_predefined=1,
operationalstate=1
)
r1_apply_grant_result = [
0,
json.JSONEncoder().encode(const.instantiate_grant_result),
'200'
]
mock_call_req.side_effect = [
r1_apply_grant_result,
]
mock_call.side_effect = [
const.c1_data_get_tenant_id,
const.c6_data_create_port,
const.c7_data_create_flavor,
const.c8_data_list_image,
const.c9_data_create_vm,
const.c10_data_get_vm
]
self.job_id = JobUtil.create_job('NF', 'SCALE', self.nf_inst_id)
JobUtil.add_job_status(self.job_id, 0, "VNF_SCALE_READY", )
ScaleVnfToLevel(
{"instantiationLevelId": "instantiation_level_2"},
nf_inst_id=self.nf_inst_id,
job_id=self.job_id
).run()
NfInstModel.objects.filter(nfinstid=self.nf_inst_id).delete()
self.assert_job_result(
self.job_id,
100,
'Scale Vnf success.'
)
@mock.patch.object(restcall, 'call_req')
@mock.patch.object(api, 'call')
def test_scale_to_inlevel_vnf_success(self, mock_call, mock_call_req):
self.nf_inst_id = '6789'
res_cache = {"volume": {}, "flavor": {}, "port": {}}
res_cache["port"]["ext_cp"] = "port1"
NfInstModel(nfinstid=self.nf_inst_id,
nf_name='VNF1',
nf_desc="VNF DESC",
vnfdid="1",
netype="XGW",
vendor="ZTE",
vnfSoftwareVersion="V1",
version="V1",
package_id="2",
status='INSTANTIATED',
vnfd_model=json.dumps(const.vnfd_for_scale),
vimInfo=json.dumps({}),
resInfo=json.dumps(res_cache)).save()
StorageInstModel.objects.create(
storageid="1",
vimid="1",
resourceid="11",
insttype=0,
instid=self.nf_inst_id,
is_predefined=1
)
NetworkInstModel.objects.create(
networkid='1',
vimid='1',
resourceid='1',
name='pnet_network',
is_predefined=1,
tenant='admin',
insttype=0,
instid=self.nf_inst_id
)
SubNetworkInstModel.objects.create(
subnetworkid='1',
vimid='1',
resourceid='1',
networkid='1',
is_predefined=1,
name='sub_pnet',
tenant='admin',
insttype=0,
instid=self.nf_inst_id
)
PortInstModel.objects.create(
portid='1',
networkid='1',
subnetworkid='1',
vimid='1',
resourceid='1',
is_predefined=1,
name='ext_cp',
tenant='admin',
insttype=0,
instid=self.nf_inst_id
)
FlavourInstModel.objects.create(
flavourid="1",
vimid="1",
resourceid="11",
instid=self.nf_inst_id,
is_predefined=1,
name="Flavor_sunshine"
)
VmInstModel.objects.create(
vmid="1",
vimid="1",
resourceid="11",
insttype=0,
instid=self.nf_inst_id,
vmname="test_01",
is_predefined=1,
operationalstate=1
)
VmInstModel.objects.create(
vmid="2",
vimid="1",
resourceid="22",
insttype=0,
instid=self.nf_inst_id,
vmname="test_02",
is_predefined=1,
operationalstate=1
)
VNFCInstModel.objects.create(
vnfcinstanceid="1",
instid=self.nf_inst_id,
vmid="1"
)
VNFCInstModel.objects.create(
vnfcinstanceid="2",
instid=self.nf_inst_id,
vmid="2"
)
r1_apply_grant_result = [
0,
json.JSONEncoder().encode(const.instantiate_grant_result),
'200'
]
mock_call_req.side_effect = [
r1_apply_grant_result,
]
mock_call.side_effect = [
const.c1_data_get_tenant_id,
const.c7_data_create_flavor,
const.c8_data_list_image,
const.c9_data_create_vm,
const.c10_data_get_vm
]
self.job_id = JobUtil.create_job('NF', 'SCALE', self.nf_inst_id)
JobUtil.add_job_status(self.job_id, 0, "VNF_SCALE_READY", )
ScaleVnfToLevel(
{"scaleInfo": {"aspectId": "sunshine_aspect",
"scaleLevel": 0}},
nf_inst_id=self.nf_inst_id,
job_id=self.job_id
).run()
NfInstModel.objects.filter(nfinstid=self.nf_inst_id).delete()
self.assert_job_result(
self.job_id,
100,
'Scale Vnf success.'
)
|
import torch
import torch.nn as nn
from torch.utils.model_zoo import load_url
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
}
class ADL(nn.Module):
def __init__(self, drop_rate=0.75, drop_thr=0.8):
super(ADL, self).__init__()
assert 0 <= drop_rate <= 1 and 0 <= drop_thr <= 1
self.drop_rate = drop_rate
self.drop_thr = drop_thr
self.attention = None
self.drop_mask = None
def extra_repr(self):
return 'drop_rate={}, drop_thr={}'.format(
self.drop_rate, self.drop_thr
)
def forward(self, x):
if self.training:
b = x.size(0)
# Generate self-attention map
attention = torch.mean(x, dim=1, keepdim=True)
self.attention = attention
# Generate importance map
importance_map = torch.sigmoid(attention)
# Generate drop mask
max_val, _ = torch.max(attention.view(b, -1), dim=1, keepdim=True)
thr_val = max_val * self.drop_thr
thr_val = thr_val.view(b, 1, 1, 1).expand_as(attention)
drop_mask = (attention < thr_val).float()
self.drop_mask = drop_mask
# Random selection
random_tensor = torch.rand([], dtype=torch.float32) + self.drop_rate
binary_tensor = random_tensor.floor()
selected_map = (1. - binary_tensor) * importance_map + binary_tensor * drop_mask
# Spatial multiplication to input feature map
output = x.mul(selected_map)
else:
output = x
return output
def get_maps(self):
return self.attention, self.drop_mask
class VGG(nn.Module):
def __init__(self, features, num_classes=1000, mode='base', init_weights=True, **kwargs):
super(VGG, self).__init__()
self.features = features
self.mode = mode
if self.mode == 'base':
self.avgpool = nn.AdaptiveAvgPool2d((7,7))
self.classifier = make_classifier(mode = self.mode, num_classes=num_classes)
elif self.mode == 'GAP' or self.mode == 'ADL':
self.conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=1)
self.relu = nn.ReLU(inplace=True)
if self.mode == 'ADL' and kwargs['lst_ADL']:
self.lst_adl = True
self.adl = ADL(kwargs['drop_rate'], kwargs['drop_thr'])
else:
self.lst_adl = False
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(1024, num_classes)
self.feature_map = None
self.pred = None
if init_weights:
self._initialize_weights()
def forward(self, x):
x = self.features(x)
if self.mode == 'base':
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
elif self.mode == 'GAP' or self.mode == 'ADL':
x = self.conv6(x)
x = self.relu(x)
if self.lst_adl:
x = self.adl(x)
self.feature_map = x
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
self.pred = x
else:
raise Exception("No mode matching")
return x
def get_cam(self):
return self.feature_map, self.pred
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
def make_layers(cfg, batch_norm=False, **kwargs):
layers = []
in_channels = 3
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
elif v == 'A':
layers += [ADL(kwargs['drop_rate'], kwargs['drop_thr'])]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return nn.Sequential(*layers)
def make_classifier(mode, num_classes):
if mode == 'base':
return nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(),
nn.Dropout(),
nn.Linear(4096, num_classes)
)
elif mode == 'GAP':
pass
else:
raise Exception("No mode matching")
cfgs = {
'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
'D_GAP': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512],
}
def make_cfg(cfg, targets):
pos_list = list('0')
block, element = 1, 1
for i in cfg:
if isinstance(i, int):
pos_list.append(str(block) + str(element))
element += 1
elif isinstance(i, str):
pos_list.append(str(block) + 'M')
block += 1
element = 1
pos_dict = dict()
for i, key in enumerate(pos_list):
pos_dict[key] = i
target_list = [pos_dict[key] for key in targets]
target_list = sorted(target_list, reverse=True)
for key in target_list:
cfg.insert(key, 'A')
return cfg
def remove_layer(state_dict, keyword):
keys = [key for key in state_dict.keys()]
for key in keys:
if keyword in key:
state_dict.pop(key)
return state_dict
def make_config_align(state_dict, base_cfg, target_cfg):
base_list = []
replace_list = []
start = 0
for layer in base_cfg:
if type(layer) is int:
base_list.append(start)
start += 2
elif type(layer) is str:
start += 1
else:
raise Exception("No state_dict match")
start = 0
for layer in target_cfg:
if type(layer) is int:
replace_list.append(start)
start += 2
elif type(layer) is str:
start += 1
else:
raise Exception("No state_dict match")
target_list = {}
for base, replace in zip(base_list, replace_list):
target_list[str(base)] = str(replace)
keys = [key for key in state_dict.keys()]
for i, key in enumerate(reversed(keys)):
key_ = key.split('.')
new_key = key.replace('.'+key_[1]+'.', '.'+target_list[key_[1]]+'.')
state_dict[new_key] = state_dict.pop(key)
return state_dict
def _vgg(arch, cfg, mode, batch_norm, pretrained, progress, **kwargs):
kwargs['init_weights'] = True
if mode == 'ADL':
if '6' in kwargs['ADL_position']:
kwargs['lst_ADL'] = True
kwargs['ADL_position'].remove('6')
else:
kwargs['lst_ADL'] = False
new_cfg = make_cfg(cfgs[cfg], kwargs['ADL_position'])
else:
new_cfg = cfgs[cfg]
model = VGG(make_layers(new_cfg, batch_norm=batch_norm, **kwargs), mode=mode, **kwargs)
if pretrained:
state_dict = load_url(model_urls[arch], progress=progress)
# remove classifier part
if mode == 'ADL':
state_dict = remove_layer(state_dict, 'classifier.')
make_config_align(state_dict, cfgs[cfg[0]], cfgs[cfg])
strict_rule = False
elif mode == 'GAP':
state_dict = remove_layer(state_dict, 'classifier.')
strict_rule = False
else:
strict_rule = True
# If not ImageNet, remove pretrained dict.
if kwargs['num_classes'] != 1000:
remove_layer(state_dict, 'classifier.6')
strict_rule = False
model.load_state_dict(state_dict, strict=strict_rule)
return model
def vgg11(pretrained=False, progress=True, **kwargs):
r"""VGG 11-layer model (configuration "A") from
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vgg('vgg11', 'A', 'base', False, pretrained, progress, **kwargs)
def vgg11_bn(pretrained=False, progress=True, **kwargs):
r"""VGG 11-layer model (configuration "A") with batch normalization
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vgg('vgg11_bn', 'A', 'base', True, pretrained, progress, **kwargs)
def vgg13(pretrained=False, progress=True, **kwargs):
r"""VGG 13-layer model (configuration "B")
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vgg('vgg13', 'B', 'base', False, pretrained, progress, **kwargs)
def vgg13_bn(pretrained=False, progress=True, **kwargs):
r"""VGG 13-layer model (configuration "B") with batch normalization
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vgg('vgg13_bn', 'B', 'base', True, pretrained, progress, **kwargs)
def vgg16(pretrained=False, progress=True, **kwargs):
r"""VGG 16-layer model (configuration "D")
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vgg('vgg16', 'D', 'base', False, pretrained, progress, **kwargs)
def vgg16_ADL(pretrained=False, progress=True, **kwargs):
r"""VGG 16-layer model (configuration "D")
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vgg('vgg16', 'D_GAP', 'ADL', False, pretrained, progress, **kwargs)
def vgg16_bn(pretrained=False, progress=True, **kwargs):
r"""VGG 16-layer model (configuration "D") with batch normalization
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vgg('vgg16_bn', 'D', 'base', True, pretrained, progress, **kwargs)
def vgg16_GAP(pretrained=False, progress=True, **kwargs):
r"""VGG 16-layer model (configuration "D")
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vgg('vgg16', 'D_GAP', 'GAP', False, pretrained, progress, **kwargs)
def vgg19(pretrained=False, progress=True, **kwargs):
r"""VGG 19-layer model (configuration "E")
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vgg('vgg19', 'E', 'base', False, pretrained, progress, **kwargs)
def vgg19_bn(pretrained=False, progress=True, **kwargs):
r"""VGG 19-layer model (configuration 'E') with batch normalization
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vgg('vgg19_bn', 'E', 'base', True, pretrained, progress, **kwargs)
|
/*
* /MathJax-v2/jax/output/SVG/fonts/STIX-Web/Shapes/Regular/Main.js
*
* Copyright (c) 2009-2018 The MathJax Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
MathJax.OutputJax.SVG.FONTDATA.FONTS.STIXMathJax_Shapes={directory:"Shapes/Regular",family:"STIXMathJax_Shapes",id:"STIXWEBSHAPES",32:[0,0,250,0,0,""],160:[0,0,250,0,0,""],9251:[16,120,500,40,460,"460 -120h-420v136h30v-76h360v76h30v-136"],9472:[340,-267,708,-11,719,"719 267h-730v73h730v-73"],9474:[910,303,708,317,390,"390 -303h-73v1213h73v-1213"],9478:[910,303,708,317,390,"390 544h-73v366h73v-366zM390 121h-73v366h73v-366zM390 -303h-73v367h73v-367"],9480:[340,-267,708,-11,719,"719 267h-145v73h145v-73zM524 267h-145v73h145v-73zM329 267h-145v73h145v-73zM134 267h-145v73h145v-73"],9482:[910,303,708,317,390,"390 650h-73v260h73v-260zM390 333h-73v260h73v-260zM390 15h-73v260h73v-260zM390 -303h-73v260h73v-260"],9500:[910,303,708,317,719,"719 267h-329v-570h-73v1213h73v-570h329v-73"],9508:[910,303,708,-11,390,"390 -303h-73v570h-328v73h328v570h73v-1213"],9516:[340,303,708,-11,719,"719 267h-329v-570h-73v570h-328v73h730v-73"],9524:[910,-267,708,-11,719,"719 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167h-54v138h-462v-138h-54v330h54v-138h462v138h54v-330"],57514:[702,-506,376,30,346,"346 506h-201l-115 165l44 31l100 -142h172v-54"],57515:[662,156,496,114,371,"371 -156h-55v240l-165 -236l-37 30l201 286l1 -1v499h55v-818"],57516:[702,-506,470,30,440,"440 547l-41 -41l-110 117l-109 -117l-150 158l41 38l109 -115l109 115"],57517:[662,156,638,35,513,"513 -156h-55v512l-184 219l-196 -233l-43 35l239 285l184 -220v220h55v-818"],57518:[662,0,423,55,345,"345 0h-55v500l-183 -500l-52 19l235 643h55v-662"],57519:[662,0,423,55,345,"345 0h-55l-235 643l52 19l183 -500v500h55v-662"],57520:[735,-531,0,100,400,"400 680h-245v-149h-55v204h300v-55"],57521:[-50,254,0,0,300,"300 -254h-300v55h245v149h55v-204"],57522:[-50,254,0,0,300,"300 -254h-300v204h55v-149h245v-55"],57559:[583,79,762,50,712,"712 252c0 -183 -148 -331 -331 -331s-331 148 -331 331s148 331 331 331s331 -148 331 -331zM575 250c0 107 -86 194 -192 194c-110 0 -196 -84 -196 -192c0 -110 85 -196 194 -196c108 0 194 86 194 194"],57571:[638,134,842,35,807,"807 603l-135 -135c50 -58 80 -134 80 -216c0 -183 -148 -331 -331 -331c-83 0 -158 30 -216 80l-135 -135l-35 35l135 135c-50 58 -80 133 -80 216c0 183 148 331 331 331c82 0 158 -30 216 -80l135 135zM686 252c0 146 -119 265 -265 265s-265 -119 -265 -265 s119 -265 265 -265s265 119 265 265"],57573:[690,189,523,72,450,"450 690l-52 -141h-57l50 141h59zM360 444l-52 -141h-57l50 141h59zM271 198l-52 -141h-57l50 141h59zM181 -48l-52 -141h-57l50 141h59"],57574:[690,189,523,72,450,"181 549h-57l-52 141h59zM271 303h-57l-52 141h59zM360 57h-57l-52 141h59zM450 -189h-57l-52 141h59"],57575:[811,127,772,35,737,"737 811l-351 -938l-351 938h702zM642 745h-512l256 -684"],57576:[532,26,1077,55,1022,"1022 220h-180v-180h-66v180h-180v66h180v180h66v-180h180v-66zM612 51v-77c-120 161 -354 230 -557 246v66c203 16 437 85 557 246v-77c-50 -86 -211 -167 -334 -202c123 -35 284 -116 334 -202"],57577:[547,41,685,48,636,"636 192h-233v-233h-122v233h-233v122h233v233h122v-233h233v-122zM598 230v46h-233v233h-46v-233h-233v-46h233v-233h46v233h233"],57578:[661,158,910,45,865,"865 220h-101l-276 -276v-102h-66v102l-275 276h-102v66h102l275 273v102h66v-102l275 -273h102v-66zM705 255l-250 240l-251 -240l251 -252"],57579:[567,58,716,45,671,"671 -12l-47 -46l-72 72h-388l-72 -72l-47 47l72 72v388l-72 72l47 46l72 -72h388l72 72l46 -47l-71 -71v-389zM534 79v351h-352v-351h352"],57581:[819,312,511,192,319,"319 756c0 -34 -25 -64 -63 -64s-64 25 -64 64c0 38 32 63 64 63s63 -26 63 -63zM319 505c0 -34 -25 -64 -63 -64s-64 25 -64 64c0 38 32 63 64 63s63 -26 63 -63zM319 254c0 -34 -25 -64 -63 -64s-64 25 -64 64c0 38 32 63 64 63s63 -26 63 -63zM319 3 c0 -34 -25 -64 -63 -64s-64 25 -64 64c0 38 32 63 64 63s63 -26 63 -63zM319 -248c0 -34 -25 -64 -63 -64s-64 25 -64 64c0 38 32 63 64 63s63 -26 63 -63"],57582:[751,156,926,85,841,"841 -156h-756v50h344v268c-61 15 -107 70 -107 136s46 121 107 136v267h-344v50h756v-50h-346v-267c61 -15 107 -70 107 -136s-46 -121 -107 -136v-268h346v-50"],57583:[547,41,686,49,637,"637 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0h-327v135h327v-135"],58000:[135,0,633,-1,634,"634 0h-635v135h635v-135"],58001:[955,-820,325,-1,326,"326 820h-327v135h327v-135"],58311:[662,156,902,0,863,"863 662l-128 -133h-77l128 133h77zM645 435l-128 -133h-77l128 133h77zM429 209l-128 -133h-77l128 133h77zM205 -23l-128 -133h-77l128 133h77"],58312:[662,156,902,0,863,"205 529h-77l-128 133h77zM423 302h-77l-128 133h77zM639 76h-77l-128 133h77zM863 -156h-77l-128 133h77"]};MathJax.Ajax.loadComplete(MathJax.OutputJax.SVG.fontDir+"/Shapes/Regular/Main.js");
|
import subprocess
import sys
import setup_util
def start(args, logfile, errfile):
setup_util.replace_text("wicket/src/main/webapp/WEB-INF/resin-web.xml", "mysql:\/\/.*:3306", "mysql://" + args.database_host + ":3306")
try:
subprocess.check_call("mvn clean compile war:war", shell=True, cwd="wicket", stderr=errfile, stdout=logfile)
subprocess.check_call("rm -rf $RESIN_HOME/webapps/*", shell=True, stderr=errfile, stdout=logfile)
subprocess.check_call("cp wicket/target/hellowicket-1.0-SNAPSHOT.war $RESIN_HOME/webapps/wicket.war", shell=True, stderr=errfile, stdout=logfile)
subprocess.check_call("$RESIN_HOME/bin/resinctl start", shell=True, stderr=errfile, stdout=logfile)
return 0
except subprocess.CalledProcessError:
return 1
def stop(logfile, errfile):
try:
subprocess.check_call("$RESIN_HOME/bin/resinctl shutdown", shell=True, stderr=errfile, stdout=logfile)
return 0
except subprocess.CalledProcessError:
return 1
|
# Copyright 2011 Denali Systems, Inc.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""
Monitors interface (1.1 extension).
"""
import urllib
from vsmclient import base
class Monitor(base.Resource):
def __repr__(self):
try:
return "<MON: %s>" % self.id
except AttributeError:
return "<MON: Summary>"
def delete(self):
"""Delete this mon."""
self.manager.delete(self)
def update(self, **kwargs):
"""Update the display_name or display_description for this mds."""
self.manager.update(self, **kwargs)
class MonitorsManager(base.ManagerWithFind):
"""
Manage :class:`Monitor` resources.
"""
resource_class = Monitor
def get(self, mon_id):
"""
Get a mon.
:param mon_id: The ID of the monitor.
:rtype: :class:`Monitor`
"""
return self._get("/monitors/%s" % mon_id, "monitor")
def list(self, detailed=False, search_opts=None):
"""
Get a list of all mons.
:rtype: list of :class:`Monitor`
"""
if search_opts is None:
search_opts = {}
qparams = {}
for opt, val in search_opts.iteritems():
if val:
qparams[opt] = val
query_string = "?%s" % urllib.urlencode(qparams) if qparams else ""
detail = ""
if detailed:
detail = "/detail"
ret = self._list("/monitors%s%s" % (detail, query_string),
"monitors")
return ret
def delete(self, mon):
self._delete("/monitors/%s" % base.getid(mon))
def summary(self):
"""
summary
"""
url = "/monitors/summary"
return self._get(url, 'monitor-summary')
|
import { h } from 'vue'
export default {
name: "CircleSlice2",
vendor: "Mdi",
type: "",
tags: ["circle","slice","2"],
render() {
return h(
"svg",
{"xmlns":"http://www.w3.org/2000/svg","width":"24","height":"24","viewBox":"0 0 24 24","class":"v-icon","fill":"currentColor","data-name":"mdi-circle-slice-2","innerHTML":"<path d='M12 2C17.5 2 22 6.5 22 12C22 17.5 17.5 22 12 22C6.5 22 2 17.5 2 12C2 6.5 6.5 2 12 2M12 4C7.58 4 4 7.58 4 12C4 16.42 7.58 20 12 20C16.42 20 20 16.42 20 12C20 7.58 16.42 4 12 4M12 5C15.87 5 19 8.13 19 12H12V5Z' />"},
)
}
}
|
'use strict';
// NOTE: any test that hits the pathfinder CTE should be considered for serialising. This is
// because the rate limits on this environment are quite low, therefore parallel tests might cause
// failures due to these rate limits, which could be misleading. At the time of writing, all are.
// TODO:
// - test module crashes fatally upon failed reconnect
const test = require('ava');
const Config = require('../config');
const _conf = new Config();
const Pathfinder = require('../');
const mockSocket = require('./_mocksocket');
const Chance = require('chance');
const dnsPacket = require('dns-packet');
const nullLogger = () => {};
// Set l = consoleLogger to log output
const l = nullLogger;
const opts = { logger: { warn: l, info: l, debug: l, error: l } }; // eslint-disable-line no-console
// const opts = { logger: console.log.bind(console) }; // eslint-disable-line no-console
// TODO: considering the following note, tests using known good responses, and known bad responses
// should probably be updated. Perhaps just to perform a more nuanced check on the result of the
// pathfinder query. For example, checking that when the aa (authoritative answer) flag is set in
// the response, we get an mcc and mnc in the response.
