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"""WizardKit: Hardware objects (mostly)"""
# vim: sts=2 sw=2 ts=2
import logging
import pathlib
import plistlib
import re
from collections import OrderedDict
from wk.cfg.hw import (
ATTRIBUTE_COLORS,
KEY_NVME,
KEY_SMART,
KNOWN_DISK_ATTRIBUTES,
KNOWN_DISK_MODELS,
KNOWN_RAM_VENDOR_IDS,
REGEX_POWER_ON_TIME,
)
from wk.cfg.main import KIT_NAME_SHORT
from wk.exe import get_json_from_command, run_program
from wk.std import (
PLATFORM,
bytes_to_string,
color_string,
sleep,
string_to_bytes,
)
# STATIC VARIABLES
LOG = logging.getLogger(__name__)
NVME_WARNING_KEYS = (
'spare_below_threshold',
'reliability_degraded',
'volatile_memory_backup_failed',
)
SMART_SELF_TEST_START_TIMEOUT_IN_SECONDS = 120
WK_LABEL_REGEX = re.compile(
fr'{KIT_NAME_SHORT}_(LINUX|UFD)',
re.IGNORECASE,
)
# Exception Classes
class CriticalHardwareError(RuntimeError):
"""Exception used for critical hardware failures."""
class SMARTNotSupportedError(TypeError):
"""Exception used for disks lacking SMART support."""
class SMARTSelfTestInProgressError(RuntimeError):
"""Exception used when a SMART self-test is in progress."""
# Classes
class BaseObj():
"""Base object for tracking device data."""
def __init__(self):
self.tests = OrderedDict()
def all_tests_passed(self):
"""Check if all tests passed, returns bool."""
return all(results.passed for results in self.tests.values())
def any_test_failed(self):
"""Check if any test failed, returns bool."""
return any(results.failed for results in self.tests.values())
class CpuRam(BaseObj):
"""Object for tracking CPU & RAM specific data."""
def __init__(self):
super().__init__()
self.description = 'Unknown'
self.details = {}
self.ram_total = 'Unknown'
self.ram_dimms = []
self.tests = OrderedDict()
# Update details
self.get_cpu_details()
self.get_ram_details()
def generate_report(self):
"""Generate CPU & RAM report, returns list."""
report = []
report.append(color_string('Device', 'BLUE'))
report.append(f' {self.description}')
# Include RAM details
report.append(color_string('RAM', 'BLUE'))
report.append(f' {self.ram_total} ({", ".join(self.ram_dimms)})')
# Tests
for test in self.tests.values():
report.extend(test.report)
return report
def get_cpu_details(self):
"""Get CPU details using OS specific methods."""
if PLATFORM == 'Darwin':
cmd = 'sysctl -n machdep.cpu.brand_string'.split()
proc = run_program(cmd, check=False)
self.description = re.sub(r'\s+', ' ', proc.stdout.strip())
elif PLATFORM == 'Linux':
cmd = ['lscpu', '--json']
json_data = get_json_from_command(cmd)
for line in json_data.get('lscpu', [{}]):
_field = line.get('field', '').replace(':', '')
_data = line.get('data', '')
if not (_field or _data):
# Skip
continue
self.details[_field] = _data
self.description = self.details.get('Model name', '')
# Replace empty description
if not self.description:
self.description = 'Unknown CPU'
def get_ram_details(self):
"""Get RAM details using OS specific methods."""
if PLATFORM == 'Darwin':
dimm_list = get_ram_list_macos()
elif PLATFORM == 'Linux':
dimm_list = get_ram_list_linux()
details = {'Total': 0}
for dimm_details in dimm_list:
size, manufacturer = dimm_details
if size <= 0:
# Skip empty DIMMs
continue
description = f'{bytes_to_string(size)} {manufacturer}'
details['Total'] += size
if description in details:
details[description] += 1
else:
details[description] = 1
# Save details
self.ram_total = bytes_to_string(details.pop('Total', 0))
self.ram_dimms = [
f'{count}x {desc}' for desc, count in sorted(details.items())
]
class Disk(BaseObj):
"""Object for tracking disk specific data."""
def __init__(self, path):
super().__init__()
self.attributes = {}
self.description = 'Unknown'
self.details = {}
self.notes = []
self.path = pathlib.Path(path).resolve()
self.smartctl = {}
self.tests = OrderedDict()
# Update details
self.get_details()
self.enable_smart()
self.update_smart_details()
if self.details['bus'] == 'USB' and not self.attributes:
# Try using SAT
LOG.warning('Using SAT for smartctl for %s', self.path)
self.enable_smart(use_sat=True)
self.update_smart_details(use_sat=True)
if not self.is_4k_aligned():
self.add_note('One or more partitions are not 4K aligned', 'YELLOW')
def abort_self_test(self):
"""Abort currently running non-captive self-test."""
cmd = ['sudo', 'smartctl', '--abort', self.path]
run_program(cmd, check=False)
def add_note(self, note, color=None):
"""Add note that will be included in the disk report."""
if color:
note = color_string(note, color)
if note not in self.notes:
self.notes.append(note)
self.notes.sort()
def check_attributes(self, only_blocking=False):
"""Check if any known attributes are failing, returns bool."""
attributes_ok = True
known_attributes = get_known_disk_attributes(self.details['model'])
for attr, value in self.attributes.items():
# Skip unknown attributes
if attr not in known_attributes:
continue
# Get thresholds
blocking_attribute = known_attributes[attr].get('Blocking', False)
err_thresh = known_attributes[attr].get('Error', None)
max_thresh = known_attributes[attr].get('Maximum', None)
if not max_thresh:
max_thresh = float('inf')
# Skip non-blocking attributes if necessary
if only_blocking and not blocking_attribute:
continue
# Skip informational attributes
if not err_thresh:
continue
# Check attribute
if known_attributes[attr].get('PercentageLife', False):
if 0 <= value['raw'] <= err_thresh:
attributes_ok = False
elif err_thresh <= value['raw'] < max_thresh:
attributes_ok = False
# Done
return attributes_ok
def disable_disk_tests(self):
"""Disable all tests."""
LOG.warning('Disabling all tests for: %s', self.path)
for test in self.tests.values():
if test.status in ('Pending', 'Working'):
test.set_status('Denied')
test.disabled = True
def enable_smart(self, use_sat=False):
"""Try enabling SMART for this disk."""
cmd = [
'sudo',
'smartctl',
f'--device={"sat,auto" if use_sat else "auto"}',
'--tolerance=permissive',
'--smart=on',
self.path,
]
run_program(cmd, check=False)
def generate_attribute_report(self):
"""Generate attribute report, returns list."""
known_attributes = get_known_disk_attributes(self.details['model'])
report = []
for attr, value in sorted(self.attributes.items()):
note = ''
value_color = 'GREEN'
# Skip attributes not in our list
if attr not in known_attributes:
continue
# Check for attribute note
note = known_attributes[attr].get('Note', '')
# ID / Name
label = f'{attr:>3}'
if isinstance(attr, int):
# Assuming SMART, include hex ID and name
label += f' / {str(hex(attr))[2:].upper():0>2}: {value["name"]}'
label = f' {label.replace("_", " "):38}'
# Value color
if known_attributes[attr].get('PercentageLife', False):
# PercentageLife values
if 0 <= value['raw'] <= known_attributes[attr]['Error']:
value_color = 'RED'
note = '(failed, % life remaining)'
elif value['raw'] < 0 or value['raw'] > 100:
value_color = 'PURPLE'
note = '(invalid?)'
else:
for threshold, color in ATTRIBUTE_COLORS:
threshold_val = known_attributes[attr].get(threshold, None)
if threshold_val and value['raw'] >= threshold_val:
value_color = color
if threshold == 'Error':
note = '(failed)'
elif threshold == 'Maximum':
note = '(invalid?)'
# 199/C7 warning
if str(attr) == '199' and value['raw'] > 0:
note = '(bad cable?)'
# Build colored string and append to report
line = color_string(
[label, value['raw_str'], note],
[None, value_color, 'YELLOW'],
)
report.append(line)
# Done
return report
def generate_report(self, header=True):
"""Generate Disk report, returns list."""
report = []
if header:
report.append(color_string(f'Device ({self.path.name})', 'BLUE'))
report.append(f' {self.description}')
# Attributes
if self.attributes:
if header:
report.append(color_string('Attributes', 'BLUE'))
report.extend(self.generate_attribute_report())
# Notes
if self.notes:
report.append(color_string('Notes', 'BLUE'))
for note in self.notes:
report.append(f' {note}')
# Tests
for test in self.tests.values():
report.extend(test.report)
return report
def get_details(self):
"""Get disk details using OS specific methods.
Required details default to generic descriptions
and are converted to the correct type.
"""
if PLATFORM == 'Darwin':
self.details = get_disk_details_macos(self.path)
elif PLATFORM == 'Linux':
self.details = get_disk_details_linux(self.path)
# Set necessary details
self.details['bus'] = str(self.details.get('bus', '???')).upper()
self.details['bus'] = self.details['bus'].replace('IMAGE', 'Image')
self.details['bus'] = self.details['bus'].replace('NVME', 'NVMe')
self.details['fstype'] = self.details.get('fstype', 'Unknown')
self.details['log-sec'] = self.details.get('log-sec', 512)
self.details['model'] = self.details.get('model', 'Unknown Model')
self.details['name'] = self.details.get('name', self.path)
self.details['phy-sec'] = self.details.get('phy-sec', 512)
self.details['serial'] = self.details.get('serial', 'Unknown Serial')
self.details['size'] = self.details.get('size', -1)
self.details['ssd'] = self.details.get('ssd', False)
# Ensure certain attributes types
for attr in ['bus', 'model', 'name', 'serial']:
if not isinstance(self.details[attr], str):
self.details[attr] = str(self.details[attr])
for attr in ['phy-sec', 'size']:
if not isinstance(self.details[attr], int):
try:
self.details[attr] = int(self.details[attr])
except (TypeError, ValueError):
LOG.error('Invalid disk %s: %s', attr, self.details[attr])
self.details[attr] = -1
# Set description
self.description = (
f'{bytes_to_string(self.details["size"], use_binary=False)}'
f' ({self.details["bus"]})'
f' {self.details["model"]}'
f' {self.details["serial"]}'
)
def get_labels(self):
"""Build list of labels for this disk, returns list."""
labels = []
# Add all labels from lsblk
for disk in [self.details, *self.details.get('children', [])]:
labels.append(disk.get('label', ''))
labels.append(disk.get('partlabel', ''))
# Remove empty labels
labels = [str(label) for label in labels if label]
# Done
return labels
def get_smart_self_test_details(self):
"""Shorthand to get deeply nested self-test details, returns dict."""
details = {}
try:
details = self.smartctl['ata_smart_data']['self_test']
except (KeyError, TypeError):
# Assuming disk lacks SMART support, ignore and return empty dict.
pass
# Done
return details
def is_4k_aligned(self):
"""Check that all disk partitions are aligned, returns bool."""
aligned = True
if PLATFORM == 'Darwin':
aligned = is_4k_aligned_macos(self.details)
elif PLATFORM == 'Linux':
aligned = is_4k_aligned_linux(self.path, self.details['phy-sec'])
return aligned
def safety_checks(self):
"""Run safety checks and raise an exception if necessary."""
blocking_event_encountered = False
self.update_smart_details()
# Attributes
if not self.check_attributes(only_blocking=True):
blocking_event_encountered = True
LOG.error('%s: Blocked for failing attribute(s)', self.path)
# NVMe status
nvme_status = self.smartctl.get('smart_status', {}).get('nvme', {})
if nvme_status.get('media_read_only', False):
blocking_event_encountered = True
msg = 'Media has been placed in read-only mode'
self.add_note(msg, 'RED')
LOG.error('%s %s', self.path, msg)
for key in NVME_WARNING_KEYS:
if nvme_status.get(key, False):
msg = key.replace('_', ' ')
self.add_note(msg, 'YELLOW')
LOG.warning('%s %s', self.path, msg)
# SMART overall assessment
smart_passed = True
try:
smart_passed = self.smartctl['smart_status']['passed']
except (KeyError, TypeError):
# Assuming disk doesn't support SMART overall assessment
pass
if not smart_passed:
blocking_event_encountered = True
msg = 'SMART overall self-assessment: Failed'
self.add_note(msg, 'RED')
LOG.error('%s %s', self.path, msg)
# Raise blocking exception if necessary
if blocking_event_encountered:
raise CriticalHardwareError(f'Critical error(s) for: {self.path}')
# SMART self-test status
test_details = self.get_smart_self_test_details()
if 'remaining_percent' in test_details.get('status', ''):
msg = f'SMART self-test in progress for: {self.path}'
LOG.error(msg)
raise SMARTSelfTestInProgressError(msg)
def run_self_test(self, log_path):
"""Run disk self-test and check if it passed, returns bool.
NOTE: This function is here to reserve a place for future
NVMe self-tests announced in NVMe spec v1.3.
"""
result = self.run_smart_self_test(log_path)
return result
def run_smart_self_test(self, log_path):
"""Run SMART self-test and check if it passed, returns bool.
NOTE: An exception will be raised if the disk lacks SMART support.
"""
finished = False
result = None
started = False
status_str = 'Starting self-test...'
test_details = self.get_smart_self_test_details()
test_minutes = 15
size_str = bytes_to_string(self.details["size"], use_binary=False)
header_str = color_string(
['[', self.path.name, ' ', size_str, ']'],
[None, 'BLUE', None, 'CYAN', None],
sep='',
)
# Check if disk supports self-tests
if not test_details:
raise SMARTNotSupportedError(
f'SMART self-test not supported for {self.path}')
# Get real test length
test_minutes = test_details.get('polling_minutes', {}).get('short', 5)
test_minutes = int(test_minutes) + 10
# Start test
with open(log_path, 'w', encoding='utf-8') as _f:
_f.write(f'{header_str}\nInitializing...')
cmd = [
'sudo',
'smartctl',
'--tolerance=normal',
'--test=short',
self.path,
]
run_program(cmd, check=False)
# Monitor progress (in five second intervals)
for _i in range(int(test_minutes*60/5)):
sleep(5)
# Update status
self.update_smart_details()
test_details = self.get_smart_self_test_details()
# Check test progress
if started:
status_str = test_details.get('status', {}).get('string', 'Unknown')
status_str = status_str.capitalize()
# Update log
with open(log_path, 'w', encoding='utf-8') as _f:
_f.write(f'{header_str}\nSMART self-test status:\n {status_str}')
# Check if finished
if 'remaining_percent' not in test_details.get('status', {}):
finished = True
break
elif 'remaining_percent' in test_details.get('status', {}):
started = True
elif _i * 5 >= SMART_SELF_TEST_START_TIMEOUT_IN_SECONDS:
# Test didn't start within limit, stop waiting
break
# Check result
if finished:
result = test_details.get('status', {}).get('passed', False)
elif started:
raise TimeoutError(f'SMART self-test timed out for {self.path}')
# Done
return result
def update_smart_details(self, use_sat=False):
"""Update SMART details via smartctl."""
self.attributes = {}
# Check if SAT is needed
if not use_sat:
# use_sat not set, check previous run (if possible)
for arg in self.smartctl.get('smartctl', {}).get('argv', []):
if arg == '--device=sat,auto':
use_sat = True
break
# Get SMART data
cmd = [
'sudo',
'smartctl',
f'--device={"sat,auto" if use_sat else "auto"}',
'--tolerance=verypermissive',
'--all',
'--json',
self.path,
]
self.smartctl = get_json_from_command(cmd, check=False)
# Check for attributes
if KEY_NVME in self.smartctl:
for name, value in self.smartctl[KEY_NVME].items():
try:
self.attributes[name] = {
'name': name,
'raw': int(value),
'raw_str': str(value),
}
except (TypeError, ValueError):
# Ignoring invalid attribute
LOG.error('Invalid NVMe attribute: %s %s', name, value)
elif KEY_SMART in self.smartctl:
for attribute in self.smartctl[KEY_SMART].get('table', {}):
try:
_id = int(attribute['id'])
except (KeyError, ValueError):
# Ignoring invalid attribute
LOG.error('Invalid SMART attribute: %s', attribute)
continue
name = str(attribute.get('name', 'Unknown')).replace('_', ' ').title()
raw = int(attribute.get('raw', {}).get('value', -1))
raw_str = attribute.get('raw', {}).get('string', 'Unknown')
# Fix power-on time
match = REGEX_POWER_ON_TIME.match(raw_str)
if _id == 9 and match:
raw = int(match.group(1))
# Add to dict
self.attributes[_id] = {
'name': name, 'raw': raw, 'raw_str': raw_str}
# Add note if necessary
if not self.attributes:
self.add_note('No NVMe or SMART data available', 'YELLOW')
class Test():
# pylint: disable=too-few-public-methods
"""Object for tracking test specific data."""
def __init__(self, dev, label):
self.dev = dev
self.disabled = False
self.failed = False
self.label = label
self.passed = False
self.report = []
self.status = 'Pending'
def set_status(self, status):
"""Update status string."""
if self.disabled:
# Don't change status if disabled
return
self.status = status
# Functions
def get_disk_details_linux(path):
"""Get disk details using lsblk, returns dict."""
cmd = ['lsblk', '--bytes', '--json', '--output-all', '--paths', path]
json_data = get_json_from_command(cmd, check=False)
details = json_data.get('blockdevices', [{}])[0]
# Fix details
for dev in [details, *details.get('children', [])]:
dev['bus'] = dev.pop('tran', '???')
dev['parent'] = dev.pop('pkname', None)
dev['ssd'] = not dev.pop('rota', True)
if 'loop' in str(path) and dev['bus'] is None:
dev['bus'] = 'Image'
dev['model'] = ''
dev['serial'] = ''
# Done
return details
def get_disk_details_macos(path):
"""Get disk details using diskutil, returns dict."""
details = {}
# Get "list" details
cmd = ['diskutil', 'list', '-plist', path]
proc = run_program(cmd, check=False, encoding=None, errors=None)
try:
plist_data = plistlib.loads(proc.stdout)
except (TypeError, ValueError):
# Invalid / corrupt plist data? return empty dict to avoid crash
LOG.error('Failed to get diskutil list for %s', path)
return details
# Parse "list" details
details = plist_data.get('AllDisksAndPartitions', [{}])[0]
details['children'] = details.pop('Partitions', [])
details['path'] = path
for child in details['children']:
child['path'] = path.with_name(child.get('DeviceIdentifier', 'null'))
# Get "info" details
for dev in [details, *details['children']]:
cmd = ['diskutil', 'info', '-plist', dev['path']]
proc = run_program(cmd, check=False, encoding=None, errors=None)
try:
plist_data = plistlib.loads(proc.stdout)
except (TypeError, ValueError):
LOG.error('Failed to get diskutil info for %s', path)
continue #Skip
# Parse "info" details
dev.update(plist_data)
dev['bus'] = dev.pop('BusProtocol', '???')
dev['fstype'] = dev.pop('FilesystemType', '')
dev['label'] = dev.pop('VolumeName', '')
dev['model'] = dev.pop('MediaName', 'Unknown')
dev['mountpoint'] = dev.pop('MountPoint', '')
dev['name'] = dev.get('name', str(dev['path']))
dev['phy-sec'] = dev.pop('DeviceBlockSize', 512)
dev['serial'] = get_disk_serial_macos(dev['path'])
dev['size'] = dev.pop('Size', -1)
dev['ssd'] = dev.pop('SolidState', False)
dev['vendor'] = ''
if dev.get('WholeDisk', True):
dev['parent'] = None
else:
dev['parent'] = dev.pop('ParentWholeDisk', None)
# Fix details if main dev is a child
for child in details['children']:
if path == child['path']:
for key in ('fstype', 'label', 'name', 'size'):
details[key] = child[key]
break
# Done
return details
def get_disk_serial_macos(path):
"""Get disk serial using system_profiler, returns str."""
cmd = ['sudo', 'smartctl', '--info', '--json', path]
smart_info = get_json_from_command(cmd)
return smart_info.get('serial_number', 'Unknown Serial')
def get_disks(skip_kits=False):
"""Get disks using OS-specific methods, returns list."""
disks = []
if PLATFORM == 'Darwin':
disks = get_disks_macos()
elif PLATFORM == 'Linux':
disks = get_disks_linux()
# Skip WK disks
if skip_kits:
disks = [
disk_obj for disk_obj in disks
if not any(
WK_LABEL_REGEX.search(label) for label in disk_obj.get_labels()
)
]
# Done
return disks
def get_disks_linux():
"""Get disks via lsblk, returns list."""
cmd = ['lsblk', '--json', '--nodeps', '--paths']
disks = []
# Add valid disks
json_data = get_json_from_command(cmd)
for disk in json_data.get('blockdevices', []):
disk_obj = Disk(disk['name'])
# Skip loopback devices, optical devices, etc
if disk_obj.details['type'] != 'disk':
continue
# Add disk
disks.append(disk_obj)
# Done
return disks
def get_disks_macos():
"""Get disks via diskutil, returns list."""
cmd = ['diskutil', 'list', '-plist', 'physical']
disks = []
# Get info from diskutil
proc = run_program(cmd, encoding=None, errors=None, check=False)
if proc.returncode != 0:
# Assuming we're running on an older macOS version
cmd.pop(-1)
proc = run_program(cmd, encoding=None, errors=None, check=False)
# Parse plist data
try:
plist_data = plistlib.loads(proc.stdout)
except (TypeError, ValueError):
# Invalid / corrupt plist data? return empty list to avoid crash
LOG.error('Failed to get diskutil list')
return disks
# Add valid disks
for disk in plist_data['WholeDisks']:
disks.append(Disk(f'/dev/{disk}'))
# Remove virtual disks
# TODO: Test more to figure out why some drives are being marked 'Unknown'
disks = [
d for d in disks if d.details.get('VirtualOrPhysical') != 'Virtual'
]
# Done
return disks
def get_known_disk_attributes(model):
"""Get known NVMe/SMART attributes (model specific), returns str."""
known_attributes = KNOWN_DISK_ATTRIBUTES.copy()
# Apply model-specific data
for regex, data in KNOWN_DISK_MODELS.items():
if re.search(regex, model):
for attr, thresholds in data.items():
if attr in known_attributes:
known_attributes[attr].update(thresholds)
else:
known_attributes[attr] = thresholds
# Done
return known_attributes
def get_ram_list_linux():
"""Get RAM list using dmidecode."""
cmd = ['sudo', 'dmidecode', '--type', 'memory']
dimm_list = []
manufacturer = 'Unknown'
size = 0
# Get DMI data
proc = run_program(cmd)
dmi_data = proc.stdout.splitlines()
# Parse data
for line in dmi_data:
line = line.strip()
if line == 'Memory Device':
# Reset vars
manufacturer = 'Unknown'
size = 0
elif line.startswith('Size:'):
size = line.replace('Size: ', '')
try:
size = string_to_bytes(size, assume_binary=True)
except ValueError:
# Assuming empty module
size = 0
elif line.startswith('Manufacturer:'):
manufacturer = line.replace('Manufacturer: ', '')
dimm_list.append([size, manufacturer])
# Save details
return dimm_list
def get_ram_list_macos():
"""Get RAM list using system_profiler."""
dimm_list = []
# Get and parse plist data
cmd = [
'system_profiler',
'-xml',
'SPMemoryDataType',
]
proc = run_program(cmd, check=False, encoding=None, errors=None)
try:
plist_data = plistlib.loads(proc.stdout)
except (TypeError, ValueError):
# Ignore and return an empty list
return dimm_list
# Check DIMM data
dimm_details = plist_data[0].get('_items', [{}])[0].get('_items', [])
for dimm in dimm_details:
manufacturer = dimm.get('dimm_manufacturer', None)
manufacturer = KNOWN_RAM_VENDOR_IDS.get(
manufacturer,
f'Unknown ({manufacturer})')
size = dimm.get('dimm_size', '0 GB')
try:
size = string_to_bytes(size, assume_binary=True)
except ValueError:
# Empty DIMM?
LOG.error('Invalid DIMM size: %s', size)
continue
dimm_list.append([size, manufacturer])
# Save details
return dimm_list
def is_4k_aligned_macos(disk_details):
"""Check partition alignment using diskutil info, returns bool."""
aligned = True
# Check partitions
for part in disk_details.get('children', []):
offset = part.get('PartitionMapPartitionOffset', 0)
if not offset:
# Assuming offset couldn't be found and it defaulted to 0
# NOTE: Just logging the error, not bailing
LOG.error('Failed to get partition offset for %s', part['path'])
aligned = aligned and offset >= 0 and offset % 4096 == 0
# Done
return aligned
def is_4k_aligned_linux(dev_path, physical_sector_size):
"""Check partition alignment using lsblk, returns bool."""
aligned = True
cmd = [
'sudo',
'sfdisk',
'--json',
dev_path,
]
# Get partition details
json_data = get_json_from_command(cmd)
# Check partitions
for part in json_data.get('partitiontable', {}).get('partitions', []):
offset = physical_sector_size * part.get('start', -1)
aligned = aligned and offset >= 0 and offset % 4096 == 0
# Done
return aligned
if __name__ == '__main__':
print("This file is not meant to be called directly.")
|
[
"wk.std.string_to_bytes",
"wk.cfg.hw.KNOWN_RAM_VENDOR_IDS.get",
"wk.std.color_string",
"wk.cfg.hw.KNOWN_DISK_ATTRIBUTES.copy",
"wk.std.bytes_to_string",
"wk.cfg.hw.KNOWN_DISK_MODELS.items",
"plistlib.loads",
"wk.exe.get_json_from_command",
"wk.std.sleep",
"pathlib.Path",
"collections.OrderedDict",
"wk.cfg.hw.REGEX_POWER_ON_TIME.match",
"re.search",
"wk.exe.run_program",
"logging.getLogger",
"re.compile"
] |
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|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import re
import math
import networkx as nx
import logging
import timeit
from collections import deque
from visualSHARK.models import Commit
def tag_filter(tags, discard_qualifiers=True, discard_patch=False):
versions = []
# qualifiers are expected at the end of the tag and they may have a number attached
# it is very important for the b to be at the end otherwise beta would already be matched!
qualifiers = ['rc', 'alpha', 'beta', 'b']
# separators are expected to divide 2 or more numbers
separators = ['.', '_', '-']
for t in tags:
tag = t.name
c = Commit.objects.get(id=t.commit_id)
qualifier = ''
remove_qualifier = ''
for q in qualifiers:
if q in tag.lower():
tmp = tag.lower().split(q)
if tmp[-1].isnumeric():
qualifier = [q, tmp[-1]]
remove_qualifier = ''.join(qualifier)
break
else:
qualifier = [q]
remove_qualifier = q
break
# if we have a qualifier we remove it before we check for best number seperator
tmp = tag.lower()
if qualifier:
tmp = tmp.split(remove_qualifier)[0]
# we only want numbers and separators
version = re.sub('[a-z]', '', tmp)
# the best separator is the one separating the most numbers
best = -1
best_sep = None
for sep in separators:
current = 0
for v in version.split(sep):
v = ''.join(c for c in v if c.isdigit())
if v.isnumeric():
current += 1
if current > best:
best = current
best_sep = sep
version = version.split(best_sep)
final_version = []
for v in version:
v = ''.join(c for c in v if c.isdigit())
if v.isnumeric():
final_version.append(int(v))
# if we have a version we append it to our list
if final_version:
# force semver because we are sorting
if len(final_version) == 1:
final_version.append(0)
if len(final_version) == 2:
final_version.append(0)
fversion = {'version': final_version, 'original': tag, 'revision': c.revision_hash}
if qualifier:
fversion['qualifier'] = qualifier
versions.append(fversion)
# discard fliers
p_version = [int(v['version'][0]) for v in versions]
sort = sorted(p_version)
a = 0.25 * len(sort)
b = 0.75 * len(sort)
if a.is_integer():
a = int(a) # otherwise could be 6.0
x_025 = ((sort[a] + sort[a + 1]) / 2)
else:
x_025 = sort[math.floor(a) + 1]
if b.is_integer():
b = int(b)
x_075 = ((sort[b] + sort[b + 1]) / 2)
else:
x_075 = sort[math.floor(b) + 1]
iqr = x_075 - x_025
flyer_lim = 1.5 * iqr
ret = []
for version in versions:
major = int(version['version'][0])
# no fliers in final list
if major > (x_075 + flyer_lim) or major < (x_025 - flyer_lim):
print('exclude: {} because {} is not between {} and {}'.format(version['version'], major, (x_025 - flyer_lim), (x_075 + flyer_lim)))
continue
if discard_qualifiers and 'qualifier' in version.keys():
continue
ret.append(version)
# sort remaining
s = sorted(ret, key=lambda x: (x['version'][0], x['version'][1], x['version'][2]))
ret = []
for v in s:
# only minor, we discard patch releases (3rd in semver, everything after 2nd in other schemas)
if discard_patch:
if len(v['version']) > 2:
del v['version'][2:]
if v['version'] not in [v2['version'] for v2 in ret]:
ret.append(v)
return ret
class OntdekBaan3(object):
"""Discover all paths in a commitgraph represented as an NetworkX DAG.
The Problem:
High number of paths without repeated nodes (simple paths) in normal Git Workflow.
The Solution:
We reset the start node if we have no path to travel to the end node (happens for SVN -> Git Tags).
We prune the graph to the subgraph containing only paths from our start to end.
We compute the longest path (which is possible in polynomial time as we work on a DAG).
We then find all nodes nod already contained in the longest path.
For each of those nodes we find a connection to a node in the longest path which is a merge or split (because then it is cached in Volg).
"""
def __init__(self, g):
self._graph = g.copy()
self._nodes = set()
self._log = logging.getLogger(self.__class__.__name__)
def _prune_graph(self, start, end):
non_pruned = self._graph.copy()
for n in non_pruned:
if not nx.has_path(non_pruned, n, end):
if n in self._graph:
self._graph.remove_node(n)
if not nx.has_path(non_pruned, start, n):
if n in self._graph:
self._graph.remove_node(n)
def _find_parent_in_paths(self, node):
succ = deque(list(self._graph.pred[node]))
while succ:
# pop out at the right
n = succ.pop()
if n in self._nodes and (len(self._graph.pred[n]) > 1 or len(self._graph.succ[n]) > 1):
return n
# append new parents to the left
for p in self._graph.pred[n]:
succ.appendleft(p)
def _find_child_in_paths(self, node):
succ = deque(list(self._graph.succ[node]))
while succ:
# pop out at the right
n = succ.pop()
if n in self._nodes and (len(self._graph.pred[n]) > 1 or len(self._graph.succ[n]) > 1):
return n
# append new childs to the left
for s in self._graph.succ[n]:
succ.appendleft(s)
def _reset_start_node(self, start, end):
self._new_start_node = start
while not nx.has_path(self._graph, self._new_start_node, end):
self._log.info('no path from {} to {} traveling backwards'.format(self._new_start_node, end))
parents = list(self._graph.pred[self._new_start_node])
if len(parents) == 0:
raise Exception('can not travel backwards from start {}, no parents on {}: ({})!'.format(start, self._new_start_node, parents))
elif len(parents) > 1:
# if we have multiple parents, chose the one which has the shortest path to target in undirected graph
length = len(self._graph)
chosen_parent = None
un = self._graph.to_undirected()
for p in parents:
path = nx.shortest_path(un, p, end)
if len(path) < length:
length = len(path)
chosen_parent = p
else:
chosen_parent = parents[0]
self._new_start_node = chosen_parent
# do we need to reattach our real start node?
# it could lead to errors if the direction is reversed because Volg does not support the reversed direction
if self._new_start_node != start:
self._log.info('real start was {} but we travelled backwards to {}'.format(start, self._new_start_node))
return self._new_start_node
def get_all_paths(self, start, end):
# travel backwards / forwards for unreachable nodes
new_start = self._reset_start_node(start, end)
# prune graph to our required sub-graph
self._prune_graph(new_start, end)
# start / end can be pre- / appended the same as other nodes not in the longest path
lp = nx.dag_longest_path(self._graph)
# we need to ensure that start and end node are at the appropriate
# if this is raised we could find shortest path from start to lp and end to lp and pre- or append them
if lp[0] != self._new_start_node or lp[-1] != end:
raise Exception('start: {} or end {} not in first path!'.format(self._new_start_node, end))
self._nodes = set(lp)
yield lp
for n in self._graph:
if n not in self._nodes:
# find parent in lp
# find child in lp
p = self._find_parent_in_paths(n)
c = self._find_child_in_paths(n)
p1 = nx.shortest_path(self._graph, p, n)
p2 = nx.shortest_path(self._graph, n, c)
self._nodes.update(set(p1 + p2))
yield(p1[:-1] + p2) # n is in both paths so we cut one of
class OntdekBaan2(object):
"""Discover all paths in a commitgraph represented as an NetworkX DAG.
The Problem:
High number of paths without repeated nodes in normal Git Workflow.
The Solution:
Split paths at articulation points to reduce number of paths.
Problem still remaining:
- no common suffixes are cleared, without Volg caching there may be a problem
- long running branches that are merged back into master later
- release branches (because we are taking a lot of information from master)
"""
def __init__(self, graph):
self._log = logging.getLogger(self.__class__.__name__)
self._graph = graph.copy()
def _preprocess(self, start_node, end_node):
self._start_node = start_node
self._end_node = end_node
self._log.info('finding all paths between {} and {}'.format(start_node, end_node))
# we need to prune the graph beforehand, this is expensive but otherwise we would have even more paths
# we also prune common prefix in the implementation, common suffix can only be done later
st = timeit.default_timer()
self._log.info('pruning graph')
non_pruned = self._graph.copy()
for node in non_pruned:
for child in iter(non_pruned.succ[node]):
try:
nx.shortest_path(self._graph, child, self._end_node)
except nx.NetworkXNoPath:
self._graph.remove_edge(node, child)
t = timeit.default_timer() - st
self._log.info('pruning finished in {:.3f}'.format(t))
# if our start node contains no path to the end node travel backwards until it does,
# except if it has more than one parent, then its over and we bail
self._new_start_node = start_node
while not nx.has_path(non_pruned, self._new_start_node, end_node):
self._log.info('no path from {} to {} traveling backwards'.format(self._new_start_node, end_node))
parents = list(non_pruned.pred[self._new_start_node])
if len(parents) == 0:
raise Exception('can not travel backwards from start {}, no parents on {}: ({})!'.format(self._start_node, self._new_start_node, parents))
elif len(parents) > 1:
# if we have multiple parents, chose the one which has the shortest path to target in undirected graph
length = len(non_pruned)
chosen_parent = None
un = non_pruned.to_undirected()
for p in parents:
path = nx.shortest_path(un, p, end_node)
if len(path) < length:
length = len(path)
chosen_parent = p
else:
chosen_parent = parents[0]
self._new_start_node = chosen_parent
# get list of APs
self._aps = list(nx.articulation_points(self._graph.to_undirected()))
def get_all_paths(self, start_node, end_node):
"""Return every traversable path between start and end commit."""
self._preprocess(start_node, end_node)
ap = self._new_start_node
full_paths = []
while ap:
ap, paths = self._get_paths(ap, self._end_node)
# we can do this here because it does not matter in which order we traverse the graph
# we do not need full paths everywhere because of the caching in Volg
if not full_paths:
full_paths = paths
self._log.debug('non ap, assigning full_paths')
else:
self._log.debug('encountered AP {} splitting path'.format(ap)) # this is potentially happening quiet often
# first one gets the complete path, as we do not prune common suffixes
# we know that the AP (our new starting node) has to be the last element of every path
# therefore, we chose the first
full_paths[0] += paths[0][1:]
full_paths += paths[1:]
# do we need to reattach our real start node?
# it could lead to errors if the direction is reversed because Volg does not support the reversed direction
if self._new_start_node != self._start_node:
self._log.info('real start was {} but we travelled backwards to {}'.format(self._start_node, self._new_start_node))
return full_paths
def _get_paths(self, start_node, end_node):
"""Get all paths where the end_node is reachable or up to an AP."""
nodes = [start_node]
paths = [[start_node]]
# print('{}, {}'.format(nodes, paths))
new_start = None
while nodes:
node = nodes.pop()
childs = list(self._graph.succ[node])
# we bail on AP or end_node reached
if node in self._aps and node != start_node:
childs = []
new_start = node
elif node == end_node:
childs = []
# print('node {} childs {}'.format(node, childs))
if childs:
nodes += childs
npath = None
for path in paths:
if path[-1] == node:
# by creating a new list instead of copying we eleminate common prefixes in the resulting paths
npath = [node]
path.append(childs[0])
# print('[1] append {} to path {}'.format(childs[0], path))
# first one we have already
for child in childs[1:]:
# do we already have a path?
if npath:
path = npath.copy() # we need this copy here in case of childs > 2
path.append(child)
paths.append(path)
# print('[2] append {} to new path {}'.format(child, npath))
# this is just for the end node
if not childs:
for path in paths:
if path[-1] in self._graph.pred[node]:
# print('[n] append {} to {} because {}'.format(node, path, path[-1]))
path.append(node)
return new_start, paths
|
[
"visualSHARK.models.Commit.objects.get",
"timeit.default_timer",
"networkx.dag_longest_path",
"math.floor",
"networkx.shortest_path",
"networkx.has_path",
"re.sub",
"logging.getLogger"
] |
[((653, 687), 'visualSHARK.models.Commit.objects.get', 'Commit.objects.get', ([], {'id': 't.commit_id'}), '(id=t.commit_id)\n', (671, 687), False, 'from visualSHARK.models import Commit\n'), ((1392, 1416), 're.sub', 're.sub', (['"""[a-z]"""', '""""""', 'tmp'], {}), "('[a-z]', '', tmp)\n", (1398, 1416), False, 'import re\n'), ((4841, 4883), 'logging.getLogger', 'logging.getLogger', (['self.__class__.__name__'], {}), '(self.__class__.__name__)\n', (4858, 4883), False, 'import logging\n'), ((7952, 7984), 'networkx.dag_longest_path', 'nx.dag_longest_path', (['self._graph'], {}), '(self._graph)\n', (7971, 7984), True, 'import networkx as nx\n'), ((9459, 9501), 'logging.getLogger', 'logging.getLogger', (['self.__class__.__name__'], {}), '(self.__class__.__name__)\n', (9476, 9501), False, 'import logging\n'), ((9973, 9995), 'timeit.default_timer', 'timeit.default_timer', ([], {}), '()\n', (9993, 9995), False, 'import timeit\n'), ((6217, 6268), 'networkx.has_path', 'nx.has_path', (['self._graph', 'self._new_start_node', 'end'], {}), '(self._graph, self._new_start_node, end)\n', (6228, 6268), True, 'import networkx as nx\n'), ((10368, 10390), 'timeit.default_timer', 'timeit.default_timer', ([], {}), '()\n', (10388, 10390), False, 'import timeit\n'), ((10688, 10743), 'networkx.has_path', 'nx.has_path', (['non_pruned', 'self._new_start_node', 'end_node'], {}), '(non_pruned, self._new_start_node, end_node)\n', (10699, 10743), True, 'import networkx as nx\n'), ((2875, 2888), 'math.floor', 'math.floor', (['a'], {}), '(a)\n', (2885, 2888), False, 'import math\n'), ((3014, 3027), 'math.floor', 'math.floor', (['b'], {}), '(b)\n', (3024, 3027), False, 'import math\n'), ((5013, 5044), 'networkx.has_path', 'nx.has_path', (['non_pruned', 'n', 'end'], {}), '(non_pruned, n, end)\n', (5024, 5044), True, 'import networkx as nx\n'), ((5149, 5182), 'networkx.has_path', 'nx.has_path', (['non_pruned', 'start', 'n'], {}), '(non_pruned, start, n)\n', (5160, 5182), True, 'import networkx as nx\n'), ((8643, 8678), 'networkx.shortest_path', 'nx.shortest_path', (['self._graph', 'p', 'n'], {}), '(self._graph, p, n)\n', (8659, 8678), True, 'import networkx as nx\n'), ((8700, 8735), 'networkx.shortest_path', 'nx.shortest_path', (['self._graph', 'n', 'c'], {}), '(self._graph, n, c)\n', (8716, 8735), True, 'import networkx as nx\n'), ((10203, 10255), 'networkx.shortest_path', 'nx.shortest_path', (['self._graph', 'child', 'self._end_node'], {}), '(self._graph, child, self._end_node)\n', (10219, 10255), True, 'import networkx as nx\n'), ((6964, 6992), 'networkx.shortest_path', 'nx.shortest_path', (['un', 'p', 'end'], {}), '(un, p, end)\n', (6980, 6992), True, 'import networkx as nx\n'), ((11452, 11485), 'networkx.shortest_path', 'nx.shortest_path', (['un', 'p', 'end_node'], {}), '(un, p, end_node)\n', (11468, 11485), True, 'import networkx as nx\n')]
|
# Copyright 2020 Open Climate Tech Contributors
#
# 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.
# ==============================================================================
"""
Reads data from csv export of one of 3 types of data:
1) votes and polygons
2) CameraID and direction
3) Filename and x/y coordinates of fire region
For each of these, it finds the approximate location and finds the historical weather,
which is cached/saved in DB.
Weather data is merged with fire data to genrate output CSV file.
"""
import os, sys
from firecam.lib import settings
from firecam.lib import collect_args
from firecam.lib import goog_helper
from firecam.lib import db_manager
from firecam.lib import img_archive
from firecam.lib import weather
import random
import time, datetime, dateutil.parser
import logging
import csv
import json
import math
from shapely.geometry import Polygon, Point
from PIL import Image
def getCentroid(polygonStr):
polygonCoords = json.loads(polygonStr)
poly = Polygon(polygonCoords)
centerLatLong = list(zip(*poly.centroid.xy))[0]
return (round(centerLatLong[0],3), round(centerLatLong[1],3))
def getRandInterpolatedVal(percentiles):
randVal = random.random()
rand10 = randVal*10
rand10Int = int(rand10)
minVal = percentiles[rand10Int]
maxVal = percentiles[rand10Int + 1]
return minVal + (rand10 - rand10Int) * (maxVal - minVal)
def keepData(score, centroid, numPolys, isRealFire):
northMexico = Polygon([(32.533, -117.157), (32.696, -115.173), (32.174, -114.692), (32.073, -117.232)])
return not northMexico.intersects(Point(centroid))
def outputWithWeather(outFile, score, timestamp, centroid, numPolys, weatherCentroid, weatherCamera, isRealFire):
dataArr = weather.normalizeWeather(score, numPolys, weatherCentroid, weatherCamera, timestamp, centroid, isRealFire)
dataArrStr = list(map(str, dataArr))
# logging.warning('Data arrayStr: %s', dataArrStr)
dataStr = ', '.join(dataArrStr)
# logging.warning('Data str: %s', dataStr)
outFile.write(dataStr + '\n')
def patchCameraId(cameraID):
if cameraID.startswith('lo-'):
cameraID = 'm' + cameraID
elif cameraID.startswith('so-'):
cameraID = 'sojr-' + cameraID[3:]
return cameraID
def main():
reqArgs = [
["o", "outputFile", "output file name"],
["i", "inputCsv", "csvfile with fire/detection data"],
['m', "mode", "mode: votepoly or camdir or pruned"],
]
optArgs = [
["s", "startRow", "starting row"],
["e", "endRow", "ending row"],
]
args = collect_args.collectArgs(reqArgs, optionalArgs=optArgs, parentParsers=[goog_helper.getParentParser()])
startRow = int(args.startRow) if args.startRow else 0
endRow = int(args.endRow) if args.endRow else 1e9
mode = args.mode
assert mode == 'votepoly' or mode == 'camdir' or mode == 'pruned'
outFile = open(args.outputFile, 'w')
dbManager = db_manager.DbManager(sqliteFile=settings.db_file,
psqlHost=settings.psqlHost, psqlDb=settings.psqlDb,
psqlUser=settings.psqlUser, psqlPasswd=settings.psqlPasswd)
lastCam = None
lastTime = None
random.seed(0)
with open(args.inputCsv) as csvFile:
csvreader = csv.reader(csvFile)
for (rowIndex, csvRow) in enumerate(csvreader):
if rowIndex < startRow:
continue
if rowIndex > endRow:
print('Reached end row', rowIndex, endRow)
break
if mode == 'votepoly':
[cameraID, timestamp, score, polygon, sourcePolygons, isRealFire] = csvRow[:6]
timestamp = int(timestamp)
logging.warning('Processing row: %d, cam: %s, ts: %s', rowIndex, cameraID, timestamp)
if cameraID == lastCam and timestamp == lastTime:
logging.warning('Duplicate row: %d, cam: %s, ts: %s', rowIndex, cameraID, timestamp)
lastCam = cameraID
lastTime = timestamp
centroid = getCentroid(polygon)
if timestamp < 1607786165: #sourcePolygons didn't exist before this
if isRealFire:
numPolys = round(getRandInterpolatedVal(settings.percentilesNumPolyFire))
else:
numPolys = round(getRandInterpolatedVal(settings.percentilesNumPolyOther))
else:
numPolys = 1
if sourcePolygons:
sourcePolygonsArr = json.loads(sourcePolygons)
numPolys = len(sourcePolygonsArr)
cameraID = patchCameraId(cameraID)
(mapImgGCS, camLatitude, camLongitude) = dbManager.getCameraMapLocation(cameraID)
else:
if mode == 'camdir':
[cameraID, isoTime, direction] = csvRow[:3]
logging.warning('Processing row: %d, cam: %s, ts: %s', rowIndex, cameraID, isoTime)
timestamp = time.mktime(dateutil.parser.parse(isoTime).timetuple())
if 'center left' in direction:
offset = -20
elif 'center right' in direction:
offset = 20
elif 'center' in direction:
offset = 0
elif 'left' in direction:
offset = -40
elif 'right' in direction:
offset = 40
else:
logging.error('Unexpected dir row: %d, dir: %s', rowIndex, direction)
continue
elif mode == 'pruned':
[_cropName, minX, _minY, maxX, _maxY, fileName] = csvRow[:6]
minX = int(minX)
maxX = int(maxX)
nameParsed = img_archive.parseFilename(fileName)
cameraID = nameParsed['cameraID']
cameraID = patchCameraId(cameraID)
timestamp = nameParsed['unixTime']
dateStr = nameParsed['isoStr'][:nameParsed['isoStr'].index('T')]
if dateStr == lastTime and cameraID == lastCam:
# logging.warning('Skip same fire. row %s', rowIndex)
continue
lastCam = cameraID
lastTime = dateStr
localFilePath = os.path.join(settings.downloadDir, fileName)
if not os.path.isfile(localFilePath):
logging.warning('Skip missing file %s, row %s', fileName, rowIndex)
continue
img = Image.open(localFilePath)
degreesInView = 110
centerX = (minX + maxX) / 2
offset = centerX / img.size[0] * degreesInView - degreesInView/2
img.close()
(mapImgGCS, camLatitude, camLongitude) = dbManager.getCameraMapLocation(cameraID)
camHeading = img_archive.getHeading(cameraID)
heading = (camHeading + offset) % 360
angle = 90 - heading
distanceDegrees = 0.2 # approx 14 miles
fireLat = camLatitude + math.sin(angle*math.pi/180)*distanceDegrees
fireLong = camLongitude + math.cos(angle*math.pi/180)*distanceDegrees
centroid = (fireLat, fireLong)
score = getRandInterpolatedVal(settings.percentilesScoreFire)
numPolys = round(getRandInterpolatedVal(settings.percentilesNumPolyFire))
isRealFire = 1
logging.warning('Processing row: %d, heading: %s, centroid: %s, score: %s, numpoly: %s', rowIndex, heading, centroid, score, numPolys)
if not keepData(score, centroid, numPolys, isRealFire):
logging.warning('Skipping Mexico fire row %d, camera %s', rowIndex, cameraID)
continue
(weatherCentroid, weatherCamera) = weather.getWeatherData(dbManager, cameraID, timestamp, centroid, (camLatitude, camLongitude))
if not weatherCentroid:
logging.warning('Skipping row %d', rowIndex)
continue
# logging.warning('Weather %s', weatherCentroid)
outputWithWeather(outFile, score, timestamp, centroid, numPolys, weatherCentroid, weatherCamera, isRealFire)
logging.warning('Processed row: %d, cam: %s, ts: %s', rowIndex, cameraID, timestamp)
outFile.close()
if __name__=="__main__":
main()
|
[
"csv.reader",
"firecam.lib.weather.normalizeWeather",
"os.path.isfile",
"os.path.join",
"shapely.geometry.Point",
"logging.error",
"json.loads",
"shapely.geometry.Polygon",
"logging.warning",
"firecam.lib.img_archive.getHeading",
"firecam.lib.weather.getWeatherData",
"firecam.lib.db_manager.DbManager",
"random.seed",
"math.cos",
"firecam.lib.img_archive.parseFilename",
"math.sin",
"random.random",
"PIL.Image.open",
"firecam.lib.goog_helper.getParentParser"
] |
[((1465, 1487), 'json.loads', 'json.loads', (['polygonStr'], {}), '(polygonStr)\n', (1475, 1487), False, 'import json\n'), ((1499, 1521), 'shapely.geometry.Polygon', 'Polygon', (['polygonCoords'], {}), '(polygonCoords)\n', (1506, 1521), False, 'from shapely.geometry import Polygon, Point\n'), ((1697, 1712), 'random.random', 'random.random', ([], {}), '()\n', (1710, 1712), False, 'import random\n'), ((1975, 2069), 'shapely.geometry.Polygon', 'Polygon', (['[(32.533, -117.157), (32.696, -115.173), (32.174, -114.692), (32.073, -117.232)\n ]'], {}), '([(32.533, -117.157), (32.696, -115.173), (32.174, -114.692), (\n 32.073, -117.232)])\n', (1982, 2069), False, 'from shapely.geometry import Polygon, Point\n'), ((2250, 2360), 'firecam.lib.weather.normalizeWeather', 'weather.normalizeWeather', (['score', 'numPolys', 'weatherCentroid', 'weatherCamera', 'timestamp', 'centroid', 'isRealFire'], {}), '(score, numPolys, weatherCentroid, weatherCamera,\n timestamp, centroid, isRealFire)\n', (2274, 2360), False, 'from firecam.lib import weather\n'), ((3456, 3626), 'firecam.lib.db_manager.DbManager', 'db_manager.DbManager', ([], {'sqliteFile': 'settings.db_file', 'psqlHost': 'settings.psqlHost', 'psqlDb': 'settings.psqlDb', 'psqlUser': 'settings.psqlUser', 'psqlPasswd': 'settings.psqlPasswd'}), '(sqliteFile=settings.db_file, psqlHost=settings.\n psqlHost, psqlDb=settings.psqlDb, psqlUser=settings.psqlUser,\n psqlPasswd=settings.psqlPasswd)\n', (3476, 3626), False, 'from firecam.lib import db_manager\n'), ((3736, 3750), 'random.seed', 'random.seed', (['(0)'], {}), '(0)\n', (3747, 3750), False, 'import random\n'), ((3812, 3831), 'csv.reader', 'csv.reader', (['csvFile'], {}), '(csvFile)\n', (3822, 3831), False, 'import csv\n'), ((2103, 2118), 'shapely.geometry.Point', 'Point', (['centroid'], {}), '(centroid)\n', (2108, 2118), False, 'from shapely.geometry import Polygon, Point\n'), ((8633, 8731), 'firecam.lib.weather.getWeatherData', 'weather.getWeatherData', (['dbManager', 'cameraID', 'timestamp', 'centroid', '(camLatitude, camLongitude)'], {}), '(dbManager, cameraID, timestamp, centroid, (\n camLatitude, camLongitude))\n', (8655, 8731), False, 'from firecam.lib import weather\n'), ((9044, 9132), 'logging.warning', 'logging.warning', (['"""Processed row: %d, cam: %s, ts: %s"""', 'rowIndex', 'cameraID', 'timestamp'], {}), "('Processed row: %d, cam: %s, ts: %s', rowIndex, cameraID,\n timestamp)\n", (9059, 9132), False, 'import logging\n'), ((3164, 3193), 'firecam.lib.goog_helper.getParentParser', 'goog_helper.getParentParser', ([], {}), '()\n', (3191, 3193), False, 'from firecam.lib import goog_helper\n'), ((4253, 4342), 'logging.warning', 'logging.warning', (['"""Processing row: %d, cam: %s, ts: %s"""', 'rowIndex', 'cameraID', 'timestamp'], {}), "('Processing row: %d, cam: %s, ts: %s', rowIndex, cameraID,\n timestamp)\n", (4268, 4342), False, 'import logging\n'), ((7652, 7684), 'firecam.lib.img_archive.getHeading', 'img_archive.getHeading', (['cameraID'], {}), '(cameraID)\n', (7674, 7684), False, 'from firecam.lib import img_archive\n'), ((8264, 8407), 'logging.warning', 'logging.warning', (['"""Processing row: %d, heading: %s, centroid: %s, score: %s, numpoly: %s"""', 'rowIndex', 'heading', 'centroid', 'score', 'numPolys'], {}), "(\n 'Processing row: %d, heading: %s, centroid: %s, score: %s, numpoly: %s',\n rowIndex, heading, centroid, score, numPolys)\n", (8279, 8407), False, 'import logging\n'), ((8483, 8560), 'logging.warning', 'logging.warning', (['"""Skipping Mexico fire row %d, camera %s"""', 'rowIndex', 'cameraID'], {}), "('Skipping Mexico fire row %d, camera %s', rowIndex, cameraID)\n", (8498, 8560), False, 'import logging\n'), ((8779, 8823), 'logging.warning', 'logging.warning', (['"""Skipping row %d"""', 'rowIndex'], {}), "('Skipping row %d', rowIndex)\n", (8794, 8823), False, 'import logging\n'), ((4425, 4513), 'logging.warning', 'logging.warning', (['"""Duplicate row: %d, cam: %s, ts: %s"""', 'rowIndex', 'cameraID', 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|
from PIL import Image
from pathlib import Path
from glob import glob
from os.path import basename
from tqdm import tqdm
import os
class Compression:
def __init__(self, compress_level, optimize=True, log=True, resize=False, resize_params=(0, 0)):
"""
Init compression params
:param compress_level: compression % (0, 100)
:param optimize: boolean, true flag will do an extra pass on the image to find a way
to reduce its size as much as possible.
:param log: boolean, prints log information
:param resize: boolean, true if resize needed
:param resize_params: specify if resize is true
"""
self.compress_level = compress_level
self.optimize = optimize
self.resize = resize
self.resize_params = resize_params
self.log = log
self.img_format = ".bmp"
def compress(self, path, save_path):
img = Image.open(path)
if self.resize:
# downsize the image with an ANTIALIAS filter (gives the highest quality)
img = img.resize(self.resize_params, Image.ANTIALIAS)
img.save(save_path[:-4] + '.jpg', optimize=self.optimize, quality=self.compress_level)
img.close()
def compress_bulk(self, data_dir, save_dir):
try:
os.makedirs(save_dir)
except OSError as e:
print(e)
pass
if self.log:
print(f'COMPRESSION OF {data_dir} HAS STARTED')
print(f'INPUT DIR SIZE: {self.get_dir_size(data_dir)}')
for file in tqdm(glob(data_dir + "/*" + self.img_format)):
self.compress(file, save_dir + "/" + basename(file))
if self.log: print("COMPRESSED DIR SIZE: " + self.get_dir_size(save_dir))
def get_dir_size(self, path):
root_directory = Path(path)
size = sum(f.stat().st_size for f in root_directory.glob('**/*'))
return str(size) + " bytes"
|
[
"os.makedirs",
"os.path.basename",
"PIL.Image.open",
"pathlib.Path",
"glob.glob"
] |
[((928, 944), 'PIL.Image.open', 'Image.open', (['path'], {}), '(path)\n', (938, 944), False, 'from PIL import Image\n'), ((1826, 1836), 'pathlib.Path', 'Path', (['path'], {}), '(path)\n', (1830, 1836), False, 'from pathlib import Path\n'), ((1311, 1332), 'os.makedirs', 'os.makedirs', (['save_dir'], {}), '(save_dir)\n', (1322, 1332), False, 'import os\n'), ((1576, 1615), 'glob.glob', 'glob', (["(data_dir + '/*' + self.img_format)"], {}), "(data_dir + '/*' + self.img_format)\n", (1580, 1615), False, 'from glob import glob\n'), ((1667, 1681), 'os.path.basename', 'basename', (['file'], {}), '(file)\n', (1675, 1681), False, 'from os.path import basename\n')]
|
"""Defines spiders related to schools that NFL players have attended."""
import scrapy
from nfldata.common.pfr import pfr_request, PRO_FOOTBALL_REFERENCE_DOMAIN
from nfldata.items.schools import School
class SchoolsSpider(scrapy.Spider):
"""The spider that crawls and stores information about schools that players
have attended."""
name = 'schools'
allowed_domains = [PRO_FOOTBALL_REFERENCE_DOMAIN]
@classmethod
def create_table(cls, database):
"""Create the table needed for this spider."""
School.sql_create(database)
def start_requests(self):
return [pfr_request('schools')]
def parse(self, response): # pylint: disable=arguments-differ
for row in response.css(
'table#college_stats_table tbody tr:not(.thead)'):
school = row.css('td[data-stat="college_name"] a::attr(href)').get()
if school:
name = row.css('td[data-stat="college_name"] a::text').get()
yield School(school=school, name=name)
|
[
"nfldata.common.pfr.pfr_request",
"nfldata.items.schools.School.sql_create",
"nfldata.items.schools.School"
] |
[((537, 564), 'nfldata.items.schools.School.sql_create', 'School.sql_create', (['database'], {}), '(database)\n', (554, 564), False, 'from nfldata.items.schools import School\n'), ((612, 634), 'nfldata.common.pfr.pfr_request', 'pfr_request', (['"""schools"""'], {}), "('schools')\n", (623, 634), False, 'from nfldata.common.pfr import pfr_request, PRO_FOOTBALL_REFERENCE_DOMAIN\n'), ((1007, 1039), 'nfldata.items.schools.School', 'School', ([], {'school': 'school', 'name': 'name'}), '(school=school, name=name)\n', (1013, 1039), False, 'from nfldata.items.schools import School\n')]
|
"""
Created on 7/17/16 10:08 AM
@author: <NAME>, <NAME>
"""
from __future__ import division, print_function, absolute_import
import numpy as np
import psutil
import joblib
import time as tm
import h5py
import itertools
from numbers import Number
from multiprocessing import cpu_count
try:
from mpi4py import MPI
if MPI.COMM_WORLD.Get_size() == 1:
# mpi4py available but NOT called via mpirun or mpiexec => single node
MPI = None
except ImportError:
# mpi4py not even present! Single node by default:
MPI = None
mpi_serial_warning = False
from pyUSID.io.hdf_utils import check_if_main, check_for_old, get_attributes
from pyUSID.io.usi_data import USIDataset
from pyUSID.io.io_utils import recommend_cpu_cores, get_available_memory, format_time, format_size
"""
For hyperthreaded applications: need to tack on the additional flag as shown below
No need to specify -n 4 or whatever if you want to use all available processors
$ mpirun -use-hwthread-cpus python hello_world.py
Check the number of ranks per socket. If only 1 rank per socket - that rank is allowed to call joblib
Thus this paradigm will span the pure-mpi and mpi+joblib paradigm. Note that this does not prevent some sockets to run
in pure MPI mode while others run in MPI+joblib mode. Eventually, this should allow each rank to use jolib when the
number of ranks in a given socket are noticeably less than the number of logical cores....
The naive approach will be to simply allow all ranks to write data directly to file
Forcing only a single rank within a socket may negate performance benefits
Writing out to separate files and then merging them later on is the most performant option
Look into sub-communication worlds that can create mini worlds instead of the general COMM WORLD
https://stackoverflow.com/questions/50900655/mpi4py-create-multiple-groups-and-scatter-from-each-group
https://www.bu.edu/pasi/files/2011/01/Lisandro-Dalcin-mpi4py.pdf
No work will be necessary to figure out the new ranking within the new communicator / group - automatically assigned
from lowest value
When it is time to write the results chunks back to file.
a. If not master -> send data to master
b. If master -> gather from this smaller world and then write to file once. IF this is too much memory to handle,
then loop over each rank <-- how is this different from just looping over each rank within the new communicator and
asking it to write?:
i. receive
ii. write
iii. repeat.
A copy of the data will be made on Rank 0. ie - Rank 0 will have to hold N ranks worth of data. Meaning that each
rank can hold only around M/(2N) of data where M is the memory per node and N is the number of ranks per socket
http://mpitutorial.com/tutorials/introduction-to-groups-and-communicators/
https://info.gwdg.de/~ceulig/docs-dev/doku.php?id=en:services:application_services:high_performance_computing:mpi4py
https://rabernat.github.io/research_computing/parallel-programming-with-mpi-for-python.html
Create a giant low precision dataset. Instead of storing indices, let each rank set the completed indices to
True. The problem is that the smallest precision is 1 byte and NOT 1 bit. Even boolean = 1 byte!
See - http://docs.h5py.org/en/latest/faq.html#faq
https://support.hdfgroup.org/HDF5/hdf5-quest.html#bool
https://groups.google.com/a/continuum.io/forum/#!topic/anaconda/qFOGRTOxFTM
"""
def group_ranks_by_socket(verbose=False):
"""
Groups MPI ranks in COMM_WORLD by socket. Another way to think about this is that it assigns a master rank for each
rank such that there is a single master rank per socket (CPU). The results from this function can be used to split
MPI communicators based on the socket for intra-node communication.
This is necessary when wanting to carve up the memory for all ranks within a socket.
This is also relevant when trying to bring down the number of ranks that are writing to the HDF5 file.
This is all based on the premise that data analysis involves a fair amount of file writing and writing with
3 ranks is a lot better than writing with 100 ranks. An assumption is made that the communication between the
ranks within each socket would be faster than communicating across nodes / scokets. No assumption is made about the
names of each socket
Parameters
----------
verbose : bool, optional
Whether or not to print debugging statements
Returns
-------
master_ranks : 1D unsigned integer numpy array
Array with values that signify which rank a given rank should consider its master.
"""
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
# Step 1: Gather all the socket names:
sendbuf = MPI.Get_processor_name()
if verbose:
print('Rank: ', rank, ', sendbuf: ', sendbuf)
recvbuf = comm.allgather(sendbuf)
if verbose and rank == 0:
print('Rank: ', rank, ', recvbuf received: ', recvbuf)
# Step 2: Find all unique socket names:
recvbuf = np.array(recvbuf)
unique_sockets = np.unique(recvbuf)
if verbose and rank == 0:
print('Unique sockets: {}'.format(unique_sockets))
master_ranks = np.zeros(size, dtype=np.uint16)
for item in unique_sockets:
temp = np.where(recvbuf == item)[0]
master_ranks[temp] = temp[0]
if verbose and rank == 0:
print('Parent rank for all ranks: {}'.format(master_ranks))
return master_ranks
def to_ranges(iterable):
"""
Converts a sequence of iterables to range tuples
From https://stackoverflow.com/questions/4628333/converting-a-list-of-integers-into-range-in-python
Credits: @juanchopanza and @luca
Parameters
----------
iterable : collections.Iterable object
iterable object like a list
Returns
-------
iterable : generator object
Cast to list or similar to use
"""
iterable = sorted(set(iterable))
for key, group in itertools.groupby(enumerate(iterable),
lambda t: t[1] - t[0]):
group = list(group)
yield group[0][1], group[-1][1]
class Process(object):
"""
Encapsulates the typical steps performed when applying a processing function to a dataset.
"""
def __init__(self, h5_main, cores=None, max_mem_mb=4*1024, verbose=False):
"""
Parameters
----------
h5_main : h5py.Dataset instance
The dataset over which the analysis will be performed. This dataset should be linked to the spectroscopic
indices and values, and position indices and values datasets.
cores : uint, optional
Default - all available cores - 2
How many cores to use for the computation
max_mem_mb : uint, optional
How much memory to use for the computation. Default 1024 Mb
verbose : Boolean, (Optional, default = False)
Whether or not to print debugging statements
"""
if h5_main.file.mode != 'r+':
raise TypeError('Need to ensure that the file is in r+ mode to write results back to the file')
if MPI is not None:
# If we came here then, the user has intentionally asked for multi-node computation
comm = MPI.COMM_WORLD
self.mpi_comm = comm
self.mpi_rank = comm.Get_rank()
self.mpi_size = comm.Get_size()
if verbose:
print("Rank {} of {} on {} sees {} logical cores on the socket".format(comm.Get_rank(), comm.Get_size(),
MPI.Get_processor_name(),
cpu_count()))
# First, ensure that cores=logical cores in node. No point being economical / considerate
cores = psutil.cpu_count()
# It is sufficient if just one rank checks all this.
if self.mpi_rank == 0:
print('Working on {} ranks via MPI'.format(self.mpi_size))
# Ensure that the file is opened in the correct comm or something
if h5_main.file.driver != 'mpio':
raise TypeError('The HDF5 file should have been opened with driver="mpio". Current driver = "{}"'
''.format(h5_main.file.driver))
"""
# Not sure how to check for this correctly
messg = None
try:
if h5_main.file.comm != comm:
messg = 'The HDF5 file should have been opened with comm=MPI.COMM_WORLD. Currently comm={}'
''.format(h5_main.file.comm)
except AttributeError:
messg = 'The HDF5 file should have been opened with comm=MPI.COMM_WORLD'
if messg is not None:
raise TypeError(messg)
"""
else:
print('No mpi4py found or script was not called via mpixexec / mpirun. Assuming single node computation')
self.mpi_comm = None
self.mpi_size = 1
self.mpi_rank = 0
# Checking if dataset is "Main"
if not check_if_main(h5_main, verbose=verbose and self.mpi_rank == 0):
raise ValueError('Provided dataset is not a "Main" dataset with necessary ancillary datasets')
if MPI is not None:
MPI.COMM_WORLD.barrier()
# Not sure if we need a barrier here.
# Saving these as properties of the object:
self.h5_main = USIDataset(h5_main)
self.verbose = verbose
self._cores = None
self.__ranks_on_socket = 1
self.__socket_master_rank = 0
self._max_pos_per_read = None
self._max_mem_mb = None
# Now have to be careful here since the below properties are a function of the MPI rank
self.__start_pos = None
self.__rank_end_pos = None
self.__end_pos = None
self.__pixels_in_batch = None
# Determining the max size of the data that can be put into memory
# all ranks go through this and they need to have this value any
self._set_memory_and_cores(cores=cores, mem=max_mem_mb)
self.duplicate_h5_groups = []
self.partial_h5_groups = []
self.process_name = None # Reset this in the extended classes
self.parms_dict = None
# The name of the HDF5 dataset that should be present to signify which positions have already been computed
self.__status_dset_name = 'completed_positions'
self._results = None
self.h5_results_grp = None
# Check to see if the resuming feature has been implemented:
self.__resume_implemented = False
try:
self._get_existing_datasets()
except NotImplementedError:
if verbose and self.mpi_rank == 0:
print('It appears that this class may not be able to resume computations')
except:
# NameError for variables that don't exist
# AttributeError for self.var_name that don't exist
# TypeError (NoneType) etc.
self.__resume_implemented = True
if self.mpi_rank == 0:
print('Consider calling test() to check results before calling compute() which computes on the entire'
' dataset and writes back to the HDF5 file')
# DON'T check for duplicates since parms_dict has not yet been initialized.
# Sub classes will check by themselves if they are interested.
def __assign_job_indices(self):
"""
Sets the start and end indices for each MPI rank
"""
# First figure out what positions need to be computed
self._compute_jobs = np.where(self._h5_status_dset[()] == 0)[0]
if self.verbose and self.mpi_rank == 0:
print('Among the {} positions in this dataset, the following positions need to be computed: {}'
'.'.format(self.h5_main.shape[0], self._compute_jobs))
pos_per_rank = self._compute_jobs.size // self.mpi_size # integer division
if self.verbose and self.mpi_rank == 0:
print('Each rank is required to work on {} of the {} (remaining) positions in this dataset'
'.'.format(pos_per_rank, self._compute_jobs.size))
# The start and end indices now correspond to the indices in the incomplete jobs rather than the h5 dataset
self.__start_pos = self.mpi_rank * pos_per_rank
self.__rank_end_pos = (self.mpi_rank + 1) * pos_per_rank
self.__end_pos = int(min(self.__rank_end_pos, self.__start_pos + self._max_pos_per_read))
if self.mpi_rank == self.mpi_size - 1:
# Force the last rank to go to the end of the dataset
self.__rank_end_pos = self._compute_jobs.size
if self.verbose:
print('Rank {} will read positions {} to {} of {}'.format(self.mpi_rank, self.__start_pos,
self.__rank_end_pos, self.h5_main.shape[0]))
def _estimate_compute_time_per_pixel(self, *args, **kwargs):
"""
Estimates how long it takes to compute an average pixel's worth of data. This information should be used by the
user to limit the number of pixels that will be processed per batch to make best use of checkpointing. This
function is exposed to the developer of the child classes. An approximate can be derived if it is simpler
Returns
-------
"""
chosen_pos = np.random.randint(0, high=self.h5_main.shape[0]-1, size=5)
t0 = tm.time()
_ = parallel_compute(self.h5_main[chosen_pos, :], self._map_function, cores=1,
lengthy_computation=False, func_args=args, func_kwargs=kwargs, verbose=False)
return (tm.time() - t0) / len(chosen_pos)
def _get_pixels_in_current_batch(self):
"""
Returns the indices of the pixels that will be processed in this batch.
Returns
-------
pixels_in_batch : numpy.ndarray
1D array of unsigned integers denoting the pixels that will be read, processed, and written back to
"""
return self.__pixels_in_batch
def test(self, **kwargs):
"""
Tests the process on a subset (for example a pixel) of the whole data. The class can be reinstantiated with
improved parameters and tested repeatedly until the user is content, at which point the user can call
compute() on the whole dataset. This is not a function that is expected to be called in mpi
Parameters
----------
kwargs - dict, optional
keyword arguments to test the process
Returns
-------
"""
# All children classes should call super() OR ensure that they only work for self.mpi_rank == 0
raise NotImplementedError('test_on_subset has not yet been implemented')
def _check_for_duplicates(self):
"""
Checks for instances where the process was applied to the same dataset with the same parameters
Returns
-------
duplicate_h5_groups : list of h5py.Group objects
List of groups satisfying the above conditions
"""
if self.verbose and self.mpi_rank == 0:
print('Checking for duplicates:')
# This list will contain completed runs only
duplicate_h5_groups = check_for_old(self.h5_main, self.process_name, new_parms=self.parms_dict)
partial_h5_groups = []
# First figure out which ones are partially completed:
if len(duplicate_h5_groups) > 0:
for index, curr_group in enumerate(duplicate_h5_groups):
"""
Earlier, we only checked the 'last_pixel' but to be rigorous we should check self.__status_dset_name
The last_pixel attribute check may be deprecated in the future.
Note that legacy computations did not have this dataset. We can add to partially computed datasets
"""
if self.__status_dset_name in curr_group.keys():
# Case 1: Modern Process results:
status_dset = curr_group[self.__status_dset_name]
if not isinstance(status_dset, h5py.Dataset):
# We should not come here if things were implemented correctly
if self.mpi_rank == 0:
print('Results group: {} contained an object named: {} that should have been a dataset'
'.'.format(curr_group, self.__status_dset_name))
if self.h5_main.shape[0] != status_dset.shape[0] or len(status_dset.shape) > 1 or \
status_dset.dtype != np.uint8:
if self.mpi_rank == 0:
print('Status dataset: {} was not of the expected shape or datatype'.format(status_dset))
# Finally, check how far the computation was completed.
if len(np.where(status_dset[()] == 0)[0]) == 0:
# remove from duplicates and move to partial
partial_h5_groups.append(duplicate_h5_groups.pop(index))
# Let's write the legacy attribute for safety
curr_group.attrs['last_pixel'] = self.h5_main.shape[0]
# No further checks necessary
continue
else:
# Optionally calculate how much was completed:
if self.mpi_rank == 0:
percent_complete = int(100 * len(np.where(status_dset[()] == 0)[0]) / status_dset.shape[0])
print('Group: {}: computation was {}% completed'.format(curr_group, percent_complete))
# Case 2: Legacy results group:
if 'last_pixel' not in curr_group.attrs.keys():
if self.mpi_rank == 0:
# Should not be coming here at all
print('Group: {} had neither the status HDF5 dataset or the legacy attribute: "last_pixel"'
'.'.format(curr_group))
# Not sure what to do with such groups. Don't consider them in the future
duplicate_h5_groups.pop(index)
continue
# Finally, do the legacy test:
if curr_group.attrs['last_pixel'] < self.h5_main.shape[0]:
# Should we create the dataset here, to make the group future-proof?
# remove from duplicates and move to partial
partial_h5_groups.append(duplicate_h5_groups.pop(index))
if len(duplicate_h5_groups) > 0 and self.mpi_rank == 0:
print('Note: ' + self.process_name + ' has already been performed with the same parameters before. '
'These results will be returned by compute() by default. '
'Set override to True to force fresh computation')
print(duplicate_h5_groups)
if len(partial_h5_groups) > 0 and self.mpi_rank == 0:
print('Note: ' + self.process_name + ' has already been performed PARTIALLY with the same parameters. '
'compute() will resuming computation in the last group below. '
'To choose a different group call use_patial_computation()'
'Set override to True to force fresh computation or resume from a '
'data group besides the last in the list.')
print(partial_h5_groups)
return duplicate_h5_groups, partial_h5_groups
def use_partial_computation(self, h5_partial_group=None):
"""
Extracts the necessary parameters from the provided h5 group to resume computation
Parameters
----------
h5_partial_group : h5py.Group object
Group containing partially computed results
"""
# Attempt to automatically take partial results
if h5_partial_group is None:
if len(self.partial_h5_groups) < 1:
raise ValueError('No group was found with partial results and no such group was provided')
h5_partial_group = self.partial_h5_groups[-1]
else:
# Make sure that this group is among the legal ones already discovered:
if h5_partial_group not in self.partial_h5_groups:
raise ValueError('Provided group does not appear to be in the list of discovered groups')
self.parms_dict = get_attributes(h5_partial_group)
self.h5_results_grp = h5_partial_group
def _set_memory_and_cores(self, cores=None, mem=None):
"""
Checks hardware limitations such as memory, # cpus and sets the recommended datachunk sizes and the
number of cores to be used by analysis methods. This function can work with clusters with heterogeneous
memory sizes (e.g. CADES SHPC Condo).