// Note: at the time of writing these tests Neustar support was contacted for known good queries,
// and known bad queries. Their response contained a set of numbers identified as known good, and a
// set identified as known bad. When tested, numbers from the 'known good' set turned out to be bad,
// and vice versa. I decided to ignore this and assume that the data changed frequently enough that
// it couldn't be relied on.
const knownGoodResponses = {
// Authors phone numbers, and Orange, MTN test numbers
'22507008181': dnsPacket.streamDecode(Buffer.from('00c760b784000001000100020001013101380131013801300130013701300135013201320865313634656e756d036e65740000230001c00c00230001000003840048000a003201750c4532552b7073746e3a74656c33215e282e2a29242174656c3a5c313b73706e3d32333538303b6d63633d3631323b6d6e633d30333b715f737461743d3130392100c0180002000100000384000e036e7332036e6574046770727300c0180002000100000384000e036e7331036e65740467707273000000291400000000000000', 'hex')),
'22504004762': dnsPacket.streamDecode(Buffer.from('00c722a984000001000100020001013201360137013401300130013401300135013201320865313634656e756d036e65740000230001c00c00230001000003840048000a003201750c4532552b7073746e3a74656c33215e282e2a29242174656c3a5c313b73706e3d33373839353b6d63633d3631323b6d6e633d30353b715f737461743d3130392100c0180002000100000384000e036e7332036e6574046770727300c0180002000100000384000e036e7331036e65740467707273000000291400000000000000', 'hex')),
'447455888660': dnsPacket.streamDecode(Buffer.from('00c9eeba840000010001000200010130013601360138013801380135013501340137013401340865313634656e756d036e65740000230001c00c00230001000003840048000a003201750c4532552b7073746e3a74656c33215e282e2a29242174656c3a5c313b73706e3d31353838383b6d63633d3233343b6d6e633d32303b715f737461743d3131322100c0160002000100000384000e036e7332036e6574046770727300c0160002000100000384000e036e7331036e65740467707273000000291400000000000000', 'hex')),
'447710066017': dnsPacket.streamDecode(Buffer.from('00c97a61840000010001000200010137013101300136013601300130013101370137013401340865313634656e756d036e65740000230001c00c00230001000003840048000a003201750c4532552b7073746e3a74656c33215e282e2a29242174656c3a5c313b73706e3d34383334383b6d63633d3233343b6d6e633d30323b715f737461743d3131322100c0160002000100000384000e036e7332036e6574046770727300c0160002000100000384000e036e7331036e65740467707273000000291400000000000000', 'hex')),
// Numbers from Neustar
'14044560000': dnsPacket.streamDecode(Buffer.from('007a477a80000001000100000001013001310130013101340130013401380137013501310865313634656e756d036e65740000230001c00c0023000100000384002f000a003201750c4532552b7073746e3a74656c1a215e282e2a29242174656c3a5c313b715f737461743d31303321000000291400000000000000')),
'14042186358': dnsPacket.streamDecode(Buffer.from('00dee48984000001000100010001013001300130013001360135013401340130013401310865313634656e756d036e65740000230001c00c0023000100000384004e000a003201750c4532552b7073746e3a74656c39215e282e2a29242174656c3a5c313b6e7064693b73706e3d32363536393b6d63633d3331303b6d6e633d3031323b715f737461743d3030322100c00c00060001000003840039036e7331036e65740467707273000561646d696e036e7331076e6575737461720362697a00776496600001518000001c200036ee800002a3000000291400000000000000')),
'17062060133': dnsPacket.streamDecode(Buffer.from('00cd0e8a84000001000100020001013901350132013601380131013201340130013401310865313634656e756d036e65740000230001c00c0023000100000384004e000a003201750c4532552b7073746e3a74656c39215e282e2a29242174656c3a5c313b6e7064693b73706e3d32323039323b6d63633d3331303b6d6e633d3135303b715f737461743d3030312100c0140002000100000384000e036e7332036e6574046770727300c0140002000100000384000e036e7331036e65740467707273000000291400000000000000')),
'14042186259': dnsPacket.streamDecode(Buffer.from('00dc03e684000001000100010001013101370137013401350136013701330137013701310865313634656e756d036e65740000230001c00c0023000100000384004c000a003201750c4532552b7073746e3a74656c37215e282e2a29242174656c3a5c313b6e7064693b73706e3d34333235363b616c745f73706e3d36363534393b715f737461743d3030312100c00c00060001000003840039036e7331036e65740467707273000561646d696e036e7331076e6575737461720362697a00776496600001518000001c200036ee800002a3000000291400000000000000')),
'14042728246': dnsPacket.streamDecode(Buffer.from('00cecf1884000001000100010001013001350137013501340133013401310137013501310865313634656e756d036e65740000230001c00c0023000100000384003e000a003201750c4532552b7073746e3a74656c29215e282e2a29242174656c3a5c313b6e7064693b73706e3d35313538393b715f737461743d3030322100c00c00060001000003840039036e7331036e65740467707273000561646d696e036e7331076e6575737461720362697a00776496600001518000001c200036ee800002a3000000291400000000000000')),
'16786628313': dnsPacket.streamDecode(Buffer.from('00deb3bf84000001000100010001013301310133013801320136013601380137013601310865313634656e756d036e65740000230001c00c0023000100000384004e000a003201750c4532552b7073746e3a74656c39215e282e2a29242174656c3a5c313b6e7064693b73706e3d34303635343b6d63633d3331303b6d6e633d3136303b715f737461743d3030322100c00c00060001000003840039036e7331036e65740467707273000561646d696e036e7331076e6575737461720362697a00776496600001518000001c200036ee800002a3000000291400000000000000')),
'552177824301': dnsPacket.streamDecode(Buffer.from('00ce171584000001000100010001013101320133013201370133013501300137013701310865313634656e756d036e65740000230001c00c0023000100000384003e000a003201750c4532552b7073746e3a74656c29215e282e2a29242174656c3a5c313b6e7064693b73706e3d35363930313b715f737461743d3030322100c00c00060001000003840039036e7331036e65740467707273000561646d696e036e7331076e6575737461720362697a00776496600001518000001c200036ee800002a3000000291400000000000000')),
'12012048415': dnsPacket.streamDecode(Buffer.from('00c8f58684000001000100020001013501310134013801340130013201310130013201310865313634656e756d036e65740000230001c00c00230001000003840049000a003201750c4532552b7073746e3a74656c34215e282e2a29242174656c3a5c313b73706e3d34303635343b6d63633d3331303b6d6e633d3136303b715f737461743d3131332100c0120002000100000384000e036e7332036e6574046770727300c0120002000100000384000e036e7331036e65740467707273000000291400000000000000')),
'12012046732': dnsPacket.streamDecode(Buffer.from('00c8e85984000001000100020001013201330137013601340130013201310130013201310865313634656e756d036e65740000230001c00c00230001000003840049000a003201750c4532552b7073746e3a74656c34215e282e2a29242174656c3a5c313b73706e3d34373930353b6d63633d3331303b6d6e633d3132303b715f737461743d3131332100c0120002000100000384000e036e7332036e6574046770727300c0120002000100000384000e036e7331036e65740467707273000000291400000000000000')),
'19414685653': dnsPacket.streamDecode(Buffer.from('00cd1a0784000001000100020001013301350136013501380136013401310134013901310865313634656e756d036e65740000230001c00c0023000100000384004e000a003201750c4532552b7073746e3a74656c39215e282e2a29242174656c3a5c313b6e7064693b73706e3d32363536393b6d63633d3331303b6d6e633d3031323b715f737461743d3030312100c0140002000100000384000e036e7332036e6574046770727300c0140002000100000384000e036e7331036e65740467707273000000291400000000000000'))
};
const knownGoodNumbers = Object.keys(knownGoodResponses);
function getResponseFromWriteBuf(buf) {
const request = dnsPacket.streamDecode(buf);
const requestNum = request.questions[0].name.split('.').reverse().slice(2).join('');
return dnsPacket.streamEncode({ ...knownGoodResponses[requestNum], id: request.id });
}
async function setup({ connectFunc = undefined, conf = new Config() } = {}) {
conf.tls.rejectUnauthorized = _conf.env === 'live';
const pf = new Pathfinder(conf, opts);
await pf.connect(connectFunc);
return pf;
}
test.serial('Test positive pathfinder sandbox query functional', async function(t) {
// given
const pf = await setup();
// when
const results = await Promise.all(knownGoodNumbers.map(el => pf.query(el)));
// then
results.forEach(r => {
t.not(r.mnc, undefined, 'result contains mobile network code field');
t.not(r.mcc, undefined, 'result contains mobile country code field');
});
});
test.serial('Test negative pathfinder sandbox query functional', async function(t) {
// given
const knownBadNumbers = [
'355456567567',
'50945454545',
'1650555010',
'35555687678',
'13055552444',
'15784042020',
'18744100215',
'15784041010',
'12482821111',
'12162744004',
'12162744003',
'15714345750',
'17737654771',
'14795538218',
'16788271794',
'17705742609',
'17705372321',
'12177824301'
];
const pf = await setup();
// when
const results = await Promise.all(knownBadNumbers.map(n => pf.query(n)));
// then
results.forEach(r => {
// TODO: we could possibly also check whether the result contains the AA (authoritative
// answer) field.
t.is(r.mnc, undefined, 'result does not contain mobile network code field');
t.is(r.mcc, undefined, 'result does not contain mobile country code field');
});
t.pass();
});
test('Test pathfinder socket error during operation causes reconnect attempt', async function(t) {
// given
const { socket, connectFunc } = mockSocket.createSocket(opts);
const pf = await setup({ connectFunc });
pf.query(knownGoodNumbers[0]);
t.is(socket.destroyCallCount, 0, 'Expect socket to not have been destroyed before error occurs');
// when
socket.emit('error', new Error('Test error'));
// then
t.true(socket.destroyCallCount > 0, 'Expect socket to have been destroyed on pathfinder timeout');
});
test('Test pathfinder timeout causes reconnect attempt', async function(t) {
// given
// Here we simulate timeout by having our mock socket never emit a 'data' event
const { socket, connectFunc } = mockSocket.createSocket(opts);
t.is(socket.destroyCallCount, 0, 'Expect socket to not have been destroyed before test runs');
const conf = new Config();
conf.tls.rejectUnauthorized = _conf.env === 'live';
conf.pathfinderTimeoutMs = 1;
// Make this quite a bit bigger than pathfinder timeout, otherwise it might get called before
// the pathfinder timeout is hit. This does raise the question:
// TODO: test that all timeouts are cleared when any timeout occurs.
// I.e., if the query timeout is called, it should clear the pathfinder timeout, and vice versa
conf.queryTimeoutMs = 10000;
const pf = new Pathfinder(conf, opts);
await pf.connect(connectFunc);
// when
try {
await pf.query(knownGoodNumbers[0]);
} catch(err) {
// ignore
}
// then
t.true(socket.destroyCallCount > 0, 'Expect socket to have been destroyed on pathfinder timeout');
});
// pass
test('Test queries are not rejected before the queue limit is reached', async function(t) {
// given
const { connectFunc } = mockSocket.createSocket({
...opts,
writeCb: (sock, buf) => sock.emit('data', getResponseFromWriteBuf(buf))
});
const conf = new Config();
// TODO: Parametrise some of this stuff with jsverify
// TODO: maxQueueLength is limited to 5 numbers because this test was not consistently running
// to completion when run in parallel with the other tests. Another way to make it successfully
// run to completion was to make it serial. However, the root cause of this failure was never
// analysed. Changing the following line to: `const maxQueueLength = knownGoodNumbers.length`
// and running all the tests should reproduce the problem.
const maxQueueLength = Math.min(5, knownGoodNumbers.length);
const numQueries = maxQueueLength;
const numbers = knownGoodNumbers.slice(0, numQueries);
conf.maxQueriesPerSecond = 4;
conf.queryTimeoutMs = Math.round(1000 / conf.maxQueriesPerSecond) * maxQueueLength;
const pf = await setup({ conf, connectFunc });
// when
const resultsP = Promise.all(numbers.map(el => pf.query(el)));
//then
await t.notThrowsAsync(resultsP, 'No error occurs');
});
//pass
test('Test queries are rejected when the queue is full', async function(t) {
// given
const { socket, connectFunc } = mockSocket.createSocket(opts);
socket.write = buf => socket.emit('data', getResponseFromWriteBuf(buf));
const conf = new Config();
// TODO: Parametrise some of this stuff with jsverify
const maxQueueLength = knownGoodNumbers.length - 1;
const numQueries = maxQueueLength;
const numbers = knownGoodNumbers.slice(0, numQueries + 1);
conf.maxQueriesPerSecond = 4;
conf.queryTimeoutMs = Math.round(1000 / conf.maxQueriesPerSecond) * maxQueueLength;
const pf = await setup({ conf, connectFunc });
// when
const resultsP = Promise.all(numbers.map(el => pf.query(el)));
// then
const errMsg = `${maxQueueLength} messages queued. ` +
`Handling ${conf.maxQueriesPerSecond} per second. ` +
`Minimum queue processing duration ${conf.queryTimeoutMs}ms. ` +
`Message timeout ${conf.queryTimeoutMs}ms. ` +
'Service at capacity, message could not succeed.';
await t.throwsAsync(resultsP, errMsg);
});
// Note that this test is run serially to prevent timeouts induced by other tests
const _test = new Config().env === 'live' ? test.skip : test;
_test('Test the module can sustain the minimum query rate', async function(t) {
// given
// We're expecting 14 transactions per second, so we'll test for 30 pathfinder queries per
// second, for now.
const chance = new Chance();
let conf = new Config();
conf.maxQueriesPerSecond = 30;
conf.queryTimeoutMs = 2000;
const pf = await setup({ conf });
const opts = { mobile: true, formatted: false };
const prefix = { fr: '33', uk: '44', za: '27' };
// when
const numbers = ['fr','uk','za']
.reduce((arr, c) => arr.concat(Array(5).fill(c)), [])
.map(c => ({
prefix: prefix[c],
num: chance.phone({ country: c, ...opts }).replace(/^0/, '')
}));
// then
await t.notThrowsAsync(Promise.all(numbers.map(n => pf.query(n.prefix + n.num))));
});
test.serial('Test a set of random numbers, test returned country code is correct', async function(t) {
// given
const chance = new Chance();
const pf = await setup();
// Chance documentation is not caught up with chance source code. At the time of writing chance
// phone supports mobile for 'uk', 'za', 'fr'. In GH master, 'br' is also supported, but this
// doesn't appear to have been published to npm.
const mccMap = { '33': '208', '44': '234', '27': '655'};
const opts = { mobile: true, formatted: false };
const prefix = { fr: '33', uk: '44', za: '27' };
const numbers = ['fr','uk','za']
.reduce((arr, c) => arr.concat(Array(20).fill(c)), [])
.map(c => ({ prefix: prefix[c], num: chance.phone({ country: c, ...opts }).replace(/^0/, '') }));
// when
const results = await Promise.all(numbers.map(n => pf.query(n.prefix + n.num)));
// then
results.forEach((r, i) => {
t.not(r, undefined, `Expect result received for ${numbers[i]}`);
if (r.mcc) {
const mcc = mccMap[numbers[i].prefix];
t.is(r.mcc, mcc, `Expect ${numbers[i]} to have mobile country code ${mcc}`);
}
});
});
|
/*
Created by Andrew DeChristopher <drew@kiir.us> on 8/30/2016.
*/
// core libraries
const io = require('socket.io').listen(27000);
const ss = require('socket.io-stream');
const fs = require('fs');
const path = require('path');
const mime = require('mime');
const c = require('chalk');
// demo webservice libraries
const express = require('express');
const app = express();
const server = require('http').Server(app);
// custom libraries
const log = require('../modules/log');
const sms = require('../modules/sms');
const cfg = require('../modules/cfg');
// Define ERRORS and other constants
const KIWI = '[' + c.green('KIWI') + '] ';
const DEMO = '[' + c.green('DEMO') + '] ';
const DOWN = '[' + c.cyan('DOWN') + '] ';
const NJ1 = '[' + c.magenta('NJ1') + '] ';
const NJ2 = '[' + c.magenta('NJ2') + '] ';
// Log startup
log(KIWI + 'Demo Transit Server', '-demo-server');
// Demo upload socket handling and transit logistics
io.sockets.on('connection', function (socket) {
log(KIWI + 'Client connected', '-demo-server');
// For uploads from US-NJ1
ss(socket).on('us-nj1', function (stream, data) {
let filename = path.basename(data.name).split('\\');
filename = filename[filename.length - 1];
let filesize = data.size;
log(DEMO + NJ1 + 'Uploading: ' + filename + ' ~' + filesize + ' B', '-demo-server');
stream.pipe(fs.createWriteStream(cfg.demoDir + filename));
});
// For uploads from US-NJ2
ss(socket).on('us-nj2', function (stream, data) {
let filename = path.basename(data.name).split('\\');
filename = filename[filename.length - 1];
log(DEMO + NJ2 + 'Uploading: ' + filename + ' ~' + filesize + ' B', '-demo-server');
stream.pipe(fs.createWriteStream(cfg.demoDir + filename));
});
});
// Log express server start
server.listen(80, function () {
log(KIWI + 'Demo download μSrvc started.', '-demo-server');
});
// Send no demo ID given and then go back
app.get('/', function (req, res, next) {
try {
res.send('No demo ID provided. Returning to previous page.<script>setTimeout(function(){ window.history.back(); }, 3000);</script>');
// Disable logging for UptimeRobot requests to '/'
if (res.ip != '::ffff:63.143.42.242') {
log(DOWN + '[' + req.ip + '] GET /', '-demo-server');
}
} catch (e) {
next(e);
}
});
// Request for demo ID (ex. http://demo.kiir.us/15 OR http://demo.kiir.us/582)
app.get('/:id', function (req, res, next) {
try {
let id = req.params.id;
fs.exists(path.join(cfg.demoDir, 'kiwi-' + id + '.dem'), function (exists) {
if (exists) {
// res.download(path.join(cfg.demoDir, 'kiwi-' + id + '.dem'));
log(DOWN + 'Found demo @ ' + path.join(cfg.demoDir, 'kiwi-' + id + '.dem'), '-demo-server');
let file = path.join(cfg.demoDir, 'kiwi-' + id + '.dem');
let filename = path.basename(file);
let mimetype = mime.lookup(filename);
let filestream = fs.createReadStream(file);
res.setHeader('Content-disposition', 'attachment; filename=' + filename);
res.setHeader('Content-type', mimetype);
filestream.pipe(res);
} else {
log(DOWN + 'Invalid demo ID given: ' + id, '-demo-server');
res.send('Invalid demo ID. Returning to previous page.<script>setTimeout(function(){ window.history.back(); }, 3000);</script>');
}
});
log(DOWN + '[' + req.ip + '] GET /' + id + ' ~ kiwi-' + id + '.dem', '-demo-server');
} catch (e) {
next(e);
}
});
|
"""
Example demonstrating Proof Verification.
First Issuer creates Claim Definition for existing Schema.
After that, it issues a Claim to Prover (as in issue_credential.py example)
Once Prover has successfully stored its Claim, it uses Proof Request that he
received, to get Claims which satisfy the Proof Request from his wallet.
Prover uses the output to create Proof, using its Master Secret.
After that, Proof is verified against the Proof Request
"""
import asyncio
import json
import pprint
import sys
import time
from src.utils import run_coroutine, get_pool_genesis_txn_path, PROTOCOL_VERSION
from indy import pool, ledger, wallet, did, anoncreds, crypto
from indy.error import IndyError
seq_no = 1
pool_name = 'pool'
wallet_credentials = json.dumps({"key": "wallet_key"})
steward_wallet_config = json.dumps({"id": "steward_wallet"})
trust_anchor_wallet_config = json.dumps({"id": "issuer_wallet"})
pool_genesis_txn_path = get_pool_genesis_txn_path(pool_name)
pool_config = json.dumps({"genesis_txn": str(pool_genesis_txn_path)})
def print_log(value_color="", value_noncolor=""):
"""set the colors for text."""
HEADER = '\033[92m'
ENDC = '\033[0m'
print(HEADER + value_color + ENDC + str(value_noncolor))
async def proof_negotiation():
try:
# Set protocol version 2 to work with Indy Node 1.4
await pool.set_protocol_version(PROTOCOL_VERSION)
# 1.
print_log('\n1. Creates a new local pool ledger configuration that is used '
'later when connecting to ledger.\n')
await pool.create_pool_ledger_config(pool_name, pool_config)
# 2.
print_log('\n2. Open pool ledger and get handle from libindy\n')
pool_handle = await pool.open_pool_ledger(pool_name, None)
# 3.
print_log('\n3. Creating new issuer, steward, and prover secure wallet\n')
await wallet.create_wallet(trust_anchor_wallet_config, wallet_credentials)
await wallet.create_wallet(steward_wallet_config, wallet_credentials)
# 4.
print_log('\n4. Open wallet and get handle from libindy\n')
trust_anchor_wallet_handle = await wallet.open_wallet(trust_anchor_wallet_config, wallet_credentials)
steward_wallet_handle = await wallet.open_wallet(steward_wallet_config, wallet_credentials)
# 5.
print_log('\n5. Generating and storing steward DID and verkey\n')
steward_seed = '000000000000000000000000Steward1'
did_json = json.dumps({'seed': steward_seed})
steward_did, steward_verkey = await did.create_and_store_my_did(steward_wallet_handle, did_json)
print_log('Steward DID: ', steward_did)
print_log('Steward Verkey: ', steward_verkey)
# 6.
print_log(
'\n6. Generating and storing trust anchor (also issuer) DID and verkey\n')
trust_anchor_did, trust_anchor_verkey = await did.create_and_store_my_did(trust_anchor_wallet_handle, "{}")
print_log('Trust anchor DID: ', trust_anchor_did)
print_log('Trust anchor Verkey: ', trust_anchor_verkey)
# 7.
print_log('\n7. Building NYM request to add Trust Anchor to the ledger\n')
nym_transaction_request = await ledger.build_nym_request(submitter_did=steward_did,
target_did=trust_anchor_did,
ver_key=trust_anchor_verkey,
alias=None,
role='TRUST_ANCHOR')
print_log('NYM transaction request: ')
pprint.pprint(json.loads(nym_transaction_request))
# 8.
print_log('\n8. Sending NYM request to the ledger\n')
nym_transaction_response = await ledger.sign_and_submit_request(pool_handle=pool_handle,
wallet_handle=steward_wallet_handle,
submitter_did=steward_did,
request_json=nym_transaction_request)
print_log('NYM transaction response: ')
pprint.pprint(json.loads(nym_transaction_response))
(gvt_schema_id, gvt_schema) = await anoncreds.issuer_create_schema(trust_anchor_did, 'gvt', '1.0',
json.dumps(['age', 'sex', 'height', 'name']))
schema_request = await ledger.build_schema_request(trust_anchor_did, gvt_schema)
await ledger.sign_and_submit_request(pool_handle, trust_anchor_wallet_handle, trust_anchor_did, schema_request)
print_log('Create Schema and submiting to ledger')
pprint.pprint(json.loads(schema_request))
time.sleep(1)
get_schema_request = await ledger.build_get_schema_request(trust_anchor_did, gvt_schema_id)
get_schema_response = await ledger.submit_request(pool_handle, get_schema_request)
(schema_id, gvt_schema_json) = await ledger.parse_get_schema_response(get_schema_response)
# 11.
print_log(
'\n11. Creating and storing CRED DEFINITION using anoncreds as Trust Anchor, for the given Schema\n')
cred_def_tag = 'cred_def_tag'
cred_def_type = 'CL'
cred_def_config = json.dumps({"support_revocation": False})
(cred_def_id, cred_def_json) = await anoncreds.issuer_create_and_store_credential_def(trust_anchor_wallet_handle, trust_anchor_did, gvt_schema_json, cred_def_tag, cred_def_type, cred_def_config)
cred_def_request = await ledger.build_cred_def_request(trust_anchor_did, cred_def_json)
await ledger.sign_and_submit_request(pool_handle, trust_anchor_wallet_handle, trust_anchor_did, cred_def_request)
print_log('Credential definition: ')
pprint.pprint(json.loads(cred_def_json))
# 12.
print_log('\n12. Creating Prover wallet and opening it to get the handle\n')
prover_did = 'VsKV7grR1BUE29mG2Fm2kX'
prover_wallet_config = json.dumps({"id": "prover_wallet"})
prover_wallet_credentials = json.dumps({"key": "prover_wallet_key"})
await wallet.create_wallet(prover_wallet_config, prover_wallet_credentials)
prover_wallet_handle = await wallet.open_wallet(prover_wallet_config, prover_wallet_credentials)
# 13.
print_log('\n13. Prover is creating Master Secret\n')
master_secret_name = 'master_secret'
master_secret_id = await anoncreds.prover_create_master_secret(prover_wallet_handle, master_secret_name)
# 14.
print_log(
'\n14. Issuer (Trust Anchor) is creating a Credential Offer for Prover\n')
schema_json = json.dumps(gvt_schema)
cred_offer_json = await anoncreds.issuer_create_credential_offer(trust_anchor_wallet_handle, cred_def_id)
print_log('Credential Offer: ')
pprint.pprint(json.loads(cred_offer_json))
# 15.
print_log(
'\n15. Prover creates Credential Request for the given credential offer\n')
(cred_req_json, cred_req_metadata_json) = await anoncreds.prover_create_credential_req(prover_wallet_handle, prover_did, cred_offer_json, cred_def_json, master_secret_id)
print_log('Credential Request: ')
pprint.pprint(json.loads(cred_req_json))
# 16.
print_log(
'\n16. Issuer (Trust Anchor) creates Credential for Credential Request\n')
cred_values_json = json.dumps({
'sex': ['male', '5944657099558967239210949258394887428692050081607692519917050011144233115103'],
'name': ['Alex', '1139481716457488690172217916278103335'],
'height': ['175', '175'],
'age': ['28', '28']
})
(cred_json, _, _) = await anoncreds.issuer_create_credential(trust_anchor_wallet_handle, cred_offer_json, cred_req_json, cred_values_json, None, None)
print_log('Credential: ')
pprint.pprint(json.loads(cred_json))
# 17.
print_log('\n17. Prover processes and stores Credential\n')
await anoncreds.prover_store_credential(prover_wallet_handle, None, cred_req_metadata_json, cred_json, cred_def_json, None)
# 18.
print_log('\n18. Prover gets Claims for Proof Request\n')
proof_request = {
'nonce': '123432421212',
'name': 'gvt',
'version': '0.1',
'requested_attributes': {
'attr1_referent': {
'name': 'name'
}
},
'requested_predicates': {
'predicate1_referent': {
'name': 'age',
'p_type': '>=',
'p_value': 18,
}
}
}
#'restrictions': [{'issuer_did': trust_anchor_did}]
print_log('Proof Request: ')
pprint.pprint(proof_request)
proof_req_json = json.dumps(proof_request)
claims_for_proof_request_json = await anoncreds.prover_get_credentials_for_proof_req(prover_wallet_handle, \
proof_req_json)
claims_for_proof_request = json.loads(claims_for_proof_request_json)
print_log('Claims for Proof Request: ')
pprint.pprint(claims_for_proof_request)
# 19.
print_log('\n19. Prover creates Proof for Proof Request\n')
referent = claims_for_proof_request['attrs']['attr1_referent'][0]['cred_info']['referent']
print_log('Referent: ')
pprint.pprint(referent)
requested_credentials_json = json.dumps({
'self_attested_attributes': {},
'requested_attributes': {
'attr1_referent': {'cred_id': referent, 'revealed': True}
},
'requested_predicates': {
'predicate1_referent': {'cred_id': referent}
}
})
pprint.pprint(json.loads(requested_credentials_json))
schemas_json = {}
get_schema_request = await ledger.build_get_schema_request(prover_did, schema_id)
get_schema_response = await ledger.submit_request(pool_handle, get_schema_request)
(received_schema_id, received_schema) = await ledger.parse_get_schema_response(get_schema_response)
schemas_json[received_schema_id] = json.loads(received_schema)
cred_defs_json = {}
get_cred_def_request = await ledger.build_get_cred_def_request(prover_did, cred_def_id)
get_cred_def_response = await ledger.submit_request(pool_handle, get_cred_def_request)
(received_cred_def_id, received_cred_def) = await ledger.parse_get_cred_def_response(get_cred_def_response)
cred_defs_json[received_cred_def_id] = json.loads(received_cred_def)
revoc_regs_json = json.dumps({})
proof_json = await anoncreds.prover_create_proof(prover_wallet_handle, proof_req_json, requested_credentials_json, master_secret_id, json.dumps(schemas_json), json.dumps(cred_defs_json), revoc_regs_json)
proof = json.loads(proof_json)
#assert 'Alex' == proof['requested_proof']['revealed_attrs']['attr1_referent'][1]
# 20.
print_log('\n20.Verifier is verifying proof from Prover\n')
assert await anoncreds.verifier_verify_proof(proof_req_json, proof_json, json.dumps(schemas_json), json.dumps(cred_defs_json), revoc_regs_json, "{}")
# 21
print_log('\n21. Closing both wallet_handles\n')
await wallet.close_wallet(steward_wallet_handle)
await wallet.close_wallet(trust_anchor_wallet_handle)
await wallet.close_wallet(prover_wallet_handle)
# 22
print_log('\n22. Deleting created wallet_handles\n')
await wallet.delete_wallet(steward_wallet_config, wallet_credentials)
await wallet.delete_wallet(trust_anchor_wallet_config, wallet_credentials)
await wallet.delete_wallet(prover_wallet_config, prover_wallet_credentials)
except IndyError as e:
print('Error occurred: %s' % e)
def main():
loop = asyncio.get_event_loop()
loop.run_until_complete(proof_negotiation())
loop.close()
if __name__ == '__main__':
main()
|
import chai from 'chai';
chai.should();
import merge from '../src/merge';
describe('merge', () => {
it('two empty objects', () => {
merge({}, {}).should.deep.equal({});
});
it('empty and plain objects', () => {
const o = { id: 25994, name: 'Terra', visible: true };
const expected = o;
merge({}, o).should.deep.equal(expected);
merge(o, {}).should.deep.equal(expected);
});
it('two plain objects', () => {
merge({
id: 25994,
name: 'Terra'
}, {
visible: true
}).should.deep.equal({
id: 25994,
name: 'Terra',
visible: true
});
});
it('two plain objects with dates', () => {
const date = new Date('2016-10-01T15:00:00Z');
merge(
{ id: 25994, name: 'Terra' },
{ visible: true, date: date }
).should.deep.equal({
id: 25994,
name: 'Terra',
visible: true,
date: date
});
});
it('two intersected plain objects', () => {
merge({
id: 25994,
name: 'Terra',
visible: true
}, {
id: 27424,
name: 'Aqua',
group: 'Remote sensing'
}).should.deep.equal({
id: 27424,
name: 'Aqua',
visible: true,
group: 'Remote sensing'
});
});
it('plain object and object with plain nested object', () => {
merge({
language: 'ru',
sidebarPanelId: 'satellite'
}, {
map: {
name: 'Map',
visible: true
}
}).should.deep.equal({
language: 'ru',
sidebarPanelId: 'satellite',
map: {
name: 'Map',
visible: true
}
});
});
it('intersected plain object and object with plain nested object', () => {
merge({
language: 'ru',
sidebarPanelId: 'satellite'
}, {
language: {
en: 'English',
ru: 'Russian'
}
}).should.deep.equal({
language: {
en: 'English',
ru: 'Russian'
},
sidebarPanelId: 'satellite'
})
});
it('two objects with nested intersected plain objects', () => {
const o1 = {
language: {
en: 'English',
ru: 'Русский'
},
sidebarPanelId: 'satellite'
};
const o2 = {
language: {
ru: 'Russian',
de: 'German'
},
sidebarPanelId: 'geocoding'
};
merge(o1, o2).should.deep.equal({
language: {
en: 'English',
ru: 'Russian',
de: 'German'
},
sidebarPanelId: 'geocoding'
});
merge(o2, o1).should.deep.equal({
language: {
en: 'English',
ru: 'Русский',
de: 'German'
},
sidebarPanelId: 'satellite'
});
});
it('two empty arrays', () => {
merge([], []).should.deep.equal([]);
});
it('empty array and plain array', () => {
const a = [1, 2, 3];
const expected = a;
merge([], a).should.deep.equal(expected);
merge(a, []).should.deep.equal(expected);
});
it('two plain arrays', () => {
merge([1, 2, 3], ['one', 'two', 'three']).should.deep.equal([1, 2, 3, 'one', 'two', 'three']);
});
it('two mixed arrays', () => {
merge(
[1, 2, 3, { id: 25994, name: 'Terra', visible: true }],
[{ id: 25994, visible: false }, 'one', 'two', 'three'],
'id'
).should.deep.equal([1, 2, 3, { id: 25994, name: 'Terra', visible: false }, 'one', 'two', 'three']);
});
it('two arrays of objects with intersected identifiers', () => {
const a1 = [
{ id: 25994, name: 'Terra', visible: true },
{ id: 27424, name: 'Aqua', visible: false }
];
const a2 = [
{ id: 27424, name: 'AQUA' },
{ id: 25994, visible: false }
];
merge(a1, a2, 'id').should.deep.equal([
{ id: 25994, name: 'Terra', visible: false },
{ id: 27424, name: 'AQUA', visible: false }
]);
merge(a2, a1, 'id').should.deep.equal([
{ id: 27424, name: 'Aqua', visible: false },
{ id: 25994, name: 'Terra', visible: true },
]);
merge(a1, a2, 'name').should.deep.equal([
{ id: 25994, name: 'Terra', visible: true },
{ id: 27424, name: 'Aqua', visible: false },
{ id: 27424, name: 'AQUA' },
{ id: 25994, visible: false }
]);
merge(a2, a1, 'name').should.deep.equal([
{ id: 27424, name: 'AQUA' },
{ id: 25994, visible: false },
{ id: 25994, name: 'Terra', visible: true },
{ id: 27424, name: 'Aqua', visible: false }
]);
});
it('two objects with nested arrays', () => {
const o1 = {
satellites: [
{ id: 25994, name: 'Terra', visible: true },
{ id: 27424, name: 'Aqua', visible: false }
]
};
const o2 = {
satellites: [
{ id: 27424, name: 'AQUA', visible: true },
{ id: 40069, name: 'Terra' }
]
};
merge(o1, o2, 'id').should.deep.equal({
satellites: [
{ id: 25994, name: 'Terra', visible: true },
{ id: 27424, name: 'AQUA', visible: true },
{ id: 40069, name: 'Terra' }
]
});
merge(o2, o1, 'id').should.deep.equal({
satellites: [
{ id: 27424, name: 'Aqua', visible: false },
{ id: 40069, name: 'Terra' },
{ id: 25994, name: 'Terra', visible: true }
]
});
merge(o1, o2, 'id', 'name').should.deep.equal({
satellites: [
{ id: 40069, name: 'Terra', visible: true },
{ id: 27424, name: 'AQUA', visible: true }
]
});
merge(o1, o2, 'name', 'id').should.deep.equal({
satellites: [
{ id: 40069, name: 'Terra', visible: true },
{ id: 27424, name: 'AQUA', visible: true }
]
});
merge(o2, o1, 'name', 'id').should.deep.equal({
satellites: [
{ id: 27424, name: 'Aqua', visible: false },
{ id: 25994, name: 'Terra', visible: true }
]
});
});
it('two plain objects, overwriting existing only', () => {
const o1 = { id: 25994, name: 'Terra', visible: false };
const o2 = { sidebarPanelId: 'satellite', visible: true };
merge(o1, o2, true).should.deep.equal({
id: 25994, name: 'Terra', visible: true
});
merge(o2, o1, true).should.deep.equal({
sidebarPanelId: 'satellite', visible: false
});
});
it('two objects with nested objects, overwriting existing only', () => {
const o1 = {
language: { en: 'English', ru: 'Русский' },
sidebarPanelId: 'satellite',
user: { name: 'Alice', age: 30 }
};
const o2 = {
language: { ru: 'Russian', de: 'German' },
sidebarPanelId: 'geocoding'
};
merge(o1, o2, true).should.deep.equal({
language: { en: 'English', ru: 'Russian' },
sidebarPanelId: 'geocoding',
user: { name: 'Alice', age: 30 }
});
merge(o2, o1, true).should.deep.equal({
language: { ru: 'Русский', de: 'German' },
sidebarPanelId: 'satellite'
});
});
it('two objects with plain nested arrays, overwriting existing only', () => {
const o1 = {
language: ['en', 'ru'],
sidebarPanelId: 'satellite'
};
const o2 = {
language: ['de'],
sidebarPanelId: 'geocoding',
user: ['Alice', 'Bob']
};
merge(o1, o2, true).should.deep.equal({
language: ['en', 'ru', 'de'],
sidebarPanelId: 'geocoding'
});
merge(o2, o1, true).should.deep.equal({
language: ['de', 'en', 'ru'],
sidebarPanelId: 'satellite',
user: ['Alice', 'Bob']
});
});
it('two objects with nested arrays, overwriting existing only', () => {
const o1 = {
satellites: [
{ id: 25994, name: 'Terra', visible: true },
{ id: 27424, name: 'Aqua', visible: false }
]
};
const o2 = {
satellites: [
{ id: 25994, visible: false },
{ id: 40069, name: 'Meteor-M №2', visible: true }
]
};
merge(o1, o2, 'id', true).should.deep.equal({
satellites: [
{ id: 25994, name: 'Terra', visible: false },
{ id: 27424, name: 'Aqua', visible: false }
]
});
merge(o2, o1, 'id', true).should.deep.equal({
satellites: [
{ id: 25994, visible: true },
{ id: 40069, name: 'Meteor-M №2', visible: true }
]
});
});
});
|
import codecs
import os
import re
class MovieCorpus:
"""A class to load cornel movie corpus data"""
def __init__(self, data_path, input_paths, label_col, limit=None):
self.paths = [os.path.join(data_path, input_path) for input_path in input_paths]
self.label_col = label_col
self.limit = limit
def __call__(self):
# Define path to new file
# datafile = os.path.join(self.path, "formatted_movie_lines.txt")
delimiter = "\t"
# Unescape the delimiter
delimiter = str(codecs.decode(delimiter, "unicode_escape"))
# Initialize lines dict, conversations list, and field ids
lines = {}
conversations = []
MOVIE_LINES_FIELDS = ["lineID", "characterID", "movieID", "character", "text"]
MOVIE_CONVERSATIONS_FIELDS = [
"character1ID",
"character2ID",
"movieID",
"utteranceIDs",
]
# Load lines and process conversations
print("\nProcessing corpus...")
lines = self.loadLines(
os.path.join(self.paths[0], "movie_lines.txt"), MOVIE_LINES_FIELDS
)
print("\nLoading conversations...")
conversations = self.loadConversations(
os.path.join(self.paths[0], "movie_conversations.txt"),
lines,
MOVIE_CONVERSATIONS_FIELDS,
)
res = []
for pair in self.extractSentencePairs(conversations):
res.append([pair[0], {self.label_col: pair[1]}])
print("Loading data from disk finished")
if self.limit:
return [res[: self.limit]]
return [res]
# Splits each line of the file into a dictionary of fields
def loadLines(self, fileName, fields):
lines = {}
with open(fileName, "r", encoding="iso-8859-1") as f:
for line in f:
values = line.split(" +++$+++ ")
# Extract fields
lineObj = {}
for i, field in enumerate(fields):
lineObj[field] = values[i]
lines[lineObj["lineID"]] = lineObj
return lines
# Groups fields of lines from `loadLines` into conversations based on *movie_conversations.txt*
def loadConversations(self, fileName, lines, fields):
conversations = []
with open(fileName, "r", encoding="iso-8859-1") as f:
for line in f:
values = line.split(" +++$+++ ")
# Extract fields
convObj = {}
for i, field in enumerate(fields):
convObj[field] = values[i]
# Convert string to list (convObj["utteranceIDs"] == "['L598485', 'L598486', ...]")
utterance_id_pattern = re.compile("L[0-9]+")
lineIds = utterance_id_pattern.findall(convObj["utteranceIDs"])
# Reassemble lines
convObj["lines"] = []
for lineId in lineIds:
convObj["lines"].append(lines[lineId])
conversations.append(convObj)
return conversations
# Extracts pairs of sentences from conversations
def extractSentencePairs(self, conversations):
qa_pairs = []
for conversation in conversations:
# Iterate over all the lines of the conversation
for i in range(
len(conversation["lines"]) - 1
): # We ignore the last line (no answer for it)
inputLine = conversation["lines"][i]["text"].strip()
targetLine = conversation["lines"][i + 1]["text"].strip()
# Filter wrong samples (if one of the lists is empty)
if inputLine and targetLine:
qa_pairs.append([inputLine, targetLine])
return qa_pairs
|
from gpiozero import MotionSensor
from subprocess import call
from time import sleep
# Time to wait until display should turn off after last motion detected
timeUntilDisplayOff = 1800
# The GPIO data pin to which the PIR sensor is connected
pin = 14
pir = MotionSensor(pin)
timer = timeUntilDisplayOff
while True:
if pir.motion_detected:
timer = timeUntilDisplayOff
print ("Motion detected! Setting timer to " + str(timer) + " seconds.")
if timer > 0:
if timer % 10 == 0:
print ("Timer: " + str(timer) + " seconds")
timer -= 1
elif timer == 0:
call(['vcgencmd', 'display_power', '0'])
print ("Timer is 0. Display turned off. Waiting for motion...")
# display is now off. we wait for motion and turn it on
pir.wait_for_motion()
call(['vcgencmd', 'display_power', '1'])
timer = timeUntilDisplayOff
sleep(1)
|
/*
*
* Copyright (C) 2011, 2015 Cisco Systems, Inc.
* Copyright (C) 2015 CBA research group, Technical University of Catalonia.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at:
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*/
#ifndef SOCKETS_H_
#define SOCKETS_H_
#include "../defs.h"
#include "sockets-util.h"
#include "packets.h"
#include "../liblisp/lisp_address.h"
#include "lbuf.h"
typedef enum {
SOCK_READ,
SOCK_WRITE,
} sock_type_e;
/*
* inspired by quagga thread.c
* It might be a little bit of an overkill for now
* but it could prove useful in the future
*/
typedef struct sock_list {
struct sock *head;
struct sock *tail;
int count;
int maxfd;
}sock_list_t;
typedef struct sock {
sock_type_e type;
int (*recv_cb)(struct sock *);
void *arg;
int fd;
struct sock *next;
struct sock *prev;
}sock_t;
typedef struct uconn {
/* TODO: decide if la, ra should be IP */
lisp_addr_t la; /* local address */
lisp_addr_t ra; /* remote address */
uint16_t lp; /* local port */
uint16_t rp; /* remote port */
} uconn_t;
typedef struct sockmstr {
sock_list_t read;
// struct sock_list *write;
// struct sock_list *netlink;
fd_set readfds;
// fd_set *writefds;
// fd_set *netlinkfds;
} sockmstr_t;
union sockunion {
struct sockaddr_in s4;
struct sockaddr_in6 s6;
};
typedef struct fwd_entry {
lisp_addr_t *srloc;
lisp_addr_t *drloc;
int *out_sock;
} fwd_entry_t;
inline fwd_entry_t *fwd_entry_new_init(lisp_addr_t *srloc, lisp_addr_t *drloc,
int *out_socket);
inline void fwd_entry_del(fwd_entry_t *fwd_entry);
static inline void fwd_entry_set_srloc(fwd_entry_t *fwd_ent, lisp_addr_t * srloc);
static inline void fwd_entry_set_drloc(fwd_entry_t *fwd_ent, lisp_addr_t * drloc);
typedef struct iface iface_t;
sockmstr_t *sockmstr_create();
void sockmstr_destroy(sockmstr_t *sm);
struct sock *sockmstr_register_get_by_fd(sockmstr_t *m, int fd);
struct sock *sockmstr_register_read_listener(sockmstr_t *m,
int (*)(struct sock *), void *arg, int fd);
int sockmstr_unregister_read_listenedr(sockmstr_t *m, struct sock *sock);
void sockmstr_process_all(sockmstr_t *m);
void sockmstr_wait_on_all_read(sockmstr_t *m);
int open_data_raw_input_socket(int afi);
int open_data_datagram_input_socket(int afi);
int open_control_input_socket(int afi);
int sock_recv(int, lbuf_t *);
int sock_ctrl_recv(int, lbuf_t *, uconn_t *);
int sock_data_recv(int sock, lbuf_t *b, int *afi, uint8_t *ttl, uint8_t *tos);
inline int uconn_init(uconn_t *uc, int lp, int rp, lisp_addr_t *la,
lisp_addr_t *ra);
static inline void
fwd_entry_set_srloc(fwd_entry_t *fwd_ent, lisp_addr_t * srloc)
{
fwd_ent->srloc = srloc;
}
static inline void
fwd_entry_set_drloc(fwd_entry_t *fwd_ent, lisp_addr_t * drloc)
{
fwd_ent->drloc = drloc;
}
#endif /*SOCKETS_H_*/
|
# Copyright 2020 The T5 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Add Mixtures to the registry.
This module contains different mixtures for training T5 models.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import t5.data.tasks # pylint: disable=unused-import
from t5.data.utils import MixtureRegistry
from t5.data.utils import rate_num_examples
from t5.data.utils import rate_unsupervised
import tensorflow_datasets as tfds
# We omit WNLI because we train on WSC/DPR simple instead
_glue_tasks = [
"glue_%s_v002" % b.name
for b in tfds.text.glue.Glue.builder_configs.values()
if "wnli" not in b.name
]
_wsc_dpr_tasks = [
"dpr_v001_simple",
"super_glue_wsc_v102_simple_train",
"super_glue_wsc_v102_simple_eval",
]
_super_glue_tasks = _wsc_dpr_tasks + [
"super_glue_%s_v102" % b.name
for b in tfds.text.super_glue.SuperGlue.builder_configs.values()
if "wsc" not in b.name
]
_supervised_tasks = (
_glue_tasks + _super_glue_tasks +
["cnn_dailymail_v002",
"squad_v010_allanswers",
"wmt_t2t_ende_v003",
"wmt15_enfr_v003",
"wmt16_enro_v003"]
)
# Tasks is not the best description. For example, glue_v002_equal refers to a
# mixture. Calling it "finetune tasks" because we consider all glue tasks as
# a single dataset to train on.
_finetune_tasks = [
"glue_v002_proportional", # mixture
"super_glue_v102_proportional", # mixture
"cnn_dailymail_v002",
"squad_v010_allanswers",
"wmt_t2t_ende_v003",
"wmt15_enfr_v003",
"wmt16_enro_v003"
]
# ========================== GLUE and SuperGLUE ================================
MixtureRegistry.add(
"glue_v002_proportional",
_glue_tasks, default_rate=rate_num_examples)
MixtureRegistry.add(
"super_glue_v102_proportional",
_super_glue_tasks,
default_rate=rate_num_examples)
# mnli and its associated dev sets: mnli_matched and mnli_mismatched
MixtureRegistry.add(
"glue_mnli_and_dev_v002",
[t for t in _glue_tasks if "mnli" in t],
default_rate=1.0)
# ============================== Co-training ===================================
# C4, glue, squad, superglue
# The supervised tasks here are all small datasets
# Mix them proportionally to their dataset sizes.
# TODO(noam): This should be called "small_mix" or something, but we will
# keep it as en_mix to avoid restarting experiments.
# TODO(noam): some rates should be reduced - but not now to avoid restarting
# experiments. They are:
# - Tasks duplicated between glue and superglue (see _dedupe)
# - squad and glue_qnli are duplicates
# - glue_sst2 may contain overlapping phrases (related examples with itself)
# - we seem to overtrain on super_glue_record - don't know why
MixtureRegistry.add(
"en_mix",
[("c4_v020_unsupervised", rate_unsupervised)] +
_glue_tasks + _super_glue_tasks +
["squad_v010_allanswers"],
default_rate=rate_num_examples)
MixtureRegistry.add(
"all_equal",
_supervised_tasks + ["c4_v020_unsupervised"],
default_rate=1.,
)
def _dedupe(name):
if "glue" in name and "rte" in name:
return functools.partial(rate_num_examples, scale=0.5)
return rate_num_examples
MixtureRegistry.add(
"all_proportional",
[(t, _dedupe(t)) for t in _supervised_tasks + ["c4_v020_unsupervised"]],
)