Parameters
----------
cores : uint, optional
Default - 1
How many cores to use for the computation
mem : uint, optional
Default - 1024
The amount a memory in Mb to use in the computation
"""
if MPI is None:
min_free_cores = 1 + int(psutil.cpu_count() > 4)
if cores is None:
self._cores = max(1, psutil.cpu_count() - min_free_cores)
else:
if not isinstance(cores, int):
raise TypeError('cores should be an integer but got: {}'.format(cores))
cores = int(abs(cores))
self._cores = max(1, min(psutil.cpu_count(), cores))
self.__socket_master_rank = 0
self.__ranks_on_socket = 1
else:
# user-provided input cores will simply be ignored in an effort to use the entire CPU
ranks_by_socket = group_ranks_by_socket(verbose=self.verbose)
self.__socket_master_rank = ranks_by_socket[self.mpi_rank]
# which ranks in this socket?
ranks_on_this_socket = np.where(ranks_by_socket == self.__socket_master_rank)[0]
# how many in this socket?
self.__ranks_on_socket = ranks_on_this_socket.size
# Force usage of all available memory
mem = None
self._cores = 1
# Disabling the following line since mpi4py and joblib didn't play well for Bayesian Inference
# self._cores = self.__cores_per_rank = psutil.cpu_count() // self.__ranks_on_socket
# TODO: Convert all to bytes!
_max_mem_mb = get_available_memory() / 1024 ** 2 # in MB
if mem is None:
mem = _max_mem_mb
else:
if not isinstance(mem, int):
raise TypeError('mem must be a whole number')
mem = abs(mem)
self._max_mem_mb = min(_max_mem_mb, mem)
# Remember that multiple processes (either via MPI or joblib) will share this socket
max_data_chunk = self._max_mem_mb / (self._cores * self.__ranks_on_socket)
# Now calculate the number of positions OF RAW DATA ONLY that can be stored in memory in one go PER RANK
mb_per_position = self.h5_main.dtype.itemsize * self.h5_main.shape[1] / 1024 ** 2
self._max_pos_per_read = int(np.floor(max_data_chunk / mb_per_position))
if self.verbose and self.mpi_rank == self.__socket_master_rank:
# expected to be the same for all ranks so just use this.
print('Rank {} - on socket with {} logical cores and {} avail. RAM shared by {} ranks each given {} cores'
'.'.format(self.__socket_master_rank, psutil.cpu_count(), format_size(_max_mem_mb * 1024**2, 2),
self.__ranks_on_socket, self._cores))
print('Allowed to read {} pixels per chunk'.format(self._max_pos_per_read))
@staticmethod
def _map_function(*args, **kwargs):
"""
The function that manipulates the data on a single instance (position). This will be used by _unit_computation()
to process a chunk of data in parallel
Parameters
----------
args : list
arguments to the function in the correct order
kwargs : dictionary
keyword arguments to the function
Returns
-------
object
"""
raise NotImplementedError('Please override the _unit_function specific to your process')
def _read_data_chunk(self):
"""
Reads a chunk of data for the intended computation into memory
"""
if self.__start_pos < self.__rank_end_pos:
self.__end_pos = int(min(self.__rank_end_pos, self.__start_pos + self._max_pos_per_read))
# DON'T DIRECTLY apply the start and end indices anymore to the h5 dataset. Find out what it means first
self.__pixels_in_batch = self._compute_jobs[self.__start_pos: self.__end_pos]
self.data = self.h5_main[self.__pixels_in_batch, :]
if self.verbose:
print('Rank {} - Read positions: {}'.format(self.mpi_rank, self.__pixels_in_batch, self.__rank_end_pos))
# DON'T update the start position
else:
if self.verbose:
print('Rank {} - Finished reading all data!'.format(self.mpi_rank))
self.data = None
def _write_results_chunk(self):
"""
Writes the computed results into appropriate datasets.
This needs to be rewritten since the processed data is expected to be at least as large as the dataset
"""
# Now update the start position
self.__start_pos = self.__end_pos
# This line can remain as is
raise NotImplementedError('Please override the _set_results specific to your process')
def _create_results_datasets(self):
"""
Process specific call that will write the h5 group, guess dataset, corresponding spectroscopic datasets and also
link the guess dataset to the spectroscopic datasets. It is recommended that the ancillary datasets be populated
within this function.
"""
raise NotImplementedError('Please override the _create_results_datasets specific to your process')
def __create_compute_status_dataset(self):
"""
Creates a dataset that keeps track of what pixels / rows have already been computed. Users are not expected to
extend / modify this function.
"""
# TODO: This will fail for Fitter and Image Processing class which will need to run Process twice . Need to allow room for customization
# Check to make sure that such a group doesn't already exist
if self.__status_dset_name in self.h5_results_grp.keys():
self._h5_status_dset = self.h5_results_grp[self.__status_dset_name]
if not isinstance(self._h5_status_dset, h5py.Dataset):
raise ValueError('Provided results group: {} contains an expected object ({}) that is not a dataset'
'.'.format(self.h5_results_grp, self._h5_status_dset))
if self.h5_main.shape[0] != self._h5_status_dset.shape[0] or len(self._h5_status_dset.shape) > 1 or \
self._h5_status_dset.dtype != np.uint8:
if self.mpi_rank == 0:
raise ValueError('Status dataset: {} was not of the expected shape or datatype'
'.'.format(self._h5_status_dset))
else:
self._h5_status_dset = self.h5_results_grp.create_dataset(self.__status_dset_name, dtype=np.uint8,
shape=(self.h5_main.shape[0],))
# Could be fresh computation or resuming from a legacy computation
if 'last_pixel' in self.h5_results_grp.attrs.keys():
completed_pixels = self.h5_results_grp.attrs['last_pixel']
if completed_pixels > 0:
self._h5_status_dset[:completed_pixels] = 1
def _get_existing_datasets(self):
"""
The purpose of this function is to allow processes to resume from partly computed results
Start with self.h5_results_grp
"""
raise NotImplementedError('Please override the _get_existing_datasets specific to your process')
def _unit_computation(self, *args, **kwargs):
"""
The unit computation that is performed per data chunk. This allows room for any data pre / post-processing
as well as multiple calls to parallel_compute if necessary
"""
# TODO: Try to use the functools.partials to preconfigure the map function
# cores = number of processes / rank here
self._results = parallel_compute(self.data, self._map_function, cores=self._cores,
lengthy_computation=False,
func_args=args, func_kwargs=kwargs,
verbose=self.verbose)
def compute(self, override=False, *args, **kwargs):
"""
Creates placeholders for the results, applies the unit computation to chunks of the dataset
Parameters
----------
override : bool, optional. default = False
By default, compute will simply return duplicate results to avoid recomputing or resume computation on a
group with partial results. Set to True to force fresh computation.
args : list
arguments to the mapped function in the correct order
kwargs : dictionary
keyword arguments to the mapped function
Returns
-------
h5_results_grp : h5py.Group object
Group containing all the results
"""
class SimpleFIFO(object):
"""
Simple class that maintains a moving average of some numbers.
"""
def __init__(self, length=5):
"""
Create a SimpleFIFO object
Parameters
----------
length : unsigned integer
Number of values that need to be maintained for the moving average
"""
self.__queue = list()
if not isinstance(length, int):
raise TypeError('length must be a positive integer')
if length <= 0:
raise ValueError('length must be a positive integer')
self.__max_length = length
self.__count = 0
def put(self, item):
"""
Adds the item to the internal queue. If the size of the queue exceeds its capacity, the oldest
item is removed.
Parameters
----------
item : float or int
Any real valued number
"""
if (not isinstance(item, Number)) or isinstance(item, complex):
raise TypeError('Provided item: {} is not a Number'.format(item))
self.__queue.append(item)
self.__count += 1
if len(self.__queue) > self.__max_length:
_ = self.__queue.pop(0)
def get_mean(self):
"""
Returns the average of the elements within the queue
Returns
-------
avg : number.Number
Mean of all elements within the queue
"""
return np.mean(self.__queue)
def get_cycles(self):
"""
Returns the number of items that have been added to the queue in total
Returns
-------
count : int
number of items that have been added to the queue in total
"""
return self.__count
if not override:
if len(self.duplicate_h5_groups) > 0:
if self.mpi_rank == 0:
print('Returned previously computed results at ' + self.duplicate_h5_groups[-1].name)
return self.duplicate_h5_groups[-1]
elif len(self.partial_h5_groups) > 0:
if self.mpi_rank == 0:
print('Resuming computation in group: ' + self.partial_h5_groups[-1].name)
self.use_partial_computation()
resuming = False
if self.h5_results_grp is None:
# starting fresh
if self.verbose and self.mpi_rank == 0:
print('Creating HDF5 group and datasets to hold results')
self._create_results_datasets()
else:
# resuming from previous checkpoint
resuming = True
self._get_existing_datasets()
self.__create_compute_status_dataset()
if resuming and self.mpi_rank == 0:
percent_complete = int(100 * len(np.where(self._h5_status_dset[()] == 0)[0]) /
self._h5_status_dset.shape[0])
print('Resuming computation. {}% completed already'.format(percent_complete))
self.__assign_job_indices()
# Not sure if this is necessary but I don't think it would hurt either
if self.mpi_comm is not None:
self.mpi_comm.barrier()
compute_times = SimpleFIFO(5)
write_times = SimpleFIFO(5)
orig_rank_start = self.__start_pos
if self.mpi_rank == 0 and self.mpi_size == 1:
if self.__resume_implemented:
print('\tThis class (likely) supports interruption and resuming of computations!\n'
'\tIf you are operating in a python console, press Ctrl+C or Cmd+C to abort\n'
'\tIf you are in a Jupyter notebook, click on "Kernel">>"Interrupt"\n'
'\tIf you are operating on a cluster and your job gets killed, re-run the job to resume\n')
else:
print('\tThis class does NOT support interruption and resuming of computations.\n'
'\tIn order to enable this feature, simply implement the _get_existing_datasets() function')
if self.verbose and self.mpi_rank == self.__socket_master_rank:
print('Rank: {} - with nothing loaded has {} free memory'
''.format(self.mpi_rank, format_size(get_available_memory())))
self._read_data_chunk()
if self.mpi_comm is not None:
self.mpi_comm.barrier()
if self.verbose and self.mpi_rank == self.__socket_master_rank:
print('Rank: {} - with only raw data loaded has {} free memory'
''.format(self.mpi_rank, format_size(get_available_memory())))
while self.data is not None:
num_jobs_in_batch = self.__end_pos - self.__start_pos
t_start_1 = tm.time()
self._unit_computation(*args, **kwargs)
comp_time = np.round(tm.time() - t_start_1, decimals=2) # in seconds
time_per_pix = comp_time / num_jobs_in_batch
compute_times.put(time_per_pix)
if self.verbose:
print('Rank {} - computed chunk in {} or {} per pixel. Average: {} per pixel'
'.'.format(self.mpi_rank, format_time(comp_time), format_time(time_per_pix),
format_time(compute_times.get_mean())))
# Ranks can become memory starved. Check memory usage - raw data + results in memory at this point
if self.verbose and self.mpi_rank == self.__socket_master_rank:
print('Rank: {} - now holding onto raw data + results has {} free memory'
''.format(self.mpi_rank, format_size(get_available_memory())))
t_start_2 = tm.time()
self._write_results_chunk()
# NOW, update the positions. Users are NOT allowed to touch start and end pos
self.__start_pos = self.__end_pos
# Leaving in this provision that will allow restarting of processes
if self.mpi_size == 1:
self.h5_results_grp.attrs['last_pixel'] = self.__end_pos
# Child classes don't even have to worry about flushing. Process will do it.
self.h5_main.file.flush()
dump_time = np.round(tm.time() - t_start_2, decimals=2)
write_times.put(dump_time / num_jobs_in_batch)
if self.verbose:
print('Rank {} - wrote its {} pixel chunk in {}'.format(self.mpi_rank,
num_jobs_in_batch,
format_time(dump_time)))
time_remaining = (self.__rank_end_pos - self.__end_pos) * \
(compute_times.get_mean() + write_times.get_mean())
if self.verbose or self.mpi_rank == 0:
percent_complete = int(100 * (self.__end_pos - orig_rank_start) /
(self.__rank_end_pos - orig_rank_start))
print('Rank {} - {}% complete. Time remaining: {}'.format(self.mpi_rank, percent_complete,
format_time(time_remaining)))
# All ranks should mark the pixels for this batch as completed. 'last_pixel' attribute will be updated later
# Setting each section to 1 independently
for section in to_ranges(self.__pixels_in_batch):
self._h5_status_dset[section[0]: section[1]+1] = 1
self._read_data_chunk()
if self.verbose:
print('Rank {} - Finished computing all jobs!'.format(self.mpi_rank))
if self.mpi_comm is not None:
self.mpi_comm.barrier()
if self.mpi_rank == 0:
print('Finished processing the entire dataset!')
# Update the legacy 'last_pixel' attribute here:
if self.mpi_rank == 0:
self.h5_results_grp.attrs['last_pixel'] = self.h5_main.shape[0]
return self.h5_results_grp
def parallel_compute(data, func, cores=1, lengthy_computation=False, func_args=None, func_kwargs=None, verbose=False):
"""
Computes the provided function using multiple cores using the joblib library
Parameters
----------
data : numpy.ndarray
Data to map function to. Function will be mapped to the first axis of data
func : callable
Function to map to data
cores : uint, optional
Number of logical cores to use to compute
Default - 1 (serial computation)
lengthy_computation : bool, optional
Whether or not each computation is expected to take substantial time.
Sometimes the time for adding more cores can outweigh the time per core
Default - False
func_args : list, optional
arguments to be passed to the function
func_kwargs : dict, optional
keyword arguments to be passed onto function
verbose : bool, optional. default = False
Whether or not to print statements that aid in debugging
Returns
-------
results : list
List of computational results
"""
if not callable(func):
raise TypeError('Function argument is not callable')
if not isinstance(data, np.ndarray):
raise TypeError('data must be a numpy array')
if func_args is None:
func_args = list()
else:
if isinstance(func_args, tuple):
func_args = list(func_args)
if not isinstance(func_args, list):
raise TypeError('Arguments to the mapped function should be specified as a list')
if func_kwargs is None:
func_kwargs = dict()
else:
if not isinstance(func_kwargs, dict):
raise TypeError('Keyword arguments to the mapped function should be specified via a dictionary')
req_cores = cores
if MPI is not None:
rank = MPI.COMM_WORLD.Get_rank()
# Was unable to get the MPI + joblib framework to work. Did not compute anything at all. Just froze
cores = 1
else:
rank = 0
cores = recommend_cpu_cores(data.shape[0],
requested_cores=cores,
lengthy_computation=lengthy_computation,
verbose=verbose)
if verbose:
print('Rank {} starting computing on {} cores (requested {} cores)'.format(rank, cores, req_cores))
if cores > 1:
values = [joblib.delayed(func)(x, *func_args, **func_kwargs) for x in data]
results = joblib.Parallel(n_jobs=cores)(values)
# Finished reading the entire data set
print('Rank {} finished parallel computation'.format(rank))
else:
if verbose:
print("Rank {} computing serially ...".format(rank))
# List comprehension vs map vs for loop?
# https://stackoverflow.com/questions/1247486/python-list-comprehension-vs-map
results = [func(vector, *func_args, **func_kwargs) for vector in data]
return results
|
[
"numpy.floor",
"numpy.random.randint",
"numpy.mean",
"pyUSID.io.io_utils.recommend_cpu_cores",
"mpi4py.MPI.COMM_WORLD.barrier",
"numpy.unique",
"psutil.cpu_count",
"multiprocessing.cpu_count",
"mpi4py.MPI.Get_processor_name",
"mpi4py.MPI.COMM_WORLD.Get_size",
"pyUSID.io.io_utils.get_available_memory",
"mpi4py.MPI.COMM_WORLD.Get_rank",
"pyUSID.io.io_utils.format_size",
"pyUSID.io.io_utils.format_time",
"pyUSID.io.usi_data.USIDataset",
"pyUSID.io.hdf_utils.check_if_main",
"numpy.zeros",
"time.time",
"pyUSID.io.hdf_utils.get_attributes",
"pyUSID.io.hdf_utils.check_for_old",
"numpy.array",
"numpy.where",
"joblib.Parallel",
"joblib.delayed"
] |
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mpi4py import MPI\n'), ((11861, 11900), 'numpy.where', 'np.where', (['(self._h5_status_dset[()] == 0)'], {}), '(self._h5_status_dset[()] == 0)\n', (11869, 11900), True, 'import numpy as np\n'), ((23108, 23130), 'pyUSID.io.io_utils.get_available_memory', 'get_available_memory', ([], {}), '()\n', (23128, 23130), False, 'from pyUSID.io.io_utils import recommend_cpu_cores, get_available_memory, format_time, format_size\n'), ((23818, 23860), 'numpy.floor', 'np.floor', (['(max_data_chunk / mb_per_position)'], {}), '(max_data_chunk / mb_per_position)\n', (23826, 23860), True, 'import numpy as np\n'), ((35478, 35487), 'time.time', 'tm.time', ([], {}), '()\n', (35485, 35487), True, 'import time as tm\n'), ((36409, 36418), 'time.time', 'tm.time', ([], {}), '()\n', (36416, 36418), True, 'import time as tm\n'), ((41263, 41292), 'joblib.Parallel', 'joblib.Parallel', ([], {'n_jobs': 'cores'}), '(n_jobs=cores)\n', (41278, 41292), False, 'import joblib\n'), ((13977, 13986), 'time.time', 'tm.time', ([], {}), '()\n', (13984, 13986), True, 'import time as tm\n'), ((22582, 22636), 'numpy.where', 'np.where', (['(ranks_by_socket == self.__socket_master_rank)'], {}), '(ranks_by_socket == self.__socket_master_rank)\n', (22590, 22636), True, 'import numpy as np\n'), ((32123, 32144), 'numpy.mean', 'np.mean', (['self.__queue'], {}), '(self.__queue)\n', (32130, 32144), True, 'import numpy as np\n'), ((41179, 41199), 'joblib.delayed', 'joblib.delayed', (['func'], {}), '(func)\n', (41193, 41199), False, 'import joblib\n'), ((24180, 24198), 'psutil.cpu_count', 'psutil.cpu_count', ([], {}), '()\n', (24196, 24198), False, 'import psutil\n'), ((24200, 24239), 'pyUSID.io.io_utils.format_size', 'format_size', (['(_max_mem_mb * 1024 ** 2)', '(2)'], {}), '(_max_mem_mb * 1024 ** 2, 2)\n', (24211, 24239), False, 'from pyUSID.io.io_utils import recommend_cpu_cores, get_available_memory, format_time, format_size\n'), ((35575, 35584), 'time.time', 'tm.time', ([], {}), '()\n', (35582, 35584), True, 'import time as tm\n'), ((36945, 36954), 'time.time', 'tm.time', ([], {}), '()\n', (36952, 36954), True, 'import time as tm\n'), ((7714, 7738), 'mpi4py.MPI.Get_processor_name', 'MPI.Get_processor_name', ([], {}), '()\n', (7736, 7738), False, 'from mpi4py import MPI\n'), ((7827, 7838), 'multiprocessing.cpu_count', 'cpu_count', ([], {}), '()\n', (7836, 7838), False, 'from multiprocessing import cpu_count\n'), ((21771, 21789), 'psutil.cpu_count', 'psutil.cpu_count', ([], {}), '()\n', (21787, 21789), False, 'import psutil\n'), ((21863, 21881), 'psutil.cpu_count', 'psutil.cpu_count', ([], {}), '()\n', (21879, 21881), False, 'import psutil\n'), ((22138, 22156), 'psutil.cpu_count', 'psutil.cpu_count', ([], {}), '()\n', (22154, 22156), False, 'import psutil\n'), ((34984, 35006), 'pyUSID.io.io_utils.get_available_memory', 'get_available_memory', ([], {}), '()\n', (35004, 35006), False, 'from pyUSID.io.io_utils import recommend_cpu_cores, get_available_memory, format_time, format_size\n'), ((35322, 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|
import numpy as np
class TicTacToeGame:
def __init__(self, size):
self.m_SizeSize = size;
self.m_Grid = np.zeros((size, size), np.int8)
self.m_Grid.fill(-1)
self.m_CurentPlayer = 0
def Move(self, player, row, col):
if self.IsMoveAllowed(player, row, col) == True:
self.m_Grid[row][col] = player
def WillMoveWin(self, player, row, col):
if not self.IsMoveAllowed(player, row, col):
return False
# check horizontal
hasWon = True
for i in range(self.m_SizeSize):
colIdx = (col + i) % self.m_SizeSize
hasWon = hasWon and self.m_Grid[row][colIdx] == player
# Check vertical win
if not hasWon:
hasWon = True
for i in range(self.m_SizeSize):
rowIdx = (row + i) % self.m_SizeSize
hasWon = hasWon and self.m_Grid[row][colIdx] == player
if not hasWon and row == 1 and col == 1:
hasWon = True
# Test diagonal from upper left to lower right
for i in range(self.m_SizeSize):
hasWon = hasWon and self.m_Grid[i][i] == player
if hasWon:
return True
# Test diagnol from lower left to upper right
hasWon = True
for i in range(self.m_SizeSize):
hasWon = hasWon and self.m_Grid[2 - i][i] == player
return hasWon
def RankMove(self, player, row, col):
reward = 0
if not self.IsMoveAllowed(player, row, col):
reward = reward + -100
backup = self.m_Grid[row][col]
self.m_Grid[row][col] = player
if self.WillMoveWin(player, row, col):
reward = reward + 1000
self.m_Grid[row][col] = backup
return reward
def IsMoveAllowed(self, player, row, col):
if int(row) in range(self.m_SizeSize) and int(col) in range(self.m_SizeSize):
return self.m_Grid[row][col] == -1
else:
return False
def NoEmptySpaces(self):
for i in range(self.m_SizeSize):
for j in range(self.m_SizeSize):
if self.m_Grid[i][j] == -1:
return False
return True
def Render(self):
# print (self.m_Grid)
print("")
for row in range(self.m_SizeSize):
lineTxt = ""
for col in range(self.m_SizeSize):
if (self.m_Grid[row][col] == 0):
lineTxt += " O"
elif (self.m_Grid[row][col] == 0):
lineTxt += " X"
else:
lineTxt += " _"
print(lineTxt)
|
[
"numpy.zeros"
] |
[((134, 165), 'numpy.zeros', 'np.zeros', (['(size, size)', 'np.int8'], {}), '((size, size), np.int8)\n', (142, 165), True, 'import numpy as np\n')]
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
]
operations = [
migrations.CreateModel(
name='Measure',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('name', models.CharField(unique=True, max_length=255)),
('is_active', models.BooleanField(default=True)),
],
options={
'db_table': 'measures',
},
),
migrations.CreateModel(
name='MeasureValue',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('description', models.CharField(max_length=255)),
('order', models.IntegerField(default=0)),
('color', models.CharField(default=b'#337BB7', max_length=7, choices=[(b'#5CB85C', b'#5CB85C'), (b'#BAE8BA', b'#BAE8BA'), (b'#8AD38A', b'#8AD38A'), (b'#369836', b'#369836'), (b'#1B7C1B', b'#1B7C1B'), (b'#F0AD4E', b'#F0AD4E'), (b'#FFD8A0', b'#FFD8A0'), (b'#FFC675', b'#FFC675'), (b'#DE9226', b'#DE9226'), (b'#AD6D11', b'#AD6D11'), (b'#D9534F', b'#D9534F'), (b'#FFADAB', b'#FFADAB'), (b'#FC827F', b'#FC827F'), (b'#BE2F2B', b'#BE2F2B'), (b'#961512', b'#961512'), (b'#5BC1DE', b'#5BC1DE'), (b'#BAEAF8', b'#BAEAF8'), (b'#85D5EC', b'#85D5EC'), (b'#39ACCD', b'#39ACCD'), (b'#1993B6', b'#1993B6'), (b'#337BB7', b'#337BB7'), (b'#7EB1DC', b'#7EB1DC'), (b'#5393C8', b'#5393C8'), (b'#1265AB', b'#1265AB'), (b'#094B83', b'#094B83'), (b'#222222', b'#222222'), (b'#929191', b'#929191'), (b'#5E5E5E', b'#5E5E5E'), (b'#000000', b'#000000'), (b'#030202', b'#030202')])),
('measure', models.ForeignKey(to='measures.Measure')),
],
options={
'ordering': ('order',),
'db_table': 'measure_values',
},
),
]
|
[
"django.db.models.CharField",
"django.db.models.ForeignKey",
"django.db.models.BooleanField",
"django.db.models.AutoField",
"django.db.models.IntegerField"
] |
[((299, 392), 'django.db.models.AutoField', 'models.AutoField', ([], {'verbose_name': '"""ID"""', 'serialize': '(False)', 'auto_created': '(True)', 'primary_key': '(True)'}), "(verbose_name='ID', serialize=False, auto_created=True,\n primary_key=True)\n", (315, 392), False, 'from django.db import models, migrations\n'), ((416, 461), 'django.db.models.CharField', 'models.CharField', ([], {'unique': '(True)', 'max_length': '(255)'}), '(unique=True, max_length=255)\n', (432, 461), False, 'from django.db import models, migrations\n'), ((494, 527), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(True)'}), '(default=True)\n', (513, 527), False, 'from django.db import models, migrations\n'), ((742, 835), 'django.db.models.AutoField', 'models.AutoField', ([], {'verbose_name': '"""ID"""', 'serialize': '(False)', 'auto_created': '(True)', 'primary_key': '(True)'}), "(verbose_name='ID', serialize=False, auto_created=True,\n primary_key=True)\n", (758, 835), False, 'from django.db import models, migrations\n'), ((866, 898), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(255)'}), '(max_length=255)\n', (882, 898), False, 'from django.db import models, migrations\n'), ((927, 957), 'django.db.models.IntegerField', 'models.IntegerField', ([], {'default': '(0)'}), '(default=0)\n', (946, 957), False, 'from django.db import models, migrations\n'), ((986, 1880), 'django.db.models.CharField', 'models.CharField', ([], {'default': "b'#337BB7'", 'max_length': '(7)', 'choices': "[(b'#5CB85C', b'#5CB85C'), (b'#BAE8BA', b'#BAE8BA'), (b'#8AD38A',\n b'#8AD38A'), (b'#369836', b'#369836'), (b'#1B7C1B', b'#1B7C1B'), (\n b'#F0AD4E', b'#F0AD4E'), (b'#FFD8A0', b'#FFD8A0'), (b'#FFC675',\n b'#FFC675'), (b'#DE9226', b'#DE9226'), (b'#AD6D11', b'#AD6D11'), (\n b'#D9534F', b'#D9534F'), (b'#FFADAB', b'#FFADAB'), (b'#FC827F',\n b'#FC827F'), (b'#BE2F2B', b'#BE2F2B'), (b'#961512', b'#961512'), (\n b'#5BC1DE', b'#5BC1DE'), (b'#BAEAF8', b'#BAEAF8'), (b'#85D5EC',\n b'#85D5EC'), (b'#39ACCD', b'#39ACCD'), (b'#1993B6', b'#1993B6'), (\n b'#337BB7', b'#337BB7'), (b'#7EB1DC', b'#7EB1DC'), (b'#5393C8',\n b'#5393C8'), (b'#1265AB', b'#1265AB'), (b'#094B83', b'#094B83'), (\n b'#222222', b'#222222'), (b'#929191', b'#929191'), (b'#5E5E5E',\n b'#5E5E5E'), (b'#000000', b'#000000'), (b'#030202', b'#030202')]"}), "(default=b'#337BB7', max_length=7, choices=[(b'#5CB85C',\n b'#5CB85C'), (b'#BAE8BA', b'#BAE8BA'), (b'#8AD38A', b'#8AD38A'), (\n b'#369836', b'#369836'), (b'#1B7C1B', b'#1B7C1B'), (b'#F0AD4E',\n b'#F0AD4E'), (b'#FFD8A0', b'#FFD8A0'), (b'#FFC675', b'#FFC675'), (\n b'#DE9226', b'#DE9226'), (b'#AD6D11', b'#AD6D11'), (b'#D9534F',\n b'#D9534F'), (b'#FFADAB', b'#FFADAB'), (b'#FC827F', b'#FC827F'), (\n b'#BE2F2B', b'#BE2F2B'), (b'#961512', b'#961512'), (b'#5BC1DE',\n b'#5BC1DE'), (b'#BAEAF8', b'#BAEAF8'), (b'#85D5EC', b'#85D5EC'), (\n b'#39ACCD', b'#39ACCD'), (b'#1993B6', b'#1993B6'), (b'#337BB7',\n b'#337BB7'), (b'#7EB1DC', b'#7EB1DC'), (b'#5393C8', b'#5393C8'), (\n b'#1265AB', b'#1265AB'), (b'#094B83', b'#094B83'), (b'#222222',\n b'#222222'), (b'#929191', b'#929191'), (b'#5E5E5E', b'#5E5E5E'), (\n b'#000000', b'#000000'), (b'#030202', b'#030202')])\n", (1002, 1880), False, 'from django.db import models, migrations\n'), ((1857, 1897), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'to': '"""measures.Measure"""'}), "(to='measures.Measure')\n", (1874, 1897), False, 'from django.db import models, migrations\n')]
|
"""Add role seed data for flask-security
Revision ID: 7b2d863b105
Revises: <PASSWORD>
Create Date: 2015-07-02 10:48:35.805882
"""
# revision identifiers, used by Alembic.
revision = '7b2d863b105'
down_revision = '<PASSWORD>'
from alembic import op
import sqlalchemy as sa
def upgrade():
### commands auto generated by Alembic - please adjust! ###
role_table = sa.table('role',
sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
sa.Column('name', sa.String(length=80), nullable=True),
sa.Column('description', sa.String(length=255), nullable=True),
)
op.bulk_insert(role_table, [
{'id': 2, 'name': 'product_category_view', 'description': 'View product categories'},
{'id': 3, 'name': 'product_category_create', 'description': 'Create product category'},
{'id': 4, 'name': 'product_category_edit', 'description': 'Edit product category'},
{'id': 5, 'name': 'product_category_delete', 'description': 'Delete product category'},
{'id': 6, 'name': 'sales_order_view', 'description': 'View sales orders'},
{'id': 7, 'name': 'sales_order_create', 'description': 'Create sales order'},
{'id': 8, 'name': 'sales_order_edit', 'description': 'Edit sales order'},
{'id': 9, 'name': 'sales_order_delete', 'description': 'Delete sales order'},
{'id': 10, 'name': 'purchase_order_view', 'description': 'View purchase orders'},
{'id': 11, 'name': 'purchase_order_create', 'description': 'Create purchase order'},
{'id': 12, 'name': 'purchase_order_edit', 'description': 'Edit purchase order'},
{'id': 13, 'name': 'purchase_order_delete', 'description': 'Delete purchase order'},
{'id': 14, 'name': 'expense_view', 'description': 'View expenses'},
{'id': 15, 'name': 'expense_create', 'description': 'Create expense'},
{'id': 16, 'name': 'expense_edit', 'description': 'Edit expense'},
{'id': 17, 'name': 'expense_delete', 'description': 'Delete expense'},
{'id': 18, 'name': 'incoming_view', 'description': 'View incoming'},
{'id': 19, 'name': 'incoming_create', 'description': 'Create incoming'},
{'id': 20, 'name': 'incoming_edit', 'description': 'Edit incoming'},
{'id': 21, 'name': 'incoming_delete', 'description': 'Delete incoming'},
{'id': 22, 'name': 'supplier_view', 'description': 'View suppliers'},
{'id': 23, 'name': 'supplier_create', 'description': 'Create supplier'},
{'id': 24, 'name': 'supplier_edit', 'description': 'Edit supplier'},
{'id': 25, 'name': 'supplier_delete', 'description': 'Delete supplier'},
{'id': 26, 'name': 'product_view', 'description': 'View products'},
{'id': 27, 'name': 'product_create', 'description': 'Create product'},
{'id': 28, 'name': 'product_edit', 'description': 'Edit product'},
{'id': 29, 'name': 'product_delete', 'description': 'Delete product'},
{'id': 30, 'name': 'enum_values_view', 'description': 'View enum values'},
{'id': 31, 'name': 'enum_values_create', 'description': 'Create enum value'},
{'id': 32, 'name': 'enum_values_edit', 'description': 'Edit enum value'},
{'id': 33, 'name': 'enum_values_delete', 'description': 'Delete enum value'},
{'id': 34, 'name': 'preference_view', 'description': 'View system preference'},
{'id': 35, 'name': 'preference_edit', 'description': 'Update system preference'},
{'id': 36, 'name': 'user_view', 'description': 'View user'},
{'id': 37, 'name': 'user_create', 'description': 'Create user'},
{'id': 38, 'name': 'user_edit', 'description': 'Edit user'},
{'id': 39, 'name': 'user_delete', 'description': 'Delete user'},
{'id': 40, 'name': 'role_view', 'description': 'View roles'},
{'id': 41, 'name': 'role_create', 'description': 'Create role'},
{'id': 42, 'name': 'role_edit', 'description': 'Edit role'},
{'id': 43, 'name': 'role_delete', 'description': 'Delete role'},
],multiinsert=False)
from sqlalchemy.sql import text
op.get_bind().execute(text("ALTER SEQUENCE role_id_seq RESTART WITH 44;"))
### end Alembic commands ###
def downgrade():
### commands auto generated by Alembic - please adjust! ###
pass
### end Alembic commands ###
|
[
"sqlalchemy.sql.text",
"alembic.op.bulk_insert",
"alembic.op.get_bind",
"sqlalchemy.String",
"sqlalchemy.Integer"
] |
[((600, 3914), 'alembic.op.bulk_insert', 'op.bulk_insert', (['role_table', "[{'id': 2, 'name': 'product_category_view', 'description':\n 'View product categories'}, {'id': 3, 'name': 'product_category_create',\n 'description': 'Create product category'}, {'id': 4, 'name':\n 'product_category_edit', 'description': 'Edit product category'}, {'id':\n 5, 'name': 'product_category_delete', 'description':\n 'Delete product category'}, {'id': 6, 'name': 'sales_order_view',\n 'description': 'View sales orders'}, {'id': 7, 'name':\n 'sales_order_create', 'description': 'Create sales order'}, {'id': 8,\n 'name': 'sales_order_edit', 'description': 'Edit sales order'}, {'id': \n 9, 'name': 'sales_order_delete', 'description': 'Delete sales order'},\n {'id': 10, 'name': 'purchase_order_view', 'description':\n 'View purchase orders'}, {'id': 11, 'name': 'purchase_order_create',\n 'description': 'Create purchase order'}, {'id': 12, 'name':\n 'purchase_order_edit', 'description': 'Edit purchase order'}, {'id': 13,\n 'name': 'purchase_order_delete', 'description': 'Delete purchase order'\n }, {'id': 14, 'name': 'expense_view', 'description': 'View expenses'},\n {'id': 15, 'name': 'expense_create', 'description': 'Create expense'},\n {'id': 16, 'name': 'expense_edit', 'description': 'Edit expense'}, {\n 'id': 17, 'name': 'expense_delete', 'description': 'Delete expense'}, {\n 'id': 18, 'name': 'incoming_view', 'description': 'View incoming'}, {\n 'id': 19, 'name': 'incoming_create', 'description': 'Create incoming'},\n {'id': 20, 'name': 'incoming_edit', 'description': 'Edit incoming'}, {\n 'id': 21, 'name': 'incoming_delete', 'description': 'Delete incoming'},\n {'id': 22, 'name': 'supplier_view', 'description': 'View suppliers'}, {\n 'id': 23, 'name': 'supplier_create', 'description': 'Create supplier'},\n {'id': 24, 'name': 'supplier_edit', 'description': 'Edit supplier'}, {\n 'id': 25, 'name': 'supplier_delete', 'description': 'Delete supplier'},\n {'id': 26, 'name': 'product_view', 'description': 'View products'}, {\n 'id': 27, 'name': 'product_create', 'description': 'Create product'}, {\n 'id': 28, 'name': 'product_edit', 'description': 'Edit product'}, {'id':\n 29, 'name': 'product_delete', 'description': 'Delete product'}, {'id': \n 30, 'name': 'enum_values_view', 'description': 'View enum values'}, {\n 'id': 31, 'name': 'enum_values_create', 'description':\n 'Create enum value'}, {'id': 32, 'name': 'enum_values_edit',\n 'description': 'Edit enum value'}, {'id': 33, 'name':\n 'enum_values_delete', 'description': 'Delete enum value'}, {'id': 34,\n 'name': 'preference_view', 'description': 'View system preference'}, {\n 'id': 35, 'name': 'preference_edit', 'description':\n 'Update system preference'}, {'id': 36, 'name': 'user_view',\n 'description': 'View user'}, {'id': 37, 'name': 'user_create',\n 'description': 'Create user'}, {'id': 38, 'name': 'user_edit',\n 'description': 'Edit user'}, {'id': 39, 'name': 'user_delete',\n 'description': 'Delete user'}, {'id': 40, 'name': 'role_view',\n 'description': 'View roles'}, {'id': 41, 'name': 'role_create',\n 'description': 'Create role'}, {'id': 42, 'name': 'role_edit',\n 'description': 'Edit role'}, {'id': 43, 'name': 'role_delete',\n 'description': 'Delete role'}]"], {'multiinsert': '(False)'}), "(role_table, [{'id': 2, 'name': 'product_category_view',\n 'description': 'View product categories'}, {'id': 3, 'name':\n 'product_category_create', 'description': 'Create product category'}, {\n 'id': 4, 'name': 'product_category_edit', 'description':\n 'Edit product category'}, {'id': 5, 'name': 'product_category_delete',\n 'description': 'Delete product category'}, {'id': 6, 'name':\n 'sales_order_view', 'description': 'View sales orders'}, {'id': 7,\n 'name': 'sales_order_create', 'description': 'Create sales order'}, {\n 'id': 8, 'name': 'sales_order_edit', 'description': 'Edit sales order'},\n {'id': 9, 'name': 'sales_order_delete', 'description':\n 'Delete sales order'}, {'id': 10, 'name': 'purchase_order_view',\n 'description': 'View purchase orders'}, {'id': 11, 'name':\n 'purchase_order_create', 'description': 'Create purchase order'}, {'id':\n 12, 'name': 'purchase_order_edit', 'description': 'Edit purchase order'\n }, {'id': 13, 'name': 'purchase_order_delete', 'description':\n 'Delete purchase order'}, {'id': 14, 'name': 'expense_view',\n 'description': 'View expenses'}, {'id': 15, 'name': 'expense_create',\n 'description': 'Create expense'}, {'id': 16, 'name': 'expense_edit',\n 'description': 'Edit expense'}, {'id': 17, 'name': 'expense_delete',\n 'description': 'Delete expense'}, {'id': 18, 'name': 'incoming_view',\n 'description': 'View incoming'}, {'id': 19, 'name': 'incoming_create',\n 'description': 'Create incoming'}, {'id': 20, 'name': 'incoming_edit',\n 'description': 'Edit incoming'}, {'id': 21, 'name': 'incoming_delete',\n 'description': 'Delete incoming'}, {'id': 22, 'name': 'supplier_view',\n 'description': 'View suppliers'}, {'id': 23, 'name': 'supplier_create',\n 'description': 'Create supplier'}, {'id': 24, 'name': 'supplier_edit',\n 'description': 'Edit supplier'}, {'id': 25, 'name': 'supplier_delete',\n 'description': 'Delete supplier'}, {'id': 26, 'name': 'product_view',\n 'description': 'View products'}, {'id': 27, 'name': 'product_create',\n 'description': 'Create product'}, {'id': 28, 'name': 'product_edit',\n 'description': 'Edit product'}, {'id': 29, 'name': 'product_delete',\n 'description': 'Delete product'}, {'id': 30, 'name': 'enum_values_view',\n 'description': 'View enum values'}, {'id': 31, 'name':\n 'enum_values_create', 'description': 'Create enum value'}, {'id': 32,\n 'name': 'enum_values_edit', 'description': 'Edit enum value'}, {'id': \n 33, 'name': 'enum_values_delete', 'description': 'Delete enum value'},\n {'id': 34, 'name': 'preference_view', 'description':\n 'View system preference'}, {'id': 35, 'name': 'preference_edit',\n 'description': 'Update system preference'}, {'id': 36, 'name':\n 'user_view', 'description': 'View user'}, {'id': 37, 'name':\n 'user_create', 'description': 'Create user'}, {'id': 38, 'name':\n 'user_edit', 'description': 'Edit user'}, {'id': 39, 'name':\n 'user_delete', 'description': 'Delete user'}, {'id': 40, 'name':\n 'role_view', 'description': 'View roles'}, {'id': 41, 'name':\n 'role_create', 'description': 'Create role'}, {'id': 42, 'name':\n 'role_edit', 'description': 'Edit role'}, {'id': 43, 'name':\n 'role_delete', 'description': 'Delete role'}], multiinsert=False)\n", (614, 3914), False, 'from alembic import op\n'), ((4131, 4182), 'sqlalchemy.sql.text', 'text', (['"""ALTER SEQUENCE role_id_seq RESTART WITH 44;"""'], {}), "('ALTER SEQUENCE role_id_seq RESTART WITH 44;')\n", (4135, 4182), False, 'from sqlalchemy.sql import text\n'), ((411, 423), 'sqlalchemy.Integer', 'sa.Integer', ([], {}), '()\n', (421, 423), True, 'import sqlalchemy as sa\n'), ((484, 504), 'sqlalchemy.String', 'sa.String', ([], {'length': '(80)'}), '(length=80)\n', (493, 504), True, 'import sqlalchemy as sa\n'), ((551, 572), 'sqlalchemy.String', 'sa.String', ([], {'length': '(255)'}), '(length=255)\n', (560, 572), True, 'import sqlalchemy as sa\n'), ((4109, 4122), 'alembic.op.get_bind', 'op.get_bind', ([], {}), '()\n', (4120, 4122), False, 'from alembic import op\n')]
|
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import unittest
from unittest import TestCase
import pkgutil
import io
import numpy as np
import pandas as pd
from kats.consts import TimeSeriesData
from kats.models.harmonic_regression import (
HarmonicRegressionModel,
HarmonicRegressionParams,
)
def load_data(file_name):
ROOT = "kats"
if "kats" in os.getcwd().lower():
path = "data/"
else:
path = "kats/data/"
data_object = pkgutil.get_data(ROOT, path + file_name)
return pd.read_csv(io.BytesIO(data_object), encoding="utf8")
class testHarmonicRegression(TestCase):
def setUp(self):
times = pd.to_datetime(
np.arange(start=1576195200, stop=1577836801, step=60 * 60), unit="s"
)
self.series_times = pd.Series(times)
harms = HarmonicRegressionModel.fourier_series(self.series_times, 24, 3)
self.harms_sum = np.sum([1, 1, 1, 1, 1, 1] * harms, axis=1)
self.data = TimeSeriesData(
pd.DataFrame({"time": self.series_times, "values": self.harms_sum})
)
self.params = HarmonicRegressionParams(24, 3)
def test_fit_and_predict(self) -> None:
hrm = HarmonicRegressionModel(self.data, self.params)
hrm.fit()
self.assertIsNotNone(hrm.params)
# pyre-fixme[16]: `HarmonicRegressionModel` has no attribute `harms`.
self.assertIsNotNone(hrm.harms)
# pyre-fixme[6]: Expected `Series` for 1st param but got
# `Union[pd.core.frame.DataFrame, pd.core.series.Series]`.
preds = hrm.predict(self.series_times.head(1))
self.assertAlmostEqual(preds["fcst"][0], self.harms_sum[0], delta=0.0001)
if __name__ == "__main__":
unittest.main()
|
[
"unittest.main",
"pkgutil.get_data",
"io.BytesIO",
"pandas.DataFrame",
"numpy.sum",
"kats.models.harmonic_regression.HarmonicRegressionModel",
"os.getcwd",
"kats.models.harmonic_regression.HarmonicRegressionParams",
"pandas.Series",
"kats.models.harmonic_regression.HarmonicRegressionModel.fourier_series",
"numpy.arange"
] |
[((606, 646), 'pkgutil.get_data', 'pkgutil.get_data', (['ROOT', '(path + file_name)'], {}), '(ROOT, path + file_name)\n', (622, 646), False, 'import pkgutil\n'), ((1861, 1876), 'unittest.main', 'unittest.main', ([], {}), '()\n', (1874, 1876), False, 'import unittest\n'), ((670, 693), 'io.BytesIO', 'io.BytesIO', (['data_object'], {}), '(data_object)\n', (680, 693), False, 'import io\n'), ((926, 942), 'pandas.Series', 'pd.Series', (['times'], {}), '(times)\n', (935, 942), True, 'import pandas as pd\n'), ((959, 1023), 'kats.models.harmonic_regression.HarmonicRegressionModel.fourier_series', 'HarmonicRegressionModel.fourier_series', (['self.series_times', '(24)', '(3)'], {}), '(self.series_times, 24, 3)\n', (997, 1023), False, 'from kats.models.harmonic_regression import HarmonicRegressionModel, HarmonicRegressionParams\n'), ((1049, 1091), 'numpy.sum', 'np.sum', (['([1, 1, 1, 1, 1, 1] * harms)'], {'axis': '(1)'}), '([1, 1, 1, 1, 1, 1] * harms, axis=1)\n', (1055, 1091), True, 'import numpy as np\n'), ((1241, 1272), 'kats.models.harmonic_regression.HarmonicRegressionParams', 'HarmonicRegressionParams', (['(24)', '(3)'], {}), '(24, 3)\n', (1265, 1272), False, 'from kats.models.harmonic_regression import HarmonicRegressionModel, HarmonicRegressionParams\n'), ((1332, 1379), 'kats.models.harmonic_regression.HarmonicRegressionModel', 'HarmonicRegressionModel', (['self.data', 'self.params'], {}), '(self.data, self.params)\n', (1355, 1379), False, 'from kats.models.harmonic_regression import HarmonicRegressionModel, HarmonicRegressionParams\n'), ((819, 877), 'numpy.arange', 'np.arange', ([], {'start': '(1576195200)', 'stop': '(1577836801)', 'step': '(60 * 60)'}), '(start=1576195200, stop=1577836801, step=60 * 60)\n', (828, 877), True, 'import numpy as np\n'), ((1140, 1207), 'pandas.DataFrame', 'pd.DataFrame', (["{'time': self.series_times, 'values': self.harms_sum}"], {}), "({'time': self.series_times, 'values': self.harms_sum})\n", (1152, 1207), True, 'import pandas as pd\n'), ((506, 517), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (515, 517), False, 'import os\n')]
|
import argparse
import torch
import numpy as np
import os
import data
from networks import domain_generator, domain_classifier
from utils import util
def optimize(opt):
dataset_name = 'cifar10'
generator_name = 'stylegan2-cc' # class conditional stylegan
transform = data.get_transform(dataset_name, 'imval')
dset = data.get_dataset(dataset_name, opt.partition,
load_w=False, transform=transform)
total = len(dset)
if opt.indices is None:
start_idx = 0
end_idx = total
else:
start_idx = opt.indices[0]
end_idx = opt.indices[1]
generator = domain_generator.define_generator(
generator_name, dataset_name, load_encoder=False)
util.set_requires_grad(False, generator.generator)
resnet = domain_classifier.define_classifier(dataset_name,
'imageclassifier')
### iterate ###
for i in range(start_idx, end_idx):
img, label = dset[i]
print("Running img %d/%d" % (i, len(dset)))
filename = os.path.join(opt.w_path, '%s_%06d.npy' %
(opt.partition, i))
if os.path.isfile(filename):
print(filename + ' found... skipping')
continue
img = img[None].cuda()
with torch.no_grad():
pred_logit = resnet(img)
_, pred_label = pred_logit.max(1)
pred_label = pred_label.item()
print("True label %d prd label %d" % (label, pred_label))
ckpt, loss = generator.optimize(img, pred_label)
current_z = ckpt['current_z'].detach().cpu().numpy()
np.save(filename, current_z)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--partition', type=str, required=True,
help='specify train, val, or test partition')
parser.add_argument('--w_path', type=str, required=True,
help='directory to save the optimized latents')
parser.add_argument('--indices', type=int, nargs=2,
help='optimize latents for specific image range')
opt = parser.parse_args()
print(opt)
os.makedirs(opt.w_path, exist_ok=True)
optimize(opt)
|
[
"numpy.save",
"argparse.ArgumentParser",
"data.get_dataset",
"os.makedirs",
"networks.domain_classifier.define_classifier",
"data.get_transform",
"os.path.isfile",
"networks.domain_generator.define_generator",
"torch.no_grad",
"os.path.join",
"utils.util.set_requires_grad"
] |
[((283, 324), 'data.get_transform', 'data.get_transform', (['dataset_name', '"""imval"""'], {}), "(dataset_name, 'imval')\n", (301, 324), False, 'import data\n'), ((337, 422), 'data.get_dataset', 'data.get_dataset', (['dataset_name', 'opt.partition'], {'load_w': '(False)', 'transform': 'transform'}), '(dataset_name, opt.partition, load_w=False, transform=transform\n )\n', (353, 422), False, 'import data\n'), ((637, 724), 'networks.domain_generator.define_generator', 'domain_generator.define_generator', (['generator_name', 'dataset_name'], {'load_encoder': '(False)'}), '(generator_name, dataset_name,\n load_encoder=False)\n', (670, 724), False, 'from networks import domain_generator, domain_classifier\n'), ((734, 784), 'utils.util.set_requires_grad', 'util.set_requires_grad', (['(False)', 'generator.generator'], {}), '(False, generator.generator)\n', (756, 784), False, 'from utils import util\n'), ((799, 867), 'networks.domain_classifier.define_classifier', 'domain_classifier.define_classifier', (['dataset_name', '"""imageclassifier"""'], {}), "(dataset_name, 'imageclassifier')\n", (834, 867), False, 'from networks import domain_generator, domain_classifier\n'), ((1733, 1758), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (1756, 1758), False, 'import argparse\n'), ((2206, 2244), 'os.makedirs', 'os.makedirs', (['opt.w_path'], {'exist_ok': '(True)'}), '(opt.w_path, exist_ok=True)\n', (2217, 2244), False, 'import os\n'), ((1079, 1139), 'os.path.join', 'os.path.join', (['opt.w_path', "('%s_%06d.npy' % (opt.partition, i))"], {}), "(opt.w_path, '%s_%06d.npy' % (opt.partition, i))\n", (1091, 1139), False, 'import os\n'), ((1183, 1207), 'os.path.isfile', 'os.path.isfile', (['filename'], {}), '(filename)\n', (1197, 1207), False, 'import os\n'), ((1661, 1689), 'numpy.save', 'np.save', (['filename', 'current_z'], {}), '(filename, current_z)\n', (1668, 1689), True, 'import numpy as np\n'), ((1326, 1341), 'torch.no_grad', 'torch.no_grad', ([], {}), '()\n', (1339, 1341), False, 'import torch\n')]
|
################################################################################
# Module: plot.py
# Description: Plot functions
# License: Apache v2.0
# Author: <NAME>
# Web: https://github.com/pedroswits/anprx
################################################################################
import math
import adjustText
import osmnx as ox
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.colorbar as colorbar
from .utils import save_fig
from .core import Edge
from .constants import Units
from .constants import deg2distance
#
#
#
def plot_camera(
camera,
bbox_side = 100,
show_camera = True,
camera_color = "#FFFFFF",
camera_marker = "*",
camera_markersize = 10,
annotate_camera = True,
draw_radius = False,
#
fig_height = 6,
fig_width = None,
margin = 0.02,
bgcolor='k',
node_color='#999999',
node_edgecolor='none',
node_zorder=2,
node_size=50,
node_alpha = 1,
edge_color='#555555',
edge_linewidth=1.5,
edge_alpha=1,
#
probability_cmap = plt.cm.Oranges,
show_colorbar_label = True,
draw_colorbar = True,
draw_arrow = False,
#
color_near_nodes = True,
color_candidate_edges = True,
nn_color = '#66B3BA',
nedge_color = '#D0CE7C',
labels_color = "white",
annotate_nn_id = False,
annotate_nn_distance = True,
adjust_text = True,
#
save = False,
file_format = 'png',
filename = None,
dpi = 300):
"""
Plot the camera on a networkx spatial graph.
Parameters
----------
bbox_side : int
half the length of one side of the bbox (a square) in which to plot the camera. This value should usually be kept within small scales (hundreds of meters), otherwise near nodes and candidate edges become imperceptible.
camera_color : string
the color of the point representing the location of the camera
camera_marker : string
marker used to represent the camera
camera_markersize: int
the size of the marker representing the camera
annotate_camera : True
whether to annotate the camera or not using its id
draw_radius : bool
whether to draw (kind of) a circle representing the range of the camera
bgcolor : string
the background color of the figure and axis - passed to osmnx's plot_graph
node_color : string
the color of the nodes - passed to osmnx's plot_graph
node_edgecolor : string
the color of the node's marker's border - passed to osmnx's plot_graph
node_zorder : int
zorder to plot nodes, edges are always 2, so make node_zorder 1 to plot nodes beneath them or 3 to plot nodes atop them - passed to osmnx's plot_graph
node_size : int
the size of the nodes - passed to osmnx's plot_graph
node_alpha : float
the opacity of the nodes - passed to osmnx's plot_graph
edge_color : string
the color of the edges' lines - passed to osmnx's plot_graph
edge_linewidth : float
the width of the edges' lines - passed to osmnx's plot_graph
edge_alpha : float
the opacity of the edges' lines - passed to osmnx's plot_graph
probability_cmap : matplotlib colormap
Colormap used to color candidate edges by probability of observation.
show_colorbar_label : bool
whether to set the label of the colorbar or not
draw_colorbar : bool
whether to plot a colorbar as a legend for probability_cmap
nn_color : string
the color of near nodes - these are not necessarily in range of the camera, but they are part of edges that do
nedge_color : string
the color of candidate edges - nearby edges filtered by address or other condition
labels_color : string
the color of labels used to annotate nearby nodes
annotate_nn_id : bool
whether the text annotating near nodes should include their id
annotate_nn_distance : bool
whether the text annotating near nodes should include their distance from the camera
adjust_text : bool
whether to optimise the location of the annotations, using adjustText.adjust_text, so that overlaps are avoided. Notice that this incurs considerable computational cost. Turning this feature off will result in much faster plotting.
save : bool
whether to save the figure in the app folder's images directory
file_format : string
format of the image
filename : string
filename of the figure to be saved. The default value is the camera's id.
dpi : int
resolution of the image
Returns
-------
fig, ax : tuple
"""
if filename is None:
filename = camera.id
bbox = ox.bbox_from_point(point = camera.point,
distance = bbox_side)
# Set color of near nodes by index
nodes_colors = [node_color] * len(camera.network.nodes())
if color_near_nodes:
i = 0
for node in camera.network.nodes(data = False):
if node in camera.lsystem['nnodes']:
nodes_colors[i] = nn_color
i = i + 1
# Color near edges
edges_colors = [edge_color] * len(camera.network.edges())
if color_candidate_edges:
norm = colors.Normalize(vmin=0, vmax=1)
cmap = plt.cm.ScalarMappable(norm=norm, cmap=probability_cmap)
pcolor = { edge : cmap.to_rgba(p)
for edge, p in camera.p_cedges.items() }
j = 0
for u,v,k in camera.network.edges(keys = True, data = False):
edge = Edge(u,v,k)
if edge in camera.lsystem['cedges']:
edges_colors[j] = pcolor[edge]
j = j + 1
# Plot it
fig, axis = \
ox.plot_graph(
camera.network,
bbox = bbox,
margin = margin,
bgcolor = bgcolor,
node_color = nodes_colors,
node_edgecolor = node_edgecolor,
node_zorder = node_zorder,
edge_color = edges_colors,
node_alpha = node_alpha,
edge_linewidth = edge_linewidth,
edge_alpha = edge_alpha,
node_size = node_size,
save = False,
show = False,
close = False,
axis_off = True,
fig_height = fig_height,
fig_width = fig_width)
if draw_colorbar:
axis2 = fig.add_axes([0.3, 0.15, 0.15, 0.02])
cb = colorbar.ColorbarBase(
axis2,
cmap=probability_cmap,
norm=norm,
orientation='horizontal')
cb.set_ticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])
if show_colorbar_label:
cb.set_label("Probability of edge", color = labels_color, size = 9)
cb.ax.xaxis.set_tick_params(pad=0,
color = labels_color,
labelcolor = labels_color,
labelsize = 8)
# Plot Camera
if show_camera:
camera_point = axis.plot(
camera.point.lng,
camera.point.lat,
marker = camera_marker,
color = camera_color,
markersize = camera_markersize)
if show_camera and draw_radius:
radius_circle = \
plt.Circle((camera.point.lng, camera.point.lat),
radius = camera.radius/deg2distance(unit = Units.m),
color=camera_color,
fill=False)
axis.add_artist(radius_circle)
if show_camera and annotate_camera:
camera_text = axis.annotate(
str(camera.id),
xy = (camera.point.lng, camera.point.lat),
color = labels_color)
if draw_arrow:
base_x = camera.network.nodes[camera.edge.u]['x']
base_y = camera.network.nodes[camera.edge.u]['y']
end_x = camera.network.nodes[camera.edge.v]['x']
end_y = camera.network.nodes[camera.edge.v]['y']
color = pcolor[camera.edge]
axis.annotate('',
xytext = (base_x, base_y),
xy = (end_x, end_y),
arrowprops=dict(arrowstyle="->", color=color),
size = 15)
if color_near_nodes and (annotate_nn_id or annotate_nn_distance):
# Annotate nearest_neighbors
texts = []
for id in camera.lsystem['nnodes']:
distance_x = camera.lsystem['lnodes'][id][0]
distance_y = camera.lsystem['lnodes'][id][1]
distance = math.sqrt(distance_x ** 2 + distance_y ** 2)
if distance < bbox_side:
s1 = ""
s2 = ""
if annotate_nn_id:
s1 = "{}: ".format(id)
if annotate_nn_distance:
s2 = "{:,.1f}m".format(distance)
text = axis.text(camera.network.node[id]['x'],
camera.network.node[id]['y'],
s = s1 + s2,
color = labels_color)
texts.append(text)
if show_camera and annotate_camera:
texts.append(camera_text)
if adjust_text:
additional_obj = []
if draw_radius:
additional_obj.append(radius_circle)
if annotate_camera:
additional_obj.append(camera_text)
adjustText.adjust_text(
texts,
x = [ camera.network.node[id]['x'] for id in camera.lsystem['nnodes'] ],
y = [ camera.network.node[id]['y'] for id in camera.lsystem['nnodes'] ],
ax = axis,
add_objects = camera_point + additional_obj,
force_points = (0.5, 0.6),
expand_text = (1.2, 1.4),
expand_points = (1.4, 1.4))
if save:
save_fig(fig, axis, filename, file_format, dpi)
return fig, axis
|
[
"matplotlib.colors.Normalize",
"math.sqrt",
"osmnx.bbox_from_point",
"osmnx.plot_graph",
"matplotlib.pyplot.cm.ScalarMappable",
"adjustText.adjust_text",
"matplotlib.colorbar.ColorbarBase"
] |
[((4844, 4902), 'osmnx.bbox_from_point', 'ox.bbox_from_point', ([], {'point': 'camera.point', 'distance': 'bbox_side'}), '(point=camera.point, distance=bbox_side)\n', (4862, 4902), True, 'import osmnx as ox\n'), ((5863, 6257), 'osmnx.plot_graph', 'ox.plot_graph', (['camera.network'], {'bbox': 'bbox', 'margin': 'margin', 'bgcolor': 'bgcolor', 'node_color': 'nodes_colors', 'node_edgecolor': 'node_edgecolor', 'node_zorder': 'node_zorder', 'edge_color': 'edges_colors', 'node_alpha': 'node_alpha', 'edge_linewidth': 'edge_linewidth', 'edge_alpha': 'edge_alpha', 'node_size': 'node_size', 'save': '(False)', 'show': '(False)', 'close': '(False)', 'axis_off': '(True)', 'fig_height': 'fig_height', 'fig_width': 'fig_width'}), '(camera.network, bbox=bbox, margin=margin, bgcolor=bgcolor,\n node_color=nodes_colors, node_edgecolor=node_edgecolor, node_zorder=\n node_zorder, edge_color=edges_colors, node_alpha=node_alpha,\n edge_linewidth=edge_linewidth, edge_alpha=edge_alpha, node_size=\n node_size, save=False, show=False, close=False, axis_off=True,\n fig_height=fig_height, fig_width=fig_width)\n', (5876, 6257), True, 'import osmnx as ox\n'), ((5382, 5414), 'matplotlib.colors.Normalize', 'colors.Normalize', ([], {'vmin': '(0)', 'vmax': '(1)'}), '(vmin=0, vmax=1)\n', (5398, 5414), True, 'import matplotlib.colors as colors\n'), ((5430, 5485), 'matplotlib.pyplot.cm.ScalarMappable', 'plt.cm.ScalarMappable', ([], {'norm': 'norm', 'cmap': 'probability_cmap'}), '(norm=norm, cmap=probability_cmap)\n', (5451, 5485), True, 'import matplotlib.pyplot as plt\n'), ((6578, 6671), 'matplotlib.colorbar.ColorbarBase', 'colorbar.ColorbarBase', (['axis2'], {'cmap': 'probability_cmap', 'norm': 'norm', 'orientation': '"""horizontal"""'}), "(axis2, cmap=probability_cmap, norm=norm, orientation=\n 'horizontal')\n", (6599, 6671), True, 'import matplotlib.colorbar as colorbar\n'), ((8705, 8749), 'math.sqrt', 'math.sqrt', (['(distance_x ** 2 + distance_y ** 2)'], {}), '(distance_x ** 2 + distance_y ** 2)\n', (8714, 8749), False, 'import math\n'), ((9589, 9899), 'adjustText.adjust_text', 'adjustText.adjust_text', (['texts'], {'x': "[camera.network.node[id]['x'] for id in camera.lsystem['nnodes']]", 'y': "[camera.network.node[id]['y'] for id in camera.lsystem['nnodes']]", 'ax': 'axis', 'add_objects': '(camera_point + additional_obj)', 'force_points': '(0.5, 0.6)', 'expand_text': '(1.2, 1.4)', 'expand_points': '(1.4, 1.4)'}), "(texts, x=[camera.network.node[id]['x'] for id in\n camera.lsystem['nnodes']], y=[camera.network.node[id]['y'] for id in\n camera.lsystem['nnodes']], ax=axis, add_objects=camera_point +\n additional_obj, force_points=(0.5, 0.6), expand_text=(1.2, 1.4),\n expand_points=(1.4, 1.4))\n", (9611, 9899), False, 'import adjustText\n')]
|
#! ../env/bin/python
# -*- coding: utf-8 -*-
import sys
print("TestURLs sys.path: {0}".format(sys.path))
import unittest
from mathsonmars.models import db, User, Role
from mathsonmars import create_app
from mathsonmars.constants.modelconstants import RoleTypes, DefaultUserName
import logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
create_user = False
class TestURLs(unittest.TestCase):
def setUp(self):
#admin._views = []
#rest_api.resources = []
app = create_app('mathsonmars.settings.TestConfig')
self.client = app.test_client()
self.app = app
db.app = app
db.create_all()
def tearDown(self):
db.session.remove()
db.drop_all()
def test_home(self):
""" Tests if the home page loads """
rv = self.client.get('/')
assert rv.status_code == 200
def test_login(self):
""" Tests if the login page loads """
rv = self.client.get('/login')
assert rv.status_code == 200
def test_logout(self):
""" Tests if the logout page loads """
rv = self.client.get('/logout')
assert rv.status_code == 302
def test_restricted_logged_out(self):
""" Tests if the restricted page returns a 302
if the user is logged out
"""
rv = self.client.get('/restricted')
assert rv.status_code == 302
def test_restricted_logged_in(self):
""" Tests if the restricted page returns a 200
if the user is logged in
"""
with self.app.app_context():
admin_role = Role(role_name = RoleTypes.ADMIN)
db.session.add(admin_role)
db.session.flush()
admin = User(role_id = admin_role.id, user_name='admin', password='<PASSWORD>')
db.session.add(admin)
db.session.commit()
self.client.get('/login', data=dict(
username='admin',
password="<PASSWORD>"
), follow_redirects=True)
rv = self.client.get('/restricted')
logger.debug("--test_restricted_logged_in() code:{0}, rv:{1}".format(rv.status_code, rv))
assert rv.status_code == 302
if __name__ == "__main__":
unittest.main()
|
[
"unittest.main",
"logging.basicConfig",
"mathsonmars.models.db.session.remove",
"mathsonmars.models.db.drop_all",
"mathsonmars.models.db.session.flush",
"mathsonmars.models.db.session.add",
"mathsonmars.models.db.session.commit",
"mathsonmars.models.Role",
"mathsonmars.create_app",
"mathsonmars.models.User",
"logging.getLogger",
"mathsonmars.models.db.create_all"
] |
[((295, 335), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.DEBUG'}), '(level=logging.DEBUG)\n', (314, 335), False, 'import logging\n'), ((345, 372), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (362, 372), False, 'import logging\n'), ((2282, 2297), 'unittest.main', 'unittest.main', ([], {}), '()\n', (2295, 2297), False, 'import unittest\n'), ((531, 576), 'mathsonmars.create_app', 'create_app', (['"""mathsonmars.settings.TestConfig"""'], {}), "('mathsonmars.settings.TestConfig')\n", (541, 576), False, 'from mathsonmars import create_app\n'), ((678, 693), 'mathsonmars.models.db.create_all', 'db.create_all', ([], {}), '()\n', (691, 693), False, 'from mathsonmars.models import db, User, Role\n'), ((735, 754), 'mathsonmars.models.db.session.remove', 'db.session.remove', ([], {}), '()\n', (752, 754), False, 'from mathsonmars.models import db, User, Role\n'), ((763, 776), 'mathsonmars.models.db.drop_all', 'db.drop_all', ([], {}), '()\n', (774, 776), False, 'from mathsonmars.models import db, User, Role\n'), ((1664, 1695), 'mathsonmars.models.Role', 'Role', ([], {'role_name': 'RoleTypes.ADMIN'}), '(role_name=RoleTypes.ADMIN)\n', (1668, 1695), False, 'from mathsonmars.models import db, User, Role\n'), ((1710, 1736), 'mathsonmars.models.db.session.add', 'db.session.add', (['admin_role'], {}), '(admin_role)\n', (1724, 1736), False, 'from mathsonmars.models import db, User, Role\n'), ((1749, 1767), 'mathsonmars.models.db.session.flush', 'db.session.flush', ([], {}), '()\n', (1765, 1767), False, 'from mathsonmars.models import db, User, Role\n'), ((1788, 1857), 'mathsonmars.models.User', 'User', ([], {'role_id': 'admin_role.id', 'user_name': '"""admin"""', 'password': '"""<PASSWORD>"""'}), "(role_id=admin_role.id, user_name='admin', password='<PASSWORD>')\n", (1792, 1857), False, 'from mathsonmars.models import db, User, Role\n'), ((1872, 1893), 'mathsonmars.models.db.session.add', 'db.session.add', (['admin'], {}), '(admin)\n', (1886, 1893), False, 'from mathsonmars.models import db, User, Role\n'), ((1906, 1925), 'mathsonmars.models.db.session.commit', 'db.session.commit', ([], {}), '()\n', (1923, 1925), False, 'from mathsonmars.models import db, User, Role\n')]
|
from datetime import date
maior = 0
menor = 0
for c in range(1, 8):
ano = int(input('Digite o ano de nascimento: '))
if date.today().year - ano >= 18:
maior += 1
else:
menor += 1
print('Das sete pessoas digitadas {} são MAIORES DE IDADE.' .format(maior))
print('As outras {} pessoas são MENORES DE IDADE.' .format(menor))
|
[
"datetime.date.today"
] |
[((128, 140), 'datetime.date.today', 'date.today', ([], {}), '()\n', (138, 140), False, 'from datetime import date\n')]
|
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.9.1)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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"
qt_resource_name = b"\
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qt_resource_struct_v1 = b"\
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qt_resource_struct_v2 = b"\
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qt_version = QtCore.qVersion().split('.')
if qt_version < ['5', '8', '0']:
rcc_version = 1
qt_resource_struct = qt_resource_struct_v1
else:
rcc_version = 2
qt_resource_struct = qt_resource_struct_v2
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
|
[
"PyQt5.QtCore.qUnregisterResourceData",
"PyQt5.QtCore.qVersion",
"PyQt5.QtCore.qRegisterResourceData"
] |
[((408212, 408313), 'PyQt5.QtCore.qRegisterResourceData', 'QtCore.qRegisterResourceData', (['rcc_version', 'qt_resource_struct', 'qt_resource_name', 'qt_resource_data'], {}), '(rcc_version, qt_resource_struct,\n qt_resource_name, qt_resource_data)\n', (408240, 408313), False, 'from PyQt5 import QtCore\n'), ((408340, 408443), 'PyQt5.QtCore.qUnregisterResourceData', 'QtCore.qUnregisterResourceData', (['rcc_version', 'qt_resource_struct', 'qt_resource_name', 'qt_resource_data'], {}), '(rcc_version, qt_resource_struct,\n qt_resource_name, qt_resource_data)\n', (408370, 408443), False, 'from PyQt5 import QtCore\n'), ((407983, 408000), 'PyQt5.QtCore.qVersion', 'QtCore.qVersion', ([], {}), '()\n', (407998, 408000), False, 'from PyQt5 import QtCore\n')]
|
# -*- coding: utf-8 -*-
#
# Copyright (C) 2008 <NAME>
# All rights reserved.
#
# This software is licensed as described in the file COPYING, which
# you should have received as part of this distribution.
import doctest
import unittest
from couchbase_mapping import design
from couchbase_mapping.tests import testutil
class DesignTestCase(testutil.TempDatabaseMixin, unittest.TestCase):
def test_options(self):
options = {'collation': 'raw'}
view = design.ViewDefinition(
'foo', 'foo',
'function(doc) {emit(doc._id, doc._rev)}',
options=options)
_, db = self.temp_db()
view.sync(db)
design_doc = db['_design/foo'].ddoc
self.assertTrue(design_doc['views']['foo']['options'] == options)
def test_multiple_views(self):
map_by_name = 'function(doc, meta) {emit(doc.name, null)}'
view1 = design.ViewDefinition(
'test_multiple_views',
'by_name',
map_by_name)
map_by_id = 'function(doc, meta) {emit(meta.id, null)}'
view2 = design.ViewDefinition(
'test_multiple_views',
'by_id',
map_by_id)
_, db = self.temp_db()
view1.sync(db)
view2.sync(db)
design_doc = db['_design/test_multiple_views'].ddoc
self.assertEqual(design_doc['views']['by_name']['map'], map_by_name)
self.assertEqual(design_doc['views']['by_id']['map'], map_by_id)
def suite():
suite = unittest.TestSuite()
suite.addTest(unittest.makeSuite(DesignTestCase))
suite.addTest(doctest.DocTestSuite(design))
return suite
if __name__ == '__main__':
unittest.main(defaultTest='suite')
|
[
"unittest.main",
"couchbase_mapping.design.ViewDefinition",
"unittest.TestSuite",
"doctest.DocTestSuite",
"unittest.makeSuite"
] |
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|
# -*- coding: utf-8 -*-
'''
@Author : Xu
@Software: PyCharm
@File : bert_bilstm_crf_entity_extractor.py
@Time : 2019-09-26 11:09
@Desc :
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
import os
import re
import io
import typing
import pickle
from builtins import str
from typing import Any, Dict, List, Optional, Text, Tuple
from rasa.nlu.extractors import EntityExtractor
from rasa.nlu.model import Metadata
from rasa.nlu.training_data import Message
from chatbot_nlu.utils.bilstm_utils import \
char_mapping, tag_mapping, prepare_dataset, BatchManager, iob_iobes, \
iob2, save_model, create_model, input_from_line
from chatbot_nlu.models.model import Model
from multiprocessing import cpu_count
import jieba
logger = logging.getLogger(__name__)
if typing.TYPE_CHECKING:
import numpy as np
import tensorflow as tf
import tensorflow.contrib
try:
import tensorflow as tf
except ImportError:
tf = None
|
[
"logging.getLogger"
] |
[((885, 912), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (902, 912), False, 'import logging\n')]
|
from classifiers import BaseRGCN
from dgl.nn.pytorch import RelGraphConv
from functools import partial
import torch
import torch.nn.functional as F
import torch.nn as nn
from dgl.nn import RelGraphConv
from layers import RelGraphConvHetero, EmbeddingLayer, RelGraphAttentionHetero,MiniBatchRelGraphEmbed
class EncoderRGCN(BaseRGCN):
def create_features(self):
features = torch.arange(self.num_nodes)
if self.use_cuda:
features = features.cuda()
return features
def build_input_layer(self):
return RelGraphConv(self.inp_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
dropout=self.dropout)
# TODO different layers may have different number of hidden units current implementation prevents
def build_hidden_layer(self, idx):
return RelGraphConv(self.h_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
dropout=self.dropout)
def build_class_output_layer(self):
return RelGraphConv(self.h_dim, self.out_dim, self.num_rels, "basis",
self.num_bases, activation=partial(F.softmax, dim=1),
self_loop=self.use_self_loop)
def build_reconstruct_output_layer(self):
return RelGraphConv(self.h_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=partial(F.softmax, dim=1),
self_loop=self.use_self_loop)
def build_output_layer(self):
return self.build_reconstruct_output_layer()
class EncoderRelGraphAttentionHetero(nn.Module):
def __init__(self,
h_dim,
in_size_dict,
etypes,
ntypes,
num_hidden_layers=1,
dropout=0,
use_self_loop=False):
super(EncoderRelGraphAttentionHetero, self).__init__()
self.h_dim = h_dim
self.rel_names = list(set(etypes))
self.rel_names.sort()
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
self.use_self_loop = use_self_loop
self.in_size_dict = in_size_dict
self.embed_layer = EmbeddingLayer(self.in_size_dict, h_dim, ntypes)
self.layers = nn.ModuleList()
# h2h
for i in range(self.num_hidden_layers):
self.layers.append(RelGraphAttentionHetero(
self.h_dim, self.h_dim, etypes, activation=F.relu, self_loop=self.use_self_loop,
dropout=self.dropout))
def forward(self,G, corrupt=False):
if corrupt:
# create local variable do not permute the original graph
g = G.local_var()
for key in self.in_size_dict:
# TODO possibly high complexity here??
# The following implements the permutation of features within each node class.
# for the negative sample in the information maximization step
perm = torch.randperm(g.nodes[key].data['features'].shape[0])
g.nodes[key].data['features'] = g.nodes[key].data['features'][perm]
else:
g = G
h = self.embed_layer(g)
for layer in self.layers:
h = layer(g, h)
return h
class EncoderRelGraphConvHomo(nn.Module):
def __init__(self,
device,
num_nodes,
h_dim,
num_rels,
num_bases=None,
num_hidden_layers=1,
dropout=0,
use_self_loop=False,
low_mem=False,
layer_norm=False):
super(EncoderRelGraphConvHomo, self).__init__()
self.device = torch.device(device)
self.num_nodes = num_nodes
self.h_dim = h_dim
self.num_rels = num_rels
self.num_bases = None if num_bases < 0 else num_bases
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
self.use_self_loop = use_self_loop
self.low_mem = low_mem
self.layer_norm = layer_norm
self.layers = nn.ModuleList()
# i2h
self.layers.append(RelGraphConv(
self.h_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
low_mem=self.low_mem, dropout=self.dropout, layer_norm=layer_norm))
# h2h
for idx in range(self.num_hidden_layers):
self.layers.append(RelGraphConv(
self.h_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
low_mem=self.low_mem, dropout=self.dropout, layer_norm=layer_norm))
# h2o
#self.layers.append(RelGraphConv(
# self.h_dim, self.out_dim, self.num_rels, "basis",
# self.num_bases, activation=None,
# self_loop=self.use_self_loop,
# low_mem=self.low_mem, layer_norm=layer_norm))
def forward(self, blocks, feats, corrupt=False, norm=None):
h = feats
if corrupt:
perm = torch.randperm(len(feats))
h = h[perm]
if blocks is None:
# full graph training
blocks = [self.g] * len(self.layers)
for layer, block in zip(self.layers, blocks):
block = block.to(self.device)
h = layer(block, h, block.edata['etype'], block.edata['norm'])
return h
class EncoderRelGraphConvHetero(nn.Module):
def __init__(self,
h_dim,
etypes,
ntypes,
device,
g,
num_bases=-1,
num_hidden_layers=1,
dropout=0,
use_self_loop=False):
super(EncoderRelGraphConvHetero, self).__init__()
self.h_dim = h_dim
self.rel_names = list(set(etypes))
self.rel_names.sort()
self.num_bases = None if num_bases < 0 else num_bases
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
self.use_self_loop = use_self_loop
self.embed_layer = MiniBatchRelGraphEmbed(g=g,device=device,embed_size=h_dim)
self.layers = nn.ModuleList()
# h2h
for i in range(self.num_hidden_layers):
self.layers.append(RelGraphConvHetero(
self.h_dim, self.h_dim, self.rel_names, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
dropout=self.dropout))
def forward(self,G, corrupt=False):
if corrupt:
# create local variable do not permute the original graph
g = G.local_var()
for key in self.g.ntypes:
# TODO possibly high complexity here??
# The following implements the permutation of features within each node class.
# for the negative sample in the information maximization step
perm = torch.randperm(g.nodes[key].data['h_f'].shape[0])
g.nodes[key].data['h_f'] = g.nodes[key].data['h_f'][perm]
else:
g = G
h = self.embed_layer(g,full=True)
for layer in self.layers:
h = layer(g, h)
return h
def forward_mb(self,blocks,permute=False):
h = self.embed_layer(blocks[0])
if permute:
for key in h.keys():
perm = torch.randperm(h[key].shape[0])
h[key] = h[key][perm]
for layer, block in zip(self.layers, blocks):
# print(h)
h = layer.forward_mb(block, h)
return h
class HGTLayer(nn.Module):
def __init__(self,
in_dim,
out_dim,
node_dict,
edge_dict,
n_heads,
dropout=0.2,
use_norm=False):
super(HGTLayer, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.node_dict = node_dict
self.edge_dict = edge_dict
self.num_types = len(node_dict)
self.num_relations = len(edge_dict)
self.total_rel = self.num_types * self.num_relations * self.num_types
self.n_heads = n_heads
self.d_k = out_dim // n_heads
self.sqrt_dk = math.sqrt(self.d_k)
self.att = None
self.k_linears = nn.ModuleList()
self.q_linears = nn.ModuleList()
self.v_linears = nn.ModuleList()
self.a_linears = nn.ModuleList()
self.norms = nn.ModuleList()
self.use_norm = use_norm
for t in range(self.num_types):
self.k_linears.append(nn.Linear(in_dim, out_dim))
self.q_linears.append(nn.Linear(in_dim, out_dim))
self.v_linears.append(nn.Linear(in_dim, out_dim))
self.a_linears.append(nn.Linear(out_dim, out_dim))
if use_norm:
self.norms.append(nn.LayerNorm(out_dim))
self.relation_pri = nn.Parameter(torch.ones(self.num_relations, self.n_heads))
self.relation_att = nn.Parameter(torch.Tensor(self.num_relations, n_heads, self.d_k, self.d_k))
self.relation_msg = nn.Parameter(torch.Tensor(self.num_relations, n_heads, self.d_k, self.d_k))
self.skip = nn.Parameter(torch.ones(self.num_types))
self.drop = nn.Dropout(dropout)
nn.init.xavier_uniform_(self.relation_att)
nn.init.xavier_uniform_(self.relation_msg)
def edge_attention(self, edges):
etype = edges.data['id'][0]
'''
Step 1: Heterogeneous Mutual Attention
'''
relation_att = self.relation_att[etype]
relation_pri = self.relation_pri[etype]
key = torch.bmm(edges.src['k'].transpose(1, 0), relation_att).transpose(1, 0)
att = (edges.dst['q'] * key).sum(dim=-1) * relation_pri / self.sqrt_dk
'''
Step 2: Heterogeneous Message Passing
'''
relation_msg = self.relation_msg[etype]
val = torch.bmm(edges.src['v'].transpose(1, 0), relation_msg).transpose(1, 0)
return {'a': att, 'v': val}
def message_func(self, edges):
return {'v': edges.data['v'], 'a': edges.data['a']}
def reduce_func(self, nodes):
'''
Softmax based on target node's id (edge_index_i).