# all_mix is the same as all_proportional except it uses rate_unsupervised
# for c4_v020_unsupervised. This is useful if you want to specify a specific
# rate for the unsupervised task which is different from the global value for
# rate_num_examples.maximum
# If you use this task, you should set a maximum rate value via gin e.g.
# --gin_param="dataset_utils.rate_num_examples.maximum = 1e6"
MixtureRegistry.add(
"all_mix",
([("c4_v020_unsupervised", rate_unsupervised)] +
[(t, _dedupe(t)) for t in _supervised_tasks]),
)
# ================== Leave-one-out cotrain then finetune =======================
for task_name in _finetune_tasks:
task_names = set(_supervised_tasks + ["c4_v020_unsupervised"])
# Special case to treat all GLUE tasks as one task.
if task_name == "glue_v002_proportional":
task_names -= set(_glue_tasks)
# No de-duping needed
tasks = [(t, rate_num_examples) for t in task_names]
# Special case to treat all Super GLUE tasks as one task.
elif task_name == "super_glue_v102_proportional":
task_names -= set(_super_glue_tasks)
# No de-duping needed
tasks = [(t, rate_num_examples) for t in task_names]
else:
task_names -= {task_name}
# Use de-duping since we have GLUE and SuperGLUE
tasks = [(t, _dedupe(t)) for t in task_names]
MixtureRegistry.add("leave_one_out_{}".format(task_name), tasks)
# ================= Pre-train on supervised tasks ==============================
_large_translation_tasks = ["wmt_t2t_ende_v003",
"wmt15_enfr_v003"]
_large_supervised_tasks = _large_translation_tasks + ["cnn_dailymail_v002"]
MixtureRegistry.add(
"large_supervised_equal",
_large_supervised_tasks,
default_rate=1.0)
MixtureRegistry.add(
"large_supervised_proportional",
_large_supervised_tasks,
default_rate=rate_num_examples)
MixtureRegistry.add(
"large_translation_equal",
_large_translation_tasks,
default_rate=1.0)
# =========================== Squad + Trivia QA ================================
MixtureRegistry.add(
"squad_trivia_qa_equal",
["squad_v010_allanswers", "trivia_qa_v010"],
default_rate=1.0)
# ================================= WSC + DPR ==================================
MixtureRegistry.add(
"wsc_dpr_simple_proportional",
_wsc_dpr_tasks,
default_rate=rate_num_examples)
|
const { Joi } = require('../../../utils/validation');
module.exports = {
tx_uid: Joi.object()
.bignumber()
.required(),
id: Joi.string()
.base58()
.required(),
height: Joi.number().required(),
tx_type: Joi.number()
.min(1)
.max(16)
.required(),
tx_version: Joi.number()
.required()
.allow(null),
fee: Joi.object()
.bignumber()
.required(),
time_stamp: Joi.date().required(),
signature: Joi.string()
.base58()
.required()
.allow(null),
proofs: Joi.array().required(),
sender: Joi.string()
.base58()
.required(),
sender_public_key: Joi.string()
.base58()
.required(),
};
|
from __future__ import print_function
import glob
import itertools as it
import numpy as np
import os
import sys
import xgboost as xgb
try:
from sklearn import datasets
from sklearn.preprocessing import scale
except ImportError:
None
class Dataset:
def __init__(self, name, get_dataset, objective, metric, use_external_memory=False):
self.name = name
self.objective = objective
self.metric = metric
self.X, self.y = get_dataset()
self.use_external_memory = use_external_memory
def get_boston():
data = datasets.load_boston()
return data.data, data.target
def get_digits():
data = datasets.load_digits()
return data.data, data.target
def get_cancer():
data = datasets.load_breast_cancer()
return data.data, data.target
def get_sparse():
rng = np.random.RandomState(199)
n = 5000
sparsity = 0.75
X, y = datasets.make_regression(n, random_state=rng)
X = np.array([[0.0 if rng.uniform(0, 1) < sparsity else x for x in x_row] for x_row in X])
from scipy import sparse
X = sparse.csr_matrix(X)
return X, y
def train_dataset(dataset, param_in, num_rounds=10, scale_features=False):
param = param_in.copy()
param["objective"] = dataset.objective
if dataset.objective == "multi:softmax":
param["num_class"] = int(np.max(dataset.y) + 1)
param["eval_metric"] = dataset.metric
if scale_features:
X = scale(dataset.X, with_mean=isinstance(dataset.X, np.ndarray))
else:
X = dataset.X
if dataset.use_external_memory:
np.savetxt('tmptmp_1234.csv', np.hstack((dataset.y.reshape(len(dataset.y), 1), X)),
delimiter=',')
dtrain = xgb.DMatrix('tmptmp_1234.csv?format=csv&label_column=0#tmptmp_')
else:
dtrain = xgb.DMatrix(X, dataset.y)
print("Training on dataset: " + dataset.name, file=sys.stderr)
print("Using parameters: " + str(param), file=sys.stderr)
res = {}
bst = xgb.train(param, dtrain, num_rounds, [(dtrain, 'train')],
evals_result=res, verbose_eval=False)
# Free the booster and dmatrix so we can delete temporary files
bst_copy = bst.copy()
del bst
del dtrain
# Cleanup temporary files
if dataset.use_external_memory:
for f in glob.glob("tmptmp_*"):
os.remove(f)
return {"dataset": dataset, "bst": bst_copy, "param": param.copy(),
"eval": res['train'][dataset.metric]}
def parameter_combinations(variable_param):
"""
Enumerate all possible combinations of parameters
"""
result = []
names = sorted(variable_param)
combinations = it.product(*(variable_param[Name] for Name in names))
for set in combinations:
param = {}
for i, name in enumerate(names):
param[name] = set[i]
result.append(param)
return result
def run_suite(param, num_rounds=10, select_datasets=None, scale_features=False):
"""
Run the given parameters on a range of datasets. Objective and eval metric will be automatically set
"""
datasets = [
Dataset("Boston", get_boston, "reg:linear", "rmse"),
Dataset("Digits", get_digits, "multi:softmax", "merror"),
Dataset("Cancer", get_cancer, "binary:logistic", "error"),
Dataset("Sparse regression", get_sparse, "reg:linear", "rmse"),
Dataset("Boston External Memory", get_boston, "reg:linear", "rmse",
use_external_memory=True)
]
results = [
]
for d in datasets:
if select_datasets is None or d.name in select_datasets:
results.append(
train_dataset(d, param, num_rounds=num_rounds, scale_features=scale_features))
return results
def non_increasing(L, tolerance):
return all((y - x) < tolerance for x, y in zip(L, L[1:]))
def assert_results_non_increasing(results, tolerance=1e-5):
for r in results:
assert non_increasing(r['eval'], tolerance), r
|
# -*- coding: utf-8 -*-
from ogs5py import OGS
model = OGS(
task_root='diff_aniso_root',
task_id='diff_aniso',
output_dir='out',
)
model.msh.read_file('diff_aniso.msh')
model.gli.read_file('diff_aniso.gli')
model.pcs.add_block(
main_key='PROCESS',
PCS_TYPE='LIQUID_FLOW',
NUM_TYPE='NEW',
)
model.pcs.add_block(
main_key='PROCESS',
PCS_TYPE='MASS_TRANSPORT',
NUM_TYPE='NEW',
)
model.bc.add_block(
main_key='BOUNDARY_CONDITION',
PCS_TYPE='MASS_TRANSPORT',
PRIMARY_VARIABLE='Tracer',
GEO_TYPE=['POLYLINE', 'PLY_0'],
DIS_TYPE=['CONSTANT', 1],
)
model.bc.add_block(
main_key='BOUNDARY_CONDITION',
PCS_TYPE='MASS_TRANSPORT',
PRIMARY_VARIABLE='Tracer',
GEO_TYPE=['POLYLINE', 'PLY_1'],
DIS_TYPE=['CONSTANT', 1],
)
model.bc.add_block(
main_key='BOUNDARY_CONDITION',
PCS_TYPE='LIQUID_FLOW',
PRIMARY_VARIABLE='PRESSURE1',
GEO_TYPE=['POLYLINE', 'PLY_2'],
DIS_TYPE=['CONSTANT', 0],
)
model.bc.add_block(
main_key='BOUNDARY_CONDITION',
PCS_TYPE='LIQUID_FLOW',
PRIMARY_VARIABLE='PRESSURE1',
GEO_TYPE=['POLYLINE', 'PLY_3'],
DIS_TYPE=['CONSTANT', 0],
)
model.ic.add_block(
main_key='INITIAL_CONDITION',
PCS_TYPE='LIQUID_FLOW',
PRIMARY_VARIABLE='PRESSURE1',
GEO_TYPE='DOMAIN',
DIS_TYPE=['CONSTANT', 0],
)
model.ic.add_block(
main_key='INITIAL_CONDITION',
PCS_TYPE='MASS_TRANSPORT',
PRIMARY_VARIABLE='Tracer',
GEO_TYPE='DOMAIN',
DIS_TYPE=['CONSTANT', 0],
)
model.ic.add_block(
main_key='INITIAL_CONDITION',
PCS_TYPE='MASS_TRANSPORT',
PRIMARY_VARIABLE='Tracer',
GEO_TYPE=['POLYLINE', 'PLY_0'],
DIS_TYPE=['CONSTANT', 1],
)
model.ic.add_block(
main_key='INITIAL_CONDITION',
PCS_TYPE='MASS_TRANSPORT',
PRIMARY_VARIABLE='Tracer',
GEO_TYPE=['POLYLINE', 'PLY_1'],
DIS_TYPE=['CONSTANT', 1],
)
model.st.add_block(
main_key='SOURCE_TERM',
)
model.mmp.add_block(
main_key='MEDIUM_PROPERTIES',
NAME='LAYER0',
GEO_TYPE=['LAYER', 0],
GEOMETRY_DIMENSION=2,
GEOMETRY_AREA=1.0,
POROSITY=[1, 0.4],
TORTUOSITY=['ANISOTROPIC', 1.0, 0, 0, 0.1],
PERMEABILITY_TENSOR=['ISOTROPIC', 1e-15],
MASS_DISPERSION=[1, 10, 10],
DENSITY=[1, 2000.0],
)
model.msp.add_block(
main_key='SOLID_PROPERTIES',
DENSITY=[1, 2000.0],
THERMAL=[
['EXPANSION:'],
[1e-05],
['CAPACITY:'],
[1, 1605.064889],
['CONDUCTIVITY:'],
[1, 0.00118799939],
],
)
model.mfp.add_block(
main_key='FLUID_PROPERTIES',
FLUID_TYPE='LIQUID',
PCS_TYPE='PRESSURE1',
DENSITY=[1, 1000.0],
VISCOSITY=[1, 0.001],
)
model.mcp.add_block(
main_key='COMPONENT_PROPERTIES',
NAME='Tracer',
MOBILE=1,
TRANSPORT_PHASE=0,
DIFFUSION=[1, 6e-10],
)
model.num.add_block(
main_key='NUMERICS',
PCS_TYPE='PRESSURE1',
ELE_UPWINDING=0.5,
ELE_MASS_LUMPING=1,
LINEAR_SOLVER=[3, 6, 1e-10, 1000, 1.0, 101, 4],
NON_LINEAR_SOLVER=['PICARD', 0.001, 10, 0.0],
ELE_GAUSS_POINTS=3,
)
model.num.add_block(
main_key='NUMERICS',
PCS_TYPE='FLUID_MOMENTUM',
ELE_GAUSS_POINTS=3,
LINEAR_SOLVER=[2, 6, 1e-14, 1000, 1.0, 1, 2],
)
model.num.add_block(
main_key='NUMERICS',
PCS_TYPE='MASS_TRANSPORT',
LINEAR_SOLVER=[2, 6, 1e-14, 1000, 0.5, 1, 2],
ELE_GAUSS_POINTS=3,
)
model.tim.add_block(
main_key='TIME_STEPPING',
PCS_TYPE='LIQUID_FLOW',
TIME_STEPS=[30, 10000000.0],
TIME_START=0.0,
TIME_END=300000000.0,
)
model.tim.add_block(
main_key='TIME_STEPPING',
PCS_TYPE='MASS_TRANSPORT',
TIME_STEPS=[30, 10000000.0],
TIME_START=0.0,
TIME_END=300000000.0,
)
model.out.add_block(
main_key='OUTPUT',
NOD_VALUES=[
['PRESSURE1'],
['Tracer'],
],
GEO_TYPE='DOMAIN',
TIM_TYPE=['STEPS', 1],
DAT_TYPE='TECPLOT',
)
model.out.add_block(
main_key='OUTPUT',
ELE_VALUES=[
['VELOCITY1_X'],
['VELOCITY1_Y'],
['VELOCITY1_Z'],
],
GEO_TYPE='DOMAIN',
TIM_TYPE=['STEPS', 1],
DAT_TYPE='TECPLOT',
)
model.write_input()
model.run_model()
|
#ifndef PrepareRawSpectra_H
#define PrepareRawSpectra_H
#include "AliUtilTOFParams.h"
#include "RooPlot.h"
#include "RooRealVar.h"
#include "TCanvas.h"
#include "TF1.h"
#include "TFile.h"
#include "TH1F.h"
#include "TList.h"
#include "TOFMacroUtils.h"
#include "TROOT.h"
#include "TString.h"
#include "TStyle.h"
#include "UtilFiles.h"
#include "UtilHisto.h"
#include "UtilMessages.h"
#include "Utilities/UtilPlots.h"
#include "iostream"
#include "libs/TOFsignal.C"
#include "AliTOFTemplateFitter.h"
using std::cout;
using std::endl;
////////////////////////////////////////////////////////////////////////////
/// //
/// //
/// Include macro to compute the raw yields of pions, kaons and protons //
/// Using the TOF signal //
/// //
/// Authors: //
/// N. Jacazio, nicolo.jacazio[AROBASe]bo.infn.it //
////////////////////////////////////////////////////////////////////////////
void SetColor(TH1* h, Int_t c, Int_t f);
void PrepareRawSpectra(TString dirname = latestDir[0], TString dataname = "TOFDataHisto.root", TString fitprefix = "", UInt_t iCharge = 0, UInt_t iSpecies = 0, UInt_t iMult = 11, UInt_t fitmode = 0, TString prefix = "", const Bool_t fitsigma = kTRUE, const Bool_t save = kFALSE)
{
Infomsg("PrepareRawSpectra", Form("TString dirname %s, TString dataname %s, TString fitprefix %s, const UInt_t iCharge %i, const UInt_t iSpecies %i, const UInt_t iMult %i, UInt_t fitmode %i, TString prefix %s, const Bool_t fitsigma %i", dirname.Data(), dataname.Data(), fitprefix.Data(), iCharge, iSpecies, iMult, fitmode, prefix.Data(), fitsigma));
if (iCharge >= kCharges)
Fatalmsg("PrepareRawSpectra", Form("Wrong charge: %i", iCharge));
//
if (iSpecies >= kSpecies)
Fatalmsg("PrepareRawSpectra", Form("Wrong particle: %i", iSpecies));
//
if (fitmode >= kFModes)
Fatalmsg("PrepareRawSpectra", Form("Wrong fitmode: %i", fitmode));
//
if (iMult >= nMultBin)
Fatalmsg("PrepareRawSpectra", Form("Multiplicity bin %i out of bounds", iMult));
//
const Bool_t drawresult = kTRUE; //To draw the magenta histogram of the total fit result
const Bool_t samemismatch = iMult == 11 || iMult < 2 ? kFALSE : kFALSE; //To use the mismatch template from the Centrality integrated case
const Int_t mismatchindex = 0; //Which mismatch template to use in case we want to use the same one as for other multiplicity bins
const Int_t useReduced = 0; //1: reduced 2: replaced To use the mismatch template reduced in the signal contribution
const Bool_t geteventinfo = kFALSE; //Set to take the event wide information, this shall not be mandatory as it is more for checks on normalization
const Bool_t drawlines = kTRUE; //Set to draw lines representing fitrange in the fitted distribution
const Int_t filltemplates = 3001; //Set to fill the templates with shaded colors if negative nothing is done
const Bool_t normfromlist = kFALSE; //Flag to normalize from the list
const Bool_t showchi2 = kFALSE; //Flag to write the chi2 of the fit on each plot
const Bool_t showratiomean = kFALSE; //Flag to write the mean and other parameters of the ratio between the fit and the data
const Bool_t limitrange = kTRUE; //Flag to limit the plotting range of the th2 to the requested range
const Bool_t showtemplateusage = kTRUE; //Flag to show the template usage for each pt bin
const Bool_t T0fillOnly = dataname.Contains("0T0") || dataname.Contains("_T0") ? kTRUE : kFALSE; //Flag to check if the file has T0 fill only, in this case it should work as for peripheral Pb--Pb
gROOT->SetStyle("Plain");
gStyle->SetOptTitle(0);
gStyle->SetOptStat(0);
//
const TString speclab = pC[iCharge] + pS[iSpecies];
const TString speclabel = pCharge[iCharge] + pSpecies[iSpecies];
const TString spectitle = pCharge[iCharge] + " " + pSpecies[iSpecies];
//
TString fNameDataMismatch = "";
if (dataname.Contains(" ")) {
fNameDataMismatch = dataname;
fNameDataMismatch.Replace(0, fNameDataMismatch.Index(" ") + 1, "");
fNameDataMismatch = dirname + Form("Processed/%s", fNameDataMismatch.Data());
while (dataname.Contains(" ") && dataname.Sizeof() > 2)
dataname.Chop();
}
TString fNameData = dirname + Form("Processed/%s", dataname.Data());
TString fNameDataEvt = dirname + "List/TListTOF.root";
TFile* finData = GetFile(fNameData, "READ");
TList* linData = 0x0;
if (outputintolists)
GetListFromFile(finData, Form("TOFList_%s", MultBinString[iMult].Data()), linData);
TFile* finDataMismatch = 0x0;
if (!fNameDataMismatch.EqualTo(""))
finDataMismatch = GetFile(fNameDataMismatch, "READ");
//Common path for macro output
const TString outpath = Form("./Spectra/%s/Yields/%s/", systemString[optpp].Data(), speclabel.Data());
TString fname = "Yield";
TString identifier = pCharge[iCharge];
identifier.Append(pSpecies[iSpecies]);
identifier.Append("_");
identifier.Append(fitmodes[fitmode]);
identifier.Append("_");
identifier.Append(MultBinString[iMult]);
if (!prefix.IsNull() && !prefix.BeginsWith('_'))
identifier.Append("_");
//
identifier.Append(prefix);
//
fname.Append(identifier);
fname.Append(".root");
fname.Prepend(outpath);
//
TFile* fout = nullptr;
if (save) {
fout = GetFile(fname, "RECREATE");
Infomsg("PrepareRawSpectra", Form("Saving output to file %s", fout->GetName()));
} else
Infomsg("PrepareRawSpectra", Form("NOT Saving output. Would like to save it in %s", fname.Data()));
//
TFile* foutfits = nullptr;
if (save && !fitprefix.EqualTo("")) {
foutfits = GetFile(Form("%sFits/Fits%s_%s.root", outpath.Data(), identifier.Data(), fitprefix.Data()), "RECREATE");
Infomsg("PrepareRawSpectra", Form("Saving fit results to file %s", foutfits->GetName()));
}
//
if (fout)
fout->cd();
//
TList* lHistograms = new TList();
lHistograms->SetOwner();
TList* lCanvas = new TList();
lCanvas->SetOwner();
//
//Define all needed histograms
//
TH1D* hNEvt = nullptr;
TH1D* hEvtMult = nullptr;
TH1D* hEvtMultAftEvSel = nullptr;
TH1F* fEntries = nullptr; //Event information
TH1F* hTOF[kPtBins] = { nullptr }; //TOF distribution (time or n-sigma)
TH1F* hTOFMismatch[kPtBins] = { nullptr }; //TOF mismatch template
TH1F* hTOFExpected[kPtBins][kExpSpecies]; //TOF signal/background templates
//Fitted results
TH1F* hFitted[kPtBins] = { nullptr }; //Total fitted result
TH1F* hMismatchFitted[kPtBins] = { nullptr }; //Mismatch fitted
TH1F* hExpectedFitted[kPtBins][kExpSpecies]; //signal/background fitted
//Ratio to fitted
TH1F* hRatioToFitted[kPtBins] = { nullptr };
//Total yield
TH1F* hYield = nullptr;
//Total yield residual in the +- 3 sigma range
TH1F* hYieldResidual = nullptr;
//Total background
TH1F* hBackground[kExpSpecies + 1] = { nullptr };
TH1F* hBackgroundOverlap[kExpSpecies + 1] = { nullptr };
TH2F* hTOFPt = nullptr;
TH2F* hTOFPtMismatch = nullptr;
TH2F* hTOFPtSignal[kExpSpecies] = { nullptr };
TH2F* hTOFPtFittedMismatch = nullptr;
TH2F* hTOFPtFittedSignal[kExpSpecies] = { nullptr };
TH2F* hTOFPtFitted = nullptr;
//Functions
TF1* fTOFsignal[kExpSpecies] = { nullptr };
TF1* fTOFsignalSum = nullptr;
TF1* fTOFbackground = nullptr;
//
//Track information
//
//Get the histograms
TString hname = "";
//Helper macro to get
#define GetMyH(H, L, F) \
{ \
if (outputintolists && L || !F) \
GetHistogram(static_cast<TList*>(L), hname, H); \
else \
GetHistogram(static_cast<TFile*>(F), hname, H); \
H->SetDirectory(0); \
lHistograms->Add(H); \
}
hname = Form("fEntries_%s", MultBinString[iMult].Data());
GetMyH(fEntries, linData, finData);
for (Int_t ptbin = 0; ptbin < kPtBins; ptbin++) { //Pt loop
hname = "hTOF";
if (fitsigma)
hname += "Sigma";
//
hname += Form("%s_%i_%s", speclab.Data(), ptbin, MultBinString[iMult].Data());
GetMyH(hTOF[ptbin], linData, finData);
//
hname = "hTOFMismatch";
if (fitsigma)
hname += "Sigma";
//
hname += Form("%s_%i_%s", speclab.Data(), ptbin, MultBinString[samemismatch ? mismatchindex : iMult].Data());
if (fitsigma && (useReduced == 1 || useReduced == 2)) { //Take the coocked background
TFile auxifile(Form("./TOFMismatch/SignalRemoved/%s/%s%i%s.root", pSpecies[iSpecies].Data(), pCharge[iCharge].Data(), ptbin, useReduced == 2 ? "Bkg" : "Sig"), "READ");
if (auxifile.IsOpen()) {
GetHistogram(&auxifile, useReduced == 2 ? "FitReplaced" : "FitReduced", hTOFMismatch[ptbin]);
hTOFMismatch[ptbin]->SetName(hname);
hTOFMismatch[ptbin]->SetDirectory(0);
auxifile.Close();
}
} else {
if (finDataMismatch) { //Get histogram for mismatch from other file
GetMyH(hTOFMismatch[ptbin], 0x0, finDataMismatch);
} else { //Get the mismacth from the same file
GetMyH(hTOFMismatch[ptbin], linData, finData);
}
}
//Templates for each species
for (Int_t species = 0; species < kExpSpecies; species++) { //Species loop
hname = "hTOF";
if (fitsigma)
hname += "Sigma";
//
hname += Form("Expected%s_%s_%i_%s", speclab.Data(), pS_all[species].Data(), ptbin, MultBinString[iMult].Data());
GetMyH(hTOFExpected[ptbin][species], linData, finData);
}
}
//
//Event wide information
//
//Get the histograms for the whole event
if (geteventinfo) {
TFile finDataEvt(fNameDataEvt, "READ");
TList* lin = 0x0;
GetListFromFile(&finDataEvt, "cOutputList", lin);
//
hname = Form("hNEvt");
GetMyH(hNEvt, lin, 0x0);
//
hname = Form("hEvtMult");
GetMyH(hEvtMult, lin, 0x0);
//
hname = Form("hEvtMultAftEvSel");
GetMyH(hEvtMultAftEvSel, lin, 0x0);
//
delete lin;
finDataEvt.Close();
}
#undef GetMyH
//Closing input files
if (finDataMismatch) {
finDataMismatch->Close();
delete finDataMismatch;
}
finData->Close();
delete finData;
//
//Define histogram to show which template is included in the fit
//
TH2I* husage = 0x0;
if (showtemplateusage) {
husage = new TH2I("husage", Form("TemplateUsage;Template;%s", ptstring.Data()), kExpSpecies + 1, -0.5, -0.5 + kExpSpecies + 1, kPtBins, -0.5, -0.5 + kPtBins);
for (Int_t i = 0; i < kExpSpecies; i++)
husage->GetXaxis()->SetBinLabel(i + 1, Form("Temp. %s", speciesRoot_all[3 * i + iCharge].Data()));
husage->GetXaxis()->SetBinLabel(kExpSpecies + 1, "Temp. Mismatch");
for (Int_t i = 0; i < kPtBins; i++)
husage->GetYaxis()->SetBinLabel(i + 1, Form("[%.2f,%.2f]", fBinPt[i], fBinPt[i + 1]));
}
//
//Fitting section
//
Int_t FI = TColor::CreateGradientColorTable(NRGBs, stops, red, green, blue, kExpSpecies + 1);
//Define the histograms
hYield = new TH1F("hYield" + speclab, Form("Yield for %s;%s;Counts", spectitle.Data(), ptstring.Data()), kPtBins, fBinPt);
lHistograms->AddFirst(hYield);
hYieldResidual = new TH1F("hYieldResidual" + speclab, Form("Residual Yield for %s;%s;Counts", spectitle.Data(), ptstring.Data()), kPtBins, fBinPt);
hYieldResidual->SetLineColor(kRed);
hYieldResidual->SetMarkerColor(kRed);
hYieldResidual->Sumw2();
lHistograms->AddFirst(hYieldResidual);
for (Int_t i = 0; i < kExpSpecies + 1; i++) {
hBackground[i] = new TH1F(Form("hBackground%s%i", speclab.Data(), i), Form("Background for %s %i;%s;%s Yield", spectitle.Data(), i, ptstring.Data(), i == 0 ? "Mismatch" : pSpecies_all[i - 1].Data()), kPtBins, fBinPt);
lHistograms->Add(hBackground[i]);
hBackgroundOverlap[i] = new TH1F(Form("hBackgroundOverlap%s%i", speclab.Data(), i), Form("Background Overlap for %s %i;%s;%s Overlap Yield", spectitle.Data(), i, ptstring.Data(), i == 0 ? "Mismatch" : pSpecies_all[i - 1].Data()), kPtBins, fBinPt);
lHistograms->Add(hBackgroundOverlap[i]);
}
Int_t rows = 7, columns = 7;
TCanvas* cTOF = new TCanvas("cTOF" + speclab, "Canvas with TOF distributions for " + spectitle, screendim[0], screendim[1]);
cTOF->Divide(rows, columns);
lCanvas->Add(cTOF);
TCanvas* cFit = new TCanvas("cFit" + speclab, "Fitted distribution for " + spectitle, screendim[0], screendim[1]);
lCanvas->Add(cFit);
cFit->Divide(rows, columns);
TCanvas* cFitOnly = new TCanvas("cFitOnly" + speclab, "Only Fitted distribution for " + spectitle, screendim[0], screendim[1]);
lCanvas->Add(cFitOnly);
cFitOnly->Divide(rows, columns);
TCanvas* cFitRatio = new TCanvas("cFitRatio" + speclab, "Fitted distribution Ratio for " + spectitle, screendim[0], screendim[1]);
lCanvas->Add(cFitRatio);
cFitRatio->Divide(rows, columns);
TCanvas* cTOFPt = new TCanvas("cTOFPt" + speclab, "Canvas with Pt TOF distributions for " + spectitle, screendim[0], screendim[1]);
lCanvas->Add(cTOFPt);
TCanvas* cYield = new TCanvas("cYield" + speclab, "Canvas with Yield for " + spectitle);
cYield->Divide(3);
lCanvas->Add(cYield);
TCanvas* cYieldResidual = new TCanvas("cYieldResidual" + speclab, "Canvas with YieldResidual for " + spectitle);
cYieldResidual->Divide(2, 2);
lCanvas->Add(cYieldResidual);
TCanvas* cBackground = new TCanvas("cBackground" + speclab, "Canvas with Background for " + spectitle);
cBackground->Divide(4, 2);
lCanvas->Add(cBackground);
TCanvas* cBackgroundOverlap = new TCanvas("cBackgroundOverlap" + speclab, "Canvas with BackgroundOverlap for " + spectitle);
cBackgroundOverlap->Divide(4, 2);
lCanvas->Add(cBackgroundOverlap);
TCanvas* cEvent = new TCanvas("cEvent", "Canvas with Events");
cEvent->Divide(2, 2);
lCanvas->Add(cEvent);
Int_t padcounter = 1;
//Pt ranges where to fit
//RooFit pt ranges
const Int_t MinPtRoofit_PbPb[nSpecies] = { 10, 10, 12, 26 };
const Int_t MaxPtRoofit_PbPb[nSpecies] = { 43, 43, 43, 59 };
const Int_t MinPtRoofit_pp[nSpecies] = { 10, 10, 16, 26 };
const Int_t MaxPtRoofit_pp[nSpecies] = { 43, 43, 43, 59 };
//Functions pt ranges
const Int_t MinPtFunctions[nSpecies] = { 10, 12, 21, 26 };
const Int_t MaxPtFunctions[nSpecies] = { 37, 40, 40, 59 };
//
Int_t MinPtFit = optpp ? MinPtRoofit_pp[iSpecies] : MinPtRoofit_PbPb[iSpecies];
Int_t MaxPtFit = optpp ? MaxPtRoofit_pp[iSpecies] : MaxPtRoofit_PbPb[iSpecies];
for (Int_t ptbin = 0; ptbin < kPtBins; ptbin++) { //Pt loop
Bool_t status = ptbin >= MinPtFit && ptbin <= MaxPtFit;
Printf("###############################");
Printf("Pt bin %i/%i: [%.2f, %.2f]", ptbin + 1, kPtBins, fBinPt[ptbin], fBinPt[ptbin + 1]);
Printf("###############################");
//Drawing Delta T
Int_t colorcounter = 0;
cTOF->cd(padcounter);
gPad->SetLogy();
SetColor(hTOF[ptbin], kBlack, 0);
hTOF[ptbin]->DrawCopy();
SetColor(hTOFMismatch[ptbin], FI + colorcounter, filltemplates);
colorcounter++;
hTOFMismatch[ptbin]->DrawCopy("same");
for (Int_t species = 0; species < kExpSpecies; species++) { //Species loop
SetColor(hTOFExpected[ptbin][species], FI + colorcounter, filltemplates);
colorcounter++;
hTOFExpected[ptbin][species]->DrawCopy("same");
}
DrawLabel(Form("pt %i [%.2f,%.2f]", ptbin, fBinPt[ptbin], fBinPt[ptbin + 1]));
//
//Define data and template histograms
//
TH1F* datahisto = hTOF[ptbin];
TH1F* mismatchhisto = hTOFMismatch[ptbin];
TH1F* templatehhisto[kExpSpecies] = { 0x0 };
for (Int_t species = 0; species < kExpSpecies; species++)
templatehhisto[species] = hTOFExpected[ptbin][species];
//
//Fit Parameters
//
const Double_t showrange[2] = { fitsigma ? -40. : -3000., fitsigma ? 40. : 3000. }; //Range where to show the actual fit in the saved figure
const Bool_t setstart = optpp ? kTRUE : kTRUE; //Gets the first bin above zero as a fitrange
const Bool_t setstop = optpp ? kFALSE : (iMult < 3 || iMult == nMultBin - 1) ? kFALSE : kTRUE; //Gets the last bin above zero as a fitrange
Double_t range[2] = { fitsigma ? -300. : -10000., fitsigma ? 300. : 10000. }; //Integration range to extract yields only if using TFF
Double_t fitrange[2] = { fitsigma ? -300. : -3000., fitsigma ? 300. : 6000. }; //Fit range
const Int_t rebin = (fitmode == 3) ? -2 : -2;
const Bool_t setzeroerrors = kFALSE;
const Bool_t useMismatch = optpp ? kTRUE : kTRUE;
const Bool_t fixMismatch = kFALSE; //If true Mismatch template is scaled before the fit and then fixed
const Bool_t useOptions = kTRUE; //Using the pt/Centrality dependet options
const Bool_t setTemplateWithRanges = kTRUE; //Using the fitranges to set if or not to use the templates
Bool_t useit[kExpSpecies] = { 0, 0, 1, 1, 1, 0 }; //Choose if use or not the template for that particular particle species
const Bool_t usefun[kExpSpecies] = { 0, 0, 0, 0, 0, 0 }; //Choose if use or not a full function instead of the template for fitting
Int_t ntemplates = 0;
Int_t indexoffset = 0;
if (useOptions && status) { //Custom pt and mult dependent parameters
if (fitsigma) {
if (ptbin < 25)
useit[0] = kTRUE; //Low pt electrons
if (ptbin < 20)
useit[1] = kTRUE; //Low pt muons
if (ptbin < 5)
useit[4] = kFALSE; //low pt protons
if (ptbin > 30)
useit[5] = kTRUE; //high pt Deuteron
if (fitmode == 1) { //RooFit
if (iSpecies == 0) {
// rebin = 4;
fitrange[0] = ptbin > 10 ? -10 : -25;
if (ptbin >= 27)
fitrange[0] = -5;
if (ptbin > 40)
fitrange[0] = -3;
if (ptbin < 34)
fitrange[1] = 200;
else if (ptbin < 37)
fitrange[1] = 100;
else
fitrange[1] = 40;
if (0) {
if (ptbin < 10)
useit[4] = kFALSE;
} else {
fitrange[0] = -30;
if (ptbin >= 29)
fitrange[0] = -5;
fitrange[1] = 50;
if (ptbin < 12)
useit[3] = kFALSE; // low pt Kaons
if (ptbin < 24)
useit[4] = kFALSE; // low pt Protons
if (ptbin < 32)
useit[5] = kFALSE; // low pt Deuterons
if (ptbin > 39) {
fitrange[1] = 10;
useit[5] = kFALSE; // low pt Deuterons
}
}
if (iMult >= 9 && iMult != nMultBin - 1 && ptbin >= 32)
fitrange[1] = 20;
if (iMult >= 9 && iMult != nMultBin - 1 && ptbin > 33)
fitrange[1] = 15;
if (iMult >= 9 && iMult != nMultBin - 1 && ptbin >= 35)
fitrange[1] = 10;
if (iMult >= 9 && iMult != nMultBin - 1 && ptbin >= 38)
fitrange[1] = 8;
if (iMult >= 8 && iMult != nMultBin - 1 && ptbin > 39)
fitrange[1] = 5;
if (iMult >= 8 && iMult != nMultBin - 1 && ptbin < 10)
useit[3] = kFALSE; //peripheral Kaons
if (iMult >= 9 && iMult != nMultBin - 1 && ptbin < 43)
useit[5] = kFALSE; //peripheral Deuteron
} else if (iSpecies == 1) {
// rebin = 5;
fitrange[0] = ptbin > 37 ? -5 : ptbin > 33 ? -8 : ptbin > 20 ? -10 : ptbin > 10 ? -25 : -40;
if (iCharge == 1 && ptbin > 30)
fitrange[0] = -7;
if (iCharge == 1 && ptbin >= 37)
fitrange[0] = -4;
fitrange[1] = ptbin > 20 ? 100 : 200;
if (ptbin >= 40)
fitrange[1] = 40;
if (iMult >= 7 && iMult != nMultBin - 1 && ptbin > 15)
fitrange[1] = 10;
if (iMult >= 7 && iMult != nMultBin - 1 && ptbin > 19)
fitrange[1] = 5;
if (optpp) {
fitrange[0] = -70;
fitrange[1] = 50;
}
useit[0] = kFALSE; //Low pt electrons
useit[1] = kFALSE; //Low pt muons
// if(ptbin < 15) useit[2] = kFALSE;//low pt pions
if (ptbin < 5)
useit[4] = kFALSE; //low pt protons
if (ptbin <= 34)
useit[5] = kFALSE; //high pt Deuteron
if (iCharge == 1 && ptbin < 37)
useit[5] = kFALSE; //high pt Deuteron
if (iMult >= 8 && iMult != nMultBin - 1 && ptbin < 26)
useit[4] = kFALSE; //peripheral Protons
if (iMult >= 7 && iMult != nMultBin - 1 && ptbin > 15 && ptbin < 35)
useit[4] = kFALSE; //peripheral Protons
if (iMult >= 7 && iMult != nMultBin - 1 && ptbin < 43)
useit[5] = kFALSE; //peripheral Deuteron
if (iMult == 9 && ptbin < 15) {
// useit[2] = kFALSE;//peripheral Deuteron
fitrange[1] = 5; //peripheral Deuteron
}
if (optpp)
useit[5] = kFALSE; //high pt Deuteron
} else if (iSpecies == 2) {
// rebin = 5;
//
//Setting fit range
//
if (0) {
fitrange[0] = -50;
if (ptbin >= 15)
fitrange[0] = -40;
if (ptbin >= 20)
fitrange[0] = -30;
if (ptbin >= 25)
fitrange[0] = -25;
if (ptbin >= 28)
fitrange[0] = -10;
if (ptbin >= 40)
fitrange[0] = -5;
} else {
fitrange[0] = -30;
fitrange[0] = templatehhisto[0]->GetXaxis()->GetBinCenter(templatehhisto[0]->GetMaximumBin());
}
fitrange[1] = ptbin > 35 ? 30 : 150;
// fitrange[0] = -15;
// fitrange[1] = 15;
fitrange[1] = 40;
fitrange[0] = -20;
fitrange[1] = 50;
if (iMult >= 8 && iMult != nMultBin - 1 && ptbin >= 35)
fitrange[1] = 6;
if (iMult >= 9 && iMult != nMultBin - 1 && ptbin > 40)
fitrange[1] = 4;
//
//Setting templates
//
//Unused templates
useit[0] = kFALSE; //Low pt electrons
useit[1] = kFALSE; //Low pt muons
//Concurrent templates
// // if(ptbin < 25) useit[3] = kFALSE;
// if(ptbin < 30) useit[2] = kFALSE;//high pt Deuteron
// if(ptbin < 30) useit[3] = kFALSE;//high pt Deuteron
// if(ptbin > 40) useit[5] = kTRUE;//high pt Deuteron
// else useit[5] = kFALSE;//high pt Deuteron
// if(/*iCharge == 0 && */iMult >= 6 && iMult != nMultBin -1 && ptbin < 43) useit[5] = kFALSE;//peripheral Deuteron
} else if (iSpecies == 3) {
// rebin = 5;
fitrange[0] = -50;
if (ptbin >= 15)
fitrange[0] = -40;
if (ptbin >= 20)
fitrange[0] = -30;
if (ptbin >= 25)
fitrange[0] = -25;
if (ptbin >= 28)
fitrange[0] = -10;
if (ptbin >= 40)
fitrange[0] = -5;
fitrange[1] = ptbin > 35 ? 30 : 150;
useit[5] = kTRUE; //Include Deuterons!!
}
} else if (fitmode == 3) { //Functions
for (Int_t species = 0; species < kExpSpecies; species++)
useit[species] = (species == kpi || species == kK || species == kp) ? kTRUE : kFALSE;
if (ptbin < 20)
useit[kp] = kFALSE;
}
} else {
if (fitmode == 1) { //RooFit
useit[kpi] = kTRUE;
switch (iSpecies) {
case 0: //Pions
{
if (ptbin < 12)
useit[ke] = kFALSE;
useit[kpi] = kTRUE;
if (ptbin < 12)
useit[kK] = kFALSE;
if (ptbin < 15)
useit[kp] = kFALSE;
else
useit[kp] = kTRUE;
useit[4] = kFALSE;
useit[kp] = kTRUE;
break;
}
case 1: //Kaons
{
useit[kK] = kTRUE;
useit[ke] = kFALSE;
useit[kmu] = kFALSE;
if (ptbin < 15)
useit[kpi] = kFALSE;
else
useit[kpi] = kTRUE;
if (ptbin < 20)
useit[kp] = kFALSE;
else
useit[kp] = kTRUE;
if (ptbin < 15) {
fitrange[0] = -1000;
fitrange[1] = 1000;
}
break;
}
case 2: //Protons
{
if (ptbin < 20) {
useit[2] = kFALSE;
useit[3] = kFALSE;
}
break;
}
}
if (ptbin > 10)
useit[3] = kTRUE;
// if(ptbin > 12) useit[4] = kTRUE;
} else if (fitmode == 3) { //Functions
for (Int_t species = 0; species < kExpSpecies; species++)
useit[species] = (species == kpi || species == kK || species == kp) ? kTRUE : kFALSE;
if (ptbin < 16)
useit[kK] = kFALSE;
if (ptbin < 20)
useit[kp] = kFALSE;
}
}
}
if (setTemplateWithRanges) {
for (Int_t i = 0; i < kExpSpecies; i++) {
if (useit[i] && !IsHistogramInRange(templatehhisto[i], fitrange[0], fitrange[1], /*threshold*/ 1., /*verbose*/ kFALSE)) {
useit[i] = kFALSE;
cout << "Template " << templatehhisto[i]->GetName() << " is not in range" << endl;
} else if (useit[i])
cout << "Template " << templatehhisto[i]->GetName() << " is in range" << endl;
}
}
//
//Signal definition
//
if (fitmode == 3 && status) { //Fit with functions
for (Int_t species = 0; species < kExpSpecies; species++) {
fTOFsignal[species] = new TF1(Form("expTOFsignal%s%s", pCharge[iCharge].Data(), pSpecies_all[species].Data()), TOFsignal, range[0], range[1], 4);
fTOFsignal[species]->SetLineColor(templatehhisto[species]->GetLineColor());
//Normalization
fTOFsignal[species]->SetParameter(0, templatehhisto[species]->GetMaximum());
fTOFsignal[species]->SetParLimits(0, 0, (Double_t)datahisto->GetEntries());
//Mean
fTOFsignal[species]->SetParameter(1, templatehhisto[species]->GetMean());
fTOFsignal[species]->SetParLimits(1, templatehhisto[species]->GetMean() - 2. * templatehhisto[species]->GetRMS(), templatehhisto[species]->GetMean() + 2. * templatehhisto[species]->GetRMS());
//Sigma
fTOFsignal[species]->SetParameter(2, fitsigma ? 1. : tofReso);
fTOFsignal[species]->SetParLimits(2, fitsigma ? 0.5 : 40., fitsigma ? 2.0 : 300.);
//Tail
fTOFsignal[species]->SetParameter(3, fitsigma ? 1. : tofTail);
fTOFsignal[species]->SetParLimits(3, fitsigma ? 0.5 : 40., fitsigma ? 2.0 : 200.);
fTOFsignal[species]->SetParNames("Norm", "Mean", "Sigma", "Tail");
}
Int_t usable = 0;
for (Int_t species = 0; species < kExpSpecies; species++) {
cout << "Species " << species << " : " << useit[species] << endl;
if (useit[species])
usable++;
}
if (useMismatch) {
if (usable == 1)
fTOFsignalSum = new TF1(Form("expTOFsignalSum"), TOFsignalBkg, range[0], range[1], 4 + 1);
else if (usable == 2)
fTOFsignalSum = new TF1(Form("expTOFsignalSum"), TOFsignalBkg_double, range[0], range[1], 8 + 1);
else if (usable == 3)
fTOFsignalSum = new TF1(Form("expTOFsignalSum"), TOFsignalBkg_triple, range[0], range[1], 12 + 1);
else
Fatalmsg("PrepareRawSpectra", Form("Too many (%i) requested functions!", usable));
fTOFbackground = new TF1(Form("expTOFbackground"), "pol0", range[0], range[1]);
fTOFbackground->SetParameter(0, 0);
fTOFbackground->SetParLimits(0, 0, datahisto->GetEffectiveEntries());
fTOFbackground->SetParName(0, "Bkg");
} else {
if (usable == 1)
fTOFsignalSum = new TF1(Form("expTOFsignalSum"), TOFsignal, range[0], range[1], 4);
else if (usable == 2)
fTOFsignalSum = new TF1(Form("expTOFsignalSum"), TOFsignal_double, range[0], range[1], 8);
else if (usable == 3)
fTOFsignalSum = new TF1(Form("expTOFsignalSum"), TOFsignal_triple, range[0], range[1], 12);
else
Fatalmsg("PrepareRawSpectra", Form("Too many (%i) requested functions!", usable));
}
Int_t counter = 0;
for (Int_t species = 0; species < kExpSpecies; species++) {
if (!useit[species])
continue;
for (Int_t i = 0; i < fTOFsignal[species]->GetNpar(); i++) {
cout << "For parameter " << counter << " using " << species << endl;
Double_t l[2];
fTOFsignal[species]->GetParLimits(i, l[0], l[1]);
fTOFsignalSum->SetParameter(counter, fTOFsignal[species]->GetParameter(i));
fTOFsignalSum->SetParLimits(counter, l[0], l[1]);
fTOFsignalSum->SetParName(counter, fTOFsignal[species]->GetParName(i));
counter++;
}
}
if (useMismatch) {
for (Int_t i = 0; i < fTOFbackground->GetNpar(); i++) {
Double_t l[2];
fTOFbackground->GetParLimits(i, l[0], l[1]);
fTOFsignalSum->SetParameter(counter, fTOFbackground->GetParameter(i));
fTOFsignalSum->SetParLimits(counter, l[0], l[1]);
fTOFsignalSum->SetParName(counter, fTOFbackground->GetParName(i));
counter++;
}
}
if (counter != fTOFsignalSum->GetNpar())
Fatalmsg("PrepareRawSpectra", "Problem in the initial conditions for conv. fit funct.");
fTOFsignalSum->SetLineColor(kMagenta);
}
//Checking the templates before running the fit
const Bool_t checkrange = kFALSE;
Bool_t inrange[kExpSpecies] = { 0 };
//To choose the which templates are in range it is important that they all have the same bin limits, the selection is made with overflow and underflow fraction
if (checkrange) {
Double_t templateentries[kExpSpecies] = { -999 };
Double_t templateoverflow[kExpSpecies] = { -999 };
Double_t templateunderflow[kExpSpecies] = { -999 };
Double_t templatemean[kExpSpecies] = { -999 };
Double_t templatesigma[kExpSpecies] = { -999 };
for (Int_t species = 0; species < kExpSpecies; species++) { //Species loop
templateentries[species] = templatehhisto[species]->GetEntries();
templateoverflow[species] = templatehhisto[species]->GetBinContent(templatehhisto[species]->GetNbinsX() + 1);
templateunderflow[species] = templatehhisto[species]->GetBinContent(0);
templatemean[species] = templatehhisto[species]->GetMean();
templatesigma[species] = templatehhisto[species]->GetRMS();
if (templateentries[species] == 0)
continue;
Double_t overflowfraction = templateoverflow[species] / templateentries[species];
Double_t underflowfraction = templateunderflow[species] / templateentries[species];
Double_t diffplus = templatemean[species] + templatesigma[species];
Double_t diffminus = templatemean[species] - templatesigma[species];
Printf("AliPID %i mean %f rms %f difference %f", species, templatemean[species], templatesigma[species], diffplus);
Printf("\t\t entries %f overflow %f (frac. %f) underflow %f (frac. %f)", templateentries[species], templateoverflow[species], overflowfraction, templateunderflow[species], underflowfraction);
if (overflowfraction < 0.99 && underflowfraction < 0.99)
inrange[species] = kTRUE;
else
inrange[species] = kFALSE;
if (useit[species] && !inrange[species])
useit[species] = kFALSE;
}
}
//
//Count the templates used
//
if (useMismatch) { //Mismatch
if (husage)
husage->Fill(kExpSpecies, ptbin);
indexoffset++;
ntemplates++;
Printf("Using Mismatch template: %i/%i", indexoffset, ntemplates);
}
for (UInt_t species = 0; species < kExpSpecies; species++) { //Signal for particles
if (useit[species]) {
if (husage)
husage->Fill(species, ptbin);