NOTE: Using DGL's API, there is a minor difference with this softmax with the original one.
This implementation will do softmax only on edges belong to the same relation type, instead of for all of the edges.
'''
att = F.softmax(nodes.mailbox['a'], dim=1)
h = torch.sum(att.unsqueeze(dim=-1) * nodes.mailbox['v'], dim=1)
return {'t': h.view(-1, self.out_dim)}
def forward(self, G, h):
with G.local_scope():
node_dict, edge_dict = self.node_dict, self.edge_dict
for srctype, etype, dsttype in G.canonical_etypes:
k_linear = self.k_linears[node_dict[srctype]]
v_linear = self.v_linears[node_dict[srctype]]
q_linear = self.q_linears[node_dict[dsttype]]
G.nodes[srctype].data['k'] = k_linear(h[srctype]).view(-1, self.n_heads, self.d_k)
G.nodes[srctype].data['v'] = v_linear(h[srctype]).view(-1, self.n_heads, self.d_k)
G.nodes[dsttype].data['q'] = q_linear(h[dsttype]).view(-1, self.n_heads, self.d_k)
G.apply_edges(func=self.edge_attention, etype=etype)
G.multi_update_all({etype: (self.message_func, self.reduce_func) \
for etype in edge_dict}, cross_reducer='mean')
new_h = {}
for ntype in G.ntypes:
'''
Step 3: Target-specific Aggregation
x = norm( W[node_type] * gelu( Agg(x) ) + x )
'''
n_id = node_dict[ntype]
alpha = torch.sigmoid(self.skip[n_id])
trans_out = self.drop(self.a_linears[n_id](G.nodes[ntype].data['t']))
trans_out = trans_out * alpha + h[ntype] * (1 - alpha)
if self.use_norm:
new_h[ntype] = self.norms[n_id](trans_out)
else:
new_h[ntype] = trans_out
return new_h
class EncoderHGT(nn.Module):
def __init__(self, G, node_dict, edge_dict, n_inp, n_hid, n_out, n_layers, n_heads, use_norm=True):
super(EncoderHGT, self).__init__()
self.node_dict = node_dict
self.edge_dict = edge_dict
self.gcs = nn.ModuleList()
self.n_inp = n_inp
self.n_hid = n_hid
self.n_out = n_out
self.n_layers = n_layers
self.adapt_ws = nn.ModuleList()
for t in range(len(node_dict)):
self.adapt_ws.append(nn.Linear(n_inp, n_hid))
for _ in range(n_layers):
self.gcs.append(HGTLayer(n_hid, n_hid, node_dict, edge_dict, n_heads, use_norm=use_norm))
self.out = nn.Linear(n_hid, n_out)
def forward(self, G, out_key):
h = {}
for ntype in G.ntypes:
n_id = self.node_dict[ntype]
h[ntype] = F.gelu(self.adapt_ws[n_id](G.nodes[ntype].data['inp']))
for i in range(self.n_layers):
h = self.gcs[i](G, h)
return self.out(h[out_key])
|
[
"torch.nn.Dropout",
"torch.ones",
"functools.partial",
"torch.nn.ModuleList",
"layers.EmbeddingLayer",
"torch.nn.init.xavier_uniform_",
"layers.MiniBatchRelGraphEmbed",
"layers.RelGraphConvHetero",
"layers.RelGraphAttentionHetero",
"torch.nn.functional.softmax",
"torch.nn.Linear",
"torch.sigmoid",
"dgl.nn.RelGraphConv",
"torch.Tensor",
"torch.arange",
"torch.randperm",
"torch.device",
"torch.nn.LayerNorm"
] |
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|
#!/usr/bin/env python
#textMyself.py - Defines the textmyself() function that texts a message passed to it as a string
from twilio.rest import TwilioRestClient
# Read in account information
with open('/Users/RyanRobertson21/PycharmProjects/CoolProjects/twilioAccountInfo') as f:
info=f.read().splitlines()
# Preset Values
accountSID = info[0]
authToken = info[1]
myNumber = info[2]
twilioNumber = info[3]
# Send message from twilio to my number
def textmyself(message):
twilioCli = TwilioRestClient(accountSID, authToken)
twilioCli.messages.create(body=message, from_=twilioNumber, to=myNumber)
|
[
"twilio.rest.TwilioRestClient"
] |
[((494, 533), 'twilio.rest.TwilioRestClient', 'TwilioRestClient', (['accountSID', 'authToken'], {}), '(accountSID, authToken)\n', (510, 533), False, 'from twilio.rest import TwilioRestClient\n')]
|
from random import randint
class Solution:
'''
TASK DESCRIPTION
Преобразуйте список целых чисел: оставьте только кратные пяти.
Примечание, ввод производится в синтаксисе списка
EXAMPLES:
Sample Input:
[4, 5, 7, 237895, 32, 432, 45, 0]
Sample Output:
5 237895 45 0
'''
def eval_list_filter(self, line: str):
# или from ast import literal_eval
number_list = eval(line)
return tuple(filter(lambda number: not number % 5, number_list))
def core_list_filter(self, line: str) -> tuple:
number_list = list(map(int, line.strip('[]').split(', ')))
return tuple(filter(lambda number: not number % 5, number_list))
if __name__ == '__main__':
line = input()
result = Solution()
print(*result.core_list_filter(line))
# заменить на юнит тесты
for i in range(100):
temporary_list = [randint(-500, 500) for _ in range(randint(5, 30))]
temporary_line = str(temporary_list)
assert result.core_list_filter(temporary_line) == result.eval_list_filter(temporary_line), 'Test failed'
|
[
"random.randint"
] |
[((898, 916), 'random.randint', 'randint', (['(-500)', '(500)'], {}), '(-500, 500)\n', (905, 916), False, 'from random import randint\n'), ((932, 946), 'random.randint', 'randint', (['(5)', '(30)'], {}), '(5, 30)\n', (939, 946), False, 'from random import randint\n')]
|
"""Base classes for paper rock scissors game
"""
# Author: <NAME> <<EMAIL>>
from abc import ABCMeta, abstractmethod
from enum import Enum, auto
import time
import warnings
class MoveChoice(Enum):
ROCK = auto()
PAPER = auto()
SCISSORS = auto()
class Outcome(Enum):
WIN = auto()
LOSE = auto()
DRAW = auto()
class ListInstanceMixin:
"""Mixin class for all class in paper_rock_scissors."""
def __attrnames(self):
return ''.join('\t%s=%s\n' % (attr, self.__dict__[attr])
for attr in sorted(self.__dict__))
def __repr__(self):
return '<Instance of %s, address %s:\n%s>' % (
self.__class__.__name__,
id(self),
self.__attrnames())
class BaseRole(metaclass=ABCMeta):
"""Base class for roles in paper_rock_scissors.
Warning: This class should not be used directly.
Use derived classes instead.
"""
@abstractmethod
def __init__(self, role, name, score):
self.role = role
self.name = name
self.score = score
@abstractmethod
def _check_params(self):
# name
if not isinstance(self.name, str):
raise ValueError(f"name should be string, "
f"got {self.name} instead.")
# score
if not isinstance(self.score, int) or self.score < 0:
warnings.warn(
"Score must be positive integer or zero; "
f"got {self.score} instead.",
RuntimeWarning,
)
self.score = 0
@abstractmethod
def get_move(self, prompt):
"""Generate current move."""
pass
class GameEnvironment(ListInstanceMixin):
"""GameEnvironment class for paper_rock_scissors.
This class controls the flow of the game.
Parameters
----------
_player : _role.Player
Player role in the GameEnvironment.
_computer : _role.Computer
Computer role in the GameEnvironment.
_target_score : int, default=10
Target score of the game. Anyone reaches this score
will end the game.
_curr_round : int, default=0
Current round of the game.
_max_rounds : int, default=20
Maximum round of the game. Once _curr_round reaches
the max the game ends.
_sleep : int, default=1
Sleep time between each rounds.
_verbose : int, default=0
Verbosity level.
_winner : {_role.Player, _role.Computer}
Winner of the game.
"""
_ROLES_MAPPING = {
MoveChoice.ROCK: [MoveChoice.SCISSORS],
MoveChoice.SCISSORS: [MoveChoice.PAPER],
MoveChoice.PAPER: [MoveChoice.ROCK],
}
def __init__(
self,
player,
computer,
target_score=10,
curr_round=0,
max_rounds=20,
sleep=1,
verbose=0,
winner=None):
self._player = player
self._computer = computer
self._target_score = target_score
self._curr_round = curr_round
self._max_rounds = max_rounds
self._sleep = sleep
self._verbose = verbose
self._winner = winner
@property
def player(self):
return self._player
@property
def computer(self):
return self._computer
@property
def target_score(self):
return self._target_score
@target_score.setter
def target_score(self, value):
self._target_score = value
@property
def curr_round(self):
return self._curr_round
@curr_round.setter
def curr_round(self, value):
self._curr_round = value
@property
def max_rounds(self):
return self._max_rounds
@max_rounds.setter
def max_rounds(self, value):
self._max_rounds = value
@property
def winner(self):
return self._winner
@winner.setter
def winner(self, value):
self._winner = value
@property
def sleep(self):
return self._sleep
@sleep.setter
def sleep(self, value):
self._sleep = value
@property
def verbose(self):
return self._verbose
@verbose.setter
def verbose(self, value):
self._verbose = value
def _check_params(self):
# target_score
if not isinstance(self.target_score, int) or self.target_score <= 0:
warnings.warn(
"target_score must be positive integer; "
f"got {self.target_score} instead.",
RuntimeWarning,
)
# Default target_score set to 10
self.target_score = 10
# max_rounds
if not isinstance(self.max_rounds, int) or \
self.max_rounds < self.target_score:
warnings.warn(
"max_rounds must greater or equal to target_score; "
f"got {self.max_rounds} instead.",
RuntimeWarning,
)
# Default max_rounds set to target_score
self.max_rounds = self.target_score
# sleep
if not isinstance(self._sleep, int) or self._sleep < 0:
warnings.warn(
"sleep must be positive integer;"
f"got {self._sleep} instead.",
RuntimeWarning,
)
# Default sleep set to 1
self._sleep = 1
# verbose
if not isinstance(self._verbose, int) or \
self._verbose < 0 or self._verbose > 3:
warnings.warn(
"verbose must be positive integer between 0 and 3"
f"got {self._verbose} instead.",
RuntimeWarning,
)
# Default verbose set to 1
self._verbose = 1
@staticmethod
def _pprint_rules():
"""Return rules of the current game."""
return "Current winning conditions of the Paper-Rock-Scissors: \n" \
"Paper beats (wraps) rock \n" \
"Rock beats (blunts) scissors \n " \
"Scissors beats (cuts) paper. \n" \
"Game ends while there is a winner " \
"(score reach preset target score) or " \
"total rounds reach the maximum. \n" \
"Press ctrl + C quit the game."
def _pprint_state(self):
"""Return state of the current game."""
return "Current state of the game: \n" \
"%(player_name)s score: %(player_score)d \n" \
"%(computer_name)s score: %(computer_score)d \n" \
"Target Score: %(target_score)d\n" \
"Current Round: %(curr_round)d\n" \
"Maximum Rounds: %(max_rounds)d\n" \
"Sleep: %(sleep)d\nWinner: %(winner)s\n" %\
{'player_name': self.player.name,
'player_score': self.player.score,
'computer_name': self.computer.name,
'computer_score': self.computer.score,
'target_score': self.target_score,
'curr_round': self.curr_round,
'max_rounds': self.max_rounds,
'sleep': self._sleep,
'winner': self.winner}
@staticmethod
def _outcome(player_move, ai_move):
"""Determine the outcome of current round.
Paper beats (wraps) rock.
Rock beats (blunts) scissors.
Scissors beats (cuts) paper.
Parameters
----------
player_move : MoveChoice
Player's move for current round.
ai_move : MoveChoice
AI's move for current round.
Returns
-------
outcome : Outcome
Outcome of the current round.
"""
if player_move is ai_move:
return Outcome.DRAW
elif ai_move in GameEnvironment._ROLES_MAPPING[player_move]:
return Outcome.WIN
else:
return Outcome.LOSE
def play(self):
# Validate roles input parameters
self.player._check_params()
self.computer._check_params()
# Validate input parameters
self._check_params()
# Display game rules
print(self._pprint_rules())
# Game ends while there is a winner or total rounds reach the maximum
while not self.winner and self.curr_round <= self.max_rounds:
# Print current game state
if self.verbose >= 1:
print(self._pprint_state())
# Prompt input from player
# Return MoveChoice
move = self.player.get_move("Choose a move for this round: ")
if self.verbose >= 1:
# Notify player's choice
print("%s's move: %s" % (self.player.name, move.name))
# Computer's turn
print("\n%s is making a decision..." % self.computer.name)
time.sleep(self._sleep)
ai_move = self.computer.get_move("Choose a move for this round: ")
if self.verbose >= 1:
print("%s's move: %s" % (self.computer.name,
ai_move.name))
print("Current round is: %s vs %s" % (move.name,
ai_move.name))
outcome = GameEnvironment._outcome(move, ai_move)
if outcome is Outcome.WIN:
if self.verbose >= 1:
print("Winner of the current round is: %s \n"
% self.player.name)
self.player.score += 1
elif outcome is Outcome.LOSE:
if self.verbose >= 1:
print("Winner of the current round is: %s \n"
% self.computer.name)
self.computer.score += 1
else:
if self.verbose >= 1:
print("It's a draw for this round")
self.curr_round += 1
if self.player.score == self.target_score:
self.winner = self.player
elif self.computer.score == self.target_score:
self.winner = self.computer
# Display final winner of the game
# Whoever has the highest score is the winner
# if there is not winner decided
# If draw then computer wins
if not self.winner:
self.winner = self.player if \
self.player.score > self.computer.score else self.computer
print(f"Winner of the game: {self.winner.name}\n")
print(self._pprint_state())
|
[
"enum.auto",
"warnings.warn",
"time.sleep"
] |
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|
import time
import sys
import quimb.tensor as qtn
import cotengra as ctg
import tqdm
from opt_einsum import contract, contract_expression, contract_path, helpers
from opt_einsum.paths import linear_to_ssa, ssa_to_linear
def load_circuit(
n=53,
depth=10,
seed=0 ,
elided=0,
sequence='ABCDCDAB',
swap_trick=False
):
file = f'circuit_n{n}_m{depth}_s{seed}_e{elided}_p{sequence}.qsim'
if swap_trick:
gate_opts={'contract': 'swap-split-gate', 'max_bond': 2}
else:
gate_opts={}
# instantiate the `Circuit` object that
# constructs the initial tensor network:
return qtn.Circuit.from_qasm_file(file, gate_opts=gate_opts)
circ = load_circuit(depth=12, swap_trick=True)
sampler = qtn.MPS_computational_state('0' * (circ.N))
tn = circ.psi & sampler
tn.full_simplify_(output_inds=[])
tn.astype_('complex64')
ctg.hyper._HYPER_SEARCH_SPACE['kahypar']['imbalance']['max'] = 0.1
opt = ctg.HyperOptimizer(
methods=['kahypar'],
max_time=120, # just search for 2 minutes
max_repeats=1000,
progbar=True,
minimize='flops',
slicing_opts={'target_slices': int(sys.argv[1])}
)
info = tn.contract(all, optimize=opt, get='path-info', output_inds=[])
sf = ctg.SliceFinder(info, target_slices=int(sys.argv[1]))
ix_sl, cost_sl = sf.search(temperature=1.0)
ix_sl, cost_sl = sf.search(temperature=0.1)
ix_sl, cost_sl = sf.search(temperature=0.01)
arrays = [t.data for t in tn]
sc = sf.SlicedContractor(arrays)
start = time.time()
c = sc.contract_slice(0, backend="jax")
end = time.time()
print(f"t_0(contract_slice[0])={end-start}")
print(f"res_0(contract_slice[0])={c}")
print("#########################################################")
for i in tqdm.tqdm(range(1, sc.nslices)):
start = time.time()
c = c + sc.contract_slice(i, backend="jax")
end = time.time()
print(f"t_0(contract_slice[{i}])={end-start}")
print(f"res_0(sum to contract_slice[{i}])={c}")
print("#########################################################")
print("#########################################################")
print("#########################################################")
# second run
tn = circ.psi & qtn.MPS_rand_computational_state(circ.N, seed=42)
tn.full_simplify_(output_inds=[]).astype_('complex64')
# update the SlicedContractor's arrays
sc.arrays = tuple(t.data for t in tn)
# perform the contraction
start = time.time()
c = sc.contract_slice(0, backend="jax")
end = time.time()
print(f"t_0(contract_slice[0])={end-start}")
print(f"res_0(contract_slice[0])={c}")
print("#########################################################")
res=0
for i in tqdm.tqdm(range(sc.nslices)):
start = time.time()
res += sc.contract_slice(i, backend="jax")
end = time.time()
print(f"t_1(contract_slice[{i}])={end-start}")
print(f"res_1(contract_slice[{i}])={res}")
# update the SlicedContractor's arrays
sc.arrays = tuple(t.data for t in tn)
print("#########################################################")
# perform the contraction
res=0
for i in tqdm.tqdm(range(sc.nslices)):
start = time.time()
res += sc.contract_slice(i, backend="jax")
end = time.time()
print(f"t_2(contract_slice[{i}])={end-start}")
print(f"res_2(contract_slice[{i}])={res}")
|
[
"quimb.tensor.MPS_rand_computational_state",
"quimb.tensor.Circuit.from_qasm_file",
"time.time",
"quimb.tensor.MPS_computational_state"
] |
[((748, 789), 'quimb.tensor.MPS_computational_state', 'qtn.MPS_computational_state', (["('0' * circ.N)"], {}), "('0' * circ.N)\n", (775, 789), True, 'import quimb.tensor as qtn\n'), ((1511, 1522), 'time.time', 'time.time', ([], {}), '()\n', (1520, 1522), False, 'import time\n'), ((1569, 1580), 'time.time', 'time.time', ([], {}), '()\n', (1578, 1580), False, 'import time\n'), ((2425, 2436), 'time.time', 'time.time', ([], {}), '()\n', (2434, 2436), False, 'import time\n'), ((2483, 2494), 'time.time', 'time.time', ([], {}), '()\n', (2492, 2494), False, 'import time\n'), ((636, 689), 'quimb.tensor.Circuit.from_qasm_file', 'qtn.Circuit.from_qasm_file', (['file'], {'gate_opts': 'gate_opts'}), '(file, gate_opts=gate_opts)\n', (662, 689), True, 'import quimb.tensor as qtn\n'), ((1788, 1799), 'time.time', 'time.time', ([], {}), '()\n', (1797, 1799), False, 'import time\n'), ((1858, 1869), 'time.time', 'time.time', ([], {}), '()\n', (1867, 1869), False, 'import time\n'), ((2206, 2255), 'quimb.tensor.MPS_rand_computational_state', 'qtn.MPS_rand_computational_state', (['circ.N'], {'seed': '(42)'}), '(circ.N, seed=42)\n', (2238, 2255), True, 'import quimb.tensor as qtn\n'), ((2704, 2715), 'time.time', 'time.time', ([], {}), '()\n', (2713, 2715), False, 'import time\n'), ((2773, 2784), 'time.time', 'time.time', ([], {}), '()\n', (2782, 2784), False, 'import time\n'), ((3111, 3122), 'time.time', 'time.time', ([], {}), '()\n', (3120, 3122), False, 'import time\n'), ((3180, 3191), 'time.time', 'time.time', ([], {}), '()\n', (3189, 3191), False, 'import time\n')]
|
'''Provide fundamental geometry calculations used by the scheduling.
'''
import math
import numpy as np
import brahe.data_models as bdm
from brahe.utils import fcross
from brahe.constants import RAD2DEG
from brahe.coordinates import sECEFtoENZ, sENZtoAZEL, sECEFtoGEOD, sGEODtoECEF
from brahe.relative_coordinates import rCARTtoRTN
def azelrng(sat_ecef: np.ndarray,
loc_ecef: np.ndarray,
use_degrees: bool = True) -> np.ndarray:
'''Compute satellite azimuth, elevation, and range as viewed from the specified location.
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame
loc_ecef (:obj:`np.ndarray`): Location in ECEF (ITRF) frame.
use_degrees (:obj:`bool`, optional): Return output in degrees. Default: `True`
Returns:
np.ndarray: azimuth elevation and range as array [deg, deg, m]
'''
# Ensure np-ness
sat_ecef = np.asarray(sat_ecef)
loc_ecef = np.asarray(loc_ecef)
# Compute Satellite State in ENZ frame
sat_enz = sECEFtoENZ(loc_ecef[0:3], sat_ecef[0:3], conversion='geodetic')
# Compute Satellite Elevation
azelrng = sENZtoAZEL(sat_enz, use_degrees=use_degrees)[0:3]
return azelrng
def azimuth(sat_ecef: np.ndarray,
loc_ecef: np.ndarray,
use_degrees: bool = True) -> np.ndarray:
'''Compute satellite azimuth as viewed from the specified location.
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame
loc_ecef (:obj:`np.ndarray`): Location in ECEF (ITRF) frame.
use_degrees (:obj:`bool`, optional): Return output in degrees. Default: `True`
Returns:
float: Azimuth [deg]
'''
return azelrng(sat_ecef, loc_ecef, use_degrees=use_degrees)[0]
def elevation(sat_ecef: np.ndarray,
loc_ecef: np.ndarray,
use_degrees: bool = True) -> np.ndarray:
'''Compute satellite elevation as viewed from the specified location.
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame
loc_ecef (:obj:`np.ndarray`): Location in ECEF (ITRF) frame.
use_degrees (:obj:`bool`, optional): Return output in degrees. Default: `True`
Returns:
float: Elevation [deg]
'''
return azelrng(sat_ecef, loc_ecef, use_degrees=use_degrees)[1]
def range(sat_ecef: np.ndarray, loc_ecef: np.ndarray,
use_degrees: bool = True) -> np.ndarray:
'''Compute satellite range from the specified location.
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame.
loc_ecef (:obj:`np.ndarray`): Location in ECEF (ITRF) frame.
use_degrees (:obj:`bool`, optional): Return output in degrees. Default: `True`
Returns:
float: Range [m]
'''
return azelrng(sat_ecef, loc_ecef, use_degrees=use_degrees)[2]
def look_angle(sat_ecef: np.ndarray, loc_ecef: np.ndarray, use_degrees: bool = True) -> np.ndarray:
'''Compute the look angle angle between the satellite and the specific location.
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame.
loc_ecef (:obj:`np.ndarray`): Location in ECEF (ITRF) frame.
use_degrees (:obj:`bool`, optional): Return output in degrees. Default: `True`
Returns:
float: look angle angle [deg]
'''
# Ensure np-ness
sat_ecef = np.asarray(sat_ecef)
loc_ecef = np.asarray(loc_ecef)
# Satellite state
r_sat = sat_ecef[0:3]
# Geodetic sub-satellte point
sat_geod = sECEFtoGEOD(r_sat)
sub_sat_geod = np.array([sat_geod[0], sat_geod[1], 0.0])
sub_sat_ecef = sGEODtoECEF(sub_sat_geod)
# look angle
nadir_dir = (sub_sat_ecef - r_sat) / np.linalg.norm(sub_sat_ecef - r_sat)
target_dir = (loc_ecef - r_sat) / np.linalg.norm(loc_ecef - r_sat)
look_angle = math.acos(np.dot(nadir_dir, target_dir)) * RAD2DEG
return look_angle
def ascdsc(sat_ecef: np.ndarray) -> bdm.AscendingDescending:
'''Compute whether whether satellite is ascending or descending in current
state.
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame.
use_degrees (:obj:`bool`, optional): Return output in degrees. Default: `True`
Returns:
bdm.AscendingDescending: ascending or descending state
'''
# Ensure np-ness
sat_ecef = np.asarray(sat_ecef)
if sat_ecef[5] > 0:
return bdm.AscendingDescending.ascending
elif sat_ecef[5] < 0:
return bdm.AscendingDescending.descending
else:
# Handle unlikely case that satellite is exaclty at 0 Z-velocity
if sat_ecef[2] < 0:
return bdm.AscendingDescending.ascending
else:
return bdm.AscendingDescending.descending
def look_direction(sat_ecef: np.ndarray,
loc_ecef: np.ndarray) -> bdm.LookDirection:
'''Compute the look direction for viewing the startet
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame.
loc_ecef (:obj:`np.ndarray`): Location in ECEF (ITRF) frame.\
Returns:
bdm.LookDirection: Look direction. 'left' or 'right'
'''
# Ensure np-ness
sat_ecef = np.asarray(sat_ecef)
loc_ecef = np.asarray(loc_ecef)
# Line of Sight Vector in ECEF Frame
los_ecef = loc_ecef[0:3] - sat_ecef[0:3]
# Apply ECEF to RTN rotation
los_rtn = rCARTtoRTN(sat_ecef) @ los_ecef
# Compute cross product of RTN velocity and RTN LOS
cp = fcross([0, 1, 0], los_rtn)
if np.sign(cp[0]) < 0:
return bdm.LookDirection.right
else:
return bdm.LookDirection.left
|
[
"brahe.coordinates.sENZtoAZEL",
"brahe.relative_coordinates.rCARTtoRTN",
"numpy.asarray",
"brahe.coordinates.sECEFtoGEOD",
"brahe.coordinates.sGEODtoECEF",
"brahe.coordinates.sECEFtoENZ",
"brahe.utils.fcross",
"numpy.array",
"numpy.linalg.norm",
"numpy.sign",
"numpy.dot"
] |
[((923, 943), 'numpy.asarray', 'np.asarray', (['sat_ecef'], {}), '(sat_ecef)\n', (933, 943), True, 'import numpy as np\n'), ((959, 979), 'numpy.asarray', 'np.asarray', (['loc_ecef'], {}), '(loc_ecef)\n', (969, 979), True, 'import numpy as np\n'), ((1038, 1101), 'brahe.coordinates.sECEFtoENZ', 'sECEFtoENZ', (['loc_ecef[0:3]', 'sat_ecef[0:3]'], {'conversion': '"""geodetic"""'}), "(loc_ecef[0:3], sat_ecef[0:3], conversion='geodetic')\n", (1048, 1101), False, 'from brahe.coordinates import sECEFtoENZ, sENZtoAZEL, sECEFtoGEOD, sGEODtoECEF\n'), ((3395, 3415), 'numpy.asarray', 'np.asarray', (['sat_ecef'], {}), '(sat_ecef)\n', (3405, 3415), True, 'import numpy as np\n'), ((3431, 3451), 'numpy.asarray', 'np.asarray', (['loc_ecef'], {}), '(loc_ecef)\n', (3441, 3451), True, 'import numpy as np\n'), ((3551, 3569), 'brahe.coordinates.sECEFtoGEOD', 'sECEFtoGEOD', (['r_sat'], {}), '(r_sat)\n', (3562, 3569), False, 'from brahe.coordinates import sECEFtoENZ, sENZtoAZEL, sECEFtoGEOD, sGEODtoECEF\n'), ((3589, 3630), 'numpy.array', 'np.array', (['[sat_geod[0], sat_geod[1], 0.0]'], {}), '([sat_geod[0], sat_geod[1], 0.0])\n', (3597, 3630), True, 'import numpy as np\n'), ((3650, 3675), 'brahe.coordinates.sGEODtoECEF', 'sGEODtoECEF', (['sub_sat_geod'], {}), '(sub_sat_geod)\n', (3661, 3675), False, 'from brahe.coordinates import sECEFtoENZ, sENZtoAZEL, sECEFtoGEOD, sGEODtoECEF\n'), ((4384, 4404), 'numpy.asarray', 'np.asarray', (['sat_ecef'], {}), '(sat_ecef)\n', (4394, 4404), True, 'import numpy as np\n'), ((5228, 5248), 'numpy.asarray', 'np.asarray', (['sat_ecef'], {}), '(sat_ecef)\n', (5238, 5248), True, 'import numpy as np\n'), ((5264, 5284), 'numpy.asarray', 'np.asarray', (['loc_ecef'], {}), '(loc_ecef)\n', (5274, 5284), True, 'import numpy as np\n'), ((5518, 5544), 'brahe.utils.fcross', 'fcross', (['[0, 1, 0]', 'los_rtn'], {}), '([0, 1, 0], los_rtn)\n', (5524, 5544), False, 'from brahe.utils import fcross\n'), ((1151, 1195), 'brahe.coordinates.sENZtoAZEL', 'sENZtoAZEL', (['sat_enz'], {'use_degrees': 'use_degrees'}), '(sat_enz, use_degrees=use_degrees)\n', (1161, 1195), False, 'from brahe.coordinates import sECEFtoENZ, sENZtoAZEL, sECEFtoGEOD, sGEODtoECEF\n'), ((3735, 3771), 'numpy.linalg.norm', 'np.linalg.norm', (['(sub_sat_ecef - r_sat)'], {}), '(sub_sat_ecef - r_sat)\n', (3749, 3771), True, 'import numpy as np\n'), ((3810, 3842), 'numpy.linalg.norm', 'np.linalg.norm', (['(loc_ecef - r_sat)'], {}), '(loc_ecef - r_sat)\n', (3824, 3842), True, 'import numpy as np\n'), ((5420, 5440), 'brahe.relative_coordinates.rCARTtoRTN', 'rCARTtoRTN', (['sat_ecef'], {}), '(sat_ecef)\n', (5430, 5440), False, 'from brahe.relative_coordinates import rCARTtoRTN\n'), ((5553, 5567), 'numpy.sign', 'np.sign', (['cp[0]'], {}), '(cp[0])\n', (5560, 5567), True, 'import numpy as np\n'), ((3870, 3899), 'numpy.dot', 'np.dot', (['nadir_dir', 'target_dir'], {}), '(nadir_dir, target_dir)\n', (3876, 3899), True, 'import numpy as np\n')]
|
# -*- coding: utf-8 -*-
import importlib
def gen_task_name_via_func(func):
"""生成函数对象对应的 task name"""
return '{name}'.format(name=func.__name__)
def import_object_from_path(path, default_obj_name='app'):
"""从定义的字符串信息中导入对象
:param path: ``task.app``
"""
module_name, obj_name = path.rsplit('.', 1)
if not obj_name:
obj_name = default_obj_name
module = importlib.import_module(module_name)
return getattr(module, obj_name)
|
[
"importlib.import_module"
] |
[((395, 431), 'importlib.import_module', 'importlib.import_module', (['module_name'], {}), '(module_name)\n', (418, 431), False, 'import importlib\n')]
|
import unittest
import graph
class BreadthFirstSearchTest(unittest.TestCase):
__runSlowTests = False
def testTinyGraph(self):
g = graph.Graph.from_file('tinyG.txt')
bfs = graph.BreadthFirstSearch(g, 0)
self.assertEqual(7, bfs.count())
self.assertFalse(bfs.connected(7))
self.assertIsNone(bfs.path_to(7))
self.assertFalse(bfs.connected(8))
self.assertIsNone(bfs.path_to(8))
self.assertFalse(bfs.connected(9))
self.assertIsNone(bfs.path_to(9))
self.assertFalse(bfs.connected(12))
self.assertIsNone(bfs.path_to(12))
self.assertEqual([2, 0], bfs.path_to(2))
self.assertEqual(1, bfs.distance(2))
self.assertEqual([3, 5, 0], bfs.path_to(3))
self.assertEqual(2, bfs.distance(3))
self.assertEqual([4, 5, 0], bfs.path_to(4))
self.assertEqual(2, bfs.distance(4))
self.assertEqual([5, 0], bfs.path_to(5))
self.assertEqual(1, bfs.distance(5))
def testMedGraph(self):
g = graph.Graph.from_file('mediumG.txt')
bfs = graph.BreadthFirstSearch(g, 0)
self.assertEqual(250, bfs.count())
self.assertTrue(bfs.connected(123))
self.assertEqual(9, bfs.distance(123))
self.assertEqual([123, 246, 244, 207, 122, 92, 171, 165, 68, 0], bfs.path_to(123))
def testTinyDG(self):
g = graph.Graph.from_file('tinyDG.txt', directed=True)
bfs = graph.BreadthFirstSearch(g, 0)
self.assertEqual(6, bfs.count())
self.assertTrue(bfs.connected(4))
self.assertIsNotNone(bfs.path_to(4))
self.assertFalse(bfs.connected(7))
self.assertIsNone(bfs.path_to(7))
self.assertEqual([2, 4, 5, 0], bfs.path_to(2))
self.assertEqual(3, bfs.distance(2))
def testTinyDAG(self):
g = graph.Graph.from_file('tinyDAG.txt', directed=True)
bfs = graph.BreadthFirstSearch(g, 0)
self.assertEqual(9, bfs.count())
self.assertTrue(bfs.connected(4))
self.assertIsNotNone(bfs.path_to(4))
self.assertFalse(bfs.connected(7))
self.assertIsNone(bfs.path_to(7))
self.assertEqual([12, 9, 6, 0], bfs.path_to(12))
self.assertEqual(3, bfs.distance(12))
if __name__ == '__main__':
unittest.main()
|
[
"unittest.main",
"graph.Graph.from_file",
"graph.BreadthFirstSearch"
] |
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|
import logging
import sys
import yaml
def load_config(filename):
with open(filename, 'r') as stream:
try:
return yaml.safe_load(stream)
except yaml.YAMLError as exc:
print('Invalid configuration')
print(exc)
sys.exit(1)
class LoadAndPreprocessConfig:
def __init__(self, raw) -> None:
expname = raw['name']
self.outputdatadir = raw['data']['output_dir'].rstrip('/')
self.outputdatadir = f'{self.outputdatadir}/{expname}'
self.preprocdir = f'/tmp/{expname}'
self.src_lgs = raw['data']['src_lgs']
self.tgt_lgs = raw['data']['tgt_lgs']
self.corpora = raw['data']['corpora']
self.datadir = raw['data']['dir'].rstrip('/')
self.on_missing_data = raw['data'].get('on_missing_data', [])
self.max_entries_per_corpus = int(raw['data']['max_entries_per_corpus'])
self.preprocessing_steps = raw['preprocessing']['steps']
class StepConfig:
def __init__(this, step_raw) -> None:
this.corpora = step_raw['corpora']
this.script = step_raw['script']
self.step_config = { step: StepConfig(raw[f'preprocessing_{step}']) for step in raw['preprocessing']['steps'] }
self.scriptsdir = raw['preprocessing']['scripts_dir']
self.final_files = raw['preprocessing']['final_files']
class TokenizeConfig:
def __init__(self, raw) -> None:
self.final_files = raw['preprocessing']['final_files']
expname = raw['name']
self.outputdatadir = raw['data']['output_dir'].rstrip('/')
self.outputdatadir = f'{self.outputdatadir}/{expname}'
class BuildVocabConfig:
def __init__(self, raw) -> None:
expname = raw['name']
self.outputdatadir = raw['data']['output_dir'].rstrip('/')
self.outputdatadir = f'{self.outputdatadir}/{expname}'
class VocabConfig:
def __init__(this, vocab_raw) -> None:
this.output = vocab_raw['save_to']
this.files = vocab_raw['files']
self.vocabs = { vocab: VocabConfig(vocab_raw) for vocab, vocab_raw in raw['vocab'].items() }
class SplitConfig:
def __init__(self, raw) -> None:
self.files = raw['splitting']['files']
self.parts = raw['splitting']['parts']
self.remain = raw['splitting']['remain']
self.seed = int(raw['splitting']['seed'])
expname = raw['name']
self.outputdatadir = raw['data']['output_dir'].rstrip('/')
self.outputdatadir = f'{self.outputdatadir}/{expname}'
class TrainConfig:
def __init__(self, raw) -> None:
expname = raw['name']
self.outputdir = raw['train']['output_dir'].rstrip('/')
self.outputdir = f'{self.outputdir}/{expname}'
model_file = raw['model'].get('file')
if model_file is not None:
self.model_file = model_file
self.custom_model = True
else:
self.model_type = raw['model']['type']
self.custom_model = False
self.model_config_file = raw['model']['config']
self.options = raw['train']['options']
class ConfigFactory:
configs = {
'load': LoadAndPreprocessConfig,
'tokenize': TokenizeConfig,
'build_vocab': BuildVocabConfig,
'split': SplitConfig,
'train': TrainConfig
}
def __init__(self, raw) -> None:
self.raw = raw
def build_for(self, step):
if step not in ConfigFactory.configs.keys():
raise NotImplementedError(f'Factory cannot build config for step {step}')
try:
result = ConfigFactory.configs[step](self.raw)
except KeyError as err:
logging.warning(f'Missing config element: {err}')
return None
return result
|
[
"logging.warning",
"yaml.safe_load",
"sys.exit"
] |
[((139, 161), 'yaml.safe_load', 'yaml.safe_load', (['stream'], {}), '(stream)\n', (153, 161), False, 'import yaml\n'), ((278, 289), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (286, 289), False, 'import sys\n'), ((3726, 3775), 'logging.warning', 'logging.warning', (['f"""Missing config element: {err}"""'], {}), "(f'Missing config element: {err}')\n", (3741, 3775), False, 'import logging\n')]
|
from collections import Counter
from itertools import product
with open('02.txt') as fd:
inp = [l.strip() for l in fd.readlines()]
twos = 0
thre = 0
for row in inp:
c = Counter(row)
if 2 in c.values():
twos += 1
if 3 in c.values():
thre += 1
print(twos*thre)
def diff(sa,sb):
c = 0
for (a, b) in zip(sa,sb):
if a != b: c+= 1
return c
for (a, b) in product(inp, inp):
if diff(a,b) == 1:
print(a)
print(b)
break
|
[
"collections.Counter",
"itertools.product"
] |
[((402, 419), 'itertools.product', 'product', (['inp', 'inp'], {}), '(inp, inp)\n', (409, 419), False, 'from itertools import product\n'), ((184, 196), 'collections.Counter', 'Counter', (['row'], {}), '(row)\n', (191, 196), False, 'from collections import Counter\n')]
|
import numpy as np
class LidarTools(object):
'''
Collection of helpers for processing LiDAR point cloud.
'''
def get_bev(self, points, resolution=0.1, pixel_values=None, generate_img=None):
'''
Returns bird's eye view of a LiDAR point cloud for a given resolution.
Optional pixel_values can be used for giving color coded info the point cloud.
Optional generate_img function can be used for creating images.
'''
x = points[:, 0]
y = points[:, 1]
z = points[:, 2]
x_range = -1 * np.ceil(y.max()).astype(np.int), ((y.min()/np.abs(y.min())) * np.floor(y.min())).astype(np.int)
y_range = np.floor(x.min()).astype(np.int), np.ceil(x.max()).astype(np.int)
# Create mapping from a 3D point to a pixel based on resolution
# floor() used to prevent issues with -ve vals rounding upwards causing index out bound error
x_img = (-y / resolution).astype(np.int32) - int(np.floor(x_range[0]/resolution))
y_img = (x / resolution).astype(np.int32) - int(np.floor(y_range[0]/resolution))
img_width = int((x_range[1] - x_range[0])/resolution)
img_height = int((y_range[1] - y_range[0])/resolution)
if pixel_values is None:
pixel_values = (((z - z.min()) / float(z.max() - z.min())) * 255).astype(np.uint8)
if generate_img is None:
img = np.zeros([img_height, img_width], dtype=np.uint8)
img[-y_img, x_img] = pixel_values
return img
return generate_img(img_height, img_width, -y_img, x_img, pixel_values)
def filter_points(self, points, side_range=None, fwd_range=None, \
height_range=None, horizontal_fov=None, vertical_fov=None):
'''
Returns filtered points based on side, forward and height range, and, horizontal and vertical field of view.
'''
x = points[:, 0]
y = points[:, 1]
z = points[:, 2]
r = points[:, 3]
mask = np.full_like(x, True)
if side_range is not None:
side_mask = np.logical_and((y > -side_range[1]), (y < -side_range[0]))
mask = np.logical_and(mask, side_mask)
if fwd_range is not None:
fwd_mask = np.logical_and((x > fwd_range[0]), (x < fwd_range[1]))
mask = np.logical_and(mask, fwd_mask)
if height_range is not None:
height_mask = np.logical_and((z > height_range[0]), (z < height_range[1]))
mask = np.logical_and(mask, height_mask)
if horizontal_fov is not None:
horizontal_fov_mask = np.logical_and(np.arctan2(y, x) > (-horizontal_fov[1] * np.pi / 180), \
np.arctan2(y, x) < (-horizontal_fov[0] * np.pi / 180))
mask = np.logical_and(mask, horizontal_fov_mask)
if vertical_fov is not None:
distance = np.sqrt(x ** 2 + y ** 2 + z ** 2)
vertical_fov_mask = np.logical_and(np.arctan2(z,distance) < (vertical_fov[1] * np.pi / 180), \
np.arctan2(z,distance) > (vertical_fov[0] * np.pi / 180))
mask = np.logical_and(mask, vertical_fov_mask)
indices = np.argwhere(mask).flatten()
return points[indices, :]
|
[
"numpy.full_like",
"numpy.arctan2",
"numpy.logical_and",
"numpy.floor",
"numpy.zeros",
"numpy.argwhere",
"numpy.sqrt"
] |
[((2055, 2076), 'numpy.full_like', 'np.full_like', (['x', '(True)'], {}), '(x, True)\n', (2067, 2076), True, 'import numpy as np\n'), ((1428, 1477), 'numpy.zeros', 'np.zeros', (['[img_height, img_width]'], {'dtype': 'np.uint8'}), '([img_height, img_width], dtype=np.uint8)\n', (1436, 1477), True, 'import numpy as np\n'), ((2145, 2199), 'numpy.logical_and', 'np.logical_and', (['(y > -side_range[1])', '(y < -side_range[0])'], {}), '(y > -side_range[1], y < -side_range[0])\n', (2159, 2199), True, 'import numpy as np\n'), ((2223, 2254), 'numpy.logical_and', 'np.logical_and', (['mask', 'side_mask'], {}), '(mask, side_mask)\n', (2237, 2254), True, 'import numpy as np\n'), ((2313, 2363), 'numpy.logical_and', 'np.logical_and', (['(x > fwd_range[0])', '(x < fwd_range[1])'], {}), '(x > fwd_range[0], x < fwd_range[1])\n', (2327, 2363), True, 'import numpy as np\n'), ((2387, 2417), 'numpy.logical_and', 'np.logical_and', (['mask', 'fwd_mask'], {}), '(mask, fwd_mask)\n', (2401, 2417), True, 'import numpy as np\n'), ((2482, 2538), 'numpy.logical_and', 'np.logical_and', (['(z > height_range[0])', '(z < height_range[1])'], {}), '(z > height_range[0], z < height_range[1])\n', (2496, 2538), True, 'import numpy as np\n'), ((2562, 2595), 'numpy.logical_and', 'np.logical_and', (['mask', 'height_mask'], {}), '(mask, height_mask)\n', (2576, 2595), True, 'import numpy as np\n'), ((2856, 2897), 'numpy.logical_and', 'np.logical_and', (['mask', 'horizontal_fov_mask'], {}), '(mask, horizontal_fov_mask)\n', (2870, 2897), True, 'import numpy as np\n'), ((2967, 3000), 'numpy.sqrt', 'np.sqrt', (['(x ** 2 + y ** 2 + z ** 2)'], {}), '(x ** 2 + y ** 2 + z ** 2)\n', (2974, 3000), True, 'import numpy as np\n'), ((3213, 3252), 'numpy.logical_and', 'np.logical_and', (['mask', 'vertical_fov_mask'], {}), '(mask, vertical_fov_mask)\n', (3227, 3252), True, 'import numpy as np\n'), ((998, 1031), 'numpy.floor', 'np.floor', (['(x_range[0] / resolution)'], {}), '(x_range[0] / resolution)\n', (1006, 1031), True, 'import numpy as np\n'), ((1087, 1120), 'numpy.floor', 'np.floor', (['(y_range[0] / resolution)'], {}), '(y_range[0] / resolution)\n', (1095, 1120), True, 'import numpy as np\n'), ((3272, 3289), 'numpy.argwhere', 'np.argwhere', (['mask'], {}), '(mask)\n', (3283, 3289), True, 'import numpy as np\n'), ((2697, 2713), 'numpy.arctan2', 'np.arctan2', (['y', 'x'], {}), '(y, x)\n', (2707, 2713), True, 'import numpy as np\n'), ((2782, 2798), 'numpy.arctan2', 'np.arctan2', (['y', 'x'], {}), '(y, x)\n', (2792, 2798), True, 'import numpy as np\n'), ((3048, 3071), 'numpy.arctan2', 'np.arctan2', (['z', 'distance'], {}), '(z, distance)\n', (3058, 3071), True, 'import numpy as np\n'), ((3136, 3159), 'numpy.arctan2', 'np.arctan2', (['z', 'distance'], {}), '(z, distance)\n', (3146, 3159), True, 'import numpy as np\n')]
|
"""
This example requires uvicorn and fastapi.
pip install fastapi uvicorn
Run:
uvicorn examples.fast_api:app
then open http://localhost:8000
Access http://localhost:8000 to list all users.
Access http://localhost:8000/create to create a new user.
"""
import os
import sqlalchemy as sa
import typing as t
from fastapi import Depends, FastAPI
from aerie import Aerie, Base, DbSession
DATABASE_URL = os.environ.get('DATABASE_URL', 'sqlite+aiosqlite:///:memory:')
db = Aerie(DATABASE_URL)
class User(Base):
__tablename__ = 'users'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.String)
def __str__(self) -> str:
return self.name or 'n/a'
app = FastAPI(on_startup=[db.schema.create_tables], on_shutdown=[db.schema.drop_tables])
@app.get("/create")
async def create_user_view(session: DbSession = Depends(db.session)) -> t.Mapping:
count = await session.query(User).count()
user = User(id=count, name=f'User {count}')
session.add(user)
await session.commit()
return {"id": user.id, 'name': user.name}
@app.get("/")
async def list_users_view(session: DbSession = Depends(db.session)) -> t.List:
users = await session.query(User).all()
return [u.name for u in users]
|
[
"aerie.Aerie",
"os.environ.get",
"fastapi.Depends",
"sqlalchemy.Column",
"fastapi.FastAPI"
] |
[((408, 470), 'os.environ.get', 'os.environ.get', (['"""DATABASE_URL"""', '"""sqlite+aiosqlite:///:memory:"""'], {}), "('DATABASE_URL', 'sqlite+aiosqlite:///:memory:')\n", (422, 470), False, 'import os\n'), ((477, 496), 'aerie.Aerie', 'Aerie', (['DATABASE_URL'], {}), '(DATABASE_URL)\n', (482, 496), False, 'from aerie import Aerie, Base, DbSession\n'), ((699, 786), 'fastapi.FastAPI', 'FastAPI', ([], {'on_startup': '[db.schema.create_tables]', 'on_shutdown': '[db.schema.drop_tables]'}), '(on_startup=[db.schema.create_tables], on_shutdown=[db.schema.\n drop_tables])\n', (706, 786), False, 'from fastapi import Depends, FastAPI\n'), ((554, 593), 'sqlalchemy.Column', 'sa.Column', (['sa.Integer'], {'primary_key': '(True)'}), '(sa.Integer, primary_key=True)\n', (563, 593), True, 'import sqlalchemy as sa\n'), ((605, 625), 'sqlalchemy.Column', 'sa.Column', (['sa.String'], {}), '(sa.String)\n', (614, 625), True, 'import sqlalchemy as sa\n'), ((852, 871), 'fastapi.Depends', 'Depends', (['db.session'], {}), '(db.session)\n', (859, 871), False, 'from fastapi import Depends, FastAPI\n'), ((1140, 1159), 'fastapi.Depends', 'Depends', (['db.session'], {}), '(db.session)\n', (1147, 1159), False, 'from fastapi import Depends, FastAPI\n')]
|
import argparse
import os
import os.path as osp
import pickle
import shutil
import tempfile
import mmcv
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, load_checkpoint
from mmdet.apis import init_dist
from mmdet.core import coco_eval, results2json, wrap_fp16_model, get_classes, tensor2imgs
from mmdet.datasets import build_dataloader, build_dataset
from mmdet.models import build_detector
from DOTA_devkit.ResultMerge_multi_process import mergebypoly_multiprocess
import numpy as np
import os
import cv2
from DOTA_devkit.dota_utils import GetFileFromThisRootDir, custombasename
def draw_bbox(img, bboxes, labels, path, class_names):
img_show = mmcv.image.imread(img)
# bgr
bbox_color = [(0, 255, 0), # green
(255, 0, 0), #深蓝
(255, 255, 0), # 浅蓝，亮
(0, 0, 255), #红
(255, 0, 255), # purple
(255, 128, 0), #天蓝（比浅蓝深一点）
(0, 255, 255), #黄
(207, 203, 211), #white
(128, 255, 0), # 青色
(128, 0, 255), #玫红
(255, 0, 128), # 紫
(0, 128, 255), # 橘色
(0, 255, 128), #草绿
(0, 0, 128), #深红
(128, 0, 0)] #藏蓝
text_color = (255, 0, 0) # green
for bbox, label in zip(bboxes, labels):
bbox_int = bbox.astype(np.int32)
pts = np.array([[bbox_int[0], bbox_int[1]],
[bbox_int[2], bbox_int[3]],
[bbox_int[4], bbox_int[5]],
[bbox_int[6], bbox_int[7]]], dtype=np.int32)
cv2.polylines(img_show, [pts], True, bbox_color[label], thickness=2)
# cv2.polylines(img_show, [pts], True, text_color, thickness=2)
label_text = class_names[
label] if class_names is not None else 'cls {}'.format(label)
font = 0.5
# cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2),
# cv2.FONT_HERSHEY_COMPLEX, font, text_color)
cv2.imwrite(path, img)
def draw_result(data, result, outdir, class_names, score_thr=0.001):
bbox_result = result
img_metas = data['img_meta'][0].data[0]
if not os.path.exists(outdir):
os.makedirs(outdir)
for img_meta in img_metas:
h, w, _ = img_meta['ori_shape']
filename = img_meta['filename']
img = mmcv.imread(filename)
img_show = img[:h, :w, :]
path = os.path.basename(os.path.splitext(img_meta['filename'])[0])
path = os.path.join(outdir, path + '.jpg')
bboxes = np.vstack(bbox_result)
labels = [
np.full(bbox.shape[0], i, dtype=np.int32)
for i, bbox in enumerate(bbox_result)
]
labels = np.concatenate(labels)
if score_thr > 0:
assert bboxes.shape[1] == 9
scores = bboxes[:, -1]
inds = scores > score_thr
bboxes = bboxes[inds, :]
labels = labels[inds]
draw_bbox(img_show, bboxes, labels, path, class_names)
def single_gpu_test(model, data_loader, outdir, show=False):
model.eval()
# model.eval()，让model变成测试模式，对dropout和batch normalization的操作在训练和测试的时候是不一样的
results = []
dataset = data_loader.dataset
prog_bar = mmcv.ProgressBar(len(dataset))
for i, data in enumerate(data_loader):
with torch.no_grad():
result = model(return_loss=False, rescale=not show, **data)
results.append(result)
if show:
draw_result(data, result, osp.join(outdir, 'images'), dataset.CLASSES, score_thr=0.001)
batch_size = data['img'][0].size(0)
for _ in range(batch_size):
prog_bar.update()
return results
def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
"""Test model with multiple gpus.
This method tests model with multiple gpus and collects the results
under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'
it encodes results to gpu tensors and use gpu communication for results
collection. On cpu mode it saves the results on different gpus to 'tmpdir'
and collects them by the rank 0 worker.
Args:
model (nn.Module): Model to be tested.
data_loader (nn.Dataloader): Pytorch data loader.
tmpdir (str): Path of directory to save the temporary results from
different gpus under cpu mode.
gpu_collect (bool): Option to use either gpu or cpu to collect results.
Returns:
list: The prediction results.
"""
model.eval()
results = []
dataset = data_loader.dataset
rank, world_size = get_dist_info()
if rank == 0:
prog_bar = mmcv.ProgressBar(len(dataset))
for i, data in enumerate(data_loader):
with torch.no_grad():
result = model(return_loss=False, rescale=True, **data)
results.append(result)
if rank == 0:
batch_size = data['img'][0].size(0)
for _ in range(batch_size * world_size):
prog_bar.update()
# collect results from all ranks
if gpu_collect:
results = collect_results_gpu(results, len(dataset))
else:
results = collect_results_cpu(results, len(dataset), tmpdir)
return results
def collect_results_cpu(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
def collect_results_gpu(result_part, size):
rank, world_size = get_dist_info()
# dump result part to tensor with pickle
part_tensor = torch.tensor(
bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
# gather all result part tensor shape
shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
shape_list = [shape_tensor.clone() for _ in range(world_size)]
dist.all_gather(shape_list, shape_tensor)
# padding result part tensor to max length
shape_max = torch.tensor(shape_list).max()
part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda')
part_send[:shape_tensor[0]] = part_tensor
part_recv_list = [
part_tensor.new_zeros(shape_max) for _ in range(world_size)
]
# gather all result part
dist.all_gather(part_recv_list, part_send)
if rank == 0:
part_list = []
for recv, shape in zip(part_recv_list, shape_list):
part_list.append(
pickle.loads(recv[:shape[0]].cpu().numpy().tobytes()))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
return ordered_results
def parse_args():
parser = argparse.ArgumentParser(description='MMDet test detector')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('--outdir', help='output dir')
parser.add_argument('--out', help='output result file') # .pkl文件
parser.add_argument(
'--gpu_collect',
action='store_true',
help='whether to use gpu to collect results')
parser.add_argument('--show', action='store_true', help='show results')
parser.add_argument('--tmpdir', help='tmp dir for writing some results')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
def write_dota_results(path, boxes, dataset, threshold=0.001):
'''
:param path: output dir path
:param boxes: list(list(ndarray))
:param threshold: 置信度下限，小于此置信度的bbox不输出
:return:
'''
classes = dataset.CLASSES
img_infos = dataset.img_infos
assert len(boxes) == len(img_infos)
print("write no merge results\n")
for i, img_info in enumerate(img_infos):
# print("img {}: {}".format(i, img_info['id']))
img_id = img_info['id']
for j, cls in enumerate(classes):
txt_path = osp.join(path, 'Task1_' + cls + '.txt')
with open(txt_path, 'a') as f:
box = boxes[i][j] # (n, 9)
inds = box[:, 8] > threshold
box = box[inds]
for k in range(box.shape[0]):
# print(cls)
# print('{} {} {} {} {} {} {} {} {} {}\n'.format(
# img_id, box[k, 8],
# int(box[k, 0]), int(box[k, 1]),
# int(box[k, 2]), int(box[k, 3]),
# int(box[k, 4]), int(box[k, 5]),
# int(box[k, 6]), int(box[k, 7])))
f.write('{} {} {} {} {} {} {} {} {} {}\n'.format(
img_id, box[k, 8],
int(box[k, 0]), int(box[k, 1]),
int(box[k, 2]), int(box[k, 3]),
int(box[k, 4]), int(box[k, 5]),
int(box[k, 6]), int(box[k, 7])))
def main():
args = parse_args()
assert args.out , \
('Please specify at least one operation (save or show the results) '
'with the argument "--out"')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
# old versions did not save class info in checkpoints, this walkaround is
# for backward compatibility
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
model.CLASSES = dataset.CLASSES
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, args.outdir, args.show)
# outputs:list(list(ndarray)),外层list:图片，内层list:类别
else:
model = MMDistributedDataParallel(model.cuda())
outputs = multi_gpu_test(model, data_loader, args.tmpdir,
args.gpu_collect)
rank, _ = get_dist_info()
# 将结果保存到.pkl文件中
if args.out and rank == 0:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, osp.join(args.outdir, args.out))
if __name__ == '__main__':
# os.environ['CUDA_VISIBLE_DEVICES'] = '0'
main()
|
[
"mmcv.runner.get_dist_info",
"argparse.ArgumentParser",
"mmcv.mkdir_or_exist",
"torch.full",
"torch.distributed.all_gather",
"mmcv.Config.fromfile",
"shutil.rmtree",
"torch.no_grad",
"os.path.join",
"mmcv.imread",
"numpy.full",
"mmdet.models.build_detector",
"cv2.imwrite",
"os.path.exists",
"tempfile.mkdtemp",
"torch.zeros",
"mmdet.apis.init_dist",
"mmdet.datasets.build_dataloader",
"pickle.dumps",
"mmcv.image.imread",
"mmcv.runner.load_checkpoint",
"numpy.vstack",
"numpy.concatenate",
"mmdet.datasets.build_dataset",
"cv2.polylines",
"os.makedirs",
"mmcv.load",
"torch.distributed.barrier",
"mmdet.core.wrap_fp16_model",
"mmcv.parallel.MMDataParallel",
"numpy.array",
"os.path.splitext",
"torch.distributed.broadcast",
"torch.tensor"
] |
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|
import numpy as np
import gym
import torch
import random
from argparse import ArgumentParser
import os
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
from scipy.ndimage.filters import gaussian_filter1d
class Stats():
def __init__(self, num_episodes=20000, num_states = 6, log_dir='./', continuous=False):
self.episode_rewards = np.zeros(num_episodes)
self.episode_lengths = np.zeros(num_episodes)
if not continuous:
self.visitation_count = np.zeros((num_states, num_episodes))
self.target_count = np.zeros((num_states, num_episodes))
self.log_dir = log_dir
def log_data(self, file_name):
save_name = self.log_dir + file_name
np.savez(save_name, reward=self.episode_rewards, step=self.episode_lengths)
def plot_rewards(ax, episodes_ydata, smoothing_window = 100, label="",c='b', alpha=0.5):
#smoothing_window = 100
overall_stats_q_learning = []
for trialdata in episodes_ydata:
overall_stats_q_learning.append(pd.Series(trialdata.episode_rewards).rolling(smoothing_window, min_periods=smoothing_window).mean())
#overall_stats_q_learning.append(pd.Series(trialdata.episode_rewards).rolling(smoothing_window, min_periods=smoothing_window).mean().data)
m_stats_q_learning = np.mean(overall_stats_q_learning, axis=0)
std_stats_q_learning = np.std(overall_stats_q_learning, axis=0)
ax.plot(range(len(m_stats_q_learning)), m_stats_q_learning, label=label, c=c)
ax.fill_between(range(len(std_stats_q_learning)), m_stats_q_learning - std_stats_q_learning, m_stats_q_learning + std_stats_q_learning, alpha=alpha, edgecolor=c, facecolor=c)
#ax.set_ylabel('Score')
#ax.set_xlabel('Episode #')
#ax.grid()
def plot_steps(ax, episodes_ydata, smoothing_window = 100, label="",c='g',alpha=0.5):
#smoothing_window = 100
overall_stats_q_learning = []
for trialdata in episodes_ydata:
overall_stats_q_learning.append(pd.Series(trialdata.episode_lengths).rolling(smoothing_window, min_periods=smoothing_window).mean())
#overall_stats_q_learning.append(pd.Series(trialdata.episode_lengths).rolling(smoothing_window, min_periods=smoothing_window).mean().data)
m_stats_q_learning = np.mean(overall_stats_q_learning, axis=0)
std_stats_q_learning = np.std(overall_stats_q_learning, axis=0)
ax.plot(range(len(m_stats_q_learning)), m_stats_q_learning, label=label, c=c)
ax.fill_between(range(len(std_stats_q_learning)), m_stats_q_learning - std_stats_q_learning, m_stats_q_learning + std_stats_q_learning, alpha=alpha, edgecolor=c, facecolor=c)
#ax.set_ylabel('Steps')
#ax.set_xlabel('Episode #')
#ax.grid()
def plot_visitation_counts(episodes_ydata, smoothing_window = 1000, c=['b', 'g', 'r', 'y', 'k', 'c'], num_states = None):
if not num_states:
num_states = len(episodes_ydata[0].visitation_count)
overall_stats_q_learning = [[] for i in range(num_states)]
for trialdata in episodes_ydata:
for state in range(num_states):
overall_stats_q_learning[state].append(pd.Series(trialdata.visitation_count[state]).rolling(smoothing_window, min_periods=smoothing_window).mean().data)
for state in range(num_states):
m_stats_q_learning = np.mean(overall_stats_q_learning[state], axis=0)
std_stats_q_learning = np.std(overall_stats_q_learning[state], axis=0)
plt.plot(range(len(m_stats_q_learning)), m_stats_q_learning, c=c[state])
plt.fill_between(range(len(std_stats_q_learning)), m_stats_q_learning - std_stats_q_learning, m_stats_q_learning + std_stats_q_learning, alpha=0.5, edgecolor=c[state], facecolor=c[state])
def plot_target_counts(episodes_ydata, smoothing_window = 1000, c=['b', 'g', 'r', 'y', 'k', 'c']):
num_states = len(episodes_ydata[0].target_count)
overall_stats_q_learning = [[] for i in range(num_states)]
for trialdata in episodes_ydata:
for state in range(num_states):
overall_stats_q_learning[state].append(pd.Series(trialdata.target_count[state]).rolling(smoothing_window, min_periods=smoothing_window).mean().data)
for state in range(num_states):
m_stats_q_learning = np.mean(overall_stats_q_learning[state], axis=0)
std_stats_q_learning = np.std(overall_stats_q_learning[state], axis=0)
plt.plot(range(len(m_stats_q_learning)), m_stats_q_learning, c=c[state])
plt.fill_between(range(len(std_stats_q_learning)), m_stats_q_learning - std_stats_q_learning, m_stats_q_learning + std_stats_q_learning, alpha=0.5, edgecolor=c[state], facecolor=c[state])
def plot_q_values(model, observation_space, action_space):
res = 100
test_observations = np.linspace(observation_space.low, observation_space.high, res)
print((action_space.n, res))
q_values = np.zeros((action_space.n, res))
for action in range(action_space.n):
for obs in range(res):
q_values[action, obs] = model.predict(test_observations[obs])[0, action]
plt.plot(test_observations, q_values[action])
def arguments():
parser = ArgumentParser()
parser.add_argument('--env', default = 'BipedalWalker-v3')
return parser.parse_args()
def save(agent, rewards, args):
path = './runs/{}/'.format(args.env)
try:
os.makedirs(path)
except:
pass
torch.save(agent.q.state_dict(), os.path.join(path, 'model_state_dict'))
plt.cla()
plt.plot(rewards, c = 'r', alpha = 0.3)
plt.plot(gaussian_filter1d(rewards, sigma = 5), c = 'r', label = 'Rewards')
plt.xlabel('Episodes')
plt.ylabel('Cumulative reward')
plt.title('Branching DDQN: {}'.format(args.env))
plt.savefig(os.path.join(path, 'reward.png'))
pd.DataFrame(rewards, columns = ['Reward']).to_csv(os.path.join(path, 'rewards.csv'), index = False)
class AgentConfig:
def __init__(self,
epsilon_start = 1.,
epsilon_final = 0.01,
epsilon_decay = 8000,
gamma = 0.99,
lr = 1e-4,
target_net_update_freq = 1000,
memory_size = 100000,
batch_size = 128,
learning_starts = 5000,
max_frames = 10000000):
self.epsilon_start = epsilon_start
self.epsilon_final = epsilon_final
self.epsilon_decay = epsilon_decay
self.epsilon_by_frame = lambda i: self.epsilon_final + (self.epsilon_start - self.epsilon_final) * np.exp(-1. * i / self.epsilon_decay)
self.gamma =gamma
self.lr =lr
self.target_net_update_freq =target_net_update_freq
self.memory_size =memory_size
self.batch_size =batch_size
self.learning_starts = learning_starts
self.max_frames = max_frames
class ExperienceReplayMemory:
def __init__(self, capacity):
self.capacity = capacity
self.memory = []
def push(self, transition):
self.memory.append(transition)
if len(self.memory) > self.capacity:
del self.memory[0]
def sample(self, batch_size):
batch = random.sample(self.memory, batch_size)
states = []
actions = []
rewards = []
next_states = []
dones = []
for b in batch:
states.append(b[0])
actions.append(b[1])
rewards.append(b[2])
next_states.append(b[3])
dones.append(b[4])
return states, actions, rewards, next_states, dones
def __len__(self):
return len(self.memory)
import torch
import collections
import random
class ReplayBuffer():
def __init__(self,buffer_limit,action_space,device):
self.buffer = collections.deque(maxlen=buffer_limit)
self.action_space = action_space
self.device = device
def put(self, transition):
self.buffer.append(transition)
def sample(self, n):
mini_batch = random.sample(self.buffer, n)
state_lst, reward_lst, next_state_lst, done_mask_lst = [], [], [], []
actions_lst = [[] for i in range(self.action_space)]
for transition in mini_batch:
state, actions,reward, next_state, done_mask = transition
state_lst.append(state)
for idx in range(self.action_space):
actions_lst[idx].append(actions[idx])
reward_lst.append([reward])
next_state_lst.append(next_state)
done_mask_lst.append([done_mask])
actions_lst = [torch.tensor(x,dtype= torch.float).to(self.device) for x in actions_lst]
return torch.tensor(state_lst, dtype=torch.float).to(self.device),\
actions_lst ,torch.tensor(reward_lst).to(self.device),\
torch.tensor(next_state_lst, dtype=torch.float).to(self.device),\
torch.tensor(done_mask_lst).to(self.device)
def size(self):
return len(self.buffer)
class TensorEnv(gym.Wrapper):
def __init__(self, env_name):
super().__init__(gym.make(env_name))
def process(self, x):
return torch.tensor(x).reshape(1,-1).float()
def reset(self):
return self.process(super().reset())
def step(self, a):
ns, r, done, infos = super().step(a)
return self.process(ns), r, done, infos
class BranchingTensorEnv(TensorEnv):
def __init__(self, env_name, n):
super().__init__(env_name)
self.n = n
self.discretized = np.linspace(-1.,1., self.n)
def step(self, a):
action = np.array([self.discretized[aa] for aa in a])
return super().step(action)
|
[
"scipy.ndimage.filters.gaussian_filter1d",
"argparse.ArgumentParser",
"random.sample",
"matplotlib.pyplot.style.use",
"numpy.mean",
"numpy.exp",
"os.path.join",
"collections.deque",
"pandas.DataFrame",
"numpy.std",
"matplotlib.pyplot.cla",
"numpy.linspace",
"pandas.Series",
"matplotlib.pyplot.ylabel",
"numpy.savez",
"os.makedirs",
"matplotlib.pyplot.plot",
"gym.make",
"numpy.zeros",
"numpy.array",
"matplotlib.pyplot.xlabel",
"torch.tensor"
] |
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'os.path.join', (['path', '"""reward.png"""'], {}), "(path, 'reward.png')\n", (5609, 5629), False, 'import os\n'), ((5687, 5720), 'os.path.join', 'os.path.join', (['path', '"""rewards.csv"""'], {}), "(path, 'rewards.csv')\n", (5699, 5720), False, 'import os\n'), ((7043, 7081), 'random.sample', 'random.sample', (['self.memory', 'batch_size'], {}), '(self.memory, batch_size)\n', (7056, 7081), False, 'import random\n'), ((7650, 7688), 'collections.deque', 'collections.deque', ([], {'maxlen': 'buffer_limit'}), '(maxlen=buffer_limit)\n', (7667, 7688), False, 'import collections\n'), ((7880, 7909), 'random.sample', 'random.sample', (['self.buffer', 'n'], {}), '(self.buffer, n)\n', (7893, 7909), False, 'import random\n'), ((9413, 9443), 'numpy.linspace', 'np.linspace', (['(-1.0)', '(1.0)', 'self.n'], {}), '(-1.0, 1.0, self.n)\n', (9424, 9443), True, 'import numpy as np\n'), ((9484, 9528), 'numpy.array', 'np.array', (['[self.discretized[aa] for aa in a]'], {}), '([self.discretized[aa] for aa in a])\n', (9492, 9528), True, 'import numpy as np\n'), ((490, 526), 'numpy.zeros', 'np.zeros', (['(num_states, num_episodes)'], {}), '((num_states, num_episodes))\n', (498, 526), True, 'import numpy as np\n'), ((550, 586), 'numpy.zeros', 'np.zeros', (['(num_states, num_episodes)'], {}), '((num_states, num_episodes))\n', (558, 586), True, 'import numpy as np\n'), ((5636, 5677), 'pandas.DataFrame', 'pd.DataFrame', (['rewards'], {'columns': "['Reward']"}), "(rewards, columns=['Reward'])\n", (5648, 5677), True, 'import pandas as pd\n'), ((8960, 8978), 'gym.make', 'gym.make', (['env_name'], {}), '(env_name)\n', (8968, 8978), False, 'import gym\n'), ((6407, 6444), 'numpy.exp', 'np.exp', (['(-1.0 * i / self.epsilon_decay)'], {}), '(-1.0 * i / self.epsilon_decay)\n', (6413, 6444), True, 'import numpy as np\n'), ((8452, 8486), 'torch.tensor', 'torch.tensor', (['x'], {'dtype': 'torch.float'}), '(x, dtype=torch.float)\n', (8464, 8486), False, 'import torch\n'), ((8540, 8582), 'torch.tensor', 'torch.tensor', (['state_lst'], {'dtype': 'torch.float'}), '(state_lst, dtype=torch.float)\n', (8552, 8582), False, 'import torch\n'), ((8629, 8653), 'torch.tensor', 'torch.tensor', (['reward_lst'], {}), '(reward_lst)\n', (8641, 8653), False, 'import torch\n'), ((8688, 8735), 'torch.tensor', 'torch.tensor', (['next_state_lst'], {'dtype': 'torch.float'}), '(next_state_lst, dtype=torch.float)\n', (8700, 8735), False, 'import torch\n'), ((8769, 8796), 'torch.tensor', 'torch.tensor', (['done_mask_lst'], {}), '(done_mask_lst)\n', (8781, 8796), False, 'import torch\n'), ((9024, 9039), 'torch.tensor', 'torch.tensor', (['x'], {}), '(x)\n', (9036, 9039), False, 'import torch\n'), ((979, 1015), 'pandas.Series', 'pd.Series', (['trialdata.episode_rewards'], {}), '(trialdata.episode_rewards)\n', (988, 1015), True, 'import pandas as pd\n'), ((1885, 1921), 'pandas.Series', 'pd.Series', (['trialdata.episode_lengths'], {}), '(trialdata.episode_lengths)\n', (1894, 1921), True, 'import pandas as pd\n'), ((2979, 3023), 'pandas.Series', 'pd.Series', (['trialdata.visitation_count[state]'], {}), '(trialdata.visitation_count[state])\n', (2988, 3023), True, 'import pandas as pd\n'), ((3915, 3955), 'pandas.Series', 'pd.Series', (['trialdata.target_count[state]'], {}), '(trialdata.target_count[state])\n', (3924, 3955), True, 'import pandas as pd\n')]
|
import os
import pandas as pd
os.system(f"{sys.executable} -m pip install -U pytd==0.8.0 td-client")
import pytd
from tdclient.errors import NotFoundError
def database_exists(database, client):
try:
client.api_client.database(database)
return True
except NotFoundError:
pass
return False
def create_database_if_not_exists(database, client):
if database_exists(database, client):
print(f"DB {database} already exists")
return False
else:
client.api_client.create_database(database)
print(f"Created DB: {database}")
return True
def table_exists(database, table, client):
try:
client.api_client.table(database, table)
return True
except NotFoundError:
pass
return False
def upload_dataset(database, table):
apikey = os.environ["TD_API_KEY"]
apiserver = os.environ["TD_API_SERVER"]
client = pytd.Client(apikey=apikey, endpoint=apiserver)
if database_exists(database, client) and table_exists(database, table, client):
print("Target database and table exist. Skip")
return True
target_url = "https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv"
df = pd.read_csv(target_url)
create_database_if_not_exists(database, client)
client.load_table_from_dataframe(df, f"{database}.{table}", if_exists="overwrite")
return True
|
[
"pytd.Client",
"pandas.read_csv",
"os.system"
] |
[((32, 102), 'os.system', 'os.system', (['f"""{sys.executable} -m pip install -U pytd==0.8.0 td-client"""'], {}), "(f'{sys.executable} -m pip install -U pytd==0.8.0 td-client')\n", (41, 102), False, 'import os\n'), ((933, 979), 'pytd.Client', 'pytd.Client', ([], {'apikey': 'apikey', 'endpoint': 'apiserver'}), '(apikey=apikey, endpoint=apiserver)\n', (944, 979), False, 'import pytd\n'), ((1262, 1285), 'pandas.read_csv', 'pd.read_csv', (['target_url'], {}), '(target_url)\n', (1273, 1285), True, 'import pandas as pd\n')]
|
# Generated by Django 3.2 on 2021-06-26 00:30
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('backend', '0004_alter_item_description'),
]
operations = [
migrations.AlterField(
model_name='item',
name='category',
field=models.CharField(default='Dinosaurs', max_length=200),
),
]
|
[
"django.db.models.CharField"
] |
[((338, 391), 'django.db.models.CharField', 'models.CharField', ([], {'default': '"""Dinosaurs"""', 'max_length': '(200)'}), "(default='Dinosaurs', max_length=200)\n", (354, 391), False, 'from django.db import migrations, models\n')]
|
# coding=utf-8
"""
Data and actions for user
"""
from typing import List
import pypi_xmlrpc
from pypi_librarian.class_package import Package
class User(object):
"""
Properties and methods
"""
def __init__(self, name: str) -> None:
"""
Initialize values
:param name:
"""
self.name = name
def get_packages_name(self) -> List[str]:
"""
xmlprc call to get user info, but just names
:return:
"""
packages = pypi_xmlrpc.user_packages(self.name)
return packages
def get_packages(self, name: str, version: str) -> List[Package]:
"""
Load all packages for user, entire package objects
:param name:
:param version:
:return:
"""
raise NotImplementedError()
|
[
"pypi_xmlrpc.user_packages"
] |
[((508, 544), 'pypi_xmlrpc.user_packages', 'pypi_xmlrpc.user_packages', (['self.name'], {}), '(self.name)\n', (533, 544), False, 'import pypi_xmlrpc\n')]
|
from app.game_state.game_state_models import (
FibbingItQuestion,
FibbingItState,
GameState,
NextQuestion,
UpdateQuestionRoundState,
)
from app.player.player_models import Player
from app.room.games.abstract_game import AbstractGame
from app.room.games.exceptions import UnexpectedGameStateType
from app.room.room_events_models import GotNextQuestion, GotQuestionFibbingIt
class FibbingIt(AbstractGame):
def got_next_question(self, player: Player, game_state: GameState, next_question: NextQuestion) -> GotNextQuestion:
if not isinstance(game_state.state, FibbingItState):
raise UnexpectedGameStateType("expected `game_state.state` to be of type `FibbingItState`")
is_player_fibber = player.player_id == game_state.state.current_fibber_id
got_next_question = self._get_got_next_question(is_player_fibber, next_question)
return got_next_question
@staticmethod
def _get_got_next_question(is_player_fibber: bool, next_question: NextQuestion) -> GotNextQuestion:
if not isinstance(next_question.next_question, FibbingItQuestion):
raise UnexpectedGameStateType("expected `next_question.next_question` to be of type `FibbingItQuestion`")
question = next_question.next_question.question
if is_player_fibber:
question = next_question.next_question.fibber_question
got_next_question = GotNextQuestion(
question=GotQuestionFibbingIt(
is_fibber=is_player_fibber,
question=question,
answers=next_question.next_question.answers,
),
updated_round=UpdateQuestionRoundState(**next_question.updated_round.dict()),
timer_in_seconds=next_question.timer_in_seconds,
)
return got_next_question
|
[
"app.room.games.exceptions.UnexpectedGameStateType",
"app.room.room_events_models.GotQuestionFibbingIt"
] |
[((625, 715), 'app.room.games.exceptions.UnexpectedGameStateType', 'UnexpectedGameStateType', (['"""expected `game_state.state` to be of type `FibbingItState`"""'], {}), "(\n 'expected `game_state.state` to be of type `FibbingItState`')\n", (648, 715), False, 'from app.room.games.exceptions import UnexpectedGameStateType\n'), ((1132, 1236), 'app.room.games.exceptions.UnexpectedGameStateType', 'UnexpectedGameStateType', (['"""expected `next_question.next_question` to be of type `FibbingItQuestion`"""'], {}), "(\n 'expected `next_question.next_question` to be of type `FibbingItQuestion`')\n", (1155, 1236), False, 'from app.room.games.exceptions import UnexpectedGameStateType\n'), ((1452, 1569), 'app.room.room_events_models.GotQuestionFibbingIt', 'GotQuestionFibbingIt', ([], {'is_fibber': 'is_player_fibber', 'question': 'question', 'answers': 'next_question.next_question.answers'}), '(is_fibber=is_player_fibber, question=question, answers\n =next_question.next_question.answers)\n', (1472, 1569), False, 'from app.room.room_events_models import GotNextQuestion, GotQuestionFibbingIt\n')]
|
import os
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import math
import argparse
import math
import h5py
import numpy as np
import tensorflow as tf
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# tf.logging.set_verbosity(tf.logging.ERROR)
import socket
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(BASE_DIR)
sys.path.append(ROOT_DIR)
sys.path.append(os.path.join(ROOT_DIR, 'utils'))
import provider
import tf_util
from model import *
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
parser.add_argument('--log_dir', default='log', help='Log dir [default: log]')
parser.add_argument('--num_point', type=int, default=4096, help='Point number [default: 4096]')
parser.add_argument('--max_epoch', type=int, default=50, help='Epoch to run [default: 50]')
parser.add_argument('--batch_size', type=int, default=12, help='Batch Size during training [default: 12]')
parser.add_argument('--learning_rate', type=float, default=0.000001, help='Initial learning rate [default: 0.000001]')
parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]')
parser.add_argument('--optimizer', default='momentum', help='adam or momentum [default: adam]')
parser.add_argument('--decay_step', type=int, default=300000, help='Decay step for lr decay [default: 300000]')
parser.add_argument('--decay_rate', type=float, default=0.5, help='Decay rate for lr decay [default: 0.5]')
parser.add_argument('--test_recordings', type=str, default='11', help='Which recording numbers to use for test, i.e "1,2", "1", "3", "3,4,5" [default: 11]')
parser.add_argument('--dir_path_h5', type=str, default='data/apollo_sem_seg_hdf5_data', help='directory containing the h5 files')
parser.add_argument('--use_saved_model', type=str, default='no', help='yes or no')
FLAGS = parser.parse_args()
LOAD_FULL_DATA = False
BATCH_SIZE = FLAGS.batch_size
NUM_POINT = FLAGS.num_point
MAX_EPOCH = FLAGS.max_epoch
BASE_LEARNING_RATE = FLAGS.learning_rate
GPU_INDEX = FLAGS.gpu
MOMENTUM = FLAGS.momentum
OPTIMIZER = FLAGS.optimizer
DECAY_STEP = FLAGS.decay_step
DECAY_RATE = FLAGS.decay_rate
LOG_DIR = FLAGS.log_dir
if not os.path.exists(LOG_DIR):
os.mkdir(LOG_DIR)
USE_SAVED_MODEL = False
if FLAGS.use_saved_model == 'yes':
USE_SAVED_MODEL = True
print('using saved model')
elif FLAGS.use_saved_model != 'no':
raise ValueError('use_saved_model param must be eitehr yes or no')
os.system('cp model.py %s' % (LOG_DIR)) # bkp of model def
os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
MAX_NUM_POINT = 4096
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
#BN_DECAY_DECAY_STEP = float(DECAY_STEP * 2)
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
HOSTNAME = socket.gethostname()
# DIR_PATH_H5 = os.path.join(ROOT_DIR, 'data/apollo_sem_seg_hdf5_data_test')
DIR_PATH_H5 = FLAGS.dir_path_h5
if not os.path.exists(DIR_PATH_H5):
raise ValueError('the given h5 directory is invalid')
H5_FILES = [os.path.join(DIR_PATH_H5, file_h5) for file_h5 in os.listdir(DIR_PATH_H5) if file_h5[-2:] == 'h5']
#ALL_FILES = provider.getDataFiles('data/apollo_sem_seg_hdf5_data')
room_filelist = [line.rstrip() for line in open(os.path.join(DIR_PATH_H5, 'room_filelist.txt'))]
classMappings = [line.rstrip() for line in open(os.path.join(DIR_PATH_H5, 'class_mappings.txt'))]
NUM_CLASSES = len(classMappings)
BATCH_SIZE_H5 = provider.loadDataFile(H5_FILES[0])[0].shape[0]
# Load ALL data
# if LOAD_FULL_DATA:
# data_batch_list = []
# label_batch_list = []
# for i,h5_filename in enumerate(H5_FILES):
# if i%10 == 0:
# print("loading h5 file: " , i, h5_filename)
# data_batch, label_batch = provider.loadDataFile(h5_filename)
# data_batch_list.append(data_batch)
# label_batch_list.append(label_batch)
# if LOAD_FULL_DATA:
# print('---all loaded---')
# data_batches = np.concatenate(data_batch_list, 0)
# data_batch_list = None
# label_batches = np.concatenate(label_batch_list, 0)
# label_batch_list = None
# print(data_batches.shape)
# print(label_batches.shape)
data_for_training = np.empty(len(room_filelist), dtype=bool)
test_recordings = [str(int(recording_number)).zfill(3) for recording_number in FLAGS.test_recordings.split(',')]
#test_recordings = 'Area_'+str(FLAGS.test_area)
# if LOAD_FULL_DATA:
# train_idxs = []
# test_idxs = []
total_training_data = 0
total_testing_data = 0
for i,room_name in enumerate(room_filelist):
#remove this
if i%4==0:
total_testing_data += 1
data_for_training[i] = False
#if room_name[6:9] in test_recordings:
# if LOAD_FULL_DATA:
# test_idxs.append(i)
else:
total_training_data += 1
data_for_training[i] = True
# if LOAD_FULL_DATA:
# train_idxs.append(i)
# if LOAD_FULL_DATA:
# train_data = data_batches[train_idxs,...]
# train_label = label_batches[train_idxs]
# test_data = data_batches[test_idxs,...]
# test_label = label_batches[test_idxs]
# data_batches = None
# label_batches = None
# print(train_data.shape, train_label.shape)
# print(test_data.shape, test_label.shape)
current_train_idx = 0
current_test_idx = 0
last_loaded_file_index = None
last_loaded_file_data = None
last_loaded_file_label = None
def reset_train_data():
global current_train_idx
current_train_idx = 0
def reset_test_data():
global current_test_idx
current_test_idx = 0
def can_get_test_data():
global current_test_idx
return current_test_idx < data_for_training.shape[0]
def can_get_train_data():
global current_train_idx
global last_loaded_file_index
global last_loaded_file_data
global last_loaded_file_label
return current_train_idx < data_for_training.shape[0]
# h5_fileindex = int(math.floor( current_train_idx / float(BATCH_SIZE_H5) ))
# if h5_fileindex + 1 < len(H5_FILES):
# return True
# if last_loaded_file_index != h5_fileindex:
# h5_filename = H5_FILES[h5_fileindex]
# last_loaded_file_data, last_loaded_file_label = provider.loadDataFile(h5_filename)
# last_loaded_file_index = h5_fileindex
# start_idx_batch = current_train_idx - (h5_fileindex * BATCH_SIZE_H5)
# h5_remaining_batch_size = BATCH_SIZE_H5 - start_idx_batch
# return h5_remaining_batch_size > 0
def get_train_or_test_data(amount, for_training):
global current_train_idx
global current_test_idx
global last_loaded_file_index
global last_loaded_file_data
global last_loaded_file_label
local_data_batch_list = []
local_label_batch_list = []
total_retrieved = 0
if for_training:
index_for_run = current_train_idx
else:
index_for_run = current_test_idx
while total_retrieved < amount and index_for_run < data_for_training.shape[0]:
#total_retrieved += 1
h5_fileindex = int(math.floor( index_for_run / float(BATCH_SIZE_H5) ))
if last_loaded_file_index != h5_fileindex:
h5_filename = H5_FILES[h5_fileindex]
last_loaded_file_data, last_loaded_file_label = provider.loadDataFile(h5_filename)
last_loaded_file_index = h5_fileindex
amount_to_retrieve = amount - total_retrieved
start_idx_batch = index_for_run - (h5_fileindex * BATCH_SIZE_H5)
h5_remaining_batch_size = BATCH_SIZE_H5 - start_idx_batch
total_remaining_size = data_for_training.shape[0] - start_idx_batch
amount_to_fetch_from_batch = min(amount_to_retrieve, h5_remaining_batch_size, total_remaining_size)
start_idx_total = index_for_run
end_idx_total = start_idx_total + amount_to_fetch_from_batch
end_idx_batch = start_idx_batch + amount_to_fetch_from_batch
if for_training:
data_batch = (last_loaded_file_data[start_idx_batch:end_idx_batch]) [data_for_training[start_idx_total:end_idx_total],:,:]
label_batch = (last_loaded_file_label[start_idx_batch:end_idx_batch]) [data_for_training[start_idx_total:end_idx_total],:]
else:
arr = data_for_training[start_idx_total:end_idx_total] == False
data_batch = (last_loaded_file_data[start_idx_batch:end_idx_batch]) [arr,:,:]
label_batch = (last_loaded_file_label[start_idx_batch:end_idx_batch]) [arr,:]
total_retrieved += data_batch.shape[0]
index_for_run += amount_to_fetch_from_batch
local_data_batch_list.append(data_batch)
local_label_batch_list.append(label_batch)
local_data_batches = np.concatenate(local_data_batch_list, 0)
local_label_batches = np.concatenate(local_label_batch_list, 0)
if for_training:
current_train_idx = index_for_run
else:
current_test_idx = index_for_run
return local_data_batches, local_label_batches
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
def get_learning_rate(batch):
learning_rate = tf.train.exponential_decay(
BASE_LEARNING_RATE, # Base learning rate.
batch * BATCH_SIZE, # Current index into the dataset.
DECAY_STEP, # Decay step.
DECAY_RATE, # Decay rate.
staircase=True)
learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!!
return learning_rate
def get_bn_decay(batch):
bn_momentum = tf.train.exponential_decay(
BN_INIT_DECAY,
batch*BATCH_SIZE,
BN_DECAY_DECAY_STEP,
BN_DECAY_DECAY_RATE,
staircase=True)
bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
return bn_decay
def train(use_saved_model ):
with tf.Graph().as_default():
with tf.device('/gpu:'+str(GPU_INDEX)):
pointclouds_pl, labels_pl = placeholder_inputs(BATCH_SIZE, NUM_POINT)
is_training_pl = tf.placeholder(tf.bool, shape=())
# Note the global_step=batch parameter to minimize.
# That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains.
batch = tf.Variable(0)
bn_decay = get_bn_decay(batch)
tf.summary.scalar('bn_decay', bn_decay)
# Get model and loss
pred = get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay, num_classes=NUM_CLASSES)
loss = get_loss(pred, labels_pl)
tf.summary.scalar('loss', loss)
correct = tf.equal(tf.argmax(pred, 2), tf.to_int64(labels_pl))
accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE*NUM_POINT)
tf.summary.scalar('accuracy', accuracy)
# Get training operator
learning_rate = get_learning_rate(batch)
tf.summary.scalar('learning_rate', learning_rate)
if OPTIMIZER == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM)
elif OPTIMIZER == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=batch)
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Create a session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
sess = tf.Session(config=config)
# Init variables
init = tf.global_variables_initializer()
sess.run(init, {is_training_pl:True})
if use_saved_model:
saver.restore(sess, os.path.join(LOG_DIR,'model.ckpt'))
# Add summary writers
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'),
sess.graph)
test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'))
ops = {'pointclouds_pl': pointclouds_pl,
'labels_pl': labels_pl,
'is_training_pl': is_training_pl,
'pred': pred,
'loss': loss,
'train_op': train_op,
'merged': merged,
'step': batch}
if use_saved_model:
eval_one_epoch(sess, ops, test_writer)
for epoch in range(MAX_EPOCH):
log_string('**** EPOCH %03d ****' % (epoch))
sys.stdout.flush()
train_one_epoch(sess, ops, train_writer)
save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"))
log_string("Model saved in file: %s" % save_path)
eval_one_epoch(sess, ops, test_writer)
# # Save the variables to disk.
# if epoch % 1 == 0:
def train_one_epoch(sess, ops, train_writer):
reset_train_data()
""" ops: dict mapping from string to tf ops """
is_training = True
log_string('----')
#checking to confirm get_train_data is functioning correctly
# if LOAD_FULL_DATA:
# current_data = train_data
# current_label = train_label
# file_size = current_data.shape[0]
# num_batches = file_size // BATCH_SIZE
# num_batches = total_training_data / BATCH_SIZE
total_correct = 0
total_seen = 0
loss_sum = 0
batch_idx = -1
# for batch_idx in range(num_batches):
while can_get_train_data():
batch_idx += 1
if batch_idx % 10 == 0:
print('Current batch: %d'%(batch_idx))
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
data_for_loop, label_for_loop = get_train_or_test_data(BATCH_SIZE, True)
#this is in case the last batch has insufficient blocks, so we simply bail
if not can_get_train_data():
break;
#checking to confirm get_train_data is functioning correctly
# check_data_for_loop = current_data[start_idx:end_idx, :, :]
# check_label_for_loop = current_label[start_idx:end_idx]
# if sum(sum(sum(data_for_loop == check_data_for_loop))) != 442368:
# z = 32131
# log_string('check data for loop not match what it should be')
# raise ValueError('check data for loop not match what it should be')
#remove below comments
feed_dict = {ops['pointclouds_pl']: data_for_loop,
ops['labels_pl']: label_for_loop,
ops['is_training_pl']: is_training,}
summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'], ops['pred']],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 2)
correct = np.sum(pred_val == label_for_loop)
total_correct += correct
total_seen += (BATCH_SIZE*NUM_POINT)
loss_sum += loss_val
#remove below comments
# log_string('mean loss: %f' % (loss_sum / float(num_batches)))
# log_string('accuracy: %f' % (total_correct / float(total_seen)))
def eval_one_epoch(sess, ops, test_writer):
reset_test_data()
""" ops: dict mapping from string to tf ops """
is_training = False
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
log_string('----')
# current_data = test_data[:,0:NUM_POINT,:]
# current_label = np.squeeze(test_label)
# file_size = current_data.shape[0]
# num_batches = file_size // BATCH_SIZE
batch_idx = -1
# for batch_idx in range(num_batches):
while can_get_test_data():
batch_idx += 1
data_for_loop, label_for_loop = get_train_or_test_data(BATCH_SIZE, False)
#this is in case the last batch has insufficient blocks
if not can_get_test_data():
break
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
feed_dict = {ops['pointclouds_pl']: data_for_loop,
ops['labels_pl']: label_for_loop,
ops['is_training_pl']: is_training}
summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['loss'], ops['pred']],
feed_dict=feed_dict)
test_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 2)
correct = np.sum(pred_val == label_for_loop)
total_correct += correct
total_seen += (BATCH_SIZE*NUM_POINT)
loss_sum += (loss_val*BATCH_SIZE)
for i in range(start_idx, end_idx):
for j in range(NUM_POINT):
try:
l = label_for_loop[i - start_idx, j - start_idx]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i-start_idx, j] == l)
except:
l = label_for_loop[i - start_idx, j - start_idx]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i-start_idx, j] == l)
log_string('eval mean loss: %f' % (loss_sum / float(total_seen/NUM_POINT)))
log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
print('total correct class')
print(total_correct_class)
print('total seen class')
print(total_seen_class)
if __name__ == "__main__":
train(USE_SAVED_MODEL)
LOG_FOUT.close()
|
[
"os.mkdir",
"numpy.sum",
"argparse.ArgumentParser",
"numpy.argmax",
"tensorflow.maximum",
"tensorflow.ConfigProto",
"tensorflow.Variable",
"sys.stdout.flush",
"os.path.join",
"provider.loadDataFile",
"sys.path.append",
"os.path.abspath",
"os.path.dirname",
"tensorflow.to_int64",
"os.path.exists",
"tensorflow.minimum",
"socket.gethostname",
"tensorflow.placeholder",
"tensorflow.cast",
"tensorflow.summary.merge_all",
"tensorflow.summary.scalar",
"tensorflow.train.Saver",
"tensorflow.global_variables_initializer",
"tensorflow.Session",
"os.system",
"tensorflow.train.MomentumOptimizer",
"tensorflow.Graph",
"tensorflow.train.exponential_decay",
"os.listdir",
"numpy.concatenate",
"tensorflow.argmax",
"numpy.array",
"tensorflow.train.AdamOptimizer"
] |
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|
from datetime import datetime
from pprint import pprint
import extensible_provn.view.mutable_prov
import annotations as prov
HIDE = prov.HIDE
SPECIFIC = prov.SPECIFIC
prov.reset_prov("../generated/mutable_prov/")
prov.STATS_VIEW = 1
def time():
return datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f")
def cond(ents):
return ents
# Line 1
m = 10000 # max value
with prov.desc("L1 - assign", line=1) as line:
e_n10000 = prov.entity("10000", None, prov.SCRIPT + "literal", "10000", line, attrs=HIDE)
v_10000 = prov.value("v10000", "10000", attrs=SPECIFIC)
prov.defined(e_n10000, v_10000, time(), attrs=SPECIFIC)
e_m = prov.entity("m", None, prov.SCRIPT + "name", "m", line, attrs=HIDE)
prov.activity("assign", [(e_m, e_n10000)], attrs=HIDE)
prov.accessed(e_m, v_10000, time(), attrs=SPECIFIC)
# Line 2
result = dist = [
[0, 1, 4],
[m, 0, 2],
[2, m, 0]
]
with prov.desc("L2 - list definition / assign", line=2) as line:
with prov.desc("L2 - list definition"):
e_n0 = prov.entity("0", None, prov.SCRIPT + "literal", "0", line + 1, attrs=HIDE)
v_0 = prov.value("v0", "0", attrs=SPECIFIC)
prov.defined(e_n0, v_0, time(), attrs=SPECIFIC)
e_n1 = prov.entity("1", None, prov.SCRIPT + "literal", "1", line + 1, attrs=HIDE)
v_1 = prov.value("v1", "1", attrs=SPECIFIC)
prov.defined(e_n1, v_1, time(), attrs=SPECIFIC)
e_n4 = prov.entity("4", None, prov.SCRIPT + "literal", "4", line + 1, attrs=HIDE)
v_4 = prov.value("v4", "4", attrs=SPECIFIC)
prov.defined(e_n4, v_4, time(), attrs=SPECIFIC)
e_n2 = prov.entity("2", None, prov.SCRIPT + "literal", "2", line + 2, attrs=HIDE)
v_2 = prov.value("v2", "2", attrs=SPECIFIC)
prov.defined(e_n2, v_2, time(), attrs=SPECIFIC)
prov_dist = [
[e_n0, e_n1, e_n4],
[e_m, e_n0, e_n2],
[e_n2, e_m, e_n0]
]
prov_label = [
["0", "1", "4"],
["m", "0", "2"],
["2", "m", "0"]
]
e_list = prov.entity("matrix", None, prov.SCRIPT + "list", prov.calc_label(prov_label), line)
rows = []
for i, row in enumerate(prov_dist):
v_row = prov.value("row{}".format(i), repr(dist[i]), attrs=SPECIFIC)
prov.derivedByInsertion(
e_list, v_row,
[(str(j), prov.VALUES[v]) for j, v in enumerate(row)],
time(), attrs=SPECIFIC
)
rows.append((str(i), v_row))
ti = time()
v_list = prov.value("vmatrix", repr(dist), attrs=SPECIFIC)
prov.derivedByInsertion(
e_list, v_list, rows, ti, attrs=SPECIFIC
)
prov.defined(e_list, v_list, ti, attrs=SPECIFIC)
with prov.desc("L2 - assign"):
e_dist = prov.entity("dist", None, prov.SCRIPT + "name", "dist", line)
prov.accessed(e_dist, v_list, time(), attrs=SPECIFIC)
prov.activity("assign", [(e_dist, e_list)], attrs=HIDE)
e_result = prov.entity("result", None, prov.SCRIPT + "name", "result", line)
prov.accessed(e_result, v_list, time(), attrs=SPECIFIC)
prov.activity("assign", [(e_result, e_list)], attrs=HIDE)
# Line 6
nodes = len(dist)
with prov.desc("L6 - func call / assign", line=6) as line:
e_ret = prov.entity("len_dist", None, prov.SCRIPT + "eval", "len(dist)", line)
v_3 = prov.value("v3", "3", attrs=SPECIFIC)
prov.defined(e_ret, v_3, time(), attrs=SPECIFIC)
prov.activity("call", [], [e_dist], [e_ret], label="len", attrs=HIDE)
e_nodes = prov.entity("nodes", None, prov.SCRIPT + "name", "nodes", line)
prov.accessed(e_nodes, v_3, time(), attrs=SPECIFIC)
prov.activity("assign", [(e_nodes, e_ret)], attrs=HIDE)
# Line 7
indexes = range(nodes)
with prov.desc("L7 - func call / list assign", line=7) as line:
e_ret = prov.entity("range_nodes", None, prov.SCRIPT + "eval", "range(nodes)", line)
vs = [(str(i), prov.value("v{}".format(x), repr(x), attrs=SPECIFIC)) for i, x in enumerate(indexes)]
v_range = prov.value("v_range", repr(list(indexes)), attrs=SPECIFIC)
ti = time()
prov.derivedByInsertion(
e_ret, v_range, vs, ti, attrs=SPECIFIC
)
prov.defined(e_ret, v_range, ti, attrs=SPECIFIC)
prov.activity("call", [], [e_nodes], [e_ret], label="range", attrs=HIDE)
e_indexes = prov.entity("indexes", None, prov.SCRIPT + "name", "indexes", line)
prov.accessed(e_indexes, v_range, time(), attrs=SPECIFIC)
prov.activity("assign", [(e_indexes, e_ret)], attrs=HIDE)
# Line 8
for k in indexes:
with prov.desc("L8 - loop access", line=8) as line:
e_k = prov.entity("k", None, prov.SCRIPT + "name", "k", line, show1=True, attrs=HIDE)
v_k = prov.DICTS[v_range][repr(k)]
prov.accessedPart(e_k, v_range, repr(k), v_k, time(), attrs=SPECIFIC)
prov.activity("access", used=[e_indexes], generated=[e_k], attrs=HIDE)
# Line 9
distk = dist[k]
with prov.desc("L9 - access / assign", line=9) as line:
e_dist_ak = prov.entity("dist@k", None, prov.SCRIPT + "access", "dist[k]", line, show1=True)
v_dist_ak = prov.DICTS[v_list][repr(k)]
prov.accessedPart(e_dist_ak, v_list, repr(k), v_dist_ak, time(), attrs=SPECIFIC)
prov.activity("access", used=[e_dist, e_k], generated=[e_dist_ak], attrs=HIDE)
e_distk = prov.entity("distk", None, prov.SCRIPT + "name", "distk", line, show1=True)
prov.accessed(e_distk, v_dist_ak, time(), attrs=SPECIFIC)
prov.activity("assign", [(e_distk, e_dist_ak)], attrs=HIDE)
# Line 10
for i in indexes:
with prov.desc("L10 - loop access", line=10) as line:
e_i = prov.entity("i", None, prov.SCRIPT + "name", "i", line, show1=True, attrs=HIDE)
v_i = prov.DICTS[v_range][repr(i)]
prov.accessedPart(e_i, v_range, repr(i), v_i, time(), attrs=SPECIFIC)
prov.activity("access", used=[e_indexes], generated=[e_i], attrs=HIDE)
# Line 11
with prov.desc("L11 - condition", line=11) as line:
cond([e_i, e_k])
if i == k: continue
# Line 12
disti = dist[i]
with prov.desc("L12 - access / assign", line=12) as line:
e_dist_ai = prov.entity("dist@i", None, prov.SCRIPT + "access", "dist[i]", line, show1=True)
v_dist_ai = prov.DICTS[v_list][repr(i)]
prov.accessedPart(e_dist_ai, v_list, repr(i), v_dist_ai, time(), attrs=SPECIFIC)
prov.activity("access", used=[e_dist, e_i], generated=[e_dist_ai], attrs=HIDE)
e_disti = prov.entity("disti", None, prov.SCRIPT + "name", "disti", line, show1=True)
prov.accessed(e_disti, v_dist_ai, time(), attrs=SPECIFIC)
prov.activity("assign", [(e_disti, e_dist_ai)], attrs=HIDE)
# Line 13
for j in indexes:
with prov.desc("L13 - loop access", line=13) as line:
e_j = prov.entity("j", None, prov.SCRIPT + "name", "j", line, show1=True, attrs=HIDE)
v_j = prov.DICTS[v_range][repr(j)]
prov.accessedPart(e_j, v_range, repr(j), v_j, time(), attrs=SPECIFIC)
prov.activity("access", used=[e_indexes], generated=[e_j], attrs=HIDE)
# Line 14
with prov.desc("L14 - condition", line=14) as line:
cond([e_j, e_k, e_i])
if j == k or j == i: continue
# Line 15
ikj = disti[k] + distk[j]
with prov.desc("L15 - access / access / operation / assign", line=15) as line:
e_disti_ak = prov.entity("disti@k", None, prov.SCRIPT + "access", "disti[k]", line, show1=True, attrs=HIDE)
v_disti_ak = prov.DICTS[v_dist_ai][repr(k)]
prov.accessedPart(e_disti_ak, v_dist_ai, repr(k), v_disti_ak, time(), attrs=SPECIFIC)
prov.activity("access", used=[e_disti, e_k], generated=[e_disti_ak], attrs=HIDE)
e_distk_aj = prov.entity("distk@j", None, prov.SCRIPT + "access", "distk[j]", line, show1=True, attrs=HIDE)
v_distk_aj = prov.DICTS[v_dist_ak][repr(j)]
prov.accessedPart(e_distk_aj, v_dist_ak, repr(j), v_distk_aj, time(), attrs=SPECIFIC)
prov.activity("access", used=[e_distk, e_j], generated=[e_distk_aj], attrs=HIDE)
e_sum = prov.entity("sum", None, prov.SCRIPT + "operation", "disti[k] + distk[j]", line, show1=True, attrs=HIDE)
vikj = prov.value("vsum", repr(ikj), attrs=SPECIFIC)
prov.defined(e_sum, vikj, time(), attrs=SPECIFIC)
prov.activity("+", [(e_sum, e_disti_ak, e_distk_aj)], attrs=HIDE)
e_ikj = prov.entity("ikj", None, prov.SCRIPT + "name", "ikj", line, show1=True, attrs=HIDE)
prov.accessed(e_ikj, vikj, time(), attrs=SPECIFIC)
prov.activity("assign", [(e_ikj, e_sum)], attrs=HIDE)
# Line 16
with prov.desc("L16 - access", line=16) as line:
e_disti_aj = prov.entity("disti@j", None, prov.SCRIPT + "access", "disti[j]", line, show1=True, attrs=HIDE)
v_disti_aj = prov.DICTS[v_dist_ai][repr(j)]
prov.accessedPart(e_disti_aj, v_dist_ai, repr(j), v_disti_aj, time(), attrs=SPECIFIC)
prov.activity("access", used=[e_disti, e_j], generated=[e_disti_aj], attrs=HIDE)
ucond = cond([e_disti_aj, e_ikj])
if disti[j] > ikj:
# Line 17
disti[j] = ikj
with prov.desc("L17 - part assign with propagation", line=17) as line:
used = [e_j]
used += ucond # from if
generated = []
e_disti_aj = prov.entity("disti@j", None, prov.SCRIPT + "access", "disti[j]", line, show1=True)
ti = time()
prov.derivedByInsertion(
e_disti_aj, v_dist_ai,
[(str(j), vikj)],
ti, attrs=SPECIFIC
)
prov.accessed(e_disti_aj, vikj, ti, attrs=SPECIFIC)
prov.activity("assign", [(e_disti_aj, e_ikj)], used=[e_disti], shared=True)
# Line 18
print(result[0][2])
with prov.desc("L18 - access / access / call", line=18) as line:
e_result_a0 = prov.entity("result@0", None, prov.SCRIPT + "access", "result[0]", line, attrs=HIDE)
v_result_a0 = prov.DICTS[v_list]["0"]
prov.accessedPart(e_result_a0, v_list, "0", v_result_a0, time(), attrs=SPECIFIC)
prov.activity("access", used=[e_result, e_n0], generated=[e_result_a0], attrs=HIDE)
e_result_a02 = prov.entity("result@0@2", None, prov.SCRIPT + "access", "result[0][2]", line, attrs=HIDE)
v_result_a02 = prov.DICTS[v_result_a0]["2"]
prov.accessedPart(e_result_a02, v_result_a0, "2", v_result_a02, time(), attrs=SPECIFIC)
prov.activity("access", used=[e_result_a0, e_n2], generated=[e_result_a02], attrs=HIDE)
prov.activity("print", [], [e_result_a02], attrs=HIDE)
prov.finish(show_count=False)
|
[
"annotations.entity",
"annotations.accessed",
"annotations.desc",
"annotations.calc_label",
"annotations.activity",
"annotations.reset_prov",
"annotations.value",
"annotations.finish",
"datetime.datetime.now",
"annotations.derivedByInsertion",
"annotations.defined"
] |
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access"""'], {'line': '(16)'}), "('L16 - access', line=16)\n", (8995, 9020), True, 'import annotations as prov\n'), ((9059, 9157), 'annotations.entity', 'prov.entity', (['"""disti@j"""', 'None', "(prov.SCRIPT + 'access')", '"""disti[j]"""', 'line'], {'show1': '(True)', 'attrs': 'HIDE'}), "('disti@j', None, prov.SCRIPT + 'access', 'disti[j]', line,\n show1=True, attrs=HIDE)\n", (9070, 9157), True, 'import annotations as prov\n'), ((9332, 9417), 'annotations.activity', 'prov.activity', (['"""access"""'], {'used': '[e_disti, e_j]', 'generated': '[e_disti_aj]', 'attrs': 'HIDE'}), "('access', used=[e_disti, e_j], generated=[e_disti_aj], attrs=HIDE\n )\n", (9345, 9417), True, 'import annotations as prov\n'), ((9573, 9629), 'annotations.desc', 'prov.desc', (['"""L17 - part assign with propagation"""'], {'line': '(17)'}), "('L17 - part assign with propagation', line=17)\n", (9582, 9629), True, 'import annotations as prov\n'), ((9785, 9871), 'annotations.entity', 'prov.entity', (['"""disti@j"""', 'None', "(prov.SCRIPT + 'access')", '"""disti[j]"""', 'line'], {'show1': '(True)'}), "('disti@j', None, prov.SCRIPT + 'access', 'disti[j]', line,\n show1=True)\n", (9796, 9871), True, 'import annotations as prov\n'), ((10119, 10170), 'annotations.accessed', 'prov.accessed', (['e_disti_aj', 'vikj', 'ti'], {'attrs': 'SPECIFIC'}), '(e_disti_aj, vikj, ti, attrs=SPECIFIC)\n', (10132, 10170), True, 'import annotations as prov\n'), ((10191, 10266), 'annotations.activity', 'prov.activity', (['"""assign"""', '[(e_disti_aj, e_ikj)]'], {'used': '[e_disti]', 'shared': '(True)'}), "('assign', [(e_disti_aj, e_ikj)], used=[e_disti], shared=True)\n", (10204, 10266), True, 'import annotations as prov\n')]
|
# -*- coding: utf-8 -*-
"""
201901, Dr. <NAME>, Beijing & Xinglong, NAOC
202101-? Dr. <NAME> & Dr./Prof. <NAME>
Light_Curve_Pipeline
v3 (2021A) Upgrade from former version, remove unused code
"""
import numpy as np
import matplotlib
#matplotlib.use('Agg')
from matplotlib import pyplot as plt
from .JZ_utils import meanclip
def plot_im_star(ini, img, x, y, mag, err, title, filename):
"""
Plot observed image and overplot stars
:param ini:
:param img:
:param x:
:param y:
:param mag:
:param err:
:param title:
:param filename: file to save
:return:
"""
ny, nx = img.shape
fig = plt.figure(figsize=(nx/50.0, ny/50.0))
ax = fig.add_subplot(111)
d_m, d_s = meanclip(img)
ax.imshow(img, cmap="gray",
vmin=d_m - d_s * ini["plot_img_lowsigma"],
vmax=d_m + d_s * ini["plot_img_highsigma"])
ax.set_xlim(0, nx)
ax.set_ylim(0, ny)
ix_g = np.where(err < 0.1)
ix_b = np.where(err >= 0.1)
ms = (25.0 - mag) * 5
ms[mag > 25] = 1.0
# ms[mag < 10] = 15.0
ax.scatter(x[ix_g], y[ix_g], marker="o", s=ms[ix_g], c="", edgecolors="r")
ax.scatter(x[ix_b], y[ix_b], marker="o", s=ms[ix_b], c="", edgecolors="c")
ax.set_title(title)
fig.savefig(filename, bbox_inches='tight')
plt.close()
def plot_magerr(ini, mag, err, title, filename):
"""
Plot mag-err figure
:param ini:
:param mag:
:param err:
:param title:
:param filename: file to save
:return:
"""
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(111)
ax.plot(mag, err, '.')
ax.set_xlim(10, 25)
ax.set_ylim(-0.001, 1.0)
ax.set_xlabel("Mag (Inst)")
ax.set_ylabel("Error")
ax.set_title(title)
fig.savefig(filename)
plt.close()
def plot_im_target(ini, img,
target_x, target_y,
ref_x, ref_y,
chk_x, chk_y,
title, filename,
target_marker=("s", "r"),
ref_marker=("s", "y"),
chk_marker=("o", "y"),
noplot=False,
):
"""
Plot image and mark target, referenece, and check stars
:param ini:
:param img:
:param target_x:
:param target_y:
:param ref_x:
:param ref_y:
:param chk_x:
:param chk_y:
:param title:
:param filename:
:param target_marker: 2-tuple for marker, marker type and border color
:param ref_marker:
:param chk_marker:
:param noplot:
:return:
"""
ny, nx = img.shape
fig = plt.figure(figsize=(nx / 100.0, ny / 100.0))
ax = fig.add_subplot(111)
fsize = nx / 100 # font size
msize = fsize * 5 # marker size
d_m, d_s = meanclip(img)
ax.imshow(img, cmap="gray",
vmin=d_m - d_s * ini["plot_img_lowsigma"],
vmax=d_m + d_s * ini["plot_img_highsigma"])
ax.set_xlim(0, nx)
ax.set_ylim(0, ny)
if target_x is not None:
ax.scatter(target_x, target_y, marker=target_marker[0], s=msize, c=None, edgecolors=target_marker[1])
if np.isscalar(target_x): target_x = (target_x, )
if np.isscalar(target_y): target_y = (target_y, )
for i in range(len(target_x)):
ax.text(target_x[i]+fsize/2, target_y[i]+fsize/2, "T-{}".format(i),
color=target_marker[1], fontsize=fsize)
if ref_x is not None:
ax.scatter(ref_x, ref_y, marker=ref_marker[0], s=msize, c=None, edgecolors=ref_marker[1])
if np.isscalar(ref_x): ref_x = (ref_x, )
if np.isscalar(ref_y): ref_y = (ref_y, )
for i in range(len(ref_x)):
ax.text(ref_x[i]+fsize/2, ref_y[i]+fsize/2, "R-{}".format(i),
color=ref_marker[1], fontsize=fsize)
if chk_x is not None:
ax.scatter(chk_x, chk_y, marker=chk_marker[0], s=msize, c=None, edgecolors=chk_marker[1])
if np.isscalar(chk_x): chk_x = (chk_x, )
if np.isscalar(chk_y): chk_y = (chk_y, )
for i in range(len(chk_x)):
ax.text(chk_x[i]+fsize/2, chk_y[i]+fsize/2, "C-{}".format(i),
color=chk_marker[1], fontsize=fsize)
ax.set_title(title)
fig.savefig(filename, bbox_inches='tight')
if noplot:
plt.close(fig)
def plot_im_obj(ini, img,
obj_x, obj_y,
title, filename,
target_marker=("s", "r"),
noplot=False,
):
"""
Plot only objects, without using ref or check
:param ini:
:param img:
:param obj_x:
:param obj_y:
:param title:
:param filename:
:param target_marker:
:param noplot:
:return:
"""
plot_im_target(ini, img, obj_x, obj_y,
None, None, None, None,
title, filename,
target_marker,
noplot=noplot,
)
|
[
"numpy.isscalar",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure",
"numpy.where"
] |
[((655, 697), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(nx / 50.0, ny / 50.0)'}), '(figsize=(nx / 50.0, ny / 50.0))\n', (665, 697), True, 'from matplotlib import pyplot as plt\n'), ((959, 978), 'numpy.where', 'np.where', (['(err < 0.1)'], {}), '(err < 0.1)\n', (967, 978), True, 'import numpy as np\n'), ((990, 1010), 'numpy.where', 'np.where', (['(err >= 0.1)'], {}), '(err >= 0.1)\n', (998, 1010), True, 'import numpy as np\n'), ((1321, 1332), 'matplotlib.pyplot.close', 'plt.close', ([], {}), '()\n', (1330, 1332), True, 'from matplotlib import pyplot as plt\n'), ((1548, 1575), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(10, 5)'}), '(figsize=(10, 5))\n', (1558, 1575), True, 'from matplotlib import pyplot as plt\n'), ((1802, 1813), 'matplotlib.pyplot.close', 'plt.close', ([], {}), '()\n', (1811, 1813), True, 'from matplotlib import pyplot as plt\n'), ((2618, 2662), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(nx / 100.0, ny / 100.0)'}), '(figsize=(nx / 100.0, ny / 100.0))\n', (2628, 2662), True, 'from matplotlib import pyplot as plt\n'), ((3139, 3160), 'numpy.isscalar', 'np.isscalar', (['target_x'], {}), '(target_x)\n', (3150, 3160), True, 'import numpy as np\n'), ((3197, 3218), 'numpy.isscalar', 'np.isscalar', (['target_y'], {}), '(target_y)\n', (3208, 3218), True, 'import numpy as np\n'), ((3559, 3577), 'numpy.isscalar', 'np.isscalar', (['ref_x'], {}), '(ref_x)\n', (3570, 3577), True, 'import numpy as np\n'), ((3608, 3626), 'numpy.isscalar', 'np.isscalar', (['ref_y'], {}), '(ref_y)\n', (3619, 3626), True, 'import numpy as np\n'), ((3949, 3967), 'numpy.isscalar', 'np.isscalar', (['chk_x'], {}), '(chk_x)\n', (3960, 3967), True, 'import numpy as np\n'), ((3998, 4016), 'numpy.isscalar', 'np.isscalar', (['chk_y'], {}), '(chk_y)\n', (4009, 4016), True, 'import numpy as np\n'), ((4299, 4313), 'matplotlib.pyplot.close', 'plt.close', (['fig'], {}), '(fig)\n', (4308, 4313), True, 'from matplotlib import pyplot as plt\n')]
|
from collections import defaultdict
class Leaf: # pylint: disable=too-few-public-methods,missing-class-docstring
def __init__(self):
self.payloads = []
self.children = defaultdict(Leaf)
class Trie:
"""
`Trie <https://en.wikipedia.org/wiki/Trie>`_ is a data structure for effective prefix search. It
is used in Spylls to store prefixes and suffixes. For example, if we have suffixes "s", "ions",
"ications", they are stored (reversed) this way:
.. code-block:: text
root
+-s ... metadata for suffix "s"
+-noi ... metadata for suffix "ions"
+-taci ... metadata for suffix "ications"
So, for the word "complications", we can receive all its possible suffixes (all three) in one
pass through trie.
**Important:** Profiling shows that search through Trie of suffixes/prefixes is the center of
Spylls performance, that's why it is very primitive and fast implementation instead of some
library like `pygtrie <https://github.com/google/pygtrie>`_. Probably, by choosing fast (C)
implementation of trie, the whole spylls can be make much faster.
"""
def __init__(self, data=None):
self.root = Leaf()
if data:
for key, val in data.items():
self.set(key, val)
def put(self, path, payload):
cur = self.root
for p in path:
cur = cur.children[p]
cur.payloads.append(payload)
def set(self, path, payloads):
cur = self.root
for p in path:
cur = cur.children[p]
cur.payloads = payloads
def lookup(self, path):
for _, leaf in self.traverse(self.root, path):
yield from leaf.payloads
def traverse(self, cur, path, traversed=[]):
yield (traversed, cur)
if not path or path[0] not in cur.children:
return
yield from self.traverse(cur.children[path[0]], path[1:], [*traversed, path[0]])
|
[
"collections.defaultdict"
] |
[((194, 211), 'collections.defaultdict', 'defaultdict', (['Leaf'], {}), '(Leaf)\n', (205, 211), False, 'from collections import defaultdict\n')]
|
# -*- coding: utf-8 -*-
# Generated by Django 1.9.6 on 2016-09-15 15:42
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import vmprofile.models
import uuid
def forward_func(apps, schema_editor):
RuntimeData = apps.get_model("vmprofile", "RuntimeData")
CPUProfile = apps.get_model("vmprofile", "CPUProfile")
for prof in CPUProfile.objects.all():
rd = RuntimeData.objects.create()
rd.created = prof.created
rd.user = prof.user
rd.name = prof.name
rd.vm = prof.vm
rd.completed = True
rd.save()
prof.runtime_data = rd
prof.save()
def backward_func(apps, schema_editor):
RuntimeData = apps.get_model("vmprofile", "RuntimeData")
CPUProfile = apps.get_model("vmprofile", "CPUProfile")
for rd in RuntimeData.objects.all():
cpup = rd.cpu_profile
cpup.created = rd.created
cpup.user = rd.user
cpup.name = rd.name
cpup.vm = rd.vm
cpup.save()
RuntimeData.objects.delete()
class Migration(migrations.Migration):
atomic = False
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('vmprofile', '0006_auto_20160915_1531'),
]
operations = [
migrations.CreateModel(
name='RuntimeData',
fields=[
('runtime_id', models.CharField(default=uuid.uuid4, max_length=64, primary_key=True, unique=True, serialize=False)),
('created', models.DateTimeField(auto_now_add=True)),
('vm', models.CharField(blank=True, max_length=32)),
('name', models.CharField(blank=True, max_length=256)),
('user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
options={
'ordering': ['-created'],
},
),
migrations.RenameField(
model_name='cpuprofile',
old_name='checksum',
new_name='cpuprofile_id',
),
migrations.AlterField(
model_name='cpuprofile',
name='cpuprofile_id',
field=models.CharField(default=uuid.uuid4, max_length=64, primary_key=True, serialize=False),
),
migrations.AddField(
model_name='cpuprofile',
name='runtime_data',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
related_name='cpu_profile', to='vmprofile.RuntimeData'),
),
migrations.RunPython(forward_func, backward_func),
migrations.AlterModelOptions(
name='cpuprofile',
options={},
),
migrations.RemoveField(
model_name='cpuprofile',
name='created',
),
migrations.RemoveField(
model_name='cpuprofile',
name='name',
),
migrations.RemoveField(
model_name='cpuprofile',
name='user',
),
migrations.RemoveField(
model_name='cpuprofile',
name='vm',
),
migrations.AddField(
model_name='cpuprofile',
name='file',
field=models.FileField(null=True, upload_to=vmprofile.models.get_profile_storage_directory),
),
migrations.AlterField(
model_name='cpuprofile',
name='data',
field=models.TextField(null=True),
),
migrations.AddField(
model_name='runtimedata',
name='completed',
field=models.BooleanField(default=False),
),
]
|
[
"django.db.migrations.RunPython",
"django.db.models.OneToOneField",
"django.db.models.FileField",
"django.db.migrations.swappable_dependency",
"django.db.models.TextField",
"django.db.migrations.RenameField",
"django.db.migrations.RemoveField",
"django.db.models.CharField",
"django.db.models.ForeignKey",
"django.db.models.BooleanField",
"django.db.migrations.AlterModelOptions",
"django.db.models.DateTimeField"
] |
[((1200, 1257), 'django.db.migrations.swappable_dependency', 'migrations.swappable_dependency', (['settings.AUTH_USER_MODEL'], {}), '(settings.AUTH_USER_MODEL)\n', (1231, 1257), False, 'from django.db import migrations, models\n'), ((2007, 2105), 'django.db.migrations.RenameField', 'migrations.RenameField', ([], {'model_name': '"""cpuprofile"""', 'old_name': '"""checksum"""', 'new_name': '"""cpuprofile_id"""'}), "(model_name='cpuprofile', old_name='checksum',\n new_name='cpuprofile_id')\n", (2029, 2105), False, 'from django.db import migrations, models\n'), ((2690, 2739), 'django.db.migrations.RunPython', 'migrations.RunPython', (['forward_func', 'backward_func'], {}), '(forward_func, backward_func)\n', (2710, 2739), False, 'from django.db import migrations, models\n'), ((2749, 2808), 'django.db.migrations.AlterModelOptions', 'migrations.AlterModelOptions', ([], {'name': '"""cpuprofile"""', 'options': '{}'}), "(name='cpuprofile', options={})\n", (2777, 2808), False, 'from django.db import migrations, models\n'), ((2853, 2916), 'django.db.migrations.RemoveField', 'migrations.RemoveField', ([], {'model_name': '"""cpuprofile"""', 'name': '"""created"""'}), "(model_name='cpuprofile', name='created')\n", (2875, 2916), False, 'from django.db import migrations, models\n'), ((2961, 3021), 'django.db.migrations.RemoveField', 'migrations.RemoveField', ([], {'model_name': '"""cpuprofile"""', 'name': '"""name"""'}), "(model_name='cpuprofile', name='name')\n", (2983, 3021), False, 'from django.db import migrations, models\n'), ((3066, 3126), 'django.db.migrations.RemoveField', 'migrations.RemoveField', ([], {'model_name': '"""cpuprofile"""', 'name': '"""user"""'}), "(model_name='cpuprofile', name='user')\n", (3088, 3126), False, 'from django.db import migrations, models\n'), ((3171, 3229), 'django.db.migrations.RemoveField', 'migrations.RemoveField', ([], {'model_name': '"""cpuprofile"""', 'name': '"""vm"""'}), "(model_name='cpuprofile', name='vm')\n", (3193, 3229), False, 'from django.db import migrations, models\n'), ((2270, 2360), 'django.db.models.CharField', 'models.CharField', ([], {'default': 'uuid.uuid4', 'max_length': '(64)', 'primary_key': '(True)', 'serialize': '(False)'}), '(default=uuid.uuid4, max_length=64, primary_key=True,\n serialize=False)\n', (2286, 2360), False, 'from django.db import migrations, models\n'), ((2486, 2635), 'django.db.models.OneToOneField', 'models.OneToOneField', ([], {'blank': '(True)', 'null': '(True)', 'on_delete': 'django.db.models.deletion.CASCADE', 'related_name': '"""cpu_profile"""', 'to': '"""vmprofile.RuntimeData"""'}), "(blank=True, null=True, on_delete=django.db.models.\n deletion.CASCADE, related_name='cpu_profile', to='vmprofile.RuntimeData')\n", (2506, 2635), False, 'from django.db import migrations, models\n'), ((3375, 3465), 'django.db.models.FileField', 'models.FileField', ([], {'null': '(True)', 'upload_to': 'vmprofile.models.get_profile_storage_directory'}), '(null=True, upload_to=vmprofile.models.\n get_profile_storage_directory)\n', (3391, 3465), False, 'from django.db import migrations, models\n'), ((3584, 3611), 'django.db.models.TextField', 'models.TextField', ([], {'null': '(True)'}), '(null=True)\n', (3600, 3611), False, 'from django.db import migrations, models\n'), ((3739, 3773), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(False)'}), '(default=False)\n', (3758, 3773), False, 'from django.db import migrations, models\n'), ((1451, 1554), 'django.db.models.CharField', 'models.CharField', ([], {'default': 'uuid.uuid4', 'max_length': '(64)', 'primary_key': '(True)', 'unique': '(True)', 'serialize': '(False)'}), '(default=uuid.uuid4, max_length=64, primary_key=True,\n unique=True, serialize=False)\n', (1467, 1554), False, 'from django.db import migrations, models\n'), ((1581, 1620), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'auto_now_add': '(True)'}), '(auto_now_add=True)\n', (1601, 1620), False, 'from django.db import migrations, models\n'), ((1646, 1689), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(True)', 'max_length': '(32)'}), '(blank=True, max_length=32)\n', (1662, 1689), False, 'from django.db import migrations, models\n'), ((1717, 1761), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(True)', 'max_length': '(256)'}), '(blank=True, max_length=256)\n', (1733, 1761), False, 'from django.db import migrations, models\n'), ((1789, 1895), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'null': '(True)', 'on_delete': 'django.db.models.deletion.CASCADE', 'to': 'settings.AUTH_USER_MODEL'}), '(null=True, on_delete=django.db.models.deletion.CASCADE,\n to=settings.AUTH_USER_MODEL)\n', (1806, 1895), False, 'from django.db import migrations, models\n')]
|
from Classes.Wrappers.PlayerDisplayData import PlayerDisplayData
class BattleLogPlayerEntry:
def encode(calling_instance, fields):
pass
def decode(calling_instance, fields):
fields["BattleLogEntry"] = {}
fields["BattleLogEntry"]["Unkown1"] = calling_instance.readVInt()
fields["BattleLogEntry"]["Unkown2"] = calling_instance.readLong()
fields["BattleLogEntry"]["Unkown3"] = calling_instance.readVInt()
fields["BattleLogEntry"]["Unkown4"] = calling_instance.readBoolean()
countVal = calling_instance.readVInt()
fields["BattleLogEntry"]["Unkown5"] = countVal
fields["BattleLogEntry"]["Entries"] = {}
for i in range(countVal):
fields["BattleLogEntry"]["Entries"][str(i)] = {}
fields["BattleLogEntry"]["Entries"][str(i)]["Unknown1"] = calling_instance.readDataReference()
fields["BattleLogEntry"]["Entries"][str(i)]["Unknown2"] = calling_instance.readVInt()
fields["BattleLogEntry"]["Entries"][str(i)]["Unknown3"] = calling_instance.readVInt()
fields["BattleLogEntry"]["Entries"][str(i)]["Unknown4"] = calling_instance.readVInt()
fields["BattleLogEntry"]["Unkown6"] = calling_instance.readVInt()
PlayerDisplayData.decode(calling_instance, fields)
|
[
"Classes.Wrappers.PlayerDisplayData.PlayerDisplayData.decode"
] |
[((1258, 1308), 'Classes.Wrappers.PlayerDisplayData.PlayerDisplayData.decode', 'PlayerDisplayData.decode', (['calling_instance', 'fields'], {}), '(calling_instance, fields)\n', (1282, 1308), False, 'from Classes.Wrappers.PlayerDisplayData import PlayerDisplayData\n')]
|
# -*- coding: utf-8 -*-
"""
Beeline.ru
"""
from html2text import convert
from . import by_subj, NBSP, BUTTONS
MARK_INBOX = 'В Ваш почтовый ящик '
MARK_CLOUD_GO = 'Прослушать сообщение можно в web-интерфейсе управления услугой'
def voice_mail(_subj, text):
"""
voice mail
"""
pos_start = text.index(MARK_INBOX)
pos_end = text.index(MARK_CLOUD_GO)
result = text[pos_start:pos_end]
if 'отабонента' in result:
result = result.replace('отабонента', 'от абонента')
return [
result,
'\n' + BUTTONS + '\n' + "[Прослушать](https://cloudpbx.beeline.ru/)",
]
SUBJ_HANDLERS = [
(('Облачная АТС - У вас новое сообщение голосовой почты', ), voice_mail),
]
def start(subj, body):
"""
parse Beeline
"""
return by_subj(
subj,
body,
convert(body).replace(NBSP, ' '),
'beeline',
'Beeline Облачная АТС\n',
SUBJ_HANDLERS
)
|
[
"html2text.convert"
] |
[((816, 829), 'html2text.convert', 'convert', (['body'], {}), '(body)\n', (823, 829), False, 'from html2text import convert\n')]
|
import bcrypt
from sqlalchemy import (
Column,
Index,
Integer,
Unicode,
Date,
)
from .meta import Base
class Entry(Base):
__tablename__ = 'entries'
id = Column(Integer, primary_key=True)
title = Column(Unicode)
body = Column(Unicode)
category = Column(Unicode)
tags = Column(Unicode)
creation_date = Column(Date)
|
[
"sqlalchemy.Column"
] |
[((184, 217), 'sqlalchemy.Column', 'Column', (['Integer'], {'primary_key': '(True)'}), '(Integer, primary_key=True)\n', (190, 217), False, 'from sqlalchemy import Column, Index, Integer, Unicode, Date\n'), ((230, 245), 'sqlalchemy.Column', 'Column', (['Unicode'], {}), '(Unicode)\n', (236, 245), False, 'from sqlalchemy import Column, Index, Integer, Unicode, Date\n'), ((257, 272), 'sqlalchemy.Column', 'Column', (['Unicode'], {}), '(Unicode)\n', (263, 272), False, 'from sqlalchemy import Column, Index, Integer, Unicode, Date\n'), ((288, 303), 'sqlalchemy.Column', 'Column', (['Unicode'], {}), '(Unicode)\n', (294, 303), False, 'from sqlalchemy import Column, Index, Integer, Unicode, Date\n'), ((315, 330), 'sqlalchemy.Column', 'Column', (['Unicode'], {}), '(Unicode)\n', (321, 330), False, 'from sqlalchemy import Column, Index, Integer, Unicode, Date\n'), ((351, 363), 'sqlalchemy.Column', 'Column', (['Date'], {}), '(Date)\n', (357, 363), False, 'from sqlalchemy import Column, Index, Integer, Unicode, Date\n')]
|
# Generated by Django 3.2.8 on 2021-11-20 23:06
import django.db.models.deletion
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
("players", "0001_initial"),
]
operations = [
migrations.CreateModel(
name="Member",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("discord_id", models.CharField(max_length=50, unique=True)),
("name", models.CharField(max_length=255, verbose_name="name")),
("last_seen", models.DateTimeField(blank=True, null=True)),
("is_bot", models.BooleanField(default=False)),
("can_admin_bot", models.BooleanField(default=False)),
(
"player",
models.OneToOneField(
blank=True,
null=True,
on_delete=django.db.models.deletion.CASCADE,
related_name="discord_member",
to="players.player",
),
),
],
),
]
|
[
"django.db.models.OneToOneField",
"django.db.models.BigAutoField",
"django.db.models.CharField",
"django.db.models.BooleanField",
"django.db.models.DateTimeField"
] |
[((413, 509), 'django.db.models.BigAutoField', 'models.BigAutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (432, 509), False, 'from django.db import migrations, models\n'), ((676, 720), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(50)', 'unique': '(True)'}), '(max_length=50, unique=True)\n', (692, 720), False, 'from django.db import migrations, models\n'), ((748, 801), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(255)', 'verbose_name': '"""name"""'}), "(max_length=255, verbose_name='name')\n", (764, 801), False, 'from django.db import migrations, models\n'), ((834, 877), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'blank': '(True)', 'null': '(True)'}), '(blank=True, null=True)\n', (854, 877), False, 'from django.db import migrations, models\n'), ((907, 941), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(False)'}), '(default=False)\n', (926, 941), False, 'from django.db import migrations, models\n'), ((978, 1012), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(False)'}), '(default=False)\n', (997, 1012), False, 'from django.db import migrations, models\n'), ((1083, 1228), 'django.db.models.OneToOneField', 'models.OneToOneField', ([], {'blank': '(True)', 'null': '(True)', 'on_delete': 'django.db.models.deletion.CASCADE', 'related_name': '"""discord_member"""', 'to': '"""players.player"""'}), "(blank=True, null=True, on_delete=django.db.models.\n deletion.CASCADE, related_name='discord_member', to='players.player')\n", (1103, 1228), False, 'from django.db import migrations, models\n')]
|
import warnings
class AuthlibDeprecationWarning(DeprecationWarning):
pass
warnings.simplefilter('always', AuthlibDeprecationWarning)
def deprecate(message, version=None, link_uid=None, link_file=None):
if version:
message += '\nIt will be compatible before version {}.'.format(version)
if link_uid and link_file:
message += '\nRead more <https://git.io/{}#file-{}-md>'.format(link_uid, link_file)
warnings.warn(AuthlibDeprecationWarning(message), stacklevel=2)
|
[
"warnings.simplefilter"
] |
[((82, 140), 'warnings.simplefilter', 'warnings.simplefilter', (['"""always"""', 'AuthlibDeprecationWarning'], {}), "('always', AuthlibDeprecationWarning)\n", (103, 140), False, 'import warnings\n')]
|
import boto.ec2
import sys
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('aws_access_key_id')
parser.add_argument('aws_secret_access_key')
parser.add_argument('region')
config = parser.parse_args()
conn = boto.ec2.connect_to_region(config.region,
aws_access_key_id=config.aws_access_key_id,
aws_secret_access_key=config.aws_secret_access_key)
images = conn.get_all_images(owners=['self'])
values = []
for image in images:
values.append('"%s": "%s"' % (image.name, image.id))
print( ','.join(values))
|
[
"argparse.ArgumentParser"
] |
[((54, 79), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (77, 79), False, 'import argparse\n')]
|
# -*- coding: utf-8 -*-
#
# Copyright (c) 2020 by <NAME> <<EMAIL>>
# All rights reserved.
# This file is part of vagrancyCtrl (https://github.com/seeraven/vagrancyCtrl)
# and is released under the "BSD 3-Clause License". Please see the LICENSE file
# that is included as part of this package.
#
"""Command line interface used by vagrancyCtrl."""
# -----------------------------------------------------------------------------
# Module Import
# -----------------------------------------------------------------------------
import sys
import argcomplete
from .parser_cmd_delete import get_subparser_delete
from .parser_cmd_download import get_subparser_download
from .parser_cmd_print import get_subparser_print
from .parser_cmd_upload import get_subparser_upload
from .parser_main import get_main_parser
# -----------------------------------------------------------------------------
# Exported Functions
# -----------------------------------------------------------------------------
def get_parser():
"""Get the command line argument parser for vagrancyCtrl.
Returns:
argparse.ArgumentParser: A new ArgumentParser object of the parser.
"""
parser = get_main_parser()
subparsers = parser.add_subparsers()
get_subparser_delete(subparsers)
get_subparser_download(subparsers)
get_subparser_print(subparsers)
get_subparser_upload(subparsers)
return parser
def vagrancy_ctrl_main():
"""Handle the vagrancyCtrl actions."""
parser = get_parser()
argcomplete.autocomplete(parser)
args = parser.parse_args()
if hasattr(args, 'func'):
args.func(args)
else:
parser.print_help(sys.stderr)
sys.exit(1)
# -----------------------------------------------------------------------------
# EOF
# -----------------------------------------------------------------------------
|
[
"argcomplete.autocomplete",
"sys.exit"
] |
[((1524, 1556), 'argcomplete.autocomplete', 'argcomplete.autocomplete', (['parser'], {}), '(parser)\n', (1548, 1556), False, 'import argcomplete\n'), ((1698, 1709), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (1706, 1709), False, 'import sys\n')]
|
# *-* coding: utf-8 *-*
"""Context manager for easily using a pymemcache mutex.
The `acquire_lock` context manager makes it easy to use :mod:`pymemcache` (which
uses memcached) to create a mutex for a certain portion of code. Of course,
this requires the :mod:`pymemcache` library to be installed, which in turn
requires `memcached <https://memcached.org>`_ to be installed.
"""
import json
import logging
from contextlib import contextmanager
from time import sleep
from pymemcache.client.base import Client
__all__ = ['acquire_lock', 'LockUnavailable']
class LockUnavailable(Exception):
"""Raised when a cached lock is already in use."""
def json_serializer(key, value):
# Borrowed from the pymemcache docs: https://pymemcache.readthedocs.io/en/latest/getting_started.html#serialization
if type(value) == str:
return value, 1
return json.dumps(value), 2
def json_deserializer(key, value, flags):
# Borrowed from the pymemcache docs: https://pymemcache.readthedocs.io/en/latest/getting_started.html#serialization
if flags == 1:
return value
if flags == 2:
return json.loads(value)
raise Exception("Unknown serialization format")
cache_client = Client(('localhost', 11211), serializer=json_serializer, deserializer=json_deserializer)
@contextmanager
def acquire_lock(lock_id, wait=0, max_retries=0):
"""Acquire a lock on the given lock ID, or raise an exception.
This context manager can be used as a mutex by doing something like the
following:
>>> from time import sleep
>>> job_done = False
>>> while not job_done:
... try:
... with acquire_lock('some id'):
... sensitive_function()
... job_done = True
... except LockUnavailable:
... # Sleep for a couple seconds while the other code runs and
... # hopefully completes
... sleep(2)
In the above example, ``sensitive_function()`` should only be run if no
other code is also running it. A more concise way of writing the above
example would be to use the other parameters, like this:
>>> with acquire_lock('some id', wait=2):
... sensitive_function()
:param lock_id: The ID for this lock. See :mod:`pymemcache`'s documentation
on `key constraints
<https://pymemcache.readthedocs.io/en/latest/getting_started.html#key-constraints>`_
for more info.
:type lock_id: str or bytes
:param int wait: Indicates how many seconds after failing to acquire the
lock to wait (sleep) before retrying. When set to 0 (default), will
immediately raise a `LockUnavailable` exception.
:param int max_retries: Maximum number of times to retry to acquire the
lock before raising a `LockUnavailable` exception. When set to 0
(default), will always retry. Has essentially no effect if ``wait`` is
0.
:raises LockUnavailable: when a lock with the same ID already exists and
``wait`` is set to 0.
"""
assert isinstance(lock_id, str) or isinstance(lock_id, bytes)
if (not isinstance(wait, int)) or wait < 0:
wait = 0
if (not isinstance(max_retries, int)) or max_retries < 0:
max_retries = 0
# Get lock
retries = 0
while retries <= max_retries:
if cache_client.add(lock_id, str('Locked by dbling')): # We got the lock
break
if wait == 0:
raise LockUnavailable
if max_retries > 0:
retries += 1
logging.info('Unable to acquire lock "{}". Will retry in {} seconds.'.format(lock_id, wait))
sleep(wait)
# Tell the `with` statement to execute
yield
# Release lock, don't wait for the reply
cache_client.delete(lock_id, noreply=True)
|
[
"pymemcache.client.base.Client",
"json.loads",
"json.dumps",
"time.sleep"
] |
[((1213, 1306), 'pymemcache.client.base.Client', 'Client', (["('localhost', 11211)"], {'serializer': 'json_serializer', 'deserializer': 'json_deserializer'}), "(('localhost', 11211), serializer=json_serializer, deserializer=\n json_deserializer)\n", (1219, 1306), False, 'from pymemcache.client.base import Client\n'), ((867, 884), 'json.dumps', 'json.dumps', (['value'], {}), '(value)\n', (877, 884), False, 'import json\n'), ((1126, 1143), 'json.loads', 'json.loads', (['value'], {}), '(value)\n', (1136, 1143), False, 'import json\n'), ((3643, 3654), 'time.sleep', 'sleep', (['wait'], {}), '(wait)\n', (3648, 3654), False, 'from time import sleep\n')]
|
'''
#*************************************************************************
Useless App:
#*************************************************************************
Description: - useless but hopefully beautiful;
- app that changes its color and themes;
- UI Modules.
#*************************************************************************
Author: <NAME>
<EMAIL>
License: MIT https://github.com/laurasiviero/UselessApp/blob/main/LICENSE
Date 2021.04.17
#*************************************************************************
'''
import sys
import maya.cmds as cmds
import useless_theme_functions as uth
import useless_functions as ufx
# *************************************************************************
# UI:
# *************************************************************************
def useless_app(USERPATH):
PATH_ICONS = USERPATH + r"\icon"
sys.path.append(USERPATH)
sys.path.append(PATH_ICONS)
print("directories have been updated")
ui_title = "Useless App"
theme_color = [0.286, 0.286, 0.286]
analogue_color = [0.2, 0.2, 0.2]
complementary_color = [0.792, 0.195, 0.203]
# DELETE if it already exists:
if cmds.window(ui_title, exists=True):
cmds.deleteUI(ui_title)
window = cmds.window(ui_title, title="USELESS APP",
backgroundColor=theme_color,
resizeToFitChildren=True)
# ************************************************************************
# LAYOUT
# ************************************************************************
cmds.formLayout("useless_form_layout", backgroundColor=theme_color,
numberOfDivisions=100)
theme_column = cmds.columnLayout("theme_column", adjustableColumn=True,
rowSpacing=5)
# THEME PANEL:
# ************************************************************************
theme_title = cmds.text("Change Theme:",
font="boldLabelFont", align="left")
cmds.separator("theme_separator", backgroundColor=complementary_color,
style="none", height=3)
cmds.iconTextButton("button_day", style='iconOnly',
image=PATH_ICONS + r'\day_icon.png',
backgroundColor=analogue_color,
command="useless_ui.uth.change_theme('day', USERPATH)")
cmds.iconTextButton("button_night", style='iconOnly',
image=PATH_ICONS + r'\night_icon.png',
backgroundColor=analogue_color,
command="useless_ui.uth.change_theme('night', USERPATH)")
cmds.iconTextButton("button_user", style='iconOnly',
image=PATH_ICONS + r'\user_icon.png',
backgroundColor=analogue_color,
command="useless_ui.uth.change_theme('user', USERPATH)")
cmds.iconTextButton("button_default", style='iconOnly',
image=PATH_ICONS + r'\default_icon.png',
backgroundColor=analogue_color,
command="useless_ui.uth.change_theme('default', USERPATH)")
cmds.setParent("..")
# APP COLUMN:
#************************************************************************
app_column = cmds.columnLayout(adjustableColumn=True, rowSpacing=5)
cmds.text("This is the space for the title:",
font="boldLabelFont", align="center")
cmds.separator("title_separator", backgroundColor=complementary_color,
style="none", height=3)
cmds.text("This is the place where it should be the most powerful tool ever made",
font="boldLabelFont", align="center")
cmds.text("Sorry, I don't have to create it for this contest",
font="boldLabelFont", align="center")
cmds.separator("stuff_separator", backgroundColor=complementary_color,
style="none", height=3)
# BUTTONS:
cmds.rowLayout(numberOfColumns=2, adjustableColumn1=True)
cmds.iconTextButton("useless_stuff_button", label="Set Idle",
width=190, style="textOnly",
backgroundColor=analogue_color,
command="useless_ui.ufx.set_idle()")
cmds.iconTextButton("useless_random_button", label="I feel lucky",
style="textOnly",
width=190,
backgroundColor=analogue_color,
command='useless_ui.ufx.get_random_quotes()')
cmds.setParent("..")
cmds.columnLayout(adjustableColumn=True, rowSpacing=5)
cmds.iconTextButton("useless_credits", label="CREDITS!",
style="textOnly",
width=190,
backgroundColor=analogue_color,
command='useless_ui.ufx.show_credits()')
cmds.separator("buttons_separator", backgroundColor=complementary_color,
style="none", height=3)
cmds.setParent("..")