ntemplates++;
if (species < iSpecies + kpi)
indexoffset++;
Printf("Using template %i %s: %i/%i", species, pSpecies_all[species].Data(), indexoffset, ntemplates);
}
}
if (rebin > 0)
datahisto->Rebin(rebin);
TObjArray* templates = new TObjArray(ntemplates);
TObjArray* prediction = new TObjArray(ntemplates + 1); //Has also the total fitted template
if (fitmode == 3) { //Fit with functions only
for (Int_t species = 0; species < kExpSpecies; species++) {
if (useit[species])
templates->Add(fTOFsignal[species]);
}
if (useMismatch)
templates->Add(fTOFbackground);
} else { //Fit with templates
if (useMismatch) {
if (rebin > 0)
mismatchhisto->Rebin(rebin);
if (setzeroerrors)
for (Int_t i = 1; i <= mismatchhisto->GetNbinsX(); i++)
mismatchhisto->SetBinError(i, 0);
templates->Add(mismatchhisto);
}
for (Int_t species = 0; species < kExpSpecies; species++) {
if (rebin > 0)
templatehhisto[species]->Rebin(rebin);
if (!useit[species])
continue;
if (usefun[species]) {
// TF1 *funct = new TF1(Form("signal%s%s", pC[iCharge].Data(), pS_all[species].Data()), TOFsignalNorm, range[0], range[1], 3);
// funct->SetTitle(Form("%s", speciesRoot_all[3*species + iCharge].Data()));
const Double_t m = templatehhisto[species]->GetXaxis()->GetBinCenter(templatehhisto[species]->GetMaximumBin()); //templatehhisto[species]->GetMean() > 0 ? templatehhisto[species]->GetMean() : 0;
const Double_t mLow = fitsigma ? m - 3 : m - 1;
const Double_t mUp = fitsigma ? m + 3 : m + 1;
const Double_t s = fitsigma ? 1 : 56;
const Double_t sLow = fitsigma ? s - .5 : s - 1;
const Double_t sUp = fitsigma ? s + .5 : s + 1;
const Double_t t = 1.25;
const Double_t tLow = t - 1;
const Double_t tUp = t + .01;
//
RooRealVar* x = new RooRealVar("x", "x" + speciesRoot_all[3 * species + iCharge],
datahisto->GetXaxis()->GetBinLowEdge(1),
datahisto->GetXaxis()->GetBinUpEdge(datahisto->GetNbinsX()));
RooRealVar* mean = new RooRealVar(
"mean" + speciesRoot_all[3 * species + iCharge],
"mean" + speciesRoot_all[3 * species + iCharge],
m);
// m, mLow, mUp);
RooRealVar* sigma = new RooRealVar(
"sigma" + speciesRoot_all[3 * species + iCharge],
"sigma" + speciesRoot_all[3 * species + iCharge],
// s);
s, s * .95, 400);
// s, sLow, sUp);
cout << "RMS IS " << templatehhisto[species]->GetRMS() << endl;
RooRealVar* qL = new RooRealVar(
"qL" + speciesRoot_all[3 * species + iCharge],
"qL" + speciesRoot_all[3 * species + iCharge],
1.0);
// 1.0, 0.9999, 1.0001);
RooRealVar* qR = new RooRealVar(
"qR" + speciesRoot_all[3 * species + iCharge],
"qR" + speciesRoot_all[3 * species + iCharge],
1.667);
// 1.667, 1.5, 1.7);
RooqGaussian* funct = new RooqGaussian("qGaussian" + speciesRoot_all[3 * species + iCharge], speciesRoot_all[3 * species + iCharge], *x, *mean, *sigma, *qR, *qL);
// funct->SetParameters(m, s, t);
// funct->SetParLimits(0, mLow, mUp);
// funct->SetParLimits(1, sLow, sUp);
// funct->SetParLimits(2, tLow, tUp);
// funct->SetParNames("Mean", "Sigma", "Tail");
templates->Add(funct);
// new TCanvas();
// RooPlot *plot = x.frame();
// funct->plotOn(plot);
// plot->Draw();
// lHistograms->Add(funct);
// return;
} else
templates->Add(templatehhisto[species]);
}
}
//
//Set fit ranges
//
if (useOptions && status) { //Auto-Set start/stop for fit
//Sets the first bin above a certain value as the start for the fit
if (setstart) {
const Double_t mincounts = 10;
const Int_t shift = 0;
const Int_t databin = GetHistoLowRange(datahisto, mincounts);
if (fitmode == 3) { //With functions only data is important
fitrange[0] = datahisto->GetXaxis()->GetBinLowEdge(databin);
Printf("Autoset start for function fit to bin %i [%f, %f]", databin, datahisto->GetXaxis()->GetBinLowEdge(databin), datahisto->GetXaxis()->GetBinUpEdge(databin));
} else {
const Int_t firstindex = GetFirstHistogram(templates, mincounts); //First template from the left with non zero values
TH1F* firsthisto = dynamic_cast<TH1F*>(templates->At(firstindex));
const Int_t templatebin = GetHistoLowRange(firsthisto, mincounts);
const Int_t templatebinAfter = GetHistoLowRangeAfter(firsthisto, mincounts, templatebin);
const Int_t templatebinNoHoles = GetHistoNoHolesAfter(firsthisto, templatebin);
if (templatebin > templatebinAfter)
Fatalmsg("PrepareRawSpectra", "Wrongly defined bins");
if (!SameBinning(datahisto, firsthisto))
Fatalmsg("PrepareRawSpectra", Form("Histograms have different binning : datahisto %s (%i) and the %s (%i)", datahisto->GetName(), datahisto->GetNbinsX(), firsthisto->GetName(), firsthisto->GetNbinsX()));
const Int_t binlimits[2] = { databin, templatebinNoHoles };
const TString bins[2] = {
Form("databin %i [%f, %f] -> %f", binlimits[0], datahisto->GetXaxis()->GetBinLowEdge(binlimits[0]), datahisto->GetXaxis()->GetBinUpEdge(binlimits[0]), datahisto->GetBinContent(binlimits[0])),
Form("templates %i [%f, %f] -> %f", binlimits[1], datahisto->GetXaxis()->GetBinLowEdge(binlimits[1]), datahisto->GetXaxis()->GetBinUpEdge(binlimits[1]), datahisto->GetBinContent(binlimits[1]))
};
TString type = "";
cout << "First histogram with required counts is " << firstindex << " " << templates->At(firstindex)->GetName() << endl;
cout << "Setting start to first bin above " << mincounts << " " << bins[0] << ", " << bins[1] << endl;
Double_t x = -999;
if (binlimits[0] > 0 && binlimits[1] < binlimits[0]) {
x = datahisto->GetXaxis()->GetBinLowEdge(binlimits[0] + shift);
type = "from templates";
} else if (binlimits[0] > 0) {
x = datahisto->GetXaxis()->GetBinLowEdge(binlimits[1] + shift);
type = "from data";
}
Bool_t inrange = kTRUE;
const Double_t min = iSpecies == 0 ? 2.2 : 2.;
if (fitsigma) {
if (x != -999 && x > -min) {
inrange = kFALSE;
fitrange[0] = -min;
}
} else {
if (x != -999 && x > -min * 80) {
inrange = kFALSE;
fitrange[0] = -min * 80;
}
}
if (inrange && x != -999 && fitrange[0] < x)
fitrange[0] = x;
// if(x != -999 && fitrange[0] < x) fitrange[0] = x;
const Int_t bin = datahisto->GetXaxis()->FindBin(fitrange[0]);
Infomsg("PrepareRawSpectra", Form("Required to autoset the start point %s for the fit from %f (%f) bin %i content %f", type.Data(), fitrange[0], x, bin, datahisto->GetBinContent(bin)));
}
}
//Sets the last bin above a certain value as the stop for the fit
if (setstop) {
const Double_t mincounts = 5;
const Int_t databin = datahisto->FindLastBinAbove(mincounts);
Double_t x = datahisto->GetXaxis()->GetBinUpEdge(databin);
x = GetHistoNoHolesBefore(datahisto, x);
cout << "Setting stop to last bin above " << mincounts << endl;
Bool_t inrange = kTRUE;
if (fitsigma) {
if (x > 0 && x < 5.) {
inrange = kFALSE;
fitrange[1] = 5.;
}
} else {
if (x > 0 && x < 5. * 80) {
inrange = kFALSE;
fitrange[1] = 30. * 80;
}
}
if (inrange && databin > 0 && fitrange[1] > x)
fitrange[1] = x;
const Int_t bin = datahisto->GetXaxis()->FindBin(fitrange[1]);
Infomsg("PrepareRawSpectra", Form("Required to autoset the stop point for the fit to %f bin %i content %f", fitrange[1], bin, datahisto->GetBinContent(bin)));
}
//Check the definition of the fit ranges
if (fitrange[1] < fitrange[0]) {
TCanvas* RangeAttempt = new TCanvas("RangeAttempt", "Attempt");
datahisto->DrawCopy();
TLine* l[2];
for (Int_t counter = 0; counter < 2; counter++) {
l[counter] = new TLine(fitrange[counter], 0, fitrange[counter], datahisto->GetMaximum());
l[counter]->Draw();
}
DrawLabel(Form("Wrong range defined [%f,%f]", fitrange[0], fitrange[1]));
RangeAttempt->SaveAs("./Images/RangeAttempt.pdf");
Fatalmsg("PrepareRawSpectra", Form("Fit range auto defined but wrongly.. %f < %f and should be the opposite", fitrange[1], fitrange[0]));
}
}
TArrayD fraction(ntemplates);
TArrayD fractionErr(ntemplates);
for (Int_t i = 0; i < ntemplates; i++) {
fraction[i] = 0;
fractionErr[i] = 0;
if (fitmode == 1) { //fraction are used to set the initial conditions
if (useMismatch && i == 0) {
fraction[i] = datahisto->GetEffectiveEntries() * 0.01;
if (fixMismatch) {
Int_t binlow = datahisto->GetXaxis()->FindBin(150);
Int_t binup = datahisto->GetXaxis()->FindBin(210);
Double_t tempintegral = mismatchhisto->Integral(fitrange[0], fitrange[1]);
fraction[i] = -tempintegral * datahisto->Integral(binlow, binup) / (datahisto->GetXaxis()->GetBinUpEdge(210) - datahisto->GetXaxis()->GetBinLowEdge(150));
}
} else
fraction[i] = datahisto->GetEffectiveEntries();
}
}
cFit->cd(padcounter);
//
//Fitting
//
Double_t chi2 = showchi2 ? indexoffset : -10;
// Double_t chi2 = -1;
if (status == kTRUE) {
switch (fitmode) {
case 0:
status = PerformFitWithTFF(datahisto, templates, range, fitrange, fraction, fractionErr, prediction);
break;
case 1:
status = PerformFitWithRooFit(datahisto, templates, range, fitrange, fraction, fractionErr, prediction, chi2);
break;
case 2:
status = PerformFitWithCD(datahisto, templates, range, fitrange, fraction, fractionErr, prediction);
break;
case 3:
status = PerformFitWithFunctions(datahisto, templates, fTOFsignalSum, range, fitrange, fraction, fractionErr, prediction);
break;
default:
Fatalmsg("PrepareRawSpectra", Form("The fitmode selected (%i) is not available", fitmode));
break;
}
} else
Infomsg("PrepareRawSpectra", "Required not to fit!");
gPad->SetLogy();
//Drawing labels on the pad
TString label = Form("%s %i [%.2f,%.2f]", ptstring.Data(), ptbin, fBinPt[ptbin], fBinPt[ptbin + 1]);
if (!status)
label += "Fit not performed!";
DrawLabel(label);
if (showchi2)
DrawLabel(Form("Chi2 = %f", chi2), 0.14, 0.82, 0.33, 0.92);
//Saving pad to file
SavePad(Form("%sFits/%i_%s%s.pdf", outpath.Data(), ptbin, MultBinString[iMult].Data(), fitmodes[fitmode].Data()), showrange[0], showrange[1], 1, -1, kTRUE);
//
//Get the yield from the fit
//
const Double_t ptmean = (fBinPt[ptbin + 1] + fBinPt[ptbin]) / 2.;
const Int_t ptmeanindex = hYield->GetXaxis()->FindBin(ptmean);
const Double_t Yield = ntemplates > 0 ? fraction[indexoffset] : 0;
const Double_t YieldError = ntemplates > 0 ? fractionErr[indexoffset] : 0;
const Double_t YieldResidual = status ? GetResidualYield(datahisto, (TH1*)prediction->At(0), -3, 3) : 0;
//Yield histograms
if (ptmeanindex != ptbin + 1)
Fatalmsg("PrepareRawSpectra", Form("Wrong index %i compare to %i", ptmeanindex, ptbin + 1));
hYield->SetBinContent(ptmeanindex, status ? Yield : -10);
hYield->SetBinError(ptmeanindex, status ? YieldError : 0);
hYieldResidual->SetBinContent(ptmeanindex, YieldResidual);
Int_t fractionindex = 0;
for (Int_t index = 0; index < kExpSpecies + 1; index++) {
hBackground[index]->SetBinContent(ptmeanindex, -10);
hBackground[index]->SetBinError(ptmeanindex, 0);
if (index == 0 && !useMismatch)
continue;
else if (index >= 1 && !useit[index - 1])
continue;
hBackground[index]->SetBinContent(ptmeanindex, fraction[fractionindex]);
hBackground[index]->SetBinError(ptmeanindex, fractionErr[fractionindex]);
fractionindex++;
}
//Retrieve fit histograms
if (status) {
Infomsg("PrepareRawSpectra", Form("Fit went OK with %i templates", ntemplates));
cout << "In output:" << endl;
prediction->ls();
cout << "In input:" << endl;
templates->ls();
Int_t counter = 0;
hFitted[ptbin] = (TH1F*)prediction->At(counter++);
if (useMismatch)
hMismatchFitted[ptbin] = (TH1F*)prediction->At(counter++);
else {
hMismatchFitted[ptbin] = (TH1F*)(mismatchhisto->Clone(Form("unfittedprediction%i", counter)));
hMismatchFitted[ptbin]->Reset();
}
for (Int_t species = 0; species < kExpSpecies; species++) {
if (useit[species])
hExpectedFitted[ptbin][species] = (TH1F*)prediction->At(counter++);
else {
hExpectedFitted[ptbin][species] = (TH1F*)(templatehhisto[species]->Clone(Form("unfittedprediction%i", counter)));
hExpectedFitted[ptbin][species]->Reset();
}
}
} else {
Int_t counter = 0;
hFitted[ptbin] = (TH1F*)datahisto->Clone("results");
hFitted[ptbin]->Reset();
hMismatchFitted[ptbin] = (TH1F*)(mismatchhisto->Clone(Form("prediction%i", counter++)));
hMismatchFitted[ptbin]->Reset();
for (Int_t species = 0; species < kExpSpecies; species++) {
hExpectedFitted[ptbin][species] = (TH1F*)(templatehhisto[species]->Clone(Form("prediction%i", counter++)));
hExpectedFitted[ptbin][species]->Reset();
}
}
TH1F* hlines[2] = { 0x0 };
if (drawlines) { //Histograms with the fit range
hlines[0] = (TH1F*)datahisto->Clone(Form("fitranges%i_%i_%i_%i", iCharge, iSpecies, iMult, ptbin));
hlines[0]->SetDirectory(0);
hlines[0]->Reset();
hlines[0]->SetTitle("Fit range");
hlines[0]->SetLineColor(kCyan);
hlines[0]->Fill(fitrange[0], datahisto->GetMaximum());
hlines[0]->Fill(fitrange[1], datahisto->GetMaximum());
hlines[1] = (TH1F*)hlines[0]->Clone(Form("ranges%i_%i_%i_%i", iCharge, iSpecies, iMult, ptbin));
hlines[1]->SetDirectory(0);
hlines[1]->Reset();
hlines[1]->SetTitle("Integration range");
hlines[1]->SetLineColor(kOrange);
hlines[1]->Fill(range[0], datahisto->GetMaximum());
hlines[1]->Fill(range[1], datahisto->GetMaximum());
}
//
//Finally drawing the result of the fit
//
cFitOnly->cd(padcounter);
gPad->SetLogy();
datahisto->SetTitle("Data");
datahisto->DrawCopy()->GetXaxis()->SetRangeUser(showrange[0], showrange[1]);
hFitted[ptbin]->SetLineColor(kMagenta);
hFitted[ptbin]->SetMarkerColor(kMagenta);
if (filltemplates > 0) {
hFitted[ptbin]->SetFillStyle(filltemplates);
hFitted[ptbin]->SetFillColor(kMagenta);
}
hFitted[ptbin]->DrawCopy("same");
hMismatchFitted[ptbin]->DrawCopy("same");
Double_t bkgerror = 0;
hBackgroundOverlap[0]->SetBinContent(ptmeanindex, GetOverlapFraction(hExpectedFitted[ptbin][iSpecies + 2], hMismatchFitted[ptbin], bkgerror, ptbin == 20 ? kFALSE : kFALSE));
hBackgroundOverlap[0]->SetBinError(ptmeanindex, bkgerror);
for (Int_t species = 0; species < kExpSpecies; species++) {
hBackgroundOverlap[species + 1]->SetBinContent(ptmeanindex, GetOverlapFraction(hExpectedFitted[ptbin][iSpecies + 2], hExpectedFitted[ptbin][species], bkgerror));
hBackgroundOverlap[species + 1]->SetBinError(ptmeanindex, bkgerror);
if (useit[species])
hExpectedFitted[ptbin][species]->DrawCopy("same");
}
if (drawlines) { //Histograms with the fit range
hlines[0]->DrawCopy("same");
hlines[1]->DrawCopy("same");
}
DrawLabel(label);
if (showchi2)
DrawLabel(Form("Chi2 = %f", chi2), 0.14, 0.82, 0.33, 0.92);
//Saving pads to figures
SavePad(Form("%sFits/FITONLY_%i_%s%s.pdf", outpath.Data(), ptbin, MultBinString[iMult].Data(), fitmodes[fitmode].Data()), showrange[0], showrange[1], 1, -1, kTRUE, Form("Fit with %s", fitmodes[fitmode].Data()), kTRUE);
gPad->SetLogy(kFALSE);
SavePad(Form("%sFits/FITONLY_linear_%i_%s%s.pdf", outpath.Data(), ptbin, MultBinString[iMult].Data(), fitmodes[fitmode].Data()), showrange[0], showrange[1], 1, -1, kTRUE, Form("Fit with %s", fitmodes[fitmode].Data()), kTRUE);
gPad->SetLogy();
//Drawing ratio between the fit and the data
cFitRatio->cd(padcounter);
hRatioToFitted[ptbin] = (TH1F*)hFitted[ptbin]->Clone(Form("RatioToFitted%s%s_%i", pS[iSpecies].Data(), pS[iSpecies].Data(), ptbin));
hRatioToFitted[ptbin]->SetTitle(Form("Ratio to Fit;%s;Fitted/Data", fitsigma ? nsigmastringSpecies[iSpecies + kpi].Data() : tofsignalstringSpecies[iSpecies + kpi].Data()));
hRatioToFitted[ptbin]->Sumw2();
hRatioToFitted[ptbin]->Divide(hFitted[ptbin], datahisto, 1, 1);
hRatioToFitted[ptbin]->DrawCopy()->GetXaxis()->SetRangeUser(fitrange[0], fitrange[1]);
;
DrawLabel(label);
if (showratiomean) {
TF1* meanvalue = new TF1("fmean", "pol0", -100, 100);
meanvalue->SetParameter(0, 0);
meanvalue->SetParLimits(0, 0, 100);
hRatioToFitted[ptbin]->Fit(meanvalue, "0 N R M S");
meanvalue->DrawCopy("same");
DrawLabel(Form("Mean = %f", meanvalue->GetParameter(0)), 0.14, 0.82, 0.33, 0.92);
}
padcounter++;
}
cTOFPt->cd();
//2D histograms
TH1F* auxiarray[kPtBins];
for (Int_t ptbin = 0; ptbin < kPtBins; ptbin++)
auxiarray[ptbin] = hTOF[ptbin];
hTOFPt = Form2DHisto(auxiarray, kPtBins, fBinPt, Form("hTOFPt%s%s", pC[iCharge].Data(), pS[iSpecies].Data()), Form("hTOFPt%s%s;%s;%s", pC[iCharge].Data(), pS[iSpecies].Data(), fitsigma ? nsigmastringSpecies[iSpecies + kpi].Data() : tofsignalstringSpecies[iSpecies + kpi].Data(), ptstring.Data()));
CopyStyle(hTOFPt, auxiarray[0]);
for (Int_t ptbin = 0; ptbin < kPtBins; ptbin++)
auxiarray[ptbin] = hMismatchFitted[ptbin];
hTOFPtFittedMismatch = Form2DHisto(auxiarray, kPtBins, fBinPt, Form("hTOFPtFittedMismatch%s%s", pC[iCharge].Data(), pS[iSpecies].Data()), Form("hTOFPtFittedMismatch%s%s", pC[iCharge].Data(), pS[iSpecies].Data()));
CopyStyle(hTOFPtFittedMismatch, auxiarray[0]);
for (Int_t species = 0; species < kExpSpecies; species++) {
for (Int_t ptbin = 0; ptbin < kPtBins; ptbin++)
auxiarray[ptbin] = hExpectedFitted[ptbin][species];
hTOFPtFittedSignal[species] = Form2DHisto(auxiarray, kPtBins, fBinPt, Form("hTOFPtFittedSignal%s%s%s", pC[iCharge].Data(), pS[iSpecies].Data(), pS_all[species].Data()), Form("hTOFPtFittedSignal%s%s%s", pC[iCharge].Data(), pS[iSpecies].Data(), pS_all[species].Data()));
CopyStyle(hTOFPtFittedSignal[species], auxiarray[0]);
}
for (Int_t ptbin = 0; ptbin < kPtBins; ptbin++)
auxiarray[ptbin] = hFitted[ptbin];
hTOFPtFitted = Form2DHisto(auxiarray, kPtBins, fBinPt, Form("hTOFPtFitted%s%s", pC[iCharge].Data(), pS[iSpecies].Data()), Form("hTOFPtFitted%s%s", pC[iCharge].Data(), pS[iSpecies].Data()));
CopyStyle(hTOFPtFitted, auxiarray[0]);
if (limitrange) {
hTOFPt->GetXaxis()->SetRangeUser(fitsigma ? -10 : -500, fitsigma ? 10 : 500);
hTOFPt->GetYaxis()->SetRangeUser(PtRange[optpp][2 * iSpecies], PtRange[optpp][2 * iSpecies + 1]);
}
hTOFPt->DrawCopy();
hTOFPtFittedMismatch->DrawCopy("same");
for (Int_t species = 0; species < kExpSpecies; species++)
hTOFPtFittedSignal[species]->DrawCopy("same");
if (drawresult)
hTOFPtFitted->DrawCopy("same");
cTOFPt->AddExec("dynamic", "DynamicExec()");
//
//Computing normalization factor
//
Double_t evtnorm = 1;
if (geteventinfo) { //Compute the normalization from the list of events
if (iMult != nMultBin - 1) { //MB case
Double_t temp = hEvtMultAftEvSel->GetEffectiveEntries();
hEvtMultAftEvSel->GetXaxis()->SetRangeUser(MultBin[iMult] + 0.0001, MultBin[iMult + 1] - 0.0001);
hEvtMult->GetXaxis()->SetRangeUser(MultBin[iMult] + 0.0001, MultBin[iMult + 1] - 0.0001);
if (temp == hEvtMultAftEvSel->GetEffectiveEntries())
Fatalmsg("PrepareRawSpectra", "Normalization");
} else { //Centrality bin
hEvtMultAftEvSel->GetXaxis()->SetRangeUser(-1, hEvtMultAftEvSel->GetNbinsX() + 1);
hEvtMult->GetXaxis()->SetRangeUser(-1, hEvtMult->GetNbinsX() + 1);
if (hEvtMultAftEvSel->GetSumOfWeights() != hEvtMultAftEvSel->GetEffectiveEntries())
Fatalmsg("PrepareRawSpectra", "Normalization");
}
evtnorm = 1. / hEvtMultAftEvSel->GetEffectiveEntries();
// evtnorm = 1./hEvtMultAftEvSel->GetSumOfWeights()
// evtnorm = 1./hEvtMult->GetEffectiveEntries()
}
//Compute the normalization from the list of analysed tracks
const Double_t norm = 1. / fEntries->GetBinContent(1);
if (geteventinfo && norm != evtnorm) {
Warningmsg("PrepareRawSpectra", Form("The two Normalizations differ! (evt) %f (processed) %f evt/processed ratio %f%%\n\t The number of events in the multiplicity class is: (evt) %f (processed) %f", evtnorm, norm, norm > 0 ? evtnorm / norm : -1, hEvtMultAftEvSel->GetEffectiveEntries(), fEntries->GetBinContent(1)));
}
//
//Draw the yield
//
cYield->cd(1);
gPad->SetLogy();
hYield->SetTitle("Raw Yield Unbinned");
hYield->GetXaxis()->SetRangeUser(0, 5);
hYield->DrawCopy()->GetYaxis()->SetTitle("Unscaled unbinned counts");
lHistograms->AddFirst(((TH1F*)hYield->Clone(Form("hUnscaledYieldUnbinned%s%s", pC[iCharge].Data(), pS[iSpecies].Data()))));
DrawLabel("Raw Yield Unbinned");
cYield->cd(2);
gPad->SetLogy();
hYield->Scale(1, "width"); //Scale to the bin width
hYield->SetTitle("Raw Yield");
hYield->DrawCopy()->GetYaxis()->SetTitle("Unscaled counts");
lHistograms->AddFirst(((TH1F*)hYield->Clone(Form("hUnscaledYield%s%s", pC[iCharge].Data(), pS[iSpecies].Data()))));
DrawLabel("Unscaled counts");
cYield->cd(3);
gPad->SetLogy();
if (normfromlist) {
hYield->Scale(evtnorm);
hYieldResidual->Scale(evtnorm, "width");
hYield->SetTitle("Yield List Evt. Norm.");
} else {
hYield->Scale(norm);
hYieldResidual->Scale(norm, "width");
hYield->SetTitle("Yield Evt. Norm.");
}
hYield->DrawCopy()->GetYaxis()->SetTitle("Evt. Scaled counts");
DrawLabel("Evt. Scaled counts");
cYield->SaveAs(Form("%s%s%s_%s.pdf", outpath.Data(), pCharge[iCharge].Data(), pSpecies[iSpecies].Data(), MultBinString[iMult].Data()));
//
//Draw the residual of the yield
//
TH1* H = 0x0;
cYieldResidual->cd(1);
H = hYieldResidual->DrawCopy("");
H->GetXaxis()->SetRangeUser(0, 5);
H->SetLineColor(kRed);
H->GetYaxis()->SetTitle("Residual");
DrawLabel("Residual yield");
cYieldResidual->cd(2);
H->DrawCopy("")->Divide(hYield);
DrawLabel("Ratio to yield");
cYieldResidual->cd(3);
H = hYieldResidual->DrawCopy("");
H->SetLineColor(kRed);
H->GetXaxis()->SetRangeUser(0, 5);
hYield->DrawCopy("same");
H->Add(hYield);
DrawLabel("Yield and Yield + Residual");
cYieldResidual->cd(4);
H = H->DrawCopy("");
H->Divide(hYield);
H->GetYaxis()->SetRangeUser(.9, 1.1);
DrawLabel("Yield + Residual Ratio to Yield");
//
//Draw the background
//
for (Int_t i = 0; i < kExpSpecies + 1; i++) {
cBackground->cd(i + 1);
hBackground[i]->SetMinimum(0);
hBackground[i]->GetXaxis()->SetRangeUser(0, 5);
hBackground[i]->DrawCopy()->Scale(1, "width");
DrawLabel(i == 0 ? "Mismatch" : pSpecies_all[i - 1].Data());
cBackgroundOverlap->cd(i + 1);
hBackgroundOverlap[i]->SetMinimum(0);
hBackgroundOverlap[i]->GetXaxis()->SetRangeUser(0, 5);
hBackgroundOverlap[i]->DrawCopy()->Scale(1, "width");
DrawLabel(i == 0 ? "Mismatch" : pSpecies_all[i - 1].Data());
}
//
//Draw event info
//
cEvent->cd(1);
if (geteventinfo)
hNEvt->DrawCopy();
else
DrawLabel("Required with no event info");
cEvent->cd(2);
if (geteventinfo) {
hEvtMult->GetXaxis()->SetRangeUser(0, 210);
hEvtMult->DrawCopy();
} else
DrawLabel("Required with no event info");
cEvent->cd(3);
if (geteventinfo) {
hEvtMultAftEvSel->GetXaxis()->SetRangeUser(0, 102);
hEvtMultAftEvSel->DrawCopy();
} else
DrawLabel("Required with no event info");
cEvent->cd(4);
fEntries->DrawCopy();
//Draw used templates
if (husage) {
TCanvas* cUsage = new TCanvas("cUsage", "Templates");
husage->DrawCopy("COLZ");
if (productionmode)
delete cUsage;
}
if (!fitprefix.EqualTo("")) { //Save fitted histos to file for fits
if (foutfits)
foutfits->cd();
for (Int_t ptbin = 0; ptbin < kPtBins; ptbin++) {
TH1F* auxihisto = hTOF[ptbin];
auxihisto->SetName(Form("TOFData%s%s%s_pt%i", pC[iCharge].Data(), pS[iSpecies].Data(), MultBinString[iMult].Data(), ptbin));
auxihisto->SetTitle(Form("TOF for %s %s in %s ptbin %i [%.2f,%.2f]", pCharge[iCharge].Data(), pSpecies[iSpecies].Data(), MultBinString[iMult].Data(), ptbin, fBinPt[ptbin], fBinPt[ptbin + 1]));
if (foutfits)
auxihisto->Write(auxihisto->GetName());
hFitted[ptbin]->SetName(Form("TOFFitted%s%s%s_pt%i", pC[iCharge].Data(), pS[iSpecies].Data(), MultBinString[iMult].Data(), ptbin));
hFitted[ptbin]->SetTitle(Form("Fitted TOF for %s %s in %s ptbin %i [%.2f,%.2f]", pCharge[iCharge].Data(), pSpecies[iSpecies].Data(), MultBinString[iMult].Data(), ptbin, fBinPt[ptbin], fBinPt[ptbin + 1]));
if (foutfits)
hFitted[ptbin]->Write(hFitted[ptbin]->GetName());
hMismatchFitted[ptbin]->SetName(Form("TOFFitted_Mismatch_%s%s%s_pt%i", pC[iCharge].Data(), pS[iSpecies].Data(), MultBinString[iMult].Data(), ptbin));
hMismatchFitted[ptbin]->SetTitle(Form("Fitted TOF Mismatch for %s %s in %s ptbin %i [%.2f,%.2f]", pCharge[iCharge].Data(), pSpecies[iSpecies].Data(), MultBinString[iMult].Data(), ptbin, fBinPt[ptbin], fBinPt[ptbin + 1]));
if (hMismatchFitted[ptbin]->GetEffectiveEntries() > 1 && foutfits)
hMismatchFitted[ptbin]->Write(hMismatchFitted[ptbin]->GetName());
for (Int_t species = 0; species < kExpSpecies; species++) {
hExpectedFitted[ptbin][species]->SetName(Form("TOFFitted_%s_%s%s%s_pt%i", pS_all[species].Data(), pC[iCharge].Data(), pS[iSpecies].Data(), MultBinString[iMult].Data(), ptbin));
hExpectedFitted[ptbin][species]->SetTitle(Form("Fitted TOF %s for %s %s in %s ptbin %i [%.2f,%.2f]", pSpecies[species].Data(), pCharge[iCharge].Data(), pSpecies[iSpecies].Data(), MultBinString[iMult].Data(), ptbin, fBinPt[ptbin], fBinPt[ptbin + 1]));
if (hExpectedFitted[ptbin][species]->GetEffectiveEntries() <= 1)
continue;
if (foutfits)
hExpectedFitted[ptbin][species]->Write(hExpectedFitted[ptbin][species]->GetName());
}
hRatioToFitted[ptbin]->SetName(Form("TOFFitted_Ratio_%s%s%s_pt%i", pC[iCharge].Data(), pS[iSpecies].Data(), MultBinString[iMult].Data(), ptbin));
hRatioToFitted[ptbin]->SetTitle(Form("Fitted TOF Mismatch for %s %s in %s ptbin %i [%.2f,%.2f]", pCharge[iCharge].Data(), pSpecies[iSpecies].Data(), MultBinString[iMult].Data(), ptbin, fBinPt[ptbin], fBinPt[ptbin + 1]));
if (foutfits)
hRatioToFitted[ptbin]->Write(hRatioToFitted[ptbin]->GetName());
}
if (foutfits) {
foutfits->Close();
delete foutfits;
}
}
if (fout) {
fout->cd();
lCanvas->Write(Form("lCanvasRawSpectra%s%s", pCharge[iCharge].Data(), pSpecies[iSpecies].Data()), 1);
lHistograms->Write(Form("lHistogramsRawSpectra%s%s", pCharge[iCharge].Data(), pSpecies[iSpecies].Data()), 1);
}
if (productionmode) {
if (lHistograms)
delete lHistograms;
if (lCanvas)
delete lCanvas;
}
if (linData)
delete linData;
if (fout) {
fout->Close();
delete fout;
}
}
void SetColor(TH1* h, Int_t c, Int_t f)
{
h->SetMarkerColor(c);
h->SetLineColor(c);
if (f > 0) {
h->SetFillStyle(f);
h->SetFillColor(c);
}
}
#endif
|
from django.contrib import admin
from user import models
admin.site.register(models.UserPreferences, admin.ModelAdmin)
|
#ifndef __HELLOWORLD_SCENE_H__
#define __HELLOWORLD_SCENE_H__
#include "cocos2d.h"
class HelloWorld : public cocos2d::Layer
{
public:
// there's no 'id' in cpp, so we recommend returning the class instance pointer
static cocos2d::Scene* createScene();
// Here's a difference. Method 'init' in cocos2d-x returns bool, instead of returning 'id' in cocos2d-iphone
virtual bool init();
void mouseDown(cocos2d::Event *event);
// implement the "static create()" method manually
CREATE_FUNC(HelloWorld);
};
#endif // __HELLOWORLD_SCENE_H__
|
# Copyright 2020 The Magenta Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for midi_hub."""
import collections
import threading
import time
from magenta.common import concurrency
from magenta.interfaces.midi import midi_hub
from magenta.music import testing_lib
from magenta.music.protobuf import music_pb2
import mido
from six.moves import queue as Queue
import tensorflow.compat.v1 as tf
Note = collections.namedtuple('Note', ['pitch', 'velocity', 'start', 'end'])
class MockMidiPort(mido.ports.BaseIOPort):
def __init__(self):
super(MockMidiPort, self).__init__()
self.message_queue = Queue.Queue()
def send(self, msg):
msg.time = time.time()
self.message_queue.put(msg)
class MidiHubTest(tf.test.TestCase):
def setUp(self):
self.maxDiff = None # pylint:disable=invalid-name
self.capture_messages = [
mido.Message(type='note_on', note=0, time=0.01),
mido.Message(type='control_change', control=1, value=1, time=0.02),
mido.Message(type='note_on', note=1, time=2.0),
mido.Message(type='note_off', note=0, time=3.0),
mido.Message(type='note_on', note=2, time=3.0),
mido.Message(type='note_on', note=3, time=4.0),
mido.Message(type='note_off', note=2, time=4.0),
mido.Message(type='note_off', note=1, time=5.0),
mido.Message(type='control_change', control=1, value=1, time=6.0),
mido.Message(type='note_off', note=3, time=100)]
self.port = MockMidiPort()
self.midi_hub = midi_hub.MidiHub([self.port], [self.port],
midi_hub.TextureType.POLYPHONIC)
# Burn in Sleeper for calibration.
for _ in range(5):
concurrency.Sleeper().sleep(0.05)
def tearDown(self):
self.midi_hub.__del__()
def send_capture_messages(self):
for msg in self.capture_messages:
self.port.callback(msg)
def testMidiSignal_ValidityChecks(self):
# Unsupported type.
with self.assertRaises(midi_hub.MidiHubError):
midi_hub.MidiSignal(type='sysex')
with self.assertRaises(midi_hub.MidiHubError):
midi_hub.MidiSignal(msg=mido.Message(type='sysex'))
# Invalid arguments.
with self.assertRaises(midi_hub.MidiHubError):
midi_hub.MidiSignal()
with self.assertRaises(midi_hub.MidiHubError):
midi_hub.MidiSignal(type='note_on', value=1)
with self.assertRaises(midi_hub.MidiHubError):
midi_hub.MidiSignal(type='control', note=1)
with self.assertRaises(midi_hub.MidiHubError):
midi_hub.MidiSignal(msg=mido.Message(type='control_change'), value=1)
# Non-inferrale type.
with self.assertRaises(midi_hub.MidiHubError):
midi_hub.MidiSignal(note=1, value=1)
def testMidiSignal_Message(self):
sig = midi_hub.MidiSignal(msg=mido.Message(type='note_on', note=1))
self.assertEqual(
r'^note_on channel=0 note=1 velocity=64 time=\d+.\d+$', str(sig))
sig = midi_hub.MidiSignal(msg=mido.Message(type='note_off', velocity=127))
self.assertEqual(r'^note_off channel=0 note=0 velocity=127 time=\d+.\d+$',
str(sig))
sig = midi_hub.MidiSignal(
msg=mido.Message(type='control_change', control=1, value=2))
self.assertEqual(
r'^control_change channel=0 control=1 value=2 time=\d+.\d+$', str(sig))
def testMidiSignal_Args(self):
sig = midi_hub.MidiSignal(type='note_on', note=1)
self.assertEqual(
r'^note_on channel=\d+ note=1 velocity=\d+ time=\d+.\d+$', str(sig))
sig = midi_hub.MidiSignal(type='note_off', velocity=127)
self.assertEqual(
r'^note_off channel=\d+ note=\d+ velocity=127 time=\d+.\d+$', str(sig))
sig = midi_hub.MidiSignal(type='control_change', value=2)
self.assertEqual(
r'^control_change channel=\d+ control=\d+ value=2 time=\d+.\d+$',
str(sig))
def testMidiSignal_Args_InferredType(self):
sig = midi_hub.MidiSignal(note=1)
self.assertEqual(
r'^.* channel=\d+ note=1 velocity=\d+ time=\d+.\d+$', str(sig))
sig = midi_hub.MidiSignal(value=2)
self.assertEqual(
r'^control_change channel=\d+ control=\d+ value=2 time=\d+.\d+$',
str(sig))
def testMetronome(self):
start_time = time.time() + 0.1
qpm = 180
self.midi_hub.start_metronome(start_time=start_time, qpm=qpm)
time.sleep(0.8)
self.midi_hub.stop_metronome()
self.assertEqual(7, self.port.message_queue.qsize())
msg = self.port.message_queue.get()
self.assertEqual(msg.type, 'program_change')
next_tick_time = start_time
while not self.port.message_queue.empty():
msg = self.port.message_queue.get()
if self.port.message_queue.qsize() % 2:
self.assertEqual(msg.type, 'note_on')
self.assertAlmostEqual(msg.time, next_tick_time, delta=0.01)
next_tick_time += 60. / qpm
else:
self.assertEqual(msg.type, 'note_off')
def testStartPlayback_NoUpdates(self):
# Use a time in the past to test handling of past notes.
start_time = time.time() - 0.05
seq = music_pb2.NoteSequence()
notes = [Note(12, 100, 0.0, 1.0), Note(11, 55, 0.1, 0.5),
Note(40, 45, 0.2, 0.6)]
notes = [Note(note.pitch, note.velocity, note.start + start_time,
note.end + start_time) for note in notes]
testing_lib.add_track_to_sequence(seq, 0, notes)
player = self.midi_hub.start_playback(seq, allow_updates=False)
player.join()
note_events = []
for note in notes:
note_events.append((note.start, 'note_on', note.pitch))
note_events.append((note.end, 'note_off', note.pitch))
# The first note on will not be sent since it started before
# `start_playback` is called.
del note_events[0]
note_events = collections.deque(sorted(note_events))
while not self.port.message_queue.empty():
msg = self.port.message_queue.get()
note_event = note_events.popleft()
self.assertEqual(msg.type, note_event[1])
self.assertEqual(msg.note, note_event[2])
self.assertAlmostEqual(msg.time, note_event[0], delta=0.01)
self.assertTrue(not note_events)
def testStartPlayback_NoUpdates_UpdateError(self):
# Use a time in the past to test handling of past notes.
start_time = time.time()
seq = music_pb2.NoteSequence()
notes = [Note(0, 100, start_time + 100, start_time + 101)]
testing_lib.add_track_to_sequence(seq, 0, notes)
player = self.midi_hub.start_playback(seq, allow_updates=False)
with self.assertRaises(midi_hub.MidiHubError):
player.update_sequence(seq)
player.stop()
def testStartPlayback_Updates(self):
start_time = time.time() + 0.1
seq = music_pb2.NoteSequence()
notes = [Note(0, 100, start_time, start_time + 101),
Note(1, 100, start_time, start_time + 101)]
testing_lib.add_track_to_sequence(seq, 0, notes)
player = self.midi_hub.start_playback(seq, allow_updates=True)
# Sleep past first note start.
concurrency.Sleeper().sleep_until(start_time + 0.2)
new_seq = music_pb2.NoteSequence()
notes = [Note(1, 100, 0.0, 0.8), Note(2, 100, 0.0, 1.0),
Note(11, 55, 0.3, 0.5), Note(40, 45, 0.4, 0.6)]
notes = [Note(note.pitch, note.velocity, note.start + start_time,
note.end + start_time) for note in notes]
testing_lib.add_track_to_sequence(new_seq, 0, notes)
player.update_sequence(new_seq)
# Finish playing sequence.
concurrency.Sleeper().sleep(0.8)
# Start and end the unclosed note from the first sequence.
note_events = [(start_time, 'note_on', 0),
(start_time + 0.3, 'note_off', 0)]
# The second note will not be played since it started before the update
# and was not in the original sequence.
del notes[1]
for note in notes:
note_events.append((note.start, 'note_on', note.pitch))
note_events.append((note.end, 'note_off', note.pitch))
note_events = collections.deque(sorted(note_events))
while not self.port.message_queue.empty():
msg = self.port.message_queue.get()
note_event = note_events.popleft()
self.assertEqual(msg.type, note_event[1])
self.assertEqual(msg.note, note_event[2])
self.assertAlmostEqual(msg.time, note_event[0], delta=0.01)
self.assertTrue(not note_events)
player.stop()
def testCaptureSequence_StopSignal(self):
start_time = 1.0
threading.Timer(0.1, self.send_capture_messages).start()
captured_seq = self.midi_hub.capture_sequence(
120, start_time,
stop_signal=midi_hub.MidiSignal(type='control_change', control=1))
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = 6.0
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, 6)])
self.assertProtoEquals(captured_seq, expected_seq)
def testCaptureSequence_StopTime(self):
start_time = 1.0
stop_time = time.time() + 1.0
self.capture_messages[-1].time += time.time()
threading.Timer(0.1, self.send_capture_messages).start()
captured_seq = self.midi_hub.capture_sequence(
120, start_time, stop_time=stop_time)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = stop_time
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, stop_time)])
self.assertProtoEquals(captured_seq, expected_seq)
def testCaptureSequence_Mono(self):
start_time = 1.0
threading.Timer(0.1, self.send_capture_messages).start()
self.midi_hub = midi_hub.MidiHub([self.port], [self.port],
midi_hub.TextureType.MONOPHONIC)
captured_seq = self.midi_hub.capture_sequence(
120, start_time,
stop_signal=midi_hub.MidiSignal(type='control_change', control=1))
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = 6
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 3), Note(2, 64, 3, 4), Note(3, 64, 4, 6)])
self.assertProtoEquals(captured_seq, expected_seq)
def testStartCapture_StopMethod(self):
start_time = 1.0
captor = self.midi_hub.start_capture(120, start_time)
self.send_capture_messages()
time.sleep(0.1)
stop_time = 5.5
captor.stop(stop_time=stop_time)
captured_seq = captor.captured_sequence()
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = stop_time
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, stop_time)])
self.assertProtoEquals(captured_seq, expected_seq)
def testStartCapture_Multiple(self):
captor_1 = self.midi_hub.start_capture(
120, 0.0, stop_signal=midi_hub.MidiSignal(note=3))
captor_2 = self.midi_hub.start_capture(
120, 1.0,
stop_signal=midi_hub.MidiSignal(type='control_change', control=1))
self.send_capture_messages()
captor_1.join()
captor_2.join()
captured_seq_1 = captor_1.captured_sequence()
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = 4.0
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(0, 64, 0.01, 3), Note(1, 64, 2, 4), Note(2, 64, 3, 4)])
self.assertProtoEquals(captured_seq_1, expected_seq)
captured_seq_2 = captor_2.captured_sequence()
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = 6.0
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, 6)])
self.assertProtoEquals(captured_seq_2, expected_seq)
def testStartCapture_IsDrum(self):
start_time = 1.0
captor = self.midi_hub.start_capture(120, start_time)
# Channels are 0-indexed in mido.
self.capture_messages[2].channel = 9
self.send_capture_messages()
time.sleep(0.1)
stop_time = 5.5
captor.stop(stop_time=stop_time)
captured_seq = captor.captured_sequence()
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = stop_time
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, stop_time)])
expected_seq.notes[0].is_drum = True
self.assertProtoEquals(captured_seq, expected_seq)
def testStartCapture_MidCapture(self):
start_time = 1.0
captor = self.midi_hub.start_capture(120, start_time)
# Receive the first 6 messages.
for msg in self.capture_messages[0:6]:
self.port.callback(msg)
time.sleep(0.1)
end_time = 3.5
captured_seq = captor.captured_sequence(end_time)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(
expected_seq, 0, [Note(1, 64, 2, 3.5), Note(2, 64, 3, 3.5)])
self.assertProtoEquals(captured_seq, expected_seq)
end_time = 4.5
captured_seq = captor.captured_sequence(end_time)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 4.5), Note(2, 64, 3, 4.5), Note(3, 64, 4, 4.5)])
self.assertProtoEquals(captured_seq, expected_seq)
end_time = 6.0
captured_seq = captor.captured_sequence(end_time)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 6), Note(2, 64, 3, 6), Note(3, 64, 4, 6)])
self.assertProtoEquals(captured_seq, expected_seq)