# SLIDERS:
cmds.rowLayout(numberOfColumns=2, adjustableColumn1=True)
cmds.intSliderGrp("useless_number_slider", field=True, label='Numbers',
value=0, min=0, max=10,
columnWidth=(1, 50),
columnAlign=[(1, "left"), (2, "left")])
cmds.iconTextButton("useless_number", label="Pick it out!",
style="textOnly",
width=80,
backgroundColor=analogue_color,
command='useless_ui.ufx.pick_numbers()')
cmds.setParent("..")
cmds.separator("end_separator", backgroundColor=complementary_color,
style="none", height=3)
# MAIN LAYOUT:
# *********************************************************************
cmds.formLayout("useless_form_layout", edit=True,
attachForm=[(theme_column, 'left', 5),
(app_column, 'right', 10)],
attachControl=[(app_column, 'left', 10, theme_column)])
cmds.showWindow(window)
|
[
"sys.path.append",
"maya.cmds.iconTextButton",
"maya.cmds.deleteUI",
"maya.cmds.rowLayout",
"maya.cmds.text",
"maya.cmds.intSliderGrp",
"maya.cmds.separator",
"maya.cmds.window",
"maya.cmds.formLayout",
"maya.cmds.columnLayout",
"maya.cmds.showWindow",
"maya.cmds.setParent"
] |
[((988, 1013), 'sys.path.append', 'sys.path.append', (['USERPATH'], {}), '(USERPATH)\n', (1003, 1013), False, 'import sys\n'), ((1019, 1046), 'sys.path.append', 'sys.path.append', (['PATH_ICONS'], {}), '(PATH_ICONS)\n', (1034, 1046), False, 'import sys\n'), ((1297, 1331), 'maya.cmds.window', 'cmds.window', (['ui_title'], {'exists': '(True)'}), '(ui_title, exists=True)\n', (1308, 1331), True, 'import maya.cmds as cmds\n'), ((1382, 1483), 'maya.cmds.window', 'cmds.window', (['ui_title'], {'title': '"""USELESS APP"""', 'backgroundColor': 'theme_color', 'resizeToFitChildren': '(True)'}), "(ui_title, title='USELESS APP', backgroundColor=theme_color,\n resizeToFitChildren=True)\n", (1393, 1483), True, 'import maya.cmds as cmds\n'), ((1715, 1809), 'maya.cmds.formLayout', 'cmds.formLayout', (['"""useless_form_layout"""'], {'backgroundColor': 'theme_color', 'numberOfDivisions': '(100)'}), "('useless_form_layout', backgroundColor=theme_color,\n numberOfDivisions=100)\n", (1730, 1809), True, 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4235), True, 'import maya.cmds as cmds\n'), ((4241, 4409), 'maya.cmds.iconTextButton', 'cmds.iconTextButton', (['"""useless_stuff_button"""'], {'label': '"""Set Idle"""', 'width': '(190)', 'style': '"""textOnly"""', 'backgroundColor': 'analogue_color', 'command': '"""useless_ui.ufx.set_idle()"""'}), "('useless_stuff_button', label='Set Idle', width=190,\n style='textOnly', backgroundColor=analogue_color, command=\n 'useless_ui.ufx.set_idle()')\n", (4260, 4409), True, 'import maya.cmds as cmds\n'), ((4481, 4664), 'maya.cmds.iconTextButton', 'cmds.iconTextButton', (['"""useless_random_button"""'], {'label': '"""I feel lucky"""', 'style': '"""textOnly"""', 'width': '(190)', 'backgroundColor': 'analogue_color', 'command': '"""useless_ui.ufx.get_random_quotes()"""'}), "('useless_random_button', label='I feel lucky', style=\n 'textOnly', width=190, backgroundColor=analogue_color, command=\n 'useless_ui.ufx.get_random_quotes()')\n", (4500, 4664), True, 'import maya.cmds as cmds\n'), ((4760, 4780), 'maya.cmds.setParent', 'cmds.setParent', (['""".."""'], {}), "('..')\n", (4774, 4780), True, 'import maya.cmds as cmds\n'), ((4788, 4842), 'maya.cmds.columnLayout', 'cmds.columnLayout', ([], {'adjustableColumn': '(True)', 'rowSpacing': '(5)'}), '(adjustableColumn=True, rowSpacing=5)\n', (4805, 4842), True, 'import maya.cmds as cmds\n'), ((4850, 5017), 'maya.cmds.iconTextButton', 'cmds.iconTextButton', (['"""useless_credits"""'], {'label': '"""CREDITS!"""', 'style': '"""textOnly"""', 'width': '(190)', 'backgroundColor': 'analogue_color', 'command': '"""useless_ui.ufx.show_credits()"""'}), "('useless_credits', label='CREDITS!', style='textOnly',\n width=190, backgroundColor=analogue_color, command=\n 'useless_ui.ufx.show_credits()')\n", (4869, 5017), True, 'import maya.cmds as cmds\n'), ((5114, 5214), 'maya.cmds.separator', 'cmds.separator', (['"""buttons_separator"""'], {'backgroundColor': 'complementary_color', 'style': '"""none"""', 'height': '(3)'}), "('buttons_separator', backgroundColor=complementary_color,\n style='none', height=3)\n", (5128, 5214), True, 'import maya.cmds as cmds\n'), ((5236, 5256), 'maya.cmds.setParent', 'cmds.setParent', (['""".."""'], {}), "('..')\n", (5250, 5256), True, 'import maya.cmds as cmds\n'), ((5278, 5335), 'maya.cmds.rowLayout', 'cmds.rowLayout', ([], {'numberOfColumns': '(2)', 'adjustableColumn1': '(True)'}), '(numberOfColumns=2, adjustableColumn1=True)\n', (5292, 5335), True, 'import maya.cmds as cmds\n'), ((5343, 5507), 'maya.cmds.intSliderGrp', 'cmds.intSliderGrp', (['"""useless_number_slider"""'], {'field': '(True)', 'label': '"""Numbers"""', 'value': '(0)', 'min': '(0)', 'max': '(10)', 'columnWidth': '(1, 50)', 'columnAlign': "[(1, 'left'), (2, 'left')]"}), "('useless_number_slider', field=True, label='Numbers',\n value=0, min=0, max=10, columnWidth=(1, 50), columnAlign=[(1, 'left'),\n (2, 'left')])\n", (5360, 5507), True, 'import maya.cmds as cmds\n'), ((5576, 5746), 'maya.cmds.iconTextButton', 'cmds.iconTextButton', (['"""useless_number"""'], {'label': '"""Pick it out!"""', 'style': '"""textOnly"""', 'width': '(80)', 'backgroundColor': 'analogue_color', 'command': '"""useless_ui.ufx.pick_numbers()"""'}), "('useless_number', label='Pick it out!', style=\n 'textOnly', width=80, backgroundColor=analogue_color, command=\n 'useless_ui.ufx.pick_numbers()')\n", (5595, 5746), True, 'import maya.cmds as cmds\n'), ((5842, 5862), 'maya.cmds.setParent', 'cmds.setParent', (['""".."""'], {}), "('..')\n", (5856, 5862), True, 'import maya.cmds as cmds\n'), ((5868, 5965), 'maya.cmds.separator', 'cmds.separator', (['"""end_separator"""'], {'backgroundColor': 'complementary_color', 'style': '"""none"""', 'height': '(3)'}), "('end_separator', backgroundColor=complementary_color, style=\n 'none', height=3)\n", (5882, 5965), True, 'import maya.cmds as cmds\n'), ((6077, 6257), 'maya.cmds.formLayout', 'cmds.formLayout', (['"""useless_form_layout"""'], {'edit': '(True)', 'attachForm': "[(theme_column, 'left', 5), (app_column, 'right', 10)]", 'attachControl': "[(app_column, 'left', 10, theme_column)]"}), "('useless_form_layout', edit=True, attachForm=[(theme_column,\n 'left', 5), (app_column, 'right', 10)], attachControl=[(app_column,\n 'left', 10, theme_column)])\n", (6092, 6257), True, 'import maya.cmds as cmds\n'), ((6332, 6355), 'maya.cmds.showWindow', 'cmds.showWindow', (['window'], {}), '(window)\n', (6347, 6355), True, 'import maya.cmds as cmds\n'), ((1342, 1365), 'maya.cmds.deleteUI', 'cmds.deleteUI', (['ui_title'], {}), '(ui_title)\n', (1355, 1365), True, 'import maya.cmds as cmds\n')]
|
import torch
import torch.nn as nn
class OurModule(nn.Module):
def __init__(self, num_inputs, num_classes, dropout_prob=0.3):
super().__init__()
self.pipe = nn.Sequential(nn.Linear(num_inputs, 5),
nn.ReLU(),
nn.Linear(5, 20),
nn.ReLU(),
nn.Linear(20, num_classes),
nn.Dropout(p=dropout_prob),
nn.Softmax(dim=1))
def forward(self, x):
return self.pipe(x)
if __name__ == "__main__":
net = OurModule(num_inputs=2, num_classes=3)
v = torch.FloatTensor([[2, 3]])
out = net(v)
print(net)
print("*"*20)
print(out)
|
[
"torch.nn.Dropout",
"torch.nn.ReLU",
"torch.FloatTensor",
"torch.nn.Softmax",
"torch.nn.Linear"
] |
[((680, 707), 'torch.FloatTensor', 'torch.FloatTensor', (['[[2, 3]]'], {}), '([[2, 3]])\n', (697, 707), False, 'import torch\n'), ((194, 218), 'torch.nn.Linear', 'nn.Linear', (['num_inputs', '(5)'], {}), '(num_inputs, 5)\n', (203, 218), True, 'import torch.nn as nn\n'), ((254, 263), 'torch.nn.ReLU', 'nn.ReLU', ([], {}), '()\n', (261, 263), True, 'import torch.nn as nn\n'), ((299, 315), 'torch.nn.Linear', 'nn.Linear', (['(5)', '(20)'], {}), '(5, 20)\n', (308, 315), True, 'import torch.nn as nn\n'), ((351, 360), 'torch.nn.ReLU', 'nn.ReLU', ([], {}), '()\n', (358, 360), True, 'import torch.nn as nn\n'), ((396, 422), 'torch.nn.Linear', 'nn.Linear', (['(20)', 'num_classes'], {}), '(20, num_classes)\n', (405, 422), True, 'import torch.nn as nn\n'), ((458, 484), 'torch.nn.Dropout', 'nn.Dropout', ([], {'p': 'dropout_prob'}), '(p=dropout_prob)\n', (468, 484), True, 'import torch.nn as nn\n'), ((520, 537), 'torch.nn.Softmax', 'nn.Softmax', ([], {'dim': '(1)'}), '(dim=1)\n', (530, 537), True, 'import torch.nn as nn\n')]
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jul 15 15:16:06 2018
@author: Arpit
"""
import numpy as np
import matplotlib.pyplot as plt
import threading
from settings import charts_folder
class GraphPlot:
lock = threading.Lock()
def __init__(self, name, xCnt=1, yCnt=1, labels=None):
self.name = name
self.xCnt = xCnt
self.yCnt = yCnt
self.labels = labels
self.X = []
self.Ys = np.empty((yCnt,), dtype=object)
for i,v in enumerate(self.Ys): self.Ys[i] = list()
def add(self, X, Y):
self.X.append(X)
for i in range(self.yCnt):
self.Ys[i].append(Y[i])
def save(self):
try:
with self.lock:
fig = plt.figure()
for i in range(self.yCnt):
plt.plot(self.X, self.Ys[i], label=self.labels[i] if self.labels is not None else i)
plt.legend(loc = "best")
plt.savefig(charts_folder + str(self.name) + '.png')
plt.close(fig)
except Exception as e:
print("error: " + str(e))
plt.close()
|
[
"matplotlib.pyplot.plot",
"numpy.empty",
"matplotlib.pyplot.close",
"matplotlib.pyplot.legend",
"threading.Lock",
"matplotlib.pyplot.figure"
] |
[((239, 255), 'threading.Lock', 'threading.Lock', ([], {}), '()\n', (253, 255), False, 'import threading\n'), ((471, 502), 'numpy.empty', 'np.empty', (['(yCnt,)'], {'dtype': 'object'}), '((yCnt,), dtype=object)\n', (479, 502), True, 'import numpy as np\n'), ((781, 793), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (791, 793), True, 'import matplotlib.pyplot as plt\n'), ((975, 997), 'matplotlib.pyplot.legend', 'plt.legend', ([], {'loc': '"""best"""'}), "(loc='best')\n", (985, 997), True, 'import matplotlib.pyplot as plt\n'), ((1085, 1099), 'matplotlib.pyplot.close', 'plt.close', (['fig'], {}), '(fig)\n', (1094, 1099), True, 'import matplotlib.pyplot as plt\n'), ((1198, 1209), 'matplotlib.pyplot.close', 'plt.close', ([], {}), '()\n', (1207, 1209), True, 'import matplotlib.pyplot as plt\n'), ((857, 945), 'matplotlib.pyplot.plot', 'plt.plot', (['self.X', 'self.Ys[i]'], {'label': '(self.labels[i] if self.labels is not None else i)'}), '(self.X, self.Ys[i], label=self.labels[i] if self.labels is not\n None else i)\n', (865, 945), True, 'import matplotlib.pyplot as plt\n')]
|
"""
Test for launch config's personality validation.
"""
import base64
from test_repo.autoscale.fixtures import AutoscaleFixture
class LaunchConfigPersonalityTest(AutoscaleFixture):
"""
Verify launch config.
"""
def setUp(self):
"""
Create a scaling group.
"""
super(LaunchConfigPersonalityTest, self).setUp()
self.path = '/root/test.txt'
def test_launch_config_personality_without_encoding(self):
"""
Create a scaling group such that the server's personality in the
launch config is not base64 encoded.
"""
file_contents = 'This is a test file.'
personality = [{'path': '/root/.csivh',
'contents': file_contents}]
self._assert_create_group(personality)
def test_launch_config_personality_with_invalid_personality(self):
"""
Create a scaling group with invalid personality and verify the creation
fails with an error 400.
"""
personalities = ['abc', 0, {'path': '/abc'}, {'contents': 'test'},
[{'path': self.path}], [{'content': 'test'}]]
for personality in personalities:
self._assert_create_group(personality)
def test_launch_config_personality_with_max_path_size(self):
"""
Create a scaling group with path over 255 characters and verify the
creation fails with an error 400.
"""
long_path = 'z' * (self.personality_maxlength + 1)
personality = [{'path': '/root/{0}.txt'.format(long_path),
'contents': base64.b64encode('tests')}]
self._assert_create_group(personality)
def test_launch_config_personality_with_max_file_content_size(self):
"""
Create a scaling group with file contents over 1000 characters and
verify the creation fails with an error 400.
"""
file_content = 'z' * (self.personality_max_file_size + 1)
personality = [{'path': self.path,
'contents': base64.b64encode(file_content)}]
self._assert_create_group(personality)
def test_launch_config_personality_with_max_personalities(self):
"""
Create a scaling group with over max personalities allowed and
verify the creation fails with an error 400.
"""
personality_content = {'path': self.path,
'contents': base64.b64encode('tests')}
personality = [personality_content
for _ in range(self.max_personalities + 1)]
self._assert_create_group(personality)
def _assert_create_group(self, personality, response=400):
"""
Creates a group with the given server personality.
"""
group_response = self.autoscale_behaviors.create_scaling_group_given(
lc_personality=personality)
self.assertEquals(group_response.status_code, response, msg='Create group '
'with invalid lc_personality returned {0} as against '
'{1}'.format(group_response.status_code, response))
if response is 200:
group = group_response.entity
self.resources.add(group, self.empty_scaling_group)
return group
|
[
"base64.b64encode"
] |
[((2455, 2480), 'base64.b64encode', 'base64.b64encode', (['"""tests"""'], {}), "('tests')\n", (2471, 2480), False, 'import base64\n'), ((1618, 1643), 'base64.b64encode', 'base64.b64encode', (['"""tests"""'], {}), "('tests')\n", (1634, 1643), False, 'import base64\n'), ((2064, 2094), 'base64.b64encode', 'base64.b64encode', (['file_content'], {}), '(file_content)\n', (2080, 2094), False, 'import base64\n')]
|
import os
import random
import numpy as np
from scipy.spatial.distance import cdist
import cv2
import time
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
# import torch.multiprocessing as mp
from torch.utils.data import DataLoader
from torch.optim import Adam, SGD
# from torch.utils.tensorboard import SummaryWriter
from scipy.spatial.distance import cdist
from package.model.cmt import CMT
from package.loss.cmt_loss import _CMT_loss
from package.dataset.data_cmt import *
from package.args.cmt_args import parse_config
from package.dataset.utils import make_logger
from package.model.utils import *
from package.loss.regularization import _Regularization
import numpy as np
from sklearn.neighbors import NearestNeighbors as NN
DEBUG = False
def dr_dec(optimizer, args):
args.lr *= 0.5
args.lr = max(args.lr, 5e-5)
optimizer.param_groups[0]['lr'] = args.lr
def _get_pre_from_matches(matches):
"""
:param matches: A n-by-m matrix. n is number of test samples, m is the top m elements used for evaluation
:return: precision
"""
return np.mean(matches)
def _map_change(inputArr):
dup = np.copy(inputArr)
for idx in range(inputArr.shape[1]):
if idx != 0:
# dup cannot be bool type
dup[:,idx] = dup[:,idx-1] + dup[:,idx]
return np.multiply(dup, inputArr)
def _get_map_from_matches(matches):
"""
mAP's calculation refers to https://github.com/ShivaKrishnaM/ZS-SBIR/blob/master/trainCVAE_pre.py.
:param matches: A n-by-m matrix. n is number of test samples, m is the top m elements used for evaluation
matches[i][j] == 1 indicates the j-th retrieved test image j belongs to the same class as test sketch i,
otherwise, matches[i][j] = 0.
:return: mAP
"""
temp = [np.arange(matches.shape[1]) for _ in range(matches.shape[0])]
mAP_term = 1.0 / (np.stack(temp, axis=0) + 1.0)
precisions = np.multiply(_map_change(matches), mAP_term)
mAP = np.mean(precisions, axis=1)
return np.mean(mAP)
def _eval(feats_labels_sk, feats_labels_im, n=200):
"""
:param feats_labels_sk: a two-element tuple [features_of_sketches, labels_of_sketches]
labels_of_sketches and labels_of_images are scalars(class id).
:param feats_labels_im: a two-element tuple [features_of_images, labels_of_images]
features_of_images and features_of_sketches are used for distance calculation.
:param n: the top n elements used for evaluation
:return: precision@n, mAP@n, mAP@all
"""
nn = NN(n_neighbors=feats_labels_im[0].shape[0], metric='cosine', algorithm='brute').fit(feats_labels_im[0])
_, indices = nn.kneighbors(feats_labels_sk[0])
retrieved_classes = np.array(feats_labels_im[1])[indices]
matches = np.vstack([(retrieved_classes[i] == feats_labels_sk[1][i])
for i in range(retrieved_classes.shape[0])]).astype(np.uint16)
return _get_pre_from_matches(matches[:, :n]), _get_map_from_matches(matches[:, :n])
def _test_and_save(epochs, optimizer, data_test, model, logger, args, loss_sum):
if not hasattr(_test_and_save, 'best_acc'):
_test_and_save.best_acc = 0
n = 200
start_cpu_t = time.time()
feats_labels_sk, feats_labels_im = _extract_feats_sk_im(data=data_test, model=model,
batch_size=args.batch_size)
pre, mAPn = _eval(feats_labels_sk, feats_labels_im, n)
logger.info("Precision@{}: {}, mAP@{}: {}, bestPrecsion: {}".format(n, pre, n, mAPn, max(pre, _test_and_save.best_acc)) +
" " + 'epochs: {}, loss_sk: {}, loss_im: {}, (eval cpu time: {}s)'.
format(epochs, np.mean(loss_sum[SK]), np.mean(loss_sum[IM]), time.time() - start_cpu_t))
if pre > _test_and_save.best_acc:
_test_and_save.best_acc = pre
torch.save({'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'epochs': epochs,
'args': args},
save_fn(args.save_dir, epochs, pre, mAPn))
torch.cuda.empty_cache()
def save_fn(save_dir, it, pre=0, mAP=0):
return join(mkdir(join(save_dir, 'models')), 'Iter__{}__{}_{}.pkl'.format(it, int(pre * 1000), int(mAP * 1000)))
def _try_load(args, logger, model, optimizer):
if args.start_from is None:
# try to find the latest checkpoint
files = os.listdir(mkdir(join(mkdir(args.save_dir), 'models')))
if len(files) == 0:
logger.info("Cannot find any checkpoint. Start new training.")
return 0
latest = max(files, key=lambda name: int(name.split('\\')[-1].split('/')[-1].split('.')[0].split('__')[1]))
checkpoint = join(args.save_dir, 'models', latest)
else:
try: checkpoint = save_fn(args.save_dir, str(int(args.start_from)))
except: checkpoint = args.start_from
logger.info("Load model from {}".format(checkpoint))
ckpt = torch.load(checkpoint, map_location='cpu')
model.load_state_dict(ckpt['model'])
optimizer.load_state_dict(ckpt['optimizer'])
return ckpt['epochs']
def _extract_feats_sk_im(data, model, batch_size=64):
skip = 1
model.eval()
feats_labels_sk = _extract_feats(data, lambda x: model(sk=x), SK, skip=skip,
batch_size=batch_size)
feats_labels_im = _extract_feats(data, lambda x: model(im=x), IM, skip=skip,
batch_size=batch_size)
model.train()
return feats_labels_sk, feats_labels_im
def _extract_feats(data_test, model, what, skip=1, batch_size=16):
"""
:param data_test: test Dataset
:param model: network model
:param what: SK or IM
:param skip: skip a certain number of image/sketches to reduce computation
:return: a two-element list [extracted_features, extracted_labels]
"""
labels = []
feats = []
for batch_idx, (xs, id) in \
enumerate(data_test.traverse(what, skip=skip, batch_size=batch_size)):
feats.append(model(xs.cuda()).data.cpu().numpy())
# print(type(labels[0]), labels[0].shape)# <class 'numpy.ndarray'> (16, 256)
# print(type(id), id) # <class 'torch.Tensor'> tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
labels.append(id.numpy())
# print(feats[-1][-1][:4])
return np.concatenate(feats), np.concatenate(labels)
def _parse_args_paths(args):
if args.dataset == 'sketchy':
sketch_folder = SKETCH_FOLDER_SKETCHY
im_folder = IMAGE_FOLDER_SKETCHY
path_semantic = PATH_SEMANTIC_SKETCHY
train_class = TRAIN_CLASS_SKETCHY
test_class = TEST_CLASS_SKETCHY
npy_folder = NPY_FOLDER_SKETCHY
elif args.dataset == 'tuberlin':
sketch_folder = SKETCH_FOLDER_TUBERLIN
im_folder = IMAGE_FOLDER_TUBERLIN
path_semantic = PATH_SEMANTIC_TUBERLIN
train_class = TRAIN_CLASS_TUBERLIN
test_class = TEST_CLASS_TUBERLIN
npy_folder = NPY_FOLDER_TUBERLIN
else: raise Exception("dataset args error!")
if args.sketch_dir != '': sketch_folder = args.sketch_dir
if args.image_dir != '': im_folder = args.image_dir
if args.path_semantic != '': im_folder = args.path_semantic
if args.npy_dir == '0': args.npy_dir = npy_folder
elif args.npy_dir == '': args.npy_dir = None
if args.ni_path == '0': args.ni_path = PATH_NAMES
return sketch_folder, im_folder, path_semantic, train_class, test_class
def train(args):
# srun -p gpu --gres=gpu:1 --exclusive --output=san10.out python main_san.py --epochs 50000 --print_every 500 --save_every 2000 --batch_size 96 --dataset sketchy --margin 10 --npy_dir 0 --save_dir san_sketchy10
# srun -p gpu --gres=gpu:1 --exclusive --output=san1.out python main_san.py --epochs 50000 --print_every 500 --save_every 2000 --batch_size 96 --dataset sketchy --margin 1 --npy_dir 0 --save_dir san_sketchy1
# srun -p gpu --gres=gpu:1 --output=san_sketchy03.out python main_san.py --epochs 30000 --print_every 200 --save_every 3000 --batch_size 96 --dataset sketchy --margin 0.3 --npy_dir 0 --save_dir san_sketchy03 --lr 0.0001
sketch_folder, image_folder, path_semantic, train_class, test_class = _parse_args_paths(args)
if DEBUG:
args.back_bone = 'default'
args.npy_dir = NPY_FOLDER_SKETCHY
args.ni_path = PATH_NAMES
args.print_every = 1
args.save_every = 5
args.paired = True
args.epochs = 20000
# args.lr = 0.001
args.sz = 32
# args.l2_reg = 0.0001
args.back_bone = 'default'
args.batch_size = 32
args.h = 500
test_class = train_class[5:7]
train_class = train_class[:5]
logger = make_logger(join(mkdir(args.save_dir), curr_time_str() + '.log'))
data_train = CMT_dataloader(folder_sk=sketch_folder, clss=train_class, folder_nps=args.npy_dir,
path_semantic=path_semantic, paired=args.paired, names=args.ni_path,
folder_im=image_folder, normalize01=False, doaug=False, logger=logger,
sz=None if args.back_bone=='vgg' else args.sz)
dataloader_train = DataLoader(dataset=data_train, batch_size=args.batch_size, shuffle=True)
data_test = CMT_dataloader(folder_sk=sketch_folder, clss=test_class, folder_nps=args.npy_dir,
path_semantic=path_semantic, folder_im=image_folder, normalize01=False, doaug=False,
logger=logger, sz=None if args.back_bone=='vgg' else args.sz)
model = CMT(d=data_train.d(), h=args.h, back_bone=args.back_bone, batch_normalization=args.bn, sz=args.sz)
model.cuda()
if not args.ft:
model.fix_vgg()
optimizer = SGD(params=model.parameters(), lr=args.lr, momentum=0.6)
epochs = _try_load(args, logger, model, optimizer)
logger.info(str(args))
args.epochs += epochs
cmt_loss = _CMT_loss()
model.train()
l2_regularization = _Regularization(model, args.l2_reg, p=2, logger=None)
loss_sum = [[0], [0]]
logger.info("Start training:\n train_classes: {}\n test_classes: {}".format(train_class, test_class))
_test_and_save(epochs=epochs, optimizer=optimizer, data_test=data_test,
model=model, logger=logger, args=args, loss_sum=loss_sum)
while True:
for mode, get_feat in [[IM, lambda data: model(im=data)],
[SK, lambda data: model(sk=data)]]:
data_train.mode = mode
for _, (data, semantics) in enumerate(dataloader_train):
# Skip one-element batch in consideration of batch normalization
if data.shape[0] == 1:
continue
# print(data.shape)
optimizer.zero_grad()
loss = cmt_loss(get_feat(data.cuda()),
semantics.cuda()) \
+ l2_regularization()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
loss_sum[mode].append(float(loss.item()))
epochs += 1
dr_dec(optimizer=optimizer, args=args)
if (epochs + 1) % args.save_every == 0:
_test_and_save(epochs=epochs, optimizer=optimizer, data_test=data_test,
model=model, logger=logger, args=args, loss_sum=loss_sum)
if (epochs + 1) % args.print_every == 0:
logger.info('epochs: {}, loss_sk: {}, loss_im: {},'.
format(epochs, np.mean(loss_sum[SK]), np.mean(loss_sum[IM])))
loss_sum = [[], []]
if epochs >= args.epochs: break
def gen_args(h=500, dataset='sketchy', back_bone='vgg', sz=32, ft=True, paired=False):
ft = int(ft)
paired = int(paired)
return \
"""
###
#!/bin/bash
#SBATCH --job-name=ZXLing
#SBATCH --partition=gpu
#SBATCH --gres=gpu:1
#SBATCH --output=cmt_%j.out
#SBATCH --time=7-00:00:00
module load gcc/7.3.0 anaconda/3 cuda/9.2 cudnn/7.1.4
source activate lzxtc2
python main_cmt.py --npy_dir 0 --dataset {} --save_dir cmts/cmt{}{}_{}_{}_{}_{} --h {} --back_bone {} --sz {} --ft {} --paired {} --ni_path 0
""".format(dataset, int(ft), int(paired) , dataset, h, back_bone, sz if back_bone=='default' else "", h, back_bone, sz, ft, paired)
if __name__ == '__main__':
if False:
print(gen_args(back_bone='vgg', ft=False, paired=True))
print(gen_args(back_bone='vgg', ft=True, paired=False))
print(gen_args(back_bone='vgg', ft=True, paired=True))
print(gen_args(back_bone='vgg', ft=False, paired=False))
print(gen_args(back_bone='default'))
exit()
args = parse_config()
print(str(args))
# train(args)
# srun --gres=gpu:1 --output=cmt_%j.out python main_cmt.py
'''
#!/bin/bash
#SBATCH --job-name=ZXLing
#SBATCH --partition=gpu
#SBATCH --gres=gpu:1
#SBATCH --output=cmt_%j.out
#SBATCH --time=7-00:00:00
module load gcc/7.3.0 anaconda/3 cuda/9.2 cudnn/7.1.4
source activate lzxtc2
python main_cmt.py --npy_dir 0 --dataset sketchy --save_dir cmts/cmt11_sketchy_500_default_32 --h 500 --back_bone default --sz 32 --ft 1 --paired 1 --ni_path 0
python main_cmt.py --npy_dir 0 --dataset sketchy --save_dir cmts/cmt01_sketchy_500_vgg_ --h 500 --back_bone vgg --sz 32 --ft 0 --paired 1 --ni_path 0
python main_cmt.py --npy_dir 0 --dataset sketchy --save_dir cmts/cmt10_sketchy_500_vgg_ --h 500 --back_bone vgg --sz 32 --ft 1 --paired 0 --ni_path 0
python main_cmt.py --npy_dir 0 --dataset sketchy --save_dir cmts/cmt11_sketchy_500_vgg_ --h 500 --back_bone vgg --sz 32 --ft 1 --paired 1 --ni_path 0
python main_cmt.py --npy_dir 0 --dataset sketchy --save_dir cmts/cmt00_sketchy_500_vgg_ --h 500 --back_bone vgg --sz 32 --ft 0 --paired 0 --ni_path 0
python main_cmt.py --npy_dir 0 --dataset sketchy --save_dir cmts/cmt10_sketchy_500_default_32 --h 500 --back_bone default --sz 32 --ft 1 --paired 0 --ni_path 0
'''
|
[
"package.loss.regularization._Regularization",
"numpy.stack",
"numpy.multiply",
"numpy.copy",
"torch.utils.data.DataLoader",
"torch.load",
"time.time",
"numpy.mean",
"package.loss.cmt_loss._CMT_loss",
"package.args.cmt_args.parse_config",
"torch.cuda.empty_cache",
"torch.nn.kneighbors",
"numpy.arange",
"numpy.array",
"sklearn.neighbors.NearestNeighbors",
"numpy.concatenate"
] |
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|
import pandas as pd
from utils import new_RF_model
# since processing of the symptoms data has several related elements, I deceided to wrap it into a class
# this makes it easier for someone reading the code that all these function address on the synptoms data and has nothing to do with the image data
class ProcessSymptomsData:
def __init__(self, cough, temperature, sore_throat, shortness_of_breath, head_ache,
age, test_indication):
'''
the attributes of the '''
self.cough = cough
self.temperature = temperature
self.sore_throat= sore_throat
self.shortness_of_breath = shortness_of_breath
self.head_ache = head_ache
self.age_60_and_above = age
self.test_indication = test_indication
self.fever = None
self.new_test_indication = None
def convert_temperature_to_categories(self):
'''
This functions takes users temerature and generate categorical data of fever presence
'''
if self.temperature >= 38.0:
self.fever = 'yes'
else:
self.fever = 'no'
return self.fever
def convert_age_to_category(self):
'''
This functions takes users age and covert it into category of younger than 60 and older than 60.
It helps to discourage the user from feeling discriminated against
'''
if self.age_60_and_above < 60:
age_60_and_above = 'no'
else:
age_60_and_above = 'yes'
return age_60_and_above
def convert_test_indication_to_category(self):
'''
This functions takes users test indication (four possibilities) and converts to three categories needed by the model
'''
if self.test_indication == 'I had contact with someone that tested positive for COVID-19':
self.new_test_indication = 'Contact with confirmed'
elif self.test_indication == 'I traveled abroad to a region with high COVID incidence':
self.new_test_indication = 'Abroad'
elif self.test_indication == 'both of the above':
self.new_test_indication = 'Contact with confirmed'
else:
self.new_test_indication = 'Other'
return self.new_test_indication
def convert_symptoms_to_dataframe(self):
'''
function to conver the input data of users into a dataframe that can be used to predict outcome
'''
user_input = {
'cough': self.cough,
'fever': self.fever,
'sore_throat': self.sore_throat,
'shortness_of_breath': self.shortness_of_breath,
'head_ache': self.head_ache,
'age_60_and_above': self.age_60_and_above,
'test_indication': self.new_test_indication,
}
self.dataframe = pd.DataFrame([user_input])
return self.dataframe
def predict_probability(self):
'''
This function imports Random forest model from utils and predict the probability of COVID-19 infection oucome using symptoms of user.
it takes a dataframe as input
'''
predicted_probability = new_RF_model.predict_proba(self.dataframe)
return predicted_probability
def predict_symptoms_outcome(self):
'''
This function imports Random forest model from utils and predict class with hihest probability using symptoms of user.
it takes a dataframe as input
'''
predicted_class = new_RF_model.predict(self.dataframe)
return predicted_class
# def search_conditions(fuzzy_condition):
# '''
# does a fuzzy search of the underlying conditions and returns best matched conditions in a list of defined conditions
# '''
# extracted = []
# defined_conditions = ['hypertension', 'diabetes', 'Immunocompromised', 'hiv', 'pregnant', 'overweight', 'cardiovascular', 'lung', 'heart', 'kidney', 'liver','stroke', 'cancer']
# for condition in defined_conditions:
# ratio1 = fuzz.ratio(fuzzy_condition, condition)
# if ratio1 > 40:
# extracted.append(condition)
# else:
# pass
# return extracted
|
[
"pandas.DataFrame",
"utils.new_RF_model.predict",
"utils.new_RF_model.predict_proba"
] |
[((2813, 2839), 'pandas.DataFrame', 'pd.DataFrame', (['[user_input]'], {}), '([user_input])\n', (2825, 2839), True, 'import pandas as pd\n'), ((3143, 3185), 'utils.new_RF_model.predict_proba', 'new_RF_model.predict_proba', (['self.dataframe'], {}), '(self.dataframe)\n', (3169, 3185), False, 'from utils import new_RF_model\n'), ((3479, 3515), 'utils.new_RF_model.predict', 'new_RF_model.predict', (['self.dataframe'], {}), '(self.dataframe)\n', (3499, 3515), False, 'from utils import new_RF_model\n')]
|
import os
import sys
import glob
import tqdm
import pickle
import logging
from indra_world.corpus import Corpus
from indra_world.assembly.operations import *
from indra_world.sources.dart import process_reader_outputs
from indra.pipeline import AssemblyPipeline
logger = logging.getLogger('dec2020_compositional')
HERE = os.path.dirname(os.path.abspath(__file__))
# December experiment
reader_versions = {'flat':
{'cwms': '2020.10.22',
'hume': 'r2020_10_26_2.flat',
# Note that this just matches the version on the
# bioexp machine dart drive and was manually renamed
# On DART, these entries appear as 1.1 and can only
# be differentiated by date.
'sofia': '1.1_old',
'eidos': '1.0.3'},
'compositional':
{'cwms': '2020.10.22',
'hume': 'r2020_10_28.compositional',
'sofia': '1.1',
'eidos': '1.0.3'}}
DART_STORAGE = '/dart'
def load_reader_outputs(reader_versions):
logger.info('Loading outputs based on %s' % str(reader_versions))
reader_outputs = {}
for reader, version in reader_versions.items():
logger.info('Loading %s/%s' % (reader, version))
reader_outputs[reader] = {}
reader_folder = os.path.join(DART_STORAGE, reader, version)
fnames = glob.glob('%s/*' % reader_folder)
logger.info('Found %d files' % len(fnames))
for fname in tqdm.tqdm(fnames):
doc_id = os.path.basename(fname)
with open(fname, 'r') as fh:
doc_str = fh.read()
reader_outputs[reader][doc_id] = doc_str
return reader_outputs
if __name__ == '__main__':
corpus_id = 'compositional_dec2020'
logger.info('Processing reader output...')
reader_outputs = load_reader_outputs(reader_versions['compositional'])
stmts = process_reader_outputs(reader_outputs, corpus_id)
'''
stmts = []
for reader in reader_versions['compositional']:
logger.info('Loading %s' % reader)
if os.path.exists('compositional_dec2020_%s_raw.pkl' % reader):
with open('compositional_dec2020_%s_raw.pkl' % reader, 'rb') as fh:
stmts += pickle.load(fh)
'''
logger.info('Got a total of %s statements' % len(stmts))
assembly_config_file = os.path.join(
HERE, os.pardir, 'indra_wm_service', 'resources',
'assembly_compositional_december2020.json')
pipeline = AssemblyPipeline.from_json_file(assembly_config_file)
assembled_stmts = pipeline.run(stmts)
num_docs = 44591
meta_data = {
'corpus_id': corpus_id,
'description': 'Compositional grounding assembly for the December '
'2020 documents.',
'display_name': 'Compositional grounding assembly December 2020',
'readers': list(reader_versions['compositional'].keys()),
'assembly': {
'level': 'grounding_location',
'grounding_threshold': 0.6,
},
'num_statements': len(assembled_stmts),
'num_documents': num_docs
}
corpus = Corpus(corpus_id=corpus_id,
statements=assembled_stmts,
raw_statements=stmts,
meta_data=meta_data)
corpus.s3_put()
|
[
"os.path.abspath",
"tqdm.tqdm",
"os.path.basename",
"indra_world.sources.dart.process_reader_outputs",
"indra.pipeline.AssemblyPipeline.from_json_file",
"glob.glob",
"indra_world.corpus.Corpus",
"os.path.join",
"logging.getLogger"
] |
[((272, 314), 'logging.getLogger', 'logging.getLogger', (['"""dec2020_compositional"""'], {}), "('dec2020_compositional')\n", (289, 314), False, 'import logging\n'), ((338, 363), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (353, 363), False, 'import os\n'), ((2034, 2083), 'indra_world.sources.dart.process_reader_outputs', 'process_reader_outputs', (['reader_outputs', 'corpus_id'], {}), '(reader_outputs, corpus_id)\n', (2056, 2083), False, 'from indra_world.sources.dart import process_reader_outputs\n'), ((2491, 2601), 'os.path.join', 'os.path.join', (['HERE', 'os.pardir', '"""indra_wm_service"""', '"""resources"""', '"""assembly_compositional_december2020.json"""'], {}), "(HERE, os.pardir, 'indra_wm_service', 'resources',\n 'assembly_compositional_december2020.json')\n", (2503, 2601), False, 'import os\n'), ((2630, 2683), 'indra.pipeline.AssemblyPipeline.from_json_file', 'AssemblyPipeline.from_json_file', (['assembly_config_file'], {}), '(assembly_config_file)\n', (2661, 2683), False, 'from indra.pipeline import AssemblyPipeline\n'), ((3274, 3377), 'indra_world.corpus.Corpus', 'Corpus', ([], {'corpus_id': 'corpus_id', 'statements': 'assembled_stmts', 'raw_statements': 'stmts', 'meta_data': 'meta_data'}), '(corpus_id=corpus_id, statements=assembled_stmts, raw_statements=\n stmts, meta_data=meta_data)\n', (3280, 3377), False, 'from indra_world.corpus import Corpus\n'), ((1439, 1482), 'os.path.join', 'os.path.join', (['DART_STORAGE', 'reader', 'version'], {}), '(DART_STORAGE, reader, version)\n', (1451, 1482), False, 'import os\n'), ((1500, 1533), 'glob.glob', 'glob.glob', (["('%s/*' % reader_folder)"], {}), "('%s/*' % reader_folder)\n", (1509, 1533), False, 'import glob\n'), ((1607, 1624), 'tqdm.tqdm', 'tqdm.tqdm', (['fnames'], {}), '(fnames)\n', (1616, 1624), False, 'import tqdm\n'), ((1647, 1670), 'os.path.basename', 'os.path.basename', (['fname'], {}), '(fname)\n', (1663, 1670), False, 'import os\n')]
|
import argparse
import torch
def get_args():
parser = argparse.ArgumentParser(
description='Goal-Oriented-Semantic-Exploration')
# General Arguments
parser.add_argument('--seed', type=int, default=1,
help='random seed (default: 1)')
parser.add_argument('--auto_gpu_config', type=int, default=1)
parser.add_argument('--total_num_scenes', type=str, default="auto")
parser.add_argument('-n', '--num_processes', type=int, default=5,
help="""how many training processes to use (default:5)
Overridden when auto_gpu_config=1
and training on gpus""")
parser.add_argument('--num_processes_per_gpu', type=int, default=6)
parser.add_argument('--num_processes_on_first_gpu', type=int, default=1)
parser.add_argument('--eval', type=int, default=0,
help='0: Train, 1: Evaluate (default: 0)')
parser.add_argument('--num_training_frames', type=int, default=10000000,
help='total number of training frames')
parser.add_argument('--num_eval_episodes', type=int, default=200,
help="number of test episodes per scene")
parser.add_argument('--num_train_episodes', type=int, default=10000,
help="""number of train episodes per scene
before loading the next scene""")
parser.add_argument('--no_cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument("--sim_gpu_id", type=int, default=0,
help="gpu id on which scenes are loaded")
parser.add_argument("--sem_gpu_id", type=int, default=-1,
help="""gpu id for semantic model,
-1: same as sim gpu, -2: cpu""")
# Logging, loading models, visualization
parser.add_argument('--log_interval', type=int, default=10,
help="""log interval, one log per n updates
(default: 10) """)
parser.add_argument('--save_interval', type=int, default=1,
help="""save interval""")
parser.add_argument('-d', '--dump_location', type=str, default="./tmp/",
help='path to dump models and log (default: ./tmp/)')
parser.add_argument('--exp_name', type=str, default="exp1",
help='experiment name (default: exp1)')
parser.add_argument('--save_periodic', type=int, default=500000,
help='Model save frequency in number of updates')
parser.add_argument('--load', type=str, default="0",
help="""model path to load,
0 to not reload (default: 0)""")
parser.add_argument('-v', '--visualize', type=int, default=0,
help="""1: Render the observation and
the predicted semantic map,
2: Render the observation with semantic
predictions and the predicted semantic map
(default: 0)""")
parser.add_argument('--print_images', type=int, default=0,
help='1: save visualization as images')
# Environment, dataset and episode specifications
parser.add_argument('-efw', '--env_frame_width', type=int, default=640,
help='Frame width (default:640)')
parser.add_argument('-efh', '--env_frame_height', type=int, default=480,
help='Frame height (default:480)')
parser.add_argument('-fw', '--frame_width', type=int, default=160,
help='Frame width (default:160)')
parser.add_argument('-fh', '--frame_height', type=int, default=120,
help='Frame height (default:120)')
parser.add_argument('-el', '--max_episode_length', type=int, default=500,
help="""Maximum episode length""")
parser.add_argument("--task_config", type=str,
default="tasks/objectnav_gibson.yaml",
help="path to config yaml containing task information")
parser.add_argument("--split", type=str, default="train",
help="dataset split (train | val | val_mini) ")
parser.add_argument('--camera_height', type=float, default=0.88,
help="agent camera height in metres")
parser.add_argument('--hfov', type=float, default=79.0,
help="horizontal field of view in degrees")
parser.add_argument('--turn_angle', type=float, default=30,
help="Agent turn angle in degrees")
parser.add_argument('--min_depth', type=float, default=0.5,
help="Minimum depth for depth sensor in meters")
parser.add_argument('--max_depth', type=float, default=5.0,
help="Maximum depth for depth sensor in meters")
parser.add_argument('--success_dist', type=float, default=1.0,
help="success distance threshold in meters")
parser.add_argument('--floor_thr', type=int, default=50,
help="floor threshold in cm")
parser.add_argument('--min_d', type=float, default=1.5,
help="min distance to goal during training in meters")
parser.add_argument('--max_d', type=float, default=100.0,
help="max distance to goal during training in meters")
parser.add_argument('--version', type=str, default="v1.1",
help="dataset version")
# Model Hyperparameters
parser.add_argument('--agent', type=str, default="sem_exp")
parser.add_argument('--lr', type=float, default=2.5e-5,
help='learning rate (default: 2.5e-5)')
parser.add_argument('--global_hidden_size', type=int, default=256,
help='global_hidden_size')
parser.add_argument('--eps', type=float, default=1e-5,
help='RL Optimizer epsilon (default: 1e-5)')
parser.add_argument('--alpha', type=float, default=0.99,
help='RL Optimizer alpha (default: 0.99)')
parser.add_argument('--gamma', type=float, default=0.99,
help='discount factor for rewards (default: 0.99)')
parser.add_argument('--use_gae', action='store_true', default=False,
help='use generalized advantage estimation')
parser.add_argument('--tau', type=float, default=0.95,
help='gae parameter (default: 0.95)')
parser.add_argument('--entropy_coef', type=float, default=0.001,
help='entropy term coefficient (default: 0.01)')
parser.add_argument('--value_loss_coef', type=float, default=0.5,
help='value loss coefficient (default: 0.5)')
parser.add_argument('--max_grad_norm', type=float, default=0.5,
help='max norm of gradients (default: 0.5)')
parser.add_argument('--num_global_steps', type=int, default=20,
help='number of forward steps in A2C (default: 5)')
parser.add_argument('--ppo_epoch', type=int, default=4,
help='number of ppo epochs (default: 4)')
parser.add_argument('--num_mini_batch', type=str, default="auto",
help='number of batches for ppo (default: 32)')
parser.add_argument('--clip_param', type=float, default=0.2,
help='ppo clip parameter (default: 0.2)')
parser.add_argument('--use_recurrent_global', type=int, default=0,
help='use a recurrent global policy')
parser.add_argument('--num_local_steps', type=int, default=25,
help="""Number of steps the local policy
between each global step""")
parser.add_argument('--reward_coeff', type=float, default=0.1,
help="Object goal reward coefficient")
parser.add_argument('--intrinsic_rew_coeff', type=float, default=0.02,
help="intrinsic exploration reward coefficient")
parser.add_argument('--num_sem_categories', type=float, default=16)
parser.add_argument('--sem_pred_prob_thr', type=float, default=0.9,
help="Semantic prediction confidence threshold")
# Mapping
parser.add_argument('--global_downscaling', type=int, default=2)
parser.add_argument('--vision_range', type=int, default=100)
parser.add_argument('--map_resolution', type=int, default=5)
parser.add_argument('--du_scale', type=int, default=1)
parser.add_argument('--map_size_cm', type=int, default=2400)
parser.add_argument('--cat_pred_threshold', type=float, default=5.0)
parser.add_argument('--map_pred_threshold', type=float, default=1.0)
parser.add_argument('--exp_pred_threshold', type=float, default=1.0)
parser.add_argument('--collision_threshold', type=float, default=0.20)
# parse arguments
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
if args.auto_gpu_config:
num_gpus = torch.cuda.device_count()
if args.total_num_scenes != "auto":
args.total_num_scenes = int(args.total_num_scenes)
elif "objectnav_gibson" in args.task_config and \
"train" in args.split:
args.total_num_scenes = 25
elif "objectnav_gibson" in args.task_config and \
"val" in args.split:
args.total_num_scenes = 5
else:
assert False, "Unknown task config, please specify" + \
" total_num_scenes"
# GPU Memory required for the SemExp model:
# 0.8 + 0.4 * args.total_num_scenes (GB)
# GPU Memory required per thread: 2.6 (GB)
min_memory_required = max(0.8 + 0.4 * args.total_num_scenes, 2.6)
# Automatically configure number of training threads based on
# number of GPUs available and GPU memory size
gpu_memory = 1000
for i in range(num_gpus):
gpu_memory = min(gpu_memory,
torch.cuda.get_device_properties(
i).total_memory
/ 1024 / 1024 / 1024)
assert gpu_memory > min_memory_required, \
"""Insufficient GPU memory for GPU {}, gpu memory ({}GB)
needs to be greater than {}GB""".format(
i, gpu_memory, min_memory_required)
num_processes_per_gpu = int(gpu_memory / 2.6)
num_processes_on_first_gpu = \
int((gpu_memory - min_memory_required) / 2.6)
if args.eval:
max_threads = num_processes_per_gpu * (num_gpus - 1) \
+ num_processes_on_first_gpu
assert max_threads >= args.total_num_scenes, \
"""Insufficient GPU memory for evaluation"""
if num_gpus == 1:
args.num_processes_on_first_gpu = num_processes_on_first_gpu
args.num_processes_per_gpu = 0
args.num_processes = num_processes_on_first_gpu
assert args.num_processes > 0, "Insufficient GPU memory"
else:
num_threads = num_processes_per_gpu * (num_gpus - 1) \
+ num_processes_on_first_gpu
num_threads = min(num_threads, args.total_num_scenes)
args.num_processes_per_gpu = num_processes_per_gpu
args.num_processes_on_first_gpu = max(
0,
num_threads - args.num_processes_per_gpu * (num_gpus - 1))
args.num_processes = num_threads
args.sim_gpu_id = 1
print("Auto GPU config:")
print("Number of processes: {}".format(args.num_processes))
print("Number of processes on GPU 0: {}".format(
args.num_processes_on_first_gpu))
print("Number of processes per GPU: {}".format(
args.num_processes_per_gpu))
else:
args.sem_gpu_id = -2
if args.num_mini_batch == "auto":
args.num_mini_batch = max(args.num_processes // 2, 1)
else:
args.num_mini_batch = int(args.num_mini_batch)
return args
|
[
"torch.cuda.get_device_properties",
"torch.cuda.is_available",
"argparse.ArgumentParser",
"torch.cuda.device_count"
] |
[((60, 133), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Goal-Oriented-Semantic-Exploration"""'}), "(description='Goal-Oriented-Semantic-Exploration')\n", (83, 133), False, 'import argparse\n'), ((9196, 9221), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (9219, 9221), False, 'import torch\n'), ((9297, 9322), 'torch.cuda.device_count', 'torch.cuda.device_count', ([], {}), '()\n', (9320, 9322), False, 'import torch\n'), ((10389, 10424), 'torch.cuda.get_device_properties', 'torch.cuda.get_device_properties', (['i'], {}), '(i)\n', (10421, 10424), False, 'import torch\n')]
|
#
# Control of the Forktools configuration and services
#
from flask import Flask, jsonify, abort, request, flash, g
from common.models import alerts as a
from web import app, db, utils
from . import worker as wk
def load_config(farmer, blockchain):
return utils.send_get(farmer, "/configs/tools/"+ blockchain, debug=False).content
def save_config(farmer, blockchain, config):
try:
utils.send_put(farmer, "/configs/tools/" + blockchain, config, debug=False)
except Exception as ex:
flash('Failed to save config to farmer. Please check log files.', 'danger')
flash(str(ex), 'warning')
else:
flash('Nice! Tools config validated and saved successfully. Worker services now restarting. Please allow 10-15 minutes to take effect.', 'success')
|
[
"flask.flash",
"web.utils.send_get",
"web.utils.send_put"
] |
[((265, 332), 'web.utils.send_get', 'utils.send_get', (['farmer', "('/configs/tools/' + blockchain)"], {'debug': '(False)'}), "(farmer, '/configs/tools/' + blockchain, debug=False)\n", (279, 332), False, 'from web import app, db, utils\n'), ((403, 478), 'web.utils.send_put', 'utils.send_put', (['farmer', "('/configs/tools/' + blockchain)", 'config'], {'debug': '(False)'}), "(farmer, '/configs/tools/' + blockchain, config, debug=False)\n", (417, 478), False, 'from web import app, db, utils\n'), ((644, 801), 'flask.flash', 'flash', (['"""Nice! Tools config validated and saved successfully. Worker services now restarting. Please allow 10-15 minutes to take effect."""', '"""success"""'], {}), "(\n 'Nice! Tools config validated and saved successfully. Worker services now restarting. Please allow 10-15 minutes to take effect.'\n , 'success')\n", (649, 801), False, 'from flask import Flask, jsonify, abort, request, flash, g\n'), ((515, 591), 'flask.flash', 'flash', (['"""Failed to save config to farmer. Please check log files."""', '"""danger"""'], {}), "('Failed to save config to farmer. Please check log files.', 'danger')\n", (520, 591), False, 'from flask import Flask, jsonify, abort, request, flash, g\n')]
|
'''
This module handles the covid API, covid data, key statistics calculations and
scheduling covid updates.
'''
import logging
import sched
import datetime
import time
from re import match
import requests
from uk_covid19 import Cov19API
import uk_covid19
covid_data = {}
national_covid_data = {}
scheduled_updates = {}
config_covid_location = {}
scheduler = sched.scheduler(time.time, time.sleep)
def parse_csv_data(filename:str) -> list[str]:
'''
Take a csv file and return a list split by each row of data
Parameters:
filename (str): The name of the Covid data CSV file
Returns:
data_lines (list[str]): The list containing each row of data as a string
'''
headers = {
"areaCode":"area_code",
"areaName":"area_name",
"areaType":"area_type",
"date": "date",
"cumDailyNsoDeathsByDeathDate":"cum_deaths",
"hospitalCases":"hospital_cases",
"newCasesBySpecimenDate":"new_cases"
}
try:
with open(str(filename), encoding="ascii") as file:
data_lines = file.read().splitlines() # file to list split by new lines (rows)
logging.info("CSV file opened successfully: %s", filename)
except IOError:
logging.warning("Cannot open CSV file")
else:
file_headers = data_lines[0].split(",")
new_headers = []
for header in file_headers:
if header in headers:
new_headers.append(headers[header])
else:
logging.warning("Unknown header in the CSV file: %s", header)
new_headers.append(header)
data_lines[0] = ",".join(new_headers)
# renaming headers - API does this automatically, but currently reading from CSV
covid_data_local = convert_covid_csv_data_to_list_dict(data_lines)
return covid_data_local
def convert_covid_csv_data_to_list_dict(covid_csv_data:list[str]) -> list[dict]:
'''
Takes the parsed csv covid data and split rows into lists appendding each row to a new list
This function is only necessary when reading from a CSV. The function turns the CSV file into
the same data structure that is returned from the API.
Parameters:
covid_csv_data (list[str]): Covid data parsed through the function parse_csv_data
the data is each row of data as a string of the entire row
Returns:
covid_data_local (list[dict]): Covid data seperated in list by row and
converted to a dictionary
'''
logging.info("""convert_covid_csv_data_to_list_dict called:
Converting CSV file to list of dictionaries for further data processing.""")
covid_data_headers = covid_csv_data[0].split(',') # save covid data headers for dict
covid_csv_data_local = covid_csv_data[1:] # store data excluding headers in another list
covid_data_local = []
for row in covid_csv_data_local:
row_data = row.split(',') # split row into individual pieces of data
data = {}
for header, data_entry in zip(covid_data_headers, row_data):
data[header] = data_entry
# take individual data and map header (data title) to data in dict
covid_data_local.append(data)
# add dict to list of Covid data
covid_data_local.sort(key = lambda x: x['date'], reverse=True)
# just in case data is not in order sort by date, most recent date as index 0.
return covid_data_local
def process_covid_csv_data(covid_data_local:list[dict]) -> tuple[int|str, int|str, int|str]:
'''
Takes the Covid data processed from parse_csv_data and returns the number of cases for the past
3 days, the number of hospital cases and the number of cumulative deaths
Parameters:
covid_data (list): The Covid data from parse_csv_data - Covid data in a list containing
dictionaries in header:data form
Returns:
total_cases_last_7_days (int|str): The total number of cases in the past 7 days -
ignoring empty data entries and the first day or N/A if not applicable
hospital_cases (int|str): The number of hospital cases from most recent data
or N/A if not applicable
cum_deaths (int|str): The number of cumulative deaths from the most recent data
or N/A if not applicable
'''
logging.info("""process_covid_csv_data called:
Processing COVID data to generate 3 key statistics""")
first_date = next(
(index for index, item in enumerate(covid_data_local) if item['new_cases']), None
) # finding the index of the first non empty entry of data.
# if there is valid entry, return none.
if first_date is not None: # test to mkae sure there is data
first_date += 1 # skip the first day
if len(covid_data_local) - first_date > 7:
days = 7 # if there are 7 days worth of data
else:
days = len(covid_data_local) - first_date
# if not, then just calculate the remaining amounts of data
total_cases_last_7_days = 0
for days in range(days):
total_cases_last_7_days += int(
covid_data_local[first_date+days]['new_cases']
) # loop through 7 days and add all of them to total
else: # if there is no data
logging.info("There is no data to calculate the 7 day covid rate.")
total_cases_last_7_days = "N/A"
# The following is the while loop as above but for the next statistics, without adding 1 day
first_date = next(
(i for i, item in enumerate(covid_data_local) if item['hospital_cases']), None
) # this is the same as the next() statement as above but for hospital cases
if first_date is not None: # makes sure data is there as some API calls don't have this data.
hospital_cases = int(covid_data_local[first_date]['hospital_cases'])
else: # if API call doesn't have this data, simply diplay N/A to user.
logging.info("There is insufficient data to show hospital cases.")
hospital_cases = "N/A"
first_date = next(
(i for i, item in enumerate(covid_data_local) if item['cum_deaths']), None
) # this is the same as the next() statement as above but for cumulative deaths
if first_date is not None: # makes sure data is there as some API calls don't have this data.
cum_deaths = int(covid_data_local[first_date]["cum_deaths"])
else: # if API call doesn't have this data, simply display N/A to user.
logging.info("There is insufficient data to show cumulative deaths.")
cum_deaths = "N/A"
return total_cases_last_7_days, hospital_cases, cum_deaths
def covid_API_request(location:str = "Exeter", location_type:str = "ltla") -> dict:
'''
This requests information from the UK Covid API
Parameters:
location (str): The location for information to be request about, default=Exeter
location_type (str): The type of location, default=ltla (Lower-tier local
authority data)
Returns:
data (dict): The data the API returns based on the filter and structure provided
'''
logging.info("Beginning API request to update COVID data.")
if location_type != "overview":
location_data = [
"areaType="+location_type,
"areaName="+location
]
else: # if areaType is overview, there is no need for areaName in request
location_data = ["areaType=overview"]
# generate a filter as required by covid API
structure_data = {
"area_name": "areaName",
"date": "date",
"cum_deaths": "cumDailyNsoDeathsByDeathDate",
"hospital_cases": "hospitalCases",
"new_cases": "newCasesBySpecimenDate"
} # information needed from API and renaming as per API parameters
try:
api = Cov19API(filters=location_data, structure=structure_data)
data = api.get_json() # json data already processed by API.
logging.info("API call completed")
return data
except uk_covid19.exceptions.FailedRequestError as error:
# may occur if there is a connection error
logging.warning("COVID API call failed: %s", error)
print("COVID API call failed: Check internet connection")
print("Retrying in 30 seconds...")
schedule_covid_updates(30, "API Retry")
return {"data": None}
except requests.exceptions.ConnectionError as error:
# may occur if there is a connection error
logging.warning("COVID API call failed: %s", error)
print("COVID API call failed: Check internet connection")
print("Retrying in 30 seconds...")
schedule_covid_updates(30, "API Retry")
return {"data": None}
def sch_update_covid_data(update_time: datetime.datetime, update_name: int, repeat: bool) -> None:
'''
This procedure is called by the scheduler to run an update and determine whether to schedule
a new update depending on whether this was a repeating update
Parameters:
update_interval (int|datetime.datetime): the datetime object of the update time
update_name (str): the name of the scheduled update
repeat (bool): whether the update is repeating
'''
global covid_data
global national_covid_data
# no way around using global variables here. They needs to be assigned on update
logging.info("Running scheduled COVID update %s", update_name)
del scheduled_updates[update_name] # scheduled update called, delete from dict
if config_covid_location: # make sure that covid API requests use config data if it is there
location_type = config_covid_location["area_type"]
location = config_covid_location["area_name"]
api_response = covid_API_request(location, location_type)
else:
api_response = covid_API_request()
national_api_response = covid_API_request(location_type="overview")
if api_response:
covid_data = api_response
if national_api_response:
national_covid_data = national_api_response
if repeat: # this is for if the user requested a repeating update
update_time = update_time + datetime.timedelta(days=1)
logging.info("Covid update (%s) to be repeated. Scheduling next update", update_name)
schedule_covid_updates(update_time, update_name, repeat)
def cancel_scheduled_update(update_name:str) -> None:
'''
This procedure simply cancels a scheduled update and remoevd it from the scheduled update dict
Parameters:
update_name(str): The key of the scheduled update in dict
'''
logging.info("Cancelling schduled COVID update named: %s", update_name)
if update_name in scheduled_updates:
# if the update exists, then find the event and remove it from the scheduler and
# list of scheduled updates
event = scheduled_updates[update_name]["event"]
scheduler.cancel(event)
del scheduled_updates[update_name]
logging.info("%s successfully removed from scheduled COVID updates", update_name)
logging.debug("COVID scheduled_updates = %s", scheduled_updates)
logging.debug("COVID Scheduler queue = %s", scheduler.queue)
else:
logging.warning("""Attempted to remove scheduled update event from scheduler
but event does not exist: %s""", update_name)
def schedule_covid_updates(update_interval: int|str|datetime.datetime,
update_name: int, repeat=False) -> None:
'''
This procedure is called when the user requests to schedule an update. All scheduled events
are added to the scheduled_updates dictionary with the name as the key.
Parameters:
update_interval (int|str|datetime.datetime):
if int, time to update in seconds
if str, time of next update in the format HH:MM
if datetime.datetime, the datetime of next update
update_name (str): the name of the scheduled update
repeat (bool): whether the update is repeating
'''
logging.info("Scheduling covid update: %s", update_name)
if isinstance(update_interval, str):
logging.info("Recieved string. Attempting to parse...")
# if it's a string, test if its coming from the dashboard and therefore HH:MM format
if match("^([0-1]?[0-9]|2[0-3]):[0-5][0-9]$", update_interval):
time_to_update, update_time = time_to_update_interval(update_interval)
logging.debug("time_to_update = %s", str(time_to_update))
logging.debug("update_time = %s", str(update_time))
elif update_interval.isdigit():
update_interval = int(update_interval)
# this will trigger the if statement below for int types
else:
logging.warning("Can't parse update time. Cancelling update scheduling")
# If we can't parse the update time parameter, cancel and exit function
return None
if isinstance(update_interval, datetime.datetime):
# if datetime object, calcuate time to next update
logging.info("Recieved datetime object.")
update_time = update_interval
if update_time < datetime.datetime.now():
update_time = datetime.datetime.now().replace(
hour=update_time.hour, minute=update_time.minute, second=0, microsecond=0
)
if update_time < datetime.datetime.now():
update_time += datetime.timedelta(days=1)
# if the datetime object is in the past, we assume the next point where that
# hour and minute occur
time_to_update = (update_time - datetime.datetime.now()).total_seconds()
if isinstance(update_interval, int):
# if int, calculate datetime object of update
logging.info("Recieved int. Parsing as seconds from now.")
time_to_update = abs(update_interval)
# if number is negative, assume absolute value anyways
update_time = datetime.datetime.now() + datetime.timedelta(seconds = update_interval)
logging.info("Covid update time has been parsed")
logging.debug("Update time parsed as %s", str(update_time))
if update_name not in scheduled_updates:
# make sure we are not trying to create an update with a duplicate name
event = scheduler.enter(
time_to_update,1,sch_update_covid_data,(update_time, update_name, repeat, )
)
scheduled_updates[update_name] = {
"event": event,
"update_time":update_time,
"repeat":repeat
}
logging.info("Scheduled COVID update: %s", update_name)
logging.debug("Scheduler Queue (covid): %s", str(scheduler.queue))
else:
# should modify HTML to tell user that the app cannot schedule update as the
# update name is already in use but outside bounds of CA
logging.warning("Tried to schedule update with same name as existing update")
logging.debug("Update Name: %s", update_name)
logging.debug("Scheduler Queue (covid): %s", str(scheduler.queue))
def time_to_update_interval(update_interval:str) -> tuple[int, datetime.datetime]:
'''
Function to convert the data taken from the website form into a datetime object and
a integer variable with the amount of time from now to the update time recieved.
Parameters:
update_interval (str): The time in "HH:MM" format.
Returns:
time_to_update (int): The amount of seconds from now to the update time
update_time (datetime.datetime): datetime object that corresponds to the update time
'''
logging.info("Converting string to datetime object and seconds to update")
logging.debug("update_interval = %s", str(update_interval))
hrs, mins = map(int, update_interval.split(":"))
update_time = datetime.datetime.now().replace(hour=hrs, minute=mins, second=0, microsecond=0)
if update_time < datetime.datetime.now():
update_time = update_time + datetime.timedelta(days=1)
time_to_update = (update_time - datetime.datetime.now()).total_seconds()
return time_to_update, update_time
if __name__ == "__main__":
# if file is run individually, run these tests.
print("Running self tests")
TEST_FILE = "nation_2021-10-28.csv"
data_covid = parse_csv_data(TEST_FILE)
last_7_days_cases, current_hospital_cases, total_deaths = (
process_covid_csv_data(data_covid)
)
print(f"""{last_7_days_cases = :,} (expected 240,299)\n
{current_hospital_cases = :,} (expeced 7,019)\n
{total_deaths = :,} (expected 141,544)""")
|
[
"logging.debug",
"logging.warning",
"re.match",
"datetime.datetime.now",
"sched.scheduler",
"logging.info",
"datetime.timedelta",
"uk_covid19.Cov19API"
] |
[((383, 421), 'sched.scheduler', 'sched.scheduler', (['time.time', 'time.sleep'], {}), '(time.time, time.sleep)\n', (398, 421), False, 'import sched\n'), ((2644, 2794), 'logging.info', 'logging.info', (['"""convert_covid_csv_data_to_list_dict called:\n Converting CSV file to list of dictionaries for further data processing."""'], {}), '(\n """convert_covid_csv_data_to_list_dict called:\n Converting CSV file to list of dictionaries for further data processing."""\n )\n', (2656, 2794), False, 'import logging\n'), ((4525, 4640), 'logging.info', 'logging.info', (['"""process_covid_csv_data called:\n Processing COVID data to generate 3 key statistics"""'], {}), '(\n """process_covid_csv_data called:\n Processing COVID data to generate 3 key statistics"""\n )\n', (4537, 4640), False, 'import logging\n'), ((7413, 7472), 'logging.info', 'logging.info', (['"""Beginning API request to update COVID data."""'], {}), "('Beginning API request to update COVID data.')\n", (7425, 7472), False, 'import logging\n'), ((9722, 9784), 'logging.info', 'logging.info', (['"""Running scheduled COVID update %s"""', 'update_name'], {}), "('Running scheduled COVID update %s', update_name)\n", (9734, 9784), False, 'import logging\n'), ((10988, 11059), 'logging.info', 'logging.info', (['"""Cancelling schduled COVID update named: %s"""', 'update_name'], {}), "('Cancelling schduled COVID update named: %s', update_name)\n", (11000, 11059), False, 'import logging\n'), ((12483, 12539), 'logging.info', 'logging.info', (['"""Scheduling covid update: %s"""', 'update_name'], {}), "('Scheduling covid update: %s', update_name)\n", (12495, 12539), False, 'import logging\n'), ((14534, 14583), 'logging.info', 'logging.info', (['"""Covid update time has been parsed"""'], {}), "('Covid update time has been parsed')\n", (14546, 14583), False, 'import logging\n'), ((16169, 16243), 'logging.info', 'logging.info', (['"""Converting string to datetime object and seconds to update"""'], {}), "('Converting string to datetime object and seconds to update')\n", (16181, 16243), False, 'import logging\n'), ((5520, 5587), 'logging.info', 'logging.info', (['"""There is no data to calculate the 7 day covid rate."""'], {}), "('There is no data to calculate the 7 day covid rate.')\n", (5532, 5587), False, 'import logging\n'), ((6189, 6255), 'logging.info', 'logging.info', (['"""There is insufficient data to show hospital cases."""'], {}), "('There is insufficient data to show hospital cases.')\n", (6201, 6255), False, 'import logging\n'), ((6740, 6809), 'logging.info', 'logging.info', (['"""There is insufficient data to show cumulative deaths."""'], {}), "('There is insufficient data to show cumulative deaths.')\n", (6752, 6809), False, 'import logging\n'), ((8129, 8186), 'uk_covid19.Cov19API', 'Cov19API', ([], {'filters': 'location_data', 'structure': 'structure_data'}), '(filters=location_data, structure=structure_data)\n', (8137, 8186), False, 'from uk_covid19 import Cov19API\n'), ((8265, 8299), 'logging.info', 'logging.info', (['"""API call completed"""'], {}), "('API call completed')\n", (8277, 8299), False, 'import logging\n'), ((10562, 10651), 'logging.info', 'logging.info', (['"""Covid update (%s) to be repeated. Scheduling next update"""', 'update_name'], {}), "('Covid update (%s) to be repeated. 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Attempting to parse..."""'], {}), "('Recieved string. Attempting to parse...')\n", (12603, 12646), False, 'import logging\n'), ((12753, 12812), 're.match', 'match', (['"""^([0-1]?[0-9]|2[0-3]):[0-5][0-9]$"""', 'update_interval'], {}), "('^([0-1]?[0-9]|2[0-3]):[0-5][0-9]$', update_interval)\n", (12758, 12812), False, 'from re import match\n'), ((13533, 13574), 'logging.info', 'logging.info', (['"""Recieved datetime object."""'], {}), "('Recieved datetime object.')\n", (13545, 13574), False, 'import logging\n'), ((14264, 14322), 'logging.info', 'logging.info', (['"""Recieved int. Parsing as seconds from now."""'], {}), "('Recieved int. Parsing as seconds from now.')\n", (14276, 14322), False, 'import logging\n'), ((15082, 15137), 'logging.info', 'logging.info', (['"""Scheduled COVID update: %s"""', 'update_name'], {}), "('Scheduled COVID update: %s', update_name)\n", (15094, 15137), False, 'import logging\n'), ((15386, 15463), 'logging.warning', 'logging.warning', (['"""Tried to schedule update with same name as existing update"""'], {}), "('Tried to schedule update with same name as existing update')\n", (15401, 15463), False, 'import logging\n'), ((15473, 15518), 'logging.debug', 'logging.debug', (['"""Update Name: %s"""', 'update_name'], {}), "('Update Name: %s', update_name)\n", (15486, 15518), False, 'import logging\n'), ((16484, 16507), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (16505, 16507), False, 'import datetime\n'), ((1218, 1276), 'logging.info', 'logging.info', (['"""CSV file opened successfully: %s"""', 'filename'], {}), "('CSV file opened successfully: %s', filename)\n", (1230, 1276), False, 'import logging\n'), ((1307, 1346), 'logging.warning', 'logging.warning', (['"""Cannot open CSV file"""'], {}), "('Cannot open CSV file')\n", (1322, 1346), False, 'import logging\n'), ((8445, 8496), 'logging.warning', 'logging.warning', (['"""COVID API call failed: %s"""', 'error'], {}), "('COVID API call failed: %s', error)\n", (8460, 8496), False, 'import logging\n'), ((8807, 8858), 'logging.warning', 'logging.warning', (['"""COVID API call failed: %s"""', 'error'], {}), "('COVID API call failed: %s', error)\n", (8822, 8858), False, 'import logging\n'), ((10526, 10552), 'datetime.timedelta', 'datetime.timedelta', ([], {'days': '(1)'}), '(days=1)\n', (10544, 10552), False, 'import datetime\n'), ((13640, 13663), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (13661, 13663), False, 'import datetime\n'), ((14457, 14480), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (14478, 14480), False, 'import datetime\n'), ((14483, 14526), 'datetime.timedelta', 'datetime.timedelta', ([], {'seconds': 'update_interval'}), '(seconds=update_interval)\n', (14501, 14526), False, 'import datetime\n'), ((16382, 16405), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (16403, 16405), False, 'import datetime\n'), ((16546, 16572), 'datetime.timedelta', 'datetime.timedelta', ([], {'days': '(1)'}), '(days=1)\n', (16564, 16572), False, 'import datetime\n'), ((1594, 1655), 'logging.warning', 'logging.warning', (['"""Unknown header in the CSV file: %s"""', 'header'], {}), "('Unknown header in the CSV file: %s', header)\n", (1609, 1655), False, 'import logging\n'), ((13225, 13297), 'logging.warning', 'logging.warning', (['"""Can\'t parse update time. Cancelling update scheduling"""'], {}), '("Can\'t parse update time. Cancelling update scheduling")\n', (13240, 13297), False, 'import logging\n'), ((13865, 13888), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (13886, 13888), False, 'import datetime\n'), ((13922, 13948), 'datetime.timedelta', 'datetime.timedelta', ([], {'days': '(1)'}), '(days=1)\n', (13940, 13948), False, 'import datetime\n'), ((16610, 16633), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (16631, 16633), False, 'import datetime\n'), ((13692, 13715), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (13713, 13715), False, 'import datetime\n'), ((14117, 14140), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (14138, 14140), False, 'import datetime\n')]
|
#!/usr/bin/env python
# coding: utf-8
# This software component is licensed by ST under BSD 3-Clause license,
# the "License"; You may not use this file except in compliance with the
# License. You may obtain a copy of the License at:
# https://opensource.org/licenses/BSD-3-Clause
"""
Optimze Full int8 - with reference dataset
Fully quantized model tflite ASC - TF 1.14.0ASC 3CL Training script from Pre calculated features.
"""
import numpy as np
import tensorflow as tf
# load ASC training Set as representative quantization dataset (100 samples)
# reduced 'dummy' data set is provided , a full representative one should be provided instead
x_train_dataset = np.load('Asc_quant_representative_data_dummy.npz')
x_train = x_train_dataset['x_train']
ASC_SHAPE = (30, 32, 1)
N_CLASSES = 3
def representative_dataset_gen():
for i in range(len(x_train)):
# Get sample input data as a numpy array in a method of your choosing.
yield [x_train[i].reshape((-1, ) + ASC_SHAPE)]
converter = tf.lite.TFLiteConverter.from_keras_model_file("Session_keras_mod_93_Model.h5" )
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset_gen
converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
print("\nConverting the model...", flush=True)
tflite_model = converter.convert()
open('asc_keras_mod_93_to_tflite_int8_xtrain.tflite','wb').write(tflite_model)
|
[
"numpy.load",
"tensorflow.lite.TFLiteConverter.from_keras_model_file"
] |
[((723, 773), 'numpy.load', 'np.load', (['"""Asc_quant_representative_data_dummy.npz"""'], {}), "('Asc_quant_representative_data_dummy.npz')\n", (730, 773), True, 'import numpy as np\n'), ((1075, 1153), 'tensorflow.lite.TFLiteConverter.from_keras_model_file', 'tf.lite.TFLiteConverter.from_keras_model_file', (['"""Session_keras_mod_93_Model.h5"""'], {}), "('Session_keras_mod_93_Model.h5')\n", (1120, 1153), True, 'import tensorflow as tf\n')]
|
# Generated by Django 3.1.8 on 2021-07-20 13:34
from django.db import migrations, models
import django.db.models.deletion
import uuid
class Migration(migrations.Migration):
dependencies = [
('django_workflow_system', '0004_auto_20210701_0910'),
]
operations = [
migrations.CreateModel(
name='WorkflowCollectionDependency',
fields=[
('created_date', models.DateTimeField(auto_now_add=True)),
('modified_date', models.DateTimeField(auto_now=True)),
('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)),
('source', models.ForeignKey(help_text='The collection for which we want to create a dependency.', on_delete=django.db.models.deletion.PROTECT, related_name='source_workflow_collection', to='django_workflow_system.workflowcollection')),
('target', models.ForeignKey(help_text="The collection which we want to require be completed before the user can create engagements for the 'source' collection.", on_delete=django.db.models.deletion.PROTECT, related_name='target_workflow_collection', to='django_workflow_system.workflowcollection')),
],
options={
'verbose_name_plural': 'Workflow Collection Dependencies',
'db_table': 'workflow_collection_dependency',
'unique_together': {('source', 'target')},
},
),
migrations.AddField(
model_name='workflowcollection',
name='collection_dependencies',
field=models.ManyToManyField(blank=True, help_text='Specify which collections a user must complete before accessing this Collection.', through='django_workflow_system.WorkflowCollectionDependency', to='django_workflow_system.WorkflowCollection'),
),
]
|
[
"django.db.models.ForeignKey",
"django.db.models.DateTimeField",
"django.db.models.ManyToManyField",
"django.db.models.UUIDField"
] |
[((1614, 1868), 'django.db.models.ManyToManyField', 'models.ManyToManyField', ([], {'blank': '(True)', 'help_text': '"""Specify which collections a user must complete before accessing this Collection."""', 'through': '"""django_workflow_system.WorkflowCollectionDependency"""', 'to': '"""django_workflow_system.WorkflowCollection"""'}), "(blank=True, help_text=\n 'Specify which collections a user must complete before accessing this Collection.'\n , through='django_workflow_system.WorkflowCollectionDependency', to=\n 'django_workflow_system.WorkflowCollection')\n", (1636, 1868), False, 'from django.db import migrations, models\n'), ((422, 461), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'auto_now_add': '(True)'}), '(auto_now_add=True)\n', (442, 461), False, 'from django.db import migrations, models\n'), ((498, 533), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'auto_now': '(True)'}), '(auto_now=True)\n', (518, 533), False, 'from django.db import migrations, models\n'), ((559, 650), 'django.db.models.UUIDField', 'models.UUIDField', ([], {'default': 'uuid.uuid4', 'editable': '(False)', 'primary_key': '(True)', 'serialize': '(False)'}), '(default=uuid.uuid4, editable=False, primary_key=True,\n serialize=False)\n', (575, 650), False, 'from django.db import migrations, models\n'), ((676, 919), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'help_text': '"""The collection for which we want to create a dependency."""', 'on_delete': 'django.db.models.deletion.PROTECT', 'related_name': '"""source_workflow_collection"""', 'to': '"""django_workflow_system.workflowcollection"""'}), "(help_text=\n 'The collection for which we want to create a dependency.', on_delete=\n django.db.models.deletion.PROTECT, related_name=\n 'source_workflow_collection', to=\n 'django_workflow_system.workflowcollection')\n", (693, 919), False, 'from django.db import migrations, models\n'), ((929, 1236), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'help_text': '"""The collection which we want to require be completed before the user can create engagements for the \'source\' collection."""', 'on_delete': 'django.db.models.deletion.PROTECT', 'related_name': '"""target_workflow_collection"""', 'to': '"""django_workflow_system.workflowcollection"""'}), '(help_text=\n "The collection which we want to require be completed before the user can create engagements for the \'source\' collection."\n , on_delete=django.db.models.deletion.PROTECT, related_name=\n \'target_workflow_collection\', to=\n \'django_workflow_system.workflowcollection\')\n', (946, 1236), False, 'from django.db import migrations, models\n')]
|
import os
import time
breakout=False
crimeseverity=False
crimesevereaction=False
# variables =
# text
# gender
# name
# age
# height
# drunk
print ("Welcome to the test, Citizen.")
time.sleep(1)
print("Today you are applying for a job at the Agency.")
time.sleep(1)
print("By participating in this test, you agree to the terms and conditions.")
time.sleep(1)
print("Please type 'I agree.' if you agree to the terms above. ")
while True:
try:
text = (input("Your Answer: "))
if text == ("I agree."):
print("Then we may begin.")
time.sleep(1)
break;
elif text == ("I agree"):
print("Then let's begin.")
time.sleep(1)
break;
else:
print("Please follow instructions.")
except ValueError:
print("Answer properly!")
continue
os.system("clear")
print("You will fill out a survey for us.")
time.sleep(1)
print("It will be reviewed.")
time.sleep(1)
print("Please answer honestly.")
time.sleep(1)
name = (input('What is your name, Citizen? '))
print('Hello, %s.' % name)
time.sleep(1)
print ('How old are you?')
while True:
try:
age = int(input("Your answer: "))
if age <= 40 and age >=18:
break;
elif age > 40:
print ("You are too old to enlist!")
elif age < 18:
print("You are too young to enlist!")
except ValueError:
print("Please input your age.")
continue
print("Okay.")
time.sleep(0.9)
os.system("clear")
time.sleep(1)
print("Are you a male or female?")
while True:
try:
gender = (input("Answer honestly: "))
if gender == "Male":
print("So you are a man?")
time.sleep(1)
break;
elif gender == "Female":
print("So you are a woman?")
time.sleep(1)
break;
elif gender == "female":
print("So you are a woman?")
time.sleep(1)
break;
elif gender == "male":
print("So you are a man?")
time.sleep(1)
break;
else:
print("Please input your gender.")
except ValueError:
print("Please answer correctly.")
continue
print("I see.")
time.sleep(1)
os.system("clear")
print("Have you drunk before?")
while True:
try:
drunk = (input('Answer : '))
if drunk == "Y":
print ("So you have drunk before?")
break;
elif drunk == "N":
print ("So you have not drunk before?")
break;
else:
print("Answer with Y/N")
except ValueError:
print("Answer with Y/N")
continue
time.sleep(1)
os.system("clear")
print("Do you have any experience with firearms?")
while True:
try:
experience = (input("Answer : "))
if experience == ("Y"):
print("So you have shot a gun before?")
break;
elif experience == ("N"):
print("So you have not shot a gun before?")
break;
else:
print("Please answer with [Y/N]")
except ValueError:
print("Please answer with [Y/N]")
continue
time.sleep(1)
print("Very well.")
time.sleep(1)
os.system("clear")
print("How tall are you?")
while True:
try:
height = int(input('cm : '))
if height >= 165 and height <=195:
break;
elif height < 165:
print("You are too short!")
elif height > 195:
print("You are too tall!")
else:
print("Please enter your height in cm.")
except ValueError:
print ("Please enter your height in cm.")
continue
print ("You are %d cm tall?" % height)
time.sleep(1)
print ("Very well.")
time.sleep(1)
os.system("clear")
print ("Have you commited any crimes?")
while True:
try:
crime = (input("Answer : "))
if crime == "N":
print("So you have not comitted any crimes?")
time.sleep(1)
break;
elif crime == "Y":
print("Was it a severe, or minor one?")
while True:
try:
severity = (input("Answer : "))
if severity == "Minor":
crimeseverity=True
print("Very well then.")
time.sleep(1)
breakout=True
break;
elif severity == "Severe":
crimeseverity=True
crimesevereaction=True
print("So, you have comitted a severe crime?")
time.sleep(1)
print("Like what?")
time.sleep(1)
os.system("clear")
print("1. Homicide")
print("2. Extortion")
print("3. Blackmail")
print("4. Use of drugs")
print("5. Rape")
print("6. Other")
while True:
try:
actions = (input("Answer : "))
if actions == "1":
print ("So you have killed someone before?")
time.sleep(1)
print ("That's okay, we do that alot here.")
time.sleep(1)
breakout=True
break
elif actions == "2":
print("So you have extorted someone before?")
time.sleep(1)
print("Do not be ashamed, we do that alot here.")
time.sleep(1)
breakout=True
break
elif actions == "3":
print("So you have blackmailed people before?")
time.sleep(1)
print("We do that alot here, do not be ashamed.")
time.sleep(1)
breakout=True
break
elif actions == "4":
print("You have consumed illegal drugs?")
time.sleep(1)
print("I guess it is okay, as long as you do not do it here.")
time.sleep(1)
breakout=True
break
elif actions == "5":
print("Rape? uh. We will note that down.")
time.sleep(1)
print("Very well.")
time.sleep(1)
breakout=True
break
elif actions == "6":
print("Very well.")
time.sleep(1)
breakout=True
break
else:
print("Answer the question with (1,2,3,4,5,6)")
except ValueError:
print("Answer the question with (1,2,3,4,5,6)")
continue
if breakout:
break
else:
print("Answer with [Severe\Minor]")
except ValueError:
print("Answer with [Severe\Minor]")
continue
if breakout:
break
else:
print("Answer with Y/N")
except ValueError:
print("Answer with Y/N")
continue
if breakout:
break
os.system("clear")
time.sleep(1)
print ("Now.")
time.sleep(1)
print("%s" % name)
time.sleep(1)
print("Here is a summary of all your answers.")
time.sleep(1)
os.system("clear")
print("Name : %s" % name)
time.sleep(0.5)
print("Gender : %s" % gender)
time.sleep(0.5)
print("Age : %s" % age)
time.sleep(0.5)
print("Height : %s cm" % height)
time.sleep(0.5)
print("Experience with firearms? : %s" % experience)
time.sleep(0.5)
print("Alcohol before? : %s" % drunk)
time.sleep(0.5)
print("Crime before? : %s" % crime)
time.sleep(0.5)
if crimeseverity is True:
print("Crime severity : %s" % severity)
time.sleep(0.5)
if crimesevereaction is True:
if actions == "1":
print("Type : Homicide")
time.sleep(0.5)
elif actions == "2":
time.sleep(0.5)
print("Type : Extortion")
elif actions == "3":
print("Type : Blackmail")
time.sleep(0.5)
elif actions == "4":
print("Type : Illegal substances")
time.sleep(0.5)
elif actions == "5":
print("Type : Rape")
time.sleep(1)
elif actions == "6":
print("Type : Other")
time.sleep(0.5)
print("Summary Evaluation")
time.sleep(1.5)
os.system("clear")
loop = 0
while loop <=2:
loop = loop + 1
print("Evaluating results")
time.sleep(0.5)
os.system("clear")
print("Evaluating results.")
time.sleep(0.5)
os.system("clear")
print("Evaluating results..")
time.sleep(0.5)
os.system("clear")
print("Evaluating results...")
time.sleep(0.5)
os.system("clear")
score = 1
#EVALUATION
if height >170:
score = score + 1
elif height <170:
score = score - 1
if drunk == "Y":
score = score - 1
elif drunk == "N":
score = score + 1
if experience == "Y":
score = score + 1
elif experience == "N":
score = score + 0
if crime == "N":
score = score + 1
elif crime == "Y":
if severity == "Minor":
score = score + 0
elif severity == "Severe":
score = score - 1
if actions == "1":
score = score + 1
elif actions == "2":
score = score + 1
elif actions == "3":
score = score + 1
elif actions == "4":
score = score + 0
elif actions == "5":
score = score + 0
elif actions == "6":
score = score + 0
if score >=3: #pass
print ('We have come back to tell you.')
time.sleep(1)
print ('That you have passed the test!')
time.sleep(1)
print ('Your final score was %d' % score)
time.sleep(1)
print ('For more info regarding this application')
time.sleep(1)
print ('Please visit bit.ly/agencysummary')
elif score <=2: #nopass
print ("We regret to inform you.")
time.sleep(1)
print("That you have failed the test.")
time.sleep(1)
print("Please re-evaluate your ways and try again.")
|
[
"os.system",
"time.sleep"
] |
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|
# -*- coding: utf-8 -*-
import networkx as nx
import itertools
def is_subset(node_types):
"""Judge if the given aspect is a subset of the Selected ones"""
global Selected_Aspects
nt_set = set(node_types)
for sa in Selected_Aspects:
if nt_set.issubset(sa):
return True
return False
def is_rational(type_graph):
"""The rationality of the given aspect is determined by its connectivity"""
return nx.is_connected(type_graph)
def center_nodes(type_graph):
"""Return the center node types of an aspect"""
centers = []
for node in type_graph.nodes():
if len([n for n in type_graph[node]]) > 1:
centers.append(node)
return centers
def Incompatibility(graph, node_types, edge_types, center_types):
"""Calculate Incompatitance for the given aspect
Each bloody aspect is determined by its node types"""
center_nodes_dict = {}
for c_type in center_types:
center_nodes_dict[c_type] = []
for node in graph.nodes():
if node[0] in center_nodes_dict.keys():
center_nodes_dict[node[0]].append(node)
inc = 0.
num_nonzero = 0
for c_type, node_list in center_nodes_dict.items():
accessable_nodetypes = extract_accessable_edgetypes(c_type, node_types, edge_types)
count = 0
total = len(node_list)
for u in node_list:
if count % 1000 == 0:
print('{} / {}'.format(count, total))
inc_u, nonzero = Inc_score(graph, u, node_list, accessable_nodetypes)
inc += inc_u
num_nonzero += nonzero
count += 1
return inc / num_nonzero
def extract_accessable_edgetypes(c, node_types, edge_types):
a_types = []
for e_t in edge_types:
if c == e_t[0] and e_t[-1] in node_types:
a_types.append(e_t[-1])
continue
if c == e_t[-1] and e_t[0] in node_types:
a_types.append(e_t[0])
return a_types
def Inc_score(graph, u, node_list, accessable):
"""Calculate gamma(u) for a single node u"""
numerator = 0.
denominator = 0.
for v in node_list:
if u == v:
continue
# compute the reachability through all accessable edge types
reachability = Num_Cn(graph, u, v, accessable)
numerator += max(reachability)
denominator += min(reachability)
if -0.1 <= denominator <= 0.1:
return 0, 0
else:
return numerator / denominator - 1, 1
def Num_Cn(graph, u, v, accessable):
neighbors_u = set([n for n in graph[u]])
neighbors_v = set([n for n in graph[v]])
cn = neighbors_u & neighbors_v
count = [0] * len(accessable)
for n in cn:
assert n[0] in accessable
count[accessable.index(n[0])] += 1
return count
# node types : ['A', 'P', 'P', 'V'], P appears multiple times because the P-P edge type
# edge types : ['A-P', 'P-P', 'P-V', ...]
def Select_Aspect(graph, node_types, edge_types, threshold):
"""Se个粑粑"""
global Selected_Aspects
if is_subset(node_types):
return
type_graph = nx.Graph()
for et in edge_types:
if et[0] in node_types and et[-1] in node_types:
type_graph.add_edge(et[0], et[-1])
if is_rational(type_graph):
# whether it is a valid aspect
center_types = center_nodes(type_graph)
Inc = Incompatibility(graph, node_types, edge_types, center_types)
if Inc > threshold:
Selected_Aspects.append(node_types)
return
if len(node_types) <= 3:
# It takes at least 3 node types to make an aspect
return
else:
for c in itertools.combinations(node_types, len(node_types)-1):
Select_Aspect(graph, list(c), edge_types, threshold)
def show_Inc_aspects(graph, node_types, edge_types, aspects):
for a in aspects:
type_graph = nx.Graph()
for et in edge_types:
if et[0] in a and et[-1] in a:
type_graph.add_edge(et[0], et[-1])
center_types = center_nodes(type_graph)
print(Incompatibility(graph, node_types, edge_types, center_types))
if __name__ == '__main__':
datasets = ['dblp/']
using = datasets[0]
graph = nx.read_edgelist(
'data/' + using + 'graph.edgelist', delimiter=',',
create_using=nx.Graph(), nodetype=str, data=False
)
Select_Aspect(
graph=graph,
node_types=['A', 'P', 'P', 'V'],
edge_types=['A-P', 'P-V', 'P-P'],
threshold=1.
)
|
[
"networkx.is_connected",
"networkx.Graph"
] |
[((445, 472), 'networkx.is_connected', 'nx.is_connected', (['type_graph'], {}), '(type_graph)\n', (460, 472), True, 'import networkx as nx\n'), ((3108, 3118), 'networkx.Graph', 'nx.Graph', ([], {}), '()\n', (3116, 3118), True, 'import networkx as nx\n'), ((3911, 3921), 'networkx.Graph', 'nx.Graph', ([], {}), '()\n', (3919, 3921), True, 'import networkx as nx\n'), ((4358, 4368), 'networkx.Graph', 'nx.Graph', ([], {}), '()\n', (4366, 4368), True, 'import networkx as nx\n')]
|
'''
Copyright (c) 2011-2018, Hortonworks Inc. All rights reserved.
Except as expressly permitted in a written agreement between you
or your company and Hortonworks, Inc, any use, reproduction,
modification,
redistribution, sharing, lending or other exploitation
of all or any part of the contents of this file is strictly prohibited.
'''
from resource_management import *
from ambari_commons import OSConst
from ambari_commons.os_family_impl import OsFamilyFuncImpl, OsFamilyImpl
@OsFamilyFuncImpl(os_family=OsFamilyImpl.DEFAULT)
def hst_service(action='start'):
import params
if action == 'start':
daemon_cmd = "hst start"
no_op_test = format("ls {hst_pid_file} >/dev/null 2>&1 && ps -p `cat {zk_pid_file}` >/dev/null 2>&1")
Execute(daemon_cmd, not_if=no_op_test)
elif action == 'stop':
daemon_cmd = "hst stop"
rm_pid = format("rm -f {hst_pid_file}")
Execute(daemon_cmd)
Execute(rm_pid)
@OsFamilyFuncImpl(os_family=OSConst.WINSRV_FAMILY)
def hst_service(action='start'):
import params
if action == 'start':
Service(params.hst_win_service_name, action="start")
elif action == 'stop':
Service(params.hst_win_service_name, action="stop")
|
[
"ambari_commons.os_family_impl.OsFamilyFuncImpl"
] |
[((484, 532), 'ambari_commons.os_family_impl.OsFamilyFuncImpl', 'OsFamilyFuncImpl', ([], {'os_family': 'OsFamilyImpl.DEFAULT'}), '(os_family=OsFamilyImpl.DEFAULT)\n', (500, 532), False, 'from ambari_commons.os_family_impl import OsFamilyFuncImpl, OsFamilyImpl\n'), ((927, 976), 'ambari_commons.os_family_impl.OsFamilyFuncImpl', 'OsFamilyFuncImpl', ([], {'os_family': 'OSConst.WINSRV_FAMILY'}), '(os_family=OSConst.WINSRV_FAMILY)\n', (943, 976), False, 'from ambari_commons.os_family_impl import OsFamilyFuncImpl, OsFamilyImpl\n')]
|
import spacy
import typer
from pathlib import Path
def main(
input_vectors: Path, input_model: Path, input_oracle: Path, output_vectors: Path
):
nlp = spacy.load(input_model)
vectors = {}
with open(input_vectors) as fileh:
for line in fileh.readlines():
parts = line.strip().split()
vectors[parts[0]] = " ".join(parts[1:])
with open(input_oracle) as fileh:
lines = fileh.readlines()
words = [line.split()[0] for line in lines]
for word in words:
if word not in vectors:
vectors[word] = " ".join(str(v) for v in nlp.vocab[word].vector)
with open(output_vectors, "w") as fileh:
for word in sorted(vectors.keys()):
fileh.write(word + " " + vectors[word] + "\n")
if __name__ == "__main__":
typer.run(main)
|
[
"spacy.load",
"typer.run"
] |
[((161, 184), 'spacy.load', 'spacy.load', (['input_model'], {}), '(input_model)\n', (171, 184), False, 'import spacy\n'), ((810, 825), 'typer.run', 'typer.run', (['main'], {}), '(main)\n', (819, 825), False, 'import typer\n')]
|
from flowjax.flows import Flow, RealNVPFlow, NeuralSplineFlow
from flowjax.bijections.utils import Permute
import jax.numpy as jnp
from jax import random
import pytest
def test_Flow():
key = random.PRNGKey(0)
bijection = Permute(jnp.array([2, 1, 0]))
dim = 3
flow = Flow(bijection, dim)
x = flow.sample(key, n=1)
assert x.shape == (1, dim)
x = flow.sample(random.PRNGKey(0), n=2)
assert x.shape == (2, dim)
# Note condition is ignored for transformation (but can be used to infer sample size)
x = flow.sample(key, condition=jnp.zeros((0,)), n=5)
assert x.shape == (5, dim)
x = flow.sample(key, condition=jnp.zeros((5, 0)))
assert x.shape == (5, dim)
with pytest.raises(AssertionError):
flow.sample(key, condition=jnp.zeros((5, 0)), n=3)
with pytest.raises(AssertionError):
flow.sample(key, condition=jnp.zeros((0,)))
# Test log prob work for vector and matrices input too
x1, x2 = x[0], x[None, 0]
lp1, lp2 = [flow.log_prob(x).item() for x in (x1, x2)]
assert lp1 == pytest.approx(lp2)
def test_broadcast():
# Matrices
size_pairs = [((5,2), (5,3)), ((1,2), (5,3)), ((5,2), (1,3)), ((2,), (5,3)), ((5,2), (3,))]
out_sizes = [((5,2), (5,3))] * len(size_pairs)
for in_s, out_s in zip(size_pairs, out_sizes):
a,b = Flow._broadcast(jnp.ones(in_s[0]), jnp.ones(in_s[1]))
assert (a.shape, b.shape) == out_s
def test_NeuralSplineFlow():
# Unconditional
n = 10
dim = 3
key = random.PRNGKey(2)
flow = NeuralSplineFlow(key, dim, num_layers=2)
x = flow.sample(key, n=n)
assert x.shape == (n, dim)
lp = flow.log_prob(x)
assert lp.shape == (n,)
# Conditional
cond_dim = 2
flow = NeuralSplineFlow(key, dim, condition_dim=cond_dim, num_layers=2)
cond = random.uniform(key, (n, cond_dim))
x = flow.sample(key, condition=cond)
lp = flow.log_prob(x, cond)
assert lp.shape == (n,)
lp = flow.log_prob(x, jnp.ones(cond_dim))
assert lp.shape == (n,)
lp = flow.log_prob(jnp.ones(dim), cond)
assert lp.shape == (n,)
x = flow.sample(key, condition=jnp.ones(2), n=n)
assert x.shape == (n, dim)
def test_RealNVPFlow():
key = random.PRNGKey(1)
flow = RealNVPFlow(key, 3)
x = flow.sample(key, n=10)
assert x.shape == (10, 3)
lp = flow.log_prob(x)
assert lp.shape == (10,)
|
[
"flowjax.flows.Flow",
"jax.random.uniform",
"jax.numpy.array",
"flowjax.flows.NeuralSplineFlow",
"flowjax.flows.RealNVPFlow",
"jax.random.PRNGKey",
"pytest.raises",
"jax.numpy.ones",
"jax.numpy.zeros",
"pytest.approx"
] |
[((197, 214), 'jax.random.PRNGKey', 'random.PRNGKey', (['(0)'], {}), '(0)\n', (211, 214), False, 'from jax import random\n'), ((284, 304), 'flowjax.flows.Flow', 'Flow', (['bijection', 'dim'], {}), '(bijection, dim)\n', (288, 304), False, 'from flowjax.flows import Flow, RealNVPFlow, NeuralSplineFlow\n'), ((1518, 1535), 'jax.random.PRNGKey', 'random.PRNGKey', (['(2)'], {}), '(2)\n', (1532, 1535), False, 'from jax import random\n'), ((1547, 1587), 'flowjax.flows.NeuralSplineFlow', 'NeuralSplineFlow', (['key', 'dim'], {'num_layers': '(2)'}), '(key, dim, num_layers=2)\n', (1563, 1587), False, 'from flowjax.flows import Flow, RealNVPFlow, NeuralSplineFlow\n'), ((1751, 1815), 'flowjax.flows.NeuralSplineFlow', 'NeuralSplineFlow', (['key', 'dim'], {'condition_dim': 'cond_dim', 'num_layers': '(2)'}), '(key, dim, condition_dim=cond_dim, num_layers=2)\n', (1767, 1815), False, 'from flowjax.flows import Flow, RealNVPFlow, NeuralSplineFlow\n'), ((1827, 1861), 'jax.random.uniform', 'random.uniform', (['key', '(n, cond_dim)'], {}), '(key, (n, cond_dim))\n', (1841, 1861), False, 'from jax import random\n'), ((2231, 2248), 'jax.random.PRNGKey', 'random.PRNGKey', (['(1)'], {}), '(1)\n', (2245, 2248), False, 'from jax import random\n'), ((2260, 2279), 'flowjax.flows.RealNVPFlow', 'RealNVPFlow', (['key', '(3)'], {}), '(key, 3)\n', (2271, 2279), False, 'from flowjax.flows import Flow, RealNVPFlow, NeuralSplineFlow\n'), ((239, 259), 'jax.numpy.array', 'jnp.array', (['[2, 1, 0]'], {}), '([2, 1, 0])\n', (248, 259), True, 'import jax.numpy as jnp\n'), ((387, 404), 'jax.random.PRNGKey', 'random.PRNGKey', (['(0)'], {}), '(0)\n', (401, 404), False, 'from jax import random\n'), ((717, 746), 'pytest.raises', 'pytest.raises', (['AssertionError'], {}), '(AssertionError)\n', (730, 746), False, 'import pytest\n'), ((817, 846), 'pytest.raises', 'pytest.raises', (['AssertionError'], {}), '(AssertionError)\n', (830, 846), False, 'import pytest\n'), ((1067, 1085), 'pytest.approx', 'pytest.approx', (['lp2'], {}), '(lp2)\n', (1080, 1085), False, 'import pytest\n'), ((1990, 2008), 'jax.numpy.ones', 'jnp.ones', (['cond_dim'], {}), '(cond_dim)\n', (1998, 2008), True, 'import jax.numpy as jnp\n'), ((2062, 2075), 'jax.numpy.ones', 'jnp.ones', (['dim'], {}), '(dim)\n', (2070, 2075), True, 'import jax.numpy as jnp\n'), ((568, 583), 'jax.numpy.zeros', 'jnp.zeros', (['(0,)'], {}), '((0,))\n', (577, 583), True, 'import jax.numpy as jnp\n'), ((657, 674), 'jax.numpy.zeros', 'jnp.zeros', (['(5, 0)'], {}), '((5, 0))\n', (666, 674), True, 'import jax.numpy as jnp\n'), ((1353, 1370), 'jax.numpy.ones', 'jnp.ones', (['in_s[0]'], {}), '(in_s[0])\n', (1361, 1370), True, 'import jax.numpy as jnp\n'), ((1372, 1389), 'jax.numpy.ones', 'jnp.ones', (['in_s[1]'], {}), '(in_s[1])\n', (1380, 1389), True, 'import jax.numpy as jnp\n'), ((2147, 2158), 'jax.numpy.ones', 'jnp.ones', (['(2)'], {}), '(2)\n', (2155, 2158), True, 'import jax.numpy as jnp\n'), ((783, 800), 'jax.numpy.zeros', 'jnp.zeros', (['(5, 0)'], {}), '((5, 0))\n', (792, 800), True, 'import jax.numpy as jnp\n'), ((883, 898), 'jax.numpy.zeros', 'jnp.zeros', (['(0,)'], {}), '((0,))\n', (892, 898), True, 'import jax.numpy as jnp\n')]
|
#!/usr/bin/python
import subprocess
subprocess.call("ifconfig enp2s0 down",shell=True)
subprocess.call("ifconfig enp2s0 hw ether 00:11:22:33:44:55",shell=True)
subprocess.call("ifconfig enp2s0 up",shell=True)
|
[
"subprocess.call"
] |
[((38, 89), 'subprocess.call', 'subprocess.call', (['"""ifconfig enp2s0 down"""'], {'shell': '(True)'}), "('ifconfig enp2s0 down', shell=True)\n", (53, 89), False, 'import subprocess\n'), ((89, 162), 'subprocess.call', 'subprocess.call', (['"""ifconfig enp2s0 hw ether 00:11:22:33:44:55"""'], {'shell': '(True)'}), "('ifconfig enp2s0 hw ether 00:11:22:33:44:55', shell=True)\n", (104, 162), False, 'import subprocess\n'), ((162, 211), 'subprocess.call', 'subprocess.call', (['"""ifconfig enp2s0 up"""'], {'shell': '(True)'}), "('ifconfig enp2s0 up', shell=True)\n", (177, 211), False, 'import subprocess\n')]
|
#!/usr/bin/python3
import os
import sys
import subprocess
import logging
import time
from djangoroku.djangoroku.linux import DeployOnLinux
class DjangoHerokuDeploy():
#I: SELECTING OS
os_name = input('Which OS are you using?\n1.Linux\n2.Windows')
if os_name == '1':
DeployOnLinux()
# I:THE DJANGO PART-SETTING UP EVERYTHING
# ask the user to enter the project-name
project_name = input('Whats your project name:')
try:
os.system('pip install gunicorn psycopg2-binary django-heroku dj-database-url')
logger.debug('DONE: All packages are installed successfully')
except FileExistsError:
logger.debug('DONE: All packages are installed successfully')
time.sleep(4)
# create a requirements.txt file
try:
os.system('pip freeze > requirements.txt')
logger.debug('DONE: requirements.txt file created')
except FileExistsError:
logger.debug('DONE: requirements.txt file created')
time.sleep(4)
# create a Procfile
try:
with open('Procfile', 'x') as f:
# make sure the project name is correct
f.write('web: gunicorn ' + project_name + '.wsgi:application')
logger.debug('DONE: Procfile created')
except FileExistsError:
logger.debug('DONE: Procfile created')
time.sleep(3)
# a function to prepend the import statement
to_settings = os.chdir(project_name)
def line_prepender(filename, line):
with open(filename, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write(line.rstrip('\r\n') + '\n' + content)
try:
line_prepender('settings.py', 'import dj_database_url')
line_prepender('settings.py', 'import django_heroku')
except FileExistsError:
logger.debug('DONE: All packages are imported')
time.sleep(3)
logger.debug('Remember to push everything on Github')
# II: HEROKU PART-DEPLOYMENT
try:
logger.debug("INFO: Please login to heroku...")
# os.system('heroku login')
except:
logger.debug('INFO: Please login to heroku')
time.sleep(2)
# creating a heroku domain-name
domain_name = input('Choose the app name: ')
os.system('heroku create' +' '+ domain_name)
reading_file = open('settings.py', 'r')
new_file_content = ""
ALLOWED_HOSTS = domain_name + '.herokuapp.com'
link = ALLOWED_HOSTS.split(' ')
for line in reading_file:
stripped_line = line.strip()
new_line = stripped_line.replace(
'ALLOWED_HOSTS = []', f'ALLOWED_HOSTS = {link}') # user should not rewrite ALLOWED_HOSTS
# before the script. Let it handle everything
new_file_content += new_line + "\n"
reading_file.close()
writing_file = open('settings.py', 'w')
writing_file.write(new_file_content)
writing_file.close()
# push to heroku
logger.debug('INFO: Deploying...')
time.sleep(4)
os.system('heroku config:set DISABLE_COLLECTSTATIC=1')
os.system('heroku git:remote -a' + ' ' + domain_name)
os.system('heroku config:set DISABLE_COLLECTSTATIC=1')
os.system('git push heroku master')
logger.debug('Setting up database...')
time.sleep(3)
os.system('heroku run python manage.py makemigrations')
os.system('heroku run python manage.py migrate')
time.sleep(2)
logger.debug('DONE: SUCCESSFUL DEPLOYED!')
elif os_name == '2':
print('two')
# condition
# windows
else:
# condition
print('last')
|
[
"djangoroku.djangoroku.linux.DeployOnLinux",
"os.system",
"os.chdir",
"time.sleep"
] |
[((286, 301), 'djangoroku.djangoroku.linux.DeployOnLinux', 'DeployOnLinux', ([], {}), '()\n', (299, 301), False, 'from djangoroku.djangoroku.linux import DeployOnLinux\n'), ((747, 760), 'time.sleep', 'time.sleep', (['(4)'], {}), '(4)\n', (757, 760), False, 'import time\n'), ((1037, 1050), 'time.sleep', 'time.sleep', (['(4)'], {}), '(4)\n', (1047, 1050), False, 'import time\n'), ((1412, 1425), 'time.sleep', 'time.sleep', (['(3)'], {}), '(3)\n', (1422, 1425), False, 'import time\n'), ((1499, 1521), 'os.chdir', 'os.chdir', (['project_name'], {}), '(project_name)\n', (1507, 1521), False, 'import os\n'), ((1987, 2000), 'time.sleep', 'time.sleep', (['(3)'], {}), '(3)\n', (1997, 2000), False, 'import time\n'), ((2302, 2315), 'time.sleep', 'time.sleep', (['(2)'], {}), '(2)\n', (2312, 2315), False, 'import time\n'), ((2415, 2461), 'os.system', 'os.system', (["('heroku create' + ' ' + domain_name)"], {}), "('heroku create' + ' ' + domain_name)\n", (2424, 2461), False, 'import os\n'), ((3270, 3283), 'time.sleep', 'time.sleep', (['(4)'], {}), '(4)\n', (3280, 3283), False, 'import time\n'), ((3293, 3347), 'os.system', 'os.system', (['"""heroku config:set DISABLE_COLLECTSTATIC=1"""'], {}), "('heroku config:set DISABLE_COLLECTSTATIC=1')\n", (3302, 3347), False, 'import os\n'), ((3356, 3409), 'os.system', 'os.system', (["('heroku git:remote -a' + ' ' + domain_name)"], {}), "('heroku git:remote -a' + ' ' + domain_name)\n", (3365, 3409), False, 'import os\n'), ((3418, 3472), 'os.system', 'os.system', (['"""heroku config:set DISABLE_COLLECTSTATIC=1"""'], {}), "('heroku config:set DISABLE_COLLECTSTATIC=1')\n", (3427, 3472), False, 'import os\n'), ((3481, 3516), 'os.system', 'os.system', (['"""git push heroku master"""'], {}), "('git push heroku master')\n", (3490, 3516), False, 'import os\n'), ((3573, 3586), 'time.sleep', 'time.sleep', (['(3)'], {}), '(3)\n', (3583, 3586), False, 'import time\n'), ((3596, 3651), 'os.system', 'os.system', (['"""heroku run python manage.py makemigrations"""'], {}), "('heroku run python manage.py makemigrations')\n", (3605, 3651), False, 'import os\n'), ((3660, 3708), 'os.system', 'os.system', (['"""heroku run python manage.py migrate"""'], {}), "('heroku run python manage.py migrate')\n", (3669, 3708), False, 'import os\n'), ((3718, 3731), 'time.sleep', 'time.sleep', (['(2)'], {}), '(2)\n', (3728, 3731), False, 'import time\n'), ((478, 557), 'os.system', 'os.system', (['"""pip install gunicorn psycopg2-binary django-heroku dj-database-url"""'], {}), "('pip install gunicorn psycopg2-binary django-heroku dj-database-url')\n", (487, 557), False, 'import os\n'), ((825, 867), 'os.system', 'os.system', (['"""pip freeze > requirements.txt"""'], {}), "('pip freeze > requirements.txt')\n", (834, 867), False, 'import os\n')]
|
import numpy as np
from .utils import Timer
def run(size='large', repeats=3 ):
sizes = {'huge': 28000, 'large': 15000, 'small': 6000, 'tiny': 2000, 'test': 2}
n = sizes[size]
A = np.array(np.random.rand(n,n))
A = A@A.T
num_runs = repeats
print('num_runs =', num_runs)
results = []
for i in range(num_runs):
print("run ", i)
with Timer() as t:
L = np.linalg.cholesky(A)
run_time=t.elapsed
print(f'Time {t.elapsed} seconds from Timer')
ops = 1E-9 * (n**3/3.0)
gflops = ops/run_time
results.append({'run_time': run_time, 'gflops': gflops})
return results
if __name__ == '__main__':
run()
|
[
"numpy.random.rand",
"numpy.linalg.cholesky"
] |
[((208, 228), 'numpy.random.rand', 'np.random.rand', (['n', 'n'], {}), '(n, n)\n', (222, 228), True, 'import numpy as np\n'), ((417, 438), 'numpy.linalg.cholesky', 'np.linalg.cholesky', (['A'], {}), '(A)\n', (435, 438), True, 'import numpy as np\n')]
|
"""ST-Link/V2 USB communication"""
import logging as _logging
import usb.core as _usb
import pyswd.swd._log as _log
class StlinkComException(Exception):
"""Exception"""
class StlinkComNotFound(Exception):
"""Exception"""
class StlinkComV2Usb():
"""ST-Link/V2 USB communication class"""
ID_VENDOR = 0x0483
ID_PRODUCT = 0x3748
PIPE_OUT = 0x02
PIPE_IN = 0x81
DEV_NAME = "V2"
_LOGGER_LEVEL3 = _logging.DEBUG - 3
def __init__(self):
self._dev = _usb.find(idVendor=self.ID_VENDOR, idProduct=self.ID_PRODUCT)
if self._dev is None:
raise StlinkComNotFound()
@_log.log(_log.DEBUG4)
def write(self, data, tout=200):
"""Write data to USB pipe"""
_logging.log(_log.DEBUG4, "%s", ', '.join(['0x%02x' % i for i in data]))
try:
count = self._dev.write(self.PIPE_OUT, data, tout)
except _usb.USBError as err:
self._dev = None
raise StlinkComException("USB Error: %s" % err)
_logging.log(_log.DEBUG4, "count=%d", count)
if count != len(data):
raise StlinkComException("Error Sending data")
@_log.log(_log.DEBUG4)
def read(self, size, tout=200):
"""Read data from USB pipe"""
read_size = size
_logging.log(_log.DEBUG4, "size=%d, read_size=%d", size, read_size)
try:
data = self._dev.read(self.PIPE_IN, read_size, tout).tolist()[:size]
except _usb.USBError as err:
self._dev = None
raise StlinkComException("USB Error: %s" % err)
_logging.log(_log.DEBUG4, "%s", ', '.join(['0x%02x' % i for i in data]))
return data
def __del__(self):
if self._dev is not None:
self._dev.finalize()
class StlinkComV21Usb(StlinkComV2Usb):
"""ST-Link/V2-1 USB communication"""
ID_VENDOR = 0x0483
ID_PRODUCT = 0x374b
PIPE_OUT = 0x01
PIPE_IN = 0x81
DEV_NAME = "V2-1"
class StlinkCom():
"""ST-Link communication class"""
_STLINK_CMD_SIZE = 16
_COM_CLASSES = [StlinkComV2Usb, StlinkComV21Usb]
def __init__(self):
self._dev = None
for com_cls in self._COM_CLASSES:
try:
self._dev = com_cls()
break
except StlinkComNotFound:
continue
else:
raise StlinkComNotFound()
@property
def version(self):
"""property with device version"""
return self._dev.DEV_NAME
@_log.log(_log.DEBUG3)
def xfer(self, command, data=None, rx_length=0, tout=200):
"""Transfer command between ST-Link
Arguments:
command: is an list of bytes with command (max 16 bytes)
data: data will be sent after command
rx_length: number of expected data to receive after command and data transfer
tout: maximum waiting time for received data
Return:
received data
Raises:
StlinkComException
"""
if len(command) > self._STLINK_CMD_SIZE:
raise StlinkComException(
"Error too many Bytes in command (maximum is %d Bytes)"
% self._STLINK_CMD_SIZE)
# pad to _STLINK_CMD_SIZE
command += [0] * (self._STLINK_CMD_SIZE - len(command))
self._dev.write(command, tout)
if data:
self._dev.write(data, tout)
if rx_length:
return self._dev.read(rx_length)
return None
|
[
"pyswd.swd._log.log",
"logging.log",
"usb.core.find"
] |
[((633, 654), 'pyswd.swd._log.log', '_log.log', (['_log.DEBUG4'], {}), '(_log.DEBUG4)\n', (641, 654), True, 'import pyswd.swd._log as _log\n'), ((1161, 1182), 'pyswd.swd._log.log', '_log.log', (['_log.DEBUG4'], {}), '(_log.DEBUG4)\n', (1169, 1182), True, 'import pyswd.swd._log as _log\n'), ((2503, 2524), 'pyswd.swd._log.log', '_log.log', (['_log.DEBUG3'], {}), '(_log.DEBUG3)\n', (2511, 2524), True, 'import pyswd.swd._log as _log\n'), ((497, 558), 'usb.core.find', '_usb.find', ([], {'idVendor': 'self.ID_VENDOR', 'idProduct': 'self.ID_PRODUCT'}), '(idVendor=self.ID_VENDOR, idProduct=self.ID_PRODUCT)\n', (506, 558), True, 'import usb.core as _usb\n'), ((1020, 1064), 'logging.log', '_logging.log', (['_log.DEBUG4', '"""count=%d"""', 'count'], {}), "(_log.DEBUG4, 'count=%d', count)\n", (1032, 1064), True, 'import logging as _logging\n'), ((1290, 1357), 'logging.log', '_logging.log', (['_log.DEBUG4', '"""size=%d, read_size=%d"""', 'size', 'read_size'], {}), "(_log.DEBUG4, 'size=%d, read_size=%d', size, read_size)\n", (1302, 1357), True, 'import logging as _logging\n')]
|
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from other import keys_and_strings
def convert_to_cap_greek( s : str ) -> str:
dict_accented_caps = { 'Ό' : 'Ο', 'Ά' : 'Α', 'Ί' : 'Ι', 'Έ' : 'Ε', 'Ύ' : 'Υ', 'Ή' : 'Η', 'Ώ' : 'Ω'}
res = s.upper()
for orig, new in dict_accented_caps.items():
res = res.replace(orig , new)
#print(s + ' -->\n' + res)
return res
class SeleniumWebParser:
def __init__(self):
chrome_options = webdriver.ChromeOptions()
prefs = {"profile.managed_default_content_settings.images": 2}
chrome_options.add_experimental_option("prefs", prefs)
self.driver = webdriver.Chrome(keys_and_strings.PATH_TO_DRIVER, options=chrome_options)
self.driver.set_window_size(width=1280, height=720) # if window too narrow : dropdown doesnt appear !
# todo: headless?? problem with width (left menu) ^^^ ?
def login_website(self, site : int):
# mystudies
if site == 1 :
from other import mycredentials
url = 'https://my-studies.uoa.gr/secr3w/connect.aspx'
elem_usr = 'username'
elem_pass = 'password'
val_usr = mycredentials.hidden_u
val_pass = mycredentials.hidden_p
else : # site == 2:
url = 'https://eclass-sandbox.noc.uoa.gr/'
elem_usr = 'uname'
elem_pass = '<PASSWORD>'
val_usr = 'stud11'
val_pass = '<PASSWORD>'
# initiate
self.driver.get(url) # go to the url
# login
username_field = self.driver.find_element_by_name(elem_usr)
password_field = self.driver.find_element_by_name(elem_pass)
username_field.send_keys(val_usr)
password_field.send_keys(val_pass)
password_field.send_keys(Keys.RETURN)
def get_average_grades(self) -> str:
# mystudies : get average grade
self.login_website(1)
sum_grades: float = 0
counter = 0
self.driver.get('https://my-studies.uoa.gr/Secr3w/app/accHistory/default.aspx')
self.driver.switch_to.frame('accmain')
all_tr_rows = self.driver.find_elements_by_xpath('//table/tbody/tr')
for row in all_tr_rows:
if not str(row.text).endswith('\n '):
continue # this row is not a course-grade
td_columns = row.text.split('\n')
course: str = td_columns[0]
course = course[course.find('- ') + 2: course.rfind('(')]
grade: str = td_columns[1]
grade = grade[grade.find('(') + 1: grade.find(')')]
if ',' in grade or '.' in grade:
grade: float = float(grade.replace(',', '.'))
else:
grade: int = int(grade)
if grade < 5:
continue
sum_grades = sum_grades + grade
counter = counter + 1
print("\t__WB__ //mystudies: ", course, '\t= ', grade)
self.driver.close()
# this takes alot of time :: self.driver.quit()
return str( (sum_grades / counter).__round__(2) if counter != 0 else 0)
def get_grade_of(self, param_target_course: str = '') -> str:
self.login_website(1)
# mystudies : get grade
grade: str = ''
self.driver.get('https://my-studies.uoa.gr/Secr3w/app/accHistory/default.aspx')
self.driver.switch_to.frame('accmain')
all_tr_rows = self.driver.find_elements_by_xpath('//table/tbody/tr')
for row in all_tr_rows:
if not str(row.text).endswith('\n '):
continue # this row is not a course-grade
td_columns = row.text.split('\n')
course: str = td_columns[0]
course = course[course.find('- ') + 2: course.rfind('(')]
# string comparison: check if this course == {:param_target_course}
if param_target_course.upper() in convert_to_cap_greek(course):
grade = td_columns[1]
grade = grade[grade.find('(') + 1: grade.find(')')]
print("\t__WB__ //mystudies found : ", param_target_course, '\t= ', grade)
break
self.driver.close()
return grade
def get_eclass_element(self, type_element, param_target_course: str = '') -> str:
self.login_website(2)
# eclass : get anakoinwseis + ergasies + plhrofories ma8hmatos
# get list of courses from main page
webelem_courses = self.driver.find_elements_by_xpath('//table/tbody/tr/td/b/a')
# #webelem_courses = self.driver.find_elements_by_class_name('text-left')
# (string comparison) click on the course with name == [ most similar to the string parameter {:param_target_course} ]
# https://www.datacamp.com/community/tutorials/fuzzy-string-python
for c in webelem_courses:
if convert_to_cap_greek(param_target_course) in convert_to_cap_greek(c.text):
c.click()
w_side_categories = self.driver.find_elements_by_class_name('list-group-item')
if w_side_categories is None:
print("!course: |"+ param_target_course+"| no side category=", type_element)
self.driver.close()
return 'not-found'
result : str
# indexes ::: 0=anakoinwseis 1=ergasies 2=ergasies 5=plhrofories
w_side_categories[type_element].click()
self.driver.implicitly_wait(0.7)
if type_element == 0:
#latest anouncement
elem = self.driver.find_elements_by_xpath("//*[@id=\"ann_table3\"]/tbody/tr[1]/td[1]/div")
announcement : str = elem[0].text
elem = self.driver.find_elements_by_xpath("//*[@id=\"ann_table3\"]/tbody/tr[1]/td[2]")
date_of_announcement =elem[0].text
result = date_of_announcement + " :\n " + announcement.replace('\n' , ' ')
if type_element == 1:
#latest deadline
pass
self.driver.close()
return result
if __name__ == "__main__":
wb = SeleniumWebParser()
test = wb.get_eclass_element( 0 , 'Εισαγωγή στον Προγραμματισμό' )
print("=" + test)
#print("\n\n", wb.get_average_grades(), "/10") # ok
|
[
"selenium.webdriver.ChromeOptions",
"selenium.webdriver.Chrome"
] |
[((469, 494), 'selenium.webdriver.ChromeOptions', 'webdriver.ChromeOptions', ([], {}), '()\n', (492, 494), False, 'from selenium import webdriver\n'), ((634, 707), 'selenium.webdriver.Chrome', 'webdriver.Chrome', (['keys_and_strings.PATH_TO_DRIVER'], {'options': 'chrome_options'}), '(keys_and_strings.PATH_TO_DRIVER, options=chrome_options)\n', (650, 707), False, 'from selenium import webdriver\n')]
|
# coding: utf-8
from os.path import dirname, realpath, join
from subprocess import check_output
from hamcrest import assert_that, equal_to
BASE_DIR = dirname(realpath(__file__))
DATA_DIR = join(BASE_DIR, 'data')
MODEL_DIR = join(DATA_DIR, 'model')
PATTERN_DIR = join(DATA_DIR, 'pattern')
MATCH_DIR = join(DATA_DIR, 'match')
MATCH_PATTERN_DIR = join(BASE_DIR, '../match_pattern')
PATTERN_MODEL = join(MATCH_PATTERN_DIR, 'pattern_model.py')
MATCH_PATTERN = join(MATCH_PATTERN_DIR, 'match_pattern.py')
def make_pattern_model(name):
model = check_output(['python2', PATTERN_MODEL, name])
pattern_path = join(PATTERN_DIR, name + '.yaml')
open(pattern_path, 'w').write(model)
return pattern_path
def match_pattern(target, pattern, limit=None):
command = ['python2', MATCH_PATTERN]
if limit:
command += ['-l', str(limit)]
return check_output(command + [target, pattern])
def load_match_result(name):
return open(join(MATCH_DIR, name + '.log')).read()
def base_test_match_pattern(name, target_name, pattern_name, limit=None):
pattern_path = make_pattern_model(pattern_name)
target_path = join(MODEL_DIR, target_name + '.yaml')
match_result = match_pattern(target_path, pattern_path, limit)
assert_that(match_result, equal_to(load_match_result(name)))
def test_match_empty_in_empty():
base_test_match_pattern('empty', 'empty', 'Empty')
def test_match_base_derived():
base_test_match_pattern('base_derived_in_extends', 'extends', 'BaseDerived')
def test_match_overridden_method_call():
base_test_match_pattern('overridden_method_call', 'overridden_method_call',
'OverriddenMethodCall')
def test_match_all_base_derived_in_hierarchy():
base_test_match_pattern('all_base_derived_in_hierarchy', 'hierarchy',
'BaseDerived')
def test_match_one_base_derived_in_hierarchy():
base_test_match_pattern('one_base_derived_in_hierarchy', 'hierarchy',
'BaseDerived', 1)
def test_match_three_base_derived_in_hierarchy():
base_test_match_pattern('three_base_derived_in_hierarchy', 'hierarchy',
'BaseDerived', 3)
|
[
"os.path.realpath",
"os.path.join",
"subprocess.check_output"
] |
[((191, 213), 'os.path.join', 'join', (['BASE_DIR', '"""data"""'], {}), "(BASE_DIR, 'data')\n", (195, 213), False, 'from os.path import dirname, realpath, join\n'), ((226, 249), 'os.path.join', 'join', (['DATA_DIR', '"""model"""'], {}), "(DATA_DIR, 'model')\n", (230, 249), False, 'from os.path import dirname, realpath, join\n'), ((264, 289), 'os.path.join', 'join', (['DATA_DIR', '"""pattern"""'], {}), "(DATA_DIR, 'pattern')\n", (268, 289), False, 'from os.path import dirname, realpath, join\n'), ((302, 325), 'os.path.join', 'join', (['DATA_DIR', '"""match"""'], {}), "(DATA_DIR, 'match')\n", (306, 325), False, 'from os.path import dirname, realpath, join\n'), ((346, 380), 'os.path.join', 'join', (['BASE_DIR', '"""../match_pattern"""'], {}), "(BASE_DIR, '../match_pattern')\n", (350, 380), False, 'from os.path import dirname, realpath, join\n'), ((397, 440), 'os.path.join', 'join', (['MATCH_PATTERN_DIR', '"""pattern_model.py"""'], {}), "(MATCH_PATTERN_DIR, 'pattern_model.py')\n", (401, 440), False, 'from os.path import dirname, realpath, join\n'), ((457, 500), 'os.path.join', 'join', (['MATCH_PATTERN_DIR', '"""match_pattern.py"""'], {}), "(MATCH_PATTERN_DIR, 'match_pattern.py')\n", (461, 500), False, 'from os.path import dirname, realpath, join\n'), ((160, 178), 'os.path.realpath', 'realpath', (['__file__'], {}), '(__file__)\n', (168, 178), False, 'from os.path import dirname, realpath, join\n'), ((545, 591), 'subprocess.check_output', 'check_output', (["['python2', PATTERN_MODEL, name]"], {}), "(['python2', PATTERN_MODEL, name])\n", (557, 591), False, 'from subprocess import check_output\n'), ((611, 644), 'os.path.join', 'join', (['PATTERN_DIR', "(name + '.yaml')"], {}), "(PATTERN_DIR, name + '.yaml')\n", (615, 644), False, 'from os.path import dirname, realpath, join\n'), ((864, 905), 'subprocess.check_output', 'check_output', (['(command + [target, pattern])'], {}), '(command + [target, pattern])\n', (876, 905), False, 'from subprocess import check_output\n'), ((1138, 1176), 'os.path.join', 'join', (['MODEL_DIR', "(target_name + '.yaml')"], {}), "(MODEL_DIR, target_name + '.yaml')\n", (1142, 1176), False, 'from os.path import dirname, realpath, join\n'), ((953, 983), 'os.path.join', 'join', (['MATCH_DIR', "(name + '.log')"], {}), "(MATCH_DIR, name + '.log')\n", (957, 983), False, 'from os.path import dirname, realpath, join\n')]
|
##########################################################################
#
# Copyright (c) 2013, Image Engine Design Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# * Neither the name of Image Engine Design nor the names of any
# other contributors to this software may be used to endorse or
# promote products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
##########################################################################
from __future__ import with_statement
import os
import maya.cmds
import imath
import IECore
import IECoreScene
import IECoreMaya
class FnSceneShapeTest( IECoreMaya.TestCase ) :
__testFile = "test/test.scc"
def setUp( self ) :
scene = IECoreScene.SceneCache( FnSceneShapeTest.__testFile, IECore.IndexedIO.OpenMode.Write )
sc = scene.createChild( str(1) )
mesh = IECoreScene.MeshPrimitive.createBox(imath.Box3f(imath.V3f(0),imath.V3f(1)))
mesh["Cd"] = IECoreScene.PrimitiveVariable( IECoreScene.PrimitiveVariable.Interpolation.Uniform, IECore.V3fVectorData( [ imath.V3f( 1, 0, 0 ) ] * 6 ) )
sc.writeObject( mesh, 0.0 )
matrix = imath.M44d().translate( imath.V3d( 1, 0, 0 ) )
sc.writeTransform( IECore.M44dData( matrix ), 0.0 )
sc = sc.createChild( "child" )
mesh = IECoreScene.MeshPrimitive.createBox(imath.Box3f(imath.V3f(0),imath.V3f(1)))
mesh["Cd"] = IECoreScene.PrimitiveVariable( IECoreScene.PrimitiveVariable.Interpolation.Uniform, IECore.V3fVectorData( [ imath.V3f( 0, 1, 0 ) ] * 6 ) )
sc.writeObject( mesh, 0.0 )
matrix = imath.M44d().translate( imath.V3d( 2, 0, 0 ) )
sc.writeTransform( IECore.M44dData( matrix ), 0.0 )
sc = sc.createChild( str( 3 ) )
mesh = IECoreScene.MeshPrimitive.createBox(imath.Box3f(imath.V3f(0),imath.V3f(1)))
mesh["Cd"] = IECoreScene.PrimitiveVariable( IECoreScene.PrimitiveVariable.Interpolation.Uniform, IECore.V3fVectorData( [ imath.V3f( 0, 0, 1 ) ] * 6 ) )
sc.writeObject( mesh, 0.0 )
matrix = imath.M44d().translate( imath.V3d( 3, 0, 0 ) )
sc.writeTransform( IECore.M44dData( matrix ), 0.0 )
return scene
def __setupTableProp( self ):
boxSize = imath.Box3f( imath.V3f( -.5, -.5, -.5 ), imath.V3f( .5, .5, .5 ) )
table = IECoreScene.SceneCache( FnSceneShapeTest.__testFile, IECore.IndexedIO.Write )
table.writeAttribute( 'scene:visible', IECore.BoolData( True ), 0 )
table.writeAttribute( 'user:testBool', IECore.BoolData( True ), 0 )
table.writeAttribute( 'user:testShort', IECore.ShortData( 2 ), 0 )
table.writeAttribute( 'user:testInt', IECore.IntData( 3 ), 0 )
table.writeAttribute( 'user:testInt64', IECore.Int64Data( 4 ), 0 )
table.writeAttribute( 'user:testFloat', IECore.FloatData( 5 ), 0 )
table.writeAttribute( 'user:testDouble', IECore.DoubleData( 6 ), 0 )
table.writeAttribute( 'user:testString', IECore.StringData( 'seven' ), 0 )
mat = imath.M44d( ( 8, 9, 10, 11 ), ( 12, 13, 14, 15 ), ( 16, 17, 18, 19 ), ( 20, 21, 22, 23 ) )
table.writeAttribute( 'user:testMatrixd', IECore.M44dData(mat), 0 )
mat = imath.M44f( ( 24, 25, 26, 27 ), ( 28, 29, 30, 31 ), ( 32, 33, 34, 35 ), ( 36, 37, 38, 39 ) )
table.writeAttribute( 'user:testMatrixf', IECore.M44fData(mat), 0 )
pedestal_GEO = table.createChild( 'pedestal_GEO' )
pedestal_GEO.writeObject( IECoreScene.MeshPrimitive.createBox(boxSize), 0 )
s = imath.V3d(15, 1, 15)
r = imath.Eulerd()
t = imath.V3d(0, .5, 0)
mat = IECore.TransformationMatrixd( s, r, t )
pedestal_GEO.writeTransform( IECore.TransformationMatrixdData(mat), 0 )
column_GEO = pedestal_GEO.createChild( 'column_GEO' )
column_GEO.writeObject( IECoreScene.MeshPrimitive.createBox(boxSize), 0 )
s = imath.V3d(.25, 20, .25)
r = imath.Eulerd()
t = imath.V3d(0, 10.5, 0)
mat = IECore.TransformationMatrixd( s, r, t )
column_GEO.writeTransform( IECore.TransformationMatrixdData(mat), 0 )
tableTop_GEO = column_GEO.createChild( 'tableTop_GEO' )
tableTop_GEO.writeObject( IECoreScene.MeshPrimitive.createBox(boxSize), 0 )
s = imath.V3d(10, 0.05, 10)
r = imath.Eulerd()
t = imath.V3d(0, .525, 0)
mat = IECore.TransformationMatrixd( s, r, t )
tableTop_GEO.writeTransform( IECore.TransformationMatrixdData(mat), 0 )
def testSceneInterface( self ) :
maya.cmds.file( new=True, f=True )
node = maya.cmds.createNode( "ieSceneShape" )
maya.cmds.setAttr( node+'.file', FnSceneShapeTest.__testFile,type='string' )
fn = IECoreMaya.FnSceneShape( node )
# Check scene for a wrong path
maya.cmds.setAttr( node+'.root', 'blabla', type='string' )
scene = fn.sceneInterface()
self.assertEqual( scene, None )
maya.cmds.setAttr( node+'.root', '/', type='string' )
scene = fn.sceneInterface()
self.assertTrue( isinstance( scene, IECoreScene.SceneCache ) )
self.assertEqual( scene.childNames(), ['1'] )
self.assertFalse( scene.hasObject() )
maya.cmds.setAttr( node+'.root', '/1', type='string' )
scene = fn.sceneInterface()
self.assertTrue( isinstance( scene, IECoreScene.SceneCache ) )
self.assertEqual( scene.childNames(), ['child'] )
self.assertTrue( scene.hasObject() )
def testCreationName( self ) :
maya.cmds.file( new=True, f=True )
fn = IECoreMaya.FnSceneShape.create( "bob" )
self.assertEqual( fn.fullPathName(), u"|bob|bobSceneShape" )
fn = IECoreMaya.FnSceneShape.create( "bob1")
self.assertEqual( fn.fullPathName(), u"|bob1|bobSceneShape1" )
fn = IECoreMaya.FnSceneShape.create( "bob" )
self.assertEqual( fn.fullPathName(), u"|bob2|bobSceneShape2" )
def testCreationSetup( self ) :
maya.cmds.file( new=True, f=True )
fn = IECoreMaya.FnSceneShape.create( "test" )
self.assertTrue( maya.cmds.sets( fn.fullPathName(), isMember="initialShadingGroup" ) )
self.assertTrue( maya.cmds.getAttr( fn.fullPathName()+".objectOnly", l=True ) )
self.assertFalse( maya.cmds.getAttr( fn.fullPathName()+".objectOnly" ) )
self.assertTrue( maya.cmds.isConnected( "time1.outTime", fn.fullPathName()+".time" ) )
def testExpandOnce( self ) :
maya.cmds.file( new=True, f=True )
fn = IECoreMaya.FnSceneShape.create( "test" )
maya.cmds.setAttr( fn.fullPathName()+'.file', FnSceneShapeTest.__testFile,type='string' )
result = fn.expandOnce()
self.assertTrue( maya.cmds.getAttr( fn.fullPathName()+".objectOnly" ) )
self.assertEqual( maya.cmds.getAttr( fn.fullPathName()+".queryPaths[0]" ), "/1" )
self.assertTrue( len(result) == 1 )
childFn = result[0]
self.assertTrue( isinstance( childFn, IECoreMaya.FnSceneShape ) )
self.assertEqual( childFn.fullPathName(), "|test|sceneShape_1|sceneShape_SceneShape1" )
self.assertEqual( maya.cmds.getAttr( childFn.fullPathName()+".file" ), FnSceneShapeTest.__testFile )
self.assertEqual( maya.cmds.getAttr( childFn.fullPathName()+".root" ), "/1" )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTransform[0].outTranslate", "|test|sceneShape_1.translate" ) )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTransform[0].outRotate", "|test|sceneShape_1.rotate" ) )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTransform[0].outScale", "|test|sceneShape_1.scale" ) )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTime", childFn.fullPathName()+".time" ) )
maya.cmds.setAttr( childFn.fullPathName()+".drawGeometry", 1 )
result = childFn.expandOnce()
self.assertTrue( maya.cmds.getAttr( childFn.fullPathName()+".objectOnly" ) )
self.assertEqual( maya.cmds.getAttr( childFn.fullPathName()+".queryPaths[0]" ), "/child" )
self.assertTrue( len(result) == 1 )
self.assertTrue( isinstance( result[0], IECoreMaya.FnSceneShape ) )
self.assertEqual( result[0].fullPathName(), "|test|sceneShape_1|child|childSceneShape" )
self.assertEqual( maya.cmds.getAttr( result[0].fullPathName()+".file" ), FnSceneShapeTest.__testFile )
self.assertEqual( maya.cmds.getAttr( result[0].fullPathName()+".root" ), "/1/child" )
self.assertTrue( maya.cmds.isConnected( childFn.fullPathName()+".outTransform[0].outTranslate", "|test|sceneShape_1|child.translate" ) )
self.assertTrue( maya.cmds.isConnected( childFn.fullPathName()+".outTransform[0].outRotate", "|test|sceneShape_1|child.rotate" ) )
self.assertTrue( maya.cmds.isConnected( childFn.fullPathName()+".outTransform[0].outScale", "|test|sceneShape_1|child.scale" ) )
self.assertEqual( maya.cmds.getAttr( result[0].fullPathName()+".drawGeometry"), 1 )
self.assertTrue( maya.cmds.isConnected( childFn.fullPathName()+".outTime", result[0].fullPathName()+".time" ) )
def testExpandOnceNamespace( self ) :
maya.cmds.file( new=True, f=True )
namespace = "INPUT"
if not maya.cmds.namespace( exists=namespace ):
maya.cmds.namespace( addNamespace=namespace )
def addnamespace( path ):
return path.replace( "|", "|" + namespace + ":" )
fn = IECoreMaya.FnSceneShape.create( namespace + ":" + "test" )
maya.cmds.setAttr( fn.fullPathName()+'.file', FnSceneShapeTest.__testFile, type='string' )
result = fn.expandOnce( preserveNamespace=True )
self.assertTrue( len(result) == 1 )
childFn = result[ 0 ]
self.assertTrue( isinstance( childFn, IECoreMaya.FnSceneShape ) )
self.assertEqual( childFn.fullPathName(), addnamespace ( "|test|sceneShape_1|sceneShape_SceneShape1" ) )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTransform[0].outTranslate", addnamespace ( "|test|sceneShape_1.translate" ) ) )
def testExpandAll( self ) :
maya.cmds.file( new=True, f=True )
fn = IECoreMaya.FnSceneShape.create( "test" )
maya.cmds.setAttr( fn.fullPathName()+'.file', FnSceneShapeTest.__testFile,type='string' )
maya.cmds.setAttr( fn.fullPathName()+".drawGeometry", 1 )
result = fn.expandAll()
self.assertTrue( maya.cmds.getAttr( fn.fullPathName()+".objectOnly" ) )
self.assertEqual( maya.cmds.getAttr( fn.fullPathName()+".queryPaths[0]" ), "/1" )
self.assertTrue( len(result) == 3 )
childFn = result[0]
self.assertTrue( isinstance( childFn, IECoreMaya.FnSceneShape ) )
self.assertEqual( childFn.fullPathName(), "|test|sceneShape_1|sceneShape_SceneShape1" )
self.assertEqual( maya.cmds.getAttr( childFn.fullPathName()+".file" ), FnSceneShapeTest.__testFile )
self.assertEqual( maya.cmds.getAttr( childFn.fullPathName()+".root" ), "/1" )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTransform[0].outTranslate", "|test|sceneShape_1.translate" ) )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTransform[0].outRotate", "|test|sceneShape_1.rotate" ) )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTransform[0].outScale", "|test|sceneShape_1.scale" ) )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTime", childFn.fullPathName()+".time" ) )
self.assertTrue( maya.cmds.getAttr( childFn.fullPathName()+".objectOnly" ) )
self.assertEqual( maya.cmds.getAttr( childFn.fullPathName()+".queryPaths[0]" ), "/child" )
self.assertEqual( maya.cmds.getAttr( childFn.fullPathName()+".drawGeometry"), 1 )
self.assertTrue( isinstance( result[1], IECoreMaya.FnSceneShape ) )
self.assertEqual( result[1].fullPathName(), "|test|sceneShape_1|child|childSceneShape" )
self.assertEqual( maya.cmds.getAttr( result[1].fullPathName()+".file" ), FnSceneShapeTest.__testFile )
self.assertEqual( maya.cmds.getAttr( result[1].fullPathName()+".root" ), "/1/child" )
self.assertTrue( maya.cmds.isConnected( childFn.fullPathName()+".outTransform[0].outTranslate", "|test|sceneShape_1|child.translate" ) )
self.assertTrue( maya.cmds.isConnected( childFn.fullPathName()+".outTransform[0].outRotate", "|test|sceneShape_1|child.rotate" ) )
self.assertTrue( maya.cmds.isConnected( childFn.fullPathName()+".outTransform[0].outScale", "|test|sceneShape_1|child.scale" ) )
self.assertEqual( maya.cmds.getAttr( result[1].fullPathName()+".drawGeometry"), 1 )
self.assertTrue( maya.cmds.isConnected( childFn.fullPathName()+".outTime", result[1].fullPathName()+".time" ) )
def testExpandAllNamespace( self ) :
namespace = "INPUT"
if not maya.cmds.namespace( exists=namespace ):
maya.cmds.namespace( addNamespace=namespace )
def addnamespace( path ):
return path.replace( "|", "|" + namespace + ":" )
maya.cmds.file( new=True, f=True )
fn = IECoreMaya.FnSceneShape.create( namespace + ":" + "test" )
maya.cmds.setAttr( fn.fullPathName()+'.file', FnSceneShapeTest.__testFile,type='string' )
maya.cmds.setAttr( fn.fullPathName()+".drawGeometry", 1 )
result = fn.expandAll( preserveNamespace=True )
self.assertTrue( maya.cmds.getAttr( fn.fullPathName()+".objectOnly" ) )
self.assertEqual( maya.cmds.getAttr( fn.fullPathName()+".queryPaths[0]" ), "/1" )
self.assertTrue( len(result) == 3 )
childFn = result[0]
self.assertTrue( isinstance( childFn, IECoreMaya.FnSceneShape ) )
self.assertEqual( childFn.fullPathName(), addnamespace( "|test|sceneShape_1|sceneShape_SceneShape1" ) )
self.assertEqual( maya.cmds.getAttr( childFn.fullPathName()+".file" ), FnSceneShapeTest.__testFile )
self.assertEqual( maya.cmds.getAttr( childFn.fullPathName()+".root" ), "/1" )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTransform[0].outTranslate", addnamespace( "|test|sceneShape_1.translate" ) ) )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTransform[0].outRotate", addnamespace( "|test|sceneShape_1.rotate" ) ) )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTransform[0].outScale", addnamespace( "|test|sceneShape_1.scale" ) ) )
self.assertTrue( maya.cmds.isConnected( fn.fullPathName()+".outTime", childFn.fullPathName()+".time" ) )
self.assertTrue( maya.cmds.getAttr( childFn.fullPathName()+".objectOnly" ) )
self.assertEqual( maya.cmds.getAttr( childFn.fullPathName()+".queryPaths[0]" ), "/child" )
self.assertEqual( maya.cmds.getAttr( childFn.fullPathName()+".drawGeometry"), 1 )
self.assertTrue( isinstance( result[1], IECoreMaya.FnSceneShape ) )
self.assertEqual( result[1].fullPathName(), addnamespace( "|test|sceneShape_1|child|childSceneShape" ) )
self.assertEqual( maya.cmds.getAttr( result[1].fullPathName()+".file" ), FnSceneShapeTest.__testFile )
self.assertEqual( maya.cmds.getAttr( result[1].fullPathName()+".root" ), "/1/child" )
self.assertTrue( maya.cmds.isConnected( childFn.fullPathName()+".outTransform[0].outTranslate", addnamespace( "|test|sceneShape_1|child.translate" ) ) )
self.assertTrue( maya.cmds.isConnected( childFn.fullPathName()+".outTransform[0].outRotate", addnamespace( "|test|sceneShape_1|child.rotate" ) ) )
self.assertTrue( maya.cmds.isConnected( childFn.fullPathName()+".outTransform[0].outScale", addnamespace( "|test|sceneShape_1|child.scale" ) ) )
self.assertEqual( maya.cmds.getAttr( result[1].fullPathName()+".drawGeometry"), 1 )
self.assertTrue( maya.cmds.isConnected( childFn.fullPathName()+".outTime", result[1].fullPathName()+".time" ) )
def testCollapse( self ) :
maya.cmds.file( new=True, f=True )
fn = IECoreMaya.FnSceneShape.create( "test" )
maya.cmds.setAttr( fn.fullPathName()+'.file', FnSceneShapeTest.__testFile,type='string' )
result = fn.expandOnce()
result[0].expandOnce()
children = set( ["|test|testSceneShape", "|test|sceneShape_1", "|test|sceneShape_1|sceneShape_SceneShape1", "|test|sceneShape_1|child", "|test|sceneShape_1|child|childSceneShape"] )
self.assertEqual( set(maya.cmds.listRelatives( "|test", ad=True, f=True )), children )
fn.collapse()
self.assertEqual( maya.cmds.listRelatives( "|test", ad=True, f=True ), ["|test|testSceneShape"] )
self.assertEqual( maya.cmds.getAttr( fn.fullPathName()+".objectOnly" ), 0 )
self.assertEqual( maya.cmds.getAttr( fn.fullPathName()+".visibility" ), 1 )
def testConvertAllToGeometry( self ):
maya.cmds.file( new=True, f=True )
fn = IECoreMaya.FnSceneShape.create( "test" )
maya.cmds.setAttr( fn.fullPathName()+'.file', FnSceneShapeTest.__testFile,type='string' )
fn.convertAllToGeometry()
children = ["|test|testSceneShape", "|test|sceneShape_1"]
self.assertEqual( maya.cmds.listRelatives( "|test", f=True ), children )
self.assertEqual( maya.cmds.getAttr( fn.fullPathName()+".intermediateObject" ), 0 )
children = ["|test|sceneShape_1|sceneShape_SceneShape1", "|test|sceneShape_1|child", "|test|sceneShape_1|sceneShape_Shape1"]
self.assertEqual( maya.cmds.listRelatives( "|test|sceneShape_1", f=True ), children )
self.assertEqual( maya.cmds.getAttr( "|test|sceneShape_1|sceneShape_SceneShape1.intermediateObject" ), 1 )
self.assertEqual( maya.cmds.nodeType( "|test|sceneShape_1|sceneShape_Shape1" ), "mesh")
self.assertEqual( maya.cmds.getAttr( "|test|sceneShape_1|sceneShape_SceneShape1.queryPaths[1]" ), "/" )
self.assertTrue( maya.cmds.isConnected( "|test|sceneShape_1|sceneShape_SceneShape1.outObjects[1]", "|test|sceneShape_1|sceneShape_Shape1.inMesh" ) )
def testComponentNames( self ):
maya.cmds.file( new=True, f=True )
fn = IECoreMaya.FnSceneShape.create( "test" )
maya.cmds.setAttr( fn.fullPathName()+'.file', FnSceneShapeTest.__testFile,type='string' )
maya.cmds.setAttr( fn.fullPathName()+".drawGeometry", 0 )
self.assertEqual( fn.componentNames(), [] )
maya.cmds.setAttr( fn.fullPathName()+".drawGeometry", 1 )
self.assertEqual( fn.componentNames(), ['/', '/1', '/1/child', '/1/child/3'] )
fn.selectComponentNames( ['/', '/1', '/1/child/3'] )
self.assertEqual( fn.selectedComponentNames(), set( ['/', '/1', '/1/child/3'] ) )
def testQuery( self ):
maya.cmds.file( new=True, f=True )
def createSceneFile():
scene = IECoreScene.SceneCache( FnSceneShapeTest.__testFile, IECore.IndexedIO.OpenMode.Write )
sc = scene.createChild( str(1) )
curves = IECoreScene.CurvesPrimitive.createBox(imath.Box3f(imath.V3f(0),imath.V3f(1))) # 6 curves.
sc.writeObject( curves, 0.0 )
matrix = imath.M44d().translate( imath.V3d( 0, 0, 0 ) )
sc.writeTransform( IECore.M44dData( matrix ), 0.0 )
createSceneFile()
node = maya.cmds.createNode( "ieSceneShape" )
maya.cmds.setAttr( node+'.file', FnSceneShapeTest.__testFile,type='string' )
maya.cmds.setAttr( node+'.root', '/',type='string' )
fn = IECoreMaya.FnSceneShape( node )
self.assertEqual( maya.cmds.getAttr(fn.fullPathName()+".outObjects[0]", type=True), None )
self.assertEqual( maya.cmds.getAttr(fn.fullPathName()+".outObjects[1]", type=True), None )
maya.cmds.setAttr( fn.fullPathName()+".queryPaths[0]" , "/1", type="string")
maya.cmds.setAttr( fn.fullPathName()+".queryPaths[1]" , "/1", type="string")
maya.cmds.setAttr( fn.fullPathName()+".queryConvertParameters[0]", "-index 0", type="string" ) # Set it to output 0 th box curve.
maya.cmds.setAttr( fn.fullPathName()+".queryConvertParameters[1]", "-index 1", type="string" ) # Set it to output 1 th box curve.
self.assertEqual( maya.cmds.getAttr(fn.fullPathName()+".outObjects[0]", type=True), "nurbsCurve" )
self.assertEqual( maya.cmds.getAttr(fn.fullPathName()+".outObjects[1]", type=True), "nurbsCurve" )
curveShape0 = maya.cmds.createNode( "nurbsCurve" )
curveShape1 = maya.cmds.createNode( "nurbsCurve" )
maya.cmds.connectAttr( fn.fullPathName()+ ".outObjects[0]", curveShape0 + '.create' )
maya.cmds.connectAttr( fn.fullPathName()+ ".outObjects[1]", curveShape1 + '.create' )
self.assertNotEqual( maya.cmds.pointPosition(curveShape0 + '.cv[0]' ), maya.cmds.pointPosition(curveShape1 + '.cv[0]' ) )
maya.cmds.setAttr( fn.fullPathName()+".queryConvertParameters[1]", "-index 0", type="string" )
self.assertEqual( maya.cmds.pointPosition(curveShape0 + '.cv[0]' ), maya.cmds.pointPosition(curveShape1 + '.cv[0]' ) )
def testPromotableAttributeNames( self ):
maya.cmds.file( new=True, force=True )
self.__setupTableProp()
sceneShapeFn = IECoreMaya.FnSceneShape.create( 'table' )
sceneShapeFn.findPlug( 'file' ).setString( FnSceneShapeTest.__testFile )
expectedAttrs = [
'user:testBool', 'user:testShort', 'user:testInt', 'user:testInt64', 'user:testFloat',
'user:testDouble', 'user:testString', 'user:testMatrixd', 'user:testMatrixf', 'scene:visible'
]
self.assertEquals( set( sceneShapeFn.promotableAttributeNames() ), set( expectedAttrs ) )
def testPromoteAttribute( self ):
maya.cmds.file( new=True, force=True )
self.__setupTableProp()
sceneShapeFn = IECoreMaya.FnSceneShape.create( 'table' )
sceneShapeFn.findPlug( 'file' ).setString( FnSceneShapeTest.__testFile )
for pAttr in sceneShapeFn.promotableAttributeNames():
sceneShapeFn.promoteAttribute( pAttr )
sceneShape = sceneShapeFn.fullPathName()
table = maya.cmds.listRelatives( sceneShape, parent=True )[0]
testVisibility = maya.cmds.getAttr( table + '.' + str( IECoreMaya.LiveScene.visibilityOverrideName ) )
testBool = maya.cmds.getAttr( table + '.ieAttr_testBool' )
testShort = maya.cmds.getAttr( table + '.ieAttr_testShort' )
testInt = maya.cmds.getAttr( table + '.ieAttr_testInt' )
testInt64 = maya.cmds.getAttr( table + '.ieAttr_testInt64' )
testFloat = maya.cmds.getAttr( table + '.ieAttr_testFloat' )
testDouble = maya.cmds.getAttr( table + '.ieAttr_testDouble' )
testString = maya.cmds.getAttr( table + '.ieAttr_testString' )
testMatrixd = maya.cmds.getAttr( table + '.ieAttr_testMatrixd' )
testMatrixf = maya.cmds.getAttr( table + '.ieAttr_testMatrixf' )
self.assertTrue( testVisibility )
self.assertTrue( testBool )
self.assertEquals( testShort, 2 )
self.assertEquals( testInt, 3 )
self.assertEquals( testInt64, 4 )
self.assertEquals( testFloat, 5. )
self.assertEquals( testDouble, 6. )
self.assertEquals( testString, 'seven' )
self.assertEquals( testMatrixd, [ 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23. ] )
self.assertEquals( testMatrixf, [ 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39. ] )
def tearDown( self ) :
if os.path.exists( FnSceneShapeTest.__testFile ) :
os.remove( FnSceneShapeTest.__testFile )
if __name__ == "__main__":
IECoreMaya.TestProgram( plugins = [ "ieCore" ] )
|
[
"IECore.TransformationMatrixdData",
"os.remove",
"IECore.M44dData",
"IECore.DoubleData",
"IECoreMaya.FnSceneShape",
"IECore.M44fData",
"IECoreMaya.TestProgram",
"IECoreScene.MeshPrimitive.createBox",
"IECoreMaya.FnSceneShape.create",
"IECore.FloatData",
"os.path.exists",
"IECore.Int64Data",
"imath.M44d",
"imath.V3f",
"imath.Eulerd",
"IECore.IntData",
"IECore.StringData",
"IECoreScene.SceneCache",
"IECore.BoolData",
"imath.M44f",
"IECore.TransformationMatrixd",
"imath.V3d",
"IECore.ShortData"
] |
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|
'''Module to load and use GloVe Models.