# Receive the rest of the messages.
for msg in self.capture_messages[6:]:
self.port.callback(msg)
time.sleep(0.1)
end_time = 6.0
captured_seq = captor.captured_sequence(end_time)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, 6)])
self.assertProtoEquals(captured_seq, expected_seq)
captor.stop()
def testStartCapture_Iterate_Signal(self):
start_time = 1.0
captor = self.midi_hub.start_capture(
120, start_time,
stop_signal=midi_hub.MidiSignal(type='control_change', control=1))
for msg in self.capture_messages[:-1]:
threading.Timer(0.2 * msg.time, self.port.callback, args=[msg]).start()
captured_seqs = []
for captured_seq in captor.iterate(
signal=midi_hub.MidiSignal(type='note_off')):
captured_seqs.append(captured_seq)
self.assertEqual(4, len(captured_seqs))
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = 3
testing_lib.add_track_to_sequence(expected_seq, 0, [Note(1, 64, 2, 3)])
self.assertProtoEquals(captured_seqs[0], expected_seq)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = 4
testing_lib.add_track_to_sequence(
expected_seq, 0, [Note(1, 64, 2, 4), Note(2, 64, 3, 4)])
self.assertProtoEquals(captured_seqs[1], expected_seq)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = 5
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, 5)])
self.assertProtoEquals(captured_seqs[2], expected_seq)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = 6
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, 6)])
self.assertProtoEquals(captured_seqs[3], expected_seq)
def testStartCapture_Iterate_Period(self):
start_time = 1.0
captor = self.midi_hub.start_capture(
120, start_time,
stop_signal=midi_hub.MidiSignal(type='control_change', control=1))
for msg in self.capture_messages[:-1]:
threading.Timer(0.1 * msg.time, self.port.callback, args=[msg]).start()
period = 0.26
captured_seqs = []
wall_start_time = time.time()
for captured_seq in captor.iterate(period=period):
if len(captured_seqs) < 2:
self.assertAlmostEqual(0, (time.time() - wall_start_time) % period,
delta=0.01)
time.sleep(0.1)
captured_seqs.append(captured_seq)
self.assertEqual(3, len(captured_seqs))
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
end_time = captured_seqs[0].total_time
self.assertAlmostEqual(wall_start_time + period, end_time, delta=0.005)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(
expected_seq, 0, [Note(1, 64, 2, end_time)])
self.assertProtoEquals(captured_seqs[0], expected_seq)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
end_time = captured_seqs[1].total_time
self.assertAlmostEqual(wall_start_time + 2 * period, end_time, delta=0.005)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, end_time)])
self.assertProtoEquals(captured_seqs[1], expected_seq)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = 6
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, 6)])
self.assertProtoEquals(captured_seqs[2], expected_seq)
def testStartCapture_Iterate_Period_Overrun(self):
start_time = 1.0
captor = self.midi_hub.start_capture(
120, start_time,
stop_signal=midi_hub.MidiSignal(type='control_change', control=1))
for msg in self.capture_messages[:-1]:
threading.Timer(0.1 * msg.time, self.port.callback, args=[msg]).start()
period = 0.26
captured_seqs = []
wall_start_time = time.time()
for captured_seq in captor.iterate(period=period):
time.sleep(0.5)
captured_seqs.append(captured_seq)
self.assertEqual(2, len(captured_seqs))
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
end_time = captured_seqs[0].total_time
self.assertAlmostEqual(wall_start_time + period, end_time, delta=0.005)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(
expected_seq, 0, [Note(1, 64, 2, end_time)])
self.assertProtoEquals(captured_seqs[0], expected_seq)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = 6
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, 6)])
self.assertProtoEquals(captured_seqs[1], expected_seq)
def testStartCapture_Callback_Period(self):
start_time = 1.0
captor = self.midi_hub.start_capture(120, start_time)
for msg in self.capture_messages[:-1]:
threading.Timer(0.1 * msg.time, self.port.callback, args=[msg]).start()
period = 0.26
wall_start_time = time.time()
captured_seqs = []
def fn(captured_seq):
self.assertAlmostEqual(0, (time.time() - wall_start_time) % period,
delta=0.01)
captured_seqs.append(captured_seq)
name = captor.register_callback(fn, period=period)
time.sleep(1.0)
captor.cancel_callback(name)
self.assertEqual(3, len(captured_seqs))
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
end_time = captured_seqs[0].total_time
self.assertAlmostEqual(wall_start_time + period, end_time, delta=0.005)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(
expected_seq, 0, [Note(1, 64, 2, end_time)])
self.assertProtoEquals(captured_seqs[0], expected_seq)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
end_time = captured_seqs[1].total_time
self.assertAlmostEqual(wall_start_time + 2 * period, end_time, delta=0.005)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, end_time)])
self.assertProtoEquals(captured_seqs[1], expected_seq)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
end_time = captured_seqs[2].total_time
self.assertAlmostEqual(wall_start_time + 3 * period, end_time, delta=0.005)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, end_time)])
self.assertProtoEquals(captured_seqs[2], expected_seq)
def testStartCapture_Callback_Period_Overrun(self):
start_time = 1.0
captor = self.midi_hub.start_capture(
120, start_time)
for msg in self.capture_messages[:-1]:
threading.Timer(0.1 * msg.time, self.port.callback, args=[msg]).start()
period = 0.26
wall_start_time = time.time()
captured_seqs = []
def fn(captured_seq):
time.sleep(0.5)
captured_seqs.append(captured_seq)
name = captor.register_callback(fn, period=period)
time.sleep(1.3)
captor.cancel_callback(name)
self.assertEqual(2, len(captured_seqs))
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
end_time = captured_seqs[0].total_time
self.assertAlmostEqual(wall_start_time + period, end_time, delta=0.005)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(
expected_seq, 0, [Note(1, 64, 2, end_time)])
self.assertProtoEquals(captured_seqs[0], expected_seq)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
end_time = captured_seqs[1].total_time
self.assertAlmostEqual(wall_start_time + 2 * period, end_time, delta=0.005)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, end_time)])
self.assertProtoEquals(captured_seqs[1], expected_seq)
def testPassThrough_Poly(self):
self.midi_hub.passthrough = False
self.send_capture_messages()
self.assertTrue(self.port.message_queue.empty())
self.midi_hub.passthrough = True
self.send_capture_messages()
passed_messages = []
while not self.port.message_queue.empty():
passed_messages.append(self.port.message_queue.get().bytes())
self.assertListEqual(
passed_messages, [m.bytes() for m in self.capture_messages])
def testPassThrough_Mono(self):
self.midi_hub = midi_hub.MidiHub([self.port], [self.port],
midi_hub.TextureType.MONOPHONIC)
self.midi_hub.passthrough = False
self.send_capture_messages()
self.assertTrue(self.port.message_queue.empty())
self.midi_hub.passthrough = True
self.send_capture_messages()
passed_messages = []
while not self.port.message_queue.empty():
passed_messages.append(self.port.message_queue.get())
passed_messages[-1].time = 0
expected_messages = [
mido.Message(type='note_on', note=0),
mido.Message(type='control_change', control=1, value=1),
mido.Message(type='note_off', note=0),
mido.Message(type='note_on', note=1),
mido.Message(type='note_off', note=1),
mido.Message(type='note_on', note=2),
mido.Message(type='note_off', note=2),
mido.Message(type='note_on', note=3),
mido.Message(type='control_change', control=1, value=1),
mido.Message(type='note_off', note=3)]
self.assertListEqual(passed_messages, expected_messages)
def testWaitForEvent_Signal(self):
for msg in self.capture_messages[3:-1]:
threading.Timer(0.2 * msg.time, self.port.callback, args=[msg]).start()
wait_start = time.time()
self.midi_hub.wait_for_event(
signal=midi_hub.MidiSignal(type='control_change', value=1))
self.assertAlmostEqual(time.time() - wait_start, 1.2, delta=0.01)
def testWaitForEvent_Time(self):
for msg in self.capture_messages[3:-1]:
threading.Timer(0.1 * msg.time, self.port.callback, args=[msg]).start()
wait_start = time.time()
self.midi_hub.wait_for_event(timeout=0.3)
self.assertAlmostEqual(time.time() - wait_start, 0.3, delta=0.01)
def testSendControlChange(self):
self.midi_hub.send_control_change(0, 1)
sent_messages = []
while not self.port.message_queue.empty():
sent_messages.append(self.port.message_queue.get())
self.assertListEqual(
sent_messages,
[mido.Message(type='control_change', control=0, value=1,
time=sent_messages[0].time)])
if __name__ == '__main__':
tf.test.main()
|
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from aliyunsdkcore.request import RpcRequest
from aliyunsdkgpdb.endpoint import endpoint_data
class DescribeParametersRequest(RpcRequest):
def __init__(self):
RpcRequest.__init__(self, 'gpdb', '2016-05-03', 'DescribeParameters')
self.set_method('POST')
if hasattr(self, "endpoint_map"):
setattr(self, "endpoint_map", endpoint_data.getEndpointMap())
if hasattr(self, "endpoint_regional"):
setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional())
def get_DBInstanceId(self): # String
return self.get_query_params().get('DBInstanceId')
def set_DBInstanceId(self, DBInstanceId): # String
self.add_query_param('DBInstanceId', DBInstanceId)
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#ifndef DASH__ITERATOR_INTERNAL_CONTIGUOUSRANGE_H__
#define DASH__ITERATOR_INTERNAL_CONTIGUOUSRANGE_H__
#include <dash/GlobPtr.h>
#include <dash/internal/Macro.h>
#include <dash/Exception.h>
namespace dash {
namespace internal {
/**
* Iterator used to find consecutive memory ranges across a global memory range.
*/
template<typename IteratorT>
struct ContiguousRangeIterator {
public:
/// Iterator Traits
using iterator_category = std::forward_iterator_tag;
using pattern_type = typename IteratorT::pattern_type;
using index_type = typename pattern_type::index_type;
using size_type = typename pattern_type::size_type;
using value_type = std::pair<IteratorT, size_type>;
using Self_t = ContiguousRangeIterator<IteratorT>;
DASH_CONSTEXPR ContiguousRangeIterator() = default;
ContiguousRangeIterator(IteratorT begin, IteratorT end)
: m_pos(begin),
m_end(end)
{
m_num_copy_elems = next_range().second;
}
Self_t&
operator++() {
auto range = next_range();
m_pos = range.first;
m_num_copy_elems = range.second;
return *this;
}
std::pair<IteratorT, size_type>
operator*() noexcept {
return std::make_pair(m_pos, m_num_copy_elems);
}
DASH_CONSTEXPR bool operator<(const Self_t& other) const DASH_NOEXCEPT
{
return (m_pos < other.m_pos);
}
DASH_CONSTEXPR bool operator<=(const Self_t& other) const DASH_NOEXCEPT
{
return (m_pos <= other.m_pos);
}
DASH_CONSTEXPR bool operator>(const Self_t& other) const DASH_NOEXCEPT
{
return (m_pos > other.m_pos);
}
DASH_CONSTEXPR bool operator>=(const Self_t& other) const DASH_NOEXCEPT
{
return (m_pos >= other.m_pos);
}
DASH_CONSTEXPR bool operator==(const Self_t& other) const DASH_NOEXCEPT
{
return m_pos == other.m_pos;
}
DASH_CONSTEXPR bool operator!=(const Self_t& other) const DASH_NOEXCEPT
{
return m_pos != other.m_pos;
}
private:
std::pair<IteratorT, size_type>
next_range() const {
auto cur_first = m_pos + m_num_copy_elems;
if (cur_first == m_end) {
return std::make_pair(m_end, 0);
}
auto cur_last = cur_first;
size_type num_copy_elem = 0;
constexpr const int ndim = pattern_type::ndim();
const auto& pattern = m_pos.pattern();
const int fast_dim = (pattern.memory_order() == dash::ROW_MAJOR) ? ndim - 1 : 0;
auto lpos = m_pos.lpos();
auto prev_lpos = lpos;
do {
/* Determine coords and offset in first block */
auto global_coords = pattern.coords(cur_last.gpos());
auto block_idx = pattern.block_at(global_coords);
auto block_viewspec = pattern.block(block_idx);
auto phase_d = global_coords[fast_dim] - block_viewspec.offset(fast_dim);
auto blocksize_d = block_viewspec.extent(fast_dim);
// the number of elements to copy is the blocksize minus the offset in the block
size_type num_copy_block_elem = blocksize_d - phase_d;
// don't try to copy too many elements
size_type elems_left = dash::distance(cur_last, m_end);
if (num_copy_block_elem > elems_left) {
num_copy_block_elem = elems_left;
// nothing more to do here
num_copy_elem += num_copy_block_elem;
break;
}
cur_last += num_copy_block_elem;
auto next_lpos = cur_last.lpos();
num_copy_elem += num_copy_block_elem;
// check whether the contiguous range is over at the end of the block
if (cur_last == m_end ||
next_lpos.unit != lpos.unit ||
next_lpos.index != (prev_lpos.index + 1)) {
break;
}
prev_lpos = next_lpos;
} while (1);
DASH_LOG_TRACE("next_range<GlobIter>", "cur_first", cur_first,
"num_copy_elem", num_copy_elem);
return std::make_pair(cur_first, num_copy_elem);
}
private:
/// Start of the current contiguous range
IteratorT m_pos;
/// End position of the total range
const IteratorT m_end;
/// Number of elements in current contiguous range
size_type m_num_copy_elems = 0;
};
/**
* Specialization for GlobPtr.
*/
template<typename ValueType, typename GlobMemT>
struct ContiguousRangeIterator<dash::GlobPtr<ValueType, GlobMemT>> {
public:
/// Iterator Traits
using iterator_category = std::forward_iterator_tag;
using pointer_type = typename dash::GlobPtr<ValueType, GlobMemT>;
using index_type = typename pointer_type::index_type;
using size_type = typename pointer_type::size_type;
using value_type = std::pair<pointer_type, size_type>;
using Self_t = ContiguousRangeIterator<pointer_type>;
DASH_CONSTEXPR ContiguousRangeIterator() = default;
ContiguousRangeIterator(pointer_type begin, pointer_type end)
: m_pos(begin),
m_end(end)
{
m_num_copy_elems = next_range().second;
}
Self_t&
operator++() {
auto range = next_range();
m_pos = range.first;
m_num_copy_elems = range.second;
return *this;
}
std::pair<pointer_type, size_type>
operator*() noexcept {
return std::make_pair(m_pos, m_num_copy_elems);
}
DASH_CONSTEXPR bool operator<(const Self_t& other) const DASH_NOEXCEPT
{
return (m_pos < other.m_pos);
}
DASH_CONSTEXPR bool operator<=(const Self_t& other) const DASH_NOEXCEPT
{
return (m_pos <= other.m_pos);
}
DASH_CONSTEXPR bool operator>(const Self_t& other) const DASH_NOEXCEPT
{
return (m_pos > other.m_pos);
}
DASH_CONSTEXPR bool operator>=(const Self_t& other) const DASH_NOEXCEPT
{
return (m_pos >= other.m_pos);
}
DASH_CONSTEXPR bool operator==(const Self_t& other) const DASH_NOEXCEPT
{
return m_pos == other.m_pos;
}
DASH_CONSTEXPR bool operator!=(const Self_t& other) const DASH_NOEXCEPT
{
return m_pos != other.m_pos;
}
private:
std::pair<pointer_type, size_type>
next_range() const {
using element_type = typename pointer_type::value_type;
auto cur_first = m_pos + m_num_copy_elems;
if (cur_first == m_end) {
return std::make_pair(m_end, 0);
}
auto cur_last = cur_first;
size_type num_copy_elem = 0;
dart_gptr_t gptr = cur_first.dart_gptr();
auto& reg = dash::internal::MemorySpaceRegistry::GetInstance();
auto const * mem_space = static_cast<const GlobMemT *>(reg.lookup(gptr));
if (mem_space == nullptr) {
// TODO: how to handle this?
}
// treat offsets and sizes in Bytes, convert as late as possible
size_type offs = gptr.addr_or_offs.offset;
dash::team_unit_t current_uid{gptr.unitid};
size_type size_at_unit = mem_space->capacity(current_uid);
DASH_ASSERT_LT(offs*sizeof(element_type), size_at_unit,
"Global pointer points beyond local unit!");
size_type size_left_at_unit = (size_at_unit - offs) / sizeof(element_type);
if (size_left_at_unit == 0) {
DASH_LOG_TRACE("next_range<GlobPtr>",
"No space left at unit ", current_uid.id,
", size_at_unit ", size_at_unit,
", offs ", offs);
}
size_type elems_left = dash::distance(cur_first, m_end);
DASH_LOG_TRACE("next_range<GlobPtr>", "size_left_at_unit ", size_left_at_unit,
", elems_left ", elems_left);
// check if there is enough space at the current unit
if (elems_left <= size_left_at_unit) {
num_copy_elem = elems_left;
} else {
// go to end of current unit
num_copy_elem = size_left_at_unit;
}
DASH_LOG_TRACE("next_range<GlobPtr>", "cur_first ", cur_first,
"num_copy_elem ", num_copy_elem);
return std::make_pair(cur_first, num_copy_elem);
}
private:
/// Start of the current contiguous range
pointer_type m_pos;
/// End position of the total range
const pointer_type m_end;
/// Number of elements in current contiguous range
size_type m_num_copy_elems = 0;
};
template<typename IteratorT>
struct ContiguousRangeSet {
using iterator_type = ContiguousRangeIterator<IteratorT>;
ContiguousRangeSet(IteratorT begin, IteratorT end)
: m_range_begin(begin), m_range_end(end), m_end(end, end)
{ }
iterator_type begin() const {
return iterator_type(m_range_begin, m_range_end);
}
iterator_type end() const {
return m_end;
}
private:
const IteratorT m_range_begin;
const IteratorT m_range_end;
const iterator_type m_end;
};
} // namespace internal
} // namespace dash
#endif // DASH__ITERATOR_INTERNAL_CONTIGUOUSRANGE_H__
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import psycopg2
from datetime import datetime
import binascii
import _thread
import time
import socket
PORT1 = 8802
def serverX():
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s1:
s1.bind(('',PORT1))
s1.listen()
conn1, addr = s1.accept()
with conn1:
print('Server Substation 2 from:',addr)
conn = psycopg2.connect(host="131.180.165.7",database="CRoF",user="postgres", password="crpg")
cursor = conn.cursor()
#Value id 7-15
cursor.execute('''SELECT value from objects WHERE id=7''')
result = cursor.fetchone()
record1 = result[0]
cursor.execute('''SELECT value from objects WHERE id=8''')
result = cursor.fetchone()
record2 = result[0]
cursor.execute('''SELECT value from objects WHERE id=9''')
result = cursor.fetchone()
record3 = result[0]
cursor.execute('''SELECT value from objects WHERE id=10''')
result = cursor.fetchone()
record4 = result[0]
cursor.execute('''SELECT value from objects WHERE id=11''')
result = cursor.fetchone()
record5 = result[0]
cursor.execute('''SELECT value from objects WHERE id=12''')
result = cursor.fetchone()
record6 = result[0]
cursor.execute('''SELECT value from objects WHERE id=13''')
result = cursor.fetchone()
record7 = result[0]
cursor.execute('''SELECT value from objects WHERE id=14''')
result = cursor.fetchone()
record8 = result[0]
cursor.execute('''SELECT value from objects WHERE id=15''')
result = cursor.fetchone()
record9 = result[0]
#Value code
cursor.execute('''SELECT code from objects WHERE id=7''')
result = cursor.fetchone()
r1 = result[0]
cursor.execute('''SELECT code from objects WHERE id=8''')
result = cursor.fetchone()
r2 = result[0]
cursor.execute('''SELECT code from objects WHERE id=9''')
result = cursor.fetchone()
r3 = result[0]
cursor.execute('''SELECT code from objects WHERE id=10''')
result = cursor.fetchone()
r4 = result[0]
cursor.execute('''SELECT code from objects WHERE id=11''')
result = cursor.fetchone()
r5 = result[0]
cursor.execute('''SELECT code from objects WHERE id=12''')
result = cursor.fetchone()
r6 = result[0]
cursor.execute('''SELECT code from objects WHERE id=13''')
result = cursor.fetchone()
r7 = result[0]
cursor.execute('''SELECT code from objects WHERE id=14''')
result = cursor.fetchone()
r8 = result[0]
cursor.execute('''SELECT code from objects WHERE id=15''')
result = cursor.fetchone()
r9 = result[0]
while True:
data = "a"
dataxy = data.encode()
try:
#Format: mu01_id+value
cursor.execute('''SELECT value from objects WHERE id=7''')
result = cursor.fetchone()
if record1 != result[0]:
print(result[0])
string = "mu01_"+str(r1)+"+"+str(result[0])
datax = string.encode()
conn1.sendall(datax)
print(string)
record1 = result[0]
cursor.execute('''SELECT value from objects WHERE id=8''')
result = cursor.fetchone()
if record2 != result[0]:
print(result[0])
string = "mu02_"+str(r2)+"+"+str(result[0])
datax = string.encode()
conn1.sendall(datax)
print(string)
record2 = result[0]
cursor.execute('''SELECT value from objects WHERE id=9''')
result = cursor.fetchone()
if record3 != result[0]:
print(result[0])
string = "mu03_"+str(r3)+"+"+str(result[0])
datax = string.encode()
conn1.sendall(datax)
print(string)
record3 = result[0]
cursor.execute('''SELECT value from objects WHERE id=10''')
result = cursor.fetchone()
if record4 != result[0]:
print(result[0])
string = "mu04_"+str(r4)+"+"+str(result[0])
datax = string.encode()
conn1.sendall(datax)
print(string)
record4 = result[0]
cursor.execute('''SELECT value from objects WHERE id=11''')
result = cursor.fetchone()
if record5 != result[0]:
print(result[0])
string = "mu04_"+str(r5)+"+"+str(result[0])
datax = string.encode()
conn1.sendall(datax)
print(string)
record5 = result[0]
cursor.execute('''SELECT value from objects WHERE id=12''')
result = cursor.fetchone()
if record6 != result[0]:
print(result[0])
string = "mu05_"+str(r6)+"+"+str(result[0])
datax = string.encode()
conn1.sendall(datax)
print(string)
record6 = result[0]
cursor.execute('''SELECT value from objects WHERE id=13''')
result = cursor.fetchone()
if record7 != result[0]:
print(result[0])
string = "mu06_"+str(r7)+"+"+str(result[0])
datax = string.encode()
conn1.sendall(datax)
print(string)
record7 = result[0]
cursor.execute('''SELECT value from objects WHERE id=14''')
result = cursor.fetchone()
if record8 != result[0]:
print(result[0])
string = "mu06_"+str(r8)+"+"+str(result[0])
datax = string.encode()
conn1.sendall(datax)
print(string)
record8 = result[0]
cursor.execute('''SELECT value from objects WHERE id=15''')
result = cursor.fetchone()
if record9 != result[0]:
print(result[0])
string = "mu06_"+str(r9)+"+"+str(result[0])
datax = string.encode()
conn1.sendall(datax)
print(string)
record9 = result[0]
conn1.sendall(dataxy)
#print(record1)
time.sleep(1)
except:
conn1.close()
conn.close()
print("Connection Close Substation 2")
break
conn1.close()
print("Restart Server Substation 2")
conn.close()
s1.close()
time.sleep(1)
serverX()
serverX()
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import Vue from 'vue';
import App from '@modules/Demo';
new Vue({
el: '#app',
render: h => h(App),
});
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/* ************************************************************************** */
/* */
/* ::: :::::::: */
/* ft_putstr.c :+: :+: :+: */
/* +:+ +:+ +:+ */
/* By: flklein <marvin@42.fr> +#+ +:+ +#+ */
/* +#+#+#+#+#+ +#+ */
/* Created: 2018/09/18 11:56:08 by flklein #+# #+# */
/* Updated: 2018/09/19 10:36:53 by flklein ### ########.fr */
/* */
/* ************************************************************************** */
#include "bsq.h"
void ft_putstr(char *str)
{
write(1, str, ft_strlen(str));
}
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'use strict';
const admin = require('firebase-admin');
/* eslint-disable import/no-unresolved */
const serviceAccount = require('./../fb.json');
admin.initializeApp({
credential: admin.credential.cert(serviceAccount),
databaseURL: process.env.FIREBASE_URL,
});
module.exports = admin;
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export { default, initialize } from 'nullbase-core/initializers/nb-textarea-theme-init';
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