Code Inspiration from:
https://www.kaggle.com/jhoward/improved-lstm-baseline-glove-dropout
'''
import os
import numpy as np
import pandas as pd
import urllib.request
from zipfile import ZipFile
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.cluster import KMeans
folder = os.path.dirname(os.path.realpath(__file__))
def download(name):
'''Downloads the relevant dataset and extracts it.
Args:
name (str): Name of the model to download (options are: [twitter, wikipedia])
Returns:
True if successful, otherwise False
'''
url = None
if name == 'twitter':
url = 'http://nlp.stanford.edu/data/wordvecs/glove.twitter.27B.zip'
elif name == 'wikipedia':
url = 'http://nlp.stanford.edu/data/wordvecs/glove.840B.300d.zip'
if url is not None:
try:
urllib.request.urlretrieve(url, os.path.join(folder, '{}.zip'.format(name)))
except:
print("download failed")
return False
try:
# Create a ZipFile Object and load sample.zip in it
with ZipFile(os.path.join(folder, '{}.zip'.format(name)), 'r') as zipObj:
# Extract all the contents of zip file in current directory
zipObj.extractall(folder)
return True
except:
print("extraction failed")
return False
return False
class GloveEmbeddings:
'''Class to load embeddings model and generate it for words or sentences.'''
def __init__(self, name, dim=25):
# load data
self.emb = self.load_vectors(name, dim)
self.emb_size = dim
# calculate items for randomization (explicit convert to list to avoid numpy warning)
all_embs = np.stack(list(self.emb.values()))
self.emb_mean,self.emb_std = all_embs.mean(), all_embs.std()
def get_coefs(self, word, *arr):
'''Helper Function to transform the given vector into a float array.'''
return word, np.asarray(arr, dtype='float32')
def load_vectors(self, name, dim):
'''Load the given vector data.'''
# retrieve file name
file = None
if name == 'twitter':
file = os.path.join(folder, 'glove.{}.27B.{}d.txt'.format(name, dim))
elif name == 'wikipedia':
file = os.path.join(folder, 'glove.840B.{}d.txt'.format(dim))
else:
raise ValueError('Unkown model type ({})'.format(name))
# load the embeddings
with open(file, encoding='utf-8') as file:
embeddings_index = [self.get_coefs(*o.strip().split()) for o in file]
embeddings_index = list(filter(lambda x: len(x[1]) == dim, embeddings_index))
return dict(embeddings_index)
def word_vector(self,word):
'''Tries to retrieve the embedding for the given word, otherwise returns random vector.'''
# generate randomness otherwise
vec = self.emb.get(word)
return vec if vec is not None else np.random.normal(self.emb_mean, self.emb_std, (self.emb_size))
def sent_vector(self, sent, use_rand=True):
'''Generates a single embedding vector.
Args:
sent (list): List of tokenized words to use
use_rand (bool): Defines if unkown words should be filled with random vectors (otherwise only use known vectors)
Returns:
Single normalized Vector to be used as embedding
'''
vec = None
vec_count = 0
for word in sent:
wvec = self.emb.get(word)
if wvec is None and use_rand:
wvec = np.random.normal(self.emb_mean, self.emb_std, (self.emb_size))
if wvec is not None:
if vec is None:
vec = wvec
else:
vec += wvec
vec_count += 1
# normalize the vector
if vec is not None and vec_count > 0:
vec = vec / vec_count
# if no word is found return random vector
return vec if vec is not None else np.random.normal(self.emb_mean, self.emb_std, (self.emb_size))
def sent_matrix(self, sent, max_feat, pad, dedub=False):
'''Generates a Matrix of single embeddings for the item.
Args:
sent (list): List of tokenized words
max_feat (int): Number of maximal features to extract
pad (bool): Defines if the resulting matrix should be zero-padded to max_feat
dedub (bool): Defines if the word list should be de-duplicated
Returns:
2-D Matrix with dimensions [max_feat, embedding_size]
'''
# remove duplicates
if dedub:
sent = list(set(sent))
# setup matrix
nb_words = min(max_feat, len(sent))
embedding_matrix = np.random.normal(self.emb_mean, self.emb_std, (nb_words, self.emb_size))
# iterate through all words
for i, word in enumerate(sent):
if i >= max_feat: continue
vec = self.emb.get(word)
if vec is not None: embedding_matrix[i] = vec
# pad the matrix to max features
if pad and nb_words < max_feat:
embedding_matrix = np.pad(embedding_matrix, (max_feat, self.emb_size), 'constant', constant_values=[0])
return embedding_matrix
def centroid_vectors(self, sent, max_feat):
'''Generates a list of `max_feat` vectors to be used as representation.
Args:
sent (list): Tokenized words in the document
max_feat (int): Number of vectors to generate
Returns:
Array of centroid vectors for the given document
'''
# generate list of vectors (use set as order not relevant and to avoid duplicates)
vecs = []
for word in set(sent):
vec = self.emb.get(word)
if vec is not None: vecs.append(vec)
# return random vector if none found
if len(vecs) < max_feat:
return np.array(vecs + [np.random.normal(self.emb_mean, self.emb_std, (self.emb_size)) for i in range(max_feat - len(vecs))])
elif len(vecs) == max_feat:
return np.array(vecs)
# perform clustering
kmeans = KMeans(n_clusters=max_feat).fit(vecs)
# return the centroid vectors
return kmeans.cluster_centers_
class GloVeTransformer(BaseEstimator, TransformerMixin):
'''Transformer for the GloVe Model.'''
def __init__(self, name, dim, type, tokenizer, max_feat=None):
'''Create the Transformer.
Note that the centroid option might be slow.
Args:
name (str): Name of the model
dim (int): Number of dimensions to use
type (str): Type of the transformation (options are: ['word', 'sent', 'sent-matrix', 'centroid'])
tokenizer (fct): Function to tokenize the input data
max_feat (int): Number of maximal feature vectors used per input
'''
# safty checks
if type not in ['word', 'sent', 'sent-matrix', 'centroid']:
raise ValueError("Invalid value for type: ({})".format(type))
if type in ['sent-matrix', 'centroid'] and max_feat is None:
raise ValueError("Required value for max_feat for type ({})".format(type))
# set values
self.glove = GloveEmbeddings(name, dim)
self.type = type
self.tokenizer = tokenizer
self.max_feat = max_feat
def fit(self, x, y=None):
return self
def vectors(self, text):
'''Extracts the specified type of vector for the given input data.'''
# retrieve the vectors
tokens = self.tokenizer(text)
if self.type == 'word':
return np.concat([self.glove.word_vector(tok) for tok in tokens])
elif self.type == 'sent':
return self.glove.sent_vector(tokens)
elif self.type == 'sent-matrix':
# note: use padding to avoid pipeline problems
return self.glove.sent_matrix(tokens, self.max_feat, True).reshape([-1])
elif self.type == 'centroid':
return self.glove.centroid_vectors(tokens, self.max_feat).reshape([-1])
return np.nan
def transform(self, X):
X_tagged = pd.Series(X).apply(lambda x: pd.Series(self.vectors(x)))
df = pd.DataFrame(X_tagged).fillna(0).replace([-np.inf], -1).replace([np.inf], 1)
return df
|
[
"numpy.pad",
"pandas.DataFrame",
"sklearn.cluster.KMeans",
"numpy.asarray",
"os.path.realpath",
"numpy.array",
"pandas.Series",
"numpy.random.normal"
] |
[((354, 380), 'os.path.realpath', 'os.path.realpath', (['__file__'], {}), '(__file__)\n', (370, 380), False, 'import os\n'), ((4413, 4485), 'numpy.random.normal', 'np.random.normal', (['self.emb_mean', 'self.emb_std', '(nb_words, self.emb_size)'], {}), '(self.emb_mean, self.emb_std, (nb_words, self.emb_size))\n', (4429, 4485), True, 'import numpy as np\n'), ((1890, 1922), 'numpy.asarray', 'np.asarray', (['arr'], {'dtype': '"""float32"""'}), "(arr, dtype='float32')\n", (1900, 1922), True, 'import numpy as np\n'), ((2807, 2867), 'numpy.random.normal', 'np.random.normal', (['self.emb_mean', 'self.emb_std', 'self.emb_size'], {}), '(self.emb_mean, self.emb_std, self.emb_size)\n', (2823, 2867), True, 'import numpy as np\n'), ((3731, 3791), 'numpy.random.normal', 'np.random.normal', (['self.emb_mean', 'self.emb_std', 'self.emb_size'], {}), '(self.emb_mean, self.emb_std, self.emb_size)\n', (3747, 3791), True, 'import numpy as np\n'), ((4768, 4856), 'numpy.pad', 'np.pad', (['embedding_matrix', '(max_feat, self.emb_size)', '"""constant"""'], {'constant_values': '[0]'}), "(embedding_matrix, (max_feat, self.emb_size), 'constant',\n constant_values=[0])\n", (4774, 4856), True, 'import numpy as np\n'), ((3356, 3416), 'numpy.random.normal', 'np.random.normal', (['self.emb_mean', 'self.emb_std', 'self.emb_size'], {}), '(self.emb_mean, self.emb_std, self.emb_size)\n', (3372, 3416), True, 'import numpy as np\n'), ((5645, 5659), 'numpy.array', 'np.array', (['vecs'], {}), '(vecs)\n', (5653, 5659), True, 'import numpy as np\n'), ((5699, 5726), 'sklearn.cluster.KMeans', 'KMeans', ([], {'n_clusters': 'max_feat'}), '(n_clusters=max_feat)\n', (5705, 5726), False, 'from sklearn.cluster import KMeans\n'), ((7549, 7561), 'pandas.Series', 'pd.Series', (['X'], {}), '(X)\n', (7558, 7561), True, 'import pandas as pd\n'), ((5498, 5558), 'numpy.random.normal', 'np.random.normal', (['self.emb_mean', 'self.emb_std', 'self.emb_size'], {}), '(self.emb_mean, self.emb_std, self.emb_size)\n', (5514, 5558), True, 'import numpy as np\n'), ((7615, 7637), 'pandas.DataFrame', 'pd.DataFrame', (['X_tagged'], {}), '(X_tagged)\n', (7627, 7637), True, 'import pandas as pd\n')]
|
"""
This file contains a function to generate a single synthetic tree, prepared for
multiprocessing.
"""
import pandas as pd
import numpy as np
# import dill as pickle
# import gzip
from syn_net.data_generation.make_dataset import synthetic_tree_generator
from syn_net.utils.data_utils import ReactionSet
path_reaction_file = '/pool001/whgao/data/synth_net/st_pis/reactions_pis.json.gz'
path_to_building_blocks = '/pool001/whgao/data/synth_net/st_pis/enamine_us_matched.csv.gz'
building_blocks = pd.read_csv(path_to_building_blocks, compression='gzip')['SMILES'].tolist()
r_set = ReactionSet()
r_set.load(path_reaction_file)
rxns = r_set.rxns
# with gzip.open(path_reaction_file, 'rb') as f:
# rxns = pickle.load(f)
print('Finish reading the templates and building blocks list!')
def func(_):
np.random.seed(_)
tree, action = synthetic_tree_generator(building_blocks, rxns, max_step=15)
return tree, action
|
[
"syn_net.utils.data_utils.ReactionSet",
"pandas.read_csv",
"numpy.random.seed",
"syn_net.data_generation.make_dataset.synthetic_tree_generator"
] |
[((584, 597), 'syn_net.utils.data_utils.ReactionSet', 'ReactionSet', ([], {}), '()\n', (595, 597), False, 'from syn_net.utils.data_utils import ReactionSet\n'), ((807, 824), 'numpy.random.seed', 'np.random.seed', (['_'], {}), '(_)\n', (821, 824), True, 'import numpy as np\n'), ((844, 904), 'syn_net.data_generation.make_dataset.synthetic_tree_generator', 'synthetic_tree_generator', (['building_blocks', 'rxns'], {'max_step': '(15)'}), '(building_blocks, rxns, max_step=15)\n', (868, 904), False, 'from syn_net.data_generation.make_dataset import synthetic_tree_generator\n'), ((500, 556), 'pandas.read_csv', 'pd.read_csv', (['path_to_building_blocks'], {'compression': '"""gzip"""'}), "(path_to_building_blocks, compression='gzip')\n", (511, 556), True, 'import pandas as pd\n')]
|
import glob
import json
import os
import re
import sys
from urllib.parse import quote, quote_plus
import nbgrader.exchange.abc as abc
from dateutil import parser
from traitlets import Bool, Unicode
from .exchange import Exchange
# "outbound" is files released by instructors (.... but there may be local copies!)
# "inbound" is files submitted by students (on external service)
# "cached" is files submitted by students & collected by instructors (so on local disk)
class ExchangeList(abc.ExchangeList, Exchange):
def do_copy(self, src, dest):
pass
fetched_root = Unicode("", help="Root location for files to be fetched into")
# the list of assignments the exchange knows about
assignments = []
# for filtering on-disk items from exchange items
# (eg removed 'released' items if the 'fetched' item is on disk)
seen_assignments = {"fetched": [], "collected": []}
def query_exchange(self):
"""
This queries the database for all the assignments for a course
if self.inbound or self.cached are true, it returns all the 'submitted'
items, else it returns all the 'released' ones.
(it doesn't care about feedback or collected actions)
"""
if self.course_id:
"""List assignments for specific course"""
r = self.api_request(f"assignments?course_id={quote_plus(self.course_id)}")
else:
"""List assignments for all courses"""
r = self.api_request(f"assignments")
self.log.debug(f"Got back {r} when listing assignments")
try:
assignments = r.json()
except json.decoder.JSONDecodeError:
self.log.error(f"Got back an invalid response when listing assignments")
return []
return assignments["value"]
def init_src(self):
pass
# sets self.assignments to be the list of assignment records that match the
# released/submitted/cached criteria configured
def init_dest(self):
course_id = self.course_id if self.course_id else "*"
assignment_id = (
self.coursedir.assignment_id if self.coursedir.assignment_id else "*"
)
self.assignments = []
exchange_listed_assignments = self.query_exchange()
self.log.debug(
f"ExternalExchange.list.init_dest collected {exchange_listed_assignments}"
)
# if "inbound", looking for inbound (submitted) records
# elif 'cached', looking for already downloaded files
# else, looking for outbound (released) files
if self.inbound or self.cached:
for assignment in exchange_listed_assignments:
if assignment.get("status") == "submitted":
self.assignments.append(assignment)
else:
self.assignments = filter(
lambda x: x.get["status"] == "released", exchange_listed_assignments
)
def copy_files(self):
pass
# Add the path for notebooks on disk, and add the blank parameters
# Feedback details is listed in "submitted" records
def parse_assignment(self, assignment): # , on_disk_assignments=None):
# If the assignment was found on disk, we need to expand the metadata
if assignment.get("status") == "fetched":
# get the individual notebook details
assignment_dir = os.path.join(
self.assignment_dir, assignment.get("assignment_id")
)
if self.path_includes_course:
assignment_dir = os.path.join(
self.assignment_dir, self.course_id, assignment.get("assignment_id")
)
assignment["notebooks"] = []
# Find the ipynb files
for notebook in sorted(glob.glob(os.path.join(assignment_dir, "*.ipynb"))):
notebook_id = os.path.splitext(os.path.split(notebook)[1])[0]
assignment["notebooks"].append(
{
"path": notebook,
"notebook_id": notebook_id,
"has_local_feedback": False,
"has_exchange_feedback": False,
"local_feedback_path": None,
"feedback_updated": False,
}
)
return assignment
def parse_assignments(self):
# Set up some general variables
self.assignments = []
held_assignments = {"fetched": {}, "released": {}}
assignment_dir = os.path.join(self.assignment_dir)
if self.path_includes_course:
assignment_dir = os.path.join(self.assignment_dir, self.course_id)
course_id = self.course_id if self.course_id and self.course_id != "*" else None
assignment_id = (
self.coursedir.assignment_id
if self.coursedir.assignment_id and self.coursedir.assignment_id != "*"
else None
)
student_id = (
self.coursedir.student_id
if self.coursedir.student_id and self.coursedir.student_id != "*"
else None
)
# Get a list of everything from the exchange
exchange_listed_assignments = self.query_exchange()
# if "inbound" or "cached" are true, we're looking for inbound
# (submitted) records else we're looking for outbound (released)
# records
# (everything else is irrelevant for this method)
if self.inbound or self.cached:
for assignment in exchange_listed_assignments:
if assignment.get("status") == "submitted":
self.assignments.append(assignment)
else:
for assignment in exchange_listed_assignments:
if assignment.get("status") == "released":
self.assignments.append(assignment)
# We want to check the local disk for "fetched" items, not what the external server
# says we should have
interim_assignments = []
found_fetched = set([])
for assignment in self.assignments:
assignment_directory = (
self.fetched_root + "/" + assignment.get("assignment_id")
)
if assignment["status"] == "released":
# Has this release already been found on disk?
if assignment["assignment_id"] in found_fetched:
continue
# Check to see if the 'released' assignment is on disk
if os.path.isdir(assignment_directory):
assignment["status"] = "fetched"
# lets just take a note of having found this assignment
found_fetched.add(assignment["assignment_id"])
interim_assignments.append(self.parse_assignment(assignment))
self.log.debug(
f"parse_assignment singular assignment returned: {assignment}"
)
# now we build two sub-lists:
# - the last "released" per assignment_id - but only if they've not been "fetched"
#
my_assignments = []
for assignment in interim_assignments:
# Skip those not being seen
if assignment is None:
continue
assignment_directory = (
self.fetched_root + "/" + assignment.get("assignment_id")
)
# Hang onto the fetched assignment, if there is one
# Note, we'll only have a note of the _first_ one - but that's fine
# as the timestamp is irrelevant... we just need to know if we
# need to look to the local disk
if assignment.get("status") == "fetched":
held_assignments["fetched"][
assignment.get("assignment_id")
] = assignment
continue
# filter out all the released items:
if assignment.get("status") == "released":
# This is complicated:
# - If the user has "fetched" the assignment, don't keep it
# - otherwise keep the latest one
if assignment.get("assignment_id") in held_assignments["fetched"]:
continue
else:
latest = held_assignments["released"].get(
assignment.get("assignment_id"),
{"timestamp": "1990-01-01 00:00:00"},
)
if assignment.get("timestamp") > latest.get("timestamp"):
held_assignments["released"][
assignment.get("assignment_id")
] = assignment
continue
# "Submitted" assignments [may] have feedback
# If they do, we need to promote details of local [on disk] feedback
# to the "assignment" level. It would have been nice to match
# sumbission times to feedback directories.
# Note that the UI displays the "submitted" time in the table, but
# will provide a link to a folder that is the "feedback" time
# ("feedback-time" for all notebooks in one 'release' is the same)
if assignment.get("status") == "submitted":
assignment_dir = os.path.join(
assignment.get("assignment_id"), "feedback"
)
if self.path_includes_course:
assignment_dir = os.path.join(
self.course_id, assignment.get("assignment_id"), "feedback"
)
local_feedback_dir = None
local_feedback_path = None
has_local_feedback = False
has_exchange_feedback = False
feedback_updated = False
for notebook in assignment["notebooks"]:
nb_timestamp = notebook["feedback_timestamp"]
# This has to match timestamp in fetch_feedback.download
if nb_timestamp:
# get the individual notebook details
if os.path.isdir(
os.path.join(
assignment_dir,
nb_timestamp,
)
):
local_feedback_path = os.path.join(
assignment_dir,
quote(nb_timestamp),
f"{notebook['notebook_id']}.html",
)
has_local_feedback = os.path.isfile(
os.path.join(
assignment_dir,
nb_timestamp,
f"{notebook['notebook_id']}.html",
)
)
notebook["has_local_feedback"] = has_local_feedback
notebook["local_feedback_path"] = local_feedback_path
# Set assignment-level variables is any not the individual notebooks
# have them
if assignment["notebooks"]:
has_local_feedback = any(
[nb["has_local_feedback"] for nb in assignment["notebooks"]]
)
has_exchange_feedback = any(
[nb["has_exchange_feedback"] for nb in assignment["notebooks"]]
)
feedback_updated = any(
[nb["feedback_updated"] for nb in assignment["notebooks"]]
)
else:
has_local_feedback = False
has_exchange_feedback = False
feedback_updated = False
assignment["has_local_feedback"] = has_local_feedback
assignment["has_exchange_feedback"] = has_exchange_feedback
assignment["feedback_updated"] = feedback_updated
if has_local_feedback:
assignment["local_feedback_path"] = os.path.join(
assignment_dir,
quote(nb_timestamp),
)
else:
assignment["local_feedback_path"] = None
# We keep everything we've not filtered out
my_assignments.append(assignment)
# concatinate the "released" and "fetched" sublists to my_assignments
for assignment_type in ("released", "fetched"):
if held_assignments[assignment_type].items():
for assignment_id in held_assignments[assignment_type]:
my_assignments.append(
held_assignments[assignment_type][assignment_id]
)
if self.inbound or self.cached:
_get_key = lambda info: (
info["course_id"],
info["student_id"],
info["assignment_id"],
)
_match_key = lambda info, key: (
info["course_id"] == key[0]
and info["student_id"] == key[1]
and info["assignment_id"] == key[2]
)
assignment_keys = sorted(
list(set([_get_key(info) for info in my_assignments]))
)
assignment_submissions = []
for key in assignment_keys:
submissions = [x for x in my_assignments if _match_key(x, key)]
submissions = sorted(submissions, key=lambda x: x["timestamp"])
info = {
"course_id": key[0],
"student_id": key[1],
"assignment_id": key[2],
"status": submissions[0]["status"],
"submissions": submissions,
}
assignment_submissions.append(info)
my_assignments = assignment_submissions
else:
my_assignments = [
x for x in my_assignments if x.get("status") != "submitted"
]
return my_assignments
def list_files(self):
"""List files"""
self.log.debug(f"ExchaneList.list_file starting")
assignments = self.parse_assignments()
return assignments
def remove_files(self):
if self.course_id:
"""Delete assignment"""
url = f"assignment?course_id={quote_plus(self.course_id)}&assignment_id={quote_plus(self.coursedir.assignment_id)}"
r = self.api_request(url, method="DELETE")
self.log.debug(f"Got back {r.status_code} after assignment unrelease")
def start(self):
if self.path_includes_course:
self.coursedir.submitted_directory = os.path.join(
self.course_id, "collected"
)
r = self.course_id
else:
self.coursedir.submitted_directory = "collected"
r = "."
self.fetched_root = os.path.abspath(os.path.join("", r))
if self.remove:
return self.remove_files()
else:
return self.list_files()
|
[
"os.path.isdir",
"traitlets.Unicode",
"urllib.parse.quote",
"urllib.parse.quote_plus",
"os.path.split",
"os.path.join"
] |
[((585, 647), 'traitlets.Unicode', 'Unicode', (['""""""'], {'help': '"""Root location for files to be fetched into"""'}), "('', help='Root location for files to be fetched into')\n", (592, 647), False, 'from traitlets import Bool, Unicode\n'), ((4580, 4613), 'os.path.join', 'os.path.join', (['self.assignment_dir'], {}), '(self.assignment_dir)\n', (4592, 4613), False, 'import os\n'), ((4681, 4730), 'os.path.join', 'os.path.join', (['self.assignment_dir', 'self.course_id'], {}), '(self.assignment_dir, self.course_id)\n', (4693, 4730), False, 'import os\n'), ((14961, 15002), 'os.path.join', 'os.path.join', (['self.course_id', '"""collected"""'], {}), "(self.course_id, 'collected')\n", (14973, 15002), False, 'import os\n'), ((15204, 15223), 'os.path.join', 'os.path.join', (['""""""', 'r'], {}), "('', r)\n", (15216, 15223), False, 'import os\n'), ((6570, 6605), 'os.path.isdir', 'os.path.isdir', (['assignment_directory'], {}), '(assignment_directory)\n', (6583, 6605), False, 'import os\n'), ((3826, 3865), 'os.path.join', 'os.path.join', (['assignment_dir', '"""*.ipynb"""'], {}), "(assignment_dir, '*.ipynb')\n", (3838, 3865), False, 'import os\n'), ((14626, 14652), 'urllib.parse.quote_plus', 'quote_plus', (['self.course_id'], {}), '(self.course_id)\n', (14636, 14652), False, 'from urllib.parse import quote, quote_plus\n'), ((14669, 14709), 'urllib.parse.quote_plus', 'quote_plus', (['self.coursedir.assignment_id'], {}), '(self.coursedir.assignment_id)\n', (14679, 14709), False, 'from urllib.parse import quote, quote_plus\n'), ((1371, 1397), 'urllib.parse.quote_plus', 'quote_plus', (['self.course_id'], {}), '(self.course_id)\n', (1381, 1397), False, 'from urllib.parse import quote, quote_plus\n'), ((12355, 12374), 'urllib.parse.quote', 'quote', (['nb_timestamp'], {}), '(nb_timestamp)\n', (12360, 12374), False, 'from urllib.parse import quote, quote_plus\n'), ((3916, 3939), 'os.path.split', 'os.path.split', (['notebook'], {}), '(notebook)\n', (3929, 3939), False, 'import os\n'), ((10264, 10306), 'os.path.join', 'os.path.join', (['assignment_dir', 'nb_timestamp'], {}), '(assignment_dir, nb_timestamp)\n', (10276, 10306), False, 'import os\n'), ((10573, 10592), 'urllib.parse.quote', 'quote', (['nb_timestamp'], {}), '(nb_timestamp)\n', (10578, 10592), False, 'from urllib.parse import quote, quote_plus\n'), ((10788, 10865), 'os.path.join', 'os.path.join', (['assignment_dir', 'nb_timestamp', 'f"""{notebook[\'notebook_id\']}.html"""'], {}), '(assignment_dir, nb_timestamp, f"{notebook[\'notebook_id\']}.html")\n', (10800, 10865), False, 'import os\n')]
|
"""Contains the ansXpl class."""
import json
import pathlib
import random
import string
import weakref
from ansys.api.mapdl.v0 import mapdl_pb2
import numpy as np
from .common_grpc import ANSYS_VALUE_TYPE
from .errors import MapdlRuntimeError
def id_generator(size=6, chars=string.ascii_uppercase):
"""Generate a random string using only uppercase letters."""
return "".join(random.choice(chars) for _ in range(size))
MYCTYPE = {
np.int32: "I",
np.int64: "L",
np.single: "F",
np.double: "D",
np.complex64: "C",
np.complex128: "Z",
}
class ansXpl:
"""
ANSYS database explorer.
Examples
--------
>>> from ansys.mapdl.core import launch_mapdl
>>> mapdl = launch_mapdl()
>>> xpl = mapdl.xpl
Open a mode file and extract a vector.
>>> xpl.open('file.mode')
>>> vec = xpl.read('MASS')
>>> vec.asarray()
array([ 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43,
46, 49, 52, 55, 58, 1], dtype=int32)
"""
def __init__(self, mapdl):
"""Initialize the class."""
from ansys.mapdl.core.mapdl_grpc import MapdlGrpc
if not isinstance(mapdl, MapdlGrpc): # pragma: no cover
raise TypeError("Must be initialized using MapdlGrpc class")
self._mapdl_weakref = weakref.ref(mapdl)
self._filename = None
self._open = False
@property
def _mapdl(self):
"""Return the weakly referenced instance of mapdl."""
return self._mapdl_weakref()
def open(self, filename, option=""):
"""
Open an MAPDL file to explore.
Parameters
----------
filename : str
Name of the file to open.
Returns
-------
str
Response from MAPDL.
Examples
--------
>>> xpl.open('file.mode')
===============================================
===== ANSYS File Xplorer ======
===============================================
Opening the file.mode ANSYS File
"""
self._filename = filename
out = self._mapdl.run(f"*XPL,OPEN,{filename},,{option}")
self._open = True
return out
def close(self):
"""
Close the MAPDL file after opening.
Returns
-------
str
Response from MAPDL.
Examples
--------
>>> xpl.open("file.mode")
>>> xpl.close()
===== ANSYS File Xplorer : Close the file.mode ANSYS File
"""
response = self._mapdl.run("*XPL,CLOSE")
self._check_ignored(response)
self._open = False
return response
def list(self, nlev=1):
"""
List the records at the current level.
Parameters
----------
nlev: int
Number of levels to recursively explore.
Returns
-------
str
Listing of records from the current level.
Examples
--------
Open a full file and list the current records.
>>> xpl.open("file.full")
>>> xpl.list()
===== ANSYS File Xplorer : List Blocks in File file.full
::FULL::HEADER Size = 652 B Total Size = 180.297 KB
::FULL::DOFSBYNOD Size = 24 B
::FULL::BACK Size = 336 B
::FULL::STIFF::HEADER Size = 117.316 KB
::FULL::RHS Size = 1.910 KB
::FULL::DIAGK Size = 1.910 KB
::FULL::SCLK Size = 1.910 KB
::FULL::MRK Size = 984 B
::FULL::NODEEXT Size = 336 B
::FULL::PCGDOFS Size = 984 B
::FULL::BCDOFS Size = 984 B
::FULL::BCVALUES Size = 12 B
::FULL::MASS::HEADER Size = 50.801 KB
::FULL::DIAGM Size = 1.910 KB
::FULL::NGPH Size = 336 B
"""
response = self._mapdl.run(f"*XPL,LIST,{nlev}")
self._check_ignored(response)
return response
def _check_ignored(self, response):
"""Check for ignored in response."""
if "ignored" in response:
raise MapdlRuntimeError(response)
def help(self):
"""
XPL help message.
Examples
--------
>>> print(xpl.help())
"""
return self._mapdl.run("*XPL,HELP")
def step(self, where):
"""
Go down in the tree of records
Parameters
----------
where : str
Path to follow. This path can be composed of several
levels, for example ``"BRANCH1::SUBBRANCH2::.."``
Returns
-------
str
Response from MAPDL.
Examples
--------
>>> xpl.step('MASS')
>>> print(xpl.where())
===== ANSYS File Xplorer : Display Current Location
Current Location : FULL::MASS
File Location : 7644
"""
response = self._mapdl.run(f"*XPL,STEP,{where}")
if "Not Found" in response:
raise RuntimeError(response.strip())
return response
def info(self, recname, option=""):
"""
Gives details on a specific record, or all records (using ``"*"``)
Parameters
----------
recname : str
Record of interest
option : str
Options string.
Returns
-------
str
Response from MAPDL.
Examples
--------
>>> xpl.open('file.full')
>>> print(xpl.info('NGPH'))
===== ANSYS File Xplorer : Information about Block NGPH
::NGPH Size = 6.289 KB
- Record Size : 81
- Data type : integer values
"""
return self._mapdl.run(f"*XPL,INFO,{recname},{option}")
def print(self, recname):
"""
Print values of a given records, or all records (using ``"*"``).
Parameters
----------
recname : str
Record of interest
option : str
Options string.
Returns
-------
str
Response from MAPDL.
Examples
--------
>>> xpl.open('file.full')
>>> print(xpl.print('DOFSBYNOD'))
===== ANSYS File Xplorer : Print Block DOFSBYNOD
DOFSBYNOD :
Size : 3
1 2 3
"""
return self._mapdl.run(f"*XPL,PRINT,{recname}")
def json(self):
"""
Return a JSON representation of the tree or records.
Examples
--------
>>> xpl.json()
{'name': 'FULL',
'children': [{'name': 'DOFSBYNOD', 'size': 24},
{'name': 'BACK', 'size': 336},
{'name': 'STIFF', 'size': 120132},
{'name': 'RHS', 'size': 1956},
{'name': 'DIAGK', 'size': 1956},
{'name': 'SCLK', 'size': 36},
{'name': 'NODEEXT', 'size': 32},
{'name': 'PCGDOFS', 'size': 984},
{'name': 'BCDOFS', 'size': 984},
{'name': 'BCVALUES', 'size': 20},
{'name': 'MASS', 'size': 52020},
{'name': 'DIAGM', 'size': 1236},
{'name': 'NGPH', 'size': 6440}]}
"""
self._mapdl.run("*XPL,JSON,_mylocal_.json")
text = self._mapdl._download_as_raw("_mylocal_.json").decode()
return json.loads(text)
def where(self):
"""
Returns the current location in the MAPDL file.
Returns
-------
str
String containing the current location.
Examples
--------
>>> print(xpl.where())
===== ANSYS File Xplorer : Display Current Location
Current Location : FULL
File Location : 412
"""
return self._mapdl.run("*XPL,WHERE")
def up(self, nlev=1):
"""
Go up in the tree.
nlev : int
Number of levels to recursively go up, or TOP
Examples
--------
>>> print(xpl.up())
===== ANSYS File Xplorer : Go up to 1 level(s)
-> Already at the top level. Command is ignored
"""
if str(nlev).upper().strip() == "TOP":
return self._mapdl.run("*XPL,UP,TOP")
return self._mapdl.run(f"*XPL,UP,{nlev}")
def goto(self, path):
"""
Go directly to a new location in the file.
Parameters
----------
path : str
Absolute path to the new location.
Examples
--------
>>> print(xpl.goto('MASS'))
===== ANSYS File Xplorer : Go up to top level(s)
===== ANSYS File Xplorer : Step into Block MASS
"""
return self._mapdl.run(f"*XPL,GOTO,{path}")
def copy(self, newfile, option=""):
"""
Copy the current opened as a new file.
Parameters
----------
newfile : str
Name of the new file to create
option: str
Option.
Examples
--------
>>> xpl.copy('tmpfile.full')
===== ANSYS File Xplorer : Copy file.full ANSYS file to file tmpfile.full
>> Remove existing output file tmpfile.full
"""
return self._mapdl.run(f"*XPL,COPY,{newfile},{option}")
def save(self):
"""Save the current file, ignoring the marked records."""
response = self._mapdl.run("*XPL,SAVE").strip()
self._check_ignored(response)
return response
def extract(self, recordname, sets="ALL", asarray=False): # pragma: no cover
"""
Import a Matrix/Vector from a MAPDL result file.
At the moment, this only supports reading the displacement vectors from
a result file.
Parameters
----------
recordname : str
Record name. Currently only supports the ``"NSL"`` record,
displacement vectors.
sets : str or int
Number of sets. Can be ``"ALL"`` or the number of sets to load.
asarray : bool, optional
Return a :class:`numpy.ndarray` rather than a :class:`AnsMat
<ansys.mapdl.core.math.AnsMat>`. Default ``False``.
Returns
-------
numpy.ndarray or ansys.mapdl.core.math.AnsMat
A :class:`numpy.ndarray` or :class:`AnsMat
<ansys.mapdl.core.math.AnsMat>` of the displacement vectors,
depending on the value of ``asarray``.
Notes
-----
This only works on the ``"NSL"`` record of MAPDL result files.
Examples
--------
First, open a result file and extract the displacement vectors for all
sets.
>>> xpl.open("file.rst")
>>> mat = xpl.extract("NSL")
>>> mat
Dense APDLMath Matrix (243, 10)
Convert to a dense numpy array
>>> arr = mat.asarray()
>>> arr
array([[-9.30806802e-03, -2.39600770e-02, -5.37856729e-03, ...,
-5.61188243e-03, -7.17686067e-11, 3.71893252e-03],
[-1.60960014e-02, 2.00410618e-02, 8.05822565e-03, ...,
-1.26917511e-02, -5.14133724e-11, -1.38783485e-03],
[ 2.54040694e-02, 3.91901513e-03, -2.67965796e-03, ...,
-1.46365178e-02, 8.31735188e-11, -2.33109771e-03],
...,
[-2.80679551e-03, -1.45686692e-02, 8.05466291e-03, ...,
5.88196684e-03, 1.72211103e-02, 6.10079082e-03],
[-7.06675717e-03, 1.30455037e-02, -6.31685295e-03, ...,
1.08619340e-02, -1.72211102e-02, 2.52199472e-03],
[ 2.29726170e-02, 3.54392176e-03, -1.87020162e-03, ...,
1.20642736e-02, 2.58299321e-11, 9.14504940e-04]])
"""
if recordname.upper() != "NSL":
raise ValueError("Currently, the only supported recordname is 'NSL'")
rand_name = id_generator()
self._mapdl._log.info(
"Calling MAPDL to extract the %s matrix from %s", recordname, self._filename
)
num_first = 1
num_last = 1
if sets == "ALL":
num_last = -1
dtype = np.double
file_extension = pathlib.Path(self._filename).suffix[1:]
if file_extension.lower() != "rst":
raise RuntimeError(
"This method only supports extracting records from result files"
)
self._mapdl.run(
f"*DMAT,{rand_name},{MYCTYPE[dtype]},IMPORT,{file_extension},{self._filename},"
f"{num_first},{num_last},{recordname}",
mute=False,
)
return self._mapdl.math.mat(dtype=dtype, name=rand_name)
def read(self, recordname):
"""
Read a record and return either an APDL math matrix or an APDL math vector.
Returns
-------
ansys.mapdl.AnsMat or ansys.mapdl.AnsVec
A handle to the APDLMath object.
Examples
--------
>>> vec = xpl.read('MASS')
>>> vec.asarray()
array([ 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43,
46, 49, 52, 55, 58, 1], dtype=int32)
"""
rand_name = id_generator()
response = self._mapdl.run(f"*XPL,READ,{recordname},{rand_name}")
self._check_ignored(response)
data_info = self._mapdl._data_info(rand_name)
dtype = ANSYS_VALUE_TYPE[data_info.stype]
if dtype is None: # pragma: no cover
raise ValueError("Unknown MAPDL data type")
# return either vector or matrix type
if data_info.objtype == mapdl_pb2.DataType.VEC:
return self._mapdl.math.vec(dtype=dtype, name=rand_name)
elif data_info.objtype in [mapdl_pb2.DataType.DMAT, mapdl_pb2.DataType.SMAT]:
return self._mapdl.math.mat(dtype=dtype, name=rand_name)
else: # pragma: no cover
raise ValueError(f"Unhandled MAPDL matrix object type {data_info.objtype}")
def write(self, recordname, vecname):
"""
Write a given record back to an MAPDL file.
Use the write function at your own risk, you may corrupt an existing
file by changing the size of a record in the file. This method must be
used only on a non-compressed file.
Parameters
----------
recordname : str
Name of the record you want to overwrite. Your position
in the file must be set accordingly to this record location
(same as if you want to read it).
vecname : str
Name of the APDLMath vector you want to write in the MAPDL
file. Its size must be consistent with the existing record.
Returns
-------
str
Response from MAPDL.
Examples
--------
>>> xpl.write('MASS', vecname)
"""
response = self._mapdl.run(f"*XPL,WRITE,{recordname},{vecname}")
self._check_ignored(response)
return response
def __repr__(self):
txt = "MAPDL File Explorer\n"
if self._open:
txt += "\tOpen file:%s" % self._filename
txt += "\n".join(self.where().splitlines()[1:])
else:
txt += "\tNo open file"
return txt
|
[
"pathlib.Path",
"weakref.ref",
"random.choice",
"json.loads"
] |
[((1306, 1324), 'weakref.ref', 'weakref.ref', (['mapdl'], {}), '(mapdl)\n', (1317, 1324), False, 'import weakref\n'), ((7630, 7646), 'json.loads', 'json.loads', (['text'], {}), '(text)\n', (7640, 7646), False, 'import json\n'), ((387, 407), 'random.choice', 'random.choice', (['chars'], {}), '(chars)\n', (400, 407), False, 'import random\n'), ((12504, 12532), 'pathlib.Path', 'pathlib.Path', (['self._filename'], {}), '(self._filename)\n', (12516, 12532), False, 'import pathlib\n')]
|
import os
import random
import string
def create_init_file(base_dir):
open(os.path.join(base_dir, '__init__.py'), 'a').close()
def create_file(base_dir, name, other):
with open(os.path.join(base_dir, name), 'w') as f:
with open(other) as o:
f.write(o.read())
def create_git_ignore(base_dir):
path = os.path.join(base_dir, '.gitignore')
if not os.path.exists(path):
with open(path, 'w') as f:
f.write(
"*.py[co]\n*.egg*\nbuild\ncache\n.script\nconfig.json\n*.db\n*.log\n.project\n.pydevproject\n.settings\n*~\n\#*\#\n/.emacs.desktop\n/.emacs.desktop.lock\n.elc\nauto-save-list\ntramp\n.\#*\n*.swp\n*.swo\n.DS_Store\n._*\nThumbs.db\nDesktop.ini\n.idea\nnode_modules\n.env\nstatic")
pass
def generate_key():
return ''.join([random.SystemRandom().choice("{}{}{}".format(string.ascii_letters, string.digits, "!#$%&'()*+,-./:;<>?@[]^_{|}~")) for i in range(50)])
|
[
"os.path.join",
"os.path.exists",
"random.SystemRandom"
] |
[((314, 350), 'os.path.join', 'os.path.join', (['base_dir', '""".gitignore"""'], {}), "(base_dir, '.gitignore')\n", (326, 350), False, 'import os\n'), ((359, 379), 'os.path.exists', 'os.path.exists', (['path'], {}), '(path)\n', (373, 379), False, 'import os\n'), ((184, 212), 'os.path.join', 'os.path.join', (['base_dir', 'name'], {}), '(base_dir, name)\n', (196, 212), False, 'import os\n'), ((79, 116), 'os.path.join', 'os.path.join', (['base_dir', '"""__init__.py"""'], {}), "(base_dir, '__init__.py')\n", (91, 116), False, 'import os\n'), ((751, 772), 'random.SystemRandom', 'random.SystemRandom', ([], {}), '()\n', (770, 772), False, 'import random\n')]
|
# Conversor de temperatura de C° para F°
import colorama
colorama.init()
print('\033[32;1mConversor de temperaturas\033[m')
temp = float(input('Digite a temperatura em C°: '))
print(f'{temp} C° é equivalente a {(9*temp/5)+32} F°')
|
[
"colorama.init"
] |
[((57, 72), 'colorama.init', 'colorama.init', ([], {}), '()\n', (70, 72), False, 'import colorama\n')]
|
import logging
import numpy as np
import tensorflow as tf
from collections import OrderedDict
import utils
from clf_model_multitask import predict
def get_latest_checkpoint_and_log(logdir, filename):
init_checkpoint_path = utils.get_latest_model_checkpoint_path(logdir, filename)
logging.info('Checkpoint path: %s' % init_checkpoint_path)
last_step = int(init_checkpoint_path.split('/')[-1].split('-')[-1])
logging.info('Latest step was: %d' % last_step)
return init_checkpoint_path
def evaluate_scores(true_labels, prediction, measures_dict):
scores_one_exp = OrderedDict()
for measure_name, measure in measures_dict.items():
logging.info('evaluating ' + measure_name)
logging.info(measure)
scores_one_exp[measure_name] = measure(y_true = np.asarray(true_labels), y_pred = np.asarray(prediction))
return scores_one_exp
def map_labels_to_list(labels, label_list):
# label_list is a python list with the labels
# map labels in range(len(label_list)) to the labels in label_list
# E.g. [0,0,1,1] becomes [0,0,2,2] (if 1 doesnt exist in the data)
# label gets mapped to label_list[label]
label_lookup = tf.constant(np.array(label_list))
return tf.gather(label_lookup, labels)
def build_clf_graph(img_tensor_shape, clf_config, joint=False):
graph_classifier = tf.Graph()
with graph_classifier.as_default():
# image (batch size = 1)
x_clf_pl = tf.placeholder(tf.float32, img_tensor_shape, name='z')
# classification of the real source image and the fake target image
predicted_labels, softmax, age_softmaxs = predict(x_clf_pl, clf_config)
# scope = tf.get_variable_scope()
# scope.reuse_variables()
# map labels in range(len(label_list)) to the labels in label_list
# E.g. [0,0,1,1] becomes [0,0,2,2] (if 1 doesnt exist in the data)
predicted_labels_mapped = map_labels_to_list(predicted_labels, clf_config.label_list)
# Add the variable initializer Op.
init = tf.global_variables_initializer()
# Create a savers for writing training checkpoints.
saver = tf.train.Saver() # disc loss is scaled negative EM distance
predictions = {'label': predicted_labels_mapped, 'diag_softmax': softmax, 'age_softmaxs': age_softmaxs}
return graph_classifier, x_clf_pl, predictions, init, saver
def build_gen_graph(img_tensor_shape, gan_config):
# noise_shape
generator = gan_config.generator
graph_generator = tf.Graph()
with graph_generator.as_default():
# source image (batch size = 1)
xs_pl = tf.placeholder(tf.float32, img_tensor_shape, name='xs_pl')
if gan_config.use_generator_input_noise:
noise_shape = gan_config.generator_input_noise_shape.copy()
# adjust batch size
noise_shape[0] = img_tensor_shape[0]
noise_in_gen_pl = tf.random_uniform(shape=noise_shape, minval=-1, maxval=1)
else:
noise_in_gen_pl = None
# generated fake image batch
xf = generator(xs=xs_pl, z_noise=noise_in_gen_pl, training=False)
# Add the variable initializer Op.
init = tf.global_variables_initializer()
# Create a savers for writing training checkpoints.
saver = tf.train.Saver()
return graph_generator, xs_pl, xf, init, saver
|
[
"tensorflow.random_uniform",
"tensorflow.train.Saver",
"tensorflow.gather",
"tensorflow.global_variables_initializer",
"numpy.asarray",
"logging.info",
"utils.get_latest_model_checkpoint_path",
"tensorflow.placeholder",
"numpy.array",
"clf_model_multitask.predict",
"tensorflow.Graph",
"collections.OrderedDict"
] |
[((231, 287), 'utils.get_latest_model_checkpoint_path', 'utils.get_latest_model_checkpoint_path', (['logdir', 'filename'], {}), '(logdir, filename)\n', (269, 287), False, 'import utils\n'), ((292, 350), 'logging.info', 'logging.info', (["('Checkpoint path: %s' % init_checkpoint_path)"], {}), "('Checkpoint path: %s' % init_checkpoint_path)\n", (304, 350), False, 'import logging\n'), ((427, 474), 'logging.info', 'logging.info', (["('Latest step was: %d' % last_step)"], {}), "('Latest step was: %d' % last_step)\n", (439, 474), False, 'import logging\n'), ((591, 604), 'collections.OrderedDict', 'OrderedDict', ([], {}), '()\n', (602, 604), False, 'from collections import OrderedDict\n'), ((1229, 1260), 'tensorflow.gather', 'tf.gather', (['label_lookup', 'labels'], {}), '(label_lookup, labels)\n', (1238, 1260), True, 'import tensorflow as tf\n'), ((1350, 1360), 'tensorflow.Graph', 'tf.Graph', ([], {}), '()\n', (1358, 1360), True, 'import tensorflow as tf\n'), ((2527, 2537), 'tensorflow.Graph', 'tf.Graph', ([], {}), '()\n', (2535, 2537), True, 'import tensorflow as tf\n'), ((669, 711), 'logging.info', 'logging.info', (["('evaluating ' + measure_name)"], {}), "('evaluating ' + measure_name)\n", (681, 711), False, 'import logging\n'), ((720, 741), 'logging.info', 'logging.info', (['measure'], {}), '(measure)\n', (732, 741), False, 'import logging\n'), ((1196, 1216), 'numpy.array', 'np.array', (['label_list'], {}), '(label_list)\n', (1204, 1216), True, 'import numpy as np\n'), ((1453, 1507), 'tensorflow.placeholder', 'tf.placeholder', (['tf.float32', 'img_tensor_shape'], {'name': '"""z"""'}), "(tf.float32, img_tensor_shape, name='z')\n", (1467, 1507), True, 'import tensorflow as tf\n'), ((1635, 1664), 'clf_model_multitask.predict', 'predict', (['x_clf_pl', 'clf_config'], {}), '(x_clf_pl, clf_config)\n', (1642, 1664), False, 'from clf_model_multitask import predict\n'), ((2045, 2078), 'tensorflow.global_variables_initializer', 'tf.global_variables_initializer', ([], {}), '()\n', (2076, 2078), True, 'import tensorflow as tf\n'), ((2156, 2172), 'tensorflow.train.Saver', 'tf.train.Saver', ([], {}), '()\n', (2170, 2172), True, 'import tensorflow as tf\n'), ((2633, 2691), 'tensorflow.placeholder', 'tf.placeholder', (['tf.float32', 'img_tensor_shape'], {'name': '"""xs_pl"""'}), "(tf.float32, img_tensor_shape, name='xs_pl')\n", (2647, 2691), True, 'import tensorflow as tf\n'), ((3203, 3236), 'tensorflow.global_variables_initializer', 'tf.global_variables_initializer', ([], {}), '()\n', (3234, 3236), True, 'import tensorflow as tf\n'), ((3314, 3330), 'tensorflow.train.Saver', 'tf.train.Saver', ([], {}), '()\n', (3328, 3330), True, 'import tensorflow as tf\n'), ((2925, 2982), 'tensorflow.random_uniform', 'tf.random_uniform', ([], {'shape': 'noise_shape', 'minval': '(-1)', 'maxval': '(1)'}), '(shape=noise_shape, minval=-1, maxval=1)\n', (2942, 2982), True, 'import tensorflow as tf\n'), ((798, 821), 'numpy.asarray', 'np.asarray', (['true_labels'], {}), '(true_labels)\n', (808, 821), True, 'import numpy as np\n'), ((832, 854), 'numpy.asarray', 'np.asarray', (['prediction'], {}), '(prediction)\n', (842, 854), True, 'import numpy as np\n')]
|
import requests
import os
import zipfile
def buster_captcha_solver(dir, unzip = False):
url = "https://api.github.com/repos/dessant/buster/releases/latest"
r = requests.get(url)
# Chrome
name = r.json()["assets"][0]["name"]
dl_url = r.json()["assets"][0]["browser_download_url"]
path = dir + "//" + name
if not os.path.exists(path):
r = requests.get(dl_url, stream=True)
with open(path, 'wb') as fd:
for chunk in r.iter_content(chunk_size=128):
fd.write(chunk)
if unzip == True:
folder = os.path.splitext(path)[0]
with zipfile.ZipFile(path, 'r') as zip_ref:
zip_ref.extractall(folder)
path = os.path.abspath(folder)
else:
path = os.path.abspath(path)
return path
if __name__ == "__main__":
foo = buster_captcha_solver("..//chrome_extension")
print(foo)
|
[
"os.path.abspath",
"zipfile.ZipFile",
"os.path.exists",
"os.path.splitext",
"requests.get"
] |
[((175, 192), 'requests.get', 'requests.get', (['url'], {}), '(url)\n', (187, 192), False, 'import requests\n'), ((357, 377), 'os.path.exists', 'os.path.exists', (['path'], {}), '(path)\n', (371, 377), False, 'import os\n'), ((392, 425), 'requests.get', 'requests.get', (['dl_url'], {'stream': '(True)'}), '(dl_url, stream=True)\n', (404, 425), False, 'import requests\n'), ((733, 756), 'os.path.abspath', 'os.path.abspath', (['folder'], {}), '(folder)\n', (748, 756), False, 'import os\n'), ((784, 805), 'os.path.abspath', 'os.path.abspath', (['path'], {}), '(path)\n', (799, 805), False, 'import os\n'), ((598, 620), 'os.path.splitext', 'os.path.splitext', (['path'], {}), '(path)\n', (614, 620), False, 'import os\n'), ((638, 664), 'zipfile.ZipFile', 'zipfile.ZipFile', (['path', '"""r"""'], {}), "(path, 'r')\n", (653, 664), False, 'import zipfile\n')]
|
# -*- coding: utf-8 -*-
from __future__ import division
import gensim
import nltk
import smart_open
import json
from sentence_extracor import segment_sentences_tok
from gensim.models import TfidfModel
from gensim.corpora import Dictionary
import warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
def read_corpus(fname, tokens_only=False):
with smart_open.smart_open(fname) as f: #encoding="iso-8859-1"
for i, line in enumerate(f):
if tokens_only:
yield gensim.utils.simple_preprocess(line)
else:
# For training data, add tags
yield gensim.models.doc2vec.TaggedDocument(gensim.utils.simple_preprocess(line), [i])
def read_list_corpus(list_corp, tokens_only=False):
for i, paragraph in enumerate(list_corp):
if tokens_only:
yield gensim.utils.simple_preprocess(paragraph[0])
else:
yield gensim.models.doc2vec.TaggedDocument(gensim.utils.simple_preprocess(paragraph[0]), [i])
'''
#this block is for buildeing sentence embeddings
#f = open("dataset.txt")
#data = f.read().decode("utf8")
#f.close()
#tokens = nltk.word_tokenize(data)
#sents = segment_sentences_tok(tokens)
'''
'''
dataset = json.load(open("./datasets/dataset_paragraphs.json"))
model = gensim.models.Word2Vec(size=256, window=15, min_count=2, workers=4) #window=10 window=20
model.build_vocab(sents)
print("String training word2vec model...")
model.train(sents, total_examples=model.corpus_count, epochs=50)
model.save("./word2vec_size-100_window-5_min-count-1_workers-4.model")
model = gensim.models.FastText(size=256, window=15, min_count=2, workers=4) #window=10 window=20
model.build_vocab(sents)
print("String training fasttext model...")
model.train(sents, total_examples=model.corpus_count, epochs= 50)
model.save("./fasttext")
#train_corpus = list(read_corpus('sents_file.txt'))
train_corpus = list(read_list_corpus(dataset))
model = gensim.models.doc2vec.Doc2Vec(vector_size=256, min_count=2, workers=4)
model.build_vocab(train_corpus)
print("Starting training doc2vec model...")
model.train(train_corpus, total_examples=model.corpus_count, epochs=50)
model.save('./my_model.doc2vec')
#dataset = list(read_corpus('sents_file.txt', tokens_only=True))
dataset = list(read_list_corpus(dataset, tokens_only=True))
dct = Dictionary(dataset)
corpus = [dct.doc2bow(line) for line in dataset]
model = TfidfModel(corpus)
matrix = model[corpus]
print dir(matrix)
#model.save("./tfidf")'''
|
[
"gensim.utils.simple_preprocess",
"smart_open.smart_open",
"warnings.filterwarnings"
] |
[((255, 324), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {'message': '"""numpy.dtype size changed"""'}), "('ignore', message='numpy.dtype size changed')\n", (278, 324), False, 'import warnings\n'), ((325, 394), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {'message': '"""numpy.ufunc size changed"""'}), "('ignore', message='numpy.ufunc size changed')\n", (348, 394), False, 'import warnings\n'), ((448, 476), 'smart_open.smart_open', 'smart_open.smart_open', (['fname'], {}), '(fname)\n', (469, 476), False, 'import smart_open\n'), ((953, 997), 'gensim.utils.simple_preprocess', 'gensim.utils.simple_preprocess', (['paragraph[0]'], {}), '(paragraph[0])\n', (983, 997), False, 'import gensim\n'), ((593, 629), 'gensim.utils.simple_preprocess', 'gensim.utils.simple_preprocess', (['line'], {}), '(line)\n', (623, 629), False, 'import gensim\n'), ((1067, 1111), 'gensim.utils.simple_preprocess', 'gensim.utils.simple_preprocess', (['paragraph[0]'], {}), '(paragraph[0])\n', (1097, 1111), False, 'import gensim\n'), ((753, 789), 'gensim.utils.simple_preprocess', 'gensim.utils.simple_preprocess', (['line'], {}), '(line)\n', (783, 789), False, 'import gensim\n')]
|
import re
import json
import inject
import logging
import requests
from bs4 import BeautifulSoup
from fake_useragent import UserAgent
from celery import Celery
from block.celery import APITask
from block.config import RedisCache, Config
from block.libs.dingding import DingDing
logger = logging.getLogger(__name__)
current_app = inject.instance(Celery)
DEFAULT_OPTS = {
"bind": True,
"exchange": "block",
"base": APITask,
}
@current_app.task(name="address.query_address", **DEFAULT_OPTS)
def query_address_by_etherscan(self, address, code):
del self
# 获取当前redis存在的币种
monitor_cache = inject.instance(RedisCache)
currencys = monitor_cache.hget(code, address)
if not currencys:
return
currency_list = json.loads(currencys.decode())
ua = UserAgent()
headers = {"user-agent": ua.chrome}
url = Config.scan_url.get(code)
resp = requests.get(url + address, headers=headers)
result = resp.text
soup = BeautifulSoup(result, 'lxml')
data = soup.select("ul.list-unstyled > li.list-custom > a.link-hover")
new_list, need_push = [], False
regex1 = re.compile(r"\/token\/(.+)\?")
for i in data:
url = regex1.findall(i.get("href"))[0]
coin = (i.select("span.list-amount")[0].string).split(" ", 1)[-1]
if coin.upper() not in currency_list:
currency_list.append(coin.upper())
new_list.append((coin, url))
need_push = True
if need_push:
monitor_cache.hset(code, address, json.dumps(currency_list))
dingding = DingDing(Config.ding_url)
dingding.send_message(address, code, new_list)
return
|
[
"block.libs.dingding.DingDing",
"fake_useragent.UserAgent",
"json.dumps",
"requests.get",
"inject.instance",
"bs4.BeautifulSoup",
"block.config.Config.scan_url.get",
"logging.getLogger",
"re.compile"
] |
[((287, 314), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (304, 314), False, 'import logging\n'), ((329, 352), 'inject.instance', 'inject.instance', (['Celery'], {}), '(Celery)\n', (344, 352), False, 'import inject\n'), ((610, 637), 'inject.instance', 'inject.instance', (['RedisCache'], {}), '(RedisCache)\n', (625, 637), False, 'import inject\n'), ((785, 796), 'fake_useragent.UserAgent', 'UserAgent', ([], {}), '()\n', (794, 796), False, 'from fake_useragent import UserAgent\n'), ((847, 872), 'block.config.Config.scan_url.get', 'Config.scan_url.get', (['code'], {}), '(code)\n', (866, 872), False, 'from block.config import RedisCache, Config\n'), ((884, 928), 'requests.get', 'requests.get', (['(url + address)'], {'headers': 'headers'}), '(url + address, headers=headers)\n', (896, 928), False, 'import requests\n'), ((963, 992), 'bs4.BeautifulSoup', 'BeautifulSoup', (['result', '"""lxml"""'], {}), "(result, 'lxml')\n", (976, 992), False, 'from bs4 import BeautifulSoup\n'), ((1118, 1150), 're.compile', 're.compile', (['"""\\\\/token\\\\/(.+)\\\\?"""'], {}), "('\\\\/token\\\\/(.+)\\\\?')\n", (1128, 1150), False, 'import re\n'), ((1558, 1583), 'block.libs.dingding.DingDing', 'DingDing', (['Config.ding_url'], {}), '(Config.ding_url)\n', (1566, 1583), False, 'from block.libs.dingding import DingDing\n'), ((1512, 1537), 'json.dumps', 'json.dumps', (['currency_list'], {}), '(currency_list)\n', (1522, 1537), False, 'import json\n')]
|
# -*- coding:UTF-8 -*-
import requests
import warnings
import os
import re
from nltk import Tree
from subprocess import Popen
import subprocess
import time
import shlex
import multiprocessing
from urllib import parse
class CoreNLP:
def __init__(self, url=None, lang="en", annotators=None, corenlp_dir=None, local_port=9000, max_mem=4, threads=multiprocessing.cpu_count(), timeout=150000):
if url:
self.url = url.rstrip("/")
self.annotators_list = ["tokenize","ssplit","pos","ner","parse","depparse","openie"]
self.lang = lang
self.corenlp_subprocess = None
self.timeout = timeout
if annotators and self._check_annotators_format(annotators):
self.annotators = annotators
else:
self.annotators = ",".join(self.annotators_list)
if corenlp_dir:
try:
os.path.exists(corenlp_dir)
except:
raise OSError("please check corenlp local path is correct! ")
if self._launch_local_server(corenlp_dir, local_port, max_mem, threads):
self.url = f"http://127.0.0.1:{local_port}"
self._request_corenlp(data="", annotators=self.annotators)
def __enter__(self):
return self
def __exit__(self, type, value, trace):
if self.corenlp_subprocess:
self.corenlp_subprocess.kill()
self.corenlp_subprocess.wait()
# os.killpg(os.getpgid(self.corenlp_subprocess.pid), 9)
def __del__(self):
if self.corenlp_subprocess:
self.corenlp_subprocess.kill()
self.corenlp_subprocess.wait()
def _check_annotators_format(self, annotators):
annotators = annotators.split(",")
for i in annotators:
if i not in self.annotators_list:
return False
return True
def _check_server_status(self):
if requests.get(self.url, verify=False).status_code != 200:
raise ConnectionError("please check your network connection, or the corenlp server is started before launching!")
@staticmethod
def _deal_path_suffix(path):
if "\\" in path:
path = path.rstrip("\\") + "\\"
else:
path = path.rstrip("/") + "/"
return path
def _launch_local_server(self, corenlp_dir, port, max_mem, threads):
corenlp_dir = self._deal_path_suffix(os.path.abspath(corenlp_dir))
tmp_dir = "tmp"
if not os.path.exists("tmp"):
os.mkdir(tmp_dir)
try:
os.system("java -version")
except:
raise AssertionError("Java is required to launch corenlp server! ")
cmd = f'java -Djava.io.tmpdir={tmp_dir} -mx{max_mem}g ' + \
f'-cp "{corenlp_dir}*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer ' + \
f'-threads {threads} -port {port} -timeout 150000 -lazy false'
print(cmd)
cmd = shlex.split(cmd)
self.corenlp_subprocess = Popen(cmd)
time.sleep(1)
return True
def _request_corenlp(self, data, annotators):
params = {"properties": '{"annotators": "%s"}' % annotators, "pipelineLanguage": self.lang}
res = requests.post(url=self.url, params=params, data=parse.quote(data), timeout=self.timeout, verify=False)
ann_result = res.json()
return ann_result
def annotate(self, data):
ann_result = self._request_corenlp(data, self.annotators)
annotation = Annotation(ann_result)
return annotation
def tokenize(self, data, ssplit=True):
if ssplit:
annotators = "tokenize,ssplit"
else:
annotators = "tokenize"
ann_result = self._request_corenlp(data, annotators)
if ssplit:
annotation = [[token["word"] for token in sent["tokens"]] for sent in ann_result["sentences"]]
else:
annotation = [token["word"] for token in ann_result["tokens"]]
return annotation
def pos_tag(self, data):
annotators = "tokenize,ssplit,pos"
ann_result = self._request_corenlp(data, annotators)
annotation = [[token["pos"] for token in sent["tokens"]] for sent in ann_result["sentences"]]
return annotation
def ner(self, data):
annotators = "tokenize,ssplit,pos,ner"
ann_result = self._request_corenlp(data, annotators)
annotation = []
for sent in ann_result["sentences"]:
sent_ner = []
if "entitymentions" in sent:
for entity in sent["entitymentions"]:
span = (entity["characterOffsetBegin"], entity["characterOffsetEnd"])
ner = entity["ner"]
ner_entity = entity["text"]
sent_ner.append({(ner_entity,span): ner})
annotation.append(sent_ner)
return annotation
@staticmethod
def pretty_print_tree(tree):
Tree.fromstring(tree).pretty_print()
def close(self):
if self.corenlp_subprocess:
self.corenlp_subprocess.kill()
self.corenlp_subprocess.wait()
class Annotation():
def __init__(self, ann_result):
self.ann_result = ann_result
self.tokens=[]
self.parse_tree=[]
self.bi_parse_tree=[]
self.basic_dep=[]
self.enhanced_dep=[]
self.enhanced_pp_dep=[]
self.entities = []
self.openie = []
self._extract_ann()
def _extract_ann(self):
ann_dict = dict()
if "sentences" in self.ann_result:
for ann_sent in self.ann_result["sentences"]:
self.tokens.append(ann_sent["tokens"])
if "parse" in ann_sent:
self.parse_tree.append(re.sub(r"\s+", " ", ann_sent["parse"]))
if "binaryParse" in ann_sent:
self.bi_parse_tree.append(re.sub(r"\s+", " ", ann_sent["binaryParse"]))
if "basicDependencies" in ann_sent:
self.basic_dep.append(ann_sent["basicDependencies"])
if "enhancedDependencies" in ann_sent:
self.enhanced_dep.append(ann_sent["enhancedDependencies"])
if "enhancedPlusPlusDependencies" in ann_sent:
self.enhanced_pp_dep.append(ann_sent["enhancedPlusPlusDependencies"])
if "entitymentions" in ann_sent:
self.entities.append(ann_sent["entitymentions"])
if "openie" in ann_sent:
self.openie.append(ann_sent["openie"])
else:
self.tokens = self.ann_result["tokens"]
return ann_dict
|
[
"os.mkdir",
"os.path.abspath",
"subprocess.Popen",
"nltk.Tree.fromstring",
"shlex.split",
"os.path.exists",
"os.system",
"time.sleep",
"urllib.parse.quote",
"requests.get",
"re.sub",
"multiprocessing.cpu_count"
] |
[((348, 375), 'multiprocessing.cpu_count', 'multiprocessing.cpu_count', ([], {}), '()\n', (373, 375), False, 'import multiprocessing\n'), ((2979, 2995), 'shlex.split', 'shlex.split', (['cmd'], {}), '(cmd)\n', (2990, 2995), False, 'import shlex\n'), ((3030, 3040), 'subprocess.Popen', 'Popen', (['cmd'], {}), '(cmd)\n', (3035, 3040), False, 'from subprocess import Popen\n'), ((3049, 3062), 'time.sleep', 'time.sleep', (['(1)'], {}), '(1)\n', (3059, 3062), False, 'import time\n'), ((2440, 2468), 'os.path.abspath', 'os.path.abspath', (['corenlp_dir'], {}), '(corenlp_dir)\n', (2455, 2468), False, 'import os\n'), ((2509, 2530), 'os.path.exists', 'os.path.exists', (['"""tmp"""'], {}), "('tmp')\n", (2523, 2530), False, 'import os\n'), ((2544, 2561), 'os.mkdir', 'os.mkdir', (['tmp_dir'], {}), '(tmp_dir)\n', (2552, 2561), False, 'import os\n'), ((2587, 2613), 'os.system', 'os.system', (['"""java -version"""'], {}), "('java -version')\n", (2596, 2613), False, 'import os\n'), ((888, 915), 'os.path.exists', 'os.path.exists', (['corenlp_dir'], {}), '(corenlp_dir)\n', (902, 915), False, 'import os\n'), ((1937, 1973), 'requests.get', 'requests.get', (['self.url'], {'verify': '(False)'}), '(self.url, verify=False)\n', (1949, 1973), False, 'import requests\n'), ((3297, 3314), 'urllib.parse.quote', 'parse.quote', (['data'], {}), '(data)\n', (3308, 3314), False, 'from urllib import parse\n'), ((4987, 5008), 'nltk.Tree.fromstring', 'Tree.fromstring', (['tree'], {}), '(tree)\n', (5002, 5008), False, 'from nltk import Tree\n'), ((5803, 5841), 're.sub', 're.sub', (['"""\\\\s+"""', '""" """', "ann_sent['parse']"], {}), "('\\\\s+', ' ', ann_sent['parse'])\n", (5809, 5841), False, 'import re\n'), ((5935, 5979), 're.sub', 're.sub', (['"""\\\\s+"""', '""" """', "ann_sent['binaryParse']"], {}), "('\\\\s+', ' ', ann_sent['binaryParse'])\n", (5941, 5979), False, 'import re\n')]
|
import dash
import dash_bootstrap_components as dbc
from flask import Flask
from ai4good.runner.facade import Facade
from ai4good.webapp.model_runner import ModelRunner
flask_app = Flask(__name__)
dash_app = dash.Dash(
__name__,
server=flask_app,
routes_pathname_prefix='/sim/',
suppress_callback_exceptions=True,
external_stylesheets=[dbc.themes.BOOTSTRAP]
)
facade = Facade.simple()
model_runner = ModelRunner(facade)
|
[
"flask.Flask",
"dash.Dash",
"ai4good.runner.facade.Facade.simple",
"ai4good.webapp.model_runner.ModelRunner"
] |
[((182, 197), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (187, 197), False, 'from flask import Flask\n'), ((210, 368), 'dash.Dash', 'dash.Dash', (['__name__'], {'server': 'flask_app', 'routes_pathname_prefix': '"""/sim/"""', 'suppress_callback_exceptions': '(True)', 'external_stylesheets': '[dbc.themes.BOOTSTRAP]'}), "(__name__, server=flask_app, routes_pathname_prefix='/sim/',\n suppress_callback_exceptions=True, external_stylesheets=[dbc.themes.\n BOOTSTRAP])\n", (219, 368), False, 'import dash\n'), ((392, 407), 'ai4good.runner.facade.Facade.simple', 'Facade.simple', ([], {}), '()\n', (405, 407), False, 'from ai4good.runner.facade import Facade\n'), ((423, 442), 'ai4good.webapp.model_runner.ModelRunner', 'ModelRunner', (['facade'], {}), '(facade)\n', (434, 442), False, 'from ai4good.webapp.model_runner import ModelRunner\n')]
|
from django.contrib import admin
from .models import UserData
from .models import Posts
from .models import HazardType
from .models import Message
from .models import Comments
from .models import PostImageCollection
# Register your models here.
admin.site.register(HazardType)
admin.site.register(UserData)
admin.site.register(Posts)
admin.site.register(Comments)
admin.site.register(Message)
admin.site.register(PostImageCollection)
|
[
"django.contrib.admin.site.register"
] |
[((247, 278), 'django.contrib.admin.site.register', 'admin.site.register', (['HazardType'], {}), '(HazardType)\n', (266, 278), False, 'from django.contrib import admin\n'), ((279, 308), 'django.contrib.admin.site.register', 'admin.site.register', (['UserData'], {}), '(UserData)\n', (298, 308), False, 'from django.contrib import admin\n'), ((309, 335), 'django.contrib.admin.site.register', 'admin.site.register', (['Posts'], {}), '(Posts)\n', (328, 335), False, 'from django.contrib import admin\n'), ((336, 365), 'django.contrib.admin.site.register', 'admin.site.register', (['Comments'], {}), '(Comments)\n', (355, 365), False, 'from django.contrib import admin\n'), ((366, 394), 'django.contrib.admin.site.register', 'admin.site.register', (['Message'], {}), '(Message)\n', (385, 394), False, 'from django.contrib import admin\n'), ((395, 435), 'django.contrib.admin.site.register', 'admin.site.register', (['PostImageCollection'], {}), '(PostImageCollection)\n', (414, 435), False, 'from django.contrib import admin\n')]
|
#-*- coding: utf-8 -*-
import xmind
from xmind.core.const import TOPIC_DETACHED
from xmind.core.markerref import MarkerId
w = xmind.load("test.xmind") # load an existing file or create a new workbook if nothing is found
s1=w.getPrimarySheet() # get the first sheet
s1.setTitle("first sheet") # set its title
r1=s1.getRootTopic() # get the root topic of this sheet
r1.setTitle("we don't care of this sheet") # set its title
s2=w.createSheet() # create a new sheet
s2.setTitle("second sheet")
r2=s2.getRootTopic()
r2.setTitle("root node")
# Empty topics are created from the root element and then filled.
# Examples:
# Create a topic with a link to the first sheet given by s1.getID()
t1 = r2.addSubTopic()
t1.setTopicHyperlink(s1.getID())
t1.setTitle("redirection to the first sheet") # set its title
# Create a topic with a hyperlink
t2 = r2.addSubTopic()
t2.setTitle("second node")
t2.setURLHyperlink("https://xmind.net")
# Create a topic with notes
t3 = r2.addSubTopic()
t3.setTitle("third node")
t3.setPlainNotes("notes for this topic")
t3.setTitle("topic with \n notes")
# Create a topic with a file hyperlink
t4 = r2.addSubTopic()
t4.setFileHyperlink("logo.jpeg")
t4.setTitle("topic with a file")
# Create topic that is a subtopic of another topic
t41 = t4.addSubTopic()
t41.setTitle("a subtopic")
# create a detached topic whose (invisible) parent is the root
d1 = r2.addSubTopic(topics_type = TOPIC_DETACHED)
d1.setTitle("detached topic")
d1.setPosition(0,20)
# loop on the (attached) subTopics
topics=r2.getSubTopics()
# Demonstrate creating a marker
for topic in topics:
topic.addMarker(MarkerId.starBlue)
# create a relationship
rel=s2.createRelationship(t1.getID(),t2.getID(),"test")
# and we save
xmind.save(w,"test2.xmind")
|
[
"xmind.save",
"xmind.load"
] |
[((127, 151), 'xmind.load', 'xmind.load', (['"""test.xmind"""'], {}), "('test.xmind')\n", (137, 151), False, 'import xmind\n'), ((1739, 1767), 'xmind.save', 'xmind.save', (['w', '"""test2.xmind"""'], {}), "(w, 'test2.xmind')\n", (1749, 1767), False, 'import xmind\n')]
|
import concurrent.futures
import csv
from ctrace.utils import max_neighbors
import functools
import itertools
import logging
import time
from collections import namedtuple
from typing import Dict, Callable, List, Any, NamedTuple
import traceback
import shortuuid
import tracemalloc
from tqdm import tqdm
from ctrace import PROJECT_ROOT
DEBUG = False
def debug_memory(logger, label=""):
snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')
logger.debug(f"[{label}]: {top_stats[:5]}")
class GridExecutor():
"""
Usage: Create a new GridExecutor with config, in_schema, out_schema and func.
GridExecutor is an abstract class for running a cartesian product of lists of arguments.
Input and output arguments specified by schemas are assumed to have pretty __str__.
"""
def __init__(self, config: Dict, in_schema: List[str], out_schema: List[str], func: Callable[..., NamedTuple]):
"""
Parameters
----------
config
A dictionary mapping string attributes to arrays of different parameters.
Each item of the dictionary must be an array of arguments
in_schema
A list describing what and the order input attributes would be printed
out_schema
A list describing what and the order output attributes would be printed
func
A function to execute in parallel. Input arguments must match config keys.
Output arguments must be a namedtuple. namedtuple must encompass all attributes in out_schema
"""
self.compact_config = config.copy()
# Schemas need to be consistent with input_param_formatter and output_param_formatter
self.in_schema = in_schema.copy()
self.out_schema = out_schema.copy()
self.func = func
self.init_output_directory()
print(f"Logging Directory Initialized: {self.output_directory}")
# Expand configurations
self.expanded_config = list(GridExecutor.cartesian_product(self.compact_config))
# TODO: Hack Fix
self._track_duration = False
# TODO: Change post initialization method?
@classmethod
def init_multiple(cls, config: Dict[str, Any], in_schema: List[str],
out_schema: List[str], func: Callable, trials: int):
"""
Runs each configuration trials number of times. Each trial is indexed by a "trial_id"s
"""
compact_config = config.copy()
compact_config["trial_id"] = list(range(trials))
in_schema.append("trial_id")
return cls(compact_config, in_schema, out_schema, func)
# TODO: Find a workaround for decorations???
# <================== Problem ====================>
def track_duration(self):
"""Adds a wrapper to runner to track duration, and another column to out_schema for run_duration"""
# raise NotImplementedError
self.out_schema.append("run_duration")
self._track_duration = True
# self.runner = GridExecutor.timer(self.runner)
@staticmethod
def timer(func):
"""A decorator that adds an duration attribute to output of a runner"""
@functools.wraps(func)
def wrapper_timer(*args, **kwargs):
start_time = time.perf_counter() # 1
formatted_param, formatted_output = func(*args, **kwargs)
end_time = time.perf_counter() # 2
formatted_output["run_duration"] = str(end_time - start_time)
return formatted_param, formatted_output
return wrapper_timer
# <================== Problem ====================>
@staticmethod
def cartesian_product(dicts):
"""Expands an dictionary of lists into a list of dictionaries through a cartesian product"""
return (dict(zip(dicts, x)) for x in itertools.product(*dicts.values()))
def input_param_formatter(self, in_param):
"""Uses in_schema and __str__ to return a formatted dict"""
filtered = {}
for key in self.in_schema:
if key == "G":
filtered[key] = in_param[key].NAME
elif key == "agent":
filtered[key] = in_param[key].__name__
else:
filtered[key] = str(in_param[key])
return filtered
def output_param_formatter(self, out_param):
"""Uses out_schema and __str__ to return a formatted dict"""
filtered = {}
for key in self.out_schema:
filtered[key] = str(out_param[key])
return filtered
def init_output_directory(self):
# Initialize Output
self.run_id = shortuuid.uuid()[:5]
# Setup output directories
self.output_directory = PROJECT_ROOT / "output" / f'run_{self.run_id}'
self.output_directory.mkdir(parents=True, exist_ok=True)
self.result_path = self.output_directory / 'results.csv'
self.logging_path = self.output_directory / 'run.log'
def init_logger(self):
# Setup up Parallel Log Channel
self.logger = logging.getLogger("Executor")
self.logger.setLevel(logging.DEBUG)
# Set LOGGING_FILE as output
fh = logging.FileHandler(self.logging_path)
fh.setLevel(logging.DEBUG)
self.logger.addHandler(fh)
# TODO: Encapsulate writer and its file into one object
# TODO: Find a way to move it to the constructor (use file open and close?)
def init_writer(self, result_file):
raise NotImplementedError
# TODO: provide a single method write result and flush to file
def write_result(self, in_param, out_param):
raise NotImplementedError
def _runner(self, param: Dict[str, Any]):
"""A runner method that returns a tuple (formatted_param, formatted_output)"""
formatted_param = self.input_param_formatter(param)
self.logger.info(f"Launching => {formatted_param}")
try:
out = self.func(**param)._asdict()
except Exception as e:
# Find a way to export culprit data?
self.logger.error(traceback.format_exc())
out = {x: None for x in self.out_schema}
# TODO: Added as a hack to allow output_param_formatter not to crash
if self._track_duration:
out["run_duration"] = None
# output_param_formatter assumes out to be consistent with out_schema
formatted_output = self.output_param_formatter(out)
return formatted_param, formatted_output
def runner(self, param):
"""TODO: Temporary workaround because of multiprocessing issues with decorators and lambdas"""
if self._track_duration:
return GridExecutor.timer(self._runner)(param)
else:
return self._runner(param)
def exec(self):
raise NotImplementedError
class GridExecutorParallel(GridExecutor):
# Override the exec
def exec(self, max_workers=20):
with concurrent.futures.ProcessPoolExecutor(max_workers) as executor, \
open(self.result_path, "w+") as result_file: # TODO: Encapsulate "csv file"
self.init_logger()
# TODO: Encapsulate "initialize csv writer" - perhaps use a context managers
row_names = self.in_schema + self.out_schema
writer = csv.DictWriter(result_file, fieldnames=row_names)
writer.writeheader()
results = [executor.submit(self.runner, arg) for arg in self.expanded_config]
for finished_task in tqdm(concurrent.futures.as_completed(results), total=len(self.expanded_config)):
(in_param, out_param) = finished_task.result()
# TODO: Encapsulate "writer"
writer.writerow({**in_param, **out_param})
result_file.flush()
self.logger.info(f"Finished => {in_param}")
# debug_memory(self.logger, "run")
class GridExecutorLinear(GridExecutor):
# Override the exec
def exec(self):
with open(self.result_path, "w") as result_file: # TODO: Encapsulate "csv file"
self.init_logger()
# TODO: Encapsulate "initialize csv writer" - perhaps use a context managers
writer = csv.DictWriter(result_file, fieldnames=self.in_schema + self.out_schema)
writer.writeheader()
for arg in tqdm(self.expanded_config):
(in_param, out_param) = self.runner(arg)
# TODO: Encapsulate "writer"
writer.writerow({**in_param, **out_param})
result_file.flush()
self.logger.info(f"Finished => {in_param}")
|
[
"tqdm.tqdm",
"logging.FileHandler",
"shortuuid.uuid",
"tracemalloc.take_snapshot",
"time.perf_counter",
"traceback.format_exc",
"functools.wraps",
"logging.getLogger",
"csv.DictWriter"
] |
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|
from simulations import simulation, simulation2
from pandas import DataFrame
from pandas import Series
from pandas import concat
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Bidirectional
from keras.layers import LSTM
from math import sqrt
from matplotlib import pyplot
import numpy
# frame a sequence as a supervised learning problem
def timeseries_to_supervised(data, lag=1):
df = DataFrame(data)
columns = [df.shift(i) for i in range(1, lag + 1)]
columns.append(df)
df = concat(columns, axis=1)
df.fillna(0, inplace=True)
return df
# create a differenced series
def difference(dataset, interval=1):
diff = list()
for i in range(interval, len(dataset)):
value = dataset[i] - dataset[i - interval]
diff.append(value)
return Series(diff)
# invert differenced value
def inverse_difference(history, yhat, interval=1):
return yhat + history[-interval]
# scale train and test data to [-1, 1]
def scale(train, test):
# fit scaler
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler = scaler.fit(train)
# transform train
train = train.reshape(train.shape[0], train.shape[1])
train_scaled = scaler.transform(train)
# transform test
test = test.reshape(test.shape[0], test.shape[1])
test_scaled = scaler.transform(test)
return scaler, train_scaled, test_scaled
# inverse scaling for a forecasted value
def invert_scale(scaler, X, value):
new_row = [x for x in X] + [value]
array = numpy.array(new_row)
array = array.reshape(1, len(array))
inverted = scaler.inverse_transform(array)
return inverted[0, -1]
# fit an LSTM network to training data
def fit_lstm(train, batch_size, nb_epoch):
X, y = train[:, 0:-1], train[:, -1]
X = X.reshape(X.shape[0], 1, X.shape[1])
model = Sequential()
model.add(Bidirectional(LSTM(50, activation='relu'), batch_input_shape=(batch_size, X.shape[1], X.shape[2])))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
for i in range(nb_epoch):
model.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False)
model.reset_states()
print('Epoch {}'.format(i))
return model
# make a one-step forecast
def forecast_lstm(model, batch_size, X):
X = X.reshape(1, 1, len(X))
yhat = model.predict(X, batch_size=batch_size)
return yhat[0, 0]
# load dataset
sim = simulation2.Simulator(50)
sim.simulate()
s = simulation.Simulation([[1, 1]], # to_plot, to_report
[[0.1, [0.2, 0.1], [15, 2], [30, 2]]], # interarrivals, demand, replenishment_lead, expiry
[[70.0, 110.0, 5.0, 30.0, 100.0, 100.0]], # purchase price, sales price, handling, backorder, overflow, recycle
[[50, 35]]) # storage, reorder point
s.simulate()
#raw_values = sim.stats.inventory_vector
raw_values = s.w.products[0].stats.storage
raw_values = raw_values[0::30]
print(len(raw_values))
diff_values = difference(raw_values, 1)
# transform data to be supervised learning
supervised = timeseries_to_supervised(diff_values, 1)
supervised_values = supervised.values
# split data into train and test-sets
train, test = supervised_values[0:-30], supervised_values[-30:]
# transform the scale of the data
scaler, train_scaled, test_scaled = scale(train, test)
# fit the model
lstm_model = fit_lstm(train_scaled, 1, 10)
# forecast the entire training dataset to build up state for forecasting
train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)
#lstm_model.predict(train_reshaped, batch_size=1)
# walk-forward validation on the test data
predictions = list()
for i in range(len(test_scaled)):
# make one-step forecast
X, y = test_scaled[i, 0:-1], test_scaled[i, -1]
yhat = forecast_lstm(lstm_model, 1, X)
# invert scaling
yhat = invert_scale(scaler, X, yhat)
# invert differencing
yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i)
# store forecast
predictions.append(yhat)
expected = raw_values[len(train) + i + 1]
print('Time=%d, Predicted=%f, Expected=%f' % (i + 1, yhat, expected))
# report performance
mse = mean_squared_error(raw_values[-30:-2], predictions[1:-1])
rmse = sqrt(mse)
ape = []
real_values = raw_values[-30:-2]
raw_value = raw_values[-30:-2]
predictions = predictions[1:-1]
for i in range(len(predictions)):
value = abs(predictions[i]-real_values[i])/real_values[i]
if value < 1:
ape.append(value)
mape = sum(ape)/len(ape)*100
print('Test RMSE: %.3f' % rmse)
print('Test MSE: %.3f' % mse)
print('Mean absolute percentage error: ', round(mape,2), "%")
# plot
pyplot.plot(raw_values[-30:-2], label='simulation')
pyplot.plot(predictions[1:-1], label='predicted by LSTM neural network')
pyplot.xlabel('time')
pyplot.ylabel('inventory level')
pyplot.grid()
pyplot.legend()
pyplot.show()
|
[
"pandas.DataFrame",
"matplotlib.pyplot.show",
"math.sqrt",
"matplotlib.pyplot.plot",
"pandas.concat",
"keras.models.Sequential",
"matplotlib.pyplot.legend",
"sklearn.preprocessing.MinMaxScaler",
"keras.layers.LSTM",
"simulations.simulation.Simulation",
"keras.layers.Dense",
"numpy.array",
"pandas.Series",
"simulations.simulation2.Simulator",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.grid",
"sklearn.metrics.mean_squared_error"
] |
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|
#coding=utf-8
"""
1. SQLAlchemy-migration现在是openstack社区维护的一个项目，主要用于实现SQLAlchemy相
关数据误置的创建、版本管理、迁移等功能；它对SQLAlchemy的版本有一定要求；它对于一般项
目而言并不是必需的；
2. 下面的db_create、db_migrate、db_upgrade、db_downgrade等方法均使用SQLAlchemy-
migration实现；
3. 如果不需要实现数据库版本管理及迁移，可以不使用SQLAlchemy-migration。
"""
import os.path
# from migrate.versioning import api
import imp
# from config import SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO
from app import db, app
from sqlalchemy import create_engine
from sqlalchemy.orm import scoped_session, sessionmaker
from sqlalchemy.ext.declarative import declarative_base
engine = create_engine(app.config['SQLALCHEMY_DATABASE_URI'], convert_unicode=True, echo=True)
db_session = scoped_session(sessionmaker(autocommit=False,
autoflush=False,
bind=engine))
Base = declarative_base()
Base.query = db_session.query_property()
def init_db():
import models
Base.metadata.create_all(bind=engine)
init_db()
# def db_create():
# """
# :summary: 使用SQLAlchmy-migration进行数据库创建及版本管理
# """
# db.create_all()
# if not os.path.exists(SQLALCHEMY_MIGRATE_REPO):
# api.create(SQLALCHEMY_MIGRATE_REPO, 'database repository')
# api.version_control(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO)
# else:
# api.version_control(SQLALCHEMY_DATABASE_URI,
# SQLALCHEMY_MIGRATE_REPO,
# api.version(SQLALCHEMY_MIGRATE_REPO))
#
#
# def db_migrate():
# """
# :summary: SQLAlchemy-migrate 迁移的方式就是比较数据库(在本例中从app.db中获取)
# 与我们模型的结构(从文件 app/models.py 获取)；两者间的不同将会被记录成一个迁移
# 脚本存放在迁移仓库中；迁移脚本知道如何去迁移或撤销它，所以它始终是可能用于升级
# 或降级一个数据库。
# """
# migration = SQLALCHEMY_MIGRATE_REPO\
# + '/versions/%03d_migration.py' \
# % (api.db_version(SQLALCHEMY_DATABASE_URI,
# SQLALCHEMY_MIGRATE_REPO) + 1)
# tmp_module = imp.new_module('old_model')
# old_model = api.create_model(SQLALCHEMY_DATABASE_URI,
# SQLALCHEMY_MIGRATE_REPO)
# exec old_model in tmp_module.__dict__
# script = api.make_update_script_for_model(SQLALCHEMY_DATABASE_URI,
# SQLALCHEMY_MIGRATE_REPO,
# tmp_module.meta,
# db.metadata)
# open(migration, 'wt').write(script)
# api.upgrade(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO)
# print 'New migration saved as {0}.'.format(migration)
# print 'Current database version: {0}'.format(
# str(api.db_version(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO))
# )
#
#
# def db_upgrade():
# """
# :summary: 数据库升级，使用SQLAlchemy-migration实现。
# """
# api.upgrade(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO)
# print 'Current database version: ' + str(
# api.db_version(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO)
# )
#
#
# def db_downgrade():
# """
# :summary: 数据库降级，使用SQLAlchemy-migration实现。
# """
# v = api.db_version(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO)
# api.downgrade(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO, v - 1)
# print 'Current database version: ' + str(
# api.db_version(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO)
# )
if __name__ == '__main__':
init_db()
|
[
"sqlalchemy.create_engine",
"sqlalchemy.ext.declarative.declarative_base",
"sqlalchemy.orm.sessionmaker"
] |
[((594, 683), 'sqlalchemy.create_engine', 'create_engine', (["app.config['SQLALCHEMY_DATABASE_URI']"], {'convert_unicode': '(True)', 'echo': '(True)'}), "(app.config['SQLALCHEMY_DATABASE_URI'], convert_unicode=True,\n echo=True)\n", (607, 683), False, 'from sqlalchemy import create_engine\n'), ((860, 878), 'sqlalchemy.ext.declarative.declarative_base', 'declarative_base', ([], {}), '()\n', (876, 878), False, 'from sqlalchemy.ext.declarative import declarative_base\n'), ((708, 768), 'sqlalchemy.orm.sessionmaker', 'sessionmaker', ([], {'autocommit': '(False)', 'autoflush': '(False)', 'bind': 'engine'}), '(autocommit=False, autoflush=False, bind=engine)\n', (720, 768), False, 'from sqlalchemy.orm import scoped_session, sessionmaker\n')]
|
import boto3
import json
def get_client() -> boto3.Session:
return boto3.client("lambda")
def external_lambda_tests() -> None:
basic_call()
def basic_call() -> None:
lambda_client = get_client()
response = lambda_client.list_functions(
MaxItems=10
)
pretty_print(response)
def pretty_print(response: str) -> None:
print(json.dumps(response, indent=4, sort_keys=True))
if __name__ == "__main__":
external_lambda_tests()
|
[
"boto3.client",
"json.dumps"
] |
[((73, 95), 'boto3.client', 'boto3.client', (['"""lambda"""'], {}), "('lambda')\n", (85, 95), False, 'import boto3\n'), ((382, 428), 'json.dumps', 'json.dumps', (['response'], {'indent': '(4)', 'sort_keys': '(True)'}), '(response, indent=4, sort_keys=True)\n', (392, 428), False, 'import json\n')]
|
import re
import os
import sys
import time
import atexit
import platform
import traceback
import logging
import base64
import random
from contextlib import contextmanager
from blackfire import profiler, VERSION, agent, generate_config, DEFAULT_CONFIG_FILE
from blackfire.utils import IS_PY3, get_home_dir, ConfigParser, \
urlparse, urljoin, urlencode, get_load_avg, get_logger, quote, \
parse_qsl, Request, urlopen, json_prettify, get_probed_runtime
from blackfire.exceptions import BlackfireApiException
from blackfire import BlackfireConfiguration
log = get_logger(__name__)
# globals
_config = None
_probe = None
_DEFAULT_OMIT_SYS_PATH = True
_DEFAULT_PROFILE_TITLE = 'unnamed profile'
__all__ = [
'get_traces', 'clear_traces', 'is_enabled', 'enable', 'end', 'reset',
'disable', 'run', 'initialize'
]
class Probe(object):
def __init__(self, config):
self._config = config
self._agent_conn = None
self._enabled = False
def is_enabled(self):
return self._enabled
def get_agent_prolog_response(self):
'''Returns the first response of the Agent in prolog dialogue'''
assert self._agent_conn is not None
return self._agent_conn.agent_response
def enable(self):
if self._enabled:
raise BlackfireApiException('Another probe is already profiling')
self._enabled = True
# connect agent
if not self._agent_conn:
try:
self._agent_conn = agent.Connection(
self._config.agent_socket, self._config.agent_timeout
)
self._agent_conn.connect(config=self._config)
except Exception as e:
self._enabled = False
self._agent_conn = None
raise e
self._req_start = time.time()
# pass start options from _config.args, set defaults as necessary
builtins = not bool(int(self._config.args.get('flag_no_builtins', '0')))
profile_cpu = bool(int(self._config.args.get('flag_cpu', '0')))
profile_memory = bool(int(self._config.args.get('flag_memory', '0')))
fn_args_enabled = bool(int(self._config.args.get('flag_fn_args', '0')))
# only enable timespan if this is the last profile of multiple sample profiles.
# we look at 'continue': 'false' from the agent response
profile_timespan = False
timespan_threshold = profiler.MAX_TIMESPAN_THRESHOLD # not probable number
if self._agent_conn.agent_response.status_val_dict.get(
'first_sample'
) == 'true':
profile_timespan = bool(
int(self._config.args.get('flag_timespan', '0'))
)
timespan_threshold = int(
self._config.args.get('timespan_threshold', 10)
)
# timespan_selectors is a dict of set of prefix/equal regex selectors.
timespan_selectors = {'^': set(), '=': set()}
if profile_timespan:
ts_selectors = self._agent_conn.agent_response.args.get(
'Blackfire-Timespan', []
)
for ts_sel in ts_selectors:
if ts_sel[0] not in ['^', '=']:
log.warning(
"Ignoring invalid timespan selector '%s'.", ts_sel
)
continue
timespan_selectors[ts_sel[0]].add(ts_sel[1:])
# instrumented_funcs is a dict of {func_name:[list of argument IDs]}
instrumented_funcs = {}
if fn_args_enabled:
# convert the fn-args string to dict for faster lookups on C side
fn_args = self._agent_conn.agent_response.args.get(
'Blackfire-Fn-Args', []
)
for fn_arg in fn_args:
fn_name, arg_ids_s = fn_arg.split()
fn_name = fn_name.strip()
if fn_name in instrumented_funcs:
log.warning(
"Function '%s' is already instrumented. Ignoring fn-args directive %s.",
fn_name, fn_arg
)
continue
arg_ids = []
for arg_id in arg_ids_s.strip().split(','):
if arg_id.isdigit():
arg_ids.append(int(arg_id))
else:
arg_ids.append(arg_id)
instrumented_funcs[fn_name] = arg_ids
profiler.start(
builtins=builtins,
profile_cpu=profile_cpu,
profile_memory=profile_memory,
profile_timespan=profile_timespan,
instrumented_funcs=instrumented_funcs,
timespan_selectors=timespan_selectors,
timespan_threshold=timespan_threshold,
)
# TODO: 'Blackfire-Error: 103 Samples quota is out'
log.debug(
"profiler started. [instrumented_funcs:%s, timespan_selectors:%s]",
json_prettify(instrumented_funcs),
json_prettify(timespan_selectors),
)
def disable(self):
self._enabled = False
profiler.stop()
def clear_traces(self):
profiler.clear_traces()
def end(self, headers={}, omit_sys_path_dirs=_DEFAULT_OMIT_SYS_PATH):
if not self._agent_conn:
return
log.debug("probe.end() called.")
self.disable()
traces = get_traces(omit_sys_path_dirs=omit_sys_path_dirs)
self.clear_traces()
# write main prolog
profile_title = self._config.args.get(
'profile_title', _DEFAULT_PROFILE_TITLE
)
end_headers = {
'file-format': 'BlackfireProbe',
'Probed-Runtime': get_probed_runtime(),
'Probed-Language': 'python',
'Probed-Os': platform.platform(),
'Probe-version': VERSION,
'Probed-Features': self._config.args_raw,
'Request-Start': self._req_start,
'Request-End': time.time(),
'Profile-Title': profile_title,
}
load_avg = get_load_avg()
if load_avg:
end_headers['Request-Sys-Load-Avg'] = load_avg
end_headers.update(headers)
context_dict = {'script': sys.executable, 'argv[]': sys.argv}
# middlewares populate the Context dict?
if 'Context' in end_headers:
context_dict.update(end_headers['Context'])
end_headers['Context'] = urlencode(context_dict, doseq=True)
profile_data_req = agent.BlackfireRequest(
headers=end_headers, data=traces
)
self._agent_conn.send(profile_data_req.to_bytes())
self._agent_conn.close()
self._agent_conn = None
return traces
def get_traces(self, omit_sys_path_dirs=_DEFAULT_OMIT_SYS_PATH):
return profiler.get_traces(omit_sys_path_dirs=omit_sys_path_dirs)
def get_traces(omit_sys_path_dirs=_DEFAULT_OMIT_SYS_PATH):
return profiler.get_traces(omit_sys_path_dirs=omit_sys_path_dirs)
def clear_traces():
profiler.clear_traces()
# used from testing to set Probe state to a consistent state
def reset():
global _config, _probe
_config = None
_probe = None
def add_marker(label=''):
pass
def generate_subprofile_query():
global _config
if not _config:
raise BlackfireApiException(
'Unable to create a subprofile query as profiling is not enabled.'
)
args_copy = _config.args.copy()
parent_sid = ''
if 'sub_profile' in args_copy:
parent_sid = args_copy['sub_profile'].split(':')[1]
args_copy.pop('aggreg_samples')
s = ''.join(chr(random.randint(0, 255)) for _ in range(7))
if IS_PY3:
s = bytes(s, agent.Protocol.ENCODING)
sid = base64.b64encode(s)
sid = sid.decode("ascii")
sid = sid.rstrip('=')
sid = sid.replace('+', 'A')
sid = sid.replace('/', 'B')
sid = sid[:9]
args_copy['sub_profile'] = '%s:%s' % (parent_sid, sid)
result = "%s&signature=%s&%s" % (
_config.challenge,
_config.signature,
urlencode(args_copy),
)
return result
def initialize(
query=None,
client_id=None,
client_token=None,
agent_socket=None,
agent_timeout=None,
endpoint=None,
log_file=None,
log_level=None,
config_file=DEFAULT_CONFIG_FILE,
_method="manual",
):
global _config, log, _probe
if log_file or log_level:
log = get_logger(__name__, log_file=log_file, log_level=log_level)
log.warning(
"DeprecationWarning: 'LOG_FILE' and 'LOG_LEVEL' params are no longer used from 'probe.initialize' API. "
"Please use 'BLACKFIRE_LOG_FILE'/'BLACKFIRE_LOG_LEVEL' environment variables."
"These settings will be removed in the next version."
)
log.debug("probe.initialize called. [method:'%s']", _method)
_config = generate_config(
query,
client_id,
client_token,
agent_socket,
agent_timeout,
endpoint,
log_file,
log_level,
config_file,
)
log.debug(
"Probe Configuration initialized. [%s]",
json_prettify(_config.__dict__)
)
_probe = Probe(_config)
def is_enabled():
global _probe
if not _probe:
return False
return _probe.is_enabled()
def enable(end_at_exit=False):
global _config, _probe
if not _config:
raise BlackfireApiException(
'No configuration set. initialize should be called first.'
)
log.debug("probe.enable() called.")
_probe.enable()
if end_at_exit: # used for profiling CLI scripts
# patch sys module to get the exit code/stdout/stderr output lengths
from blackfire.hooks.sys.patch import patch
from blackfire.hooks.sys import SysHooks
patch()
def _deinitialize():
headers = {}
headers['Response-Code'] = SysHooks.exit_code
headers['Response-Bytes'
] = SysHooks.stdout_len + SysHooks.stderr_len
try:
end(headers=headers)
except:
# we do not need to return if any error happens inside end()
# but it would be nice to see the traceback
log.warn(traceback.format_exc())
logging.shutdown()
# Note: The functions registered via this module are not called when the
# program is killed by a signal not handled by Python, when a Python fatal
# internal error is detected, or when os._exit() is called.
atexit.register(_deinitialize)
def disable():
global _probe
if not _probe:
return
_probe.disable()
log.debug("probe.disable() called.")
def end(headers={}, omit_sys_path_dirs=_DEFAULT_OMIT_SYS_PATH):
'''
headers: additional headers to send along with the final profile data.
'''
global _probe
if not _probe:
return
log.debug("probe.end() called.")
return _probe.end()
@contextmanager
def run(call_end=True):
enable()
try:
yield
finally:
disable()
if call_end:
end()
|
[
"atexit.register",
"blackfire.profiler.stop",
"blackfire.profiler.clear_traces",
"blackfire.agent.Connection",
"blackfire.utils.get_load_avg",
"random.randint",
"blackfire.utils.json_prettify",
"blackfire.utils.get_probed_runtime",
"traceback.format_exc",
"blackfire.generate_config",
"blackfire.utils.get_logger",
"logging.shutdown",
"blackfire.hooks.sys.patch.patch",
"blackfire.profiler.start",
"blackfire.exceptions.BlackfireApiException",
"blackfire.utils.urlencode",
"platform.platform",
"time.time",
"blackfire.agent.BlackfireRequest",
"base64.b64encode",
"blackfire.profiler.get_traces"
] |
[((565, 585), 'blackfire.utils.get_logger', 'get_logger', (['__name__'], {}), '(__name__)\n', (575, 585), False, 'from blackfire.utils import IS_PY3, get_home_dir, ConfigParser, urlparse, urljoin, urlencode, get_load_avg, get_logger, quote, parse_qsl, Request, urlopen, json_prettify, get_probed_runtime\n'), ((7014, 7072), 'blackfire.profiler.get_traces', 'profiler.get_traces', ([], {'omit_sys_path_dirs': 'omit_sys_path_dirs'}), '(omit_sys_path_dirs=omit_sys_path_dirs)\n', (7033, 7072), False, 'from blackfire import profiler, VERSION, agent, generate_config, DEFAULT_CONFIG_FILE\n'), ((7099, 7122), 'blackfire.profiler.clear_traces', 'profiler.clear_traces', ([], {}), '()\n', (7120, 7122), False, 'from blackfire import profiler, VERSION, agent, generate_config, DEFAULT_CONFIG_FILE\n'), ((7826, 7845), 'base64.b64encode', 'base64.b64encode', (['s'], {}), '(s)\n', (7842, 7845), False, 'import base64\n'), ((8958, 9082), 'blackfire.generate_config', 'generate_config', (['query', 'client_id', 'client_token', 'agent_socket', 'agent_timeout', 'endpoint', 'log_file', 'log_level', 'config_file'], {}), '(query, client_id, client_token, agent_socket, agent_timeout,\n endpoint, log_file, log_level, config_file)\n', (8973, 9082), False, 'from blackfire import profiler, VERSION, agent, generate_config, DEFAULT_CONFIG_FILE\n'), ((1837, 1848), 'time.time', 'time.time', ([], {}), '()\n', (1846, 1848), False, 'import time\n'), ((4503, 4758), 'blackfire.profiler.start', 'profiler.start', ([], {'builtins': 'builtins', 'profile_cpu': 'profile_cpu', 'profile_memory': 'profile_memory', 'profile_timespan': 'profile_timespan', 'instrumented_funcs': 'instrumented_funcs', 'timespan_selectors': 'timespan_selectors', 'timespan_threshold': 'timespan_threshold'}), '(builtins=builtins, profile_cpu=profile_cpu, profile_memory=\n profile_memory, profile_timespan=profile_timespan, instrumented_funcs=\n instrumented_funcs, timespan_selectors=timespan_selectors,\n 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|
# MIT License
#
# Copyright (c) 2017 <NAME> and (c) 2020 Google LLC
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import os
import time
from collections import deque
import gym
import numpy as np
import torch
from third_party.a2c_ppo_acktr import algo, utils
from third_party.a2c_ppo_acktr.arguments import get_args
from third_party.a2c_ppo_acktr.envs import make_vec_envs
from third_party.a2c_ppo_acktr.model import Policy
from third_party.a2c_ppo_acktr.storage import RolloutStorage
from my_pybullet_envs import utils as gan_utils
import logging
import sys
from my_pybullet_envs.laikago import mirror_obs, mirror_action
sys.path.append("third_party")
def main():
args, extra_dict = get_args()
# this file for normal ppo training, sim-gan(gail-dyn) training in main_gail_dyn_ppo.py
assert not args.gail
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
if args.cuda and torch.cuda.is_available() and args.cuda_deterministic:
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
log_dir = os.path.expanduser(args.log_dir)
eval_log_dir = log_dir + "_eval"
utils.cleanup_log_dir(log_dir)
utils.cleanup_log_dir(eval_log_dir)
torch.set_num_threads(1)
device = torch.device("cuda:0" if args.cuda else "cpu")
Tensor = torch.cuda.FloatTensor if args.cuda else torch.FloatTensor
envs = make_vec_envs(args.env_name, args.seed, args.num_processes,
args.gamma, args.log_dir, device, False, render=False, **extra_dict)
if args.warm_start == '':
actor_critic = Policy(
envs.observation_space.shape,
envs.action_space,
base_kwargs={'recurrent': args.recurrent_policy, 'hidden_size': args.hidden_size})
actor_critic.to(device)
else:
# TODO: assume no state normalize ob_rms
if args.cuda:
actor_critic, _ = torch.load(args.warm_start)
else:
actor_critic, _ = torch.load(args.warm_start, map_location='cpu')
actor_critic.reset_critic(envs.observation_space.shape)
if args.warm_start_logstd is not None:
actor_critic.reset_variance(envs.action_space, args.warm_start_logstd)
actor_critic.to(device)
dummy = gym.make(args.env_name, render=False, **extra_dict)
save_path = os.path.join(args.save_dir, args.algo)
print("SAVE PATH:")
print(save_path)
try:
os.makedirs(save_path)
except FileExistsError:
print("warning: path existed")
# input("warning: path existed")
except OSError:
exit()
pathname = os.path.join(save_path, "source_test.py")
text_file = open(pathname, "w+")
text_file.write(dummy.getSourceCode())
text_file.close()
print("source file stored")
# input("source file stored press enter")
dummy.reset()
# dummy.close()
log_formatter = logging.Formatter("%(asctime)s [%(threadName)-12.12s] [%(levelname)-5.5s] %(message)s")
root_logger = logging.getLogger()
root_logger.setLevel(logging.INFO)
file_handler = logging.FileHandler("{0}/{1}.log".format(save_path, "console_output"))
file_handler.setFormatter(log_formatter)
root_logger.addHandler(file_handler)
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setFormatter(log_formatter)
root_logger.addHandler(console_handler)
if args.algo == 'a2c':
agent = algo.A2C_ACKTR(
actor_critic,
args.value_loss_coef,
args.entropy_coef,
lr=args.lr,
eps=args.eps,
alpha=args.alpha,
max_grad_norm=args.max_grad_norm)
elif args.algo == 'ppo':
if args.loss_sym > 0.0:
agent = algo.PPO(
actor_critic,
args.clip_param,
args.ppo_epoch,
args.num_mini_batch,
args.value_loss_coef,
args.entropy_coef,
symmetry_coef=args.loss_sym,
lr=args.lr,
eps=args.eps,
max_grad_norm=args.max_grad_norm,
mirror_act=mirror_action,
mirror_obs=mirror_obs
)
else:
agent = algo.PPO(
actor_critic,
args.clip_param,
args.ppo_epoch,
args.num_mini_batch,
args.value_loss_coef,
args.entropy_coef,
lr=args.lr,
eps=args.eps,
max_grad_norm=args.max_grad_norm)
elif args.algo == 'acktr':
agent = algo.A2C_ACKTR(
actor_critic, args.value_loss_coef, args.entropy_coef, acktr=True)
else:
agent = None
feat_select_func = None
obs = envs.reset()
obs_feat = gan_utils.replace_obs_with_feat(obs, args.cuda, feat_select_func, return_tensor=True)
feat_len = obs_feat.size(1) # TODO: multi-dim obs broken
if args.dup_sym:
buffer_np = args.num_processes * 2
else:
buffer_np = args.num_processes
rollouts = RolloutStorage(args.num_steps, buffer_np,
envs.observation_space.shape, envs.action_space,
actor_critic.recurrent_hidden_state_size,
feat_len)
rollouts.to(device)
if args.dup_sym:
obs_s = gan_utils.mirror_obsact_batch(obs, args.cuda, mirror_obs, augment=True)
obs_feat_s = obs_feat.repeat(2, 1)
rollouts.obs[0].copy_(obs_s)
rollouts.obs_feat[0].copy_(obs_feat_s)
else:
rollouts.obs[0].copy_(obs)
rollouts.obs_feat[0].copy_(obs_feat)
episode_rewards = deque(maxlen=10000)
total_num_episodes = 0
j = 0
max_num_episodes = args.num_episodes if args.num_episodes else np.infty
start = time.time()
num_updates = int(
args.num_env_steps) // args.num_steps // args.num_processes
while j < num_updates and total_num_episodes < max_num_episodes:
if args.use_linear_lr_decay:
# decrease learning rate linearly
utils.update_linear_schedule(
agent.optimizer, j, num_updates,
agent.optimizer.lr if args.algo == "acktr" else args.lr)
for step in range(args.num_steps):
# print(args.num_steps) 300*8
# Sample actions
with torch.no_grad():
value, action, action_log_prob, recurrent_hidden_states = actor_critic.act(
rollouts.obs[step, :args.num_processes, :],
rollouts.recurrent_hidden_states[step, :args.num_processes, :],
rollouts.masks[step, :args.num_processes, :])
# Obser reward and next obs
obs, reward, done, infos = envs.step(action)
obs_feat = gan_utils.replace_obs_with_feat(obs, args.cuda, feat_select_func, return_tensor=True)
for info in infos:
if 'episode' in info.keys():
episode_rewards.append(info['episode']['r'])
# If done then clean the history of observations.
masks = Tensor(
[[0.0] if done_ else [1.0] for done_ in done])
bad_masks = Tensor(
[[0.0] if 'bad_transition' in info.keys() else [1.0]
for info in infos])
if args.dup_sym:
obs_s = gan_utils.mirror_obsact_batch(obs, args.cuda, mirror_obs, augment=True)
action_s = gan_utils.mirror_obsact_batch(action, args.cuda, mirror_action, augment=True)
recurrent_hidden_states_s = recurrent_hidden_states.repeat(2, 1)
action_log_prob_s = action_log_prob.repeat(2, 1)
value_s = value.repeat(2, 1)
reward_s = reward.repeat(2, 1)
masks_s = masks.repeat(2, 1)
bad_masks_s = bad_masks.repeat(2, 1)
obs_feat_s = obs_feat.repeat(2, 1)
rollouts.insert(obs_s, recurrent_hidden_states_s, action_s,
action_log_prob_s, value_s, reward_s, masks_s, bad_masks_s, obs_feat_s)
else:
rollouts.insert(obs, recurrent_hidden_states, action,
action_log_prob, value, reward, masks, bad_masks, obs_feat)
with torch.no_grad():
next_value = actor_critic.get_value(
rollouts.obs[-1], rollouts.recurrent_hidden_states[-1],
rollouts.masks[-1]).detach()
rollouts.compute_returns(next_value, args.use_gae, args.gamma,
args.gae_lambda, not args.no_proper_time_limits)
value_loss, action_loss, dist_entropy = agent.update(rollouts)
rollouts.after_update()
# save for every interval-th episode or for the last epoch
if (j % args.save_interval == 0 or j == num_updates - 1) and args.save_dir != "":
torch.save([
actor_critic,
getattr(utils.get_vec_normalize(envs), 'ob_rms', None)
], os.path.join(save_path, args.env_name + ".pt"))
torch.save([
actor_critic,
getattr(utils.get_vec_normalize(envs), 'ob_rms', None)
], os.path.join(save_path, args.env_name + "_" + str(j) + ".pt"))
if j % args.log_interval == 0 and len(episode_rewards) > 1:
total_num_steps = (j + 1) * args.num_processes * args.num_steps
end = time.time()
root_logger.info(
("Updates {}, num timesteps {}, FPS {} \n Last {} training episodes:" +
" mean/median reward {:.1f}/{:.1f}, min/max reward {:.1f}/{:.1f}, " +
"dist en {}, l_pi {}, l_vf {} \n").format(
j, total_num_steps,
int(total_num_steps / (end - start)),
len(episode_rewards), np.mean(episode_rewards),
np.median(episode_rewards), np.min(episode_rewards),
np.max(episode_rewards), dist_entropy, value_loss,
action_loss
)
)
# actor_critic.dist.logstd._bias,
total_num_episodes += len(episode_rewards)
episode_rewards.clear()
j += 1
if __name__ == "__main__":
main()
|
[
"logging.Formatter",
"torch.set_num_threads",
"numpy.mean",
"third_party.a2c_ppo_acktr.algo.PPO",
"torch.device",
"third_party.a2c_ppo_acktr.algo.A2C_ACKTR",
"third_party.a2c_ppo_acktr.storage.RolloutStorage",
"torch.no_grad",
"os.path.join",
"third_party.a2c_ppo_acktr.utils.get_vec_normalize",
"collections.deque",
"sys.path.append",
"third_party.a2c_ppo_acktr.model.Policy",
"torch.load",
"third_party.a2c_ppo_acktr.utils.cleanup_log_dir",
"numpy.max",
"third_party.a2c_ppo_acktr.utils.update_linear_schedule",
"numpy.median",
"torch.manual_seed",
"logging.StreamHandler",
"numpy.min",
"my_pybullet_envs.utils.replace_obs_with_feat",
"torch.cuda.is_available",
"third_party.a2c_ppo_acktr.arguments.get_args",
"my_pybullet_envs.utils.mirror_obsact_batch",
"third_party.a2c_ppo_acktr.envs.make_vec_envs",
"gym.make",
"os.makedirs",
"time.time",
"torch.cuda.manual_seed_all",
"os.path.expanduser",
"logging.getLogger"
] |
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|
from search_test import SearchTest, SearchTestElastic
if __name__ == "__main__":
test = SearchTestElastic(timeout=50, file_for_save=
'/home/roman/Projects/ElasticMongoTest/test_results_csv/ElasticsearchTest.csv')
# test.search_substrings_or(['Colorado', 'USA', 'President', 'Washington', 'December',
# 'Book', 'Ford', 'million', 'Apple', 'Official',
# 'year', 'Bank', 'Study', 'University', 'blood'],
# )
# test.search_substring(['Washington', 'Russia', 'USA', 'MTV', 'London', 'Crime', 'Science',
# 'good', 'kosdfsd', 'luck'], 'news100gb')
# print(test.size_of_object('news10gb'))
test.size_of_object('news14gb', 'message')
print(test.size)
# test.search_substrings_or(['MTV', 'London'],
# )
# test.show_results()
|
[
"search_test.SearchTestElastic"
] |
[((95, 228), 'search_test.SearchTestElastic', 'SearchTestElastic', ([], {'timeout': '(50)', 'file_for_save': '"""/home/roman/Projects/ElasticMongoTest/test_results_csv/ElasticsearchTest.csv"""'}), "(timeout=50, file_for_save=\n '/home/roman/Projects/ElasticMongoTest/test_results_csv/ElasticsearchTest.csv'\n )\n", (112, 228), False, 'from search_test import SearchTest, SearchTestElastic\n')]
|
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
V1 = Parameter(torch.randn(3, 3, requires_grad=True))
V2 = Parameter(torch.randn(3, 3, requires_grad=True))
W = torch.randn(2, 2)
bias = torch.zeros(2)
def update(V, W):
V = torch.matmul(V1, V2.transpose(0, 1))
i = 0
V = V.view(-1, 1)
W = W.view(-1, 1)
for j in range(len(W)):
W[j] = V[i]
i += 1
def forward(x, W, bias):
return F.linear(x, W, bias)
print("V {}".format(V))
print("W {}".format(W))
update(V, W)
print("V {}".format(V))
print("W {}".format(W))
x = torch.randn(2)
g = torch.ones(2)
print(x)
print(forward(x, W, bias).norm)
y = forward(x, W, bias)
print(y)
print(y.reshape(-1,1))
loss_fn = F.cross_entropy(y.reshape(1, -1), torch.ones(1, 2))
print(loss_fn)
forward(x, W, bias).backward(g)
|
[
"torch.zeros",
"torch.ones",
"torch.randn",
"torch.nn.functional.linear"
] |
[((211, 228), 'torch.randn', 'torch.randn', (['(2)', '(2)'], {}), '(2, 2)\n', (222, 228), False, 'import torch\n'), ((236, 250), 'torch.zeros', 'torch.zeros', (['(2)'], {}), '(2)\n', (247, 250), False, 'import torch\n'), ((609, 623), 'torch.randn', 'torch.randn', (['(2)'], {}), '(2)\n', (620, 623), False, 'import torch\n'), ((628, 641), 'torch.ones', 'torch.ones', (['(2)'], {}), '(2)\n', (638, 641), False, 'import torch\n'), ((114, 151), 'torch.randn', 'torch.randn', (['(3)', '(3)'], {'requires_grad': '(True)'}), '(3, 3, requires_grad=True)\n', (125, 151), False, 'import torch\n'), ((168, 205), 'torch.randn', 'torch.randn', (['(3)', '(3)'], {'requires_grad': '(True)'}), '(3, 3, requires_grad=True)\n', (179, 205), False, 'import torch\n'), ((471, 491), 'torch.nn.functional.linear', 'F.linear', (['x', 'W', 'bias'], {}), '(x, W, bias)\n', (479, 491), True, 'import torch.nn.functional as F\n'), ((783, 799), 'torch.ones', 'torch.ones', (['(1)', '(2)'], {}), '(1, 2)\n', (793, 799), False, 'import torch\n')]
|
# Copyright 2019 Nine Entertainment Co.
#
# 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 secretupdater import app
import requests
class HeaderClient():
"""This is a dummy ConfidantClient that works with header auth."""
def __init__(self, **_kwargs):
super(HeaderClient, self).__init__()
app.logger.debug("Initialising HeaderClient")
def get_service(self, service):
app.logger.debug("DummyClient.get_service")
url = "{}/v1/services/{}".format(
app.config.get('CONFIDANT_SERVER_URL'), service)
headers = {
"X-CONFIDANT-USERNAME": "secretupdater",
"X-CONFIDANT-EMAIL": "<EMAIL>"
}
r = requests.get(url, headers=headers)
return r.json()
|
[
"secretupdater.app.config.get",
"requests.get",
"secretupdater.app.logger.debug"
] |
[((813, 858), 'secretupdater.app.logger.debug', 'app.logger.debug', (['"""Initialising HeaderClient"""'], {}), "('Initialising HeaderClient')\n", (829, 858), False, 'from secretupdater import app\n'), ((904, 947), 'secretupdater.app.logger.debug', 'app.logger.debug', (['"""DummyClient.get_service"""'], {}), "('DummyClient.get_service')\n", (920, 947), False, 'from secretupdater import app\n'), ((1189, 1223), 'requests.get', 'requests.get', (['url'], {'headers': 'headers'}), '(url, headers=headers)\n', (1201, 1223), False, 'import requests\n'), ((1002, 1040), 'secretupdater.app.config.get', 'app.config.get', (['"""CONFIDANT_SERVER_URL"""'], {}), "('CONFIDANT_SERVER_URL')\n", (1016, 1040), False, 'from secretupdater import app\n')]
|
import pytest
import datetime
import pandas as pd
import pyarrow as pa
import numpy as np
from arrow_pd_parser.parse import (
pa_read_csv_to_pandas,
pa_read_json_to_pandas,
)
def pd_datetime_series_to_list(s, series_type, date=False):
fmt = "%Y-%m-%d" if date else "%Y-%m-%d %H:%M:%S"
if series_type == "object":
s_ = s.apply(datetime_object_as_str).to_list()
elif series_type == "datetime64":
s_ = s.dt.strftime(fmt).to_list()
elif series_type == "period":
s_ = s.apply(lambda x: None if pd.isna(x) else x.strftime(fmt))
s_ = s_.to_list()
else:
raise ValueError(f"series_type input {series_type} not expected.")
str_dates = [None if pd.isna(x) else x for x in s_]
return str_dates
def datetime_object_as_str(x):
if pd.isna(x):
return np.nan
else:
return str(x)
@pytest.mark.parametrize(
"in_type,pd_timestamp_type,out_type",
[
("timestamp[s]", "datetime_object", "object"),
("timestamp[s]", "pd_timestamp", "datetime64[ns]"),
("timestamp[s]", "pd_period", "period[S]"),
("timestamp[ms]", "datetime_object", "object"),
("timestamp[ms]", "pd_timestamp", "datetime64[ns]"),
("timestamp[ms]", "pd_period", "period[L]"),
("timestamp[us]", "datetime_object", "object"),
("timestamp[us]", "pd_timestamp", "datetime64[ns]"),
("timestamp[us]", "pd_period", "period[U]"),
("timestamp[ns]", "datetime_object", "datetime64[ns]"),
("timestamp[ns]", "pd_timestamp", "datetime64[ns]"),
("timestamp[ns]", "pd_period", "period[N]"),
],
)
def test_datetime(in_type, pd_timestamp_type, out_type):
test_data_path = "tests/data/datetime_type.csv"
test_str_dates = pd.read_csv(test_data_path, dtype=str)["my_datetime"]
test_str_dates = [None if pd.isna(s) else s for s in test_str_dates]
type_dict = {
"timestamp[s]": pa.timestamp("s"),
"timestamp[ms]": pa.timestamp("ms"),
"timestamp[us]": pa.timestamp("us"),
"timestamp[ns]": pa.timestamp("ns"),
}
schema = pa.schema([("my_datetime", type_dict[in_type])])
# datetime_object
df = pa_read_csv_to_pandas(
test_data_path,
schema=schema,
expect_full_schema=False,
pd_timestamp_type=pd_timestamp_type,
)
test_str_dates = pd.read_csv(test_data_path, dtype=str)["my_datetime"]
test_str_dates = [None if pd.isna(s) else s for s in test_str_dates]
assert str(df.my_datetime.dtype) == out_type
if out_type == "object":
assert isinstance(df.my_datetime[0], datetime.datetime)
actual_str_dates = pd_datetime_series_to_list(
df.my_datetime, out_type.split("[")[0], date=False
)
assert test_str_dates == actual_str_dates
@pytest.mark.parametrize(
"in_type,pd_date_type,out_type",
[
("date32", "datetime_object", "object"),
("date32", "pd_timestamp", "datetime64[ns]"),
("date32", "pd_period", "object"),
("date64", "datetime_object", "object"),
("date64", "pd_timestamp", "datetime64[ns]"),
("date64", "pd_period", "period[L]"),
],
)
def test_date(in_type, pd_date_type, out_type):
test_data_path = "tests/data/date_type.csv"
test_str_dates = pd.read_csv(test_data_path, dtype=str)["my_date"]
test_str_dates = [None if pd.isna(s) else s for s in test_str_dates]
schema = pa.schema([("my_date", getattr(pa, in_type)())])
# datetime_object
if in_type == "date32" and pd_date_type == "pd_period":
with pytest.warns(UserWarning):
df = pa_read_csv_to_pandas(
test_data_path,
schema,
expect_full_schema=False,
pd_date_type=pd_date_type,
)
else:
df = pa_read_csv_to_pandas(
test_data_path, schema, expect_full_schema=False, pd_date_type=pd_date_type
)
test_str_dates = pd.read_csv(test_data_path, dtype=str)["my_date"]
test_str_dates = [None if pd.isna(s) else s for s in test_str_dates]
assert str(df.my_date.dtype) == out_type
if out_type == "object":
assert isinstance(df.my_date[0], datetime.date)
actual_str_dates = pd_datetime_series_to_list(
df.my_date, out_type.split("[")[0], date=True
)
assert test_str_dates == actual_str_dates
@pytest.mark.skip(
reason=(
"This currently fails (see issue #43), but adding in "
"test boilerplate for future fix."
)
)
def test_timestamps_as_strs():
test_data_path = "tests/data/datetime_type.csv"
test_str_dates = pd.read_csv(test_data_path, dtype="string")["my_datetime"]
schema = pa.schema([("my_datetime", pa.string())])
df = pa_read_csv_to_pandas(test_data_path, schema, expect_full_schema=False)
assert df["my_datetime"].to_list() == test_str_dates.to_list()
df = pa_read_json_to_pandas(
test_data_path.replace(".csv", ".jsonl"), schema, expect_full_schema=False
)
assert df["my_datetime"].to_list() == test_str_dates.to_list()
|
[
"pyarrow.schema",
"pyarrow.string",
"pandas.read_csv",
"pytest.warns",
"arrow_pd_parser.parse.pa_read_csv_to_pandas",
"pytest.mark.parametrize",
"pandas.isna",
"pytest.mark.skip",
"pyarrow.timestamp"
] |
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|
import sklearn
from sklearn.linear_model import LinearRegression
import catboost
import pandas as pd
import copy
import lightgbm as lgb
import xgboost as xgb
from sklearn.model_selection import train_test_split, KFold, cross_val_score, StratifiedKFold, GridSearchCV
from sklearn.metrics import mean_absolute_error, r2_score
import inspect
import numpy as np
import skopt
import datetime
import os
from sklearn.externals import joblib
jl_compress = 3
def hello_world():
print("HW!")
# Bayes Search EXample
# opt = skopt.BayesSearchCV(lgb.LGBMRegressor(n_estimators=3000, observation="mae"),
# search_spaces= ruml.utils.SKOPT_BO["lgb"], verbose=True,
# n_iter=3000,n_jobs=5,cv=folds, scoring="neg_mean_absolute_error",
# fit_params ={"early_stopping_rounds":200,"eval_set":[(X_early_stop,y_early_stop)]} )
# opt.fit(X=X_train,y=y_train, callback=[ruml.utils.Print_Callback(), skopt.callbacks.DeadlineStopper(total_time=36000)])
DEFAULT_VALUES = {
"lgb": {
"regr": lgb.LGBMRegressor,
"model_params": {
"n_estimators":2000
},
"fit_params":
{
"eval_metric":"mae",
"early_stopping_rounds":150,
"verbose":False
}
},
"metrics":{
"mae": mean_absolute_error,
"r2": r2_score
}
}
def conv_pred(preds):
if (isinstance(preds,np.ndarray) and isinstance(preds[0],np.ndarray)) or (
isinstance(preds,pd.Series) and isinstance(preds.iloc[0],np.ndarray)):
preds = preds[:,0]
return preds
def add_def_params(model_name, model_params, fit_params, def_param = DEFAULT_VALUES):
if model_name in DEFAULT_VALUES.keys():
if "model_params" in DEFAULT_VALUES[model_name]:
new_p = copy.deepcopy(DEFAULT_VALUES[model_name]["model_params"])
new_p.update(model_params)
model_params = new_p
if "fit_params" in DEFAULT_VALUES[model_name]:
new_p = copy.deepcopy(DEFAULT_VALUES[model_name]["fit_params"])
new_p.update(fit_params)
fit_params = new_p
return model_params, fit_params
#model can be str,
#instance of estiomator - we use parameters of these estimator and model_params together
#or estimator type we use model_params
def cv(model=LinearRegression, X = pd.DataFrame([]), y = pd.Series([]), folds = 5,
model_params = {},
fit_params = {},
task = "regr",
metrics=["mae"]):
model_name = None
if isinstance(model,str):
model_name = model
model_params, fit_params = add_def_params(model, model_params,fit_params)
model = DEFAULT_VALUES[model_name][task]
if not isinstance(model, type):
model_params.update(model.get_params())
model = type(model)
predictions_cv = pd.Series([0]*len(X), index = X.index)
predictions_cv_best = pd.Series([0]*len(X), index = X.index)
scores = list()
scores_best = list()
models = list()
best_iterations = list()
if folds == 0:
model_instance = model(**model_params)
if "early_stopping_rounds" in fit_params.keys():
fit_params = {k:v for k,v in fit_params.items() if k != "early_stopping_rounds"}
model_instance = model_instance.fit( X, y,
**fit_params)
return {"models":[model_instance], "scores":[], "predictions_cv":None, "score_cv":None, "best_iterations": None}
if isinstance(folds,int):
folds = KFold(n_splits=folds, shuffle=True, random_state=42)
for fold_n, (train_index, valid_index) in enumerate(folds.split(X, y)):
X_train, X_valid = X.loc[train_index], X.loc[valid_index]
y_train, y_valid = y.loc[train_index], y.loc[valid_index]
model_instance = model(**model_params)
if "eval_set" in inspect.getfullargspec( model_instance.fit).args:
fit_params['eval_set'] = [(X_valid,y_valid)]
model_instance.fit( X_train, y_train,
**fit_params)
train_predict = conv_pred(model_instance.predict(X_train))
train_score = list()
for metric in metrics:
if isinstance(metric,str):
metric = DEFAULT_VALUES["metrics"][metric]
train_score.append(metric(y_train,train_predict))
valid_predict = conv_pred(model_instance.predict(X_valid))
predictions_cv[valid_index] = pd.Series(valid_predict)
score = list()
for metric in metrics:
if isinstance(metric,str):
metric = DEFAULT_VALUES["metrics"][metric]
score.append(metric(y_valid,valid_predict))
scores.append(score)
models.append(model_instance)
if hasattr(model_instance, "best_iteration_" ):
best_iterations.append(model_instance.best_iteration_)
print("Fold ", fold_n, "score ", scores[-1], "train_score", train_score)
if hasattr(model_instance,"predict_best"):
valid_predict_best = model_instance.predict_best(X_valid, y_valid)
valid_predict_best = conv_pred(valid_predict_best)
predictions_cv_best[valid_index] = pd.Series(valid_predict_best)
score_best = list()
for metric in metrics:
if isinstance(metric,str):
metric = DEFAULT_VALUES["metrics"][metric]
score_best.append(metric(y_valid,valid_predict_best))
scores_best.append(score_best)
print("score best ", scores_best[-1], "\n")
if hasattr(model_instance,"get_cluster_models"):
clust_models = model_instance.get_cluster_models()
for i, model_cluster in clust_models.items():
valid_predict_model = conv_pred(model_cluster.predict(X_valid))
score_model = list()
for metric in metrics:
if isinstance(metric,str):
metric = DEFAULT_VALUES["metrics"][metric]
score_model.append(metric(y_valid,valid_predict_model))
print("score best for model ", i, " ", score_model, "\n")
print("#"*30)
score = list()
for metric in metrics:
if isinstance(metric,str):
metric = DEFAULT_VALUES["metrics"][metric]
score.append(metric(y,predictions_cv))
print("Final scores: ", score)
score_best = list()
if len(scores_best)>0:
for metric in metrics:
if isinstance(metric,str):
metric = DEFAULT_VALUES["metrics"][metric]
score_best.append(metric(y,predictions_cv_best))
print("Final scores best: ", score_best)
return {"models":models, "scores":scores, "predictions_cv":predictions_cv, "score_cv":score,"score_cv_best":score_best, "best_iterations": best_iterations,
"scores_best":scores_best, "model":model, "model_params":model_params, "fit_params":fit_params}
def blend_models(models,X):
res = pd.Series([0]*len(X), index = X.index)
for m in models:
preds = m.predict(X)
preds = conv_pred(preds)
res+=preds
res/=len(models)
return res
#test
#ruml.utils.cv(X = pd.DataFrame({1:[i for i in range(10)],2:[2*i for i in range(10)]}),
# y = pd.Series(i*i for i in range(10)),
# )
lgbm_bo = {
'num_leaves': (6, 1024),
# 'max_depth': (4, 20),
'learning_rate': (0.00001, 0.1),
'bagging_fraction': (0.1, 1.0),
'feature_fraction': (0.1, 1.0),
'min_data_in_leaf': (6, 200),
'bagging_freq': (0, 10),
'reg_alpha': (0,100),
'reg_lambda': (0,100),
}
# space.Integer(6, 30, name='num_leaves'),
# space.Integer(50, 200, name='min_child_samples'),
# space.Real(1, 400, name='scale_pos_weight'),
# space.Real(0.6, 0.9, name='subsample'),
# space.Real(0.6, 0.9, name='colsample_bytree')
#objectives
# regression_l2, L2 loss, aliases: regression, mean_squared_error, mse, l2_root, root_mean_squared_error, rmse
# regression_l1, L1 loss, aliases: mean_absolute_error, mae
# huber, Huber loss
# fair, Fair loss
# poisson, Poisson regression
# quantile, Quantile regression
# mape, MAPE loss, aliases: mean_absolute_percentage_error
# gamma, Gamma regression with log-link. It might be useful, e.g., for modeling insurance claims severity, or for any target that might be gamma-distributed
# tweedie
SKOPT_BO = {
"lgb" : {
'num_leaves': skopt.space.Integer(6, 512),
'min_child_samples': skopt.space.Integer(10, 200), #min_data_in_leaf
'scale_pos_weight': skopt.space.Integer(1,400),
'subsample':skopt.space.Real(0.1,1.0), #bagging_fraction
'colsample_bytree':skopt.space.Real(0.1,1.0), #feature_fraction
'reg_alpha': skopt.space.Integer(0,100),
'reg_lambda': skopt.space.Integer(0,100),
'learning_rate': skopt.space.Real(0.00001, 0.1)
}
}
lcb_bo = {
'num_leaves': (15, 1024),
'l2_leaf_reg': [2, 18],
# 'max_depth': (4, 20),
'learning_rate': (0.005, 0.1),
'bagging_fraction': (0.1, 1.0),
'feature_fraction': (0.1, 1.0),
'min_data_in_leaf': (6, 200),
'bagging_freq': (0, 10),
'reg_alpha': (0,100),
'reg_lambda': (0,100),
}
BO_SPACES = {
sklearn.linear_model.Ridge.__name__: {
'alpha': (0.001, 1000),
},
lgb.LGBMRegressor.__name__: lgbm_bo,
lgb.LGBMClassifier.__name__: lgbm_bo,
catboost.CatBoostRegressor.__name__: {
'max_depth': [4, 12],
'learning_rate': [0.001],
}
}
def subm_res(res_dic, x_text , comm = "comment",
competition = "LANL-Earthquake-Prediction"):
res_dic['prediction'] = blend_models(res_dic["models"],x_text)
sub = pd.read_csv('../input/sample_submission.csv', index_col='seg_id')
sub['time_to_failure'] = res_dic['prediction']
filename = 'submission_'+str(res_dic["score_cv"][0])+'.csv'
sub.to_csv(filename)
command = 'kaggle competitions submit '+ competition + ' -f '+filename+' -m\"' + comm + '\"'
print(sub.head())
print('\n\n')
print(command, '\n\n')
pickle_filename = res_dic["model"].__name__[:20]+"_"+str(res_dic["score_cv"][0])+".model"+".jbl"
joblib.dump(res_dic,filename=pickle_filename,compress=jl_compress)
return res_dic['prediction']
def list_models(dir="."):
f_list = os.listdir(dir)
res = [f for f in f_list if ".model" in f]
return res
def stack_models(file_list, X, X_test):
for f in file_list:
model_dict = joblib.load(f)
X[f] = model_dict["predictions_cv"]
X_test[f] = ['prediction']
return X, X_test
class Print_Callback:
def __init__(self):
pass
# self.best_index = -1
def __call__(self, x):
if min(x.func_vals) == x.func_vals[-1]:
print(datetime.datetime.now().time().strftime(format="%HH:%MM:%SS"), "new best ", x.func_vals[-1], " iter ", len(x.func_vals))
BO_RUN = {
"lgbr": {
"model":
{
"estimator" : lgb.LGBMRegressor(n_estimators=2000, observation="mae"),
"search_spaces": SKOPT_BO["lgb"],
"verbose": True,
"n_iter":3000,
"n_jobs":5,
"cv":KFold(5, shuffle=True, random_state=42),
"scoring":"neg_mean_absolute_error",
},
"fit_params" :{"early_stopping_rounds":200}
}
}
def bo(X, y, estimator = "lgbr",
search_spaces= {},
verbose=True,
n_iter=3000,
n_jobs=5,
cv=KFold(5, shuffle=True, random_state=42),
scoring="neg_mean_absolute_error",
fit_params ={},
callbacks = [Print_Callback()],
max_time = 7200,
eval_set_ratio = 0.15
):
if eval_set_ratio is not None and eval_set_ratio>0:
X_train, X_early_stop, y_train, y_early_stop = train_test_split(X, y, test_size=eval_set_ratio, random_state=42)
fit_params["eval_set"] = [(X_early_stop,y_early_stop)]
else:
X_train = X,
y_train = y
if max_time is not None and max_time>0:
callbacks.append(skopt.callbacks.DeadlineStopper(total_time=max_time))
if isinstance(estimator, str):
fit_params.update(BO_RUN[estimator]["fit_params"])
params = BO_RUN[estimator]["model"]
if search_spaces is not None and len(search_spaces)>0: params["search_spaces"] = search_spaces
if n_iter is not None: params["n_iter"] = n_iter
if n_jobs is not None: params["n_jobs"] = n_jobs
if verbose is not None: params["verbose"] = verbose
if cv is not None: params["cv"] = cv
if scoring is not None: params["scoring"] = scoring
opt = skopt.BayesSearchCV(fit_params=fit_params,**params)
else:
opt = skopt.BayesSearchCV(estimator,
search_spaces= search_spaces,
n_iter=n_iter,n_jobs=n_jobs,cv=cv, scoring=scoring,
fit_params =fit_params )
opt.fit(X=X_train,y=y_train, callback=callbacks)
print(opt.best_iteration_)
print(opt.best_score_, opt.best_params_)
print("Byes opt res "+ str(opt.best_score_) + " " + str(opt.best_params_), file=open("output.txt", "a"))
return opt
|
[
"pandas.DataFrame",
"sklearn.externals.joblib.dump",
"copy.deepcopy",
"inspect.getfullargspec",
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"skopt.space.Integer",
"skopt.BayesSearchCV",
"sklearn.model_selection.KFold",
"skopt.space.Real",
"pandas.Series",
"skopt.callbacks.DeadlineStopper",
"sklearn.externals.joblib.load",
"lightgbm.LGBMRegressor",
"datetime.datetime.now",
"os.listdir"
] |
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9014), False, 'import skopt\n'), ((9035, 9061), 'skopt.space.Real', 'skopt.space.Real', (['(0.1)', '(1.0)'], {}), '(0.1, 1.0)\n', (9051, 9061), False, 'import skopt\n'), ((9108, 9134), 'skopt.space.Real', 'skopt.space.Real', (['(0.1)', '(1.0)'], {}), '(0.1, 1.0)\n', (9124, 9134), False, 'import skopt\n'), ((9181, 9208), 'skopt.space.Integer', 'skopt.space.Integer', (['(0)', '(100)'], {}), '(0, 100)\n', (9200, 9208), False, 'import skopt\n'), ((9231, 9258), 'skopt.space.Integer', 'skopt.space.Integer', (['(0)', '(100)'], {}), '(0, 100)\n', (9250, 9258), False, 'import skopt\n'), ((9285, 9313), 'skopt.space.Real', 'skopt.space.Real', (['(1e-05)', '(0.1)'], {}), '(1e-05, 0.1)\n', (9301, 9313), False, 'import skopt\n'), ((11081, 11095), 'sklearn.externals.joblib.load', 'joblib.load', (['f'], {}), '(f)\n', (11092, 11095), False, 'from sklearn.externals import joblib\n'), ((12381, 12446), 'sklearn.model_selection.train_test_split', 'train_test_split', (['X', 'y'], {'test_size': 'eval_set_ratio', 'random_state': '(42)'}), '(X, y, test_size=eval_set_ratio, random_state=42)\n', (12397, 12446), False, 'from sklearn.model_selection import train_test_split, KFold, cross_val_score, StratifiedKFold, GridSearchCV\n'), ((13221, 13273), 'skopt.BayesSearchCV', 'skopt.BayesSearchCV', ([], {'fit_params': 'fit_params'}), '(fit_params=fit_params, **params)\n', (13240, 13273), False, 'import skopt\n'), ((13300, 13440), 'skopt.BayesSearchCV', 'skopt.BayesSearchCV', (['estimator'], {'search_spaces': 'search_spaces', 'n_iter': 'n_iter', 'n_jobs': 'n_jobs', 'cv': 'cv', 'scoring': 'scoring', 'fit_params': 'fit_params'}), '(estimator, search_spaces=search_spaces, n_iter=n_iter,\n n_jobs=n_jobs, cv=cv, scoring=scoring, fit_params=fit_params)\n', (13319, 13440), False, 'import skopt\n'), ((1808, 1865), 'copy.deepcopy', 'copy.deepcopy', (["DEFAULT_VALUES[model_name]['model_params']"], {}), "(DEFAULT_VALUES[model_name]['model_params'])\n", (1821, 1865), False, 'import copy\n'), ((2013, 2068), 'copy.deepcopy', 'copy.deepcopy', (["DEFAULT_VALUES[model_name]['fit_params']"], {}), "(DEFAULT_VALUES[model_name]['fit_params'])\n", (2026, 2068), False, 'import copy\n'), ((5266, 5295), 'pandas.Series', 'pd.Series', (['valid_predict_best'], {}), '(valid_predict_best)\n', (5275, 5295), True, 'import pandas as pd\n'), ((11592, 11647), 'lightgbm.LGBMRegressor', 'lgb.LGBMRegressor', ([], {'n_estimators': '(2000)', 'observation': '"""mae"""'}), "(n_estimators=2000, observation='mae')\n", (11609, 11647), True, 'import lightgbm as lgb\n'), ((11768, 11807), 'sklearn.model_selection.KFold', 'KFold', (['(5)'], {'shuffle': '(True)', 'random_state': '(42)'}), '(5, shuffle=True, random_state=42)\n', (11773, 11807), False, 'from sklearn.model_selection import train_test_split, KFold, cross_val_score, StratifiedKFold, GridSearchCV\n'), ((12630, 12682), 'skopt.callbacks.DeadlineStopper', 'skopt.callbacks.DeadlineStopper', ([], {'total_time': 'max_time'}), '(total_time=max_time)\n', (12661, 12682), False, 'import skopt\n'), ((3898, 3940), 'inspect.getfullargspec', 'inspect.getfullargspec', (['model_instance.fit'], {}), '(model_instance.fit)\n', (3920, 3940), False, 'import inspect\n'), ((11394, 11417), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (11415, 11417), False, 'import datetime\n')]
|
import asyncio
import pytest
from motor.motor_asyncio import AsyncIOMotorClient
from blog.repositories import PostRepository
@pytest.mark.asyncio
async def test_create_blog(db):
post_repository = PostRepository()
collection = db['posts']
result = await collection.insert_one({'name': 'Rob'})
assert result.inserted_id is not None
|
[
"blog.repositories.PostRepository"
] |
[((204, 220), 'blog.repositories.PostRepository', 'PostRepository', ([], {}), '()\n', (218, 220), False, 'from blog.repositories import PostRepository\n')]
|
import os
import xlrd
from xlrd import XLRDError
from xlrd.book import Book
from xlrd.sheet import Sheet
from collections import OrderedDict
from typing import Iterable, List, Dict, Tuple
import logging
import traceback
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
def read_xml_files(root_dir: str) -> Iterable[Tuple[str, str]]:
"""Read instance XML files found recursively in root_dir."""
for entry in os.scandir(path=root_dir):
if entry.is_dir():
yield from read_xml_files(root_dir=entry.path)
elif entry.name.endswith(".xml"):
with open(entry.path, mode='r', encoding="UTF-8") as f:
xml_file = f.read()
yield xml_file, entry.path
def read_xlsform_definitions(root_dir: str) -> Iterable[OrderedDict]:
"""Read XLSX files found recursively in root_dir"""
error_text = "Encountered an error while trying to read the XLSX file " \
"at the following path, and did not read from it: {0}.\n" \
"Error message was: {1}\n"
for entry in os.scandir(path=root_dir):
if entry.is_dir():
yield from read_xlsform_definitions(root_dir=entry.path)
elif entry.name.endswith(".xlsx"):
try:
workbook = xlrd.open_workbook(filename=entry.path)
form_def = read_xlsform_data(workbook=workbook)
except XLRDError as xle:
logger.info(error_text.format(entry.path, "{0}\n\n{1}".format(
str(xle), ''.join(traceback.format_exc()))))
continue
except ValueError as ve:
logger.info(error_text.format(entry.path, "{0}\n\n{1}".format(
str(ve), ''.join(traceback.format_exc()))))
continue
else:
yield form_def
def read_xlsform_data(workbook: Book) -> OrderedDict:
"""Return XLSForm definition data read from an XLRD Workbook."""
sheets = {x.name for x in workbook.sheets()}
required = {"survey", "choices", "settings"}
if not required.issubset(sheets):
raise ValueError(
"The required sheets for an XLSForm definition ({0}) were not "
"found in the workbook sheets ({1}).".format(required, sheets))
survey = xlrd_sheet_to_list_of_dict(
workbook.sheet_by_name(sheet_name='survey'))
choices = xlrd_sheet_to_list_of_dict(
workbook.sheet_by_name(sheet_name='choices'))
settings = xlrd_sheet_to_list_of_dict(
workbook.sheet_by_name(sheet_name='settings'))
form_def = OrderedDict()
form_def['@settings'] = settings[0]
for item in survey:
if item['type'].startswith('select'):
select_type, choice_name = item['type'].split(' ')
choice_list = [x for x in choices
if x['list_name'] == choice_name]
item['choices'] = choice_list
form_def[item['name']] = item
return form_def
def xlrd_sheet_to_list_of_dict(sheet: Sheet) -> List[Dict]:
"""Convert an xlrd sheet into a list of dicts."""
keys = [sheet.cell(0, col_index).value for col_index in range(sheet.ncols)]
dict_list = []
for row_index in range(1, sheet.nrows):
d = {keys[col_index]: sheet.cell(row_index, col_index).value
for col_index in range(sheet.ncols)}
dict_list.append(d)
return dict_list
def flatten_dict_leaf_nodes(dict_in: OrderedDict,
dict_out: OrderedDict = None) -> OrderedDict:
"""Flatten nested leaves of and/or a list of OrderedDict into one level."""
if dict_out is None:
dict_out = OrderedDict()
for k, v in dict_in.items():
if isinstance(v, OrderedDict):
if "#text" in v.keys():
dict_out[k] = v["#text"]
else:
flatten_dict_leaf_nodes(v, dict_out)
elif isinstance(v, list):
for i in v:
flatten_dict_leaf_nodes(i, dict_out)
else:
dict_out[k] = v
return dict_out
|
[
"xlrd.open_workbook",
"logging.getLogger",
"traceback.format_exc",
"logging.NullHandler",
"collections.OrderedDict",
"os.scandir"
] |
[((230, 257), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (247, 257), False, 'import logging\n'), ((276, 297), 'logging.NullHandler', 'logging.NullHandler', ([], {}), '()\n', (295, 297), False, 'import logging\n'), ((447, 472), 'os.scandir', 'os.scandir', ([], {'path': 'root_dir'}), '(path=root_dir)\n', (457, 472), False, 'import os\n'), ((1089, 1114), 'os.scandir', 'os.scandir', ([], {'path': 'root_dir'}), '(path=root_dir)\n', (1099, 1114), False, 'import os\n'), ((2609, 2622), 'collections.OrderedDict', 'OrderedDict', ([], {}), '()\n', (2620, 2622), False, 'from collections import OrderedDict\n'), ((3680, 3693), 'collections.OrderedDict', 'OrderedDict', ([], {}), '()\n', (3691, 3693), False, 'from collections import OrderedDict\n'), ((1299, 1338), 'xlrd.open_workbook', 'xlrd.open_workbook', ([], {'filename': 'entry.path'}), '(filename=entry.path)\n', (1317, 1338), False, 'import xlrd\n'), ((1557, 1579), 'traceback.format_exc', 'traceback.format_exc', ([], {}), '()\n', (1577, 1579), False, 'import traceback\n'), ((1762, 1784), 'traceback.format_exc', 'traceback.format_exc', ([], {}), '()\n', (1782, 1784), False, 'import traceback\n')]
|
from classier.decorators.has_state_decorator.options import ATTRIBUTE_OPTIONS
from classier.decorators.has_state_decorator.options import METHOD_OPTIONS
from classier.objects import ClassMarker
from classier.decorators import _MARK_ATTRIBUTE_NAME
from classier.decorators.has_state_decorator import _MARK_TYPE_NAME
import classier.utils as utils
import json
def _get_from_pointer(options):
state_transformer = METHOD_OPTIONS.METHOD_STATE_TRANSFORMER.get_option(options)
pointer_exists = METHOD_OPTIONS.METHOD_POINTER_EXISTS.get_option(options) # TODO: remove?
saver = METHOD_OPTIONS.METHOD_SAVER.get_option(options)
state_attribute_name = ATTRIBUTE_OPTIONS.ATTRIBUTE_NAME_STATE.get_option(options)
saver = METHOD_OPTIONS.METHOD_SAVER.get_option(options)
index = METHOD_OPTIONS.METHOD_INDEX.get_option(options)
index_path = METHOD_OPTIONS.PATH_INDEX.get_option(options)
from_pointer_default = METHOD_OPTIONS.METHOD_POINTER_DEFAULT.get_option(options)
def from_pointer(self, pointer, default=None):
if isinstance(pointer, type(self)):
setattr(self, state_attribute_name, getattr(pointer, state_attribute_name))
return pointer
setattr(self, state_attribute_name, None)
default = utils.convenience.set_default(default, from_pointer_default)
index_information = None
if index is not None:
index_information = index(pointer, type(self), index_path)
state = None
if isinstance(pointer, dict):
state = pointer
# TODO: add debug logs here
if state is None and isinstance(pointer, str):
# pointer could be json.dumps
state = utils.convenience.optional(lambda: json.loads(pointer))
if state is None and isinstance(pointer, str) and index_information is not None:
# pointer could be something saver knows
state = utils.convenience.call(lambda: saver.get(pointer, index_information))
if state is None and default is not None:
state = default(pointer)
if state is None:
raise ValueError(f"Could not initialize from {pointer} of type {type(pointer)}")
if state_transformer is not None:
state = state_transformer(state)
setattr(self, state_attribute_name, state)
return self
return from_pointer
def _add_from_pointer(some_class, options):
method_name_from_pointer = METHOD_OPTIONS.METHOD_NAME_FROM_POINTER.get_option(options)
if not ClassMarker.does_mark_exist(some_class, _MARK_ATTRIBUTE_NAME, _MARK_TYPE_NAME, method_name_from_pointer):
ClassMarker.add_mark_to_class(some_class, _MARK_ATTRIBUTE_NAME, _MARK_TYPE_NAME, method_name_from_pointer)
some_class = utils.convenience.add_mixin(some_class, _get_from_pointer(options), method_name_from_pointer)
return some_class
|
[
"classier.decorators.has_state_decorator.options.METHOD_OPTIONS.METHOD_POINTER_EXISTS.get_option",
"json.loads",
"classier.decorators.has_state_decorator.options.METHOD_OPTIONS.METHOD_SAVER.get_option",
"classier.decorators.has_state_decorator.options.METHOD_OPTIONS.METHOD_INDEX.get_option",
"classier.utils.convenience.set_default",
"classier.objects.ClassMarker.does_mark_exist",
"classier.decorators.has_state_decorator.options.METHOD_OPTIONS.METHOD_POINTER_DEFAULT.get_option",
"classier.decorators.has_state_decorator.options.METHOD_OPTIONS.METHOD_NAME_FROM_POINTER.get_option",
"classier.decorators.has_state_decorator.options.METHOD_OPTIONS.PATH_INDEX.get_option",
"classier.decorators.has_state_decorator.options.METHOD_OPTIONS.METHOD_STATE_TRANSFORMER.get_option",
"classier.objects.ClassMarker.add_mark_to_class",
"classier.decorators.has_state_decorator.options.ATTRIBUTE_OPTIONS.ATTRIBUTE_NAME_STATE.get_option"
] |
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